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Map showing location of the study area -location of Zimbabwe within Africa, and Lake Manyame and Lake Chivero in the Upper Manyame Catchment located west of Harare
Source publication
This study quantified the spatial and temporal variation of aquatic weeds in two lakes in an urban catchment of Zimbabwe using the automatic water extraction index (AWEI) and normalised difference vegetation index (NDVI) derived from Landsat satellite data from 1986 to 2020. Extent of aquatic weeds estimated using AWEI in Lake Chivero increased fro...
Contexts in source publication
Context 1
... this study, Lakes Chivero (formerly Lake McIlwaine) and Manyame (formerly Darwendale Dam), located 76 km and 35 km west of Harare, respectively, were selected as study sites (Fig. 1). The two reservoirs are man-made lakes, with Chivero having been established in 1956 andManyame in 1976. The surface area of Lake Chivero is 26.32 km 2 , with an estimated width of ~2km, while Lake Manyame covers an area of 292.60 km 2 . Lake Manyame has a storage capacity of 480 200 ML and an average depth of 9.4 m. On the other hand, ...
Context 2
... of the trend analysis illustrate a positive but nonsignificant trend in the area under aquatic weeds for Lake Relationship between aquatic weed extent and phosphorus concentration Figure 10 shows the relationship between aquatic weed extent and: (a) phosphorus and (b) nitrogen concentration in Lake Chivero. It can be observed that there is strong and significant positive relationship between aquatic weed extent and phosphorus (R 2 = 79.57, ...
Citations
This study assesses the relationships between vegetation dynamics and climatic variations in Pakistan from 2000 to 2023. Employing high-resolution Landsat data for Normalized Difference Vegetation Index (NDVI) assessments, integrated with climate variables from CHIRPS and ERA5 datasets, our approach leverages Google Earth Engine (GEE) for efficient processing. It combines statistical methodologies, including linear regression, Mann–Kendall trend tests, Sen's slope estimator, partial correlation, and cross wavelet transform analyses. The findings highlight significant spatial and temporal variations in NDVI, with an annual increase averaging 0.00197 per year (p < 0.0001). This positive trend is coupled with an increase in precipitation by 0.4801 mm/year (p = 0.0016). In contrast, our analysis recorded a slight decrease in temperature (− 0.01011 °C/year, p < 0.05) and a reduction in solar radiation (− 0.27526 W/m²/year, p < 0.05). Notably, cross-wavelet transform analysis underscored significant coherence between NDVI and climatic factors, revealing periods of synchronized fluctuations and distinct lagged relationships. This analysis particularly highlighted precipitation as a primary driver of vegetation growth, illustrating its crucial impact across various Pakistani regions. Moreover, the analysis revealed distinct seasonal patterns, indicating that vegetation health is most responsive during the monsoon season, correlating strongly with peaks in seasonal precipitation. Our investigation has revealed Pakistan's complex association between vegetation health and climatic factors, which varies across different regions. Through cross-wavelet analysis, we have identified distinct coherence and phase relationships that highlight the critical influence of climatic drivers on vegetation patterns. These insights are crucial for developing regional climate adaptation strategies and informing sustainable agricultural and environmental management practices in the face of ongoing climatic changes.
