Predicting land use and land cover change dynamics in the eThekwini Municipality: a machine learning approach with Landsat imagery

被引：1

作者：

Buthelezi, Mthokozisi Ndumiso Mzuzuwentokozo ^{[1
]}

Lottering, Romano Trent ^{[1
]}

Peerbhay, Kabir Yunus ^{[1
]}

Mutanga, Onisimo ^{[1
]}

机构：

[1] Univ KwaZulu Natal, Sch Agr Earth & Environm Sci, Pietermaritzburg, South Africa

来源：

JOURNAL OF SPATIAL SCIENCE | 2024年 / 69卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

Land cover; land use; remote sensing; machine learning; Landsat; DIFFERENCE WATER INDEX; BUILT-UP INDEX; CLASSIFICATION; AREAS; PERFORMANCE; ALGORITHMS; QUANTITY; ACCURACY; NDWI; TM;

D O I：

10.1080/14498596.2024.2378362

中图分类号：

P9 [自然地理学];

学科分类号：

0705 ; 070501 ;

摘要：

Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9's potential in LULC classifications.

引用

页码：1241 / 1263

页数：23

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