Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Lizao Ye"'
Publikováno v:
Forests, Vol 13, Iss 10, p 1643 (2022)
This paper intends to clarify that the spatial and temporal evolutionary patterns of regional vegetation and their relationship with climate form a premise of ecological conservation and environmental governance, and play an important role in maintai
Externí odkaz:
https://doaj.org/article/07a2910199aa4233973a61d8ea4087c5
Autor:
Yongke Yang, Pengfeng Xiao, Xueliang Zhang, Xuezhi Feng, Jiangeng Wang, Nan Ye, Zuo Wang, Guangjun He, Lizao Ye
Publikováno v:
Journal of Applied Meteorology and Climatology. 61:1881-1892
Near-surface air temperature lapse rate (NSATLR) is vital for hydrological simulation and mountain climate research in snowmelt-dominated regions. In this study, NSATLRs of two vertical zones (i.e., mountain grassland–coniferous forest belt and alp
Autor:
Rui Hu, Tengyao Ma, Yina Song, Wei Ma, Xuezhi Feng, Pengfeng Xiao, Xueliang Zhang, Haixing Li, Lizao Ye
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Because of the anisotropy of snow surface reflectance, it is essential to select a proper snow bidirectional reflectance model for extracting snow cover and inverting snow properties from remote sensing image, especially during the period of snow rap
Publikováno v:
ISPRS Journal of Photogrammetry and Remote Sensing. 169:17-28
Wetness is one of the important physical parameters of snowpack. Its spatial and temporal changes play a key role in snowmelt runoff forecast, regional climate change, and agricultural irrigation. In this study, we proposed a new method to retrieve s
Autor:
Congfang Liu, Donghua Chen, Chen Zou, Saisai Liu, Hu Li, Zhihong Liu, Wutao Feng, Naiming Zhang, Lizao Ye
Publikováno v:
Sustainability; Volume 14; Issue 20; Pages: 13006
Forest biomass estimation is an important parameter for calculating forest carbon storage, which is of great significance for formulating carbon-neutral strategies and forest resource management measures. We aimed at solving the problems of low estim