Rainfall Trend and Its Relationship with Normalized Difference Vegetation Index in a Restored Semi-Arid Wetland of South Africa

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Spatial and temporal patterns of agrometeorological indicators in maize producing provinces of South Africa

Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91-2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03-0.04 °C per year and 0.02-0.04 °C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms-1 per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50-80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Niño and La Niña events.

Long-Term Dynamics and Response to Climate Change of Different Vegetation Types Using GIMMS NDVI3g Data over Amathole District in South Africa

Monitoring vegetation dynamics is essential for improving our understanding of how natural and managed agricultural landscapes respond to climate variability and change in the long term. Amathole District Municipality (ADM) in Eastern Cape Province of South Africa has been majorly threatened by climate variability and change during the last decades. This study explored long-term dynamics of vegetation and its response to climate variations using the satellite-derived normalized difference vegetation index from the third-generation Global Inventory Modeling and Mapping Studies (GIMMS NDVI3g) and the ERA5-Land global reanalysis product. A non-parametric trend and partial correlation analyses were used to evaluate the long-term vegetation changes and the role of climatic variables (temperature, precipitation, solar radiation and wind speed) during the period 1981–2015. The results of the ADM’s seasonal NDVI3g characteristics suggested that negative vegetation changes (browning trends) dominated most of the landscape from winter to summer while positive (greening) trends dominated in autumn during the study period. Much of these changes were reflected in forest landscapes with a higher coefficient of variation (CV ≈ 15) than other vegetation types (CV ≈ 10). In addition, the pixel-wise correlation analyses indicated a positive (negative) relationship between the NDVI3g and the ERA5-Land precipitation in spring–autumn (winter) seasons, while the reverse was the case with other climatic variables across vegetation types. However, the relationships between the NDVI3g and the climatic variables were relatively low (R < 0.5) across vegetation types and seasons, the results somewhat suggest the potential role of atmospheric variations in vegetation changes in ADM. The findings of this study provide invaluable insights into potential consequences of climate change and the need for well-informed decisions that underpin the evaluation and management of regional vegetation and forest resources.

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.