Satellite-Based Assessment of Vegetation Dynamics and their Potential Drivers in Lochinvar National Park Using Google Earth Engine
DOI:
https://doi.org/10.38027/smart.v3n1-3Keywords:
Remote Sensing, Google Earth Engine, NDVI, Land Cover Change, Wetland Conservation PolicyAbstract
This study examines vegetation dynamics and their potential environmental drivers in Lochinvar National Park, Zambia, over a 41-year period (1984 to 2025), using Landsat imagery processed on Google Earth Engine (GEE). The specific objectives were to: (1) quantify long-term land-cover change across six classes, namely water, grassland, woodland, floodplain, Mimosa pigra, and mine area; (2) characterise vegetation greenness trends using the Normalized Difference Vegetation Index (NDVI); and (3) examine the spatial association between these changes and hydrological alteration, invasive species spread, and human activity. The guiding research question was: how have vegetation cover and greenness in Lochinvar National Park changed since 1984, and to what extent are these changes spatially associated with hydrological, biological, and anthropogenic pressures? Using a Random Forest classifier (500 trees) applied to nine dry-season composite periods, grassland increased from 161.85 km² (39.31%) in 1984-1988 to 185.21 km² (44.99%) in 2024-2025, a net gain of 23.36 km² (+5.68 percentage points), while floodplain area declined from 114.32 km² (27.77%) to 76.89 km² (18.68%), a net loss of 37.43 km² (-9.09 percentage points). Mimosa pigra extent fell from a peak of 42.05 km² (10.22%) in 1994-1998 to 31.87 km² (7.74%) by 2024-2025. Classification accuracy reached 92% overall in the final period. Maximum NDVI fluctuated between 0.385 and 0.447 across the record, with a shallow positive linear trend of approximately +0.0004 NDVI units per year. These patterns are spatially consistent with, though not statistically proven to be caused by, altered flooding regimes downstream of the Itezhi-Tezhi and Kafue Gorge dams, sustained Mimosa pigra control efforts, and continued anthropogenic pressure. The study contributes an empirical, cloud-based monitoring framework that can inform smart, data-driven, and evidence-based land-management policy for wetland protected areas in Zambia and comparable floodplain systems in the region.
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Copyright (c) 2026 Grace Viswamo, Timothy Mwaanga, Musoka Nyongolo, Penjani Hopkins Nyimbili, Masauso Sakala, Anastasia Kilundo, Erastus Misheng’u Mwanaumo (Author)
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