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Special Issue "Advances in Active Remote Sensing of Forests"

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

Deadline for manuscript submissions: 31 October 2019

Special Issue Editor

Guest Editor
Prof. Heiko Balzter

School of Geography, Geology and the Environment, University of Leicester
Website | E-Mail
Interests: quantitative understanding of climate change; land use change impacts on ecosystem services; spatial-temporal patterns and processes; forest aboveground biomass; logging detection; fire monitoring; mapping tree disease outbreaks

Special Issue Information

Dear Colleagues,

Active remote sensing enables the acquisition of data independent of indirect illumination. The wide availability of Synthetic Aperture Radar (SAR), Light Detection and Ranging (LiDAR) and other active remote sensing techniques has led to a phenomenal growth in active remote sensing applications. One of the dominant research applications is in remote sensing of forests, because of their global significance for the carbon cycle, mitigation of climate change and biodiversity (gaining renewed global attention through the Paris Climate Agreement), as well as a wide range of essential ecosystem services for people. This technology has also enabled a much more detailed understanding of the 3D forest structure and its dynamics over time, including effects of tree diseases on forests, impacts of storm damage, forest fires and selective logging.

This Special Issue invites research papers describing cutting-edge research on active remote sensing of forests using any active remote sensing technology from any platform—be it tripod, drone, aircraft, satellite or any other. I wish to put together a journal issue that describes out-of-the-box approaches to the sensing of forest canopies at any scale, from leaves or needles to forest stands or national, continental or global scales. Research that integrates active with passive remote sensing approaches is also relevant. This Special Issue will be open access and will provide a compendium of novel and significant active remote sensing methods and applications.

Prof. Heiko Balzter
Guest Editor

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 papers will be 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 1800 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

  • Synthetic Aperture Radar (SAR)
  • LiDAR
  • active remote sensing
  • forest biomass
  • tree height
  • vegetation structure

Published Papers (4 papers)

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Research

Open AccessArticle
Comparative Study on Variable Selection Approaches in Establishment of Remote Sensing Model for Forest Biomass Estimation
Remote Sens. 2019, 11(12), 1437; https://doi.org/10.3390/rs11121437
Received: 21 May 2019 / Revised: 11 June 2019 / Accepted: 13 June 2019 / Published: 17 June 2019
PDF Full-text (916 KB)
Abstract
In the field of quantitative remote sensing of forest biomass, a prominent phenomenon is the increasing number of explanatory variables. Then how to effectively select explanatory variables has become an important issue. Linear regression model is one of the commonly used remote sensing [...] Read more.
In the field of quantitative remote sensing of forest biomass, a prominent phenomenon is the increasing number of explanatory variables. Then how to effectively select explanatory variables has become an important issue. Linear regression model is one of the commonly used remote sensing models. In the process of establishing the linear regression model, a vital step is to select explanatory variables. Focusing on variable selection and model stability, this paper conducts a comparative study on the performance of eight linear regression parameter estimation methods (Stepwise Regression Method (SR), Criterions Based on The Bayes Method (BIC), Criterions Based on The Bayes Method (AIC), Criterions Based on Prediction Error (Cp), Least Absolute Shrinkage and Selection Operator (Lasso), Adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Non-negative garrote (NNG)) in the subtropical forest biomass remote sensing model development. For the purpose of comparison, OLS and RR, are commonly used as methods with no variable selection ability, and are also compared and discussed. The performance of five aspects are evaluated in this paper: (i) Determination coefficient, prediction error, model error, etc., (ii) significance test about the difference between determination coefficients, (iii) parameter stability, (iv) variable selection stability and (v) variable selection ability of the methods. All the results are obtained through a five ten-fold CV. Some evaluation indexes are calculated with or without degrees of freedom. The results show that BIC performs best in comprehensive evaluation, while NNG, Cp and AIC perform poorly as a whole. Other methods show a great difference in the performance on each index. SR has a strong capability in variable selection, although it is poor in commonly used indexes. The short-wave infrared band and the texture features derived from it are selected most frequently by various methods, indicating that these variables play an important role in forest biomass estimation. Some of the conclusions in this paper are likely to change as the study object changes. The ultimate goal of this paper is to introduce various model establishment methods with variable selection capability, so that we can have more choices when establishing similar models, and we can know how to select the most appropriate and effective method for specific problems. Full article
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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Graphical abstract

Open AccessArticle
Development and Validation of a Photo-Based Measurement System to Calculate the Debarking Percentages of Processed Logs
Remote Sens. 2019, 11(9), 1133; https://doi.org/10.3390/rs11091133
Received: 29 March 2019 / Revised: 29 April 2019 / Accepted: 10 May 2019 / Published: 12 May 2019
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Abstract
Within a research project investigating the applicability and performance of modified harvesting heads used during the debarking of coniferous tree species, the actual debarking percentage of processed logs needed to be evaluated. Therefore, a computer-based photo-optical measurement system (Stemsurf) designed to assess the [...] Read more.
Within a research project investigating the applicability and performance of modified harvesting heads used during the debarking of coniferous tree species, the actual debarking percentage of processed logs needed to be evaluated. Therefore, a computer-based photo-optical measurement system (Stemsurf) designed to assess the debarking percentage recorded in the field was developed, tested under laboratory conditions, and applied in live field operations. In total, 1720 processed logs of coniferous species from modified harvesting heads were recorded and analyzed within Stemsurf. With a single log image as the input, the overall debarking percentage was calculated by further estimating the un-displayed part of the log surface by defining polygons representing the differently debarked areas of the log surface. To assess the precision and bias of the developed measurement system, 480 images were captured under laboratory conditions on an artificial log with defined surface polygons. Within the laboratory test, the standard deviation of average debarking percentages remained within a 4% variation. A positive bias of 6.7% was caused by distortion and perspective effects. This resulted in an average underestimation of 1.1% for the summer debarking percentages gathered from field operations. The software generally performed as anticipated through field and lab testing and offered a suitable alternative of assessing stem debarking percentage, a task that should increase in importance as more operations are targeting debarked products. Full article
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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Open AccessArticle
Particle Swarm Optimization-Based Noise Filtering Algorithm for Photon Cloud Data in Forest Area
Remote Sens. 2019, 11(8), 980; https://doi.org/10.3390/rs11080980
Received: 14 March 2019 / Revised: 20 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
PDF Full-text (7230 KB) | HTML Full-text | XML Full-text
Abstract
The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific [...] Read more.
The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), was launched successfully in 15 September 2018. The ATLAS represents a micro-pulse photon-counting laser system, which is expected to provide more comprehensive and scientific data for carbon storage. However, the ATLAS system is sensitive to the background noise, which poses a tremendous challenge to the photon cloud noise filtering. Moreover, the Density Based Spatial Clustering of Applications with Noise (DBSCAN) is a commonly used algorithm for noise removal from the photon cloud but there has not been an in-depth study on its parameter selection yet. This paper presents an automatic photon cloud filtering algorithm based on the Particle Swarm Optimization (PSO) algorithm, which can be used to optimize the two key parameters of the DBSCAN algorithm instead of using the manual parameter adjustment. The Particle Swarm Optimization Density Based Spatial Clustering of Applications with Noise (PSODBSCAN) algorithm was tested at different laser intensities and laser pointing types using the MATLAS dataset of the forests located in Virginia, East Coast, and the West Coast, USA. The results showed that the PSODBSCAN algorithm and the localized statistical algorithm were effective in identifying the background noise and preserving the signal photons in the raw MATLAS data. Namely, the PSODBSCAN achieved the mean F value of 0.9759, and the localized statistical algorithm achieved the mean F value of 0.6978. For both laser pointing types and laser intensities, the proposed algorithm achieved better results than the localized statistical algorithm. Therefore, the PSODBSCAN algorithm could support the MATLAS photon cloud data noise filtering applicably without manually selecting parameters. Full article
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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Figure 1

Open AccessArticle
Rubber Tree Crown Segmentation and Property Retrieval Using Ground-Based Mobile LiDAR after Natural Disturbances
Remote Sens. 2019, 11(8), 903; https://doi.org/10.3390/rs11080903
Received: 5 March 2019 / Revised: 10 April 2019 / Accepted: 11 April 2019 / Published: 13 April 2019
PDF Full-text (26490 KB) | HTML Full-text | XML Full-text
Abstract
Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 [...] Read more.
Rubber trees in southern China are often impacted by natural disturbances, and accurate rubber tree crown segmentation and property retrieval are of great significance for forest cultivation treatments and silvicultural risk management. Here, three plots of different rubber tree clones, PR107, CATAS 7-20-59 and CATAS 8-7-9, that were recently impacted by hurricanes and chilling injury were taken as the study targets. Through data collection using ground-based mobile light detection and ranging (LiDAR) technology, a weighted Rayleigh entropy method based on the scanned branch data obtained from the region growing algorithm was proposed to calculate the trunk inclination angle and crown centre of each tree. A watershed algorithm based on the extracted crown centres was then adopted for tree crown segmentation, and a variety of tree properties were successfully extracted to evaluate the susceptibility of different rubber tree clones facing natural disturbances. The results show that the angles between the first-order branches and trunk ranged from 35.1–67.7° for rubber tree clone PR107, which is larger than the angles for clone CATAS 7-20-59, which ranged from 20.2–43.2°. Clone PR107 had the maximum number of scanned leaf points, lowest tree height and a crown volume that was larger than that of CATAS 7-20-59, which generates more frontal leaf area to oppose wind flow and reduces the gaps among tree crowns, inducing strong wind loading on the tree body. These factors result in more severe hurricane damage, resulting in trunk inclination angles that are larger for PR107 than CATAS 7-20-59. In addition, the rubber tree clone CATAS 8-7-9 had the minimal number of scanned leaf points and the smallest tree crown volume, reflecting its vulnerability to both hurricanes and chilling injury. The results are verified by field measurements. The work quantitatively assesses the susceptibility of different rubber tree clones under the impacts of natural disturbances using ground-based mobile LiDAR. Full article
(This article belongs to the Special Issue Advances in Active Remote Sensing of Forests)
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Graphical abstract

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