Introduction: Green spaces have been recognized for their ecological services, including air purification and biodiversity protection, which contribute to the enhancement of life quality and well-being. However, despite the surge in research evaluating the impact of blue-green spaces on health outcomes, we still lack a definitive understanding of how green and blue spaces affect human health outcomes. To assess the impact of blue-green spaces on human health outcomes, we systematically summarized and evaluated the relationship between green and blue spaces and human health through an umbrella review of epidemiological meta-analyses up to the year 2024.Methods: The study follows the PRISMA guidelines and includes meta analyses from PubMed, Embase, and Cochrane databases, focusing on evidence and methodological improvements. Inclusion criteria encompass studies on human populations, exposure to green and blue spaces, and health outcomes such as mortality, disease risk, and physiological indicators. Data extraction and quality assessment of evidence and methods are conducted using the GRADE system and AMSTAR 2 tool.Results: We find that green space exposure is associated with reduced all-cause mortality, mortality from cardiovascular diseases, incidence of diabetes and metabolic syndrome, low birth weight, and mental health improvements. Blue spaces also show positive associations with beneficial health outcomes, including reduced obesity rates and improved psychological well-being. However, the evidence regarding green space exposure and specific health outcomes such as cancer, asthma, and allergic rhinitis remains heterogeneous and unclear.Discussion: Green and blue spaces clearly have some impact on health. For some outcomes, the effects are robust. This article emphasizes the importance of improving residents’ health through urban planning in public health strategies.Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42024533346].

The Changtang Nature Reserve, located in the hinterland of the Qinghai-Tibet Plateau, plays a crucial role in researching ecological and environmental assessment on the plateau. However, the severe natural conditions in the Changtang Plateau have resulted in the absence of meteorological observation stations within the reserve, thereby leading to a lack of fundamental ecological and environmental research data. Remote sensing technology presents an opportunity for ecological monitoring in the Changtang Nature Reserve. In this study, remote sensing ecological indices (RSEI) were utilized to evaluate the ecological environment of the reserve from 2000 to 2020. The MODIS data reconstructed using the Savitzky-Golay filter on the Google Earth Engine (GEE) platform were employed. Principal component analysis was then conducted to construct the RSEI. The results reveal that the overall ecological environment quality in the Changtang Nature Reserve between 2000 and 2020 was relatively poor. Over the past two decades, the mean RSEI of the reserve exhibited a fluctuating trend of decrease and increase, indicating a deteriorating and subsequently improving ecological environment quality. Specifically, during the period of 2000–2010, the RSEI mean decreased from 0.3197 to 0.2269, suggesting degradation of the ecological environment, and the proportion of areas classified as fair and poor increased by 51.99%, while the proportion of areas classified as good and excellent decreased by 32.69%. However, from 2010 to 2020, it increased from 0.2269 to 0.3180, indicating an improvement in the ecological environment, and the proportion of areas classified as good and excellent increased by 6.11%, while the proportion of areas classified as fair and poor decreased by 2.91%. Spatially, the core zone demonstrated higher ecological environment quality compared to the experimental and buffer zones. The findings of this study provide comprehensive and accurate information about the ecological environment, which supports management, decision-making, and emergency response efforts in the Changtang Nature Reserve. Moreover, it offers a scientific basis for conservation and sustainable development strategies in the reserve. The quantitative assessment of the ecological environment dynamics contributes to the understanding of the reserve’s ecological dynamics and facilitates informed decision-making for effective conservation and management practices.

Convolutional neural networks (CNNs) have demonstrated their efficacy in remote sensing applications for mountain forest classification. However, two-dimensional convolutional neural networks (2D CNNs) require a significant manual involvement in the visual interpretation to obtain continuous polygon label data. To reduce the errors associated with manual visual interpretation and enhance classification efficiency, it is imperative to explore alternative approaches. In this research, we introduce a novel one-dimensional convolutional neural network (1D CNN) methodology that directly leverages field investigation data as labels for classifying mountain forest types based on multiple remote sensing data sources. The hyperparameters were optimised using an orthogonal table, and the model’s performance was evaluated on Mount Emei of Sichuan Province. Comparative assessments with traditional classification methods, namely, a random forest (RF) and a support vector machine (SVM), revealed superior results obtained by the proposed 1D CNN. Forest type classification using the 1D CNN achieved an impressive overall accuracy (OA) of 97.41% and a kappa coefficient (Kappa) of 0.9673, outperforming the U-Net (OA: 94.45%, Kappa: 0.9239), RF (OA: 88.99%, Kappa: 0.8488), and SVM (OA: 88.79%, Kappa: 0.8476). Moreover, the 1D CNN model was retrained using limited field investigation data from Mount Wawu in Sichuan Province and successfully classified forest types in that region, thereby demonstrating its spatial-scale transferability with an OA of 90.86% and a Kappa of 0.8879. These findings underscore the effectiveness of the proposed 1D CNN in utilising multiple remote sensing data sources for accurate mountain forest type classification. In summary, the introduced 1D CNN presents a novel, efficient, and reliable method for mountain forest type classification, offering substantial contributions to the field.

Evergreen broad-leaved forests with rich biodiversity play a key role in stabilizing global vegetation productivity and maintaining land carbon sinks. However, quantitative and accurate classification results for humid, evergreen, broad-leaved forests (HEBF) and semi-humid evergreen broad-leaved forests (SEBF) with different vegetation productivity and significant differences in species composition are lacking. Remote sensing technology brings the possibility of vegetation subtype classification. Taking the mountainous evergreen broad-leaved forests distributed in Sichuan Province as an example, this study proposed a hierarchy-based classifier combined with environmental variables to quantitatively classify the two vegetation subtypes with different ecological characteristics but similar image features. Additionally, we applied Sun–Canopy–Sensor and C parameter(SCS + C) topographic correction to preprocess the images, effectively correcting the radiometric distortion and enhancing the accuracy of vegetation classification. Finally, achieving an overall accuracy (OA) of 87.91% and a Kappa coefficient of 0.76, which is higher than that of directly using the classifier to classify the two vegetation subtypes. The study revealed the widespread distribution of evergreen broad-leaved forests in Sichuan, with a clear boundary between the distribution areas of HEBF and SEBF. The HEBF in the east is located in the basin and the low marginal mountains; the SEBF is located in the southwest dry valley. The methods employed in this study offer an effective approach to vegetation classification in mountainous areas. The findings can provide guidance for ecological engineering construction, ecological protection, and agricultural and livestock development.

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.