The Asia-Pacific region is the most dynamic part of the global economy.After over 50 years of continuously expanding trade,investment and industrial cooperation,the region has formed a highly-tied multi-level labor di...The Asia-Pacific region is the most dynamic part of the global economy.After over 50 years of continuously expanding trade,investment and industrial cooperation,the region has formed a highly-tied multi-level labor division system covering vertical integration and horizontal complementarity of industrial chains.From the V-shaped development model in the twentieth century to the East Asian production network in the twenty-first century,the intra-industry and intra-product division of labor in the Asia-Pacific region has been ever deepening.However,due to the region’s high dependence on the U.S.market,the division of labor model in the Asia-Pacific suffers structural problems and is relatively fragile.Under the shock of Trump 1.0,countries in the Asia-Pacific region were forced to make adaptive adjustments,causing short-term turmoil in the regional economic order.Trump’s strong return in 2025,driven by the“America First”principle,brought forth a series of policies with strong unilateralism,isolationism and protectionism tendencies within just a few months。展开更多
Karst wetlands play a crucial role in global biodiversity conservation,water regulation,and carbon sequestration.Accurate classification of wetland vegetation species is vital for the effective conservation and restor...Karst wetlands play a crucial role in global biodiversity conservation,water regulation,and carbon sequestration.Accurate classification of wetland vegetation species is vital for the effective conservation and restoration of these ecosystems.However,the issue of the challenges arises from the diversity of species and the spectral similarity of their canopies.This study addresses these challenges by integrating horizontal structural features from hyperspectral imagery(HSI),including vegetation indices and spectral and texture features,with vertical structural features derived from light detection and ranging(LiDAR)data,such as height variables,intensity variables,and canopy characteristics.To achieve precise vegetation species classification,we constructed adaptive ensemble learning stacking(AEL-Stacking)and deep learning models while exploring the impact of different feature datasets and classifiers on vegetation species mapping.The LIME(local interpretable model-agnostic explanations)method was utilized to assess the contribution of individual features to classification performance.Our findings reveal that(a)integrating HSI and LiDAR features achieved the highest overall accuracy(87.91% to 92.77%),surpassing their single feature datasets by 4.44% to 9.51%;(b)the AEL-Stacking outperformed the other models,with the accuracy improvements of 0.96% to 7.58% over the Swin Transformer;(c)there are more significant differences in classification results between the 4 classifiers based on HSI(the classification accuracy of Lotus is most affected by the classifier and the dataset);and(d)LiDAR features played a pivotal role in karst wetland classification,with most of vegetation species indicating the high sensitivity to DSM(digital surface model)-derived features.Our works highlight the critical role of HSI and LiDAR in improving karst wetland vegetation species classification.展开更多
文摘The Asia-Pacific region is the most dynamic part of the global economy.After over 50 years of continuously expanding trade,investment and industrial cooperation,the region has formed a highly-tied multi-level labor division system covering vertical integration and horizontal complementarity of industrial chains.From the V-shaped development model in the twentieth century to the East Asian production network in the twenty-first century,the intra-industry and intra-product division of labor in the Asia-Pacific region has been ever deepening.However,due to the region’s high dependence on the U.S.market,the division of labor model in the Asia-Pacific suffers structural problems and is relatively fragile.Under the shock of Trump 1.0,countries in the Asia-Pacific region were forced to make adaptive adjustments,causing short-term turmoil in the regional economic order.Trump’s strong return in 2025,driven by the“America First”principle,brought forth a series of policies with strong unilateralism,isolationism and protectionism tendencies within just a few months。
基金supported by the National Natural Science Foundation of China(grant number 42371341)the Natural Science Foundation of Guangxi Zhuang Autonomous Region(grant number 2024GXNSFAA010351)+1 种基金the Innovation Project of Guangxi Graduate Education(grant number YCBZ2024179)the Key Laboratory of Tropical Marine Ecosystem and Bioresource,Ministry of Natural Resources(grant number 2023ZD02).
文摘Karst wetlands play a crucial role in global biodiversity conservation,water regulation,and carbon sequestration.Accurate classification of wetland vegetation species is vital for the effective conservation and restoration of these ecosystems.However,the issue of the challenges arises from the diversity of species and the spectral similarity of their canopies.This study addresses these challenges by integrating horizontal structural features from hyperspectral imagery(HSI),including vegetation indices and spectral and texture features,with vertical structural features derived from light detection and ranging(LiDAR)data,such as height variables,intensity variables,and canopy characteristics.To achieve precise vegetation species classification,we constructed adaptive ensemble learning stacking(AEL-Stacking)and deep learning models while exploring the impact of different feature datasets and classifiers on vegetation species mapping.The LIME(local interpretable model-agnostic explanations)method was utilized to assess the contribution of individual features to classification performance.Our findings reveal that(a)integrating HSI and LiDAR features achieved the highest overall accuracy(87.91% to 92.77%),surpassing their single feature datasets by 4.44% to 9.51%;(b)the AEL-Stacking outperformed the other models,with the accuracy improvements of 0.96% to 7.58% over the Swin Transformer;(c)there are more significant differences in classification results between the 4 classifiers based on HSI(the classification accuracy of Lotus is most affected by the classifier and the dataset);and(d)LiDAR features played a pivotal role in karst wetland classification,with most of vegetation species indicating the high sensitivity to DSM(digital surface model)-derived features.Our works highlight the critical role of HSI and LiDAR in improving karst wetland vegetation species classification.
