Forests play a critical role in global carbon cycling, and continuous forest destruction together with other factors has accelerated global warming. Furthermore, continued decline of forest areas will critically hinder the accomplishment of carbon neutrality goals. Although the geographical location of deforestation can now be rapidly and accurately detected with remote sensing technology, current forest change products are still not fine-grained, especially from the perspective of carbon trading. Here, we used a deep learning method to detect deforestation in large regions based on 2 m high-resolution optical remote sensing images. Firstly, we proposed a new deforestation detection dataset, which was generated from 11 provincial regions in the Yangtze River Economic Zone of China, containing a total number of 8330 samples (the size of each sample being 512 × 512 pixels). Then, a new deforestation detection model, SiamHRnet-OCR, was designed, based on this dataset. Compared with other deep learning models, SiamHRnet-OCR achieves better results in terms of precision, F1-score, and OA indicator: 0.6482, 0.6892, and 0.9898, respectively. Finally, two large-scale scenarios of deforestation experiments in Southern China were further tested; the deforestation detection results demonstrate that SiamHRnet-OCR can not only detect deforestation effectively but also capture the accurate boundary of the changing area.

Green plants provide food, energy and oxygen sources for human beings and animals on Earth through photosynthesis, which is essential to maintain regional ecological balance. However, few studies have focused on the natural oxygen supply capacity of urban green spaces. As a companion to photosynthesis in leaves, solar-induced chlorophyll fluorescence (SIF) contains abundant photosynthetic information. Currently, satellite-based SIF observations are considered to be a rapid and nondestructive ‘indicator’ of plant photosynthesis, which provides an alternative way to quantitatively assess the spatio-temporal dynamics of oxygen supply capacity in urban green spaces. This study examined the spatial patterns, long-term trends, and environmental control factors of SIF in the nine central cities in China from 2001 to 2020 based on the time-series of the global reconstructed GOSIF-v2 SIF dataset. The results were as follows: (1) There was a contrasting spatial difference between southern and northern cities in China, and multi-year mean SIF values of the southern cities were generally higher than those of the northern cities; (2) The interannual dynamics of SIF in each city generally showed an upward trend, with fluctuations, and the intraannual seasonal differences were more significant in northern cities than those in the southern cities; (3) The spatial trend analysis showed that Beijing, Guangzhou, and Chongqing have had the most significant improvements, followed by Xi’an, Wuhan, Chengdu, and Zhengzhou, while Tianjin and Shanghai have had the least improvements; and (4) The expansion of construction land has exerted significant impacts on the dynamics of the SIF trend in several cities, but it is not the only factor. All analyses indicated that the improvement of vegetation structure and function in the area can offset its negative effect.