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Salinity and species richness determine mangrove canopy height

2020
<p>Mangrove ecosystems play an important role in carbon sequestration of coastal wetlands through litterfall and soil carbon accumulation. Maximum canopy height (<em>H<sub>max</sub></em>) is a key variable in assessing above-ground carbon stocks and productivity, and it is ...
Saverio Perri   +2 more
openaire   +1 more source

Laser altimeter canopy height profiles: methods and validation for closed-canopy, broadleaf forests

Remote Sensing of Environment, 2001
Waveform-recording laser altimeter observations of vegetated landscapes provide a time-resolved measure of laser pulse backscatter energy from canopy surfaces and the underlying ground. Airborne laser altimeter waveform data was acquired using the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER) for a successional sequence of four, closed ...
D.J Harding   +3 more
openaire   +1 more source

Canopy Height Estimation from Spaceborne Imagery Using Convolutional Encoder-Decoder

2021
The recent advances in multimedia modeling with deep learning methods have significantly affected remote sensing applications, such as canopy height mapping. Estimating canopy height maps in large-scale is an important step towards sustainable ecosystem management.
Leonidas Alagialoglou   +4 more
openaire   +1 more source

sentinel2-canopy-height

This is a data repository for the paper "Estimating vegetation canopy height from intra-annual Sentinel-2 reflectance dynamics using a 3D convolutional neural network"
openaire   +1 more source

SAR radargrammetry and scanning LiDAR in predicting forest canopy height

2012 IEEE International Geoscience and Remote Sensing Symposium, 2012
Our objective was to evaluate the accuracy of estimating forest canopy height when using scanning LiDAR and TerraSAR-X stereo radargrammetry. The study area was located in southern Finland. We used SAR radargrammetry and LiDAR to extract 3D point clouds to derive predictors used in the non-parametric prediction of forest canopy height.
Vastaranta, Mikko   +7 more
openaire   +3 more sources

Water availability predicts forest canopy height at the global scale

Ecology Letters, 2015
AbstractThe tendency of trees to grow taller with increasing water availability is common knowledge. Yet a robust, universal relationship between the spatial distribution of water availability and forest canopy height (H) is lacking. Here, we created a global water availability map by calculating an annual budget as the difference between precipitation
Klein, Tamir   +2 more
openaire   +3 more sources

A comparison between various definitions of forest stand height and aerodynamic canopy height

Agricultural and Forest Meteorology, 2010
A consistent measure of forest canopy height is required for among-site comparisons of meteorological properties and for comparison of model results such as distributions of wind and other scalars calculated by large eddy simulations with observations.
Taro Nakai   +4 more
openaire   +1 more source

Comparative analysis of SRTM–NED vegetation canopy height to LIDAR‐derived vegetation canopy metrics

International Journal of Remote Sensing, 2009
Vegetation canopy heights derived from the SRTM 30 m grid DEM minus USGS National Elevation Data (NED) DTM were compared to three vegetation metrics derived from a medium footprint LIDAR data (LVIS) for the US Sierra Nevada forest in California. Generally the SRTM minus NED was found to underestimate the vegetation canopy height. Comparing the SRTM-NED-
L. W. Kenyi   +3 more
openaire   +1 more source

Tree and Canopy Height Estimation with Scanning Lidar

2003
A large part of the research efforts concerning the remote sensing of forests has been devoted to the development of repeatable methods for the extraction of information from monoscopic, two-dimensional images. Emphasis has been on spectral pattern recognition.
Benoît St-Onge   +2 more
openaire   +1 more source

Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data

Remote Sensing of Environment, 2022
Xiaoqiang Liu   +9 more
semanticscholar   +1 more source

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