Zixia Xie

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is crucial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promotes a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and detecting mangrove area. In addition, we also present an improved model, AttCloudNet+, to identify the distribution of mangrove species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with SegNet, UNet, and DeepUNet, etc. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These include K-means clustering, ISODATA cluster analysis, Random Forest (RF), Support Vector Machines (SVM), and others. The comparison demonstrates that the mangrove species identification results obtained using AttCloudNet+ exhibit the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reaching 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provide an efficient solution based on deep learning with a dual attention mechanism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery.

Mangrove ecosystems are important in tropical and subtropical coastal zones, contributing to marine biodiversity and maintaining marine ecological balance. It is cru-cial to develop more efficient, intelligent, and accurate monitoring methods for mangroves to understand better and protect mangrove ecosystems. This study promoted a novel model, MangroveNet, for integrating multi-scale spectral and spatial information and de-tecting mangrove area. In addition, we also presented an improved model—AttCloudNet+ to identify distribution of mangroves species based on high-resolution multispectral drone images. These models incorporate spectral and spatial attention mechanisms and have been shown to effectively address the limitations of traditional methods, which have been prone to inaccuracy and low efficiency in mangrove species identification. In this study, we compare the results from MangroveNet with the SegNet, UNet, and DeepUNet models. The findings demonstrate that the MangroveNet exhibits superior generalization learning capabilities and more accurate extraction outcomes than other deep learning models. The accuracy, F1_Score, mIoU, and precision of MangroveNet were 99.13%, 98.84%, 98.11%, and 99.14%, respectively. In terms of identifying mangrove species, the prediction results from AttCloudNet+ were compared with those obtained from traditional supervised and unsupervised classifications and various machine learning and deep learning methods. These included K-means clustering, ISODATA cluster analysis, random forest (RF), sup-port vector machines (SVM), and others. The comparison demonstrated that the mangrove species identification results obtained using AttCloudNet+ exhibited the most optimal performance in terms of the Kappa coefficient and the overall accuracy (OA) index, reach-ing 0.81 and 0.87, respectively. The two comparison results confirm the effectiveness of the two models developed in this study for identifying mangroves and their species. Overall, we provided an efficient solution based on deep learning with a dual attention mecha-nism in the acceptable real-time monitoring of mangroves and their species using high-resolution multispectral drone imagery.

Climate change and air pollution pose multiple health threats to humans through complex and interacting pathways, whereas urban vegetation can improve air quality by influencing pollutant deposition and dispersion. This study estimated the amount of PM_2.5 removal by the urban forest in the city of Shanghai by using remote sensing data of vegetation and a model approach. We also identified its potential contribution of urban forest presence in relation to human population and particulate matter concentration. Results show that the urban forest in Shanghai reached 46,161 ha in 2017, and could capture 874 t of PM_2.5 with an average of 18.94 kg/ha. There are significant spatial heterogeneities in the role of different forest communities and administrative districts in removing PM_2.5. Although PM_2.5 removal was relatively harmonized with the human population distribution in terms of space, approximately 57.41% of the urban forest presented low coupling between removal capacity and PM_2.5 concentration. Therefore, we propose to plant more trees with high removal capacity of PM_2.5 in the western areas of Shanghai, and increase vertical planting in bridge pillars and building walls to compensate the insufficient amount of urban forest in the center area.