Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine
Tropical deforestation is increasingly driven by the expansion of agricultural commodity production. Mapping commodity crops is an important step towards monitoring commodity-driven deforestation. Advances in remote sensing technology, such as the availability of high-resolution imagery and the comb...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2023
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| Online Access: | https://hdl.handle.net/10356/164660 |
| _version_ | 1811686423181393920 |
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| author | Rege, Anushka Warnekar, Smita Bodhankar Lee, Janice Ser Huay |
| author2 | Asian School of the Environment |
| author_facet | Asian School of the Environment Rege, Anushka Warnekar, Smita Bodhankar Lee, Janice Ser Huay |
| author_sort | Rege, Anushka |
| collection | NTU |
| description | Tropical deforestation is increasingly driven by the expansion of agricultural commodity production. Mapping commodity crops is an important step towards monitoring commodity-driven deforestation. Advances in remote sensing technology, such as the availability of high-resolution imagery and the combination of optical and radar imagery have enabled the detection of the tree-like crops which are difficult to distinguish from forest cover. Cashew is an example of a tree-like crop that grows in areas with high forest cover and biodiversity. Cashew is reported to occupy ∼7.1 million ha globally yet mapping it has been constrained by unclear boundaries due to spatial mixing with forests, an indistinct spectral signature, and structural composition that resembles forests. We employed optical, radar, and a combination of the two imagery types to detect and map cashew monocultures in south Maharashtra, India for 2020. We performed a land cover classification on Google Earth Engine using Random Forest, Classification And Regression Trees and Support Vector Machine algorithms. The combination of Sentinel-2 and Sentinel-1 SAR imagery using Random Forest algorithm yielded the highest unbiased overall accuracy (83%) and unbiased producer's and user's accuracies of 71% and 86% respectively for cashew land cover and was considered the best approach. According to our best approach, monoculture cashew plantations occupy 53,350.37 ha of total land area in the Sawantwadi- Dodamarg landscape in India. This study shows that a combination of optical and radar imagery can be used for cashew land cover classification in the Western Ghats, and future studies could modify these methods for cashew mapping in other landscapes. This study contributes to a growing body of literature supporting the use of both optical and radar imagery for detecting tree-like crop cover. |
| first_indexed | 2024-10-01T05:00:11Z |
| format | Journal Article |
| id | ntu-10356/164660 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:00:11Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1646602023-02-11T23:32:27Z Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine Rege, Anushka Warnekar, Smita Bodhankar Lee, Janice Ser Huay Asian School of the Environment Earth Observatory of Singapore Engineering::Environmental engineering Anacardium Occidentale Random Forest Tropical deforestation is increasingly driven by the expansion of agricultural commodity production. Mapping commodity crops is an important step towards monitoring commodity-driven deforestation. Advances in remote sensing technology, such as the availability of high-resolution imagery and the combination of optical and radar imagery have enabled the detection of the tree-like crops which are difficult to distinguish from forest cover. Cashew is an example of a tree-like crop that grows in areas with high forest cover and biodiversity. Cashew is reported to occupy ∼7.1 million ha globally yet mapping it has been constrained by unclear boundaries due to spatial mixing with forests, an indistinct spectral signature, and structural composition that resembles forests. We employed optical, radar, and a combination of the two imagery types to detect and map cashew monocultures in south Maharashtra, India for 2020. We performed a land cover classification on Google Earth Engine using Random Forest, Classification And Regression Trees and Support Vector Machine algorithms. The combination of Sentinel-2 and Sentinel-1 SAR imagery using Random Forest algorithm yielded the highest unbiased overall accuracy (83%) and unbiased producer's and user's accuracies of 71% and 86% respectively for cashew land cover and was considered the best approach. According to our best approach, monoculture cashew plantations occupy 53,350.37 ha of total land area in the Sawantwadi- Dodamarg landscape in India. This study shows that a combination of optical and radar imagery can be used for cashew land cover classification in the Western Ghats, and future studies could modify these methods for cashew mapping in other landscapes. This study contributes to a growing body of literature supporting the use of both optical and radar imagery for detecting tree-like crop cover. Ministry of Education (MOE) National Research Foundation (NRF) Published version Funding for the study was provided by the Singaporean Ministry of Education Academic Tier 1 Research Funds (RG145/19 (NS)) and the Navjot Sodhi Conservation Research Award 2021. This research was supported by the Earth Observatory of Singapore via its funding from the National Research Foundation Singapore and the Singapore Ministry of Education under the Research Centres of Excellence initiative. 2023-02-08T00:30:41Z 2023-02-08T00:30:41Z 2022 Journal Article Rege, A., Warnekar, S. B. & Lee, J. S. H. (2022). Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine. Remote Sensing Applications: Society and Environment, 28, 100861-. https://dx.doi.org/10.1016/j.rsase.2022.100861 2352-9385 https://hdl.handle.net/10356/164660 10.1016/j.rsase.2022.100861 2-s2.0-85141316462 28 100861 en RG145/19 (NS) Remote Sensing Applications: Society and Environment © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
| spellingShingle | Engineering::Environmental engineering Anacardium Occidentale Random Forest Rege, Anushka Warnekar, Smita Bodhankar Lee, Janice Ser Huay Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title | Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title_full | Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title_fullStr | Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title_full_unstemmed | Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title_short | Mapping cashew monocultures in the Western Ghats using optical and radar imagery in Google Earth Engine |
| title_sort | mapping cashew monocultures in the western ghats using optical and radar imagery in google earth engine |
| topic | Engineering::Environmental engineering Anacardium Occidentale Random Forest |
| url | https://hdl.handle.net/10356/164660 |
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