Hui Huang

BACKGROUND: This study investigates the spatiotemporal dynamics of pine wilt disease (PWD) to inform data‐driven management. We implemented a time‐series monitoring framework in a township in Zhejiang Province, China, acquiring seven sequences of unmanned aerial vehicle (UAV) orthomosaics between 2022 and 2024. An enhanced YOLOX‐based change detection model was developed and trained on 300 000 samples. This model exploits phenological variations to automatically identify PWD‐discolored pines, effectively filtering confounding objects. RESULTS: The model demonstrated robust performance, achieving an Average Precision (AP) of 0.89, with Precision and Recall exceeding 85%. Analysis revealed a consistent westward expansion and a progressive increase in disease hotspots. Crucially, winter surveys detected substantial delayed‐symptom pines missed in autumn, roughly equivalent to the autumn baseline. Consequently, the annual cumulative mortality caused by PWD was nearly double (2×) the autumn count. Over 90% of trees newly identified in autumn were located within 300 m of infections detected the previous spring, indicating strong spatial clustering. Furthermore, 80% of infected trees occurred at elevations < 400 m and slopes < 29°, aligning with prevailing easterly winds. CONCLUSION: This research establishes a validated framework bridging remote sensing and on‐the‐ground sanitation. By quantifying the symptom lag effect (which doubles mortality estimates relative to traditional autumn surveys) and elucidating environmentally driven spread mechanisms, we provide a scientific basis for correcting census biases and optimizing resource allocation for precise PWD management. © 2026 Society of Chemical Industry.

Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.

Extracting buildings from very high resolution (VHR) images has attracted much attention but is still challenging due to their large varieties in appearance and scale. Convolutional neural networks (CNNs) have shown effective and superior performance in automatically learning high-level and discriminative features in extracting buildings. However, the fixed receptive fields make conventional CNNs insufficient to tolerate large scale changes. Multiscale CNN (MCNN) is a promising structure to meet this challenge. Unfortunately, the multiscale features extracted by MCNN are always stacked and fed into one classifier, which make it difficult to recognize objects with different scales. Besides, the repeated sub-sampling processes lead to a blurred boundary of the extracted features. In this study, we proposed a novel parallel support vector mechanism (SVM)-based fusion strategy to take full use of deep features at different scales as extracted by the MCNN structure. We firstly designed a MCNN structure with different sizes of input patches and kernels, to learn multiscale deep features. After that, features at different scales were individually fed into different support vector machine (SVM) classifiers to produce rule images for pre-classification. A decision fusion strategy is then applied on the pre-classification results based on another SVM classifier. Finally, superpixels are applied to refine the boundary of the fused results using region-based maximum voting. For performance evaluation, the well-known International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam dataset was used in comparison with several state-of-the-art algorithms. Experimental results have demonstrated the superior performance of the proposed methodology in extracting complex buildings in urban districts.