This document describes a class project that aims to map crop residues using hyperspectral data. It will generate estimates of crop residue cover and amount in agricultural fields in Central Indiana using Landsat 7 ETM+, EO-1 ALI, and EO-1 Hyperion data from April 12, 2003. Methods will include pre-processing the hyperspectral data through de-striping and atmospheric corrections, then calculating indices like the Cellulose Absorption Index to classify crop residues and differentiate tillage systems. The results will provide information on quantifying and mapping crop residues using remote sensing techniques.
Diffuse reflectance spectroscopy to estimate soil attributes of Brazilian wet...Agriculture Journal IJOEAR
Abstract— The study of soils and characterization of its attributes are continually evolving, however, for the condition of wetlands, such information is still scarce and poorly distributed. Thus, the objective of this work was to characterize spectrally the soils of a wetland area. On the study area were collected georeferenced soil samples and sent for chemical and physical analysis routine and then subjected to spectral evaluation. Were identified seven soil classes with hydromorphic characteristics in their spectral curves? The information contained in these curves then led the development of equations for soil attributes. Sand was the physical attribute of a better correlation with laboratory data and Cationic Exchange Capacity (CEC), the chemical attributes that showed better results.
The document summarizes previous research inverting canopy reflectance models to retrieve vegetation biophysical parameters from remote sensing data. It then describes a study that coupled the PROSPECT leaf optical model with the SAIL canopy reflectance model to retrieve sugar beet canopy parameters like chlorophyll, water, leaf area index from AVIRIS hyperspectral data. The study found it could accurately estimate chlorophyll and water but had difficulty separating effects of leaf area index and leaf angle. It also applied the approach to Landsat TM data with similar results.
This document describes a study that developed a method for mapping annual land cover and land use (LULC) in the Dry Chaco ecoregion of South America using MODIS satellite imagery. Reference data was collected by visually interpreting high-resolution QuickBird imagery in Google Earth at random sample points. LULC was classified into 8 classes using predictor variables derived from MODIS time series data and a Random Forests classifier. Annual LULC maps from 2001 to 2007 were produced at 250m resolution and assessed for accuracy. The maps showed rapid deforestation related to expansion of soybean and pasture agriculture.
NIR in Leaf Chlorophyll Concentration EstimationIRJET Journal
This document discusses using field imaging spectroscopy and random forests to estimate leaf chlorophyll concentration in soybeans. A field imaging spectrometer system was used to collect hyperspectral reflectance data from soybean leaves. The PROSPECT radiative transfer model was used to relate the spectral data to chlorophyll content. Random forests were then used to develop a model for estimating chlorophyll content based on the spectral data. The model was able to accurately retrieve chlorophyll content from validation spectral data, demonstrating the potential of this approach for remote monitoring of crop health conditions. Future work involves applying this method from field to satellite data to allow monitoring soybean health over large areas.
This study aimed to establish a supervised classification of global blue carbon mangrove ecosystems using remote sensing techniques. Four classification models were compared for two regions of interest: the Zambezi Delta and Rufiji River Delta. The models used different vegetation indices and were assessed against published classification maps for accuracy. The NDVI model achieved the highest accuracy for both regions at around 80-83%. However, all models overpredicted mangrove cover in non-mangrove areas, suggesting improvements are needed to better account for land cover variability. The study demonstrates the potential for remote sensing to map mangroves globally but highlights challenges in achieving high accuracy.
The document reviews the potential use of remote sensing for investigating maize production. It discusses how the spectral reflectance characteristics of maize leaves can be used for classification and mapping of maize fields. Specifically, it describes how remote sensing can detect the spectral differences between soil and crop canopies, allowing for estimation of crop nutrient levels and health issues. The review concludes that remote sensing provides a fast, non-destructive method for monitoring biophysical and biochemical parameters of maize crops over large areas when appropriate sensors and processing techniques are used.
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
INTEGRATED TECHNOLOGY OF DATA REMOTE SENSING AND GIS TECHNIQUES ASSESS THE LA...acijjournal
The present study focuses on the nature and pattern of urban expansion of Madurai city over its
surrounding region during the period from 2003 to 2013. Based on Its proximity to the Madurai city,
Preparation of various thematic data such Land use and Land cover using Land sat data. Create a land
use land cover map from satellite imagery using supervised classification. Find out the areas from the
classified data. The study is Based on secondary data, the satellite imagery has downloaded from GLCF
(Global Land Cover Facility) web site, for the study area (path101 row 67), the downloaded imagery
Subset using Imagery software to clip the study area. The clipped satellite imagery has Send to prepare the
land use and land cover map using supervised classification.
Diffuse reflectance spectroscopy to estimate soil attributes of Brazilian wet...Agriculture Journal IJOEAR
Abstract— The study of soils and characterization of its attributes are continually evolving, however, for the condition of wetlands, such information is still scarce and poorly distributed. Thus, the objective of this work was to characterize spectrally the soils of a wetland area. On the study area were collected georeferenced soil samples and sent for chemical and physical analysis routine and then subjected to spectral evaluation. Were identified seven soil classes with hydromorphic characteristics in their spectral curves? The information contained in these curves then led the development of equations for soil attributes. Sand was the physical attribute of a better correlation with laboratory data and Cationic Exchange Capacity (CEC), the chemical attributes that showed better results.
The document summarizes previous research inverting canopy reflectance models to retrieve vegetation biophysical parameters from remote sensing data. It then describes a study that coupled the PROSPECT leaf optical model with the SAIL canopy reflectance model to retrieve sugar beet canopy parameters like chlorophyll, water, leaf area index from AVIRIS hyperspectral data. The study found it could accurately estimate chlorophyll and water but had difficulty separating effects of leaf area index and leaf angle. It also applied the approach to Landsat TM data with similar results.
This document describes a study that developed a method for mapping annual land cover and land use (LULC) in the Dry Chaco ecoregion of South America using MODIS satellite imagery. Reference data was collected by visually interpreting high-resolution QuickBird imagery in Google Earth at random sample points. LULC was classified into 8 classes using predictor variables derived from MODIS time series data and a Random Forests classifier. Annual LULC maps from 2001 to 2007 were produced at 250m resolution and assessed for accuracy. The maps showed rapid deforestation related to expansion of soybean and pasture agriculture.
NIR in Leaf Chlorophyll Concentration EstimationIRJET Journal
This document discusses using field imaging spectroscopy and random forests to estimate leaf chlorophyll concentration in soybeans. A field imaging spectrometer system was used to collect hyperspectral reflectance data from soybean leaves. The PROSPECT radiative transfer model was used to relate the spectral data to chlorophyll content. Random forests were then used to develop a model for estimating chlorophyll content based on the spectral data. The model was able to accurately retrieve chlorophyll content from validation spectral data, demonstrating the potential of this approach for remote monitoring of crop health conditions. Future work involves applying this method from field to satellite data to allow monitoring soybean health over large areas.
This study aimed to establish a supervised classification of global blue carbon mangrove ecosystems using remote sensing techniques. Four classification models were compared for two regions of interest: the Zambezi Delta and Rufiji River Delta. The models used different vegetation indices and were assessed against published classification maps for accuracy. The NDVI model achieved the highest accuracy for both regions at around 80-83%. However, all models overpredicted mangrove cover in non-mangrove areas, suggesting improvements are needed to better account for land cover variability. The study demonstrates the potential for remote sensing to map mangroves globally but highlights challenges in achieving high accuracy.
The document reviews the potential use of remote sensing for investigating maize production. It discusses how the spectral reflectance characteristics of maize leaves can be used for classification and mapping of maize fields. Specifically, it describes how remote sensing can detect the spectral differences between soil and crop canopies, allowing for estimation of crop nutrient levels and health issues. The review concludes that remote sensing provides a fast, non-destructive method for monitoring biophysical and biochemical parameters of maize crops over large areas when appropriate sensors and processing techniques are used.
Assessment of wheat crop coefficient using remote sensing techniquesPremier Publishers
Irrigation water consumption under physical and climatic conditions for large scale will be easier with remote sensing techniques. Crop evapotranspiration (ETc) uses crop coefficient (Kc) and reference evapotranspiration (ETo). Kc plays an essential role in agricultural practices and it has been widely used to estimate ETc. In this paper Normalized Deference Vegetation Index (NDVI) used to estimate crop coefficient according to satellite data (KcSat) through simple model (KcSat = 2NDVI - 0.2). Landsat8; bands 4 and 5 provide Red (R) and Near Infra-Red (NIR) measurements and it used to calculate NDVI. Single KcFAO estimated under Egyptian conditions according to FAO 56 paper. The KcFAO used to validate KcSat. Linear relationship between KcFAO and KcSat was established and R2 was 0.96. The main objective of this paper is estimation of wheat crop coefficient using remote sensing techniques.
INTEGRATED TECHNOLOGY OF DATA REMOTE SENSING AND GIS TECHNIQUES ASSESS THE LA...acijjournal
The present study focuses on the nature and pattern of urban expansion of Madurai city over its
surrounding region during the period from 2003 to 2013. Based on Its proximity to the Madurai city,
Preparation of various thematic data such Land use and Land cover using Land sat data. Create a land
use land cover map from satellite imagery using supervised classification. Find out the areas from the
classified data. The study is Based on secondary data, the satellite imagery has downloaded from GLCF
(Global Land Cover Facility) web site, for the study area (path101 row 67), the downloaded imagery
Subset using Imagery software to clip the study area. The clipped satellite imagery has Send to prepare the
land use and land cover map using supervised classification.
The document summarizes research on using RADARSAT-2 satellite data to monitor soil moisture for agricultural risk reduction in Canada. It finds that a calibrated Integral Equation Model can estimate regional soil moisture with an average error of 3.23% and detect changes in soil moisture over time. However, site-specific estimates have higher errors of 7.71% due to spatial variability not captured. Further analysis is needed to reduce errors and better quantify relative changes in soil moisture.
1) The study aimed to establish a baseline for monitoring land health over time in Cameroon, describe patterns of land degradation, and identify implications for sustainable agricultural intensification.
2) Soil samples were collected from smallholder farms using a stratified random sampling method and analyzed using laboratory and spectroscopic methods. Textural and carbon content analyses showed potential to support intensification, though slopes were steep in places.
3) Limitations including high acidity and erosion risk on slopes will require interventions like liming and conservation to enable sustainable intensification.
Self-organzing maps in Earth Observation Data Cube AnalysisLorena Santos
Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis
have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the
utility of Self-Organizing Maps (SOM) neural network method in the
process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application
The document describes research conducted in Finland from 1996-2006 that evaluated the use of optical and radar remote sensing for cereal yield estimation. Four optical vegetation index models were developed and validated using Landsat and SPOT data to estimate baseline yields of wheat, barley, and oats. The yields from the vegetation index models were then compared to yields estimated by a crop model and composite SAR/NDVI models. The research aimed to develop remote sensing techniques to support Finland's national agricultural inventory program and the EU's Common Agricultural Policy.
Change detection analysis of Cropland using Geospatial technique -A case Stud...IJEAB
Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.
Using Artificial Neural Networks for Digital Soil Mapping – a comparison of M...Ricardo Brasil
This document discusses using artificial neural networks (ANNs) for digital soil mapping in Portugal and Spain. Specifically, it compares the performance of multi-layer perceptron (MLP) and self-organizing map (SOM) ANN approaches. Four study areas in Portugal and Spain were selected for testing and modeling soil classes using terrain and land cover data as inputs to the ANNs. Results showed that MLP generally performed better than SOM at modeling soil classes across different sampling methods and data transformations. However, MLP was also more sensitive to the input data used.
Prediction of soil urea content using rf spectroscopy and partial least squar...IAEME Publication
This document summarizes a study that used radio frequency (RF) spectroscopy and partial least squares regression (PLSR) to predict soil urea content. 20 soil samples with varying concentrations of nutrients including urea were analyzed using an RF spectrometer. 16 samples were used to calibrate a PLSR model and the remaining 4 samples were used to validate the model's predictions of urea content. The PLSR model achieved an R2 value of 0.987 and prediction errors ranging from 0.49% to 2.78% for the validation samples, indicating RF spectroscopy combined with PLSR can accurately predict soil urea content. The study demonstrated the potential of this approach for analyzing soil nutrients but was limited to laboratory-
Application of remote sensing in forest ecosystemaliya nasir
Established remote sensing systems provide opportunities to develop and apply new measurements of ecosystem function across landscapes, regions and continents.
New efforts to predict the consequences of ecosystem function change, both natural and human- induced, on the regional and global distributions and abundances of species should be a high research priority
The document outlines a framework for measuring and monitoring land health and degradation through field measurements and remote sensing. It describes a stratified sampling approach with sentinel sites, clusters, plots and sub-plots to collect biophysical data on vegetation, soils, and trace gases. Remote sensing is used to extrapolate ground measurements and characterize spatial heterogeneity. Soil and carbon models integrate field and remote sensing data for carbon accounting and analysis at local to global scales.
[International agrophysics] ground penetrating radar for underground sensing ...Minal Ghugal
This document reviews the use of ground penetrating radar (GPR) for underground sensing in agriculture. GPR provides non-invasive measurements of belowground properties and has been used to measure soil characteristics like moisture, texture, and compaction. While GPR has been widely used to detect coarse tree roots, measuring individual crop roots directly is difficult but GPR can detect root cohorts within a soil volume. GPR also shows potential for indirectly estimating root development by sensing related soil properties and coupling with soil water models. Further advances rely on integrating knowledge across disciplines to adapt GPR for agricultural management needs.
G-Range: An intermediate complexity model for simulating and forecasting ecos...ILRI
1. Improving ecosystem modeling in grazing lands will benefit forecasting of ecosystem service delivery such as livestock production and carbon storage.
2. Simpler validation approaches for ecosystem models, such as G-Range, can reduce data needs as well as uncertainty while still achieving reasonable agreement with field data.
3. The G-Range model, an intermediate complexity ecosystem model adapted for grazing lands, was successfully validated at two sites with contrasting climates, demonstrating its ability to address practical questions faced by researchers and land managers.
7 Journal of Life Sciences and Biomedicine (2710-4915 2710-3447).pdfPublisherNasir
Research article: Mapping of LC/LU changes inside the Agdam district of the Karabakh economics region applying object-based satellite image analysis
Author (s): A.A. Rasouli, M.M. Asgarova, S.H. Safarov pdf, doi.org/10.29228/jlsb.22
Landuse landcover and ndvi analysis for halia catchmentIAEME Publication
This document summarizes a study analyzing land use/land cover changes and normalized difference vegetation index (NDVI) for the Halia catchment area in India over several decades using remote sensing data. Medium to high resolution Landsat satellite imagery from 1975, 1989, and 2001 was processed to create land use/land cover maps and NDVI maps for the area. The objective was to examine changes in cropped area and land use/land cover patterns over time and understand the implications for the local environment.
43 % of Earth’s terrestrial vegetated surface is degraded with limited capacity to supply benefits to humans.
Degraded landscapes often result in lower Soil Organic Carbon and overall poor soil health.
Understanding drivers of Land Degradation and processes of Soil Organic Carbon loss are key for informing effective interventions .
MODELAÇÃO DO SOLO-PAISAGEM – A IMPORTÂNCIA DA LOCALIZAÇÃORicardo Brasil
This document describes a study that uses artificial neural networks (ANNs) to model soil landscapes and predict soil types in unmapped areas of two catchments in northern Portugal. The study evaluates how the performance of ANN models is impacted by (1) including latitude and longitude data in the input variables, (2) using a stratified sampling strategy that considers both the range of landscape variables and proximity of training sites, and (3) comparing results between the two catchments and at different spatial resolutions. Two ANN architectures - Multi-Layer Perceptron and Self-Organizing Map - were trained on landscape and soil data from the catchments. The study aims to better understand how spatial autocorrelation and location data influence ANN soil
Crop Identification Using Unsuperviesd ISODATA and K-Means from Multispectral...IJERA Editor
This document summarizes an article that examines unsupervised classification methods for identifying crops from multispectral remote sensing imagery. It discusses ISODATA and K-means clustering algorithms for classifying imagery without training data. The author applies these methods to an image using ENVI software and compares the results. ISODATA identified five classes - water body, crops, hilly area, and barren land. K-means identified the same classes but with different class distributions between methods. The author concludes unsupervised classification can successfully identify crop types from remote sensing images without prior knowledge of land cover types.
Prediction of Soil Total Nitrogen Content Using Spectraradiometer and GIS in ...Agriculture Journal IJOEAR
This document summarizes a study that used visible near-infrared (VNIR) reflectance spectroscopy and geographic information systems-kriging (GIS-Kriging) to predict total nitrogen content in soil samples from two locations in southern Iraq. Soil samples were collected and their total nitrogen content was measured using chemical analysis. The samples were then scanned using a spectroradiometer to obtain VNIR reflectance spectra. Partial least squares regression models showed strong correlations between reflectance in wavelength bands 500-600 nm and 800-1000 nm and total nitrogen content, with R2 values over 0.84 for calibration and validation sets. GIS-Kriging models performed poorly for prediction with Q2 values around 0.
From global to regional scale: Remote sensing-based concepts and methods for ...Repository Ipb
The document discusses land cover mapping and change detection in tropical regions using remote sensing. It provides an overview of existing land cover classification systems and global and regional satellite-based mapping products. The paper compares different land cover maps for a test region in Central Sulawesi, Indonesia, finding inconsistencies between products and uncertainty in estimates of land cover classes and changes between years. Future work on operational land cover mapping in the tropics needs further harmonization of definitions and products, regional validation, and a standardized multi-level classification workflow.
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Consider the vision for a successful Southwest Transit marketing tea.docxclarebernice
Consider the vision for a successful Southwest Transit marketing team composed in Topic 4. Narrow down the team selection to four individuals for presentation to the director. Decide which strategies will be most effective for leading the agreed-upon team. Compose a PowerPoint presentation (10-12 slides), then record your 5-7-minute presentation using YouTube Video, Loom, or Zoom. On the title slide of your PowerPoint presentation, provide the link to your YouTube, Loom, or Zoom video recording that you created. Your presentation should address the following:
Who are the four team members, and what was the primary reason each person was selected? How difficult was it to come to a decision regarding team selection? Which potential team member was most difficult to come to a consensus about? Why?
What are the primary strengths of the team? What are its potential weaknesses? How positive is the management team about the team's potential? Justify your answers with evidence from " Southwest Transit Team Member Profiles."
What strategies will be most effective for motivating the team, managing conflict, and ensuring success and fostering collaboration? Cite specific motivational theories, conflict-resolution strategies, and leadership strategies in your answer.
Justify how the selected team embodies the values of Conscious Capitalism how the tenet of stakeholder orientation played a role in the team selection process. Provide citations to strengthen your claims.
Describe how value is created for each stakeholder, and in what ways will the team positively impact the business as a whole?
You are required to use at least three academic references to strengthen and support your claims and recommendations. Ensure each content slide has supporting citations and specific examples.
.
Consider the various ways to create effective communication in teams.docxclarebernice
Consider the various ways to create effective communication in teams and guidelines from the text to determine how s a student group could constructively manage the situations described below. In your response for each scenario, identify which principles of effective teamwork are being disregarded, and develop responses (i.e. ways) that maintain a supportive communication climate.
A. LATENESS: At the second meeting, Peg came in a few minutes late. That was bad enough, but now she’s coming 10–15 minutes late to every meeting. What’s worse, Angelica and Robert have started arriving late, too. It makes the rest of us feel like giving up.
B. SKEPTICISM: Dan constantly makes negative comments. Our brainstorming activities fail because he makes fun of our efforts. Some people in the group are losing their enthusiasm and have stopped saying anything.
C. MONOPOLIZING: Rajiv is very opinionated. He keeps talking, and he rambles on and on. It feels like we can’t get a word in edgewise.
D. SILENT DISAGREEMENT: Adelle sits around rolling her eyes about almost everything we say. We can tell from the look on her face that she doesn’t like our ideas. It makes us feel like she doesn’t like us, either. She’s very pretty, and I think maybe she feels superior.
.
The document summarizes research on using RADARSAT-2 satellite data to monitor soil moisture for agricultural risk reduction in Canada. It finds that a calibrated Integral Equation Model can estimate regional soil moisture with an average error of 3.23% and detect changes in soil moisture over time. However, site-specific estimates have higher errors of 7.71% due to spatial variability not captured. Further analysis is needed to reduce errors and better quantify relative changes in soil moisture.
1) The study aimed to establish a baseline for monitoring land health over time in Cameroon, describe patterns of land degradation, and identify implications for sustainable agricultural intensification.
2) Soil samples were collected from smallholder farms using a stratified random sampling method and analyzed using laboratory and spectroscopic methods. Textural and carbon content analyses showed potential to support intensification, though slopes were steep in places.
3) Limitations including high acidity and erosion risk on slopes will require interventions like liming and conservation to enable sustainable intensification.
Self-organzing maps in Earth Observation Data Cube AnalysisLorena Santos
Earth Observation (EO) Data Cubes infrastructures model
analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis
have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the
utility of Self-Organizing Maps (SOM) neural network method in the
process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application
The document describes research conducted in Finland from 1996-2006 that evaluated the use of optical and radar remote sensing for cereal yield estimation. Four optical vegetation index models were developed and validated using Landsat and SPOT data to estimate baseline yields of wheat, barley, and oats. The yields from the vegetation index models were then compared to yields estimated by a crop model and composite SAR/NDVI models. The research aimed to develop remote sensing techniques to support Finland's national agricultural inventory program and the EU's Common Agricultural Policy.
Change detection analysis of Cropland using Geospatial technique -A case Stud...IJEAB
Access to accurate and up-to-date information on the extent and distribution of individual crop types, associated with land use changes and practices, has significant value in intensively agricultural regions. Explicit information of croplands can be useful for sustainable water resources, land and agriculture planning and management. Remote sensing, has been proven to be a more cost-effective alternative to the traditional statistically-based ground surveys for crop coverage areas that are costly and provide insufficient information. Satellite images along with ground surveys can provide the necessary information of spatial coverage and spectral responses of croplands for sustainable agricultural management. This study strives to differentiate different crop types and agricultural practices to achieve a higher detailed crop map of the Narsinghpur district.
Using Artificial Neural Networks for Digital Soil Mapping – a comparison of M...Ricardo Brasil
This document discusses using artificial neural networks (ANNs) for digital soil mapping in Portugal and Spain. Specifically, it compares the performance of multi-layer perceptron (MLP) and self-organizing map (SOM) ANN approaches. Four study areas in Portugal and Spain were selected for testing and modeling soil classes using terrain and land cover data as inputs to the ANNs. Results showed that MLP generally performed better than SOM at modeling soil classes across different sampling methods and data transformations. However, MLP was also more sensitive to the input data used.
Prediction of soil urea content using rf spectroscopy and partial least squar...IAEME Publication
This document summarizes a study that used radio frequency (RF) spectroscopy and partial least squares regression (PLSR) to predict soil urea content. 20 soil samples with varying concentrations of nutrients including urea were analyzed using an RF spectrometer. 16 samples were used to calibrate a PLSR model and the remaining 4 samples were used to validate the model's predictions of urea content. The PLSR model achieved an R2 value of 0.987 and prediction errors ranging from 0.49% to 2.78% for the validation samples, indicating RF spectroscopy combined with PLSR can accurately predict soil urea content. The study demonstrated the potential of this approach for analyzing soil nutrients but was limited to laboratory-
Application of remote sensing in forest ecosystemaliya nasir
Established remote sensing systems provide opportunities to develop and apply new measurements of ecosystem function across landscapes, regions and continents.
New efforts to predict the consequences of ecosystem function change, both natural and human- induced, on the regional and global distributions and abundances of species should be a high research priority
The document outlines a framework for measuring and monitoring land health and degradation through field measurements and remote sensing. It describes a stratified sampling approach with sentinel sites, clusters, plots and sub-plots to collect biophysical data on vegetation, soils, and trace gases. Remote sensing is used to extrapolate ground measurements and characterize spatial heterogeneity. Soil and carbon models integrate field and remote sensing data for carbon accounting and analysis at local to global scales.
[International agrophysics] ground penetrating radar for underground sensing ...Minal Ghugal
This document reviews the use of ground penetrating radar (GPR) for underground sensing in agriculture. GPR provides non-invasive measurements of belowground properties and has been used to measure soil characteristics like moisture, texture, and compaction. While GPR has been widely used to detect coarse tree roots, measuring individual crop roots directly is difficult but GPR can detect root cohorts within a soil volume. GPR also shows potential for indirectly estimating root development by sensing related soil properties and coupling with soil water models. Further advances rely on integrating knowledge across disciplines to adapt GPR for agricultural management needs.
G-Range: An intermediate complexity model for simulating and forecasting ecos...ILRI
1. Improving ecosystem modeling in grazing lands will benefit forecasting of ecosystem service delivery such as livestock production and carbon storage.
2. Simpler validation approaches for ecosystem models, such as G-Range, can reduce data needs as well as uncertainty while still achieving reasonable agreement with field data.
3. The G-Range model, an intermediate complexity ecosystem model adapted for grazing lands, was successfully validated at two sites with contrasting climates, demonstrating its ability to address practical questions faced by researchers and land managers.
7 Journal of Life Sciences and Biomedicine (2710-4915 2710-3447).pdfPublisherNasir
Research article: Mapping of LC/LU changes inside the Agdam district of the Karabakh economics region applying object-based satellite image analysis
Author (s): A.A. Rasouli, M.M. Asgarova, S.H. Safarov pdf, doi.org/10.29228/jlsb.22
Landuse landcover and ndvi analysis for halia catchmentIAEME Publication
This document summarizes a study analyzing land use/land cover changes and normalized difference vegetation index (NDVI) for the Halia catchment area in India over several decades using remote sensing data. Medium to high resolution Landsat satellite imagery from 1975, 1989, and 2001 was processed to create land use/land cover maps and NDVI maps for the area. The objective was to examine changes in cropped area and land use/land cover patterns over time and understand the implications for the local environment.
43 % of Earth’s terrestrial vegetated surface is degraded with limited capacity to supply benefits to humans.
Degraded landscapes often result in lower Soil Organic Carbon and overall poor soil health.
Understanding drivers of Land Degradation and processes of Soil Organic Carbon loss are key for informing effective interventions .
MODELAÇÃO DO SOLO-PAISAGEM – A IMPORTÂNCIA DA LOCALIZAÇÃORicardo Brasil
This document describes a study that uses artificial neural networks (ANNs) to model soil landscapes and predict soil types in unmapped areas of two catchments in northern Portugal. The study evaluates how the performance of ANN models is impacted by (1) including latitude and longitude data in the input variables, (2) using a stratified sampling strategy that considers both the range of landscape variables and proximity of training sites, and (3) comparing results between the two catchments and at different spatial resolutions. Two ANN architectures - Multi-Layer Perceptron and Self-Organizing Map - were trained on landscape and soil data from the catchments. The study aims to better understand how spatial autocorrelation and location data influence ANN soil
Crop Identification Using Unsuperviesd ISODATA and K-Means from Multispectral...IJERA Editor
This document summarizes an article that examines unsupervised classification methods for identifying crops from multispectral remote sensing imagery. It discusses ISODATA and K-means clustering algorithms for classifying imagery without training data. The author applies these methods to an image using ENVI software and compares the results. ISODATA identified five classes - water body, crops, hilly area, and barren land. K-means identified the same classes but with different class distributions between methods. The author concludes unsupervised classification can successfully identify crop types from remote sensing images without prior knowledge of land cover types.
Prediction of Soil Total Nitrogen Content Using Spectraradiometer and GIS in ...Agriculture Journal IJOEAR
This document summarizes a study that used visible near-infrared (VNIR) reflectance spectroscopy and geographic information systems-kriging (GIS-Kriging) to predict total nitrogen content in soil samples from two locations in southern Iraq. Soil samples were collected and their total nitrogen content was measured using chemical analysis. The samples were then scanned using a spectroradiometer to obtain VNIR reflectance spectra. Partial least squares regression models showed strong correlations between reflectance in wavelength bands 500-600 nm and 800-1000 nm and total nitrogen content, with R2 values over 0.84 for calibration and validation sets. GIS-Kriging models performed poorly for prediction with Q2 values around 0.
From global to regional scale: Remote sensing-based concepts and methods for ...Repository Ipb
The document discusses land cover mapping and change detection in tropical regions using remote sensing. It provides an overview of existing land cover classification systems and global and regional satellite-based mapping products. The paper compares different land cover maps for a test region in Central Sulawesi, Indonesia, finding inconsistencies between products and uncertainty in estimates of land cover classes and changes between years. Future work on operational land cover mapping in the tropics needs further harmonization of definitions and products, regional validation, and a standardized multi-level classification workflow.
Land Use and Land Cover change monitoring of Surajpur Wetland, Uttar Pradesh:...Arnab Saha
Abstract:
Wetlands are extremely important areas throughout the world for wildlife protection, recreation, sediment control and flood prevention. Wetlands are important bird’s habitats and birds use them for feeding, roosting, nesting and rearing their young. In Surajpur Wetland are mainly used for agriculture, fisheries, reclamation for harboring and irrigation purposes. In this paper an attempt is made to study the changes in land use and land cover in Surajpur wetland area over 11 years’ period (2003-2014). LULC is an important component in understanding the interactions of the human activities with the environment and thus it is necessary to be able to simulate changes. The land cover mapping of study area was attempted using remotely sensed images of Landsat and Google Earth imagery. The study area was classified into five categories on the basis of field study, geographical conditions, and remote sensing data. LULC changes have been detected by image processing method in EDRAS imagine 2014 and ArcGIS 10.3. The eleven years’ time period of 2003-2014 shows the major type of land use change. Vegetation area that occupied about around 60 per cent of the Surajpur wetland area in 2003 has decreased to 34.25 percent in 2014. Wetland is increased 8.17 percent and Urban area, Fallow land and Water body also have experienced change. Finally, through the work it is recommended that the wetlands need detail mapping through the use of advance remote sensing techniques like microwave and LIDAR for restoration and management of wetland.
Keywords: LULC, ArcGIS, Surajpur, ERDAS, Remote Sensing
Consider the vision for a successful Southwest Transit marketing tea.docxclarebernice
Consider the vision for a successful Southwest Transit marketing team composed in Topic 4. Narrow down the team selection to four individuals for presentation to the director. Decide which strategies will be most effective for leading the agreed-upon team. Compose a PowerPoint presentation (10-12 slides), then record your 5-7-minute presentation using YouTube Video, Loom, or Zoom. On the title slide of your PowerPoint presentation, provide the link to your YouTube, Loom, or Zoom video recording that you created. Your presentation should address the following:
Who are the four team members, and what was the primary reason each person was selected? How difficult was it to come to a decision regarding team selection? Which potential team member was most difficult to come to a consensus about? Why?
What are the primary strengths of the team? What are its potential weaknesses? How positive is the management team about the team's potential? Justify your answers with evidence from " Southwest Transit Team Member Profiles."
What strategies will be most effective for motivating the team, managing conflict, and ensuring success and fostering collaboration? Cite specific motivational theories, conflict-resolution strategies, and leadership strategies in your answer.
Justify how the selected team embodies the values of Conscious Capitalism how the tenet of stakeholder orientation played a role in the team selection process. Provide citations to strengthen your claims.
Describe how value is created for each stakeholder, and in what ways will the team positively impact the business as a whole?
You are required to use at least three academic references to strengthen and support your claims and recommendations. Ensure each content slide has supporting citations and specific examples.
.
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A. LATENESS: At the second meeting, Peg came in a few minutes late. That was bad enough, but now she’s coming 10–15 minutes late to every meeting. What’s worse, Angelica and Robert have started arriving late, too. It makes the rest of us feel like giving up.
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C. MONOPOLIZING: Rajiv is very opinionated. He keeps talking, and he rambles on and on. It feels like we can’t get a word in edgewise.
D. SILENT DISAGREEMENT: Adelle sits around rolling her eyes about almost everything we say. We can tell from the look on her face that she doesn’t like our ideas. It makes us feel like she doesn’t like us, either. She’s very pretty, and I think maybe she feels superior.
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1) It should be a minimum of 350 Words not including references
2) APA Format and References needed
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Consider the two following statements Photosynthesis and cellular .docxclarebernice
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Which statement is correct? Or are they both correct? Explain.
Autotrophs are considered to be the foundation of all ecosystems. Do you agree with that statement? Explain why.
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A toxic waste dump company wants to build a facility within a small, low-income community. The city council has to approve the placement for all new companies. The approval of the addition of this particular company will bring very little economic benefit to the community. None of the residents of the community attended the city council meeting when this company made its proposal, and the community residents were unaware of the proposal. Since no one in the community attended the city council meetings, the chief executive officer (CEO) of the toxic waste dump company says approval from the city council should be the only approval needed to move forward with building this facility.
Do you agree or disagree with the CEO? Consider the roles and responsibilities of federal, state, and local health departments in public health practice. What is one recommendation for what should have been done prior to the council meeting to ensure that the decision was fair and valid?
.
Consider the role of interest groups in the policy-making process, w.docxclarebernice
Consider the role of interest groups in the policy-making process, which often involves a wide range of stakeholders. In your opinion, what role should healthcare interest groups have in setting healthcare policy at the local and national levels?
How does the financing of interest groups impact healthcare policy in the United States? Do healthcare interest groups have too much, too little, or the appropriate amount of influence on healthcare policy?
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Consider the role of stakeholders in addressing a health problem a.docxclarebernice
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at all stagesof a health promotion initiative? (Your comments should delineate particular stages at which stakeholder participation is most critical.)
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Consider the quote by Adam Fuss in this module in which he describes.docxclarebernice
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Consider the obstacles that Phoenix Jackson had to overcome on h.docxclarebernice
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Each group needs to find 2 or more examples of each of the following conflicts that Phoenix Jackson faced.
a. Person vs Self
b. Person vs Person
c. Person vs Environment
d. Person vs Society
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Consider the nurse leader’s role in achieving the IHI Quadruple Aim for this transition of care. (Hint: Draw from resources on systems thinking and nurse leaders’ ability to influence innovation and change.)
Assignment (5–6 pages, not including title and reference page):
Write a paper in which you address the following:
Identity your selected example of a transition of care.
Describe the key stakeholders that might be involved in this transition of care and the leadership strategies you would use to engage and influence them.
Explain how you, as a nurse leader along with your healthcare team, would apply systems thinking when providing a transition of care aligned with the IHI Quadruple Aim framework in order to improve it. Explain the fourth aim and strategy you would use and why.
Explain how systems thinking would inform your improvement plan for the specific transition of care you selected.
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Consider the music business as a supply network. How has music d.docxclarebernice
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Research should include:
1) music industry structure before downloads - Create a diagram to show the supply network
2) current music industry structure - Create a diagram to show the supply network
3) Remember to include terms such as: dintermediation, co-opetition, business ecosystem, dyads and triads, vertical integration, outsourcing etc
4) Create a power point presentation:
zero plagarism and proper APA formatting..
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Please ensure to cite the Author, YYYY with any content brought into the discussion. All discussions should contain at least one reference (and matching in-text citation in APA format).
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Find a poorly designed PowerPoint slideshow and share it here. Give a short summary that identifies the problems and make suggestions on what would improve the design.
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Consider the imagery you created in your mind as you interacted with.docxclarebernice
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The Open Window
. Describe this imagery and discuss whether it helped you understand the story. Did the imagery or imagined tone change when you listened to the audio? How? Which medium did you enjoy the most?
Now, do the same exercise with Sojourner Truth's speech in text and as interpreted by Cicely Tyson in video form. Which medium did you prefer and why?
Comparing the forms you preferred in each case, what might that tell you about how you learn?
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Consider the followingContrast Soviet and post-Soviet migration.docxclarebernice
Consider the following:
Contrast Soviet and post-Soviet migration patterns within the Russian domain. Throughout the Soviet era Russian leaders relocated people to new locations, both in an attempt to extend Russian political and economic power and as a means of punishment—as prisoners were dispatched to a series of security sites: The Gulag Archipelago. Russian leaders viewed many of these relocations as part of a broader ‘Russification’ policy.
As the result of the circumstances above:
In the post-Soviet era, there has been a reversal of migration, and ethnic Russians are leaving the newly independent states (former republics) to return to Russia.
The assignment
:
Following the demise of the Soviet Union, what will be the likely outcome of ethnic Russians leaving the newly independent states to return to Russia?
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This presentation was provided by Steph Pollock of The American Psychological Association’s Journals Program, and Damita Snow, of The American Society of Civil Engineers (ASCE), for the initial session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session One: 'Setting Expectations: a DEIA Primer,' was held June 6, 2024.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Fix the Import Error in the Odoo 17Celine George
An import error occurs when a program fails to import a module or library, disrupting its execution. In languages like Python, this issue arises when the specified module cannot be found or accessed, hindering the program's functionality. Resolving import errors is crucial for maintaining smooth software operation and uninterrupted development processes.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Class ProjectMapping of Cr.docx
1. Class Project
Mapping of Crop Residues Using Hyperspectral Data:
· Techniques and indexes for quantification,
· Data sources and
· Unsupervised classification for tillage systems
2. Table of contents / INDEX
Topic
Page
1.
Problem / application
3
2.
Working hypothesis
3
3.
Project outcomes
4
4.
Literature review
4
5.
Data sources
8
6.
Methods
10
7.
Results
16
3. 8
Issues and learning
20
9
Conclusions and future works
21
10.
Annex 1. Corrected bands and columns
22
11
Annex 2. Copy of in-running matlab code for de-striping
23
12
References
25
2
4. 1. PROBLEM / APPLICATION
Agriculture is a widespread, basic activity around the world,
which main purpose is to harvest food, fiber or/and energy.
After every growing season residues are left in fields. It is
important to quantify the amount and cover of agricultural
residues for enhancing the understanding in global
biogeochemical cycles, and for applications such as their role
for preventing soil erosion and their contribution in carbon
sequestration. However, it is not completely understood yet how
to estimate crop residues cover, their discrimination under
tillage or no tillage cropping systems, and its seasonal
variability as well as their temporal changes. This class project
proposes to explore the estimation and mapping of crop residues
by remote sensing techniques using hyperspectral image data.
2. WORKING HYPOTHESIS
Crop residues cover and amount can be accurately estimated by
remote sensing techniques. A wide range of crop species and
their residues can be studied in the near future and they might
be even differentiated by spectral classification. Future work
might include description of temporal patterns upon analyzing
hyperspectral data (EO-1 Hyperion) in complement with
multispectral data (Landsat 7 ETM+ and EO-1 ALI).
5. 3
3. PROJECT OUTCOMES
This class project will generate an estimation of crop residues
cover in agricultural fields in Central Indiana in Tipton County.
In addition, the amount of crop residues will be approximately
calculated based upon yield/residues ratio assumptions. Also,
unsupervised classifications for different tillage management
(two classes: tilled areas and no-tilled areas) in agricultural
fields in Tipton County. Finally, by this study we expect to
integrate/use three different data sources (Landsat 7 ETM+, EO-
1 ALI and EO-1 Hyperion) and to calculate Cellulose
Absorption Index on hyperspectral data.
4. LITERATURE REVIEW
Crop residues are any portion of crop plants that is left in the
field after harvest. Crop residues cover is a relevant topic to be
6. studied because of three main reasons: they are widespread in
the landscape of agriculture in the Midwest, they represent one
of the most important organic inputs for soil carbon
sequestration estimating input, and also they relate to soil
conservation and reduction of soil erosion (Lal, 2002 & 2004).
Remote sensing techniques are also a potential for these
methods for monitoring compliance with conservation measures
or perhaps in the future carbon credits.
Agricultural management practices such as tillage and crop
rotation (West, 2002) are driving factors for crop residues
accumulation in field. In any case, authors such as Nagler et al
(2003) have pointed
4
out that remote sensing methods would be useful for providing
wider area coverage in a regional landscape studies as well as
estimation of spatial structure/variability of residues cover in
agricultural settings. Spectral variability is another important
outcome from remote sensing methods, which in general is
overlooked in manual methods for crop residues determination.
One of the first studies assessing crop residues estimation by a
remote sensing method was by Biard and Baret (1997). They
appealed to multiband reflectance under an algorithm called
7. Crop Residue Index Multiband (CRIM) which is based on any
set of wave bands and consists on a linear mixing model of soil-
residue complex and on soil and residue lines. However the
same authors found dependency of the near infrared and middle
infrared domains according to the aging state of the residues,
which may be due to decomposition of key macromolecular
compounds such as lignin and cellulose.
At about the same time as Biard and Baret (1997), Su et al
(1997) also proposed another way to assess crop residues by
SAIL model. It consisted in Scattering by Arbitrarily Inclined
Leaves, which simply simulated the residue reflectance in wheat
in near infrared band, and then it was possible to study the
agreement between field measured reflectance and the simulated
reflectance. They concluded that SAIL model was a promising
technique for crop residues determination at that point in time.
Daughtry et al (1997) studies recorded potential problems that
must be addressed to implement the fluorescence technique in
the field,
5
which are: adequate excitation energy must be supplied to
induce fluorescence, and the fluorescence signal is small
relative to normal, ambient sunlight. The technique must be
8. developed to either shield the system from sunlight or extract
the fluorescence.
Nagler et al (2003) studied the use of Cellulose Absorption
Index (CAI) as a way to quantify the plant litter cover after
harvest even at low cover percentage (around 10 %). Their
research conducted analysis from 0 % cover (bare soil) up to
100 % plant litter cover and using reflectance spectra (0.4 to 2.5
μm) for four crops: corn, soybean, rice and wheat; plus two tree
species.
Following Nagler et al (2003) work, Daughtry et al (2004)
established that the spectra of dry crop residues displayed a
broad band absorption feature near 2100 nm due to absorption
by cellulose/lignin compounds. Therefore, these authors
proposed the combination of Cellulose Absorption Index (CAI)
and Normalized Difference Vegetation Index (NDVI) base upon
shortwave infrared reflectance in order to estimate crop residues
cover. Also, it seems to be important to make a difference
between dry residues and wet crop residues under this approach.
However, once again these authors concurred in the usefulness
of remote sensing methods in regional landscape surveys for
crop residues.
Continuing with their work on CAI, Daughtry et al (2006) used
multi/hyperspectral data (Landsat and Hyperion data) to
distinguish two classes in corn and soybean residues. Landsat
data did not yield good fit between field measurements and
remote sensing analysis. Conversely,
6
9. Hyperion yield linear relationship for CAI. In addition,
classification accuracy resulted in 80 % when working just in
two classes of tillage (conventional: reduced + intensive, and
conservational tillages). Consequently, it seems that an
advanced multispectral or hyperspectral data is needed when
assessing crop residues in a regional landscape scenario.
In this sense, Bannari et al (2006) also concurred with Daughtry
etal (2006) when pointing out that a hyperspectral data (e.g.
Probe-1) or anadvanced multispectral data (e.g. high spatial
resolution IKONOS data) is needed for precise evaluation of
surface crop residues cover because of better spectral band
characteristics, specially when looking at lignin/cellulose
absorption features. In other words, Probe-1 hyperspectral data
outperformed the IKONOS data because of the characteristics of
the spectral bands in the hyperspectral sensor.
Lately, South et al (2004) compiled and analyzed a very
interesting comparison of classification methods based upon
spectral reflectance signatures for mapping senescent crop
residues in Eastern Cornbelt soils. They concluded that out of
five different methods (including parametric an non parametric
ones) two spectral angle methods (spectral angle mapping and
cosine of the angle) were the ones with higher performance,
specifically the cosine of the angle concept algorithm had the
highest accuracy (97.2 %) and kappa value (0.959). In contrast,
minimum distance, Mahalanobis classifier and maximum
likelihood had user’s accuracy below 84 % and producer’s
accuracy around those values. Bannari et al (2006) also
established a validation for their supervised classification with
10. a divergence D of 0.86.
7
Utilizing aircraft and satellite image data (IKONOS
hyperspectral data), Martin (2002) developed both unsupervised
(using ISODATA algorithms) and supervised classifications
(using ECHO: extraction and classification of homogenous
objects and maximum likelihood algorithms) for different
tillage and crop rotation treatments in a location in the
Midwest. This author found that to compare tillage systems as
classes is the best method to estimate residue cover. Four
different treatments were investigated and those tillage systems
were indicative of the amount of residue present within a given
date but also that the total amount of residue differs during
seasons of the year due to decomposition.
5. DATA SOURCES
Location of the study is between Windfall City and Tipton City
in Tipton County IN, respectively. Coordinates are between
40º21’44” to 40º10’32” N and 85º57’25” to 86º1’11” W. This
narrow area was defined by the EO-1 Hyperion stripe (about 7.7
11. km wide).
Images data are Landsat 7 ETM+ (a single scene corresponding
to path 21 and row 32 radiometric and geometric corrected),
EO-1 Advanced Land Imager ALI (a single stripe radiometric
correcte), and EO-1 Hyperion (radiometric corrected). Landsat 7
and EO-1 correspond to the AM constellation as shown in
Figure 1. They were acquired in the same date (April 12th,
2003) under a zero cloud cover. All three image were subset to
extract about the same narrow area (sensor have different cover;
ALI does not cover all the Hyperion view), corresponding
8
to a specific geographic location in the center of this county in
order to study agricultural fields. Figure 2 shows the three
subsets. The data sets are already downloaded in the Oxisol
hard drive computer at LARS.
AB
12. Figure 1. AM Constellation including Landsat 7 and EO-1
spaceborne platforms (A) and swath with for three different
sensors: Landsat 7 ETM, ALI and Hyperion (B).
ABC
Figure 2. Three original image data subsets showing field areas
between two cities: Tipton City in the southwest and Windfall
City in the northeast. Landsat 7 ETM (A), EO-1 ALI (B) and
EO-1 Hyperion (C). Source: www.indianaview.org
13. 9
The images in Figure 2 where prepared using RGB
representation of Landsat 7 bands: 4 (NIR 0.76 – 0.90 μm), 3
(red 0.63 – 0.69 μm) and 2 (green 0.52 – 0.60 μm).
Consequently, in case of ALI data bands 5 and
6 were averaged, and for Hyperion data average were prepared
as B42-
45, B28 – B33 and B18 – B25, respectively.
In addition to image data, we accessed data from the Annual
Crop Residue Management Survey 2002
(www.conservationinformation.org). It contains field data about
crop residues and tillage systems. Therefore, it allowed us to do
a comparison between image analysis and field data.
6. METHODS FOR DATA PROCESSING
Pre-processing include two main steps: de-striping and
conversion/corrections.
14. Problems with calibrations of the Hyperion hyperspectral
sensors results in stripes in the band images (which translates
into “bad columns or bad detectors”). They may show up as
dark stripes or clear stripes in vertical position.
Since in order to calculate the CAI index, one may need 9 SWIR
bands: (183, 184, 185, 195, 196, 197, 204, 205, 206). After
opening the bands and performing Gaussian and Linear 2%
enhancement, the bands were examining and found that bands
183, 184, 185 needed stripe correction. Figure 3 A show one
example (B196) of a band with any problematic columns, so de-
striping was not run on those other six
10
bands. Problematic columns were visually identified in the first
three bands. Annex 1 shows a list of bands and columns that
were corrected.
De-striping was performed by iterated running a MATLAB®
code (cor_str written at U. Texas) on the data. Annex 2 shows a
copy of the in-running code as example. The code uses 32
adjacent pixels from the previous columns in order to fix the
15. striped columns and bring it to a normal distribution. Figure 3
shows how the de-striping code yield positive results when
removing the stripes from bands 184 and 185. However, the
striped B185 was still noisy and during the index calculation, it
was excluded.
ABCDE
Figure 3. Hyperion Hyperspectral Bands: 196 (A), 184 before
(B) and after (C), 185 before (D) and after (E).
In order to analysis the effect of de-striping in noise reduction,
we performed minimum noise fraction (MNF) transform.
Generally MNF is used to reduce the dimensionality of
hyperspectral data; however a forward transformation can
remove the noise from data and in that way determine which
bands contain the coherent images by looking at the
16. 11
actual images or to the eigenvalues. After identifying the noisy
bands an inverse MNF can be run on a spectral subset including
just the good bands. Figures 4 and 5 show the
eigenvalues/eigenvector/linear transformation before and after
the running de-striping code. It is evident that at least one of
the linear combinations has gotten better after the de-striping
pre-processing of the three bands (B183-185) as shown by a
decrease in the seventh eigenvalue. Here, the eigenvalues or
eigenvectors are the result of linear combinations. In fact
Figures 4 and 5 are also showing that one of the linear
combinations (the first) is containing the most statistic
information. In other words, one “new band” by linear
combination through MNF transform has represented a large
portion of the information containing originally in the nine
bands. It is common that hyperspectral bands are correlated
each other introducing redundancy and MNF is in fact a way to
deal with this difficulty by reducing dimensionality.
20
19. 8
9
Eigenvalue Number
Figure 4. Eigenvalues for nine different linear combinations.
Figure 5. Image representing three of the linear combinations
before and after the de-striping.
12
The second main step in pre-processing was the conversion and
correction needed for the index calculation. Most of the crop
residues
models and indexes are built on reflectance units. Therefore,
digital number (DN) needed to be converted into radiance and
20. then into
2
reflectance. The conversion to radiance [ W / (m sr µ) ] for
Hyperion data was achieved by dividing the VNIR 50 bands
(B8-57) by the scalar 40 and the SWIR bands (B77-224) by 80.
Then, the conversion from
radiance into reflectance in order to normalize for the incoming
radiation was done in FLAASH model.
ABCD
Figure 6.Hyperion RGB images for average bands (Red: B183-
184, Green: B195-197 and Blue: B204-206) in digital number
(A), radiance (B) (units= W·sr-1·m-2), reflectance from
FLAASH without any aerosol/water corrections (C) and
reflectance from FLAASH with both corrections aerosol and
21. water retrieval by absorption band 1.1 μm (D).
Fast Line-of-sight Atmospheric Analysis of Spectral
Hypercubes (FLAASH) is an atmospheric correction modeling
tool in ENVI 4.3 for retrieving spectral reflectance from
hyperspectral radiance images. FLAASH incorporates the
MODTRAN4 radiation transfer model to compensate for
atmospheric effects. FLAASH is capable of doing aerosol
correction as well as H2O vapor corrections.
We run both aerosol correction and water vapor correction. For
H2O vapor corrections, the model has the choice of three
different water absorption bands/features: 1.1, 0.9 and 0.8 μm. .
We evaluated the first and the third features in Hyperion data
resulting in better scenes and lower water retrieval in the
feature 1.1 μm. The water retrieval in water absorption bands
770 - 870 nm resulted in 0.227 cm, while in the band 1050 -
1210 nm water retrieval was 0.120 cm. Both results can be
compared to FLAASH output without application of water
retrieval detected: 1.302 cm H2O vapor in atmosphere of scene.
In other words, FLAASH detected a total of 1.302 cm of water
average in the atmosphere of the scene, and then the two
absorption bands were able to drastically reduce the water
vapor; however, the 1.1 μm features was more efficient to
remove water vapor, so we keep using it for our correspondent
analysis. The image was taken in a no cloudy day; however,
FLAASH made it slightly better; besides it was an interesting
exercise to do. The best way to correct from radiance to
reflectance would be to set spot on the ground with different
material (white paper, water, etc...) right in the moment when
the airborne or spaceborne sensor is doing the readings, and
22. then do the correction based on this data. It will also help to do
atmospheric correction.
14
7. DATA PROCESSING: INDEX CALCULATION
It is important to understand how CAI works. The Cellulose
Absorption Index is a result from absorption of the plant
macromolecules that are cellulose (≈ 40 %) and lignin (6 – 14
%). Those plant parts exhibit absorption at around 2100 nm (as
Figure 7 shows); therefore, most crop residues on soil surface
also show this absorption feature. This index needs as input 30
nm-width spectral bands in reflectance (R). Those three bands
are centered at 2015 nm (R2.0), 2106 nm (R2.1) and 2195 nm
(R2.2). Because of the spectral resolution These 30 nm bands
for CAI are averages over three Hyperion bands each one for
R2.0: B183, B184, B185*; R2.1: B195, B196, B197; and R2.2:
B204, B205, B206. Note*: in this project Hyperion band B185
was excluded because of severe striping and it was very noisy.
The average can be done in Band Math by given more weight to
the central bands as follow: Average Reflectance =
0.25*B1+0.50*B2+0.25*B3. Then CAI can be calculated with
23. λ (μm)2.02.12.2
bare soil
residue coverReflectance
Figure 7. Reflectance at bands in the Cellulose Absorption
Index.
The CAI uses the bands at 2000 nm and 2200 nm in order to
normalize the 2100 nm wavelength absorption feature. As
Figure 7
15
shows when the 2100 nm feature gets deeper, then it implies
more absorption (in other words less reflectance is happening),
more crop residues are present, and as a result one obtains
higher index.
24. Figure 8. Cellulose Absorption Index image for central Tipton
County.
Image data results from the CAI (as a single band) for the
selected agricultural areas (central Tipton County) shows
greater abundance of crop residues as bright areas, and bare soil
as dark areas as can be seen in Figure 8. There are also mixing
pixel with intermediate colors. This can later on be classified
25. into conservative and conventional tillage and crop residues
management practices of the agricultural fields. The index
image also shows the two cities (Tipton and Windfall) and some
winter crops (C3 grasses and/or winter wheat) as bright areas
16
because of the presence of trees and growing vegetation in both
of them; therefore cellulose absorption would be present.
After running the CAI in the selected Hyperion data subset for
central Tipton County (without band 185), the next step was to
register the image to the panchromatic Landsat subset (15 m
spatial resolution) as shown in Figure 8. This was accomplished
by 20 ground control points and a RMS error of 0.511m.
7. RESULTS
Unsupervised classification by ISODATA algorithm was
performed on CAI image (previous Figure 8), and then crop
residues classes were assigned as crop management classes.
Three classification classes were assigned: one with less than
26. 30% crop residues, conservation management, one with more
than 30% crop residues, conventional management, and a class
with other areas which corresponded to urban areas of the two
cities, small houses farms scattered in the image, infrastructure,
roadways and growing crops. The result of ISODATA is
presented in Figure 9.
Figure 9. ISODATA classification on CAI image for central
Tipton County with Tillage systems classes
27. 17
Then the areas corresponding to the two tillage classes were
extracted from ISODATA statistic output and finally normalized
to 1. By comparing the classification results, table 1, with data
from the Crop Residues Management Survey
(www.conservationinformation.org) could a statistical
comparing be performed by a Z- test, z ≈ (df: 1) = 7.56, P-value
< 0.05. This showed that it were significant so both sources
behave differently. However, in numerical terms, the data
behave consistently similar for the tillage systems. The
difference is just 3 %. It is very likely that the test statistic is
not powerful enough to detect differences (z-test is a
conservative statistic test), specially when having such limited
amount of degree of freedom. In this case, we different just two
classes conservative and conventional tillage; however, more
classes can be differentiated, and then more detail and more
powerful test might identify conclusive similarity.
Table 1. Classification results vs. Crop Management Survey
field data.
Group /
CRM
29. In addition to the previous analysis it was explored supervised
classification by minimum distance on CAI image and maximum
likelihood in ALI image. It is relevant to clarify that Envy 4.2
software requests two bands to perform a parametric supervised
classification by
maximum likelihood as well as mahalanobis needs two or more
bands, while the calculated index corresponds to a single band.
Minimum distance can be performed in a single band; however,
results were consistently poor as can be seen in Figure 10. Even
after trying several times using different training sets. Minimum
distance had an overall accuracy of = 90.8 % (1241/1366) and
Kappa Coefficient = 0.82, which are good values; however, it
totally confused the urban areas and the growing crops with no
tillage areas yielding a bias result of 69 % of surface no tillage
systems and 31 % under tillage systems. These results are
totally different than field data collected during the Crop
Residues Management Survey. Minimum distance classifier is
way to simple to do a good job in this case, so we disqualified
this classification result.
31. 19
Because of the impediment of the Envy 4.2 software (requesting
two or more bands to perform maximum likelihood or
mahalanobis), we decided to use ALI image with spectral bands
5 & 6 (average), 4 and 3 as RGB (as shown in Figure 2) to
perform classification by maximum likelihood with training sets
for three classes and 1100 to 1200 pixels per class. Results were
better than in minimum distance because cities were
discriminated as well as growing crop. Maximum likelihood had
an overall accuracy of = 98.8 % (3440/3480) and Kappa
Coefficient = 0.98, which are good values; however, it
underestimate the field under conventional tillage with 43.3 %,
while the no tillage or conservational tillage system was 57 %.
These values are closer than minimum distance on CAI image;
however, not closed to the Crop Residues Management Survey.
One has to remember that this classification was done not on the
CAI image but in ALI RGB image. Even so, some agricultural
fields were clearly identified by this classification.
32. Tilled
No-tilled
Other areas
Figure 10.Maximum likelihood classification on ALI RGB
image for central Tipton County with Tillage systems classes
20
8. ISSUES AND LEARNING
During this project have we had several issues and difficulties
but managed and learn a lot from each. The first difficulties
33. were to destripe the Hyperion data to get rid of as many bad
columns/stripes and noise as possible. Also the conversion from
Digital Numbers, DN, to radiance and reflectance by the
program FLAASH were not easy managed.
We have also found some limitations for the index, which are:
living plants have also cellulose/lignin that will affect the
results. Also the soil moisture content will change over time
also that different kind of soils reacts in different ways by soil
moisture content.
During this project have we used a number of software and that
include matlab for the destriping code, Minimum Noise Fraction
(MNF), FLAASH, band math, ISODATA algorithm and the
Cellulose Absorption Index (CAI) and also ENVI as the
program we have been working in.
9. CONCLUSIONS AND FUTURE WORKS
The results from this study can we draw the conclusion that the
Cellulose Absorption Index is a very powerful tool for mapping
crop residues with hyperspectral data. The results we performed
could not statistically agree with field data at County level;
however, in numerical they behaved very similar with a
difference of just 3 %.
34. 21
We would like to see future work in some
comparisons/interpretations between hyperspectral data and
multispectral data. Also to compare multispectral data indexes
(Landsat and ALI) with hyperspectral indexes. Some indexes
that could be interesting to investigate in can be Normalized
Differential Index (NDVI), and Normalized Differential
Senecent Index (NDSVI). One step can also be to perform a
detailed supervised classification with more classes, and
compare different algorithms for example Spectral Angle
Mapper (SAM), Cosines angle, Maximum Liklehood, Minimum
distance and Mahalanobis algorithms.
A project with multitemporal studies looking at fresh surface
residues and how the decomposition rate changes with time
would also be very interesting.
37. 23
ANNEX 2. COPY OF IN-RUNNING MATLAB CODE FOR DE-
STRIPPING
>> cor_str
File to De-stripe is:
OriginalB9s
Info file is:
B9.info.txt
Methods to correct streaks
38. [1] Additive
Please wait: creating destriped file.
if you left any band, you can run this code again.
It will not affect those bands that have been corrected
Bands that need to be corrected: put them into like [ 1 2 ], (0 for
all) [3]
Enter your sliding window size, the default window size is 32
pixels
32
Band 3:
Steaked columns: put them into like [ 1 2 ], (0 for all)[12 57
162]
Band 3: 12 57 162
processing column 12
processing column 57
processing column 162
>>
39. 24
8. REFERENCES
Martin, A. 2002. Detection of Crop Residues with four tillage
systems using Remote Sensing Techniques. MS Thesis, Purdue
University. 115 p
Bannari A, A. Pacheco, K. Staenz, H. McNairn, K. Omari.
Estimating and mapping crop residues cover on agricultural
lands using hyperspectral and IKONOS data. Remote Sensing of
Environment 104 (2006) 447–459
Biard F, Baret F. Crop Residue Estimation Multiband
Reflectance Using Multiband Reflectance. Remote Sens.
Environ. 59:530-536 (1997)
Daughtry, C; J. E. McMurtrey III, M. S. Kim, E. W. Chappelle.
Estimating Crop Residue Fluorescence Imaging Cover by Blue.
40. Remote Sens. Environ. 60:14-21 (1997)
Daughtry, C., E.R. Hunt Jr., J.E. McMurtrey III. Assessing crop
residue cover using shortwave infrared reflectance. Remote
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Daughtry C., P.C. Doraiswamy a, E.R. Hunt Jr.a, A.J. Stern a,
J.E. McMurtrey IIIa, J.H. Prueger. Remote sensing of crop
residue cover and soil tillage intensity. Soil & Tillage Research
91 (2006) 101–108
Lal, R. 2002. Soil carbon dynamics in cropland and rangeland
Environmental Pollution 116:353–362
Lal, R. 2004. Soil carbon sequestration impacts on global
climate change and food security. Science 304:1623-1627.
McNairna, H.; C. Duguayb, B. Briscoc, T.J. Pultz. The effect of
soil and crop residue characteristics on polarimetric radar
response. Remote Sensing of Environment 80 (2002) 308– 320
Nagler, P. L., Y. Inoue,1, E.P. Glenn, A.L. Russ,2, C.S.T.
Daughtry, Cellulose absorption index (CAI) to quantify mixed
soil–plant litter scenes Remote Sensing of Environment 87
(2003) 310–325
25
41. South, S, Jiaguo Qi, David P. Lusch. Optimal classification
methods for mapping agricultural tillage practices. Remote
Sensing of Environment 91 (2004) 90–97
Su, H.; M. D. Ransom, E. T. Kanemasu. Simulating wheat crop
residue reflectance with the SAIL model. Int. J. Remote
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Thomas J. Jackson Peggy E. O'Neill Remote Sensing of
Environment. Microwave emission and crop residues. Volume
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66:1930– 1946.
44. 27
Using Landsat 8 Images for Estimating Crop Harvest Progress
Introduction
45. Gathering agronomic information from remotely sensed data is
not a new concept. For
decades, efforts to mine information about crop conditions have
been increasing as advancing
technology enables higher quality data to be collected. Badhwar
(1984) developed an automatic
unsupervised classification method to estimate the proportions
of agricultural land in three crop
categories: corn, soybean, and other. The method takes
advantage of the known signatures for
corn and soybeans; specifically the higher ⍴ max of soybeans.
The data were refined to determine
the thresholds for each class. A linear classifier, Ho-Kashyap
algorithm, then used the criterion
function to separate each pixel into one of the classes. This
methodology was developed and
tested using Landsat 5 data over the United States over three
years. It was determined to provide
unbiased estimates of the crop proportions. The method
performed best when the corn proportion
was between 25-50% and struggled with pixels on the borders of
fields. Conese and Maselli
(1990) utilized multitemporal information to improve the
classification of three different Landsat
46. images. 150 ground reference points were used to determine ten
classes to be used when
classifying the entire image. A maximum likelihood classifier
was used on each individual image
and also on the multitemporal set. The error probabilities were
then estimated using the error
matrix. This information was used for the modified maximum
likelihood classifier and the
process was repeated. Using this approach increased the Kappa
coefficient by 0.09 when
compared to the multitemporal classification. Bruzzone et al
(1997) used a supervised
nonparametric approach to identify land-cover changes over
time. This technique made it
possible to detect changes without classifying each individual
scene. This method also provided
better accuracy than traditional post classification methods.
Training pixels were developed
using ground control points where the ground truth was known
at both time points. A Bayesian
approach was used to determine the probabilities of a land-
cover transition. The accuracy was
47. assessed using Kappa coefficients and it was determined this
approach was more accurate than a
post classification approach, 0.86 and 0.67 respectively.
Each season, the USDA issues weekly crop progress reports.
These reports give
important details of the progression of the crops growing in the
field. National Agricultural
Statistics Service (NASS) sends questionnaires to 4,000 USDA
extension and FSA agents each
week. The data from these questionnaires are compiled to create
county, state, and national
reports. One of these reports is the harvest progress report. This
report gives the percent of the
crop that has been harvested in each state. These reports are
widely used and can affect the
commodity market prices. In 2013, these reports were
interrupted by the government shutdown
in October. Crop progress reports were not issued for the weeks
of October 7 and October 15.
Some information from those missed reports have been
interpolated; however, the use of satellite
images from those time points could rectify the data from that
time period.
48. Materials and Methods
Data from Landsat 8 acquired over Southern Indiana during the
months of September,
October, and November 2013 were selected for analysis. Data
were downloaded from USGS
Earth Explorer. The temporal spacing of these images is
important when estimating regional
harvest progress. Because of this, 30 day intervals were
selected. The acquisition dates of the
data selected were September 6, October 8, and November 9,
2013 (Figure 1). The scenes for
each month were chosen based on the quality of the data.
Images with minimal cloud cover were
selected. Since cloud cover is common in the Midwest during
the fall months, the amount of data
available was quickly limited.
A spatial subset of the images was taken to reduce the scenes to
a smaller area of interest.
Decatur County, Indiana was chosen because of its prolific crop
production. There is a large
amount of cropland located in Decatur County in relation to
urban areas. The spatial subset was
49. achieved by setting the extent to that of our National
Agricultural Statistics Service Cropland
Data Layer (downloaded for Decatur County) at the time of
performing radiometric calibration.
By subsetting the data spatially, computation time was
significantly reduced through all steps of
preprocessing the image data.
ENVI v5.1 was used to calibrate the three scenes. The data was
converted from digital
numbers to reflectance values using the radiometric calibration
tool available in ENVI. This tool
uses the multispectral metadata file to determine the correct
values to use when converting from
radiance to reflectance. Figure 2 shows screen captures of the
settings used for these radiometric
calibrations.
Figure 1. Landsat 8 data acquired for the months of September
(left), October (middle), and November
(right).
Atmospheric calibration was also completed using ENVI v5.1.
This calibration was
50. achieved using the dark pixel subtraction method. This method
uses the assumption that the
darkest pixels in the image should have a reflectance value of
zero and any difference is assumed
to be the effect of atmospheric scattering. The darkest pixels are
then adjusted to zero. The rest
of the image is then adjusted by the same factor. Figure 3 shows
the settings used in the ENVI
Dark Subtraction tool to achieve this correction.
Figure 3: Screen captures of settings used for radiometric
calibrations
Figure 2: Screenshot of settings used for darkest pixel
subtraction and results for
September image.
After these calibrations were complete, the three scenes were
co-registered. The
registration was achieved using ERDAS Imagine 2014. Ground
control points (GCPs) were
selected near the perimeter of the image and were uniformly
distributed. Five GCPs were used in
each image. Objects chosen as GCPs included building corners,
grain bins, and road
51. intersections. The scene acquired in November was used as the
reference image and the other
scenes were registered to that image. Figure 4 shows the GCPs
used for registration; the total
RMS was 0.28.
At this point, the data was prepared for classification.
Supervised classification was
preferred over unsupervised classification for the added control
in making adjustments and fine-
tuning the classification. The NASS Cropland Data Layer
(CDL) was used to identify known
soybean and corn fields. The CDL is a raster product made by
the USDA NASS which shows
Figure 4: Image registration. The September image (right) was
registered to the
November image (left). Also shown are the ground control
points and the reported
RMS error.
the type of crops grown across the lower 48 states at a 30-meter
resolution. This dataset was
used to look up whether a given field was soybeans or corn
52. when training our classifier.
Looking at the three images, it was apparent that the changes
that occur over the course
of the harvest season are substantial. The majority of the image
in September indicates healthy,
live crops that are green. In the October image, much of the
crop has senesced and is darker
brown in color. Finally, in November, most of the crop appears
a gray or tan color which
indicates that it has been harvested. Because of these temporal
effects, classification could not
be performed by using common spectral signatures across all
three images; it was necessary to
treat each image as a separate classification problem.
Beginning in September with no identifiable fields where the
crop had been harvested, 25
area of interest (AOI) polygons were drawn for soybeans field
and another 25 for corn fields. An
additional 25 area of interest polygons were drawn for urban
and forest classes to account for
non-cropland areas that should be excluded from harvest
progress calculations. Although it was
not important to distinguish between forest and urban areas for
the purpose of estimating harvest
53. progress, their spectral signatures are dissimilar; taking the
mean of the two classes by merging
them would result in a poor classification. The AOI polygons
were used to add spectral
signatures of each class through the Spectral Signature Editor.
Then, for each of the four classes
(soybean, corn, forest, urban), all of the signatures of that given
class were merged together to
generate a single spectral signature class used in the
classification. Classification was performed
with the ERDAS Supervised Classification tool using the
maximum likelihood method and
defaults for the other settings.
For the next image in October, the 75 total area-of-interest
polygons were reused from
the September image where the possible classes were now
standing corn, standing soybeans,
harvested corn, harvested soybeans, forest, and urban. While
the NASS CDL was used to
distinguish corn and soybean fields from one another, no data
was collected to ground truth
fields that had been harvested. However, it is relatively
54. straightforward to determine those fields
that had been harvested from the imagery by eye; harvested
fields have a light tan or gray
appearance while senesced, standing crops are a dark brown
color.
However, when training the classifier for the AOIs in the
November image, it was
observed that some fields had changed from a tan harvested
color in October back to a dark
brown color. This is likely attributed to tillage that often takes
place in the fall where the post-
harvest residue is worked down into the soil, exposing the moist
soil below which will produce a
dark brown appearance. Additionally, other fields appeared to
have new, green vegetation
growth; this is likely cover crops or weeds that had grown after
harvest took place and further
complicate classification.
Initially, it was observed that the classification struggled with
marginal fields that were
adjacent to forested areas along rivers and streams. Because the
25 AOI polygons used for a
given crop were merged into a single class where the mean
reflectance value is taken, these
55. marginal fields were far enough from the mean that they were
more closely related to the forest
class. To resolve this, additional classes were created to
capture these marginal soybean and
corn fields. Five AOIs were used for each of these two classes.
Results and Discussion
The classification results for each image were compared to
determine how well the
classifier distinguished corn from soybeans, and then how well
it distinguished standing from
harvested crops. Figure 5 shows the classification of corn,
soybeans, forest, and urban areas.
Overall, the classification appears to be accurate. In September,
fewer pixels were classified as
corn compared to the other months. In November, pixels
previously classified as soybeans in
September and October were being misclassified as corn. Table
1 shows the number of pixels
assigned to each crop class for each month. These values were
converted to acres based on the
30-meter pixel resolution. These estimates were then compared
56. to the NASS county estimates for
acres planted of each crop in Decatur County in 2013. The
calculated acres were expected to be
higher than the reported acres because the area classified was
approximately 20% larger than
Decatur County. According to these results, October was the
most accurate classification result
for corn, with acres being underestimated in September and
overestimated in November. The
results for soybeans were accurate and consistent for September
and October, but were
underestimated in November.
Figure 5: Classification results. September 6, 2013 (left),
October 8, 2013 (middle), and November 9,
2013 (right). Pixels classified as corn are green, pixels
classified as soybeans are red, and pixels
classified as urban or forest are black.
When looking at the classification of harvested and standing
crops for each month, the
expected trend is apparent. Figure 6 shows the classification
57. results of harvest progress of corn
over the three months while figure 7 shows the results for
soybeans. Table 2 compares the results
of the classification to the USDA reports available for 2013. For
October, when reports were not
generated due to the government shutdown, 2011 estimates were
used for comparison. The
results for September and October are very similar to the
progress reported by the USDA. The
results for November are very similar for soybeans, but appear
to be overestimating the corn
harvest progress.
Figure 6: Classification results for standing corn (green) and
harvested corn (yellow) for September,
October, and November (left to right).
Table 1: Comparison of classification results to the 2013 USDA
planting report.
* Area classified is larger than area reported
**2013 Decatur County NASS county
estimates
58. The results from September showed pixels that should have
been classified as cropland
were being misclassified as urban and forest. This resulted
because of the marginal acres near
forests and rivers, where end of season senescence was not
uniform across the entire field. To
solve this issue, more classes were added to capture the spectral
signatures of these areas. Figure
8 shows the improved classification of an area identified as
difficult to classify when these
additional classes were added.
Figure 7: Classification results for standing soybeans (red) and
harvested soybeans (yellow) for
September, October, and November (left to right).
Table 2: Comparison of monthly classification results to USDA
harvest reports.
In November, the classification results were affected by the
number of different
management practices occurring in the field. Tillage operations
and cover crops caused some
fields to be misclassified. Some pixels that had previously been
classified as soybeans were
59. classified as corn. Also some fields that had been classified as
harvested in October were
classified as standing in November. Figure 9 shows an area
identified as difficult to classify in
November where several soybean fields were misclassified in
November.
Figure 8: Comparison of classification results for September
(original image, right) when single classes were
used for each crop (middle) and when additional classes were
added (right). Corn pixels are green, soybeans
are red, and urban/forest pixels are shown in black.
Figure 9: Comparison of classification results for October (left)
and November (right). Standing
soybeans are maroon, harvested soybeans are orange, standing
corn is dark green, and harvested
corn is light green, and urban/forested areas are black.
Conclusions
Of the three months classified, October was determined to be
the most accurate.
60. Classification becomes difficult when crop senescence varies
within fields and also when late
season management practices are occurring. In order to further
improve classification for the
purpose of tracking harvest progress, a more robust
methodology is proposed. Identify the time
point when the separability of corn and soybeans is highest and
use an image acquired near this
time point to create a base classification. Use the results from
this classification to create a mask
for each crop of interest. This mask should then be used to
isolate the pixels determined to be
corn and soybeans for subsequent classifications. This method
will provide an accurate and
stable estimate of acres and will alleviate the error associated
with management practices late in
the season.
A decision tree system, similar to how crops are classified in
the NASS CDL, would also
aid in the multi-temporal, multi-image aspects of this
classification problem. For example, a
decision tree might limit the possible classifications to
“harvested,” “cover crop,” or “tilled, bare
61. earth” following a classification of “senesced.” Similarly, the
decision tree might prohibit a
classification of “standing” or “senesced” following a
classification as “harvested.” These
techniques would prevent some of these types of problems
experienced in this study.
References
Badhwar,G.D., Automatic corn-soybean classification using
landsat MSS data. I.
Near-harvest crop proportion estimation, Remote Sensing of
Environment, Volume 14,
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http://dx.doi.org/10.1016/0034-
4257(84)90004-X.
62. Bruzzone, L.; Serpico, S.B., "An iterative technique for the
detection of land-cover
transitions in multitemporal remote-sensing images,"
Geoscience and Remote Sensing,
IEEE Transactions on , vol.35, no.4, pp.858,867, Jul 1997.
Conese, Claudio; Maselli, Fabio, Use of multitemporal
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ISPRS Journal of
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ERDAS IMAGINE. 2014. Atlanta, Georgia: ERDAS, Inc.
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Solution
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