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

Deadline for manuscript submissions: closed (15 November 2017) | Viewed by 50545

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Special Issue Editors

Prof. Dr. Valerie A. Thomas

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Guest Editor

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 307A, 310 West Campus Dr, Blacksburg, VA 24061, USA
Interests: forest ecosystem remote sensing; forest disturbance; multitemporal analysis; LiDAR; imaging spectroscopy; data fusion

Prof. Dr. Randolph H. Wynne

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Guest Editor

Department of Forest Resources and Environmental Conservation, Virginia Tech, Cheatham Hall, RM 319, 310 West Campus Dr., Blacksburg, VA 24061, USA
Interests: applications of remote sensing to forestry; natural resource management; ecological modeling; and earth system science
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Special Issue Information

Dear Colleagues,

Associated with SilviLaser 2017 (http://www.cpe.vt.edu/silvilaser2017/), hosted in Blacksburg, Virginia at Virginia Tech in October 2017, this Special Issue will be focused on lidar applications for assessing and managing forest ecosystems. Allied technologies, such as phodar, are welcome, as are any methods of acquisition (piloted or unpiloted; airborne, space borne and terrestrial). We particularly welcome technological or analytical advances that have strong potential to improve forest science and silviculture.

Prof. Valerie A. Thomas
Prof. Randolph H. Wynne
Guest Editor

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Published Papers (7 papers)

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Research

20 pages, 34321 KiB

Open AccessArticle

Evaluating Different Methods for Estimating Diameter at Breast Height from Terrestrial Laser Scanning

by Chang Liu, Yanqiu Xing, Jialong Duanmu and Xin Tian

Remote Sens. 2018, 10(4), 513; https://doi.org/10.3390/rs10040513 - 25 Mar 2018

Cited by 42 | Viewed by 6399

Abstract

The accurate measurement of diameter at breast height (DBH) is essential to forest operational management, forest inventory, and carbon cycle modeling. Terrestrial laser scanning (TLS) is a measurement technique that allows rapid, automatic, and periodical estimates of DBH information. With the multitude of DBH estimation approaches available, a systematic study is needed to compare different algorithms and evaluate the ideal situations to use a specific algorithm. To contribute to such an approach, this study evaluated three commonly used DBH estimation algorithms: Hough-transform, linear least square circle fitting, and nonlinear least square circle fitting. They were each evaluated on their performance using two forest types of TLS data under numerous preprocessing conditions. The two forest types were natural secondary forest and plantation. The influences of preprocessing conditions on the performance of the algorithms were also investigated. Results showed that among the three algorithms, the linear least square circle fitting algorithm was the most appropriate for the natural secondary forest, and the nonlinear least square circle fitting algorithm was the most appropriate for the plantation. In the natural secondary forest, a moderate gray scale threshold of three and a slightly large height bin of 0.24 m were the optimal parameters for the appropriate algorithm of the multi-scan scanning method, and a moderate gray scale threshold of three and a large height bin of 1.34 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method. A small gray scale threshold of one and a small height bin of 0.1 m were the optimal parameters for the appropriate algorithm of the single-scan scanning method in the plantation. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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21 pages, 2465 KiB

Open AccessArticle

A Topography-Informed Morphology Approach for Automatic Identification of Forest Gaps Critical to the Release of Avalanches

by Jochen Ruben Breschan, Andreas Gabriel and Monika Frehner

Remote Sens. 2018, 10(3), 433; https://doi.org/10.3390/rs10030433 - 10 Mar 2018

Cited by 5 | Viewed by 4112

Abstract

Human assets in Alpine regions are prone to gravitational natural hazards such as rock fall, shallow landslides and avalanches. Forests make up a substantial share in that landscape and can mitigate those hazards. Management of avalanche protection forests must cope with avalanches potentially released in forest gaps, which can damage downslope forests. The Swiss guidelines “Sustainability and success monitoring in protection forests” prescribe forest-gap extents in slope-line direction critical to the release of avalanches in forested areas. This article proposes a topography-informed morphology approach (TIMA) to automate the detection of critical gaps based on a digital terrain model and a canopy height model (CHM) derived from airborne LiDAR-data. TIMA uses complementary information about topography to probe forest gaps computed from the CHM with templates meeting critical-gap extents adjusted to local topography. The method was applied to a test site in Klosters-Serneus (Switzerland). The comparison of a critical-gap map with the results of a field assessment at 19 sample locations resulted in 84% overall accuracy. Moreover, plausibility of gap detection could be improved by including linear features forest roads and torrent channels in TIMA to account for decoupled snow layer resulting from abrupt breaks on the hillslope. If the TIMA concept can be successfully applied to the case of avalanches, this would encourage its use in assessing other gravitational natural hazard processes. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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16 pages, 10813 KiB

Open AccessArticle

Estimating Uncertainty of Point-Cloud Based Single-Tree Segmentation with Ensemble Based Filtering

by Matthew Parkan and Devis Tuia

Remote Sens. 2018, 10(2), 335; https://doi.org/10.3390/rs10020335 - 23 Feb 2018

Cited by 9 | Viewed by 6019

Abstract

Individual tree crown segmentation from Airborne Laser Scanning data is a nodal problem in forest remote sensing. Focusing on single layered spruce and fir dominated coniferous forests, this article addresses the problem of directly estimating 3D segment shape uncertainty (i.e., without field/reference surveys), using a probabilistic approach. First, a coarse segmentation (marker controlled watershed) is applied. Then, the 3D alpha hull and several descriptors are computed for each segment. Based on these descriptors, the alpha hulls are grouped to form ensembles (i.e., groups of similar tree shapes). By examining how frequently regions of a shape occur within an ensemble, it is possible to assign a shape probability to each point within a segment. The shape probability can subsequently be thresholded to obtain improved (filtered) tree segments. Results indicate this approach can be used to produce segmentation reliability maps. A comparison to manually segmented tree crowns also indicates that the approach is able to produce more reliable tree shapes than the initial (unfiltered) segmentation. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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15 pages, 7250 KiB

Open AccessEditor’s ChoiceArticle

Exploring Multispectral ALS Data for Tree Species Classification

by Arvid Axelsson, Eva Lindberg and Håkan Olsson

Remote Sens. 2018, 10(2), 183; https://doi.org/10.3390/rs10020183 - 26 Jan 2018

Cited by 55 | Viewed by 7384

Abstract

Multispectral Airborne Laser Scanning (ALS) is a new technology and its output data have not been fully explored for tree species classification purposes. The objective of this study was to investigate what type of features from multispectral ALS data (wavelengths of 1550 nm, 1064 nm and 532 nm) are best suited for tree species classification. Remote sensing data were gathered over hemi-boreal forest in southern Sweden (58°27′18.35″N, 13°39′8.03″E) on 21 July 2016. The field data consisted of 179 solitary trees from nine genera and ten species. Two new methods for feature extraction were tested and compared to features of height and intensity distributions. The features that were most important for tree species classification were intensity distribution features. Features from the upper part of the upper and outer parts of the crown were better for classification purposes than others. The best classification model was created using distribution features of both intensity and height in multispectral data, with a leave-one-out cross-validated accuracy of 76.5%. As a comparison, only structural features resulted in an highest accuracy of 43.0%. Picea abies and Pinus sylvestris had high user’s and producer’s accuracies and were not confused with any deciduous species. Tilia cordata was the deciduous species with a large sample that was most frequently confused with many other deciduous species. The results, although based on a small and special data set, suggest that multispectral ALS is a technology with great potential for tree species classification. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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17 pages, 4461 KiB

Open AccessArticle

Tree Death Not Resulting in Gap Creation: An Investigation of Canopy Dynamics of Northern Temperate Deciduous Forests

by Jean-Francois Senécal, Frédérik Doyon and Christian Messier

Remote Sens. 2018, 10(1), 121; https://doi.org/10.3390/rs10010121 - 17 Jan 2018

Cited by 17 | Viewed by 5029

Abstract

Several decades of research have shown that canopy gaps drive tree renewal processes in the temperate deciduous forest biome. In the literature, canopy gaps are usually defined as canopy openings that are created by partial or total tree death of one or more canopy trees. In this study, we investigate linkages between tree damage mechanisms and the formation or not of new canopy gaps in northern temperate deciduous forests. We studied height loss processes in unmanaged and managed forests recovering from partial cutting with multi-temporal airborne Lidar data. The Lidar dataset was used to detect areas where canopy height reduction occurred, which were then field-studied to identify the tree damage mechanisms implicated. We also sampled the density of leaf material along transects to characterize canopy structure. We used the dataset of the canopy height reduction areas in a multi-model inference analysis to determine whether canopy structures or tree damage mechanisms most influenced the creation of new canopy gaps within canopy height reduction areas. According to our model, new canopy gaps are created mainly when canopy damage enlarges existing gaps or when height is reduced over areas without an already established dense sub-canopy tree layer. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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9314 KiB

Open AccessArticle

Bayesian and Classical Machine Learning Methods: A Comparison for Tree Species Classification with LiDAR Waveform Signatures

by Tan Zhou, Sorin C. Popescu, A. Michelle Lawing, Marian Eriksson, Bogdan M. Strimbu and Paul C. Bürkner

Remote Sens. 2018, 10(1), 39; https://doi.org/10.3390/rs10010039 - 26 Dec 2017

Cited by 30 | Viewed by 11166

Abstract

A plethora of information contained in full-waveform (FW) Light Detection and Ranging (LiDAR) data offers prospects for characterizing vegetation structures. This study aims to investigate the capacity of FW LiDAR data alone for tree species identification through the integration of waveform metrics with machine learning methods and Bayesian inference. Specifically, we first conducted automatic tree segmentation based on the waveform-based canopy height model (CHM) using three approaches including TreeVaW, watershed algorithms and the combination of TreeVaW and watershed (TW) algorithms. Subsequently, the Random forests (RF) and Conditional inference forests (CF) models were employed to identify important tree-level waveform metrics derived from three distinct sources, such as raw waveforms, composite waveforms, the waveform-based point cloud and the combined variables from these three sources. Further, we discriminated tree (gray pine, blue oak, interior live oak) and shrub species through the RF, CF and Bayesian multinomial logistic regression (BMLR) using important waveform metrics identified in this study. Results of the tree segmentation demonstrated that the TW algorithms outperformed other algorithms for delineating individual tree crowns. The CF model overcomes waveform metrics selection bias caused by the RF model which favors correlated metrics and enhances the accuracy of subsequent classification. We also found that composite waveforms are more informative than raw waveforms and waveform-based point cloud for characterizing tree species in our study area. Both classical machine learning methods (the RF and CF) and the BMLR generated satisfactory average overall accuracy (74% for the RF, 77% for the CF and 81% for the BMLR) and the BMLR slightly outperformed the other two methods. However, these three methods suffered from low individual classification accuracy for the blue oak which is prone to being misclassified as the interior live oak due to the similar characteristics of blue oak and interior live oak. Uncertainty estimates from the BMLR method compensate for this downside by providing classification results in a probabilistic sense and rendering users with more confidence in interpreting and applying classification results to real-world tasks such as forest inventory. Overall, this study recommends the CF method for feature selection and suggests that BMLR could be a superior alternative to classical machining learning methods. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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7504 KiB

Open AccessArticle

Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs

by Jake E. Simpson, Thomas E. L. Smith and Martin J. Wooster

Remote Sens. 2017, 9(11), 1101; https://doi.org/10.3390/rs9111101 - 28 Oct 2017

Cited by 40 | Viewed by 8553

Abstract

Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs. Full article

(This article belongs to the Special Issue Lidar for Forest Science and Management)

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