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The widespread construction of photovoltaic power stations within northwest China poses an environmental threat because of severe increased wind erosion and land degradation. Engineering, plant, and biocrust treatments were evaluated in this study for their effectiveness in the reduction of wind erosion. The placement of solar panels caused wind speed variation and resulted in distinct abrasion and deposition zones between the rows of the solar panels and the formation of deflation zones under the solar panels. Combined treatments (gravel and red clay mulch were applied within the abrasion and deposition zones, respectively) and moss‐crust were the optimal choices within the engineering and biocrust treatments, respectively. We found that for engineering treatments, the combined procedures led to treatments had sand transport rate reductions of 87%, while the straw checkerboard, gravel, and red clay treatments gave reductions of 51, 78, and 74%, respectively. Within the biocrust treatments, the moss‐crust decreased the sand transport rates and the sand erosion–deposit budget by 71 and 114%, respectively, while the cyanobacteria crust caused reductions of 65 and 109%, respectively, in comparison to the control. Both plant treatments decreased the sand transport rates and the sand erosion–deposit budgets, but were inferior to other optimal treatments with the best plant treatment dependent on the placement pattern used for plant establishment. All the treatments had effects on reducing wind erosion, and we strongly recommend the use of moss‐crust and combined treatments in the deflation zones and between the rows of the solar panels, respectively, to significantly reduce the severe wind erosion occurring at these photovoltaic power stations located in sandy areas.

Unmanned aerial vehicles are becoming promising platforms for disaster relief, such as providing emergency communication services in wireless sensor networks, delivering some living supplies, and mapping for disaster recovery. Dynamic task scheduling plays a very critical role in coping with emergent tasks. To solve the multi-UAV dynamic task scheduling, this paper constructs a multi-constraint mathematical model for multi-UAV dynamic task scheduling, involving task demands and platform capabilities. Three objectives are considered, which are to maximize the total profit of scheduled tasks, to minimize the time consumption, and to balance the number of scheduled tasks for multiple UAVs. The multi-objective problem is converted into single-objective optimization via the weighted sum method. Then, a novel dynamic task scheduling method based on a hybrid contract net protocol is proposed, including a buy-sell contract, swap contract, and replacement contract. Finally, extensive simulations are conducted under three scenarios with emergency tasks, pop-up obstacles, and platform failure to verify the superiority of the proposed method.