Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Xiliang Ni"'
Autor:
Xiaozhe Zhou, Minfeng Xing, Binbin He, Jinfei Wang, Yang Song, Jiali Shang, Chunhua Liao, Min Xu, Xiliang Ni
Publikováno v:
Drones, Vol 7, Iss 7, p 406 (2023)
Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV i
Externí odkaz:
https://doaj.org/article/609bad8812664388ab6092a32fcfb043
Publikováno v:
Drones, Vol 7, Iss 5, p 299 (2023)
Leaf area index (LAI) is a widely used plant biophysical parameter required for modelling plant photosynthesis and crop yield estimation. UAV remote sensing plays an increasingly important role in providing the data source needed for LAI extraction.
Externí odkaz:
https://doaj.org/article/80092399d62242119273a45c65af3488
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1132-1145 (2021)
Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR b
Externí odkaz:
https://doaj.org/article/a109bf3d9fc44a059527259b952a9f87
Publikováno v:
Remote Sensing, Vol 15, Iss 5, p 1368 (2023)
Desertification is of significant concern as one of the world’s most serious ecological and environmental problems. China has made great achievements in afforestation and desertification control in recent years. The climate varies greatly across no
Externí odkaz:
https://doaj.org/article/46128403abaa476b97b2428ddf27c4a7
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 478-495 (2017)
Although leaf area index (LAI) is one of the essential parameters employed to monitor global vegetation, no global LAI products which use a fine spatial resolution yet exist. To remedy this, we herein outline an adapted LAI retrieval algorithm which
Externí odkaz:
https://doaj.org/article/09a7fdb398444bb4ab905bac1e1801c6
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 367-383 (2017)
It is an indisputable fact that wetlands in northern China are subject to increasing pressures from climate change and other human-mediated activities, including the wetland in the Ordos Larus relictus National Nature Reserve. Dynamic monitoring the
Externí odkaz:
https://doaj.org/article/fc9ac103c40c4afbbf43906a4b18d6d8
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 8, Iss 2, Pp 466-477 (2017)
Taking the Tibetan Plateau (TP) as a study area, we developed an algorithm to generate long-term four-level snow disaster products (1982–2012) using a new daily snow depth product with a spatial resolution of 0.05° using AVHRR archival reflectance
Externí odkaz:
https://doaj.org/article/4e302b17ce7246efad93690109b0716f
Autor:
Xiaojuan Lin, Ronald van der A, Jos de Laat, Henk Eskes, Frédéric Chevallier, Philippe Ciais, Zhu Deng, Yuanhao Geng, Xuanren Song, Xiliang Ni, Da Huo, Xinyu Dou, Zhu Liu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3995db6e7e372c554040f1da3cfd4041
https://doi.org/10.5194/egusphere-2022-1490-supplement
https://doi.org/10.5194/egusphere-2022-1490-supplement
Autor:
Xiliang Ni, Yuke Zhou, Chunxiang Cao, Xuejun Wang, Yuli Shi, Taejin Park, Sungho Choi, Ranga B. Myneni
Publikováno v:
Remote Sensing, Vol 7, Iss 7, Pp 8436-8452 (2015)
Spatially-detailed forest height data are useful to monitor local, regional and global carbon cycle. LiDAR remote sensing can measure three-dimensional forest features but generating spatially-contiguous forest height maps at a large scale (e.g., con
Externí odkaz:
https://doaj.org/article/960d10f97f90428092cd72ca0354b6a7
Autor:
Yuli Shi, Lei Song, Zhen Xia, Yurong Lin, Ranga B. Myneni, Sungho Choi, Lin Wang, Xiliang Ni, Cailian Lao, Fengkai Yang
Publikováno v:
Remote Sensing, Vol 7, Iss 5, Pp 5849-5878 (2015)
Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are
Externí odkaz:
https://doaj.org/article/46ac10c90eac403880d132acda4acee9