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Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region's ecosystems and human communities. [ABSTRACT FROM AUTHOR]

The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. There are four steps in this hybrid approach. First, the ICEEMDAN is employed to decompose the link dynamic vehicle count time series data into several intrinsic components. Second, the components are predicted by the GRU model. At the same time, the Bayesian optimization method is utilized to automatically optimize the hyperparameters of the GRU model. Finally, the predicted subcomponents are reconstructed to obtain the final prediction results. The proposed hybrid deep learning method is tested on two roads of Hangzhou, China. Results show that, compared with the 12 benchmark models, the proposed hybrid deep learning model achieves the best performance in link dynamic vehicle count forecasting., Author(s): Chunguang He [1,2]; Dianhai Wang [1]; Yi Yu [1,3]; Zhengyi Cai (corresponding author) [1] 1. Introduction With the development of the social economy and urbanization, travellers' traffic demands increased [...]

Northeast China, rich in natural resources and diverse biodiversity, boasts a unique habitat for threatened bird species due to its remote location and perennial cold climate. An analysis assessed the adaptability of these species using data on their geographic distribution and functional traits collected through database queries. The results revealed that threatened bird species share similar functional traits and a stronger phylogenetic signal (Blomberg mean K = 0.39) compared to common species. The Biomod2 model analyzed potentially suitable ranges and environmental drivers under current and future climate scenarios, showing a pattern of larger suitable areas in southern regions and smaller suitable areas in the north. The most critically threatened species faced greater geographical constraints (0.989), with mean annual temperature being a key influence. Altitude and water system distribution were also key factors impacting the distribution of other threatened bird species. Simulated projections under different climate scenarios (RCP 45 and 85) indicated varying degrees of expansion in the suitable range for these species. This research sheds light on the functional traits and distribution of threatened bird species in Northeast China, providing a scientific foundation for future conservation and management efforts. [ABSTRACT FROM AUTHOR]

The eco-agricultural park is a new comprehensive agricultural technology system integrating agricultural production, rural economic development, ecological environment protection, and efficient resource utilization. Therefore, an in-depth analysis of the ecosystem structure of eco-agricultural parks will help achieve the goal of coordinated symbiosis between human development and environmental protection. This study takes the research area of the Eco-agricultural Park of Jinchuan Town, Huinan County, a typical town in the Changbai Mountains of Northeast China. Based on field surveys, market research, farmer consultation, and related data collection, emergy theory and methods are used to construct an emergy model for the park. The value evaluation index system integrates the unique emergy index of the agricultural ecosystem with the traditional emergy index system to conduct a targeted evaluation of the park's functional structure and sustainable development capabilities in order to improve the efficiency of material and energy use and provide technical reference for ecological construction and comprehensive development of agricultural industry in mountainous areas in northern China. The research results show that: (1) The annual input total emergy of the eco-agricultural park is 4.04E+24 sej/a, and the emergy of labor input, electricity input, and topsoil loss is relatively high. The park is in a labor-intensive stage. The annual output total emergy is 5.09E+24 sej/a, the park is dominated by planting and forestry industries. (2) The park's emergy utilization intensity is high—production efficiency is high, economic development is advanced, and the system's self-control, adjustment, and feedback functions are vital—and plays a significant role in promoting the development of the regional economy. However, the park relies more on investment from external resources, and production in the park puts pressure on the environment. (3) The current sustainable development capability of the study area is weak, and the factors affecting the sustainable development capability are mainly energy loss and uneven distribution of industrial areas in the park. Effective measures to promote the transformation of the park to develop technology-intensive industries and improve the sustainable development performance of the park were proposed. These include: adjusting the proportion of industries in the park; reducing high-energy external input emergy, such as industrial auxiliary emergy; reducing the loss of non-renewable natural resources through ecological engineering measures, such as reducing the depth of slope runoff in the park; and combining modern resource-based production technology and environmentally sound management methods to reduce energy loss and rational use of natural resources. [ABSTRACT FROM AUTHOR]