Long-term land use and land cover dynamics in the Okavango River Basin
Impacts on wetlands ecohydrological conditions using satellite data and machine learning
Bibliographic Data
| ID | 21540490 |
|---|---|
| Authors | Maria Sigopi (University of the Western Cape, corresponding author), Cletah Shoko (0000-0003-4222-3539, University of the Witwatersrand), Lebogang M Moropane (University of the Western Cape), Timothy Dube (0000-0003-3456-8991, University of the Western Cape) |
| Year | 2025 |
| Pages | 1-26 |
| Publication date | 2025-10-30 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | South African Geographical Journal (JOURNAL) |
| Journal identifiers | ISSN: 0373-6245 • E-ISSN: 2151-2418 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/03736245.2025.2575951 |
| OpenAlex | W4415694932 |
| Language | EN |
| References cited | 79 |
The Okavango River Basin (ORB), one of sub-Saharan Africa’s most ecologically significant and well-preserved endorheic system, is critical for sustaining biodiversity and providing ecosystem services. However, increasing anthropogenic pressure and environmental change demand continuous and precise monitoring to safeguard its natural assets. This study utilized Google Earth Engine (GEE) to present a robust 34-year (1989–2023) analysis using Landsat 5 and 8 at 30 m resolution. The study examined the relationship between the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and normalized difference phenology index (NDPI), coupled with Climate Hazards Group Infrared Precipitation (CHIRPS), ERA5 Land, and TerraClimate products. Utilizing a Random Forest (RF) classifier, we achieved accuracies of 95-98% across nine intervals. Wetlands maintained 3% coverage from 1989-2004, while forest occupied 20-26%. Water bodies declined from 1989-2016, then gained 6419 km2 (2017–2020). Wetlands gained 19144 km2 (1989–1992) and 8406 km2 (2017–2020), but lost -10986 (1993–1996) and -7734 km2 (2009–2012). Higher temperatures are correlated with NDPI (β = 0.05, p = 0.003, R2 = 0.32) and NDVI (β = 0.106, p = 0.0045, R2 = 0.29), while precipitation and evapotranspiration were not significant. SAVI presented no significant relationship (R2 = 0.27, p = 0.027). These findings underscore the urgent need for continuous LULC monitoring to inform adaptive management strategies for the ORB
Land cover · Land use · Land use, land-use change and forestry · Satellite · Wetland · Flood Risk Assessment and Management · Hydrology and Watershed Management Studies · Soil and Water Nutrient Dynamics
Applied Predictive Modeling
The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features
A soil-adjusted vegetation index (Savi)
Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery
Google Earth Engine for geo-big data applications
Use of normalized difference built-up index in automatically mapping urban areas from TM imagery
The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes
Red and photographic infrared linear combinations for monitoring vegetation
Land Use and Land Cover Classification in the Northern Region of Mozambique Based on Landsat Time Series and Machine Learning
Use of multispectral satellite data to assess impacts of land management practices on wetlands in the Limpopo Transfrontier River Basin, South Africa
Legal aspects of transboundary water management
Land-use change and socio-ecological drivers of wetland conversion in Ha Tien Plain, Mekong Delta, Vietnam
Analysis of land change in the dryland agricultural landscapes of eastern Botswana
| Highly cited | No |
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