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The implementation of an urban growth boundary (UGB) can effectively control urban sprawl and promote efficient land use, which is crucial for future urban development. However, most of existing studies overlook the reuse of existing idle and inefficient land within the city in the delineation of UGBs. With China's urban construction shifting from incremental development to stock development, this study focuses on Wuhan and presents a set of technical approaches for delineating UGBs with a stock development orientation. First, a built-up area composite index (POI&ISA) is constructed based on point of interest (POI) kernel density analysis and impervious surface index extraction to evaluate constructive levels in 2010 and 2020 and identify the urban vitality zone. Then, we combine the current land use status and control policies to divide the urban spatial development potential into five categories: urban vitality land, urban non-vitality land, other vitality land, other non-vitality land, and restricted development land. Finally, the PLUS model is applied in the analysis of the driving forces of land use change in Wuhan, simulating the UGBs in three stages of incremental development (2020–2030), incremental and stock development (2030–2040), and stock development (2040–2050). Finally, the PLUS model simulation projects the UGB areas to be 436.436 km2, 474.617 km2, and 520.396 km2 for the years 2030, 2040, and 2050, respectively. The predicted timespan of urban development extends up to 30 years, serving as a reliable reference for Wuhan's long-term and near-term planning. [ABSTRACT FROM AUTHOR]

Ecosystem services are fundamental for the sustainable management of urban environments, particularly in mining cities confronting unique socio-environmental complexities. This study explores the intricate interactions among ecosystem services in a representative mining city, focusing on the impact of mining activities. A novel approach is employed to introduce a comprehensive framework for scenario-based analysis of ecosystem services. Land use and ecosystem service values for 2050 were predicted under the following three scenarios: natural development, ecological protection, and farmland protection. Through the evaluation of four key ecosystem services, namely water yield, habitat quality, carbon storage, and soil conservation, ecosystem service bundles were identified, and the trade-offs and synergies among these bundles were explored. Moreover, ecosystem service bundles in the mining areas were analyzed compared to the region at large, underscoring how the mining of various mineral types distinctly influenced ecosystem services. The results showed a persistent decline in total ecosystem service values of the whole region during 2000–2020 due to the diminishing forest cover and the enlargement of farmland and impervious surfaces. Mining areas exhibited significant impacts, with the soil erosion bundle predominating. However, the soil erosion bundle significantly reduced in the granite, copper, and nickel mining areas. By 2050, total ecosystem service values are projected to slowly rise, except under the farmland protection scenario. The entire region is expected to be mostly occupied by the ecological vulnerability bundle. But the ecosystem vulnerability bundle of mining areas is projected to decrease, especially under the ecological protection scenario, highlighting the importance of conservation efforts. These changes will enhance the synergies between soil conservation and other ecosystem services. [ABSTRACT FROM AUTHOR]

The environmental quality of a mining city has a direct impact on regional sustainable development and has become a key indicator for assessing the effectiveness of national environmental policies. However, against the backdrop of accelerated urbanization, increased demand for resource development, and the promotion of the concept of ecological civilization, mining cities are faced with the major challenge of balancing economic development and ecological environmental protection. This study aims to deeply investigate the spatial and temporal variations of environmental quality and its driving mechanisms of mineral resource-based cities. This study utilizes the wide coverage and multitemporal capabilities of MODIS optical and thermal infrared remote sensing data. It innovatively develops the remote sensing ecological index (RSEI) algorithm on the PIE-Engine cloud platform to quickly obtain the RSEI, which reflects the quality of the ecological environment. The spatial and temporal evolution characteristics of the environmental quality in seven typical mining cities in China from 2001 to 2022 were analyzed. Combined with the vector mine surface data, the spatial and temporal variability of the impacts of mining activities on the ecological environment were quantitatively separated and explored. In particular, the characteristics of mining cities were taken into account by creating buffer zones and zoning statistics to analyze the response relationship between RSEI and these factors, including the distance to the mining area and the percentage of the mining area. In addition, the drivers and impacts of RSEI in 2019 were analyzed through Pearson correlation coefficients pixel by pixel with 10 factors, including natural, economic, and mining. Regression modeling of RSEI in 2019 was performed using the random forest (RF) model, and these drivers were ranked in order of importance through random forest factor importance assessment. The results showed that (1) the ecological quality of mining cities changed significantly during the study period, and the negative impacts of mining activities on the ecological environment were significant. (2) The areas with low RSEI values were closely related to the mining areas and cities. (3) The RSEI in the mining areas of mining cities was generally lower than the average level of the cities. The RSEI gradually increased as the distance to the mine site increased. (4) The increase in the size of the mine area initially exacerbates the impact on the ecological environment, but the impact is weakened beyond a certain threshold. (5) The distance to the mining area is the most important factor affecting the quality of the ecological environment, followed by DEM, GDP, and precipitation. This study is of great importance for advancing sustainable development in mining cities and formulating sustainable strategies. [ABSTRACT FROM AUTHOR]

Vegetation is an essential component of terrestrial ecosystems, influenced by climate change and human activities. Quantifying the relative contributions of climate change and human activities to vegetation dynamics is crucial for addressing global climate change. Sichuan Province is one of the essential ecological functional areas in the upper reaches of the Yangtze River, and its vegetation change is of great significance to the environmental function and ecological security of the Yangtze River Basin and southwest China. In this paper, the modified Carnegie-Ames-Stanford Approach(CASA) model was used to estimate the monthly NPP (Net Primary Productivity) of vegetation in Sichuan Province from 2000 to 2018, and the univariate linear regression analysis was used to analyze the temporal and spatial variation of vegetation NPP in Sichuan Province from 2000 to 2018. In addition, taking vegetation NPP as an index, Pearson correlation analysis, partial correlation analysis, and second-order partial correlation analysis were carried out to quantitatively analyze the contribution of climate change and human activities to vegetation NPP. Finally, the Hurst index and nonparametric Man-Kendall significance test were used to predict the future change trend of vegetation NPP in Sichuan Province. The results show that (1) from 2000 to 2018, the NPP of vegetation in Sichuan Province has a significant increasing trend (Slope = 6.09gC·m−2·a−1), with a multi-year average of 438.72 gC·m−2·a−1, showing a trend of low in the east and high in the middle. The response of vegetation NPP to altitude is different at different elevations; (2) the contribution rates of climate change and human activities to vegetation NPP change are 4.12gC·m−2·a−1 and 1.97gC·m−2·a−1, respectively. In contrast, the impact of human activities on NPP is more significant than climate change. Human activities are the main factors affecting vegetation restoration and degradation in Sichuan Province. However, the positive contribution to NPP change is less than climate change; (3) the future vegetation NPP change trend in Sichuan Province is mainly rising, and the same direction change trend is much larger than the reverse change trend. The areas with an increasing trend in the future account for 89.187% of the total area. This research helps understand the impact of climate change and human activities on vegetation change in Sichuan Province. It offers scientific bases for vegetation restoration and ecosystem management in Sichuan and the surrounding areas. [ABSTRACT FROM AUTHOR]

Vision transformers (ViTs) are increasingly utilized for HSI classification due to their outstanding performance. However, ViTs encounter challenges in capturing global dependencies among objects of varying sizes, and fail to effectively exploit the spatial–spectral information inherent in HSI. In response to this limitation, we propose a novel solution: the multi-scale spatial–spectral transformer (MSST). Within the MSST framework, we introduce a spatial–spectral token generator (SSTG) and a token fusion self-attention (TFSA) module. Serving as the feature extractor for the MSST, the SSTG incorporates a dual-branch multi-dimensional convolutional structure, enabling the extraction of semantic characteristics that encompass spatial–spectral information from HSI and subsequently tokenizing them. TFSA is a multi-head attention module with the ability to encode attention to features across various scales. We integrated TFSA with cross-covariance attention (CCA) to construct the transformer encoder (TE) for the MSST. Utilizing this TE to perform attention modeling on tokens derived from the SSTG, the network effectively simulates global dependencies among multi-scale features in the data, concurrently making optimal use of spatial–spectral information in HSI. Finally, the output of the TE is fed into a linear mapping layer to obtain the classification results. Experiments conducted on three popular public datasets demonstrate that the MSST method achieved higher classification accuracy compared to state-of-the-art (SOTA) methods. [ABSTRACT FROM AUTHOR]

Urban vegetation plays a crucial role in the urban ecological system. Efficient and accurate extraction of urban vegetation information has been a pressing task. Although the development of deep learning brings great advantages for vegetation extraction, there are still problems, such as ultra-fine vegetation omissions, heavy computational burden, and unstable model performance. Therefore, a Separable Dense U-Net (SD-UNet) was proposed by introducing dense connections, separable convolutions, batch normalization layers, and Tanh activation function into U-Net. Furthermore, the Fake sample set (NIR-RG), NDVI sample set (NDVI-RG), and True sample set (RGB) were established to train SD-UNet. The obtained models were validated and applied to four scenes (high-density buildings area, cloud and misty conditions area, park, and suburb) and two administrative divisions. The experimental results show that the Fake sample set can effectively improve the model's vegetation extraction accuracy. The SD-UNet achieves the highest accuracy compared to other methods (U-Net, SegNet, NDVI, RF) on the Fake sample set, whose ACC, IOU, and Recall reached 0.9581, 0.8977, and 0.9577, respectively. It can be concluded that the SD-UNet trained on the Fake sample set not only is beneficial for vegetation extraction but also has better generalization ability and transferability. [ABSTRACT FROM AUTHOR]

Landslides, the second largest geological hazard after earthquakes, result in significant loss of life and property. Extracting landslide information quickly and accurately is the basis of landslide disaster prevention. Fengjie County, Chongqing, China, is a typical landslide-prone area in the Three Gorges Reservoir Area. In this study, we newly integrate Shapley Additive Explanation (SHAP) and Optuna (OPT) hyperparameter tuning into four basic machine learning algorithms: Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Additive Boosting (AdaBoost). We construct four new models (SHAP-OPT-GBDT, SHAP-OPT-XGBoost, SHAP-OPT-LightGBM, and SHAP-OPT-AdaBoost) and apply the four new models to landslide extraction for the first time. Firstly, high-resolution remote sensing images were preprocessed, landslide and non-landslide samples were constructed, and an initial feature set with 48 features was built. Secondly, SHAP was used to select features with significant contributions, and the important features were selected. Finally, Optuna, the Bayesian optimization technique, was utilized to automatically select the basic models' best hyperparameters. The experimental results show that the accuracy (ACC) of these four SHAP-OPT models was above 92% and the training time was less than 1.3 s using mediocre computational hardware. Furthermore, SHAP-OPT-XGBoost achieved the highest accuracy (96.26%). Landslide distribution information in Fengjie County from 2013 to 2020 can be extracted by SHAP-OPT-XGBoost accurately and quickly. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The quantification of large-scale leaf-age-dependent leaf area index has been lacking in tropical and subtropical evergreen broadleaved forests (TEFs), despite the recognized importance of leaf age in influencing leaf photosynthetic capacity in this biome. Here, we simplified the canopy leaves of TEFs into three age cohorts (i.e., young, mature, and old, with different photosynthesis capacities; i.e., Vc,max) and proposed a novel neighbor-based approach to develop the first gridded dataset of a monthly leaf-age-dependent leaf area index (LAI) product (referred to as Lad-LAI) at 0.25 ∘ spatial resolution over the continental scale during 2001–2018 from satellite observations of sun-induced chlorophyll fluorescence (SIF) that was reconstructed from MODIS and TROPOMI (the TROPOspheric Monitoring Instrument). The new Lad-LAI products show good performance in capturing the seasonality of three LAI cohorts, i.e., young (LAI young ; the Pearson correlation coefficient of R=0.36), mature (LAI mature ; R=0.77), and old (LAI old ; R=0.59) leaves at eight camera-based observation sites (four in South America, three in subtropical Asia, and one in the Democratic Republic of the Congo (DRC)) and can also represent their interannual dynamics, validated only at the Barro Colorado site, with R being equal to 0.54, 0.64, and 0.49 for LAI young , LAI mature , and LAI old , respectively. Additionally, the abrupt drops in LAI old are mostly consistent with the seasonal litterfall peaks at 53 in situ measurements across the whole tropical region (R=0.82). The LAI seasonality of young and mature leaves also agrees well with the seasonal dynamics of the enhanced vegetation index (EVI; R=0.61), which is a proxy for photosynthetically effective leaves. Spatially, the gridded Lad-LAI data capture a dry-season green-up of canopy leaves across the wet Amazonian areas, where mean annual precipitation exceeds 2000 mm yr -1 , consistent with previous satellite-based analyses. The spatial patterns clustered from the three LAI cohorts also coincide with those clustered from climatic variables over the whole TEF region. Herein, we provide the average seasonality of three LAI cohorts as the main dataset and their time series as a supplementary dataset. These Lad-LAI products are available at 10.6084/m9.figshare.21700955.v4 (Yang et al., 2022). [ABSTRACT FROM AUTHOR]

Improvements in hyperspectral image technology, diversification methods, and cost reductions have increased the convenience of hyperspectral data acquisitions. However, because of their multiband and multiredundant characteristics, hyperspectral data processing is still complex. Two feature extraction algorithms, the autoencoder (AE) and restricted Boltzmann machine (RBM), were used to optimize the classification model parameters. The optimal classification model was obtained by comparing a stacked autoencoder (SAE) and a deep belief network (DBN). Finally, the SAE was further optimized by adding sparse representation constraints and GPU parallel computation to improve classification accuracy and speed. The research results show that the SAE enhanced by deep learning is superior to the traditional feature extraction algorithm. The optimal classification model based on deep learning, namely, the stacked sparse autoencoder, achieved 93.41% and 94.92% classification accuracy using two experimental datasets. The use of parallel computing increased the model's training speed by more than seven times, solving the model's lengthy training time limitation. [ABSTRACT FROM AUTHOR]

In recent years, the water–sand composition of the Yangtze River channel has changed due to the influence of human factors, especially the construction of water reservoirs such as the Three Gorges Project. Changing water–sand conditions have a long-term impact on the shaping of the river channel morphology in the middle and lower reaches of the Yangtze River, and the erosion retreat of local river sections has caused great harm to embankment projects. This paper focuses on the river evolution mechanism of the river channel from Chenglingji to Datong in the middle and lower reaches of the Yangtze River over the past 31 years. Landsat remote sensing images from 1989–2019 were used to extract and interpret water bodies, river shorelines, and central bars in the study area using the Modified Normalized Difference Water Index (MNDWI) combined with visual interpretation. We used near analysis to study the morphological evolution characteristics of the river, the channel, and selected typical river reaches for comparative analysis. We found out that the overall change in river morphology between 1989 and 2019 was small in the horizontal direction, but the local area changed significantly. Considerable scouring occurred in the vertical direction. Combining hydrological and meteorological data, we investigated the effects of the Three Gorges Dam, instream sand mining, boundary conditions, vegetation cover on both sides of the riverbanks, and aspects of storm flooding in the watershed on the evolution of the river. The study indicated that the geological conditions on both sides of the river, the implementation of the bank protection project, and the improvement of vegetation cover on both sides of the river have made the riverbanks more resistant to scouring. However, heavy rainfall floods, the operation of the Three Gorges Reservoir, and sand mining activities in the river channel make the river channel more susceptible to scouring. Based on the calculation of the slope change rate of the accumulated volume, it was found that the runoff is mainly influenced by precipitations, while the sand transport is mainly affected by human activities. This study shows that natural and anthropogenic activities affect the equilibrium state of the river's water and sediment to varying degree. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

The environment supplies water, land, biological resources, and climate resources for people's daily life and development, dramatically affecting the subsistence and development of human beings. Panzhihua City is a representative resource-based industrial city of southwestern China. The abundant mineral resources provide the material basis for the city's development. However, while the overdevelopment of the past decades has provided the preconditions for its rapid economic growth, it has also inevitably had a huge impact on its environmental quality and land use structure. In this study, the landsat remote sensing images, terrain data, socio-economic data, and mining resources exploitation data of Panzhihua were used to extract the NDVI (Normalized Difference Vegetation Index), NDBSI (Normalized Difference Build and Soil Index) and LST (Land Surface Temperature) of the past 20 years at 5-year intervals. We normalized four indicators by Principal Component Analysis to derive a remote sensing ecological index of each factor and build the Remote Sensing-based Ecological Index evaluation model. This research quantified the changes in environmental quality in the past 20 years through the range method, showing that the environmental quality of Panzhihua City first declined and then increased slowly. This research also analyzed the influence of land use types, terrain, mining area, and socio-economy on the environmental quality of Panzhihua City by grey relational analysis and buffer analysis. It is found that with the influence of its unique topographical factors and economic aspects, the environmental quality of Panzhihua City changed to varying degrees. The results provide a reliable basis for the future environmental planning of Panzhihua City and a reference for the ecological restoration of mining areas with different mineral species accurately. [ABSTRACT FROM AUTHOR]

A flash flood disaster is one of the most destructive natural disasters. With the increase in extreme rainfall events, more and more areas will be threatened by flash floods. The flash flood susceptibility assessment is the basis of flash flood risk assessment and is also an important step in flash flood disaster management. Based on Citespace analysis tools, this study made a bibliometric and visualized analysis of 305 documents collected in the core collection of Web of Science in the past 15 years, including the analysis of the number of publications and citation frequency, influence analysis, keyword analysis, author co-citation analysis, and institutional co-operation analysis. This paper summarizes the current research status and future development trend of flash flood susceptibility assessment from five key research subfields, including assessment scale, assessment unit, assessment index, assessment model, and model assessment method, discusses the analysis of the application of remote sensing and GIS in flash flood susceptibility assessment, discusses the problems encountered in the current research of the five subfields, and provides suggestions for flash flood hazard control. [ABSTRACT FROM AUTHOR]

Following significant urban expansion, the ecological problems of resource-based cities are gradually exposed. It is of great significance to study the ecosystem services of resource-based cities to achieve their sustainable development goals and to alleviate the conflicts between environmental protection and the utilization of the surrounding resources. However, in the current research on resource-based cities, few scholars have combined multiple minerals and multiple ecosystem services to explore the impact of mineral resources on the ecosystem. In this study, based on the historical data spanning from 2002 to 2018, we used the CA–Markov model to project the land use of Panzhihua City to 2030. Based on future land use projection, we quantified four ecosystem services (ESs) variables, including water yield, carbon storage, habitat quality, and soil conservation, using the InVEST model from the perspective of land use evolution in Panzhihua City. In addition, we explored the trade-offs and synergies of different ecosystem services and the correlations between different mineral species and ecosystem services using Spearman's correlation coefficient. Results showed the following: (1) During 2002–2018, water yield service, habitat quality service, and carbon storage service of Panzhihua City decreased year by year, and soil conservation service showed significant fluctuations; most of the low ESs areas were distributed in the central region of Panzhihua. On the contrary, most high ESs areas were located in the forest region. (2) The trade-offs and synergistic relationships among different ecosystem services showed significant spatial variations. There were synergistic relationships among ESs and weak trade-offs between water yield services, soil conservation, and habitat quality services. There was also significant spatial variability in the trade-offs and synergies among ecosystem services, with water production services showing "east trade-offs and west synergies" with soil conservation and habitat quality services, and most of the rest showing trade-offs in urban areas. (3) ESs in mining areas showed trade-offs in general, mainly between water production services and carbon storage services, with clay as the major negative factor of mineral species, and iron ore mines that have undergone ecological protection construction showed the lowest negative impact on ecology. [ABSTRACT FROM AUTHOR]

The Himalayan volcanic earthquake zone has significantly impacted China's Sichuan-Tibet region. Many barriers have formed as a result of the earthquake and secondary disasters, such as landslides, which have blocked the river. The breach of the barrier lake seriously threatens the lives and property safety of downstream personnel. There has been little research on the surrounding ecology for the later treatment of the barrier lake. This paper aims to scientifically predict the risk of dam break in a barrier lake as well as to explore its impact on the ecological environment and put forward controllable measures. Based on four major barrier lake events in the Sichuan-Tibet area, Diexihaizi, Tangjiashan barrier lake, and so on, we extract water bodies from remote sensing images and use the HEC-RAS (Hydrologic Engineering Center of River Analysis System) model to investigate whether there is a dam break risk and the route of the dam break is predictable. Simultaneously, from 1990 to 2020, the smallest administrative region is located. The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model is utilized to evaluate and analyze its habitat and create an evaluation based on flood inundation data. The results suggest that a stable barrier lake (such as Diexihaizi) has a sound effect on the habitat quality index following engineering treatment. The development of the barrier lake has altered the types of neighboring lands used and the natural patterns of the region's landscape. The habitat quality index will marginally deteriorate within a 1-km radius of the barrier lake. However, the quality of habitats in the area ranging within 3 km and 5 km has improved. It is necessary to discharge and strengthen the barrier lake artificially. Human-controlled regions, according to studies, will recover higher habitat quality index values than other locations. Whether the barrier lake has a positive impact on the surrounding area, on the other hand, is primarily dependent on the original ecology. The development of barrier lakes is damaging and unprofitable in Tibet, where the actual ecology is better in the short term. Still, in Sichuan Province, where the habitat quality is relatively low, the appearance of dammed lakes has played a role in correcting the ecology. [ABSTRACT FROM AUTHOR]

With the social and economic development in recent years, human activities have been more extensive and intensified. As a result, ecosystems are damaged to varying degrees, and regional ecological environments tend to be weaker. The socio-ecological system in Aba Prefecture, Western Sichuan Plateau, China, the researched area, also faces increasingly serious problems. To advance ecological civilization development in a coordinated way across the country, the national government and the competent authorities have launched a series of new strategies. Research on socio-ecological vulnerability, a major part of the ecosystem protection and restoration program, is provided with powerful spatial data observation and analysis tools thanks to the invention and development of remote sensing and geographic information system technologies. This study was based on the vulnerability scoping diagram (VSD) framework. Multi-source data such as digital elevation model (DEM), geographical data such as land use types, soil and geological disasters, remote sensing image data, meteorological data and social statistics data from 2005 to 2019 were used to construct the temporal social-ecosystem vulnerability evaluation index database of Aba Prefecture, Western Sichuan Plateau. The spatial principal component analysis (SPCA) is applied to evaluating the socio-ecological vulnerability and analyzing its spatial-temporal variation in Aba Prefecture, Western Sichuan Plateau. To probe into the driving effects of various impact factors on the socio-ecological vulnerability, the Geodetector is used to analyze the driving factors. The ordered weighted average (OWA) method is applied to the multi-scenario analysis of socio-ecological vulnerability in the researched area. The conclusions of this study are as follows: (1) from 2005 to 2019, the spatial distribution characteristics of exposure and sensitivity in Aba Prefecture were higher in the southeast and lower in the northwest, and the overall spatial distribution characteristics of socio-ecological system vulnerability showed that the degree of vulnerability increased from the north to the southeast. (2) Extreme natural climate conditions play a leading role in the driving of socio-ecosystem vulnerability, followed by human production activities and geological hazards. (3) The degree of social-ecosystem vulnerability in Aba Prefecture will increase with the increase of decision risk coefficient. The results of social-ecosystem vulnerability under the status quo scenario are similar to those in 2010 and 2019, indicating that the selected evaluation factors can reflect the actual social-ecosystem vulnerability. In the sustainable guided scenario and the unsustainable guided scenario, the proportion of the area of the social-ecosystem severe vulnerability level was at the minimum value and the maximum value, respectively. [ABSTRACT FROM AUTHOR]

Glacial lakes are important freshwater resources in southern Tibet. However, glacial lake outburst floods have significantly jeopardized the safety of local residents. To better understand the changes in glacial lakes in response to climate change, it is necessary to conduct a long-term evaluation on the areal dynamics of glacial lakes, assisted with local observations. Here, we propose an innovative method of classification and stacking extraction to accurately delineate glacial lakes in southwestern Tibet from 1990 to 2020. Based on Landsat images and meteorological data, we used geographic detectors to detect correlation factors. Multiple regression models were used to analyze the driving factors of the changes in glacier lake area. We combined bathymetric data of the glacial lakes with the changes in climatic variables and utilized HEC-RAS to determine critical circumstances for glacial lake outbursts. The results show that the area of glacial lakes in Nyalam County increased from 27.95 km2 in 1990 to 52.85 km2 in 2020, and eight more glacial lakes were observed in the study area. The glacial lake area expanded by 89.09%, where we found significant growth from 2015 to 2020. The correlation analysis between the glacial lake area and climate change throughout the period shows that temperature and precipitation dominate the expansion of these lakes from 1990 to 2020. We also discover that the progressive increase in water volume of glacial lakes can be attributed to the constant rise in temperature and freeze–thaw of surrounding glaciers. Finally, the critical conditions for the glacial lake's outburst were predicted by using HEC-RAS combined with the changes in the water volume and climatic factors. It is concluded that GangxiCo endures a maximum water flow of 4.3 × 108 m3, and the glacial lake is in a stable changing stage. This conclusion is consistent with the field investigation and can inform the prediction of glacial lake outbursts in southwestern Tibet in the future. [ABSTRACT FROM AUTHOR]

Lakes are important natural resources closely related to human survival and development. Based on PIE cloud computing platform, the study uses Landsat images and the empirical normalized water body index (ENDWI) to extract water body information of the large lakes in Sichuan province from 2000 to 2020 in the drought and rainy seasons, respectively, and uses the Mann–Kendall test to obtain the long-term trends of their area and climate. On this basis, the evolution of the lakes and their correlation with climate and human activities are analyzed. The results show that (1) In the past 20 years, the area of Lugu Lake, Qionghai Lake, and Luban Reservoir represent a decreasing trend, with Lugu Lake being the most affected. The area of Ma Lake, Three Forks Lake, and Shengzhong Reservoir increased, with the area of Shengzhong Reservoir increasing significantly; (2) During the drought season, all six lakes showed a decreasing trend in precipitation, with the most apparent decreasing trend for Lugu Lake (Slope = −0.8). Only Lugu Lake showed a decreasing trend in precipitation (Slope = −0.15) during the rainy season. The precipitation of Ma Lake, Three Forks Lake, Luban Reservoir and Shengzhong Reservoir showed a significant increasing trend (Slope value was greater than 1.96); (3) The temperatures of the remaining lakes all decreased in the drought season and increased in the rainy season, except that the temperature of Shengzhong Reservoir decreases throughout the year; (4) The area change of plain lakes is greatly affected by human activities, but the area of plateau lakes is are more impacted by climate. Our study improved the accuracy of long-term water body change monitoring with PIE-Engine Studio. Besides, the findings would provide reference for the implementation of sustainable water resources management in Sichuan Province. [ABSTRACT FROM AUTHOR]

Panzhihua City is a typical agricultural-forestry-pastoral and ecologically sensitive city in China. It is also an important ecological defense in the upper Yangtze River. It has abundant mineral resources, including vanadium, titanium, and water supplies. However, ecological and environmental problems emerge due to the excessive development of mining, agriculture, animal husbandry, and other non-natural urban economies. Therefore, a scientific understanding of the spatio-temporal changes of the eco-environment of Panzhihua is critical for environmental protection, city planning, and construction. To objectively evaluate the eco-environmental status of Panzhihua, the remote sensing-based ecological index (RSEI) was first applied to Panzhihua, a typical resource-based city, and its ecological environmental quality (EEQ) was quantitatively assessed from 1990 to 2020. This study explored the effects of mining activities and policies on EEQ and used change detection to reveal the spatial-temporal changes of EEQ in Panzhihua City over the past three decades. In addition, this study also verified the suitability of RSEI for evaluating EEQ in resource-based city using spatial autocorrelation, revealed the spatial heterogeneity of EEQ in Panzhihua City using optimized hot spot analysis, and showed different ecological clustering by hot spot analysis at two scales of urban and mining areas. According to the results: (1) From 1990 to 2020, the general eco-environmental condition of Panzhihua is improving, but there are still regional differences. (2) The Moran's I value ranges from 0.436 (1990) to 0.700 (2020), indicating that there is autocorrelation in the distribution of eco-environmental quality. (3) At the mine, the mean value of RSEI dropped by 20–40%, and the EEQ decreased significantly due to mining activities. (4) A series of ecological restoration policies can buffer the negative impact of mining activities on the ecosystem, resulting in a slight improvement in the quality of the ecological environment. This study evaluates the EEQ of resource-based city and its spatial-temporal changes using RSEI constructed by the Google Earth Engine (GEE) platform, which can provide theoretical support for ecological and environmental conditions monitoring, development planning, and environmental protection policy-making of a resource-based city. [ABSTRACT FROM AUTHOR]

Landslide prediction is one of the complicated topics recognized by the global scientific community. The research on landslide susceptibility prediction is vitally important to mitigate and prevent landslide disasters. The instability and complexity of the landslide system can cause uncertainty in the prediction process and results. Although there are many types of models for landslide susceptibility prediction, they still do not have a unified theoretical basis or accuracy test standard. In the past, models were mainly subjectively selected and determined by researchers, but the selection of models based on subjective experience often led to more significant uncertainty in the prediction process and results. To improve the universality of the model and the reliability of the prediction accuracy, it is urgent to systematically summarize and analyze the performance of different models to reduce the impact of uncertain factors on the prediction results. For this purpose, this paper made extensive use of document analysis and data mining tools for the bibliometric and knowledge mapping analysis of 600 documents collected by two data platforms, Web of Science and Scopus, in the past 40 years. This study focused on the uncertainty analysis of four key research subfields (namely disaster-causing factors, prediction units, model space data sets, and prediction models), systematically summarized the difficulties and hotspots in the development of various landslide prediction models, discussed the main problems encountered in these four subfields, and put forward some suggestions to provide references for further improving the prediction accuracy of landslide disaster susceptibility. [ABSTRACT FROM AUTHOR]

Global climate changes have a great impact on terrestrial ecosystems. Vegetation is an important component of ecosystems, and the impact of climate changes on ecosystems can be determined by studying vegetation phenology. Vegetation phenology refers to the phenomenon of periodic changes in plants, such as germination, flowering and defoliation, with the seasonal change of climate during the annual growth cycle, and it is considered to be one of the most efficient indicators to monitor climate changes. This study collected the global land surface satellite leaf area index (GLASS LAI) products, meteorological data sets and other auxiliary data in the Three-River headwaters region from 2001 to 2018; rebuilt the vegetation LAI annual growth curve by using the asymmetric Gaussian (A-G) fitting method and extracted the three vegetation phenological data (including Start of Growing Season (SOS), End of Growing Season (EOS) and Length of Growing Season (LOS)) by the maximum slope method. In addition, it also integrated Sen's trend analysis method and the Mann-Kendall test method to explore the temporal and spatial variation trends of vegetation phenology and explored the relationship between vegetation phenology and meteorological factors through a partial correlation analysis and multiple linear regression models. The results of this study showed that: (1) the SOS of vegetation in the Three-River headwaters region is concentrated between the beginning and the end of May, with an interannual change rate of −0.14 d/a. The EOS of vegetation is concentrated between the beginning and the middle of October, with an interannual change rate of 0.02 d/a. The LOS of vegetation is concentrated between 4 and 5 months, with an interannual change rate of 0.21 d/a. (2) Through the comparison and verification with the vegetation phenological data observed at the stations, it was found that the precision of the vegetation phonology extracted by the A-G method and the maximum slope method based on GLASS LAI data is higher (MAE is 7.6 d, RMSE is 8.4 d) and slightly better than the vegetation phenological data (MAE is 9.9 d, RMSE is 10.9 d) extracted based on the moderate resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) product. (3) The correlation between the SOS of vegetation and the average temperature in March–May is the strongest. The SOS of vegetation is advanced by 1.97 days for every 1 °C increase in the average temperature in March–May; the correlation between the EOS of vegetation and the cumulative sunshine duration in August–October is the strongest. The EOS of vegetation is advanced by 0.07 days for every 10-h increase in the cumulative sunshine duration in August–October. [ABSTRACT FROM AUTHOR]

Flash flood is one of the extremely destructive natural disasters in the world. In recent years, extreme rainfall events caused by global climate change have increased, and flash flood disasters are becoming the main types of natural disasters in the world. Due to the characteristics of strong suddenness, complex disaster-causing factors, great difficulty in prediction and forecast, and the lack of historical data, it is difficult to effectively prevent and control flash flood disaster. The early identification technology of flash floods is not only the basis of flash flood disaster prediction and early warning, but also an effective means of flash flood prevention and control. The paper makes a meta-analysis and visual analysis of 475 documents collected by the Web of Science Document Platform in the past 31 years by comprehensively using Citespace, Vosviewer, Origin, etc. We systematically summarize the research progress and development trend of early identification technology of flash flood disasters from five key research subfields: (1) precipitation, (2) sediment, (3) sensitivity analysis, (4) risk assessment, (5) uncertainty analysis. In addition, we analyze and discuss the main problems encountered in the current research of several subfields and put forward some suggestions to provide references for the prevention and control of flash flood disasters. [ABSTRACT FROM AUTHOR]

After the "5·12" Wenchuan earthquake in 2008, collapses and landslides have occurred continuously, resulting in the accumulation of a large quantity of loose sediment on slopes or in gullies, providing rich material source reserves for the occurrence of debris flow and flash flood disasters. Therefore, it is of great significance to build a collapse and landslide susceptibility evaluation model in Wenchuan County for local disaster prevention and mitigation. Taking Wenchuan County as the research object and according to the data of 1081 historical collapse and landslide disaster points, as well as the natural environment, this paper first selects six categories of environmental factors (13 environmental factors in total) including topography (slope, aspect, curvature, terrain relief, TWI), geological structure (lithology, soil type, distance to fault), meteorology and hydrology (rainfall, distance to river), seismic impact (PGA), ecological impact (NDVI), and impact of human activity (land use). It then builds three single models (LR, SVM, RF) and three CF-based hybrid models (CF-LR, CF-SVM, CF-RF), and makes a comparative analysis of the accuracy and reliability of the models, thereby obtaining the optimal model in the research area. Finally, this study discusses the contribution of environmental factors to the collapse and the landslide susceptibility prediction of the optimal model. The research results show that (1) the areas prone to extremely high collapse and landslide predicted by the six models (LR, CF-LR, SVM, CF-SVM, RF and CF-RF) have an area of 730.595 km2, 377.521 km2, 361.772 km2, 372.979 km2, 318.631 km2, and 306.51 km2, respectively, and the frequency ratio precision of collapses and landslides is 0.916, 0.938, 0.955, 0.956, 0.972, and 0.984, respectively; (2) the ranking of the comprehensive index based on the confusion matrix is CF-RF>RF>CF-SVM>CF-LR>SVM>LR and the ranking of the AUC value is CF-RF>RF>CF-SVM>CF-LR>SVM>LR. To a certain extent, the coupling models can improve precision more over the single models. The CF-RF model ranks the highest in all indexes, with a POA value of 257.046 and an AUC value of 0.946; (3) rainfall, soil type, and distance to river are the three most important environmental factors, accounting for 24.216%, 22.309%, and 11.41%, respectively. Therefore, it is necessary to strengthen the monitoring of mountains and rock masses close to rivers in case of rainstorms in Wenchuan county and other similar areas prone to post-earthquake landslides. [ABSTRACT FROM AUTHOR]

Southwestern Tibet is one of the areas seriously affected by glacial lake outburst floods (GLOFs). In this study, a systematic GLOF hazard management process chain for moraine-dammed lakes was constructed by integrating remote sensing (RS), geomorphology investigations, geographic information systems (GIS), statistical methods, and hydrological models. The evolution of two moraine-dammed lakes and their mother glaciers across the region from 1976 to 2018 were investigated and the susceptibility of 74 moraine-dammed lakes outburst was evaluated. The actual and potential GLOF risks of former Cirenmaco Lake GLOF was modeled based on a single hydrological model. The monitoring results showed that two moraine-dammed lakes in southwestern Tibet, including Yanongco and Rongxiazangbu, were continuing to expand and were at risk of GLOF. The parameters used for assessing GLOF risk were filtered through a grey correlation model with a modeling accuracy of 84%. The FLO-2D hydrological model was adopted to simulate GLOF hazards of a moraine-dammed lake in Southwest Tibet for the first time. Results showed that the modeled errors of fluid depth and velocity were within 10%. The combined method proposed in this study could be potentially used to assess the status of the regional GLOF in Tibet and provides effective suggestions for local governments to prevent disasters and reduce damages of GLOF. [ABSTRACT FROM AUTHOR]

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone's natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River's Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River's Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility. [ABSTRACT FROM AUTHOR]

Gridded precipitation data with a high spatiotemporal resolution are of great importance for studies in hydrology, meteorology, and agronomy. Observational data from meteorological stations cannot accurately reflect the spatiotemporal distribution and variations of precipitation over a large area. Meanwhile, radar-derived precipitation data are restricted by low accuracy in areas of complex terrain and satellite-based precipitation data by low spatial resolution. Therefore, hourly precipitation models were employed to merge data from meteorological stations, Radar, and satellites; the models used five machine learning algorithms (XGBoost, gradient boosting decision tree, random forests (RF), LightGBM, and multiple linear regression (MLR)), as well as the CoKriging method. In the north of Guangdong Province, data of four heavy rainfall events in 2018 were processed with geographic data to obtain merged hourly precipitation data. The CoKriging method secured the best prediction of spatial distribution of accumulated precipitation, followed by the tree-based machine learning (ML) algorithms, and significantly, the prediction of MLR deviated from the actual pattern. All machine learning methods showed poor performances for timepoints with little precipitation during the heavy rainfall events. The tree-based ML method showed poor performance at some timepoints when precipitation was over-related to latitude, longitude, and distance from the coast. [ABSTRACT FROM AUTHOR]

Lakes over the Qinghai-Tibet Plateau (TP) have large quantities and areas. As an important component of fragile plateau ecosystems, these lakes have attracted increasing attention. However, owing to the limitations of technology and methods, changes in smaller lakes on the TP have received less attention. In this study, we used Google Earth Engine (GEE) with the Analysis Ready Data (ARD) preparation framework to obtain preprocessed Sentinel-1 data covering the plateau. The D-LinkNet framework was introduced to achieve lake extraction, and the lake dataset was completed from 2016 to 2021. The lake dataset showed an area accuracy of 86.49% and Intersection over Union (IoU) of 0.72-0.99 in different regions. The findings were as follows: during the study period, the TP lakes tended to be stable after increasing, with an increase in area and number of +7.6% and +14.8%. Except for the northwest TP, the other regions show the same general trend. In particular, the Tarim Basin exhibited a lake variation pattern independent of the TP. Significantly, we found frequent lake activity in the Kunlun Mountains, Qaidam Basin, Mountain Qogir, etc. Effects of the ongoing La Niña event on the TP lakes may occur in the next few years. [ABSTRACT FROM AUTHOR]

Hyper-spectral image can provide precise information on land surface targets identification and classification thanks to its advanced feature on spectral resolution. However, due to its complicated hyper-dimension data structure, greater challenge is put on the conventional image classification methods for hyper-spectral images. To fill this technical knowledge gap, we introduce a deep learning-based feature extraction method for hyper-spectral data classification. Firstly, we used a Stacked De-noising Auto-encoders(SDAE) to extract the in-depth features of hyper-spectral image data: a large amount of unlabeled data was pre-trained to extract the depth characteristics of pixels. We added random noise into the input layer of the network to form a de-noising auto-encoder machine and added treated inputs to reconstruct original data. An L-BFGS (Limited-memory quasi-Newton code) was used to optimize the loss function. In the top layer, deep neural network was fine-tuned by a Softmax regression classifier. All these improvements directed towards the model to obtain the image element abstraction and robust expression in the classification task of the hyper-spectral images. We tested the model performance on a Hyspex imaging spectrometer image and found that the newly introduced model framework outperforms other traditional methods including principal component analysis (PCA), and support vector machine (SVM) classifier, combined PCA-SVM classifier, and Minimum Noise Fraction Rotation (MNF)-SVM classifier. [ABSTRACT FROM AUTHOR]

Panzhihua City, a typical eco-fragile region for agro-sylvo-pastoral industry in China, is located in the dry-hot valley of the Jinsha River, characterized by its big landform undulation, great elevation difference, uneven hydrothermal conditions, and complex geological structure. As a crucial ecological barrier in upper reaches of the Yangtze River, this area is abundant in water resources and mineral resources, such as vanadium and titanium. However, due to its over-development for nonnatural urban economy in the mining industry, agriculture, and animal husbandry, ecological problems are getting worse. Such problems as soil erosion and groundwater pollution have led obvious ecological degeneration in Panzhihua city. Therefore, for protecting the eco-environment and planning construction, it is significant to scientifically recognize that how eco-environment changes based on spatial-temporal, and how the driving mechanism affects Panzhihua city. Nowadays, there are some theories and methods that study eco-environmental protection and city construction in Panzhihua, but they are not comprehensive enough to study its spatial-temporal evolution and driving-force system. This study takes Panzhihua City as the research area of which evaluation factors, for example, topography, soil, vegetation, and meteorological factors, are chosen to construct an evaluation system suitable for the ecological environment vulnerability of Panzhihua City. These factors are selected in three aspects, which are ecological sensitivity, ecological recovery, and ecological pressure from 2005 to 2015 in this area. Then, spatial principal component analysis method, CA-Markov model, and dynamic degree model are applied to analyze the spatial-temporal evolution for ecological vulnerability based on three periods from 2005 to 2015 in Panzhihua City. Besides, GeoDetector is used to quantitatively analyze how spatial-temporal disparities change and what drives them to change. The results show that (1) during these 10 years, the overall ecological fragility of Panzhihua City is steadily increasing from northwest to southeast. The overall ecological quality is moderate, and regional differences are obvious. Places of moderate vulnerability or above are distributed in central and eastern regions of frequent human activities; places of mild vulnerability or below are distributed in the regions of Yanbian County and Miyi County. (2) The comparison of the changing rates based on vulnerability levels is severe > potential > moderate > mild > slight. The overall vulnerability changes within a small trend, showing a balanced two-way transition state between adjacent vulnerability levels. The comprehensive index for overall ecological vulnerability decreases period by period. (3) The interactions between each two factors toward spatial differentiation and explanatory power by ecological vulnerability show a two-factor-enhanced relation, indicating that multiple factors form the ecological vulnerability of Panzhihua City. [ABSTRACT FROM AUTHOR]

Reducing the dimension of the hyperspectral image data can directly reduce the redundancy of the data, thus improving the accuracy of hyperspectral image classification. In this paper, the deep belief network algorithm in the theory of deep learning is introduced to extract the in-depth features of the imaging spectral image data. Firstly, the original data is mapped to feature space by unsupervised learning methods through the Restricted Boltzmann Machine (RBM). Then, a deep belief network will be formed by superimposed multiple Restricted Boltzmann Machines and training the model parameters by using the greedy algorithm layer by layer. At the same time, as the objective of data dimensionality reduction is achieved, the underground feature construction of the original data will be formed. The final step is to connect the depth features of the output to the Softmax regression classifier to complete the fine-tuning (FT) of the model and the final classification. Experiments using imaging spectral data showing the in-depth features extracted by the profound belief network algorithm have better robustness and separability. It can significantly improve the classification accuracy and has a good application prospect in hyperspectral image information extraction. [ABSTRACT FROM AUTHOR]

Research on the service values of urban ecosystems is a hot topic of ecological studies in the current era of rapid urbanization. To quantitatively estimate the ecosystem service value in Chengdu, China from the perspectives of natural ecology and social ecology, the technologies of remote sensing (RS) and geographic information system (GIS) are utilized in this study to extract the land use type information from RS images of Chengdu in 2003, 2007, 2013 and 2018. Subsequently, a driver analysis of the ecosystem services of Chengdu was performed based on socioeconomic data from the last 16 years. The results indicated that: (1) from 2003 to 2018, the land utilization in Chengdu changed significantly, with the area of cultivated lands, forest lands and water decreasing remarkably, while the area of construction lands dramatically increased. (2) The ecosystem services value (ESV) of Chengdu decreased by 30.92% in the last 16 years, from CNY 2.4078 × 1010 in 2003 to CNY 1.6632 × 1010 in 2018. Based on a future simulation, the ESV is further predicted to be reduced to CNY 1.4261 × 1010 by 2033. (3) The ESV of Chengdu showed a negative correlation with the total population, the urbanization rate and the per capita GDP of the region, indicating that the ESV of the studied region was inter-coupled with the socioeconomic development and can be maintained at a high level through rationally regulating the socioeconomic structure. [ABSTRACT FROM AUTHOR]

The widely distributed lakes, as one of the major components of the inland water system, are the primary available freshwater resources on the earth and are sensitive to accelerated climate change and extensive human activities. Lakes play an important role in the terrestrial water cycle and biogeochemical cycle and substantially influence the health of humans living in the surrounding areas. Given the importance of lakes in the ecosystem, long-term monitoring of dynamic changes has important theoretical and practical significance. Here, we extracted water body information and monitored the long-term dynamics of Bosten Lake, which is the largest inland lake in China. We quantified the meteorological factors of the study area from the observation data of meteorological stations between 1988 and 2018. The characteristics of climate change and its correlation with the change of area in the Bosten Lake Basin in the past 30 years were analyzed. The major contributions of this study are as follows: (1) The initial water body was segmented based on the water index model Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI) with a pre-assigned threshold value. The results were evaluated with the area extracted through artificial visual interpretation. Then we conducted mathematical morphology operators, opening and closing operations, and median filter to eliminate noise to ensure the accuracy of water body information extraction from the Bosten Lake. A long-term water surface area database of the Bosten Lake was established from high-resolution remote sensing images during 1988–2018. (2) Due to the seasonal difference of snow, ice content, and other objects on images, the areadynamics of Bosten Lake in the recent 30 years were analyzed separately in dry season and rainy season. The water surface area of Bosten Lake showed large inter-annual variations between 1988–2018. (3) Based on the assumption that climatic change has more direct effects on lake than human activities, six meteorological factors were selected to analyze the impacts of climate change on the annual mean lake surface area. The result indicated that in the past 30 years, climate conditions in the Bosten Lake Basin fluctuated greatly. We conducted correlations analysis between the areal dynamics of the Bosten Lake and the meteorological factors. Here, the annual average evaporation had the highest correlation with the areal dynamics of Bosten Lake followed by air temperature, precipitation, sunshine hours, and relative humidity, while the annual average wind speed had the weakest correlation. [ABSTRACT FROM AUTHOR]