Examining spatiotemporal land cover dynamics in relation to rainfall and temperature: 1992-2022. Case of Chizarira National Park, Zimbabwe

Abstract

Abstract Understanding the spatiotemporal dynamics of land cover is crucial for establishing effective monitoring and evaluation programs to assess ecosystem changes. This study explores the intricate land cover dynamics within Chizarira National Park (CNP), Zimbabwe, spanning the period from 1992 to 2022. It seeks to elucidate the complex interplay between climate variables, land cover changes, and human impacts within the park’s ecosystem. Utilizing Google Earth Engine (GEE), we acquired time series data for Normalized Difference Vegetation Index (NDVI), rainfall, and temperature, enabling us to map changes in five key land cover classes: water, trees, shrubs, grass, and bare land. Quantum Geographic Information System (QGIS) software facilitated further classification and the computation of area changes for each land cover class. Our analysis produced intriguing results. Mean annual Normalized Difference Vegetation Index (NDVI) exhibited fluctuations, reaching its peak in 2017 and lowest in 1995. Notably, we found no significant correlation between rainfall and NDVI (rho = 0.32, p = 0.77) or between temperature and NDVI (rho = -0.16, p = 0.45). These findings indicate that climate variables alone may not be sufficient to explain NDVI variations. Notably, CNP experienced woody encroachment, with shrubs developing into trees, potentially altering ecosystem services and land cover. Additionally, the study identified bare land in areas impacted by illegal anthropogenic activities, underscoring the need for stronger conservation enforcement. This research serves as a critical baseline for understanding CNP's complex ecological system, informing strategies for conservation, and guiding future studies aimed at preserving this unique national park. It emphasizes the importance of monitoring and addressing human-induced changes, exploring fine-scale temporal dynamics, and investigating the impacts of fires and wildlife population density on vegetation cover.