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Urbanization and economic growth in node cities surged due to the Belt and Road Initiative (BRI), leading to significant environmental changes, notably in vegetation net primary productivity (NPP). Investigating the ecological impact of these urban changes was crucial, despite scarce relevant studies. We employed Sen's slope estimation and Mann–Kendall trend analysis to study NPP trends (2005–2020) in ten Belt and Road node cities. The Optimized Parameters Geographic Detector Model (OPGD) analyzed factors impacting NPP and their interactions. Results revealed significant NPP variations among the ten cities, ranging from 656.47 gCm−2a−1 to 250.55 gCm−2a−1, with over 79% showing increasing trends. Since 2013, Chongqing, Wuhan, Hefei, Nanchang, and Changsha experienced declining NPP, while the other five cities saw an increase. Natural factors like temperature, precipitation, and DEM predominantly influence rising NPP trends, while anthropogenic factors like land use changes and nighttime light drive NPP decline. Land use changes, with 39.0% explanatory power, primarily affect NPP. After 2013, construction land increased by 117.7 km2 on average, while arable land decreased by 274.8 km2, contributing to decreased vegetation cover NPP. Nighttime lights explained up to 25% of NPP variance. Regions with high nocturnal light values exhibited more developed urbanization but comparatively lower NPP levels. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) is a significantly destructive forest disease. To control the spread of PWD, an urgent need exists for a real-time and efficient method to detect infected trees. However, existing object detection models have often faced challenges in balancing lightweight design and accuracy, particularly in complex mixed forests. To address this, an improvement was made to the YOLOv5s (You Only Look Once version 5s) algorithm, resulting in a real-time and efficient model named PWD-YOLO. First, a lightweight backbone was constructed, composed of multiple connected RepVGG Blocks, significantly enhancing the model's inference speed. Second, a C2fCA module was designed to incorporate rich gradient information flow and concentrate on key features, thereby preserving more detailed characteristics of PWD-infected trees. In addition, the GSConv network was utilized instead of conventional convolutions to reduce network complexity. Last, the Bidirectional Feature Pyramid Network strategy was used to enhance the propagation and sharing of multiscale features. The results demonstrate that on a self-built dataset, PWD-YOLO surpasses existing object detection models with respective measurements of model size (2.7 MB), computational complexity (3.5 GFLOPs), parameter volume (1.09 MB), and speed (98.0 frames/s). The Precision, Recall, and F1-score on the test set are 92.5%, 95.3%, and 93.9%, respectively, which confirms the effectiveness of the proposed method. It provides reliable technical support for daily monitoring and clearing of infected trees by forestry management departments., (Copyright © 2023 Xinquan Ye et al.)