Plastic greenhouses (PGs) play a vital role in modern agricultural development by providing a controlled environment for the cultivation of food crops. Their widespread adoption has the potential to revolutionize agriculture and impact the local environment. Accurate mapping and estimation of PG coverage are critical for strategic planning in agriculture. However, the challenge lies in the extraction of small and densely distributed PGs; this is often compounded by issues like irrelevant and redundant features and spectral confusion in high-resolution remote-sensing imagery, such as Gaofen-2 data. This paper proposes an innovative approach that combines the power of a full convolutional network (FC-DenseNet103) with an image enhancement index. The image enhancement index effectively accentuates the boundary features of PGs in Gaofen-2 satellite images, enhancing the unique spectral characteristics of PGs. FC-DenseNet103, known for its robust feature propagation and extensive feature reuse, complements this by addressing challenges related to feature fusion and misclassification at the boundaries of PGs and adjacent features. The results demonstrate the effectiveness of this approach. By incorporating the image enhancement index into the DenseNet103 model, the proposed method successfully eliminates issues related to the fusion and misclassification of PG boundaries and adjacent features. The proposed method, known as DenseNet103 (Index), excels in extracting the integrity of PGs, especially in cases involving small and densely packed plastic sheds. Moreover, it holds the potential for large-scale digital mapping of PG coverage. In conclusion, the proposed method providing a practical and versatile tool for a wide range of applications related to the monitoring and evaluation of PGs, which can help to improve the precision of agricultural management and quantitative environmental assessment.

When it comes to mitigating the damaging effects of deforestation and the adverse effects of rising carbon dioxide concentrations in the atmosphere, reforestation is an environmentally responsible and effective strategy. China, which has one of the most significant rates of afforestation in the world, has increased its forest cover from 16.6% when it was last measured twenty years ago to 23.0% when it is next measured in 2020. However, there is uncertainty regarding the maximum potential forest coverage that can be achieved through tree planting and restoration. We developed a random forest regression model to map the potential tree coverage across China. The model links environmental factors with the types of forests that are most appropriate. After removing already-forested areas, urban areas, and agricultural land covers and uses, we estimate that there is a total of 67.2 million hectares of land that is currently available for tree restoration. This is a 50% increase over the current understanding of the available land. The establishment of a forest on these lands would result in the creation of 3.99 gigatons of new carbon stocks both above and below ground, which would be an important contribution toward the goal of reaching carbon neutrality. This potential is geographically unbalanced, with the largest restorable carbon potential being located in the south-west (29.5%), followed by the north-east (17.2%), and then the north-west (16.8%). The results of our study highlight the importance of coordinating the planting of trees in reforestation efforts with the uneven distribution of potential carbon storage. Reforestation should provide other environmental services in addition to acting as a biological mitigation strategy to partially offset carbon dioxide emissions caused by the burning of fossil fuels. These services include the restoration of degraded soils, conservation of biological diversity, revitalization of hydrological integrity, localized cooling, and improvement in air quality. Instead of concentrating solely on the act of planting trees, we believe it is more beneficial for forest restoration efforts to concentrate on the ecosystem as a whole rather than just the trees themselves.