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Bowei Chen
Kay Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

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Journal article
Published: 21 April 2021 in Plants
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In this study, we simulated vegetation net primary productivity (NPP) using the boreal ecosystem productivity simulator (BEPS) between 2003 and 2012 over Northeast China, a region that is significantly affected by climate change. The NPP was then validated against the measurements that were calculated from tree ring data, with a determination coefficient (R 2) = 0.84 and the root mean square error (RMSE) = 42.73 gC/m2·a. Overall, the NPP showed an increasing trend over Northeast China, with the average rate being 4.48 gC/m2·a. Subsequently, partial correlation and lag analysis were conducted between the NPP and climatic factors. The partial correlation analysis suggested that temperature was the predominant factor that accounted for changes in the forest NPP. Solar radiation was the main factor that affected the forest NPP, and the grass NPP was the most closely associated with precipitation. The relative humidity substantially affected the annual variability of the shrub and crop NPPs. The lag time of the NPP related to precipitation increased with the vegetation growth, and it was found that the lag period of the forest was longer than that of grass and crops, whereas the cumulative lag month of the forest was shorter. This comprehensive analysis of the response of the vegetation NPP to climate change can provide scientific references for the managing departments that oversee relevant resources.

ACS Style

Min Yan; Mei Xue; Li Zhang; Xin Tian; Bowei Chen; Yuqi Dong. A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China. Plants 2021, 10, 821 .

AMA Style

Min Yan, Mei Xue, Li Zhang, Xin Tian, Bowei Chen, Yuqi Dong. A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China. Plants. 2021; 10 (5):821.

Chicago/Turabian Style

Min Yan; Mei Xue; Li Zhang; Xin Tian; Bowei Chen; Yuqi Dong. 2021. "A Decade’s Change in Vegetation Productivity and Its Response to Climate Change over Northeast China." Plants 10, no. 5: 821.

Journal article
Published: 15 April 2021 in Remote Sensing
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Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination‒random forest (RFE-RF) to select the vegetation’s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation.

ACS Style

Yufeng Jiang; Li Zhang; Min Yan; Jianguo Qi; Tianmeng Fu; Shunxiang Fan; Bowei Chen. High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data. Remote Sensing 2021, 13, 1529 .

AMA Style

Yufeng Jiang, Li Zhang, Min Yan, Jianguo Qi, Tianmeng Fu, Shunxiang Fan, Bowei Chen. High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data. Remote Sensing. 2021; 13 (8):1529.

Chicago/Turabian Style

Yufeng Jiang; Li Zhang; Min Yan; Jianguo Qi; Tianmeng Fu; Shunxiang Fan; Bowei Chen. 2021. "High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data." Remote Sensing 13, no. 8: 1529.

Journal article
Published: 24 August 2020 in Remote Sensing
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Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R2) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.

ACS Style

Dan Li; Baosheng Wu; Bowei Chen; Chao Qin; Yanjun Wang; Yi Zhang; Yuan Xue. Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River. Remote Sensing 2020, 12, 2737 .

AMA Style

Dan Li, Baosheng Wu, Bowei Chen, Chao Qin, Yanjun Wang, Yi Zhang, Yuan Xue. Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River. Remote Sensing. 2020; 12 (17):2737.

Chicago/Turabian Style

Dan Li; Baosheng Wu; Bowei Chen; Chao Qin; Yanjun Wang; Yi Zhang; Yuan Xue. 2020. "Open-Surface River Extraction Based on Sentinel-2 MSI Imagery and DEM Data: Case Study of the Upper Yellow River." Remote Sensing 12, no. 17: 2737.

Journal article
Published: 27 March 2020 in Remote Sensing
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The detection of individual trees in a larch plantation could improve the management efficiency and production prediction. This study introduced a two-stage individual tree crown (ITC) segmentation method for airborne light detection and ranging (LiDAR) point clouds, focusing on larch plantation forests with different stem densities. The two-stage segmentation method consists of the region growing and morphology segmentation, which combines advantages of the region growing characteristics and the detailed morphology structures of tree crowns. The framework comprises five steps: (1) determination of the initial dominant segments using a region growing algorithm, (2) identification of segments to be redefined based on the 2D hull convex area of each segment, (3) establishment and selection of profiles based on the tree structures, (4) determination of the number of trees using the correlation coefficient of residuals between Gaussian fitting and the tree canopy shape described in each profile, and (5) k-means segmentation to obtain the point cloud of a single tree. The accuracy was evaluated in terms of correct matching, recall, precision, and F-score in eight plots with different stem densities. Results showed that the proposed method significantly increased ITC detections compared with that of using only the region growing algorithm, where the correct matching rate increased from 73.5% to 86.1%, and the recall value increased from 0.78 to 0.89.

ACS Style

Zhenyu Ma; Yong Pang; Di Wang; Xiaojun Liang; Bowei Chen; Hao Lu; Holger Weinacker; Barbara Koch. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sensing 2020, 12, 1078 .

AMA Style

Zhenyu Ma, Yong Pang, Di Wang, Xiaojun Liang, Bowei Chen, Hao Lu, Holger Weinacker, Barbara Koch. Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features. Remote Sensing. 2020; 12 (7):1078.

Chicago/Turabian Style

Zhenyu Ma; Yong Pang; Di Wang; Xiaojun Liang; Bowei Chen; Hao Lu; Holger Weinacker; Barbara Koch. 2020. "Individual Tree Crown Segmentation of a Larch Plantation Using Airborne Laser Scanning Data Based on Region Growing and Canopy Morphology Features." Remote Sensing 12, no. 7: 1078.