Land Use Classification, Prediction, and the Relationship Between Land Use and Sediment Loss in the Lam Phra Phlong Watershed, Thailand

This study aims to assess the evolution of land cover in the Lam Phra Phloeng (LPP) watershed and predict future land use patterns. By employing the Gray Level Co-occurrence Matrix (GLCM) and several spectral indices, high classification accuracy (>92%) was achieved using the Random Forest (RF) algorithm. Based on classified land use maps from 2003 and 2023, future land use predictions for 2030, and 2050 were generated using the CA-Markov chain model. The predictions suggest a gradual trend toward deforestation and the expansion of croplands, driven by population growth and increased anthropogenic activity in the region. The Sediment Delivery Ratio (SDR) model, part of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite, was used to simulate soil loss in the LPP watershed. The results indicate minimal soil loss in vegetated areas and significant erosion in regions adjacent to water bodies, primarily due to rainfall erosivity. This research highlights the social, ecological, and economic implications of land use change. Furthermore, best management practices (BMPs) are identified as effective strategies for land restoration and erosion reduction. The study also discusses three widely adopted soil erosion control techniques, providing recommendations for reforestation and erosion mitigation programmes.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2026-02-14
DOI
https://doi.org/10.3390/agriculture16040448
Citations
1
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
36.62
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Land Use Classification, Prediction, and the Relationship Between Land Use and Sediment Loss in the Lam Phra Phlong Watershed, Thailand

Prapas Thammaboribal, Uma Seeboonruang, Satya Venkata Sai Aditya Bharadwaz Ganni, Ranadheer Reddy Mandadi et al.
1 citations
Agriculture
Land Use and Ecosystem Services
36.62
article

Land Use Classification, Prediction, and the Relationship Between Land Use and Sediment Loss in the Lam Phra Phlong Watershed, Thailand

Prapas Thammaboribal, Uma Seeboonruang, Satya Venkata Sai Aditya Bharadwaz Ganni, Ranadheer Reddy Mandadi, Arlene L. Gonzales
article en
1 citations

Abstract

This study aims to assess the evolution of land cover in the Lam Phra Phloeng (LPP) watershed and predict future land use patterns. By employing the Gray Level Co-occurrence Matrix (GLCM) and several spectral indices, high classification accuracy (>92%) was achieved using the Random Forest (RF) algorithm. Based on classified land use maps from 2003 and 2023, future land use predictions for 2030, and 2050 were generated using the CA-Markov chain model. The predictions suggest a gradual trend toward deforestation and the expansion of croplands, driven by population growth and increased anthropogenic activity in the region. The Sediment Delivery Ratio (SDR) model, part of the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) suite, was used to simulate soil loss in the LPP watershed. The results indicate minimal soil loss in vegetated areas and significant erosion in regions adjacent to water bodies, primarily due to rainfall erosivity. This research highlights the social, ecological, and economic implications of land use change. Furthermore, best management practices (BMPs) are identified as effective strategies for land restoration and erosion reduction. The study also discusses three widely adopted soil erosion control techniques, providing recommendations for reforestation and erosion mitigation programmes.

AgricultureVol. 16(4)
Mariano Marcos State University (PH), Asian Institute of Technology (TH), King Mongkut's Institute of Technology Ladkrabang (TH)
Life in Land
Openalex Percentile: Top 1%
Land Use and Ecosystem Services
36.62
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.