Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
Urban Land Cover Change Detection Analysis and Modelling Spatio-Temporal Grow...Bayes Ahmed
This is my final Mater thesis presentation. The thesis defense was held on March' 07, 2011 at 15:30 in the seminar room of Universitat Jaume I (UJI), Castellón, Spain.
Spatial analysis for the assessment of the environmental changes in the lands...Universität Salzburg
Presented research is focused on the spatial analysis aimed at the assessment of the environmental changes in the landscapes of Izmir surroundings, Turkey. Methods include Landsat TM images classification using Erdas Imagine, clustering segmentation and classification, verification via the Google Earth and GIS Mapping. Tme span is 13-years (1987-2000). Images were taken from the Global Land Cover Facility (GLCF) Earth Science Data Interface. The selected area of Izmir has the most diverse landscape structure and high heterogeneity of the land cover types. Accuracy results computed. Kappa statistics for the image 2000: 0.7843, for the image 1987: 0.7923. The classification of the image 1987: accuracy 81.25%, 2000: 80,47%. The results indicate changes in land cover types affected by human activities, i.e. increased agricultural areas. Results include following findings. 1987: croplands (wheat) covered 71% of the today’s area (2000): 2382 vs. 3345 ha. Increase in barley cropland areas is noticeable as well: 1149 ha in 1987 vs. 4423 ha in 2000. Sparsely vegetated areas now also occupy more areas : 5914 ha in 2000 against 859 ha in 1987. Natural vegetation, decreased, which can be explained by the expansion of the agricultural lands. 1987: coppice areas covered 5500 ha while later on there are only 700 ha in this land type.
Current poster presents a student assignment on Course: 'GEOG6038 Calibration and Validation of Earth Observation Data'. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
3-D integrated geological modeling in the Abitibi Subprovince (Québec, Canada...FF Explore 3D
The development of robust 3-D geological models involves the integration of large amounts of public geological data, as well as additional accessible proprietary lithological, structural, geochemical, geophysical, and diamond drill hole data. 3-D models and maps have been available, particularly in the petroleum industry, for more than 10 years. Here, we demonstrate how robust 3-D maps can be used as interactive tools for mineral deposits exploration. In particular, we show how the interrogation of 3-D data sets can constrain exploration targets at depth.
The main advantages of this technique for the mining industry are the homogeneity of data treatment and the validation of geological interpretations, taking into account geophysical and geochemical data. Data integration and cross-correlation of geology and geophysics can be achieved in two dimensions in any good GIS package. However, the added strength of 3-D analysis is the integration of separate data sets in three dimensions to build more complete, more realistic models, and in delineating areas of high economic potential at depth. Furthermore, these models can be modified and improved at any time by adding new data from ongoing drilling and geoscientific surveys.
This paper presents two examples of 3-D models used for mineral exploration: the Joutel VMS mining camp and the Duparquet gold camp, Quebec. In both examples, the creation of the model is discussed and queries specific to the relevant exploration model are introduced. Eight potential exploration targets are generated at Joutel and seven at Duparquet. Although the targets defined are dependent on the details of the chosen queries, it is apparent that this technique has the potential to generate promising exploration activity that can engender new targets.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
Bringing Geospatial Analysis to the Social Studies: an Assessment of the City...Universität Salzburg
Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.
Structure-metric method FOR PREDICTIVE ESTIMATION of NATURAL RESOURCESKaterinaKaritskaya
Research Company offers performing forecast and estimation of presence of hydrocarbon fields by structurometric method. Structurometric method requires no field trips and provides significant time saving. Forecasts developed by structurometric method, in comparison with conventional exploration activities 3 times are more exact, by 1-2 orders more efficient, environment remains undisturbed.
Root-mean-square errors of definition of deposit depths and thickness of oil and gas formation according to numerous test wells do not exceed 4-5 % (at depths up to 4000 m.). There can be discovered productive formations at depths of 7 km and more, and also on a shelf at sea depth up to 450 m.
This method can be used rather productively by investors with the purpose of predictive estimations of resources of licensed sites and areas offered for right of land tenure.
Modelling Landscape Changes and Detecting Land Cover Types by Means of the Re...Universität Salzburg
The emphasis of this research is to demonstrate application of Landsat satellite imagery as a major resource for environmental research using ILWIS GIS. Landsat images are highly useful and strongly recommended for educational purposes as they are provided free of charge and timely regular geospatial data with 30-m resolution covering any places of the Earth. The case study describes mapping land cover types in ecosystems. It details how exactly satellite images can be used for geospatial research step by step. In the current research I used orthorectified Landsat Thematic Mapper (TM), MSS and Enhanced Thematic Mapper (ETM+) data in Geographic Tagged Image-File Format (GeoTIFF) acquired over the area of Bovanenkovo region, Yamal. The images cover study area for different time periods. The choice of Landsat data application for land cover mapping is explained by its 30-m high spatial resolution, well-known advantages of application of the Landsat scenes in research and cartography, almost 40 year old history of the image record, successful distribution and open availability. Landsat scenes were selected for the pair analysis: Landsat TM scenes for 1988-08-07 and 2011-07-14 and Landsat ETM+, 2001. The research methodology is based spatial analysis tools of the open source GIS software: Quantum GIS and ILWIS GIS. The images were georeferenced, preprocessed and imported to ILWIS from .img into ILWIS .mpr raster map format (ASCII) using GDAL (Geospatial Data Abstraction Library) in main ILWIS. Minimal Distance method was sued to classify images. After converting, each image contained collection of 7 Landsat raster bands, as well as theirs metadata stored in Map List (.mpl) file, information about georeference (.grf) and coordinate system in .csy file. To visualize spectral information from the Landsat image, a color composite map has been created using combination of three raster images of the individual bands. Supervised classification of the raster imagery includes image analysis aimed at recognizing class membership for each pixel. The respective pixels are selected in Sample Set Editor, ILWIS GIS. The research method used in current research is supervised classification, which enabled to assign land cover classes by adjusting classification parameters and thresholds in DN values of spectral signature of pixels. The principle of Minimum Distance method, used for pixels classification is based on the calculating of shortest straight-line distance in Euclidian coordinate system from each pixel’s DN to the pattern pixels of land cover classes.
Accurate and rapid big spatial data processing by scripting cartographic algo...Universität Salzburg
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean
Spatial analysis for the assessment of the environmental changes in the lands...Universität Salzburg
Presented research is focused on the spatial analysis aimed at the assessment of the environmental changes in the landscapes of Izmir surroundings, Turkey. Methods include Landsat TM images classification using Erdas Imagine, clustering segmentation and classification, verification via the Google Earth and GIS Mapping. Tme span is 13-years (1987-2000). Images were taken from the Global Land Cover Facility (GLCF) Earth Science Data Interface. The selected area of Izmir has the most diverse landscape structure and high heterogeneity of the land cover types. Accuracy results computed. Kappa statistics for the image 2000: 0.7843, for the image 1987: 0.7923. The classification of the image 1987: accuracy 81.25%, 2000: 80,47%. The results indicate changes in land cover types affected by human activities, i.e. increased agricultural areas. Results include following findings. 1987: croplands (wheat) covered 71% of the today’s area (2000): 2382 vs. 3345 ha. Increase in barley cropland areas is noticeable as well: 1149 ha in 1987 vs. 4423 ha in 2000. Sparsely vegetated areas now also occupy more areas : 5914 ha in 2000 against 859 ha in 1987. Natural vegetation, decreased, which can be explained by the expansion of the agricultural lands. 1987: coppice areas covered 5500 ha while later on there are only 700 ha in this land type.
Current poster presents a student assignment on Course: 'GEOG6038 Calibration and Validation of Earth Observation Data'. Study aim is image classification using ENVI GIS and remote sensing data aimed at national park area classification. Study area is Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enablesd to make an analysis of the area in 9 (nine) working steps and to produce a map based on 2 criteria: vegetation amount and altitude. Methodology includes following steps: 1) True-colour composite of the ETM+ image, bands 3,2,1; 2) Image contrast enhancement (Enhance-Gaussian); 3) SRTM-Data Upload to derive elevation model; 4) 3D surface visualization; 5) Calculating Greenness Index; 6) Creation Vegetation Layer ROI; 7) Creating Altitude Layer Zones by “Intersect Regions” for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 – 5000). These zones are shown on the map in different colors (yellow, beige, two greens) ; 8) Mapping and Design; 9) 3D-Mapping and DEM. The research was done as part of MSc studies at the University of Southampton, UK, autumn 2009.
As part of the GSP’s capacity development and improvement programme, FAO/GSP have organised a one week training in Izmir, Turkey. The main goal of the training was to increase the capacity of Turkey on digital soil mapping, new approaches on data collection, data processing and modelling of soil organic carbon. This 5 day training is titled ‘’Training on Digital Soil Organic Carbon Mapping’’ was held in IARTC - International Agricultural Research and Education Center in Menemen, Izmir on 20-25 August, 2017.
3-D integrated geological modeling in the Abitibi Subprovince (Québec, Canada...FF Explore 3D
The development of robust 3-D geological models involves the integration of large amounts of public geological data, as well as additional accessible proprietary lithological, structural, geochemical, geophysical, and diamond drill hole data. 3-D models and maps have been available, particularly in the petroleum industry, for more than 10 years. Here, we demonstrate how robust 3-D maps can be used as interactive tools for mineral deposits exploration. In particular, we show how the interrogation of 3-D data sets can constrain exploration targets at depth.
The main advantages of this technique for the mining industry are the homogeneity of data treatment and the validation of geological interpretations, taking into account geophysical and geochemical data. Data integration and cross-correlation of geology and geophysics can be achieved in two dimensions in any good GIS package. However, the added strength of 3-D analysis is the integration of separate data sets in three dimensions to build more complete, more realistic models, and in delineating areas of high economic potential at depth. Furthermore, these models can be modified and improved at any time by adding new data from ongoing drilling and geoscientific surveys.
This paper presents two examples of 3-D models used for mineral exploration: the Joutel VMS mining camp and the Duparquet gold camp, Quebec. In both examples, the creation of the model is discussed and queries specific to the relevant exploration model are introduced. Eight potential exploration targets are generated at Joutel and seven at Duparquet. Although the targets defined are dependent on the details of the chosen queries, it is apparent that this technique has the potential to generate promising exploration activity that can engender new targets.
Introduction Petrel Course (UAB-2014)
This course has been prepared as an introduction of Petrel software (Schlumberger, www.software.slb.com/products/platform/Pages/petrel.aspx), an application which allows the modeling and visualization of reservoirs, since the exploration stage until production, integrating geological and geophysical data, geological modeling (structural and stratigraphic frameworks), well planning, or property modeling ( petrophysical or petrological) among other possibilities.
The course will be focused mainly in the understanding and utilization of workflows aimed to build geological models based on superficial data (at the outcrop scale) but also with seismic data. The course contents have been subdivided in 5 modules each one developed through the combination of short explanations and practical exercises.
The duration of the course covers more or less 10h divided in three sessions. The starting data will be in the first week of December.
This course will be oriented mainly for the PhD and master students ascribed at the Geologic department of the UAB. For logistic reasons the maximum number of places for each torn are 9. The course is free from the Department members but the external interested will have to make a symbolic payment.
Those interested send an e-mail to the Doctor Griera (albert.griera@uab.cat).
The course will be imparted by Marc Diviu (Msc. Geology and Geophysics of reservoirs).
Bringing Geospatial Analysis to the Social Studies: an Assessment of the City...Universität Salzburg
Current poster presents an example of Landsat TM image processing using ENVI GIS. Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan’s core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatio- temporal analysis of urban dynamics in study area during 15 years (1990- 2005) Research objective: application of GIS methodology and remote sens- ing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS. Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2\% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality. The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005.
Structure-metric method FOR PREDICTIVE ESTIMATION of NATURAL RESOURCESKaterinaKaritskaya
Research Company offers performing forecast and estimation of presence of hydrocarbon fields by structurometric method. Structurometric method requires no field trips and provides significant time saving. Forecasts developed by structurometric method, in comparison with conventional exploration activities 3 times are more exact, by 1-2 orders more efficient, environment remains undisturbed.
Root-mean-square errors of definition of deposit depths and thickness of oil and gas formation according to numerous test wells do not exceed 4-5 % (at depths up to 4000 m.). There can be discovered productive formations at depths of 7 km and more, and also on a shelf at sea depth up to 450 m.
This method can be used rather productively by investors with the purpose of predictive estimations of resources of licensed sites and areas offered for right of land tenure.
Modelling Landscape Changes and Detecting Land Cover Types by Means of the Re...Universität Salzburg
The emphasis of this research is to demonstrate application of Landsat satellite imagery as a major resource for environmental research using ILWIS GIS. Landsat images are highly useful and strongly recommended for educational purposes as they are provided free of charge and timely regular geospatial data with 30-m resolution covering any places of the Earth. The case study describes mapping land cover types in ecosystems. It details how exactly satellite images can be used for geospatial research step by step. In the current research I used orthorectified Landsat Thematic Mapper (TM), MSS and Enhanced Thematic Mapper (ETM+) data in Geographic Tagged Image-File Format (GeoTIFF) acquired over the area of Bovanenkovo region, Yamal. The images cover study area for different time periods. The choice of Landsat data application for land cover mapping is explained by its 30-m high spatial resolution, well-known advantages of application of the Landsat scenes in research and cartography, almost 40 year old history of the image record, successful distribution and open availability. Landsat scenes were selected for the pair analysis: Landsat TM scenes for 1988-08-07 and 2011-07-14 and Landsat ETM+, 2001. The research methodology is based spatial analysis tools of the open source GIS software: Quantum GIS and ILWIS GIS. The images were georeferenced, preprocessed and imported to ILWIS from .img into ILWIS .mpr raster map format (ASCII) using GDAL (Geospatial Data Abstraction Library) in main ILWIS. Minimal Distance method was sued to classify images. After converting, each image contained collection of 7 Landsat raster bands, as well as theirs metadata stored in Map List (.mpl) file, information about georeference (.grf) and coordinate system in .csy file. To visualize spectral information from the Landsat image, a color composite map has been created using combination of three raster images of the individual bands. Supervised classification of the raster imagery includes image analysis aimed at recognizing class membership for each pixel. The respective pixels are selected in Sample Set Editor, ILWIS GIS. The research method used in current research is supervised classification, which enabled to assign land cover classes by adjusting classification parameters and thresholds in DN values of spectral signature of pixels. The principle of Minimum Distance method, used for pixels classification is based on the calculating of shortest straight-line distance in Euclidian coordinate system from each pixel’s DN to the pattern pixels of land cover classes.
Accurate and rapid big spatial data processing by scripting cartographic algo...Universität Salzburg
Accurate and rapid big spatial data processing by scripting cartographic algorithms: advanced seafloor mapping of the deep-sea trenches along the margins of the Pacific Ocean
Land use/land cover classification using machine learning modelsIJECEIAES
An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers.
Chronological Calibration Methods for Landsat Satellite Images iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Land Cover maps supply information about the physical material at the surface of the Earth (i.e. grass, trees, bare ground, asphalt, water, etc.). Usually they are 2D representations so to present variability of land covers about latitude and longitude or other type of earth coordinates. Possibility to link this variability to the terrain elevation is very useful because it permits to investigate probable correlations between the type of physical material at the surface and the relief. This paper is aimed to describe the approach to be followed to obtain 3D visualizations of land cover maps in GIS (Geographic Information System) environment. Particularly Corine Land Cover vector files concerning Campania Region (Italy) are considered: transformed raster files are overlapped to DEM (Digital Elevation Model) with adequate resolution and 3D visualizations of them are obtained using GIS tool. The resulting models are discussed in terms of their possible use to support scientific studies on Campania Land Cover.
Land Use/Land Cover Mapping Of Allahabad City by Using Remote Sensing & GIS IJMER
The present study was carried out to produce and evaluate the land use/land cover maps by on
screen visual interpretation. The studies of land cover of Allahabad city (study area) consist of 87517.47 ha
out of which 5500.35 ha is build up land (Urban / Rural) Area. In this respect, the Build up land (Urban /
Rural) area scorers 6.28% of the total area. It has also been found that about 17155.001ha (19.60 %) of
area is covered by current fallow land. The double/triple crop land of 30178.44ha (34.84%). The area
covered by gullied / ravines is 1539.20 ha (1.75 %) and that of the kharif crop land is 2828.00 ha (3.23 %).
The area covered by other wasteland is 2551.05ha (2.91%). Table 4.1 shows the area distribution of the
various land use and land cover of Allahabad city.
Class Project
Mapping of Crop Residues Using Hyperspectral Data:
· Techniques and indexes for quantification,
· Data sources and
· Unsupervised classification for tillage systems
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
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
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).
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 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 fo ...
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
Using Artificial Neural Networks for Digital Soil Mapping – a comparison of MLP and SOM approaches
1. 1 Introduction
Soils play a fundamental role in sustainable land use by
supporting valuable services, such as biodiversity, food
production, and pollution buffering. Vital human activities
depend on this important non-renewable resource [1].
The absence of soil information contributes to the
uncertainties of predicting food production, and lack of
reliable and harmonized soil data has considerably limited
environmental impact assessments, land degradation studies
and adapted sustainable land management interventions [2].
Despite soil surveys having been carried out in many
countries, the scale and spatial coverage of many conventional
soil maps are not ideal for planning applications at national
level [3]. There is also a lack of consistency across countries
concerning soil classifications and legends, which hampers
the necessary integration of soil datasets, even in Europe [4]
Portugal, like most European Union countries, has only part
of its territory covered with soil maps at semi-detailed or
reconnaissance scales [5]. While 55% of continental Portugal
has soil maps at 1:50 000 produced by traditional methods of
soil survey before the 1970s, only about 40% of the territory
has more recent soil map coverage at 1:100 000 with some
overlap (Figure 1). Therefore, in addition to the published
coverage remaining incomplete, significant problems remain
regarding the existing cartography, namely concerning
cartographic uniformity and taxonomic systems adopted [6].
Figure 1: Scale and legends of regional soil maps of
continental Portugal.
Digital Soil Mapping (DSM) is a technique that has been
successful in mapping soil data, i.e. the spatial distribution of
soil classes and spatial variation of soil properties [3]. DSM
Using Artificial Neural Networks for Digital Soil Mapping – a
comparison of MLP and SOM approaches
Sérgio Freire
e-GEO, FCSH, Univ.
Nova de Lisboa
Av. de Berna 26-C
Lisboa, Portugal
sfreire@fcsh.unl.pt
Inês Fonseca
Centro de Estudos
Geográficos - IGOT
Alameda da
Universidade
Lisboa, Portugal
i.fonseca@campus.ul.pt
Ricardo Brasil
Centro de Estudos
Geográficos - IGOT
Alameda da Universidade
Lisboa, Portugal
rmsb@campus.ul.pt
Jorge Rocha
Centro de Estudos
Geográficos - IGOT
Alameda da
Universidade
Lisboa, Portugal
jorge.rocha@campus.ul.pt
José A. Tenedório
e-GEO, FCSH, Univ. Nova de Lisboa
Av. de Berna 26-C
Lisboa, Portugal
ja.tenedorio @fcsh.unl.pt
Abstract
Portuguese soil map coverage remains incomplete, while the existing cartography has some shortcomings. Artificial Neural Networks
(ANN) are advanced computer-based techniques which are being used for Digital Soil Mapping (DSM). These techniques allow mapping
soil classes in a cheaper, more consistent and flexible way, using surrogate landscape data. This work compares the performance of two
ANN approaches, Multi-layer Perceptron (MLP) and Self-Organizing Map (SOM), for DSM. The tests were carried out in IDRISI Taiga for
three catchments in Portugal and one in Spain, using different sampling designs to obtain the training sets. Results reveal that best ANN
performance is obtained with a MLP model rather than a SOM model, independently of data transformation and sampling method.
However, MLP is also the most sensitive method to the data used to develop the models.
Keywords: Digital Soil Mapping, MLP, SOM, IDRISI Taiga, AutoMAPticS, Portugal.
2. AGILE 2013 – Leuven, May 14-17, 2013
may combine Geographical Information Systems (GIS) with
advanced techniques such as Artificial Neural Networks
(ANN), which have enabled mapping the spatial distribution
of soils in a cheaper, more accurate, reproducible, and flexible
way in terms of data storage and visualization, using surrogate
landscape data easy to obtain. Thus, ANN modelling is able to
provide the means to predict soil types at locations where
there are no current soil maps, by combining soil map data
from other areas with landscape features known to be
responsible for the spatial variation of soils [5]. ANNs are
therefore sophisticated computer programs able to model
complex functional relationships, which applied to the soil
mapping problem use a set of variables related to soil forming
factors and the respective soil type as training data in order to
constructs rules [7] that can be extended to the unmapped
areas.
Although the usefulness of ANNs for DSM has been
demonstrated (e.g., [8, 9, 10]) more needs to be known about
their relative advantages and limitations. Specifically, there
remains a lack of research on the performance of different
ANN architectures and related aspects for soil spatial
modelling at different scales and in different conditions.
While MLP and SOM are probably the most frequently-used
ANNs for DSM, these networks utilise different classification
approaches. Therefore it is important to investigate their
capabilities and limitations for DSM using different
experimental settings.
Sarmento et al. [11] compared three ANNs and a decision
tree approach for mapping soil classes at a detailed scale,
concluding that the latter method and a Multi-Layer
Perceptron (MLP) network had similar and the best
performances, obtaining the highest overall accuracies.
Albuquerque et al. [12] have tested MLP and Kohonen’s Self-
Organizing Map (SOM) models in the context of image
analysis, obtaining better results with MLP.
The present work reports the results of using different
supervised ANN methods and experimental setups for DSM at
regional scale in selected study areas in Portugal and Spain.
2 Study areas
The methodology has so far been applied in four medium-
sized catchments: three in Portugal (Mondim de Basto, Vila
Real, Castanheira) and one in Spain (El Almendro) (Figure 2).
Figure 2: Location of the study areas: Mondim de Basto (1),
Vila Real (2), Castanheira (3), and El Almendro (4).
These catchments were selected for two main reasons: (a)
their location in areas where soil maps at 1:100 000 are
available, and (b) the fact that they display varied
geomorphological and ecological features and include soil
types representative of those occurring in the respective
regions. Their main geographic characteristics are
summarized in Table 1.
While Mondim de Basto is the largest catchment, its soil
map (1:100 000) comprises only four classes, whereas
Castanheira includes seven, despite being the smallest.
Table 1: Main characteristics of the selected catchments.
Catchment Region River
Area
(km2
)
Min. elev.
(m)
Max. elev.
(m)
No. of soil
classes
Mondim de
Basto
Douro-Minho Tâmega 911 56 1298 4
Vila Real Northeast Corgo 468 67 1405 4
Castanheira Inland Center Rib. das Cabras 227 558 969 7
El Almendro
Andalucía
(Spain)
Rio Piedros 517 1 361 10
3 Data and methods
3.1 Datasets
The ANNs were trained using independent variables, which
included both continuous terrain data and categorical
(thematic) geoinformation. The terrain surrogate data were
derived from the Shuttle Radar Topography Mission (SRTM)
digital elevation data (www2.jpl.nasa.gov/srtm) with a 90 m
resolution and selected after multicollinearity tests showed
little data redundancy. Seven morphometric variables, which
are frequently used in DSM, were extracted from the terrain
3. AGILE 2013 – Leuven, May 14-17, 2013
data: slope steepness, plan and profile curvatures, upslope
catchment area, dispersal area, wetness index and potential
solar radiation. These continuous variables were rescaled to a
0-255 value range. In addition to altitude, land use from
Corine Land Cover 2006 (CLC2006) and geological data were
also included, as well as existing digital soil data at 1:100 000
(Mondim de Basto, Vila Real, e Castanheira), and 1:400 000
(El Almendro). All layers were clipped to the study areas and
converted to a raster structure with a 90-m cell size.
In order to test for the possible effects of spatial
autocorrelation which is commonly high in datasets derived
from altitude data, two sets of independent variables were
used for network training and classification. One set included
the coordinates (latitude and longitude), in addition to the ten
input variables indicated above.
3.2 Methodology
To model the spatial distribution of soil classes in each
catchment, two types of ANN architectures were employed:
the Multi-Layer Perceptron (MLP) and Kohonen’s Self-
Organizing Map (SOM). These were run in hard classification
mode, using IDRISI Taiga software (Clark Labs).
MLP is a widely used supervised method based on the back-
propagation learning algorithm [13]. Such a network typically
comprises an input layer, one output layer, and at least one
intermediary hidden layer. Independent variables are nodes in
the input layer, while final classes result as neurons in the
output layer. The experimental setup for each training set in
MLP shifted from the default specifications presented in
Table 2 used as initial values.
Table 2: Default characteristics and parameters of the ANN
MLP in IDRISI Taiga.
Group Parameter Default value
Avrg. training pixels
per class
200 / 250
Input
specifications Avrg. testing pixels per
class
200 / 250
Network
topology
Hidden layers 1
Automatic training No
Dynamic learning rate Yes
Learning rate 0.01
End learning rate 0.001
Momentum factor 0.5
Training
parameters
Sigmoid constant “a” 1
RMS 0.01
Iterations (Variable)
Stopping
criteria
Accuracy rate 100%
The SOM model in IDRISI Taiga is based on Kohonen’s
Self-Organizing Map [14, 15]. The basic architecture includes
a layer of input neurons connected by synaptic weights to
neurons in an output layer arranged in a two-dimensional
(usually square) array. The process begins with a coarse
tuning phase that is in effect a form of unsupervised
classification. In a subsequent fine tuning stage, intra-class
decision boundaries are refined using a Learning Vector
Quantization (LVQ) procedure. The default parameters of
SOM are presented in Table 3.
Table 3: Default characteristics and parameters of the ANN
SOM in IDRISI Taiga.
Group Parameter Default value
Interval in column 3Sampling in
band images Interval in row 7
Output layer neuron 15x15= 225
Initial neighborhood
radius
22.21
Min learning rate 0.5
Network
parameters
Max learning rate 1
Min gain term 0.0001
Max gain term 0.0005
Fine tuning rule LVQ2
Fine tune
parameters
Fine tuning epochs 50
Output hard
classification map
Y
Display feature map Y
Classification
Specification
Algorithm for unknown
pixels
Min Mean
Distance
Regarding sampling of soil classes for training the
networks, the potential impact due to sampling scheme was
not yet well addressed in the existing literature. Considering
the significance of spatial autocorrelation present in both the
distribution of environmental variables and resulting soil
classes, different sampling schemes were used, in addition to
testing the inclusion of latitude and longitude in the variable
set for each watershed. Therefore the ANNs were trained by
presenting them with a number of different examples of the
same soil type drawn (i) randomly (RS), or (ii) in a stratified
fashion (SS). For the latter, training pixel vectors were located
by choosing (a) random coordinates within soil types strata
(SRS), (b) random coordinates within soil types and chosen
evenly in the frequency space (SRPS), (c) nearest coordinates
within soil types and chosen evenly in the frequency space
(SNPS), and (d) farthest coordinates within soil types and
chosen evenly in the frequency space (SFPS) [16, 17].
In MLP, an average of 250 pixels per class were used for
training and testing in the Vila Real and El Almendro
catchments, while 200 were used for Mondim and
Castanheira, due to constraints in the total area covered by
some soil classes. Some of the network parameters were
progressively changed and the network performance
monitored, namely: number of layer 1 nodes, use of automatic
training, use of dynamic learning rate, and number of
iterations (maximum of 100 000). Training ended when one of
the stopping criteria was achieved: either a RMSE ≤ 0.01, an
accuracy of 100%, or the defined maximum number of
iterations. Therefore the default neural network included 10 or
12 input layer nodes, and one hidden layer with 7 nodes (see
Table 2).
In a given study area, for a specific combination of
sampling method and parameters, results of different runs can
vary due to different seeding of training pixels. Thus, five
model runs were initially performed for each combination, in
order to average their accuracies and derive the best
parameters.
4. AGILE 2013 – Leuven, May 14-17, 2013
The quality of estimated maps was assessed in Map
Comparison Kit (MCK) v3.2.2. software (Geonamica), using
the conventional soil maps as reference information. For each
study area and model run, overall accuracy was computed
from the contingency table (error matrix), as the percentage of
agreement between the classified map and the reference map.
Figure 3 shows the 1:100 000 conventional soil map for
Mondim de Bastos used to extract training samples and for
accuracy assessment.
Figure 3: Soil map for Mondim de Bastos catchment.
4 Results and discussion
The results obtained using ANNs for soil mapping in the four
study areas are presented in Table 4. For each catchment and
for each of the ANN methods employed, the sampling
strategy which obtained the highest accuracy, using 12 and 10
variables, is shown. This is shown only for the combination of
ANN model and variable set obtaining the highest overall
accuracy, as computed in MCK.
Table 4: Impact of ANN method on the performance of ANN
models.
Catchment
ANN
Method
Sampling
No. of
variables
Accuracy
(%)
MLP RS 12 67.9Mondim de
Basto SOM RS 10 64.8
MLP RS 12 74
Vila Real
SOM RS 10 72.6
MLP RS 12 60.9
Castanheira
SOM RS 12 55.2
MLP RS 12 74.3
El Almendro
SOM RS 12 72.2
In general, accuracies are rather high, with best simulations
obtained for El Almendro´s catchment, which also has the
largest number of soil classes. The lowest accuracy values
were obtained in Castanheira, the smallest catchment. It is
possible that some differences are due to (1) scale of soils
maps being rather different (1:100 000 vs 1:400 000, for
Portuguese and Spanish datasets respectively) and (2) the
number of soil classes. For instance, Castanheira catchment
has fewer soil classes which are more likely to include
different types of soils, whilst in El Almendro the opposite
happens: higher number of classes which are also pre-defined
as a combination of soil types. The latter approach to soil
classification may result in soil patches that reflect better the
differences in the landscape, i.e. higher purity of soil mapped
patches, thus resulting in better ability of the ANN to classify
correctly.
Figure 4: Modelled soil class distribution in the Mondim de
Basto catchment, using MLP and SOM.
5. AGILE 2013 – Leuven, May 14-17, 2013
Even though specific accuracy values differ for each
catchment, results show that MLP consistently outperformed
SOM in all study areas, although the level of accuracy varied
only between 1.4% (Vila Real) and 5.7% (Castanheira).
Additionally, in MLP higher accuracies were obtained when
location variables were included, while SOM performed
generally better if latitude and longitude were not included as
independent variables. In all catchments, best results were
obtained using random sampling for selecting training sites.
Inasmuch as MLP emerges as a robust method for this
purpose, it sometimes suffers from the problem of falling into
a local minimum, resulting in a poor prediction when the
whole study area is analyzed. Therefore it is important to
average accuracies while deriving the best parameters and
monitor the classification process closely. The algorithm used
by SOM model has the benefits of being faster and easier to
use.
The spatial distribution of soil classes obtained with each of
the ANN architectures, in the Mondim de Basto catchment, is
illustrated in Figure 4. The figure shows that MLP (a) maps
soil classes in larger patches, while SOM’s prediction (b) is
more detailed and ‘pixelated’. It is possible that the higher
degree of agreement between the MLP map and the reference
map may result from the scale-dependent cartographic
generalization of the latter, and/or that it is necessary to use a
set of indices that reflect not only the degree of overlap, but
also the size and location of patches, as well as the number of
classes correctly predicted. Due to the resulting SOM maps
being very fragmented (i.e., ‘pixelated’), it may be advisable
to re-asses after conducting a spatial generalization. Future
experiments using both soil data and environmental variables
at different resolutions and a set of evaluation indices will
allow testing these hypotheses.
In this work, ANN-modelled soil maps were compared to
conventional soil maps, despite the inexistence of accuracy or
confidence measures for these maps. Future work will use
available soil survey data for a true validation of the models.
5 Conclusions
Conventional mapping of soils is based on labour-intensive
field surveys, which are very costly. While the demand is
rising for high-resolution spatial soil information for
modelling and environmental planning, Portugal still lacks
complete soil-map coverage. Artificial Neural Networks are
powerful techniques for Digital Soil Mapping that can model
high-quality digital soil maps in a fast and cost-effective way.
However, there exist different ANN model architectures and
there is a shortage of studies testing and comparing their
performance for various problems, including the spatial
prediction of soil classes at a regional scale.
This research tested the modelling of soil classes using two
ANN approaches in four catchments in Portugal and Spain
and compared the results. Noteworthy conclusions are that 1)
the modelling accuracy of the ANN models in supervised
mode is highly dependent on the sampling method used to
select training sites and, 2) the best ANN performance was
obtained with a MLP model rather than a SOM model,
independently of sampling method. However, MLP is also the
most sensitive method to the data used to develop the models
and the soil class patterns it predicted produced more compact
and fewer patches than the original soil maps.
Ongoing work involves classifying soils using ANNs at
different spatial resolutions, for testing sensitivity to mapping
scales. Subsequent research will explore the application of
Fuzzy Logic for the evaluation of accuracy levels, as well as
for producing hybrid ANNs, and results obtained using both
methodologies will be compared and validated using existing
maps and soil profile data. The best model will be used to map
soil classes across areas which are currently lacking spatial
soil data, ultimately enabling the completion of the Portuguese
soil map coverage at 1:100 000.
Acknowledgements
The authors thank DRAEDM for providing digital soil data.
This work was produced in the framework of AutoMAPticS
(Automatic Mapping of Soils), a project supported by a grant
from FCT - Portugal (PTDC/CS-GEO/111929/2009), whose
PI is employed under the FCT Science 2008 Programme.
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