Facing the demands of biodiversity conservation and ecosystem service improvement, the spatial pattern optimization of nature reserves has always been a research topic of interest. However, there remains a lack of methodological guidance in the planning of nature reserves and the surrounding areas. To promote the landscape sustainability of nature reserves, we constructed ecological security patterns (ESPs) with two scenarios as a case study in Wenchuan, China. In detail, the ecological sources were identified by ecosystem service evaluation, and the resistance surface was characterized by the habitat quality. The ecological corridors were determined based on circuit theory and the minimum cumulative resistance model. The ecological sources were mainly aggregated in the protected areas, with an area of more than 1000 ha; the high-resistance values were mainly in the area with dense roads or high elevation. There were 21 corridors in the scenario of only optimizing the nature reserve, while 31 corridors were identified when considering non-nature reserves, and the landscape connectivity was enhanced accordingly. The result supported constructing the ESPs between nature and non-nature reserves in Wenchuan to further protect pandas, and a methodological contribution was made to understand the differences of ESPs between them, thus supporting a methodological formulation of sustainable landscape patterns.

Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting.