Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
SAR for Forest Mapping III
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

SAR for Forest Mapping III

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 5362

Special Issue Editors

Dr. Michele Martone

E-Mail Website
Guest Editor
Microwaves and Radar Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Interests: forest mapping with SAR interferometry (InSAR); forest change detection; SAR raw data quantization; data volume reduction methods for future SAR systems
Special Issues, Collections and Topics in MDPI journals
Dr. Armando Marino

E-Mail Website
Guest Editor
Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA, UK
Interests: remote sensing; synthetic aperture radar (SAR); polarimetric SAR; forest aboveground biomass; polarimetric target detector
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a vital natural resource, forests are of extreme importance for all living beings on our planet. They play a key role in controlling climate change, represent an essential source of energy (e.g., biomass), food, jobs, and livelihoods, and serve as natural habitats to a large variety of animal species, which is essential for biodiversity preservation.

Forest ecosystems are constantly shaped and changed by physical and biological disturbances and eventual regeneration processes. Today, forest degradation is occurring at an alarming rate, often due to illegal anthropogenic activities such as logging and fires, such that sensitive environments have been irreversibly damaged, with critical environmental and economic consequences at regional as well as at global scales. A precise and efficient assessment and monitoring of the forest resources, treatments, and recreational opportunities is therefore of crucial importance in order to develop early warning systems. In this scenario, synthetic aperture radar (SAR) remote sensing represents a unique technique for providing high-resolution images independently of daylight and almost any weather conditions. In the last few decades, SAR imaging has demonstrated its suitability for forest mapping applications. The combination of the polarimetric, interferometric, and/or tomographic information further increases its capabilities and the achievable product accuracy.

As Guest Editors, we would like to dedicate this Special Issue to documenting SAR-based methods for forest mapping. Well-prepared, unpublished submissions that address one or more of the following topics are solicited:

  • New methods and concepts for the quantitative assessment of forest biomass;
  • Combination of complementary SAR imaging methods (tomography, polarimetry, interferometry) to define novel approaches, concepts, and applications for forest mapping and monitoring;
  • Feasibility studies with new sensors, ranging from drones to spaceborne SAR systems, and their applications to forestry;
  • Combined use of multifrequency SAR imaging for forest applications;
  • Comparison and benchmarking studies using various sensors and/or processing methods for forestry;
  • New approaches for the detection of forest changes;
  • Potential of artificial intelligence-based methods for forest information retrieval;
  • Novel methodologies considering the fusion of SAR data with data from other sources.

Dr. Michele Martone
Dr. Armando Marino
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

  • drone/airborne/spaceborne synthetic aperture radar (SAR)
  • biomass estimation
  • forest mapping
  • change detection
  • SAR polarimetry, interferometry, tomography
  • artificial intelligence for forest applications

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issues

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

31 pages, 7836 KiB  
Article
Estimation of Forest Growing Stock Volume with Synthetic Aperture Radar: A Comparison of Model-Fitting Methods
by Maurizio Santoro, Oliver Cartus, Oleg Antropov and Jukka Miettinen
Remote Sens. 2024, 16(21), 4079; https://doi.org/10.3390/rs16214079 - 31 Oct 2024
Viewed by 454
Abstract
Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference [...] Read more.
Satellite-based estimation of forest variables including forest biomass relies on model-based approaches since forest biomass cannot be directly measured from space. Such models require ground reference data to adapt to the local forest structure and acquired satellite data. For wide-area mapping, such reference data are too sparse to train the biomass retrieval model and approaches for calibrating that are independent from training data are sought. In this study, we compare the performance of one such calibration approach with the traditional regression modelling using reference measurements. The performance was evaluated at four sites representative of the major forest biomes in Europe focusing on growing stock volume (GSV) prediction from time series of C-band Sentinel-1 and Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS-2 PALSAR-2) backscatter measurements. The retrieval model was based on a Water Cloud Model (WCM) and integrated two forest structural functions. The WCM trained with plot inventory GSV values or calibrated with the aid of auxiliary data products correctly reproduced the trend between SAR backscatter and GSV measurements across all sites. The WCM-predicted backscatter was within the range of measurements for a given GSV level with average model residuals being smaller than the range of the observations. The accuracy of the GSV estimated with the calibrated WCM was close to the accuracy obtained with the trained WCM. The difference in terms of root mean square error (RMSE) was less than 5% units. This study demonstrates that it is possible to predict biomass without providing reference measurements for model training provided that the modelling scheme is physically based and the calibration is well set and understood. Full article
(This article belongs to the Special Issue SAR for Forest Mapping III)
►▼ Show Figures

Figure 1

30 pages, 12064 KiB  
Article
Inversion of Forest Aboveground Biomass in Regions with Complex Terrain Based on PolSAR Data and a Machine Learning Model: Radiometric Terrain Correction Assessment
by Yonghui Nie, Rula Sa, Sergey Chumachenko, Yifan Hu, Youzhu Wang and Wenyi Fan
Remote Sens. 2024, 16(12), 2229; https://doi.org/10.3390/rs16122229 - 19 Jun 2024
Viewed by 780
Abstract
The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction [...] Read more.
The accurate estimation of forest aboveground biomass (AGB) in areas with complex terrain is very important for quantifying the carbon sequestration capacity of forest ecosystems and studying the regional or global carbon cycle. In our previous research, we proposed the radiometric terrain correction (RTC) process for introducing normalized correction factors, which has strong effectiveness and robustness in terms of the backscattering coefficient of polarimetric synthetic aperture radar (PolSAR) data and the monadic model. However, the impact of RTC on the correctness of feature extraction and the performance of regression models requires further exploration in the retrieval of forest AGB based on a machine learning multiple regression model. In this study, based on PolSAR data provided by ALOS-2, 117 feature variables were accurately extracted using the RTC process, and then Boruta and recursive feature elimination with cross-validation (RFECV) algorithms were used to perform multi-step feature selection. Finally, 10 machine learning regression models and the Optuna algorithm were used to evaluate the effectiveness and robustness of RTC in improving the quality of the PolSAR feature set and the performance of the regression models. The results revealed that, compared with the situation without RTC treatment, RTC can effectively and robustly improve the accuracy of PolSAR features (the Pearson correlation R between the PolSAR features and measured forest AGB increased by 0.26 on average) and the performance of regression models (the coefficient of determination R2 increased by 0.14 on average, and the rRMSE decreased by 4.20% on average), but there is a certain degree of overcorrection in the RTC process. In addition, in situations where the data exhibit linear relationships, linear models remain a powerful and practical choice due to their efficient and stable characteristics. For example, the optimal regression model in this study is the Bayesian Ridge linear regression model (R2 = 0.82, rRMSE = 18.06%). Full article
(This article belongs to the Special Issue SAR for Forest Mapping III)
►▼ Show Figures

Graphical abstract

18 pages, 6867 KiB  
Article
Assessment of the Impact of Surface Water Content for Temperate Forests in SAR Data at C-Band
by Costanza Cagnina, Armando Marino, Cristian Silva-Perez, Javier Ruiz-Ramos and Juan Suarez
Remote Sens. 2023, 15(24), 5723; https://doi.org/10.3390/rs15245723 - 14 Dec 2023
Viewed by 1091
Abstract
This study addresses the escalating challenges posed by forest drought and wildfires, emphasizing the critical need to monitor forest conditions to mitigate associated risks. While traditional optical sensors have proven valuable for vegetation surface water (VSW) assessment, their limitations in regions with persistent [...] Read more.
This study addresses the escalating challenges posed by forest drought and wildfires, emphasizing the critical need to monitor forest conditions to mitigate associated risks. While traditional optical sensors have proven valuable for vegetation surface water (VSW) assessment, their limitations in regions with persistent cloud cover prompt an exploration of the alternatives. The study advocates the efficacy of Synthetic Aperture Radar (SAR) systems, known for their cloud-penetrating capabilities and sensitivity to changes in dielectric properties. Leveraging Sentinel-1 C-band dual polarization SAR data, the research investigates the impact of Vegetation Surface Water (VSW) on backscatter coefficients in a temperate coniferous forest through the application of generalized linear models. Despite the challenges posed by precipitation and canopy characteristics, the study unveils detectable modulation in backscatter, particularly in VH polarization, indicating the potential of SAR-based methods in forest monitoring. The occurrence of rain on the day of Sentinel-1 image acquisition, and therefore the presence of VSW, triggers an increase of 0.35 dB in VV backscatter, and an increase of 0.45 dB in VH backscatter. The findings underscore the importance of considering surface water content in radar backscatter analyses for accurate biomass estimations and change detection, suggesting avenues for future research and potential correction mechanisms. Full article
(This article belongs to the Special Issue SAR for Forest Mapping III)
►▼ Show Figures

Figure 1

18 pages, 8912 KiB  
Article
Optimization of the Vertical Wavenumber for PolInSAR Inversion Performance Based on Numerical CRLB Analysis
by Xiao Wang and Hong Li
Remote Sens. 2023, 15(22), 5321; https://doi.org/10.3390/rs15225321 - 10 Nov 2023
Viewed by 1007
Abstract
A number of advanced SAR missions have been planned to launch, which can operate in fully polarimetric SAR interferometry mode to acquire structural parameters of global forests. Before the PolInSAR mission, the system configuration of vertical wavenumber kz must be carefully designed [...] Read more.
A number of advanced SAR missions have been planned to launch, which can operate in fully polarimetric SAR interferometry mode to acquire structural parameters of global forests. Before the PolInSAR mission, the system configuration of vertical wavenumber kz must be carefully designed because it has a significant impact on the inversion performance. To minimize the estimation error of forest height caused by the system error from the future PolInSAR campaigns, it is valuable for us to optimize the vertical wavenumber. To quantitatively investigate the impact of kz on PolInSAR inversion performance, this paper proposes the optimization of kz based on the Cramér–Rao Lower Bound (CRLB) analysis. Extensive numerical CRLB simulations have been conducted to analyze the impact of several parameters, including extinction level, incident angle, and system decorrelation, etc., on the optimum kz. Finally, by minimizing the simulated CRLB, the numerical optimum kz maps are provided for the system engineers to easily design the system parameters. Full article
(This article belongs to the Special Issue SAR for Forest Mapping III)
►▼ Show Figures

Graphical abstract

Other

Jump to: Research

13 pages, 3387 KiB  
Technical Note
Polarimetric Measures in Biomass Change Prediction Using ALOS-2 PALSAR-2 Data
by Henrik J. Persson and Ivan Huuva
Remote Sens. 2024, 16(6), 953; https://doi.org/10.3390/rs16060953 - 8 Mar 2024
Viewed by 1389
Abstract
The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the [...] Read more.
The use of multiple synthetic aperture radar polarizations can improve biomass estimations compared to using a single polarization. In this study, we compared predictions of aboveground biomass change from ALOS-2 PALSAR-2 backscatter using linear regression based on (1) the cross-polarization channels, (2) the co- and cross-polarizations from fully polarimetric SAR, (3) Freeman–Durden polarimetric decomposition, and (4) the polarimetric radar vegetation index (RVI). Additionally, the impact of forest structure on the sensitivity of the polarimetric backscatter to AGB and AGB change was assessed. The biomass consisted of mainly coniferous trees at the hemi-boreal test site Remningstorp, located in southern Sweden. We found some improvements in the predictions when quad-polarized data (RMSE = 79.4 tons/ha) were used instead of solely cross-polarized data (RMSE = 84.9 tons/ha). However, when using Freeman–Durden decomposition, the prediction accuracy improved further (RMSE = 69.7 tons/ha), and the highest accuracy was obtained with the radar vegetation index (RMSE = 50.4 tons/ha). The corresponding R2 values ranged from 0.45 to 0.82. The bias was less than 1 t/ha for all models. An analysis of forest variables showed that the sensitivity to AGB was reduced for high values of basal-area-weighted mean height, basal area, and stem density when predicting absolute AGB, but the best change prediction model was sensitive to changes larger than the apparent saturation point for AGB state estimates. We conclude that by using fully polarimetric SAR images, forest biomass changes can be estimated more accurately compared to using single- or dual-polarization images. The results were improved the most (in terms of RMSE and R2) by using the Freeman–Durden decomposition model or the RVI, which captured especially the large changes better. Full article
(This article belongs to the Special Issue SAR for Forest Mapping III)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop