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Current Techniques and Prospects for Forest Mapping and Monitoring with...
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Current Techniques and Prospects for Forest Mapping and Monitoring with Synthetic Aperture Radar (SAR)

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (25 November 2023) | Viewed by 6417

Special Issue Editors

Dr. Haiqiang Fu

E-Mail Website
Guest Editor
The Department of Geomatics Science and Technology, Central South University, Changsha 410083, China
Interests: polarimetric SAR scattering mechanism; dual-station SAR forest mapping; desert penetration mapping; soil moisture inversion
Special Issues, Collections and Topics in MDPI journals
Dr. Qinghua Xie

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: vegetation mapping; SAR; LiDAR; satellite image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Xing Peng

E-Mail Website
Guest Editor
School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
Interests: TomoSAR; forest vertical structure estimation; forest dynamic change monitoring

Special Issue Information

Dear Colleagues,

Forest parameters (such as forest height, volume, biomass, types, etc.) are important for better understanding the global carbon cycle. Synthetic aperture radar (SAR), due to its penetration ability, can record and acquire information on the vertical structure and physical properties of forests, which provides great potential for humans to understand the forest system more comprehensively and systematically. In particular, the upcoming fully polarimetric SAR satellites with a capacity for long-wavelength and short revisit periods, such as the Tandem-L, BIOMASS, and NISAR missions planned by the German Aerospace Center (DLR), European Space Agency (ESA), and National Aeronautics and Space Administration (NASA), will form penetration observations at multiple layers for the forest. This will not only provide abundant data support for the analysis of the dynamic evolution process of the forest system at the global and regional scales but also greatly promote theoretical and technological development for wide-ranging, fast, and high-precision inversion of vertical structure and physical property information about forests.

Potential topics include but are not limited to:

  • PolSAR scattering mechanisms and PolSAR decomposition model;
  • Polarimetric SAR interferometry (PolInSAR) data processing theory and methods for forest mapping and monitoring;
  • Forest height estimation and subcanopy topography mapping by InSAR/PolInSAR;
  • Forest vertical structure estimation via TomoSAR;
  • Forest dynamic change monitoring via SAR.

Dr. Haiqiang Fu
Dr. Qinghua Xie
Dr. Xing Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • SAR
  • interferometry
  • tomographic SAR
  • forest vertical structure
  • forest dynamic change

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Published Papers (3 papers)

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Research

15 pages, 14033 KiB  
Article
A Fourier–Legendre Polynomial Forest Height Inversion Model Based on a Single-Baseline Configuration
by Bing Zhang, Hongbo Zhu, Wenxuan Xu, Sairu Xu, Xinyue Chang, Weidong Song and Jianjun Zhu
Forests 2024, 15(1), 49; https://doi.org/10.3390/f15010049 - 26 Dec 2023
Cited by 6 | Viewed by 1387
Abstract
In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the [...] Read more.
In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the forest volume. This is based on the consideration that the forest height remains constant within neighboring pixels. Meanwhile, we also assume that the coefficients of the FL polynomials remain unchanged within neighboring pixels, except for the last polynomial coefficient. The idea of using neighboring pixels to increase the observations provides us with the possibility to obtain high-order FL polynomials. With this approach, it is possible to obtain a high-resolution vertical profile that is suitable for forest height estimation without losing too much spatial resolution. P-band PolInSAR data acquired in Mabounie in Gabon and Krycklan in Sweden were selected for testing the proposed algorithm. The results show that the algorithm outperforms the random volume over ground (RVoG) model by 18% and 16.7% in forest height estimation for the Mabounie and Krycklan study sites, respectively. Full article
(This article belongs to the Special Issue Current Techniques and Prospects for Forest Mapping and Monitoring with Synthetic Aperture Radar (SAR))
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10 pages, 2155 KiB  
Article
A Rapid and Easy Way for National Forest Heights Retrieval in China Using ICESat-2/ATL08 in 2019
by Shijuan Gao, Jianjun Zhu and Haiqiang Fu
Forests 2023, 14(6), 1270; https://doi.org/10.3390/f14061270 - 20 Jun 2023
Cited by 2 | Viewed by 1648
Abstract
Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, [...] Read more.
Continuous and extensive monitoring of forest height is essential for estimating forest above-ground biomass and predicting the ability of forests to absorb CO2. In particular, forest height at the national scale is an important indicator reflecting the national forestry economic construction, environmental governance, and ecological balance. However, the lack of inventory data restricts large-scale monitoring of forest height to some extent. Conducting manual surveys of forest height for large-scale areas would be labor-intensive and time-consuming. The successful launch of the new generation of spaceborne light detection and ranging (LiDAR) (The Ice, Cloud, and Land Elevation Satellite-2/the Advanced Topographic Laser Altimeter System, ICESat-2/ATLAS) has brought new opportunities for national-scale forestry resource surveys. This paper explores a method to survey national forest canopy height from the new generation of ICESat-2/ATLAS data. In view of the sparse sampling and little overlap between repeated spaceborne LiDAR data, a strategy for assessing the overall change of canopy height for large scales is provided. Some spatially continuous ancillary data were used to assist ICESat-2/ATLAS data to generate a wall-to-wall (spatially continuous) forest canopy height map in China by using the machine learning approach and then quantifying the analysis of forest canopy height in various provinces. The results show that there is a good correlation between the model forest height and the verification data, with a root mean squared error (RMSE) of 3.30 m and a coefficient of determination (R2) of 0.87. This indicates that the method for retrieving national forest canopy height is reliable. There are some limitations in areas with lower vegetation coverage or complex topography which need additional filtering or terrain correction to achieve higher accuracy in measuring forest canopy height. Our analysis suggests that ICESat-2/ATLAS data can achieve the retrieval of national forest height at an overall level, and it would be feasible to use ICESAT-2/ATLAS products to estimate forest canopy height change for large-scale areas. Full article
(This article belongs to the Special Issue Current Techniques and Prospects for Forest Mapping and Monitoring with Synthetic Aperture Radar (SAR))
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17 pages, 13404 KiB  
Article
A Dual-Baseline PolInSAR Method for Forest Height and Vertical Profile Function Inversion Based on the Polarization Coherence Tomography Technique
by Rong Zhao, Shicheng Cao, Jianjun Zhu, Longchong Fu, Yanzhou Xie, Tao Zhang and Haiqiang Fu
Forests 2023, 14(3), 626; https://doi.org/10.3390/f14030626 - 20 Mar 2023
Cited by 2 | Viewed by 2107
Abstract
Forest height and vertical structure profile functions can be estimated using polarimetric interferometric synthetic aperture radar (PolInSAR) data based on the random volume over ground (RVoG) model and polarization coherence tomography (PCT) theory, respectively. For each resolution cell, considering different forest vertical scattering [...] Read more.
Forest height and vertical structure profile functions can be estimated using polarimetric interferometric synthetic aperture radar (PolInSAR) data based on the random volume over ground (RVoG) model and polarization coherence tomography (PCT) theory, respectively. For each resolution cell, considering different forest vertical scattering structure functions to solve the corresponding forest height, the accuracy of PolInSAR forest height inversion will be improved. In this study, a forest vertical structure profile function and forest height inversion algorithm based on PCT technology was developed by using dual-baseline PolInSAR data. Then the deviation of forest height was corrected according to the inverted forest vertical structure. Finally, the LiDAR and PolInSAR data were employed to verify the proposed method. The experimental results show that the accuracy of the proposed method (tropical forest: RMSE = 5.96 m, boreal forest: RMSE = 3.11 m) is 25.5% and 30.43% higher than that of the dual-baseline RVoG model algorithm (tropical forest: RMSE = 8 m, boreal forest: RMSE = 4.47 m). Full article
(This article belongs to the Special Issue Current Techniques and Prospects for Forest Mapping and Monitoring with Synthetic Aperture Radar (SAR))
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