Rabigh Lagoon, located on the eastern coast of the Red Sea, is an ecologically rich zone in Saudi Arabia, providing habitat to Avicennia marina mangrove trees. The environmental quality of the lagoon has been decaying since the 1990s mainly from sedimentation, road construction, and camel grazing. However, because of remedial measures, the mangrove communities have shown some degree of restoration. This study aims to monitor mangrove health of Rabigh Lagoon during the time it was under stress from road construction and after the road was demolished. For this purpose, time series of EVI (Enhanced Vegetation Index), MSAVI (Modified, Soil-Adjusted Vegetation Index), NDVI (Normalized Difference Vegetation Index), and NDMI (Normalized Difference Moisture Index) have been used as a proxy to plant biomass and indicator of forest disturbance and recovery. Long-term trend patterns, through linear, least square regression, were estimated using 30 m annual Landsat surface-reflectance-derived indices from 1986 to 2019. The outcome of this study showed (1) a positive trend over most of the study region during the evaluation period; (2) most trend slopes were gradual and weakly positive, implying subtle changes as opposed to abrupt changes; (3) all four indices divided the times series into three phases: degraded mangroves, slow recovery, and regenerated mangroves; (4) MSAVI performed best in capturing various trend patterns related to the greenness of vegetation; and (5) NDMI better identified forest disturbance and recovery in terms of water stress. Validating observed patterns using only the regression slope proved to be a challenge. Therefore, water quality parameters such as salinity, pH/dissolved oxygen should also be investigated to explain the calculated trends.

In this study, we analyzed time series of a vegetation index to identify land-cover/land-use changes in HHK mountain regions administered by Pakistan. Monthly MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) series were decomposed to retrieve changes in long-term mean, peaking magnitude and seasonal characteristics. Resulting linear trend patterns were used as a baseline to map syndromes over the study area. To address non-linear changes, inter-annual variation (IAV) and short-term variation (STV) was also computed. Distributed lag-models (DLM) were used to determine significant correlation between GIMMS (Global Inventory Monitoring and Mapping) NDVI (Normalized Difference Vegetation Index) and rainfall as an additional syndrome to highlight climate sensitive regions. Our results indicate that land use is mainly controlled by two factors: elevation and river network. Based on that, there is a particular spatial distribution of agricultural intensification because of deforestation, forest degradation from unsustainable use, forest regeneration and human settlements near rivers. Most of the trends observed showed a persistent greening pattern compared to small groups of pixels with negative trends. Outcomes of DLM do not provide plausible links between rain and forest biomass. It does however suggest a positive response of crops to rainfall events in the arid zones of the study area. High short-term fluctuation in EVI residuals occurred in areas that experienced considerable land modification and where land-use patterns are not stable. IAV was high in regions around rivers and water works and its impact on forest dynamics could not be substantiated. Land change patterns described here can be used by decision makers for forest restoration programmes.

The process of land degradation needs to be understood at various spatial and temporal scales in order to protect ecosystem services and communities directly dependent on it. This is especially true for regions in sub-Saharan Africa, where socio economic and political factors exacerbate ecological degradation. This study identifies spatially explicit land change dynamics in the Copperbelt province of Zambia in a local context using satellite vegetation index time series derived from the MODIS sensor. Three sets of parameters, namely, monthly series, annual peaking magnitude, and annual mean growing season were developed for the period 2000 to 2019. Trend was estimated by applying harmonic regression on monthly series and linear least square regression on annually aggregated series. Estimated spatial trends were further used as a basis to map endemic land change processes. Our observations were as follows: (a) 15% of the study area dominant in the east showed positive trends, (b) 3% of the study area dominant in the west showed negative trends, (c) natural regeneration in mosaic landscapes (post shifting cultivation) and land management in forest reserves were chiefly responsible for positive trends, and (d) degradation over intact miombo woodland and cultivation areas contributed to negative trends. Additionally, lower productivity over areas with semi-permanent agriculture and shift of new encroachment into woodlands from east to west of Copperbelt was observed. Pivot agriculture was not a main driver in land change. Although overall greening trends prevailed across the study site, the risk of intact woodlands being exposed to various disturbances remains high. The outcome of this study can provide insights about natural and assisted landscape restoration specifically addressing the miombo ecoregion.