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|Title:||Mapping cashew monocultures in the sub-districts of Sawantwadi and Dodamarg on Google Earth Engine||Authors:||Sreekumar, Parvathi||Keywords:||Social sciences::Geography::Environmental sciences||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||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||Abstract:||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.||URI:||https://hdl.handle.net/10356/156733||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ASE Student Reports (FYP/IA/PA/PI)|
Updated on May 15, 2022
Updated on May 15, 2022
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