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Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat

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A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 35488

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Special Issue Editors

Dr. Qinghua Guo

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Guest Editor

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
Interests: remote sensing; lidar applications; GIS; UAV; climate change and terrestrial ecosystem
Special Issues, Collections and Topics in MDPI journals

Dr. Yanjun Su

E-Mail Website
Guest Editor

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Science, Beijing 100093, China
Interests: remote sensing; LiDAR; GIS; climate change; terrestrial ecosystems; forest structures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Biodiversity offers a foundation to various ecosystem services (e.g., provisioning services, regulating services, culture services) that are critical to human beings. Tropical, temperate, and boreal forests serve a diverse set of habitats for wildlife, and, consequently, harbor the majority of terrestrial species. However, in recent decades, forest wildlife abundancy and diversity have been diminishing due to the threat caused by human activities and global climate change. The importance of conserving forest wildlife abundance and diversity has been increasingly recognized, and numerous efforts have been made to improve our understanding of forest wildlife behavior. Forest vegetation biophysical features and three-dimensional (3D) structures have been demonstrated to be valuable inputs for mapping wildlife distribution. Therefore, accurate and continuous mapping of these forest parameters are essential for assessing forest wildlife habitats. Light detection and ranging (LiDAR) can provide highly accurate 3D forest structures from individual-tree to forest-stand scales, and optical imagery contains abundant spectral and textural information for mapping vegetation types and bio-physiological parameters. The integration of these two datasets opens a new era for mapping forest vegetation, both vertically and horizontally, and, therefore, assessing and protecting wildlife habitats.

This Special Issue of Forests emphasizes forest vegetation mapping through the integration of LiDAR and optical imagery, and how it can be used to assess the quality of wildlife habitats. Research articles may focus on, but are not limited to, topics such as new approaches of forest vegetation mapping for wildlife habitat assessment based on LiDAR and/or optical data, data fusion algorithms on improving the vegetation mapping accuracy for wildlife habitat assessment, and addressing how the integration of LiDAR data and optical imagery affects the niche modelling and wildlife habitat assessment results. Application studies regarding forest biodiversity and wildlife habitat management and conservation with the help of LiDAR data and optical imagery are also welcome.

Prof. Qinghua Guo
Dr. Yanjun Su
Guest Editors

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Keywords

LiDAR
aerial imagery
multispectral imagery
forest
vegetation mapping
wildlife habitat
biodiversity

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Published Papers (6 papers)

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Research

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26 pages, 6732 KiB

Open AccessArticle

Prediction of Aboveground Biomass from Low-Density LiDAR Data: Validation over P. radiata Data from a Region North of Spain

by Leyre-Torre Tojal, Aitor Bastarrika, Brian Barrett, Javier Maria Sanchez Espeso, Jose Manuel Lopez-Guede and Manuel Graña

Forests 2019, 10(9), 819; https://doi.org/10.3390/f10090819 - 19 Sep 2019

Cited by 13 | Viewed by 3793

Abstract

Estimation of forestry aboveground biomass (AGB) by means of aerial Light Detection and Ranging (LiDAR) data uses high-density point sampling data obtained in dedicated flights, which are often too costly for available research budgets. In this paper we exploit already existing public low-density LiDAR data obtained for other purposes, such as cartography. The challenge is to show that such low-density data allows accurate biomass estimation. We demonstrate the approach on data available from plantations of Pinus radiata in the Arratia-Nervión region, located in Biscay province located in the North of Spain. We use public data gathered from the low-density (0.5 pulse/m²) LiDAR flight conducted by the Basque Government in 2012 for cartographic production. We propose a linear regression model based on explanatory variables obtained from the LiDAR point cloud data. We calibrate the model using field data from the Fourth National Forest Inventory (NFI4), including the selection of the optimal model variables. The results revealed that the best model depends on two variables extracted from LiDAR data: One directly related with tree height and a second parameter with the canopy density. The model explained 80% of its variability with a standard error of 0.25 ton/ha in logarithmic units. We validate the predictions against the biomass measurements provided by the government institutions, obtaining a difference of 8%. The proposed approach would allow the exploitation of the periodic available low-density LiDAR data, collected with territorial and cartographic purposes, for a more frequent and less expensive control of the forestry biomass. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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19 pages, 2287 KiB

Open AccessArticle

Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

by Kalkidan Ayele Mulatu, Mathieu Decuyper, Benjamin Brede, Lammert Kooistra, Johannes Reiche, Brice Mora and Martin Herold

Forests 2019, 10(3), 291; https://doi.org/10.3390/f10030291 - 26 Mar 2019

Cited by 17 | Viewed by 9126

Abstract

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R² of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R² = 0.52, RRMSE = 1.4, R² = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R² = 0.28, RRMSE = 0.41, and R² = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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26 pages, 2729 KiB

Open AccessArticle

Evaluating Two Optical Methods of Woody-to-Total Area Ratio with Destructive Measurements at Five Larix gmelinii Rupr. Forest Plots in China

by Jie Zou, Peng Leng, Wei Hou, Peihong Zhong, Ling Chen, Chunna Mai, Yonggang Qian and Yong Zuo

Forests 2018, 9(12), 746; https://doi.org/10.3390/f9120746 - 29 Nov 2018

Cited by 6 | Viewed by 3327

Abstract

Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio (

α

) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods [...] Read more.

Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio (

α

) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the

α

of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the

α

of L. gmelinii forest plots was evaluated by using the reference

α

of the selected forest plots. The reference

α

of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference

α

of the selected forest plots (ranging from 0% to 56%). These

α

were also highly correlated with the site conditions and management activities in these plots. The effective

α

(

α_{e}

) or

α

estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference

α

in the five plots. The overestimation of

α

α_{e}

was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate

α

estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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13 pages, 2197 KiB

Open AccessArticle

Detecting the Competition between Moso Bamboos and Broad-Leaved Trees in Mixed Forests Using a Terrestrial Laser Scanner

by Yingjie Yan, Mingpeng Xia, Shaohui Fan, Meichun Zhan and Fengying Guan

Forests 2018, 9(9), 520; https://doi.org/10.3390/f9090520 - 29 Aug 2018

Cited by 9 | Viewed by 3973

Abstract

The growth of individual trees in a forest is affected by many factors, a crucial one being the intensity of competition among trees, because it affects the spatial structure of the forest and is in turn influenced by silvicultural practices. In a mixed forest in particular, the growth of trees is affected by multiple interactions. To analyse the competition between moso bamboo (Phyllostachys pubescens (Pradelle) Mazel ex J.Houz.) and broad-leaved trees in a mixed forest, data were extracted by sampling six spots within such a forest using terrestrial laser scanning (TLS). The convex hull algorithm was used for calculating the overlap volume between the crowns of the broad-leaved trees and the bamboo canopy. Bamboos growing at least 3 m away from any of the broad-leaved trees were the most numerous and the diameter at breast height (DBH) is larger than those growing closer than that, which suggests that broad-leaved trees suppressed the growth of bamboo if they are closer but promote it beyond 3 m up to a point at which the distance is too great for any such effect. The modified Hegyi’s competition index was constructed based on the canopy factor, which may better describe the competitive interaction among the trees and bamboos. Using TLS can enhance our understanding of the competition among trees in mixed forests and help in planning the spatial structure of such forests in general and provide a benchmark for choosing planting distances in particular. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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19 pages, 5060 KiB

Open AccessArticle

Estimating Stand Density in a Tropical Broadleaf Forest Using Airborne LiDAR Data

by Chung-Cheng Lee and Chi-Kuei Wang

Forests 2018, 9(8), 475; https://doi.org/10.3390/f9080475 - 4 Aug 2018

Viewed by 4720

Abstract

Forest-related statistics, including forest biomass, carbon sink, and the prevention of forest fires, can be obtained by estimating stand density. In this study, a dataset with the laser pulse density of 225.5 pulses/m² was obtained using airborne laser scanning in a tropical broadleaf forest. Three digital surface models (DSMs) were generated using first-echo, last-echo, and highest first-echo data. Three canopy height models (CHMs) were obtained by deducting the digital elevation model from the three DSMs. The cell sizes (Csizes) of the CHMs were 1, 0.5, and 0.2 m. In addition, stand density was estimated using CHM data and following the local maximum method. The stand density of 35 sample regions was acquired via in-situ measurement. The results indicated that the root-mean-square error (

R M S E

) ranged between 1.68 and 2.43; the

R M S E

difference was only 0.78, indicating that stand density was effectively estimated in both cases. Furthermore, regression models were used to correct the error in stand density estimations; the

R M S E

after correction was called

R M S E^{'}

. A comparison of the

R M S E

and

R M S E^{'}

showed that the average value decreased from 12.35 to 2.66, meaning that the regression model could effectively reduce the error. Finally, a comparison of the effects of different laser pulse densities on the

R M S E

value showed that, in order to obtain the minimum

R M S E

for stand density, the laser pulse density must be greater than 10, 30, and 125 pulses/m² at Csizes of 1, 0.5, and 0.2 m, respectively. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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Review

Jump to: Research

18 pages, 2003 KiB

Open AccessReview

A Review: Individual Tree Species Classification Using Integrated Airborne LiDAR and Optical Imagery with a Focus on the Urban Environment

by Kepu Wang, Tiejun Wang and Xuehua Liu

Forests 2019, 10(1), 1; https://doi.org/10.3390/f10010001 - 20 Dec 2018

Cited by 86 | Viewed by 9457

Abstract

With the significant progress of urbanization, cities and towns are suffering from air pollution, heat island effects, and other environmental problems. Urban vegetation, especially trees, plays a significant role in solving these ecological problems. To maximize services provided by vegetation, urban tree species should be properly selected and optimally arranged. Therefore, accurate classification of tree species in urban environments has become a major issue. In this paper, we reviewed the potential of light detection and ranging (LiDAR) data to improve the accuracy of urban tree species classification. In detail, we reviewed the studies using LiDAR data in urban tree species mapping, especially studies where LiDAR data was fused with optical imagery, through classification accuracy comparison, general workflow extraction, and discussion and summarizing of the specific contribution of LiDAR. It is concluded that combining LiDAR data in urban tree species identification could achieve better classification accuracy than using either dataset individually, and that such improvements are mainly due to finer segmentation, shadowing effect reduction, and refinement of classification rules based on LiDAR. Furthermore, some suggestions are given to improve the classification accuracy on a finer and larger species level, while also aiming to maintain classification costs. Full article

(This article belongs to the Special Issue Using LiDAR and Optical Imagery to Map Forest Vegetation for Assessing Wildlife Habitat)

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