Rapid mapping of land cover changes in tropical watershed in Indonesia using Google Earth Engine
Besitang watershed is one of the tropical watersheds in Indonesia. In the upstream area, Besitang watershed is part of the Gunung Leuser National Park, a conservation forest abundant with diverse wildlife, including primates and terrestrial mammals. Studying land cover changes in Besitang watershed...
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description | Besitang watershed is one of the tropical watersheds in Indonesia. In the upstream area, Besitang watershed is part of the Gunung Leuser National Park, a conservation forest abundant with diverse wildlife, including primates and terrestrial mammals. Studying land cover changes in Besitang watershed is crucial. A challenge in studying land cover changes in tropical watersheds is cloud cover, thus necessitating land cover mapping studies in Besitang watershed using Google Earth Engine (GEE). GEE is a platform for processing geo-big data, providing extensive and cloud-free satellite image data. Therefore, this study aims to map land cover in Besitang watershed using machine learning-based classification on GEE. The land cover mapping process in Besitang watershed utilizes Landsat 8 satellite imagery. The input data includes bands 1 to 7 of Landsat 8 imagery and image transformations such as NDVI, NDWI, NDBI, BSI, EVI, NDTI, SATVI. The years selected for analysis are 2001 and 2021, with machine learning techniques tested including CART and Random Forest (RF). The results of this study indicate that RF is the best machine learning method for mapping land cover in Besitang watershed, using an image combination consisting of Band 2, Band 3, Band 4, Band 5, Band 6, Band, NDVI, NDWI, NDBI, BSI. The mapping results show a forest area change of approximately 352326.29 hectares, with the majority of changes to plantation. |
doi_str_mv | 10.1088/1755-1315/1414/1/012077 |
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In the upstream area, Besitang watershed is part of the Gunung Leuser National Park, a conservation forest abundant with diverse wildlife, including primates and terrestrial mammals. Studying land cover changes in Besitang watershed is crucial. A challenge in studying land cover changes in tropical watersheds is cloud cover, thus necessitating land cover mapping studies in Besitang watershed using Google Earth Engine (GEE). GEE is a platform for processing geo-big data, providing extensive and cloud-free satellite image data. Therefore, this study aims to map land cover in Besitang watershed using machine learning-based classification on GEE. The land cover mapping process in Besitang watershed utilizes Landsat 8 satellite imagery. The input data includes bands 1 to 7 of Landsat 8 imagery and image transformations such as NDVI, NDWI, NDBI, BSI, EVI, NDTI, SATVI. The years selected for analysis are 2001 and 2021, with machine learning techniques tested including CART and Random Forest (RF). The results of this study indicate that RF is the best machine learning method for mapping land cover in Besitang watershed, using an image combination consisting of Band 2, Band 3, Band 4, Band 5, Band 6, Band, NDVI, NDWI, NDBI, BSI. The mapping results show a forest area change of approximately 352326.29 hectares, with the majority of changes to plantation.</description><identifier>ISSN: 1755-1307</identifier><identifier>EISSN: 1755-1315</identifier><identifier>DOI: 10.1088/1755-1315/1414/1/012077</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Big Data ; Cloud cover ; Forest management ; Land conservation ; Land cover ; Landsat ; Learning algorithms ; Machine learning ; Mapping ; National parks ; Normalized difference vegetative index ; Remote sensing ; Satellite imagery ; Watersheds ; Wildlife ; Wildlife conservation</subject><ispartof>IOP conference series. Earth and environmental science, 2024-12, Vol.1414 (1), p.12077</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>Published under licence by IOP Publishing Ltd. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). 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Earth and environmental science</title><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><description>Besitang watershed is one of the tropical watersheds in Indonesia. In the upstream area, Besitang watershed is part of the Gunung Leuser National Park, a conservation forest abundant with diverse wildlife, including primates and terrestrial mammals. Studying land cover changes in Besitang watershed is crucial. A challenge in studying land cover changes in tropical watersheds is cloud cover, thus necessitating land cover mapping studies in Besitang watershed using Google Earth Engine (GEE). GEE is a platform for processing geo-big data, providing extensive and cloud-free satellite image data. Therefore, this study aims to map land cover in Besitang watershed using machine learning-based classification on GEE. The land cover mapping process in Besitang watershed utilizes Landsat 8 satellite imagery. The input data includes bands 1 to 7 of Landsat 8 imagery and image transformations such as NDVI, NDWI, NDBI, BSI, EVI, NDTI, SATVI. The years selected for analysis are 2001 and 2021, with machine learning techniques tested including CART and Random Forest (RF). The results of this study indicate that RF is the best machine learning method for mapping land cover in Besitang watershed, using an image combination consisting of Band 2, Band 3, Band 4, Band 5, Band 6, Band, NDVI, NDWI, NDBI, BSI. The mapping results show a forest area change of approximately 352326.29 hectares, with the majority of changes to plantation.</description><subject>Big Data</subject><subject>Cloud cover</subject><subject>Forest management</subject><subject>Land conservation</subject><subject>Land cover</subject><subject>Landsat</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Mapping</subject><subject>National parks</subject><subject>Normalized difference vegetative index</subject><subject>Remote sensing</subject><subject>Satellite imagery</subject><subject>Watersheds</subject><subject>Wildlife</subject><subject>Wildlife conservation</subject><issn>1755-1307</issn><issn>1755-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>BENPR</sourceid><recordid>eNqFkN9LwzAQx4soOKd_gwGffKjNNc3SPMqoczAQ_PEc0yTtOrqkJp3if-9KZSIIPt1x9_3cwSeKLgHfAM7zBBilMRCgCWSQJZBgSDFjR9HksDk-9JidRmchbDCesYzwSfT6KLtGo63susbWyFWolVYj5d6NR2otbW0CaizqvesaJVv0IXvjw9roYbq02lkTGol2YcAXztWtQYX0_RoVtm6sOY9OKtkGc_Fdp9HLXfE8v49XD4vl_HYVK6BZHnOJaUrULM8pV0QTBYxQYJxxnpGU0FSXpOSGUoVz0JynFeZKkRJmZaoN52QaXY13O-_edib0YuN23u5fCgIZZSRlWb5PsTGlvAvBm0p0vtlK_ykAi0GnGESJQZoYdAoQo849SUaycd3P6f-p6z-oonj6nROdrsgXuyaCvw</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Fariz, Trida Ridho</creator><creator>Suhardono, Sapta</creator><creator>Fadhilla, Suri</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>COVID</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope></search><sort><creationdate>20241201</creationdate><title>Rapid mapping of land cover changes in tropical watershed in Indonesia using Google Earth Engine</title><author>Fariz, Trida Ridho ; Suhardono, Sapta ; Fadhilla, Suri</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1548-9a0523c68859c3d3c1735179799432352db3b9e55c081d992f09cc3b16b2de993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Big Data</topic><topic>Cloud cover</topic><topic>Forest management</topic><topic>Land conservation</topic><topic>Land cover</topic><topic>Landsat</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Mapping</topic><topic>National parks</topic><topic>Normalized difference vegetative index</topic><topic>Remote sensing</topic><topic>Satellite imagery</topic><topic>Watersheds</topic><topic>Wildlife</topic><topic>Wildlife conservation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fariz, Trida Ridho</creatorcontrib><creatorcontrib>Suhardono, Sapta</creatorcontrib><creatorcontrib>Fadhilla, Suri</creatorcontrib><collection>IOP Publishing Free Content</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><jtitle>IOP conference series. Earth and environmental science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fariz, Trida Ridho</au><au>Suhardono, Sapta</au><au>Fadhilla, Suri</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid mapping of land cover changes in tropical watershed in Indonesia using Google Earth Engine</atitle><jtitle>IOP conference series. Earth and environmental science</jtitle><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><date>2024-12-01</date><risdate>2024</risdate><volume>1414</volume><issue>1</issue><spage>12077</spage><pages>12077-</pages><issn>1755-1307</issn><eissn>1755-1315</eissn><abstract>Besitang watershed is one of the tropical watersheds in Indonesia. In the upstream area, Besitang watershed is part of the Gunung Leuser National Park, a conservation forest abundant with diverse wildlife, including primates and terrestrial mammals. Studying land cover changes in Besitang watershed is crucial. A challenge in studying land cover changes in tropical watersheds is cloud cover, thus necessitating land cover mapping studies in Besitang watershed using Google Earth Engine (GEE). GEE is a platform for processing geo-big data, providing extensive and cloud-free satellite image data. Therefore, this study aims to map land cover in Besitang watershed using machine learning-based classification on GEE. The land cover mapping process in Besitang watershed utilizes Landsat 8 satellite imagery. The input data includes bands 1 to 7 of Landsat 8 imagery and image transformations such as NDVI, NDWI, NDBI, BSI, EVI, NDTI, SATVI. The years selected for analysis are 2001 and 2021, with machine learning techniques tested including CART and Random Forest (RF). The results of this study indicate that RF is the best machine learning method for mapping land cover in Besitang watershed, using an image combination consisting of Band 2, Band 3, Band 4, Band 5, Band 6, Band, NDVI, NDWI, NDBI, BSI. The mapping results show a forest area change of approximately 352326.29 hectares, with the majority of changes to plantation.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1755-1315/1414/1/012077</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Big Data Cloud cover Forest management Land conservation Land cover Landsat Learning algorithms Machine learning Mapping National parks Normalized difference vegetative index Remote sensing Satellite imagery Watersheds Wildlife Wildlife conservation |
title | Rapid mapping of land cover changes in tropical watershed in Indonesia using Google Earth Engine |
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