The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data

Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RV...

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Published in:Remote Sensing
Main Authors: Changcheng Wang, Lei Wang, Haiqiang Fu, Qinghua Xie, Jianjun Zhu
Format: Article in Journal/Newspaper
Language:English
Published: MDPI AG 2016
Subjects:
Q
Online Access:https://doi.org/10.3390/rs8040291
https://doaj.org/article/0b17fe532bfd4ace9e4e29b7028101ff
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spelling ftdoajarticles:oai:doaj.org/article:0b17fe532bfd4ace9e4e29b7028101ff 2023-05-15T17:44:53+02:00 The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data Changcheng Wang Lei Wang Haiqiang Fu Qinghua Xie Jianjun Zhu 2016-03-01T00:00:00Z https://doi.org/10.3390/rs8040291 https://doaj.org/article/0b17fe532bfd4ace9e4e29b7028101ff EN eng MDPI AG http://www.mdpi.com/2072-4292/8/4/291 https://doaj.org/toc/2072-4292 2072-4292 doi:10.3390/rs8040291 https://doaj.org/article/0b17fe532bfd4ace9e4e29b7028101ff Remote Sensing, Vol 8, Iss 4, p 291 (2016) RVoG model three-stage inversion process forest stand density ground phase ground-to-volume scattering ratio Science Q article 2016 ftdoajarticles https://doi.org/10.3390/rs8040291 2022-12-31T15:17:52Z Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio. Article in Journal/Newspaper Northern Sweden Directory of Open Access Journals: DOAJ Articles Remote Sensing 8 4 291
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic RVoG model
three-stage inversion process
forest stand density
ground phase
ground-to-volume scattering ratio
Science
Q
spellingShingle RVoG model
three-stage inversion process
forest stand density
ground phase
ground-to-volume scattering ratio
Science
Q
Changcheng Wang
Lei Wang
Haiqiang Fu
Qinghua Xie
Jianjun Zhu
The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
topic_facet RVoG model
three-stage inversion process
forest stand density
ground phase
ground-to-volume scattering ratio
Science
Q
description Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.
format Article in Journal/Newspaper
author Changcheng Wang
Lei Wang
Haiqiang Fu
Qinghua Xie
Jianjun Zhu
author_facet Changcheng Wang
Lei Wang
Haiqiang Fu
Qinghua Xie
Jianjun Zhu
author_sort Changcheng Wang
title The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
title_short The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
title_full The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
title_fullStr The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
title_full_unstemmed The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data
title_sort impact of forest density on forest height inversion modeling from polarimetric insar data
publisher MDPI AG
publishDate 2016
url https://doi.org/10.3390/rs8040291
https://doaj.org/article/0b17fe532bfd4ace9e4e29b7028101ff
genre Northern Sweden
genre_facet Northern Sweden
op_source Remote Sensing, Vol 8, Iss 4, p 291 (2016)
op_relation http://www.mdpi.com/2072-4292/8/4/291
https://doaj.org/toc/2072-4292
2072-4292
doi:10.3390/rs8040291
https://doaj.org/article/0b17fe532bfd4ace9e4e29b7028101ff
op_doi https://doi.org/10.3390/rs8040291
container_title Remote Sensing
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