Zobrazeno 1 - 10
of 194
pro vyhledávání: '"Forest canopy height"'
Publikováno v:
Ecological Indicators, Vol 166, Iss , Pp 112566- (2024)
The study on PolInSAR forest canopy height(FCH) inversion is an essential branch within the SAR field, where the temporal decorrelation(TD) inherent in the interferometric complex coherence of repeat-pass Polarization Interferometric Synthetic Apertu
Externí odkaz:
https://doaj.org/article/e29d8d1547a84e1d8c09ad9eabaf8dcf
Publikováno v:
International Journal of Applied Earth Observations and Geoinformation, Vol 133, Iss , Pp 104123- (2024)
Forest canopy height (FCH) is crucial for monitoring forest structure and aboveground biomass. Light detecting and ranging (LiDAR), as a promising remote sensing technology, provides various forms of data for measuring and mapping FCH. Airborne laser
Externí odkaz:
https://doaj.org/article/a897296e255c45e3b7bc716cb4fcafea
Publikováno v:
GIScience & Remote Sensing, Vol 61, Iss 1 (2024)
Forest canopy height (FCH) is one of the most important variables for carbon stock estimation. While many studies have focused on extracting FCH from spaceborne LiDAR in regions with spatially continuous and large patch sizes of forested lands, limit
Externí odkaz:
https://doaj.org/article/a60163866b394d60b7557a18c72a5960
Publikováno v:
International Journal of Digital Earth, Vol 17, Iss 1 (2024)
ABSTRACTThe discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multi-temporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reli
Externí odkaz:
https://doaj.org/article/bec76a7aed254301a5eae7db25b58b62
Publikováno v:
Forests, Vol 15, Iss 7, p 1132 (2024)
Accurate estimation of forest canopy height is crucial for biomass inversion, carbon storage assessment, and forestry management. However, deep learning methods are underutilized compared to machine learning. This paper introduces the convolutional n
Externí odkaz:
https://doaj.org/article/1429848a5b4749209b58d2f7eb50abaa
Publikováno v:
Remote Sensing, Vol 16, Iss 12, p 2138 (2024)
Forest canopy height is a fundamental parameter of forest structure, and plays a pivotal role in understanding forest biomass allocation, carbon stock, forest productivity, and biodiversity. Spaceborne LiDAR (Light Detection and Ranging) systems, suc
Externí odkaz:
https://doaj.org/article/0c169bb5f2e742678b7955735252b8c7
Publikováno v:
Ecological Indicators, Vol 156, Iss , Pp 111092- (2023)
Normalized Difference Vegetation Index (NDVI) is widely used to represent the greenness indicator for the Ecological Environment Quality (EEQ) assessment based on the traditional Remote Sensing Ecological Index (RSEI). However, NDVI saturation issues
Externí odkaz:
https://doaj.org/article/dac0e356d03d45758342a0111b65181f
Publikováno v:
Redai dili, Vol 43, Iss 1, Pp 1-11 (2023)
Mangroves, which have extremely high primary productivity, are efficient coastal blue carbon ecosystems. Aboveground biomass (AGB) is an important component of vegetation carbon pools. Thus, accurate estimation of mangrove AGB is critical for studyin
Externí odkaz:
https://doaj.org/article/0c16e86a4cab4df995e02fed0415f369
Publikováno v:
Forests, Vol 15, Iss 2, p 369 (2024)
As an important component of forest parameters, forest canopy height is of great significance to the study of forest carbon stocks and carbon cycle status. There is an increasing interest in obtaining large-scale forest canopy height quickly and accu
Externí odkaz:
https://doaj.org/article/40070463db5f4106afb542d6e953399b
Autor:
Yu-Huan Zhao, Kazem Bakian-Dogaheh, Jane Whitcomb, Richard H Chen, Yonghong Yi, John S Kimball, Mahta Moghaddam
Publikováno v:
Environmental Research Letters, Vol 19, Iss 7, p 074025 (2024)
Vegetation information is essential for analyzing aboveground biomass and understanding subsurface characteristics, such as root biomass, soil organic matter, and soil moisture conditions. In this study, we mapped boreal forest canopy height (FCH) an
Externí odkaz:
https://doaj.org/article/11d37874880a437fa7cbe65f3babfb85