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Loess Landslide Detection Using Object Detection Algorithms in Northwest China 期刊论文
REMOTE SENSING, 2022, 卷号: 14, 期号: 5
作者:  Ju, Yuanzhen;  Xu, Qiang;  Jin, Shichao;  Li, Weile;  Su, Yanjun;  Dong, Xiujun;  Guo, Qinghua
Adobe PDF(11451Kb)  |  收藏  |  浏览/下载:13/0  |  提交时间:2024/03/07
loess landslide  google earth image  deep learning  automatic identification  object detection  
Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications 期刊论文
REMOTE SENSING, 2021, 卷号: 13, 期号: 1
作者:  Hu, Tianyu;  Sun, Xiliang;  Su, Yanjun;  Guan, Hongcan;  Sun, Qianhui;  Kelly, Maggi;  Guo, Qinghua
Adobe PDF(8953Kb)  |  收藏  |  浏览/下载:83/0  |  提交时间:2023/02/24
unmanned aerial vehicle (UAV)  lidar  DJI Livox  low cost  forest inventory  
Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data 期刊论文
REMOTE SENSING, 2020, 卷号: 12, 期号: 10
作者:  Hu, Tianyu;  Zhang, YingYing;  Su, Yanjun;  Zheng, Yi;  Lin, Guanghui;  Guo, Qinghua
Adobe PDF(3278Kb)  |  收藏  |  浏览/下载:91/0  |  提交时间:2022/03/01
mangrove  LiDAR  random forest  GLAS  aboveground biomass  
Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 11
作者:  Zhang, Liangxia;  Zhou, Decheng;  Fan, Jiangwen;  Guo, Qun;  Chen, Shiping;  Wang, Ranghui;  Li, Yuzhe
Adobe PDF(5477Kb)  |  收藏  |  浏览/下载:89/0  |  提交时间:2022/01/06
gross primary productivity  light use efficiency model  satellite remote sensing  MODIS  EVI  eddy covariance  grassland ecosystem  temperate steppe  alpine meadow  
The Influence of Vegetation Characteristics on Individual Tree Segmentation Methods with Airborne LiDAR Data 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 23
作者:  Yang, Qiuli;  Su, Yanjun;  Jin, Shichao;  Kelly, Maggi;  Hu, Tianyu;  Ma, Qin;  Li, Yumei;  Song, Shilin;  Zhang, Jing;  Xu, Guangcai;  Wei, Jianxin;  Guo, Qinghua
Adobe PDF(6208Kb)  |  收藏  |  浏览/下载:81/0  |  提交时间:2022/01/06
individual segmentation method  leaf area index  canopy cover  tree density  coefficient of variation of tree height  
An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications 期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 24
作者:  Guan, Hongcan;  Su, Yanjun;  Hu, Tianyu;  Chen, Jin;  Guo, Qinghua
Adobe PDF(10441Kb)  |  收藏  |  浏览/下载:96/0  |  提交时间:2022/01/06
spatiotemporal data fusion  object-based framework  similar pixel  vegetation mapping  
Artificial Mangrove Species Mapping Using Pleiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms 期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 2
作者:  Wang, Dezhi;  Wan, Bo;  Qiu, Penghua;  Su, Yanjun;  Guo, Qinghua;  Wu, Xincai
Adobe PDF(2967Kb)  |  收藏  |  浏览/下载:96/0  |  提交时间:2022/02/25
artificial mangrove  object-based  pixel-based  decision tree  random forest  support vector machine  
Evaluating the Performance of Sentinel-2, Landsat 8 and Pleiades-1 in Mapping Mangrove Extent and Species 期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 9
作者:  Wang, Dezhi;  Wan, Bo;  Qiu, Penghua;  Su, Yanjun;  Guo, Qinghua;  Wang, Run;  Sun, Fei;  Wu, Xincai
Adobe PDF(5307Kb)  |  收藏  |  浏览/下载:96/0  |  提交时间:2022/02/25
mangroves  species  Sentinel-2  Landsat 8  Pleiades-1  random forest  
Impact of Error in Lidar-Derived Canopy Height and Canopy Base Height on Modeled Wildfire Behavior in the Sierra Nevada, California, USA 期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 1
作者:  Kelly, Maggi;  Su, Yanjun;  Di Tommaso, Stefania;  Fry, Danny L.;  Collins, Brandon M.;  Stephens, Scott L.;  Guo, Qinghua
Adobe PDF(4708Kb)  |  收藏  |  浏览/下载:79/0  |  提交时间:2022/02/25
wildfire burn probability  crown fire  forest fuels  Sierra Nevada  Lidar  error  
The Transferability of Random Forest in Canopy Height Estimation from Multi-Source Remote Sensing Data 期刊论文
REMOTE SENSING, 2018, 卷号: 10, 期号: 8
作者:  Jin, Shichao;  Su, Yanjun;  Gao, Shang;  Hu, Tianyu;  Liu, Jin;  Guo, Qinghua
Adobe PDF(4477Kb)  |  收藏  |  浏览/下载:79/0  |  提交时间:2022/02/25
canopy height  Random Forest  LiDAR  multi-source  vegetation type  location  scale