Special Issue "3D Forest Structure Observation"

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

Deadline for manuscript submissions: 15 August 2020.

Special Issue Editors

Dr. Dominik Seidel
Website
Guest Editor
Department of Silviculture and Forest Ecology of the temperate Zones, Faculty of Forest Sciences, University of Göttingen, Göttingen, Germany
Interests: Forest structure; tree architecture; structural complexity; LiDAR; Structure from motion; structure-function-relationships
Special Issues and Collections in MDPI journals
Dr. Martin Ehbrecht
Website
Guest Editor
Department of Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Büsgenweg 5, D-37077 Göttingen, Germany
Interests: forest structure; forest ecology and management

Special Issue Information

Dear Colleagues,

The observation or measurement of three-dimensional (3D) vegetation structures is improving through the recent advances in remote sensing, including approaches such as RaDAR, LiDAR or structure from motion (SFM). From the assessment of single-tree architecture to descriptions of the comprehensive 3D structure of complete forest stands, a wide scale has been covered in the latest research. In this Special Issue, contributions to the above-described fields of research are invited, particularly those addressing either drivers of structure or ecosystem functions that depend on structures. Authors introducing methodical advancements are also encouraged to submit their manuscripts. All platforms, terrestrial, UAV-based, airborne, and spaceborne are welcome.

Dr. Dominik Seidel
Dr. Martin Ehbrecht
Guest Editors

Manuscript Submission Information

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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 2200 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

  • terrestrial laser scanning
  • mobile laser scanning
  • airborne laser scanning
  • structure from motion
  • spaceborne sensors
  • vegetation structure
  • forest structure
  • plant architecture
  • structure and function of ecosystems

Published Papers (9 papers)

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Research

Open AccessFeature PaperArticle
Spatial Patterns of Structural Complexity in Differently Managed and Unmanaged Beech-Dominated Forests in Central Europe
Remote Sens. 2020, 12(12), 1907; https://doi.org/10.3390/rs12121907 - 12 Jun 2020
Abstract
One of the main goals of modern silviculture is to emulate the structural complexity of old-growth forests. In this context, it is of advantage to identify a target state of structural complexity at the stand level and to analyze the spatial characteristics that [...] Read more.
One of the main goals of modern silviculture is to emulate the structural complexity of old-growth forests. In this context, it is of advantage to identify a target state of structural complexity at the stand level and to analyze the spatial characteristics that led to the desired complexity of forest structures in primary forest references. In this study, we used 3D forest scenes captured by terrestrial laser scanning (TLS) to identify spatial patterns of structural complexity of differently managed and unmanaged European forests dominated by beech (Fagus sylvatica L.). We scanned in managed even-aged and uneven-aged stands, as well as in formerly managed forests (National Parks) and primary forests. For three different forest strata, representing the understory, the midstory, and the overstory of a forest stand, we determined the structural complexity mathematically using fractal analysis. Beyond that, we analyzed the density, as well as the horizontal and vertical distribution of plant material. For all three forest strata, we observed differences in structural complexity between the different forest types. Within the lower and middle strata, the investigated primary forests showed a random to regular distribution of plant material, as well as a complex understory structure as a result of pronounced natural decay. Compared to the primary forests, the managed uneven-aged stands showed quite similar spatial patterns of distribution of plant material, but on average a higher space occupation in the lower and middle forest stratum. Our results suggest that single tree or group selection cutting is a useful management tool to imitate old-growth structures of undisturbed beech-dominated forests. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?
Remote Sens. 2020, 12(11), 1854; https://doi.org/10.3390/rs12111854 - 08 Jun 2020
Abstract
The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from [...] Read more.
The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
The Impact of Canopy Reflectance on the 3D Structure of Individual Trees in a Mediterranean Forest
Remote Sens. 2020, 12(9), 1430; https://doi.org/10.3390/rs12091430 - 01 May 2020
Abstract
The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to [...] Read more.
The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure. A heterogeneous natural environment in a Mediterranean forest, in which various tree species (pine, oak and eucalyptus) coexist, was covered using a high-resolution digital camera and a multispectral sensor. These devices were mounted on an Unmanned Aerial Vehicle (UAV) in order to observe the tree architecture and the spectral reflectance at the same time. The Structure from Motion (SfM) method was applied to model the 3D structures using RGB images from the high-resolution camera. The geometric accuracy of the resulting point cloud was validated by georeferencing the study area through multiple ground control points (GCPs). Then, the point cloud was enriched with the reflected light in four narrow-bands (green, near-infrared, red and red-edge). Furthermore, the Normalized Difference Vegetation Index (NDVI) was calculated in order to measure the tree vigor. A comprehensive analysis based on structural and spectral features of individual trees was proposed. A spatial segmentation was developed to detect single-trees in a forest and for each one to identify the crown and trunk. Consequently, structural parameters were extracted, such as the tree height, the diameter at breast height (DBH) and the crown volume. The validation of these measurements was performed by field data, which were taken using a Total Station (TS). In addition, these characteristics were correlated with the mean reflectance in the tree canopy. Regarding the observed tree species, a statistical analysis was carried out to study the impact of reflectance on the 3D tree structure. By applying our method, a more detailed knowledge of forest dynamics can be gained and the impact of available solar irradiance on single-trees can be analyzed. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Predicting Tree-Related Microhabitats by Multisensor Close-Range Remote Sensing Structural Parameters for the Selection of Retention Elements
Remote Sens. 2020, 12(5), 867; https://doi.org/10.3390/rs12050867 - 08 Mar 2020
Abstract
The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown [...] Read more.
The retention of structural elements such as habitat trees in forests managed for timber production is essential for fulfilling the objectives of biodiversity conservation. This paper seeks to predict tree-related microhabitats (TreMs) by close-range remote sensing parameters. TreMs, such as cavities or crown deadwood, are an established tool to quantify the suitability of habitat trees for biodiversity conservation. The aim to predict TreMs based on remote sensing (RS) parameters is supposed to assist a more objective and efficient selection of retention elements. The RS parameters were collected by the use of terrestrial laser scanning as well as unmanned aerial vehicles structure from motion point cloud generation to provide a 3D distribution of plant tissue. Data was recorded on 135 1-ha plots in Germany. Statistical models were used to test the influence of 28 RS predictors, which described TreM richness (R2: 0.31) and abundance (R2: 0.31) in moderate precision and described a deviance of 44% for the abundance and 38% for richness of TreMs. Our results indicate that multiple RS techniques can achieve moderate predictions of TreM occurrence. This method allows a more efficient and objective selection of retention elements such as habitat trees that are keystone features for biodiversity conservation, even if it cannot be considered a full replacement of TreM inventories due to the moderate statistical relationship at this stage. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Airborne Tree Crown Detection for Predicting Spatial Heterogeneity of Canopy Transpiration in a Tropical Rainforest
Remote Sens. 2020, 12(4), 651; https://doi.org/10.3390/rs12040651 - 16 Feb 2020
Abstract
Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across [...] Read more.
Tropical rainforests comprise complex 3D structures and encompass heterogeneous site conditions; their transpiration contributes to climate regulation. The objectives of our study were to test the relationship between tree water use and crown metrics and to predict spatial variability of canopy transpiration across sites. In a lowland rainforest of Sumatra, we measured tree water use with sap flux techniques and simultaneously assessed crown metrics with drone-based photogrammetry. We observed a close linear relationship between individual tree water use and crown surface area (R2 = 0.76, n = 42 trees). Uncertainties in predicting stand-level canopy transpiration were much lower using tree crown metrics than the more conventionally used stem diameter. 3D canopy segmentation analyses in combination with the tree crown–water use relationship predict substantial spatial heterogeneity in canopy transpiration. Among our eight study plots, there was a more than two-fold difference, with lower transpiration at riparian than at upland sites. In conclusion, we regard drone-based canopy segmentation and crown metrics to be very useful tools for the scaling of transpiration from tree- to stand-level. Our results indicate substantial spatial variation in crown packing and thus canopy transpiration of tropical rainforests. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Synthesis of L-Band SAR and Forest Heights Derived from TanDEM-X DEM and 3 Digital Terrain Models for Biomass Mapping
Remote Sens. 2020, 12(3), 349; https://doi.org/10.3390/rs12030349 - 21 Jan 2020
Cited by 1
Abstract
In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal [...] Read more.
In this study, we compared the accuracies of above-ground biomass (AGB) estimated by integrating ALOS (Advanced Land Observing Satellite) PALSAR (Phased-Array-Type L-Band Synthetic Aperture Radar) data and TanDEM-X-derived forest heights (TDX heights) at four scales from 1/4 to 25 ha in a hemi-boreal forest in Japan. The TDX heights developed in this study included nine canopy height models (CHMs) and three model-based forest heights (ModelHs); the nine CHMs were derived from the three digital surface models (DSMs) of (I) TDX 12 m DEM (digital elevation model) product, (II) TDX 90 m DEM product and (III) TDX 5 m DSM, which we developed from two TDX–TSX (TerraSAR-X) image pairs for reference, and the three digital terrain models (DTMs) of (i) an airborne Light Detection and Ranging (LiDAR)-based DTM (LiDAR DTM), (ii) a topography-based DTM and (iii) the Shuttle Radar Topography Mission (SRTM) DEM; the three ModelHs were developed from the two TDX-TSX image pairs used in (III) and the three DTMs (i to iii) with the Sinc inversion model. In total, 12 AGB estimation models were developed for comparison. In this study, we included the C-band SRTM DEM as one of the DTMs. According to Walker et al. (2007), the SRTM DEM serves as a DTM for most of the Earth’s surface, except for the areas with extensive tree and/or shrub coverage, e.g., the boreal and Amazon regions. As our test site is located in a hemi-boreal zone with medium forest cover, we tested the ability of the SRTM DEM to serve as a DTM in our test site. This study especially aimed to analyze the capability of the two TDX DEM products (I and II) to estimate AGB in practice in the hemi-boreal region, and to examine how the different forest height creation methods (the simple DSM and DTM subtraction for the nine CHMs and the Sinc inversion model-based approach for the three ModelHs) and the different spatial resolutions of the three DSMs and three DTMs affected the AGB estimation results. We also conducted the slope-class analysis to see how the varying slopes influenced the AGB estimation accuracies. The results show that the combined use of the PALSAR data and the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM achieved the highest AGB estimation accuracies across the scales (R2 ranged from 0.82 to 0.97), but the CHMs derived from (I) TDX 12 m DEM and another two DTMs, (ii) and (iii), showed low R2 values at any scales. In contrast, the two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed high R2 values > 0.87 and 0.78, respectively, at the scales > 9.0 ha, but they yielded much lower R2 values at smaller scales. The three ModelHs gave the lowest R2 values across the scales (R2 ranged from 0.39 to 0.60). Analyzed by slope class at the 1.0 ha scale, however, all the 12 AGB estimation models yielded high R2 values > 0.66 at the lowest slope class (0° to 9.9°), including the three ModelHs (R2 ranged between 0.68 to 0.69). The two CHMs derived from (II) TDX 90 m DEM and both (i) LiDAR DTM and (iii) SRTM DEM showed R2 values of 0.80 and 0.71, respectively, at the lowest slope class, while the CHM derived from (I) TDX 12 m DEM and (i) LiDAR DTM showed high R2 values across the slope classes (R2 > 0.82). The results show that (I) TDX 12 m DEM had a high capability to estimate AGB, with a high accuracy across the scales and the slope classes in the form of CHM, but the use of (i) LiDAR DTM was required. On the other hand, (II) TDX 90 m DEM was able to achieve high AGB estimation accuracies not only with (i) LiDAR DTM, but also with (iii) SRTM DEM in the form of CHM, but it was limited to large scales > 9.0 ha; however, all the models developed in this study have the possibility to achieve higher AGB estimation accuracies at the 1.0 ha scale in flat terrains with slope < 10°. The analysis showed the strengths and limitations of each model, and it also indicates that the data creation methods, the spatial resolutions of datasets and topographic features affects the effective spatial scales for AGB mapping, and the optimal combinations of these features should be chosen to obtain high AGB estimation accuracies. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Understanding Tree-to-Tree Variations in Stone Pine (Pinus pinea L.) Cone Production Using Terrestrial Laser Scanner
Remote Sens. 2020, 12(1), 173; https://doi.org/10.3390/rs12010173 - 03 Jan 2020
Abstract
Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two [...] Read more.
Kernels found in stone pinecones are of great economic value, often surpassing timber income for most forest owners. Visually evaluating cone production on standing trees is challenging since the cones are located in the sun-exposed part of the crown, and covered by two vegetative shoots. Very few studies were carried out in evaluating how new remote sensing technologies such as terrestrial laser scanners (TLS) can be used in assessing cone production, or in trying to explain the tree-to-tree variability within a given stand. Using data from 129 trees in 26 plots located in the Spanish Northern Plateau, the gain observed by using TLS data when compared to traditional inventory data in predicting the presence, the number, and the average weight of the cones in an individual tree was evaluated. The models using TLS-derived metrics consistently showed better fit statistics, when compared to models using traditional inventory data pertaining to site and tree levels. Crown dimensions such as projected crown area and crown volume, crown density, and crown asymmetry were the key TLS-derived drivers in understanding the variability in inter-tree cone production. These results underline the importance of crown characteristics in assessing cone production in stone pine. Moreover, as cone production (number of cones and average weight) is higher in crowns with lower density, the use of crown pruning, abandoned over 30 years ago, might be the key to increasing production in combination with stand density management. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Response of Beech (Fagus sylvatica L.) Trees to Competition—New Insights from Using Fractal Analysis
Remote Sens. 2019, 11(22), 2656; https://doi.org/10.3390/rs11222656 - 13 Nov 2019
Cited by 1
Abstract
Individual tree architecture and the composition of tree species play a vital role for many ecosystem functions and services provided by a forest, such as timber value, habitat diversity, and ecosystem resilience. However, knowledge is limited when it comes to understanding how tree [...] Read more.
Individual tree architecture and the composition of tree species play a vital role for many ecosystem functions and services provided by a forest, such as timber value, habitat diversity, and ecosystem resilience. However, knowledge is limited when it comes to understanding how tree architecture changes in response to competition. Using 3D-laser scanning data from the German Biodiversity Exploratories, we investigated the detailed three-dimensional architecture of 24 beech (Fagus sylvatica L.) trees that grew under different levels of competition pressure. We created detailed quantitative structure models (QSMs) for all study trees to describe their branching architecture. Furthermore, structural complexity and architectural self-similarity were measured using the box-dimension approach from fractal analysis. Relating these measures to the strength of competition, the trees are exposed to reveal strong responses for a wide range of tree architectural measures indicating that competition strongly changes the branching architecture of trees. The strongest response to competition (rho = −0.78) was observed for a new measure introduced here, the intercept of the regression used to determine the box-dimension. This measure was discovered as an integrating descriptor of the size of the complexity-bearing part of the tree, namely the crown, and proven to be even more sensitive to competition than the box-dimension itself. Future studies may use fractal analysis to investigate and quantify the response of tree individuals to competition. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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Open AccessArticle
Comparison of LiDAR and Digital Aerial Photogrammetry for Characterizing Canopy Openings in the Boreal Forest of Northern Alberta
Remote Sens. 2019, 11(16), 1919; https://doi.org/10.3390/rs11161919 - 16 Aug 2019
Abstract
Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. [...] Read more.
Forest canopy openings are a key element of forest structure, influencing a host of ecological dynamics. Light detection and ranging (LiDAR) is the de-facto standard for measuring three-dimensional forest structure, but digital aerial photogrammetry (DAP) has emerged as a viable and economical alternative. We compared the performance of LiDAR and DAP data for characterizing canopy openings and no-openings across a 1-km2 expanse of boreal forest in northern Alberta, Canada. Structural openings in canopy cover were delineated using three canopy height model (CHM) alternatives, from (i) LiDAR, (ii) DAP, and (iii) a LiDAR/DAP hybrid. From a point-based detectability perspective, the LiDAR CHM produced the best results (87% overall accuracy), followed by the hybrid and DAP models (47% and 46%, respectively). The hybrid and DAP CHMs experienced large errors of omission (9–53%), particularly with small openings up to 20m2, which are an important element of boreal forest structure. By missing these, DAP and hybrid datasets substantially under-reported the total area of openings across our site (152,470 m2 and 159,848 m2, respectively) compared to LiDAR (245,920 m2). Our results illustrate DAP’s sensitivity to occlusions, mismatched tie points, and other optical challenges inherent to using structure-from-motion workflows in complex forest scenes. These under-documented constraints currently limit the technology’s capacity to fully characterize canopy structure. For now, we recommend that operational use of DAP in forests be limited to mapping large canopy openings, and area-based attributes that are well-documented in the literature. Full article
(This article belongs to the Special Issue 3D Forest Structure Observation)
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