Modelling and Mapping of Forest Above Ground Biomass in Vietnam using Remote Sensing and GIS technology
| Meeting | VIWC2026 |
| Topic | OS9: Reducing CO2 Emissions: Experiences from ASEAN Countries |
| Author | Luong Viet NGUYEN | Enrico ZARA | Tu Trong TO | Son Mai LE | Thanh Kim Thi PHAN |
| Organization | Head Remote Sensing Application Department, Space Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam | Biological Research and Services Laboratory, Natural Sciences Research Institute, University of the Philippines Diliman | Researcher Scientist Remote Sensing Application Department, Space Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam | Researcher Scientist Remote Sensing Application Department, Space Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam | Researcher Scientist Remote Sensing Application Department, Space Technology Institute (STI), Vietnam Academy of Science and Technology (VAST), 18 Hoang Quoc Viet str., Cau Giay dist., Hanoi 100000, Vietnam |
| DOI | 2022.1669933491 |
Abstract
Climate change is no longer a prediction but has become a major challenge for all humanity. Forests are carbon sinks and affect the rate of climate change. In addition, forests are an important resource for livelihoods for millions of people and contribute to the national economic development of many countries. In this research, we used multi-source satellite data such as ALOS -2 PALSAR-2 (JAXA), Landsat 8 OLI (NASA), Sentinel 1 (ESA) and Sentinel 2 (ESA) for modelling and estimation of forest above ground biomass (AGB) with the support of sample plots data in seven main natural forest ecosystems in Vietnam. The results from the research showed that, (1) For evergreen forest on limestone mountain in Central North, the best model (R2 = 0.78) was obtained from the usage of PALSAR-2 HV polarization, (2) Evergreen forest ecosystem on limestone mountain in North West, the best model (R2 = 0.79) was obtained from the usage of PALSAR-2 HV polarization. (3) For coniferous forest ecosystem in Central Highland, the best model (R2 = 0.79) was obtained from the combination of PALSAR-2 HH, PALSAR-2 textures and Sentinel-2 NDVI, (4) For the drought forest ecosystem in Central South, the best model (R2 = 0.83) was obtained from the combination of PALSAR-2 HV, PALSAR-2 textures, and Landsat 8 NDVI, (5) For the dry open forest ecosystem in Central Highland, the best model (R2 = 0.74) was obtained from the combination of PALSAR-2 HV, PALSAR-2 textures and Landsat 8 NDVI, (6) For melaleuca forest ecosystem in Southwest, the best model (R2 = 0.84) was obtained from the combination of PALSAR-2 HV, PALSAR-2 textures and Sentinel 2 NDVI, (7) For mangrove ecosystem in South East, the best model (R2 = 0.82) was obtained by the combination of PALSAR-2 HV, PALSAR-2 textures and Sentinel-2 NDVI. <br> The outcomes of the research are expected to be the scientific basis for participating in emission trading mechanism and on implementing commitments to the international community on response to climate change in Vietnam.
Keywords: Tropical; Forest ecosystems; Biomass model; Remote Sensing and GIS; Vietnam
