Results 1 to 10 of about 11,335 (270)
MLAOS: A Multi-Point Linear Array of Optical Sensors for Coniferous Foliage Clumping Index Measurement [PDF]
Sensors, 2014 The canopy foliage clumping effect is primarily caused by the non-random distribution of canopy foliage. Currently, measurements of clumping index (CI) by handheld instruments is typically time- and labor-intensive.
Yonghua Qu +4 more
doaj +6 more sources
Modeling the Directional Clumping Index of Crop and Forest [PDF]
Remote Sensing, 2018 The Clumping Index (Ω) was introduced to quantify the spatial distribution pattern of vegetation elements. It is crucial to improve the estimation accuracy of vital vegetation parameters, such as Leaf Area Index (LAI) and Gross Primary Production (GPP ...
Jingjing Peng +6 more
doaj +3 more sources
Remote Sensing, 2017 In contrast to herbaceous canopies and forests, savannas are grassland ecosystems with sparsely distributed individual trees, so the canopy is spatially heterogeneous and open, whereas the woody cover in savannas, e.g., tree cover, adversely affects ...
Jucai Li +5 more
doaj +4 more sources
Comparison of Canopy Clumping Index Measuring Methods and Analysis of Their Impact
Remote Sensing, 2023 The clumping index (CI) is a commonly used vegetation dispersion parameter used to characterize the spatial distribution of the clumping or random distribution of leaves in canopy environments, as well as to determine the radiation transfer of the canopy,
Zhiguo Liang +3 more
doaj +3 more sources
Effects of Tree Trunks on Estimation of Clumping Index and LAI from HemiView and Terrestrial LiDAR [PDF]
Forests, 2018 Estimating clumping indices is important for determining the leaf area index (LAI) of forest canopies. The spatial distribution of the clumping index is vital for LAI estimation. However, the neglect of woody tissue can result in biased clumping index estimates when indirectly deriving them from the gap probability and LAI observations. It is difficult
Yunfei Bao, Wenjian Ni, Hans Verbeeck
exaly +5 more sources
A Robust Inversion Algorithm for Surface Leaf and Soil Temperatures Using the Vegetation Clumping Index [PDF]
Remote Sensing, 2017 The inversion of land surface component temperatures is an essential source of information for mapping heat fluxes and the angular normalization of thermal infrared (TIR) observations. Leaf and soil temperatures can be retrieved using multiple-view-angle
Zunjian Bian +7 more
doaj +3 more sources
Remote Sensing, 2022 The clumping index (CI) quantifies the foliage grouping within distinct canopies relative to randomly distributed canopies, which plays an important role in the vegetation radiative regime. Among the vegetation structure parameters, the global CI map can
Siyang Yin +10 more
doaj +3 more sources
Retrieving Forest Canopy Elements Clumping Index Using ICESat GLAS Lidar Data [PDF]
Remote Sensing, 2021 Clumping index (CI) is a canopy structural variable important for modeling the terrestrial biosphere, but its retrieval from remote sensing data remains one of the least reliable.
Lei Cui +11 more
doaj +2 more sources
Science of Remote Sensing, 2021 Canopy clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) are important vegetation structural variables. Characterization of the spatial and temporal variations of these variables is important for understanding the global ...
Hongliang Fang +4 more
doaj +3 more sources
International Journal of Applied Earth Observations and Geoinformation, 2020 Gap probability theory provides a theoretical equation to calculate fractional vegetation cover (FVC). However, the main algorithms used in present FVC products generation are still the linear mixture model and machine learning methods.
Jing Zhao +6 more
doaj +2 more sources