Special Issue "Lidar for Ecosystem Science and Management"

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

Deadline for manuscript submissions: closed (31 May 2019).

Special Issue Editor

Guest Editor
Prof. Dr. Sorin Popescu Website E-Mail
Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843, USA
Phone: (979) 862-2614
Interests: laser and LiDAR remote sensing of vegetation structure; unmanned aerial systems (UAS/UAV)

Special Issue Information

Dear Colleagues,

Lidar remote sensing has become more than just a tool used to estimate remotely vegetation biophysical parameters, as it has pushed the science of characterizing vegetation three-dimensional structure and our understanding of ecological applications and management strategies. More so, the spectrum of lidar studies has extended past height measurements to a broad range of applications, from mapping biomass and carbon to assessing fuels, biodiversity, ecological services, wood quality, and site productivity. Lidar systems and technology have evolved and sensors are collecting data from terrestrial, airborne, including unmanned systems, and satellite platforms.

The special issue, “Lidar for Ecosystem Science and Management”, is calling for original and innovative papers that demonstrate the use of lidar from all platforms to advance both remote sensing and ecosystem sciences and show the potential to challenge management paradigms and provide solutions to contemporary environmental problems. Invited topics include: 1) types of lidar data, such as discrete-return, waveform, photon counting, multi-wavelength lidar, and applications; 2) new methods, new variables and algorithms, including machine and deep learning approaches, for processing lidar data; 3) synergistic approaches with the use of lidar with optical and radar data; 4) vegetation science advancements and management strategies afforded by lidar remote sensing.

Prof. Dr. Sorin Popescu
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

  • lidar
  • ecosystem science and management
  • photon lidar, multispectral or multi-wavelength lidar
  • machine learning and deep learning methods
  • algorithms
  • lidar variables
  • forest management

Published Papers (5 papers)

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Research

Open AccessArticle
Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices
Remote Sens. 2019, 11(16), 1877; https://doi.org/10.3390/rs11161877 - 11 Aug 2019
Abstract
Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective [...] Read more.
Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure–from–motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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Open AccessArticle
Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand
Remote Sens. 2019, 11(14), 1661; https://doi.org/10.3390/rs11141661 - 12 Jul 2019
Abstract
The Douglas fir crown structure serves important ecological functions in regulating the ecosystem of the Pacific Northwest (PNW). Mapping and modeling of the Douglas-fir crown has traditionally focused on young plantations or old-growth forests. The crown description in natural regime forests is limited [...] Read more.
The Douglas fir crown structure serves important ecological functions in regulating the ecosystem of the Pacific Northwest (PNW). Mapping and modeling of the Douglas-fir crown has traditionally focused on young plantations or old-growth forests. The crown description in natural regime forests is limited by data availability. Terrestrial laser scanning (TLS) enables the acquisition of crown structural attributes, even in dense forests, at a fine scale. The certical and horizontal distributions of the fine-scale branch attributes, such as branch diameter, branch length, and branch insertion angle, will reflect the crown behaviors towards light resources availability, as a result of neighborhood competition. The main objective of the study is to compare crown structural models of a group of neighboring trees developed with two TLS-based procedures, namely: semi-automatic (Cyclone software) and automatic (TreeQSM) procedures. The estimated crown attributes are the branch diameter, branch length, branch insertion angle, height of branch insertion point, and branch azimuth. The results show that branch azimuth distribution does not differ between TreeQSM and Cyclone for most of the sample trees. However, the TreeQSM and Cyclone identified branches exhibit different distributions of insertion height. A paired t-test indicates no difference between the mean branch diameter of Cyclone and TreeQSM at an individual tree level. However, Cyclone estimated that the branch length and branch insertion angle are 0.49 m and 9.9° greater than the TreeQSM estimates, respectively. Repeat measurements of the analysis of variance (ANOVA) suggest that the height along the stem is an influential factor of the difference between the Cyclone and TreeQSM branch diameter estimates. To test whether TLS-based estimates are within the ranges of the previous observations, we computed the tree crown attributes of second- and old-growth trees using Monte Carlo simulations for diameter at breast height (DBH) class 50–55 cm, 60–65 cm, and 85–105 cm. We found that the crown attributes estimated from both of the TLS-based methods are between the simulated second- and old-growth trees, except for DBH 85–105 cm. The TLS-based crown structural models show increasingly diverse distributions of branch insertion angles and increasing branch exclusion as DBH increases. Cyclone-based crown structural models are consistent with previous studies. However, TreeQSM-based crown structural models omitted a significant number of branches and generated crown structures with reduced plausibility. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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Open AccessArticle
Synergy of ICESat-2 and Landsat for Mapping Forest Aboveground Biomass with Deep Learning
Remote Sens. 2019, 11(12), 1503; https://doi.org/10.3390/rs11121503 - 25 Jun 2019
Abstract
Spatially continuous estimates of forest aboveground biomass (AGB) are essential to supporting the sustainable management of forest ecosystems and providing invaluable information for quantifying and monitoring terrestrial carbon stocks. The launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on September 15th, [...] Read more.
Spatially continuous estimates of forest aboveground biomass (AGB) are essential to supporting the sustainable management of forest ecosystems and providing invaluable information for quantifying and monitoring terrestrial carbon stocks. The launch of the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) on September 15th, 2018 offers an unparalleled opportunity to assess AGB at large scales using along-track samples that will be provided during its three-year mission. The main goal of this study was to investigate deep learning (DL) neural networks for mapping AGB with ICESat-2, using simulated photon-counting lidar (PCL)-estimated AGB for daytime, nighttime, and no noise scenarios, Landsat imagery, canopy cover, and land cover maps. The study was carried out in Sam Houston National Forest located in south-east Texas, using a simulated PCL-estimated AGB along two years of planned ICESat-2 profiles. The primary tasks were to investigate and determine neural network architecture, examine the hyper-parameter settings, and subsequently generate wall-to-wall AGB maps. A first set of models were developed using vegetation indices calculated from single-date Landsat imagery, canopy cover, and land cover, and a second set of models were generated using metrics from one year of Landsat imagery with canopy cover and land cover maps. To compare the effectiveness of final models, comparisons with Random Forests (RF) models were made. The deep neural network (DNN) models achieved R2 values of 0.42, 0.49, and 0.50 for the daytime, nighttime, and no noise scenarios respectively. With the extended dataset containing metrics calculated from Landsat images acquired on different dates, substantial improvements in model performance for all data scenarios were noted. The R2 values increased to 0.64, 0.66, and 0.67 for the daytime, nighttime, and no noise scenarios. Comparisons with Random forest (RF) prediction models highlighted similar results, with the same R2 and root mean square error (RMSE) range (15–16 Mg/ha) for daytime and nighttime scenarios. Findings suggest that there is potential for mapping AGB using a combinatory approach with ICESat-2 and Landsat-derived products with DL. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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Open AccessArticle
Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data
Remote Sens. 2019, 11(3), 261; https://doi.org/10.3390/rs11030261 - 28 Jan 2019
Cited by 2
Abstract
This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an [...] Read more.
This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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Open AccessArticle
From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure
Remote Sens. 2018, 10(12), 1949; https://doi.org/10.3390/rs10121949 - 04 Dec 2018
Cited by 1
Abstract
Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This [...] Read more.
Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization. Full article
(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)
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