1) The chapter analyzes land use patterns in Thrissur district of Kerala, India using satellite data.
2) It finds that the predominant land uses are residential/agricultural mix (50.89%) and forest (25.46%).
3) A comparison of Thrissur's land use with neighboring districts and Kerala state shows it has a higher percentage of marshy land and lower percentage of residential land than most areas.
This document discusses using GIS to evaluate and map soil erosion. It describes several GIS-based models used to estimate soil loss, including the Distributed soil erosion model, Modified USLE, Gully Surface Growth, and Gully Head Advance models. It also discusses how the RUSLE model has been integrated with GIS for soil loss estimation by using input raster files for rainfall erosivity, soil erodibility, topographic factors, crop management factors, and practice factors. Case studies are presented on evaluating soil erosion in Taita Hills, Kenya and mapping erosion risk in dam watersheds in Turkey, with findings that erosion risk decreased over time in some areas due to increased vegetation cover or conservation measures.
This document summarizes the Brazilian experience with the EU-FP6 project SENSOR, which aimed to develop ex-ante impact assessment tools to support land use and agricultural policies. It describes how the project was adapted to test the tools in Brazil, focusing on sugarcane expansion policies in Mato Grosso do Sul state. Spatially-explicit land use change models and sustainability impact indicators were used to assess the effects of sugarcane expansion on the environment, economy and society. The results highlighted limitations in approach and data, but demonstrated the potential for participatory impact assessment frameworks to strengthen the analysis where data and modeling capacity are limited.
Smallholder livelihoods and land use in the Eastern Brazilian Amazon: Lessons...CIFOR-ICRAF
This document analyzes the outcomes of Proambiente, a Brazilian program that promoted smallholder production and environmental conservation. It found that families participating in Proambiente had higher annual incomes, more diverse crops, and obtained more agricultural income per hectare than non-participating families. However, it could not determine whether these differences were caused by the program. The implications for a proposed REDD+ project in the area are that interventions should promote more intensive and diversified agricultural production to supplement livestock income, and reforestation on degraded lands to encourage environmental compliance.
This study investigates the effects of land use changes on microclimate in the area surrounding Botanical Garden Bukit Cerakah in Shah Alam, Malaysia between 1991 and 2001. Land use/land cover maps were generated from Landsat images and showed losses of agricultural land, forest land and increases in built-up and barren areas. Land surface temperature maps were also developed and showed temperature increases in built-up and barren areas compared to cooler temperatures in forested and agricultural areas. The results demonstrate the impacts of urbanization and land use changes on local microclimates.
Atmospheric and topographic corrections improve Landsat satellite imagery for analysis of forest cover dynamics in mountainous areas. Corrections increase pixel homogeneity and reduce dependency of reflectance values on terrain illumination. Image preprocessing, including topographic correction and compositing, leads to more accurate land cover classification and change mapping over large areas. Factors controlling forest cover dynamics in the Romanian Carpathians from 1985-2010 include accessibility, demographic changes, land use policies, slope, elevation, and soil type.
Ideas Marketplace presentation from WRI - the World Resources Institute. Presented at Agriculture, Landscapes and Livelihoods Day 5 in Doha Qatar, 3 December 2012. http://www.agricultureday.org
1) The chapter analyzes land use patterns in Thrissur district of Kerala, India using satellite data.
2) It finds that the predominant land uses are residential/agricultural mix (50.89%) and forest (25.46%).
3) A comparison of Thrissur's land use with neighboring districts and Kerala state shows it has a higher percentage of marshy land and lower percentage of residential land than most areas.
This document discusses using GIS to evaluate and map soil erosion. It describes several GIS-based models used to estimate soil loss, including the Distributed soil erosion model, Modified USLE, Gully Surface Growth, and Gully Head Advance models. It also discusses how the RUSLE model has been integrated with GIS for soil loss estimation by using input raster files for rainfall erosivity, soil erodibility, topographic factors, crop management factors, and practice factors. Case studies are presented on evaluating soil erosion in Taita Hills, Kenya and mapping erosion risk in dam watersheds in Turkey, with findings that erosion risk decreased over time in some areas due to increased vegetation cover or conservation measures.
This document summarizes the Brazilian experience with the EU-FP6 project SENSOR, which aimed to develop ex-ante impact assessment tools to support land use and agricultural policies. It describes how the project was adapted to test the tools in Brazil, focusing on sugarcane expansion policies in Mato Grosso do Sul state. Spatially-explicit land use change models and sustainability impact indicators were used to assess the effects of sugarcane expansion on the environment, economy and society. The results highlighted limitations in approach and data, but demonstrated the potential for participatory impact assessment frameworks to strengthen the analysis where data and modeling capacity are limited.
Smallholder livelihoods and land use in the Eastern Brazilian Amazon: Lessons...CIFOR-ICRAF
This document analyzes the outcomes of Proambiente, a Brazilian program that promoted smallholder production and environmental conservation. It found that families participating in Proambiente had higher annual incomes, more diverse crops, and obtained more agricultural income per hectare than non-participating families. However, it could not determine whether these differences were caused by the program. The implications for a proposed REDD+ project in the area are that interventions should promote more intensive and diversified agricultural production to supplement livestock income, and reforestation on degraded lands to encourage environmental compliance.
This study investigates the effects of land use changes on microclimate in the area surrounding Botanical Garden Bukit Cerakah in Shah Alam, Malaysia between 1991 and 2001. Land use/land cover maps were generated from Landsat images and showed losses of agricultural land, forest land and increases in built-up and barren areas. Land surface temperature maps were also developed and showed temperature increases in built-up and barren areas compared to cooler temperatures in forested and agricultural areas. The results demonstrate the impacts of urbanization and land use changes on local microclimates.
Atmospheric and topographic corrections improve Landsat satellite imagery for analysis of forest cover dynamics in mountainous areas. Corrections increase pixel homogeneity and reduce dependency of reflectance values on terrain illumination. Image preprocessing, including topographic correction and compositing, leads to more accurate land cover classification and change mapping over large areas. Factors controlling forest cover dynamics in the Romanian Carpathians from 1985-2010 include accessibility, demographic changes, land use policies, slope, elevation, and soil type.
Ideas Marketplace presentation from WRI - the World Resources Institute. Presented at Agriculture, Landscapes and Livelihoods Day 5 in Doha Qatar, 3 December 2012. http://www.agricultureday.org
The document summarizes an analysis of predicting forest cover types using machine learning models. It describes the dataset containing over 500,000 observations of forest cover types and 12 descriptive variables. Various models were tested including decision forests, boosted decision trees, and neural networks. The best performing models on the test set were an ensemble approach blending multiple models and a one-vs-all decision forest, both achieving over 80% accuracy. Experiments were conducted using Microsoft Azure machine learning services.
Accuracy assessment is an essential step in any remote sensing classification. It involves collecting reference data through methods like GPS ground sampling and comparing the classifications to determine accuracy. Key aspects of accuracy assessment include error matrices to calculate overall, user's, and producer's accuracies. User's accuracy indicates errors of commission while producer's accuracy reflects errors of omission. ERDAS Imagine software can be used to import reference data and generate accuracy reports to evaluate classification performance.
Introduce variable/ Indices using landsat imageKabir Uddin
Image Index is a “synthetic image layer” created from the existing bands of a multispectral image. This new layer often provides unique and valuable information not found in any of the other individual bands.
Image index is a calculated result or generated product from satellite band/channels
It helps to identify different land cover from mathematical definition .
Band Combination of Landsat 8 Earth-observing Satellite ImagesKabir Uddin
This document discusses Landsat 8, an Earth-observing satellite launched by NASA in 2013. It provides details on Landsat 8 such as its mission to continuously archive global images of Earth since the 1970s, how it collects about 400 scenes per day, and its various spectral bands that can be used alone or combined for different analyses. Band combination examples are shown for Landsat 8 images before and after processing of Inle Lake to demonstrate the use of natural color, color infrared, and different false color composites.
Few Indicies(NDVI... etc) performed on ERDAS software using Model MakerSwetha A
The document describes how to create indices in ERDAS Model Maker to highlight different land features in satellite imagery. It provides the formulas and instructions to build models to create the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Turbidity Index (NDTI), Normalized Difference Pond Index (NDPI), and Modified Normalized Difference Water Index (MNDWI). For each index, it lists the band math formula used and explains that the output will highlight the targeted land features.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
This document provides a summary of the 2014 field-based Agricultural Resource Inventory (AgRI) mapping of the Innisfil Creek Subwatershed in Ontario. The main crops grown are potato, corn, soybean, turf, and winter wheat, covering over 75% of mapped farm fields. Soybean is the predominant crop. Irrigation systems were observed on 6% of fields, with centre pivot systems being most common. Crops are generally grown on soils with moderate to low infiltration rates. The mapping identifies spatial patterns of crop distribution and irrigation that can help inform water resource management.
This document describes a land use and land cover classification system for mapping using satellite data. It includes 14 categories organized in a three-level hierarchy with associated codes. The categories include built-up land, agricultural land, forest, wastelands, water bodies, and others. Factors like tone, size, shape, texture, and pattern are used to classify different land types captured by satellites at various scales from 1:2,50,000 to 1:4,000. Ground truthing is used to verify and modify preliminary interpretations of satellite imagery.
This document discusses strategies for stabilizing upland rice cropping and developing village forestry systems as crucial components of upland development in Laos. It recognizes upland rice cultivation and bush fallow systems as important current and future livelihood activities that can be stabilized. It argues that rotational upland systems have a neutral carbon footprint and that upland rice is an important cash crop and route to food security. The document presents examples of how participatory land use planning processes have stabilized upland cropping systems. It also notes that many villagers will lose access to lowland areas due to hydropower projects, increasing reliance on uplands. Finally, it calls for improving forest management strategies and legal definitions of forest categories to better incorporate village
TABI input on: Stabilisation and Development of upland rice cropping and villager forestry systems as a crucial component in Upland Development in the LAO PDR
National LUP workshop 4.10. - 5.10.2012, Vientiane, Lao PDR
NT2 project
Participatory Land use Planning (the case of NK resettlement area of the NT2 Project)
The document summarizes an analysis of predicting forest cover types using machine learning models. It describes the dataset containing over 500,000 observations of forest cover types and 12 descriptive variables. Various models were tested including decision forests, boosted decision trees, and neural networks. The best performing models on the test set were an ensemble approach blending multiple models and a one-vs-all decision forest, both achieving over 80% accuracy. Experiments were conducted using Microsoft Azure machine learning services.
Accuracy assessment is an essential step in any remote sensing classification. It involves collecting reference data through methods like GPS ground sampling and comparing the classifications to determine accuracy. Key aspects of accuracy assessment include error matrices to calculate overall, user's, and producer's accuracies. User's accuracy indicates errors of commission while producer's accuracy reflects errors of omission. ERDAS Imagine software can be used to import reference data and generate accuracy reports to evaluate classification performance.
Introduce variable/ Indices using landsat imageKabir Uddin
Image Index is a “synthetic image layer” created from the existing bands of a multispectral image. This new layer often provides unique and valuable information not found in any of the other individual bands.
Image index is a calculated result or generated product from satellite band/channels
It helps to identify different land cover from mathematical definition .
Band Combination of Landsat 8 Earth-observing Satellite ImagesKabir Uddin
This document discusses Landsat 8, an Earth-observing satellite launched by NASA in 2013. It provides details on Landsat 8 such as its mission to continuously archive global images of Earth since the 1970s, how it collects about 400 scenes per day, and its various spectral bands that can be used alone or combined for different analyses. Band combination examples are shown for Landsat 8 images before and after processing of Inle Lake to demonstrate the use of natural color, color infrared, and different false color composites.
Few Indicies(NDVI... etc) performed on ERDAS software using Model MakerSwetha A
The document describes how to create indices in ERDAS Model Maker to highlight different land features in satellite imagery. It provides the formulas and instructions to build models to create the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Turbidity Index (NDTI), Normalized Difference Pond Index (NDPI), and Modified Normalized Difference Water Index (MNDWI). For each index, it lists the band math formula used and explains that the output will highlight the targeted land features.
Use of remote sensing for land cover monitoring servir science applicationsKabir Uddin
This document discusses land cover mapping using remote sensing. It provides background on land cover mapping and monitoring in the Himalayan region, where deforestation and forest degradation have been issues. Remote sensing using satellite imagery and tools like GIS allows accurate land cover mapping over large areas. The document discusses different remote sensing platforms and sensors, as well as image classification techniques including unsupervised, supervised and object-based classification. It provides examples of software used for object-based image analysis, and outlines the steps involved in land cover mapping projects using remote sensing.
This document provides a summary of the 2014 field-based Agricultural Resource Inventory (AgRI) mapping of the Innisfil Creek Subwatershed in Ontario. The main crops grown are potato, corn, soybean, turf, and winter wheat, covering over 75% of mapped farm fields. Soybean is the predominant crop. Irrigation systems were observed on 6% of fields, with centre pivot systems being most common. Crops are generally grown on soils with moderate to low infiltration rates. The mapping identifies spatial patterns of crop distribution and irrigation that can help inform water resource management.
This document describes a land use and land cover classification system for mapping using satellite data. It includes 14 categories organized in a three-level hierarchy with associated codes. The categories include built-up land, agricultural land, forest, wastelands, water bodies, and others. Factors like tone, size, shape, texture, and pattern are used to classify different land types captured by satellites at various scales from 1:2,50,000 to 1:4,000. Ground truthing is used to verify and modify preliminary interpretations of satellite imagery.
This document discusses strategies for stabilizing upland rice cropping and developing village forestry systems as crucial components of upland development in Laos. It recognizes upland rice cultivation and bush fallow systems as important current and future livelihood activities that can be stabilized. It argues that rotational upland systems have a neutral carbon footprint and that upland rice is an important cash crop and route to food security. The document presents examples of how participatory land use planning processes have stabilized upland cropping systems. It also notes that many villagers will lose access to lowland areas due to hydropower projects, increasing reliance on uplands. Finally, it calls for improving forest management strategies and legal definitions of forest categories to better incorporate village
TABI input on: Stabilisation and Development of upland rice cropping and villager forestry systems as a crucial component in Upland Development in the LAO PDR
Similar to Lupws session 4 se_clu_forest cover_TABI_eng (6)
National LUP workshop 4.10. - 5.10.2012, Vientiane, Lao PDR
NT2 project
Participatory Land use Planning (the case of NK resettlement area of the NT2 Project)
Lupws session 1 progress NGD FINNMAP_short_oct2012_eng only
Lupws session 4 se_clu_forest cover_TABI_eng
1. Session 4:
Information and Assessment:
Livelihoods, Current land Use and Forest Cover
4.1: Assessment of Livelihoods (especially Ag) by re-call Interview
4.2: Current Land Use and Forest Cover assessment-mapping by
4.2.1: Sateliite or aerial photo imagery review
4.2.2: Field survey
4.2.3: Household mapping of upland field distribution
1
15.10.2012
2. Session 4.1:
Gathering Information – Assessing Livelihoods and
Land Use > (i) Group or (ii) Household Interview
4.1(i) By Group Interview
1. Agro ecosystem assessment (optional)
2. NTFP
3. Wood
4. Wildlife (protein)
5. Aquatic ife (protein)
Purposes
1. To understand and assess the natural resources
2. To provide information/data for forest land allocation
3. To use as the baseline data for natural resource monitoring, management, and planning
2
15.10.2012
3. Session 4.1:
Gathering Information – Assessing Livelihoods and Land
Use >> form (i) Group or (ii) Household Interview (Cont.)
4.1 (ii) By Household Interview
1. HH Population, and Rice Sufficieny
2. Livestock
3. Land Use - holdings and Production
4. Income (optional)
5. Goods and chattels (optional)
3
15.10.2012
5. 4.2: Current Land Use - Forest Cover Mapping by
4.2.1: Imagery review
4.2.2: Field survey
4.2.3: Mapping of upland field distribution (1-2 years)
4.2.1: imagery review
1: Upland field mapping of the year image taken
2: Paddy fields mapping (those may be seen)
3: Plantation and concession area mapping (those may be seen)
4: Forests never been cleared before (+ other areas) mapping –
those are never cleared for agriculture
5
> The remaining areas must be bush fallow“
15.10.2012
6. Example of ALOS imagery on upland fields and forest never been cleared for
agri. before
6
15.10.2012
8. 4.2.2: Field Survey
If permanent agricultural fields or plantations cannot be seen or mapped
from the Image, then they will have to be field surveyed – semi-detailed:
GPS points around the boundary of contiguous blocks.
1. For ‘forest never been cleared” and ‘upland fields’ : staff and villager
teams should undertake some field surveys to observe and verify,
2. Permanent agriculture fields not appeared in the image, field survey is
required by using GPS.
8
15.10.2012
10. 4.2.3: Mapping of upland field distribution (not appeared on the imagery)
Objectives:
1: To increase understanding of the villagers’ upland field activities
2: To serve as the reference for possible upland field re-allocation
Method: “Place the Pin/sticker on Image” (or TOPO 2.5 D)
The best village mapper and staff guide each household in turn
to place a coloured mapping pin/sticker using one color for 1
year and placing for 2 years.
Issue:
• Be able to pin only the rice upland fields, while other crop fields were
not able to do so.
10
15.10.2012
15. Current Land Use Map: Longkhan Village, Phoukoud District
15
15.10.2012
16. Table/Data of Current Land Use and Forest Cover: Donxay
Village, Phonexai District
Types of Current Land Use No. of Pieces Area (ha)
1. Virgin Forests 38 626 28.47
Mixed forests Mixed of dense, deciduous, and
37 609 27.69
bamboo forests
Jungle 1 17 0.77
Plantation areas 1 3 0.14
Agricultural land 114 1566 71.21
Wet season paddy fields 1 1 0.05
(Current) upland plantation fields 51 83 3.77
1- years bush fallows young fallows 35 936 42.56
> years bush fallows old fallows 26 538 24.47
Savanna 1 8 0.36
Construction land 1 4 0.18
Residence areas 1 4 0.18
16
15.10.2012
Total 154 2199 100.00