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2020 Applied Ecology and Environmental Research  
The quantification and timely information on aboveground biomass (AGB) of grassland are crucial for the sustainable use and management of grassland resources.  ...  In this study, AGB for main grassland types in the North Tibet was analyzed using field measurements and climatic controls of variations of aboveground biomass were explored, and the general estimate models  ...  The artificial neural network and MODIS VIs were used to estimate grassland AGB in the Three-River Headwaters Region in the northeastern TP and it was found that the backpropagation artificial neural network  ... 
doi:10.15666/aeer/1806_76557672 fatcat:sio4xzhk25gtddlissrlyxkyy4

Modeling alpine grassland above ground biomass based on remote sensing data and machine learning algorithm: A case study in the east of Tibetan Plateau, China

Baoping Meng, Shuhua Yi, Tiangang Liang, Jianpeng Yin, Xia Cui, Jing Ge, Mengjing Hou, Yanyan Lv, Yi Sun
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Effective and accurate assessment of grassland aboveground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands  ...  measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region.  ...  These results were similar to studies of Liang et al., (2016) and Yang et al., (2017) in Three-River Headwaters Region. B.  ... 
doi:10.1109/jstars.2020.2999348 fatcat:g2odli43tbaufiw34d4eks6i7q

Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

Ruiyang Yu, Yunjun Yao, Qiao Wang, Huawei Wan, Zijing Xie, Wenjia Tang, Ziping Zhang, Junming Yang, Ke Shang, Xiaozheng Guo, Xiangyi Bei
2021 Remote Sensing  
The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China.  ...  data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR.  ...  The authors thank Jie He and Kun Yang from the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (, accessed on 15 September 2020) for providing the China Meteorological  ... 
doi:10.3390/rs13152993 fatcat:t6wy6fnprjgo3nmhzokguviddu

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 6410-6423 Multidimensional Response Evaluation of Remote-Sensing Vegetation Change to Drought Stress in the Three-River Headwaters, China.  ...  Zhang, G., +, JSTARS 2020 649-658 Wind Speed Estimation From CYGNSS Using Artificial Neural Networks.  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y

Freewat, A Horizon 2020 Project To Build Open Source Tools For Water Management: A European Perspective Conference Towards Sustainable Agriculture

Borsi Iacopo Mehl Steffen
2017 Zenodo  
through all phases of a project, from scenario generation to the final stage of discussion.  ...  FREEWAT aims at promoting water resource management by simplifying the application of the Water Framework Directive and related Directives.  ...  Acknowledgements: This study is part of the PERSIST project funded by the EU Water JPI (JPIW2013-118).  ... 
doi:10.5281/zenodo.546436 fatcat:k2i5fkueorbefidbm676x74dii

Satellitenbasiertes Monitoring von Weidedegradation auf dem Tibetischen Plateau: Ein Multiskalenansatz

Lukas Lehnert, Bendix, Jörg (Prof. Dr.)
The new approach consists of three parts and incorporates different spatial and temporal scales: (i) the development and testing of an indicator system for pasture degradation on the local scale, (ii)  ...  The first part of the new approach comprised the test of the suitability of a new two-indicator system and its transferability to spaceborne data. The indicators were land-cover fractions (e.g.  ...  of the article.  ... 
doi:10.17192/z2015.0406 fatcat:uu322xxy5zedxnbcbsskqgd24e