TY - GEN
T1 - Analysis of Mangrove Forest Change from Multioral Landsat Imagery Using Google Earth Engine Application
T2 - 4th IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2021
AU - Cipta, Iqbal Maulana
AU - Sobarman, Fahmi Adnizar
AU - Sanjaya, Hartanto
AU - Darminto, Mohammad Rohmaneo
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Bangka Belitung Archipelago is a province consisting of many islands and coastal areas, including mangrove ecosystems. Mangrove forests have experienced a decline since the 1990s due to land clearing for mining. However, there was a restoration of mangrove forests due to the replanting of mangrove forests in the 2000s. Based on these problems, this research aims to identify the changes area of mangrove forests that occurred in 1990 - 2020 in the Belitung Islands. For this purpose, multi-source remote sensing data is used (Landsat 5, Landsat 7, and Landsat 8), machine learning random forest, a free cloud-based platform of Google Earth Engine, and several vegetation indices including MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) which are used as indicators of mangrove forest decline and recovery. Based on the processing results, the highest area obtained in 2020 is 119.91 Ha, with a land cover classification accuracy of 99.09%. Meanwhile, the lowest area occurred in 2000, 70.88 Ha, with a land cover classification accuracy of 98.98%.
AB - Bangka Belitung Archipelago is a province consisting of many islands and coastal areas, including mangrove ecosystems. Mangrove forests have experienced a decline since the 1990s due to land clearing for mining. However, there was a restoration of mangrove forests due to the replanting of mangrove forests in the 2000s. Based on these problems, this research aims to identify the changes area of mangrove forests that occurred in 1990 - 2020 in the Belitung Islands. For this purpose, multi-source remote sensing data is used (Landsat 5, Landsat 7, and Landsat 8), machine learning random forest, a free cloud-based platform of Google Earth Engine, and several vegetation indices including MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) which are used as indicators of mangrove forest decline and recovery. Based on the processing results, the highest area obtained in 2020 is 119.91 Ha, with a land cover classification accuracy of 99.09%. Meanwhile, the lowest area occurred in 2000, 70.88 Ha, with a land cover classification accuracy of 98.98%.
KW - Belitung Archipelago
KW - Google Earth Engine
KW - Land Cover
KW - Mangrove Forest
KW - Vegetation Indices
UR - http://www.scopus.com/inward/record.url?scp=85123610519&partnerID=8YFLogxK
U2 - 10.1109/AGERS53903.2021.9617354
DO - 10.1109/AGERS53903.2021.9617354
M3 - Conference contribution
AN - SCOPUS:85123610519
T3 - 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2021 - Proceeding
SP - 90
EP - 95
BT - 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2021 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 September 2021 through 30 September 2021
ER -
