Lidar Remote Sensing for Forestry and Terrestrial Applications
نویسنده:
, , , , , , ,سال
: 2011
چکیده: Remote sensing has facilitated extraordinary advances in modeling, mapping,
and the understanding of ecosystems. Applications of remote sensing involve
either images from passive optical systems, such as Aerial Photography and
the Landsat Thematic Mapper, or, active Radar sensors such as RADARSAT.
These types of remote sensors have proven to be satisfactory for many forest
applications, such as mapping and classifying land cover into specific classes
and, in some biomes, estimating aboveground biomass and Leaf Area Index
(LAI). However, conventional sensors have significant limitations for
ecological and forest applications. The sensitivity and accuracy of these
devices have repeatedly been shown to fall with increasing aboveground
biomass and LAI. They are also limited in their ability to represent the spatial
patterns. They produce only two-dimensional (x & y) images, which cannot
fully represent the three dimensional structure of the forest canopy. Ecologists
have long understood that the presence of specific organisms and the overall
richness of wildlife communities can be highly dependent on the threedimensional
spatial pattern of vegetation. Individual bird species, in particular,
are often associated with specific three dimensional features in riparian
forests. Additionally, aspects of forests, such as productivity, may be related to
forest canopy structure. Lidar (light detecting and ranging) is an alternative
remote sensing technology that promises to both increase the accuracy of
biophysical measurements and extend spatial analysis into the third dimension
(z). Lidar sensors directly measure the three-dimensional distribution of forest
canopies as well as sub-canopy topography, therefore providing high
resolution topographic maps and highly accurate estimates of tree height cover, and canopy structure. In addition, lidar has been shown to accurately
estimate LAI and aboveground biomass, even in those high biomass
ecosystems, where passive optical and active radar sensors typically fail to do
so. Estimation of forest structural attributes, such as LAI, is an important step
in identifying the amount of water use in forest areas.
and the understanding of ecosystems. Applications of remote sensing involve
either images from passive optical systems, such as Aerial Photography and
the Landsat Thematic Mapper, or, active Radar sensors such as RADARSAT.
These types of remote sensors have proven to be satisfactory for many forest
applications, such as mapping and classifying land cover into specific classes
and, in some biomes, estimating aboveground biomass and Leaf Area Index
(LAI). However, conventional sensors have significant limitations for
ecological and forest applications. The sensitivity and accuracy of these
devices have repeatedly been shown to fall with increasing aboveground
biomass and LAI. They are also limited in their ability to represent the spatial
patterns. They produce only two-dimensional (x & y) images, which cannot
fully represent the three dimensional structure of the forest canopy. Ecologists
have long understood that the presence of specific organisms and the overall
richness of wildlife communities can be highly dependent on the threedimensional
spatial pattern of vegetation. Individual bird species, in particular,
are often associated with specific three dimensional features in riparian
forests. Additionally, aspects of forests, such as productivity, may be related to
forest canopy structure. Lidar (light detecting and ranging) is an alternative
remote sensing technology that promises to both increase the accuracy of
biophysical measurements and extend spatial analysis into the third dimension
(z). Lidar sensors directly measure the three-dimensional distribution of forest
canopies as well as sub-canopy topography, therefore providing high
resolution topographic maps and highly accurate estimates of tree height cover, and canopy structure. In addition, lidar has been shown to accurately
estimate LAI and aboveground biomass, even in those high biomass
ecosystems, where passive optical and active radar sensors typically fail to do
so. Estimation of forest structural attributes, such as LAI, is an important step
in identifying the amount of water use in forest areas.
کلیدواژه(گان): Lidar,Canopy,Forest,Terrestrial,Airborne
کالکشن
:
-
آمار بازدید
Lidar Remote Sensing for Forestry and Terrestrial Applications
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contributor author | علیرضا فریدحسینی | en |
contributor author | آمنه میان آبادی | en |
contributor author | امین علیزاده | en |
contributor author | محمد بنایان اول | en |
contributor author | Alireza Faridhosseini | fa |
contributor author | Ameneh Mianabadi | fa |
contributor author | Amin Alizadeh | fa |
contributor author | Mohammad Bannayan Aval | fa |
date accessioned | 2020-06-06T14:36:16Z | |
date available | 2020-06-06T14:36:16Z | |
date issued | 2011 | |
identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/3403658 | |
description abstract | Remote sensing has facilitated extraordinary advances in modeling, mapping, and the understanding of ecosystems. Applications of remote sensing involve either images from passive optical systems, such as Aerial Photography and the Landsat Thematic Mapper, or, active Radar sensors such as RADARSAT. These types of remote sensors have proven to be satisfactory for many forest applications, such as mapping and classifying land cover into specific classes and, in some biomes, estimating aboveground biomass and Leaf Area Index (LAI). However, conventional sensors have significant limitations for ecological and forest applications. The sensitivity and accuracy of these devices have repeatedly been shown to fall with increasing aboveground biomass and LAI. They are also limited in their ability to represent the spatial patterns. They produce only two-dimensional (x & y) images, which cannot fully represent the three dimensional structure of the forest canopy. Ecologists have long understood that the presence of specific organisms and the overall richness of wildlife communities can be highly dependent on the threedimensional spatial pattern of vegetation. Individual bird species, in particular, are often associated with specific three dimensional features in riparian forests. Additionally, aspects of forests, such as productivity, may be related to forest canopy structure. Lidar (light detecting and ranging) is an alternative remote sensing technology that promises to both increase the accuracy of biophysical measurements and extend spatial analysis into the third dimension (z). Lidar sensors directly measure the three-dimensional distribution of forest canopies as well as sub-canopy topography, therefore providing high resolution topographic maps and highly accurate estimates of tree height cover, and canopy structure. In addition, lidar has been shown to accurately estimate LAI and aboveground biomass, even in those high biomass ecosystems, where passive optical and active radar sensors typically fail to do so. Estimation of forest structural attributes, such as LAI, is an important step in identifying the amount of water use in forest areas. | en |
language | English | |
title | Lidar Remote Sensing for Forestry and Terrestrial Applications | en |
type | Journal Paper | |
contenttype | External Fulltext | |
subject keywords | Lidar | en |
subject keywords | Canopy | en |
subject keywords | Forest | en |
subject keywords | Terrestrial | en |
subject keywords | Airborne | en |
journal title | Intenational journal of applied environmental sciences | fa |
pages | 99-114 | |
journal volume | 6 | |
journal issue | 1 | |
identifier link | https://profdoc.um.ac.ir/paper-abstract-1023159.html | |
identifier articleid | 1023159 |