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In this study, we focused on integrating remote sensing techniques and MaxEnt modeling to develop a restoration approach to determine and rank hotspots for revegetation and restoration planning of native desert plant communities. The results showed that the distribution of suitable habitats for native desert communities varied according to climatic, bioclimatic, soil, and topographic factors. Cyperus species occur in deep loose soil, while Rhanterium species predominate on gypsums or calcareous soils growing in areas that contain a thin layer of gravel. Communities such as Cyperus and Haloxylon were not influenced by the wet seasons, making them suitable for restoration because of their resiliency to grow under harsh and drought seasons. Based on the results, a vegetation restoration approach was designed, which classifies the locations into three categories: community management sites (appropriate sites for one community), restoration sites (appropriate sites for two communities), and prioritized restoration sites (appropriate sites for more than three communities). Limited sites were ranked as prioritized restoration sites, making it essential to consider these locations as optimum hotspot sites for future restoration and revegetation. We concluded that the integrated approach helped develop a detailed vegetation community map that ranked suitability habitats based on several environmental factors, which could be used as biological indicators for restoration planning. The vegetation restoration approach is also functional for regional‐scale studies to identify and rank hotspot locations for revegetation and restoration planning.

Unmanned Aerial Vehicles (UAVs) have started to receive more attention in the ecological field in the past 15 years, as they provide very high-resolution imagery that ranges from meters to millimeters. Very high-resolution multispectral imagery obtained from UAVs can help in assessing and monitoring native desert vegetation. Thus, this study use UAVs to develop a method to estimate the biomass and carbon stock of native desert shrubs. The method integrates different techniques and software to monitor native plants' coverage, biomass, and carbon stock. The techniques used in this work are also applicable for other native desert shrubs in the region and could support ecosystem managers in assessing and monitoring arid ecosystems and restoration and revegetation programs. A three-stage image and data management are discussed, including: (1) fieldwork and image acquisition using UAVs, (2) image pre-processing, and (3) image processing using different techniques and software. Determining shrub biomass is not restricted to multispectral data only but could be applicable for RGB data since it mainly depends on the DSM and DTM. Allometric parameters could help in estimating desert shrub biomass which could be measured easily and rapidly using UAV imagery. SVM Supervised classification could help in distinguishing between native shrubs and grasses.

The rapid assessment and monitoring of native desert plants are essential in restoration and revegetation projects to track the changes in vegetation patterns in terms of vegetation coverage and structure. This work investigated advanced vegetation monitoring methods utilizing UAVs and remote sensing techniques at the Al Abdali protected site in Kuwait. The study examined the effectiveness of using UAV techniques to assess the structure of desert plants. We specifically examined the use of very-high-resolution aerial imagery to estimate the vegetation structure of Rhanterium epapposum (perennial desert shrub), assess the vegetation cover density changes in desert plants after rainfall events, and investigate the relationship between the distribution of perennial shrub structure and vegetation cover density of annual plants. The images were classified using supervised classification techniques (the SVM method) to assess the changes in desert plants after extreme rainfall events. A digital terrain model (DTM) and a digital surface model (DSM) were also generated to estimate the maximum shrub heights. The classified imagery results show that a significant increase in vegetation coverage occurred in the annual plants after rainfall events. The results also show a reasonable correlation between the shrub heights estimated using UAVs and the ground-truth measurements (R² = 0.66, p < 0.01). The shrub heights were higher in the high-cover-density plots, with coverage >30% and an average height of 77 cm. However, in the medium-cover-density (MD) plots, the coverage was <30%, and the average height was 52 cm. Our study suggests that utilizing UAVs can provide several advantages to critically support future ecological studies and revegetation and restoration programs in desert ecosystems.