EVALUATION AND PREDICTION OF LAND USE, AND LAND COVER CHANGES USING REMOTE SENSING AND CA-ANN MODEL IN HUONG HOA DISTRICT, QUANG TRI PROVINCE
Vol. 132 No. 3C (2023), Original Article
Vol. 132 No. 3C (2023)

EVALUATION AND PREDICTION OF LAND USE, AND LAND COVER CHANGES USING REMOTE SENSING AND CA-ANN MODEL IN HUONG HOA DISTRICT, QUANG TRI PROVINCE

Original Article
https://doi.org/10.26459/hueunijard.v132i3C.7219
Published 2023-11-30
Nguyen Thuy Phuong
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Nguyen Phuc Khoa
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Le Thai Hung
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Pham Gia Tung
International School – Hue University, 1 Dien Bien Phu St., Hue, Vietnam
Le Đinh Huy
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Nguyen Trung Hai
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Trinh Ngan Ha
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Nguyen Huu Ngu
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
Tran Thanh Duc
University of Agriculture and Forestry, Hue University, 102 Phung Hung St., Hue, Vietnam
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Keywords

LULC change
remote sensing
CA-ANN
LULC prediction

Abstract

Evaluation of land use and land cover change (LULC) is necessary for densely vegetated areas like Huong Hoa district, Quang Tri province. It is a basis for sustainable development strategies. Therefore, the study aims to evaluate the LULC change in the 10-year period of 2013–2023 by using Landsat 8 satellite image data with the Maximum Likelihood Classification method and to predict future LULC changes. The LULC maps for 2013, 2018, and 2023 are accurate, with Kappa coefficients of 0.82–0.85. In the period of 2013–2023, the dense vegetation area tended to decrease by 1.4%. The decrease was mainly due to the transition to sparse vegetation cover. Bare land increased by 0.5%, and the built-up area decreased by 0.6%. Meanwhile, the water bodies changed slightly. The prediction of LULC change with the CA-ANN model in the QGIS MOLUSCE plugin is based on the history of LULC change and two spatial variables: DEM and distance to the road. The accuracy of the CA-ANN model is satisfactory, with an overall accuracy of 86% and a Kappa coefficient of 0.76. In the simulated LULC of 2033, dense vegetation is predicted to keep a higher decrease (2%) in the area compared with the LULC of 2023. Sparse vegetation steadily increased by 1.3% over the subsequent 10 years. Similarly, the built-up area, water boddies, and bare land extended slightly by 0.5, 0.1, and 0.1%, respectively. The CA-ANN model in the QGIS MOLUSCE plugin is suitable for the simulated LULC changes for the studied area.

https://doi.org/10.26459/hueunijard.v132i3C.7219
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