Remote Sensing and GIS Approaches to Carbon Emission Measurement as Related to Land Use Change in Phu Giao, Binh Duong Province, Vietnam

Land use in the Phu Giao district of Binh Duong province, Vietnam, has undergone significant changes recently, mainly as a result of the province’s economic growth. Corresponding changes in carbon stocks are frequently linked to changes in land use. We examined how changes in land-use categories between 2000 and 2020 affected the amount of carbon stored in vegetation and soil. This study quantified changes in vegetation (biomass) carbon storage, and soil organic carbon (SOC) as land use, land cover change (LULCC) over a 20-year period from 2000 to 2020 using remote sensing techniques, geographic information systems (GIS), and guidelines from the Intergovernmental Panel on Climate Change (IPCC). In details, an algorithm used Landsat time series data to analyze the change in land use. We applied the Random Forest (RF) model in R using variables extracted from Landsat 7 and 8 images and digital data (DEM) from 2000 to 2020 to build land-use maps for 2000 and 2020, then calculate changes in land use during the study period. Next, based on the IPCC guidelines, we calculated each land use type’s carbon emission generated displacement. The results demonstrate that carbon emissions from the land use change process were negligible within the 20-year period, and forest loss causes the majority of carbon emissions in Phu Giao, with the largest source carbon from the conversion of forests to cropland, which accounted for 0.80 Tg C of net emissions.

Authors

Institutions

Publication Details

Journal
Eurasian Soil Science
Published
2026-01-30
DOI
https://doi.org/10.1134/s1064229325603221
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Remote Sensing and GIS Approaches to Carbon Emission Measurement as Related to Land Use Change in Phu Giao, Binh Duong Province, Vietnam

Hang T. T. Nguyen, Hien T. D. Le
Eurasian Soil Science
Remote Sensing in Agriculture
article

Remote Sensing and GIS Approaches to Carbon Emission Measurement as Related to Land Use Change in Phu Giao, Binh Duong Province, Vietnam

Hang T. T. Nguyen, Hien T. D. Le
article en

Abstract

Land use in the Phu Giao district of Binh Duong province, Vietnam, has undergone significant changes recently, mainly as a result of the province’s economic growth. Corresponding changes in carbon stocks are frequently linked to changes in land use. We examined how changes in land-use categories between 2000 and 2020 affected the amount of carbon stored in vegetation and soil. This study quantified changes in vegetation (biomass) carbon storage, and soil organic carbon (SOC) as land use, land cover change (LULCC) over a 20-year period from 2000 to 2020 using remote sensing techniques, geographic information systems (GIS), and guidelines from the Intergovernmental Panel on Climate Change (IPCC). In details, an algorithm used Landsat time series data to analyze the change in land use. We applied the Random Forest (RF) model in R using variables extracted from Landsat 7 and 8 images and digital data (DEM) from 2000 to 2020 to build land-use maps for 2000 and 2020, then calculate changes in land use during the study period. Next, based on the IPCC guidelines, we calculated each land use type’s carbon emission generated displacement. The results demonstrate that carbon emissions from the land use change process were negligible within the 20-year period, and forest loss causes the majority of carbon emissions in Phu Giao, with the largest source carbon from the conversion of forests to cropland, which accounted for 0.80 Tg C of net emissions.

Eurasian Soil ScienceVol. 59(2)
Vietnam National University Ho Chi Minh City (VN), Ho Chi Minh City University of Science (VN), Dalat University (VN)
Openalex Percentile: Top 75%
Remote Sensing in Agriculture
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.