Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine

Due to the high potential of cashew as a cash group, global agricultural land devoted to cashew has increased rapidly over the past decade. To make space for these plantations, natural landscapes such as forests are cleared. One ecologically sensitive area in which cashew plantations may be con...

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Main Author: Sreekumar, Parvathi
Other Authors: Lee Ser Huay Janice Teresa
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/156733
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1567332023-02-28T16:47:18Z Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine Sreekumar, Parvathi Lee Ser Huay Janice Teresa Asian School of the Environment Anushka Rege janicelee@ntu.edu.sg Social sciences::Geography::Environmental sciences Due to the high potential of cashew as a cash group, global agricultural land devoted to cashew has increased rapidly over the past decade. To make space for these plantations, natural landscapes such as forests are cleared. One ecologically sensitive area in which cashew plantations may be contributing substantially to deforestation trends is the sub-districts of Sawantwadi and Dodamarg, located within the Western Ghats belt in India. To monitor long term changes in the landscape attributed to cashew plantations, we planned to use remote sensing data to create a map which can accurately map cashew monocultures in our study site, for the year 2010. The map can then help researchers and policymakers understand the long term trends in the area devoted to cashew cultivation and determine the impact cashew plantations have on forest cover in the region. We created 9 different maps, with 3 classification algorithms (RF, CART, and SVM) and 3 different datasets (optical data, radar data, and a combination of both), to determine which parameters can create the map that can most accurately identify cashew monocultures. We determined that a model with RF as the classification algorithm and the combined optical and radar dataset as the input data created the map with the greatest overall accuracy of 97%. Therefore, we established that cashew plantations spanned an area of 40 664 hectares in the region constituting 29% of the total region. The methodology in this study can be replicated in future remote sensing studies which may create similar maps for different time periods, thus making it possible for scientists and policy-makers to accurately monitor land cover changes in the region. Bachelor of Science in Environmental Earth Systems Science and Public Policy and Global Affairs 2022-04-23T07:10:04Z 2022-04-23T07:10:04Z 2022 Final Year Project (FYP) Sreekumar, P. (2022). Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156733 https://hdl.handle.net/10356/156733 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Social sciences::Geography::Environmental sciences
spellingShingle Social sciences::Geography::Environmental sciences
Sreekumar, Parvathi
Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
description Due to the high potential of cashew as a cash group, global agricultural land devoted to cashew has increased rapidly over the past decade. To make space for these plantations, natural landscapes such as forests are cleared. One ecologically sensitive area in which cashew plantations may be contributing substantially to deforestation trends is the sub-districts of Sawantwadi and Dodamarg, located within the Western Ghats belt in India. To monitor long term changes in the landscape attributed to cashew plantations, we planned to use remote sensing data to create a map which can accurately map cashew monocultures in our study site, for the year 2010. The map can then help researchers and policymakers understand the long term trends in the area devoted to cashew cultivation and determine the impact cashew plantations have on forest cover in the region. We created 9 different maps, with 3 classification algorithms (RF, CART, and SVM) and 3 different datasets (optical data, radar data, and a combination of both), to determine which parameters can create the map that can most accurately identify cashew monocultures. We determined that a model with RF as the classification algorithm and the combined optical and radar dataset as the input data created the map with the greatest overall accuracy of 97%. Therefore, we established that cashew plantations spanned an area of 40 664 hectares in the region constituting 29% of the total region. The methodology in this study can be replicated in future remote sensing studies which may create similar maps for different time periods, thus making it possible for scientists and policy-makers to accurately monitor land cover changes in the region.
author2 Lee Ser Huay Janice Teresa
author_facet Lee Ser Huay Janice Teresa
Sreekumar, Parvathi
format Final Year Project
author Sreekumar, Parvathi
author_sort Sreekumar, Parvathi
title Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
title_short Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
title_full Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
title_fullStr Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
title_full_unstemmed Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine
title_sort mapping cashew monocultures in the sub-districts of sawantwadi and dodamarg on google earth engine
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/156733
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