Horizontal Visibility in Forests

The important variable of horizontal visibility within forest stands is gaining increasing attention in studies and applications involving terrestrial laser scanning (TLS), photographic measurements of forest structure, and autonomous mobility. We investigated distributions of visibility distance, open arc length, and shaded arc length in three mature forest stands. Our analysis was based (1) on tree position maps and TLS data collected in 2013 and 2019 with three different scanners, and (2) on simulated digital twins of the forest stands, constructed with two pattern-generation models incorporating commonly used indices of tree position clumping. The model simulations were found to yield values for visibility almost identical to those calculated from the corresponding tree location maps. The TLS measurements, however, were found to diverge notably from the simulations. Overall, the probability of free line of sight was found to decrease exponentially with distance to target, and the probabilities of open arc length and shaded arc length were found to decrease and increase, respectively, with distance from the observer. The TLS measurements, which are sensitive to forest understory vegetation, were found to indicate increased visibility after vegetation removal. Our chosen visibility prediction models support practical forest management, being based on common forest inventory parameters and on widely used forest structure indices.

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Stratifying Forest Overstory for Improving Effective LAI Estimation Based on Aerial Imagery and Discrete Laser Scanning Data

Remote Sensing ◽

10.3390/rs12132126 ◽

2020 ◽

Vol 12 (13) ◽

pp. 2126 ◽

Cited By ~ 1

Author(s):

Zhaoshang Xu ◽

Guang Zheng ◽

L. Monika Moskal

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Structural Information ◽

Canopy Cover ◽

Remotely Sensed ◽

Background Information ◽

Estimation Accuracy ◽

Forest Stands ◽

Remotely Sensed Data ◽

Area Index

Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

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The importance of forest structure to biodiversity–productivity relationships

Royal Society Open Science ◽

10.1098/rsos.160521 ◽

2017 ◽

Vol 4 (1) ◽

pp. 160521 ◽

Cited By ~ 38

Author(s):

Friedrich J. Bohn ◽

Andreas Huth

Keyword(s):

Species Distribution ◽

Forest Structure ◽

Basal Area ◽

Tree Height ◽

National Forest Inventory ◽

National Forest ◽

Forest Stands ◽

Annual Productivity ◽

Structure Indices ◽

Modelling Approach

While various relationships between productivity and biodiversity are found in forests, the processes underlying these relationships remain unclear and theory struggles to coherently explain them. In this work, we analyse diversity–productivity relationships through an examination of forest structure (described by basal area and tree height heterogeneity). We use a new modelling approach, called ‘forest factory’, which generates various forest stands and calculates their annual productivity (above-ground wood increment). Analysing approximately 300 000 forest stands, we find that mean forest productivity does not increase with species diversity. Instead forest structure emerges as the key variable. Similar patterns can be observed by analysing 5054 forest plots of the German National Forest Inventory. Furthermore, we group the forest stands into nine forest structure classes, in which we find increasing, decreasing, invariant and even bell-shaped relationships between productivity and diversity. In addition, we introduce a new index, called optimal species distribution, which describes the ratio of realized to the maximal possible productivity (by shuffling species identities). The optimal species distribution and forest structure indices explain the obtained productivity values quite well ( R 2 between 0.7 and 0.95), whereby the influence of these attributes varies within the nine forest structure classes.

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Modelling growing stock volume of forest stands with various ALS area-based approaches

Forestry An International Journal of Forest Research ◽

10.1093/forestry/cpab011 ◽

2021 ◽

Author(s):

Karolina Parkitna ◽

Grzegorz Krok ◽

Stanisław Miścicki ◽

Krzysztof Ukalski ◽

Marek Lisańczuk ◽

...

Keyword(s):

Laser Scanning ◽

Mean Squared Error ◽

Prediction Method ◽

Forest Stand ◽

Boosted Regression Trees ◽

Forest Stands ◽

Individual Tree ◽

Growing Stock ◽

Modelling Techniques ◽

Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

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Global patterns and climatic controls of forest structural complexity

Nature Communications ◽

10.1038/s41467-020-20767-z ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Martin Ehbrecht ◽

Dominik Seidel ◽

Peter Annighöfer ◽

Holger Kreft ◽

Michael Köhler ◽

...

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Structural Complexity ◽

Terrestrial Laser Scanning ◽

Ecosystem Functions ◽

The Earth ◽

Precipitation Seasonality ◽

Latitudinal Patterns ◽

Global Patterns ◽

Primary Forests

AbstractThe complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes.

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TomoSAR Mapping of 3D Forest Structure: Contributions of L-Band Configurations

Remote Sensing ◽

10.3390/rs13122255 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2255

Author(s):

Matteo Pardini ◽

Victor Cazcarra-Bes ◽

Konstantinos Papathanassiou

Keyword(s):

Information Content ◽

Forest Structure ◽

Structural Information ◽

3D Structure ◽

Physical Structure ◽

Structure Mapping ◽

Structure Information ◽

Forest Sites ◽

L Band ◽

Structure Indices

Synthetic Aperture Radar (SAR) measurements are unique for mapping forest 3D structure and its changes in time. Tomographic SAR (TomoSAR) configurations exploit this potential by reconstructing the 3D radar reflectivity. The frequency of the SAR measurements is one of the main parameters determining the information content of the reconstructed reflectivity in terms of penetration and sensitivity to the individual vegetation elements. This paper attempts to review and characterize the structural information content of L-band TomoSAR reflectivity reconstructions, and their potential to forest structure mapping. First, the challenges in the accurate TomoSAR reflectivity reconstruction of volume scatterers (which are expected to dominate at L-band) and to extract physical structure information from the reconstructed reflectivity is addressed. Then, the L-band penetration capability is directly evaluated by means of the estimation performance of the sub-canopy ground topography. The information content of the reconstructed reflectivity is then evaluated in terms of complementary structure indices. Finally, the dependency of the TomoSAR reconstruction and of its structural information to both the TomoSAR acquisition geometry and the temporal change of the reflectivity that may occur in the time between the TomoSAR measurements in repeat-pass or bistatic configurations is evaluated. The analysis is supported by experimental results obtained by processing airborne acquisitions performed over temperate forest sites close to the city of Traunstein in the south of Germany.

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Potential for Individual Tree Monitoring in Ponderosa Pine-Dominated Forests Using Unmanned Aerial System Structure from Motion Point Clouds

Canadian Journal of Forest Research ◽

10.1139/cjfr-2020-0433 ◽

2021 ◽

Cited By ~ 1

Author(s):

Matthew B. Creasy ◽

Wade Travis Tinkham ◽

Chad M. Hoffman ◽

Jody C. Vogeler

Keyword(s):

Forest Structure ◽

Ponderosa Pine ◽

Laser Scanning ◽

Point Clouds ◽

System Structure ◽

Detection Methods ◽

Unmanned Aerial System ◽

Individual Tree ◽

Detection Rates ◽

Ponderosa Pine Forests

Characterization of forest structure is important for management-related decision making, monitoring, and adaptive management. Increasingly, observations of forest structure are needed at both finer resolutions and across greater extents to support spatially explicit management planning. Unmanned aerial system (UAS)-based photogrammetry provides an airborne method of forest structure data acquisition at a significantly lower cost and time commitment than existing methods such as airborne laser scanning (LiDAR). This study utilizes nearly 5,000 stem-mapped trees in ponderosa pine-dominated forests to evaluate several algorithms for detecting individual tree locations and characterizing crown area across tree sizes. Our results indicate that adaptive variable-window detection methods with UAS-based canopy height models have greater tree detection rates compared to fixed window analysis across a range of tree sizes. Using the UAS approach, probability of detecting individual trees decreases from 97% for dominant overstory to 67% for suppressed understory trees. Additionally, crown radii were correctly determined within 0.5 m for approximately two-thirds of sampled trees. These findings highlight the potential for UAS photogrammetry to characterize forest structure through the detection of trees and tree groups in open-canopy ponderosa pine forests. Further work should investigate how these methods transfer to more diverse species compositions and forest structures.

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A simple approach to forest structure classification using airborne laser scanning that can be adopted across bioregions

Forest Ecology and Management ◽

10.1016/j.foreco.2018.10.057 ◽

2019 ◽

Vol 433 ◽

pp. 111-121 ◽

Cited By ~ 8

Author(s):

Syed Adnan ◽

Matti Maltamo ◽

David A. Coomes ◽

Antonio García-Abril ◽

Yadvinder Malhi ◽

...

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Simple Approach ◽

Airborne Laser Scanning ◽

Airborne Laser

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Processing and analysis of airborne full-waveform laser scanning data for the characterization of forest structure and fuel properties

Revista de Teledetección ◽

10.4995/raet.2020.14551 ◽

2020 ◽

pp. 95

Author(s):

P. Crespo-Peremarch ◽

L. A. Ruiz

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Software Tool ◽

Understory Vegetation ◽

Airborne Laser Scanning ◽

Vegetation Density ◽

Regression Methods ◽

Full Waveform ◽

Airborne Laser ◽

Pulse Density

This PhD thesis addresses the development of full-waveform airborne laser scanning (ALSFW) processing and analysis methods to characterize the vertical forest structure, in particular the understory vegetation. In this sense, the influence of several factors such as pulse density, voxel parameters (voxel size and assignation value), scan angle at acquisition, radiometric correction and regression methods is analyzed on the extraction of ALSFW metric values and on the estimate of forest attributes. Additionally, a new software tool to process ALSFW data is presented, which includes new metrics related to understory vegetation. On the other hand, occlusion caused by vegetation in the ALSFW, discrete airborne laser scanning (ALSD) and terrestrial laser scanning (TLS) signal is characterized along the vertical structure. Finally, understory vegetation density is detected and determined by ALSFW data, as well as characterized by using the new proposed metrics.

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Deriving Stand Structural Complexity from Airborne Laser Scanning Data—What Does It Tell Us about a Forest?

Remote Sensing ◽

10.3390/rs12111854 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1854

Author(s):

Dominik Seidel ◽

Peter Annighöfer ◽

Martin Ehbrecht ◽

Paul Magdon ◽

Stephan Wöllauer ◽

...

Keyword(s):

Forest Structure ◽

Laser Scanning ◽

Structural Complexity ◽

Basal Area ◽

Airborne Laser Scanning ◽

Stand Age ◽

Box Dimension ◽

Forest Types ◽

3D Point Clouds ◽

Airborne Laser

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.

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