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期刊论文 [13]
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Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data
期刊论文
ECOLOGICAL INDICATORS, 2020, 卷号: 108
作者:
Xu, Kexin
;
Su, Yanjun
;
Liu, Jin
;
Hu, Tianyu
;
Jin, Shichao
收藏
  |  
浏览/下载:25/0
  |  
提交时间:2022/03/01
Aboveground biomass (AGB)
Degraded grassland
Machine learning
Northern agro-pastoral ecotone
Terrestrial laser scanning (TLS)
Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm
期刊论文
ECOLOGICAL INDICATORS, 2019, 卷号: 102, 页码: 479-487
作者:
Zeng, Na
;
Ren, Xiaoli
;
He, Honglin
;
Zhang, Li
;
Zhao, Dan
收藏
  |  
浏览/下载:49/0
  |  
提交时间:2019/09/24
Aboveground biomass (AGB)
The Tibetan Plateau
Random Forest (RF)
Mean annual temperature (MAT)
Mean annual precipitation (MAP)
Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm
期刊论文
Ecological Indicators, 2019, 卷号: Vol.102, 页码: 479-487
作者:
Zeng, N.
;
Ren, X.
;
He, H.
;
Zhang, L.
;
Zhao, D.
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2019/11/21
Aboveground biomass (AGB)
Mean annual precipitation (MAP)
Mean annual temperature (MAT)
Random Forest (RF)
The Tibetan Plateau
Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery
期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 13, 页码: 1597
作者:
He, Li
;
Li, Ainong
;
Yin, Gaofei
;
Nan, Xi
;
Bian, Jinhu
收藏
  |  
浏览/下载:47/0
  |  
提交时间:2019/09/09
aboveground biomass (AGB)
grassland
PROSAIL
MCD43A4
Estimation and Multiscale Transformation of Aboveground Biomass: An HGSU-Oriented Approach Based on Multisource Data
期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 卷号: 12, 期号: 7
作者:
Du, Yingkun
;
Wang, Jing
;
Liu, Zhengjun
;
Lin, Yifan
收藏
  |  
浏览/下载:9/0
  |  
提交时间:2019/12/05
Aboveground biomass (AGB)
homogeneous geography and spectrum unit (HGSU)
multiple scales
multisource data
random forest (RF)
Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data
期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 12
作者:
Zhang, Linjing
;
Shao, Zhenfeng
;
Liu, Jianchen
;
Cheng, Qimin
收藏
  |  
浏览/下载:11/0
  |  
提交时间:2019/12/05
LiDAR
Landsat 8 OLI
combined optical and LiDAR indices
synergy
Stacked Sparse Autoencoder network (SSAE)
forest aboveground biomass (AGB)
Estimation and Multiscale Transformation of Aboveground Biomass: An HGSU-Oriented Approach Based on Multisource Data
期刊论文
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2019, 卷号: 12, 期号: 7
作者:
Du, Yingkun
;
Wang, Jing
;
Liu, Zhengjun
;
Lin, Yifan
收藏
  |  
浏览/下载:6/0
  |  
提交时间:2019/12/05
Aboveground biomass (AGB)
homogeneous geography and spectrum unit (HGSU)
multiple scales
multisource data
random forest (RF)
Deep Learning Based Retrieval of Forest Aboveground Biomass from Combined LiDAR and Landsat 8 Data
期刊论文
REMOTE SENSING, 2019, 卷号: 11, 期号: 12
作者:
Zhang, Linjing
;
Shao, Zhenfeng
;
Liu, Jianchen
;
Cheng, Qimin
收藏
  |  
浏览/下载:4/0
  |  
提交时间:2019/12/11
LiDAR
Landsat 8 OLI
combined optical and LiDAR indices
synergy
Stacked Sparse Autoencoder network (SSAE)
forest aboveground biomass
(AGB)
Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance
期刊论文
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2018, 卷号: 7, 期号: 7, 页码: 14
作者:
Yin, Gaofei
;
Li, Ainong
;
Wu, Chaoyang
;
Wang, Jiyan
;
Xie, Qiaoyun
收藏
  |  
浏览/下载:44/0
  |  
提交时间:2019/05/23
aboveground biomass (AGB)
uncertainty
consistent adjustment of the climatology to actual observations (CACAO)
Gaussian process regression (GPR)
Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance
期刊论文
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2018, 卷号: 7, 期号: 7, 页码: 14
作者:
Yin, Gaofei
;
Li, Ainong
;
Wu, Chaoyang
;
Wang, Jiyan
;
Xie, Qiaoyun
收藏
  |  
浏览/下载:64/0
  |  
提交时间:2018/10/10
aboveground biomass (AGB)
uncertainty
consistent adjustment of the climatology to actual observations (CACAO)
Gaussian process regression (GPR)
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