The continuous prohibition of commercial logging and intensifying conservation endeavors have encompassed the implementation of the Natural Forest Conservation Program (NFCP) and the Grain-to-Green Program (GTGP) by the Chinese government since 1999. Nevertheless, the efficacy of the commercial logging ban and its effectiveness in halting deforestation remain uncertain. Likewise, the destructive aftermath of the 7.9 magnitude Wenchuan earthquake in 2008 continues to be under scrutiny, necessitating ongoing study and analysis. Thus, there exists a pressing need to comprehensively monitor the spatio-temporal evolution of the forest habitat and assess the ecological status over the past two decades. The Jiudingshan Nature Reserve (JNR) is situated in the upper reaches of the Tuojiang River basin in Sichuan province, China, constituting an integral part of the Giant Panda National Park (GPNP). In this study, we classified land cover types and conducted a meticulous monitoring of forest habitat alterations within JNR, by a multilayer perceptron model (MLP) with a highly learning-sensitive algorithm. To quantify these changes, the Simple Ratio Index (SRI) and the Normalized Difference Vegetation Index (NDVI) were computed from Landsat TM/OLI images of four years (i.e., 1997, 2007, 2008, and 2018). Additionally, elevation, slope, aspect, and other topographic data were acquired from the Digital Elevation Model (DEM). The findings of our study unveil a notable expansion in both the scope and proportion of mixed conifer and broadleaf forest from 1997 to 2004. The growth of coniferous forest and the augmented areas of mixed conifer and broadleaf forest signify a substantial improvement in panda habitat. However, the seismic event of 2008 exhibited a pronounced adverse impact on vegetation, particularly within forested regions. Although there is evidence of forest recovery spanning 21 years, concerns regarding fragmentation linger. It is pivotal to acknowledge the potential long-term adverse implications arising from widespread socio-economic development and a multitude of geohazards. Hence, sustained long-term monitoring coupled with effective management strategies remain pivotal for the preservation and rehabilitation of the Giant Panda National Park (GPNP) and giant panda habitat in the future.

Temperature condition in urban areas has a substantial impact on the well-being and safety of both urban residents and the ecosystems. Green spaces are crucial for mitigating urban heat impacts, and hence, their balanced supply and demand is essential. Due to rapid urbanization, Chengdu has had a significant increase in population, which has had an impact on the dynamic changes in its green space environment, making it a suitable case for our study. This study employs the high-temperature vulnerability framework to classify urban green spaces as entities that regulate the thermal environment. This classification is based on the analysis of ecosystem service supply and demand. The approach creates an assessment framework for managing the balance between the need for and availability of thermal environment regulation in urban green spaces. The study utilizes matching and a priority index to identify places in Chengdu where there are imbalances between supply and demand for services. Our findings suggest: (1) The demand for regulating the thermal environment in Chengdu’s green spaces is defined by three indices: thermal exposure, thermal sensitivity, and thermal adaptability. High-demand areas account for a relatively small proportion and exhibit a pattern of “global diffusion and local concentration”. (2) The supply capacity in Chengdu is determined by both the size of green spaces and the surface temperature. High-supply capacity areas are mainly located in the southeastern part of the city, and their distribution pattern is similar to that of green spaces. (3) The level of correspondence between the supply and demand in Chengdu varies significantly and can be classified into three categories: “insufficient supply and high demand”, “insufficient supply and low demand”, and “abundant supply and low demand”. Out of these, 32 units are located in the area with a shortage of supply, while 6 units are in the area with a significant imbalance of low supply and high demand. (4) The green space thermal environment regulation in Chengdu is categorized into four priority intervention levels: priority I, II, III, and IV zones. The priority IV zone, which has a high intervention priority level, consists of two units primarily located in the Inner Ring Qingyang, Inner Ring Jinjiang, Cuijiadian, Caotang, and Donghu, which are deemed crucial for needing prioritized regulation.

The alpine timberline is a component of terrestrial ecosystems and is highly susceptible to climate change. Since 2000, the Tibetan Plateau’s high-altitude zone has been experiencing a persistent warming, clarifying that the response of the alpine timberline to climate warming is important for mitigating the negative impacts of global warming. However, it is difficult for traditional field surveys to clarify changes in the alpine timberline over a wide range of historical periods. Therefore, alpine timberline sites were extracted from 2000–2021, based on remote sensing data sources (LANDSAT, MODIS), to quantify the timberline vegetation growth in the Gexigou National Nature Reserve and to explore the impacts of climate change on timberline vegetation growth. The results show that the mean temperature increased significantly from 2000 to 2021 (R² = 0.35, p = 0.0036) at a rate of +0.03 °C/year. The alpine timberline continued to shift upwards, but at a slower rate, by +22.87 m, +23.23 m, and +2.73 m in 2000–2007, 2007–2014, and 2014–2021, respectively. The sample plots of the timberline showing an upward shift experienced a decreasing trend. The timberline NDVI increased significantly from 2000 to 2021 (R² = 0.2678, p = 0.0136) with an improvement in its vegetation. The timberline NDVI is positively correlated with the annual mean temperature (p < 0.05), February mean temperature (p < 0.05), June minimum temperature (p < 0.05), February maximum temperature (p < 0.01), June maximum temperature (p < 0.01), and June mean temperature (p < 0.01). It was also found to be negatively correlated with annual precipitation (p < 0.01). The study showcases the practicality of using remote sensing techniques to investigate the alpine timberline shifts and timberline vegetation. The findings are valuable in developing approaches to the sustainable management of timberline ecosystems.