Browsing by Author "Sigopi, Maria"

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    Advancements in remote sensing technologies for accurate monitoring and management of surface water resources in Africa: an overview, limitations, and future directions
    (Taylor and Francis Ltd., 2024) Sigopi, Maria; Dube, Timothy; Shoko, Cletah
    This review presents a comprehensive examination of recent advancements in utilizing multi-date satellite data to analyze spatial and temporal variations in seasonal and inter-annual surface water dynamics within arid environments of Africa. Remote sensing offers continuous, precise, and long-term datasets for surface water research. Various sensors with differing spatial resolutions are discussed, with high-resolution multispectral sensors providing superior spatial resolution but at higher costs. Conversely, dual-sensor approaches, incuding optical sensors (Sentinel-2 and Landsat), radar satellites (Sentinel-1 and RADARSAT) and UAVs were investigated. The review further examines the efficiency and applicability of traditional algorithms such as the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), and automated water extraction index (AWEI) in detecting and delineating surface water resources. Additionally, machine learning (ML) algorithms, including support vector machines (SVM), Random Forest (RF), deep learning and emerging methodologies like recurrent tranformer networks, have been explored. Therefore, we recommend that future research endeavours focus on leveraging high-resolution satellite imagery and integrating physical models with deep learning techniques, artificial intelligence, and online big data processing platforms to improve surface water mapping capabilities.
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    Long-term land use and land cover dynamics in the Okavango River Basin: impacts on wetlands ecohydrological conditions using satellite data and machine learning
    (Routledge, 2025) Sigopi, Maria; Moropane, Lebogang Mmasechaba; Dube, Timothy
    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.