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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]

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]

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, 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]