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Special Issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function"

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

Deadline for manuscript submissions: 31 July 2019.

Special Issue Editors

Guest Editor
PD Dr. Hooman Latifi

1. Dept of Photogrammerty and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, No. 1346 Valiasr Str., Mirdamad Crossing, Postal Code 19967-15433 Tehran, Iran
2. Dept. of Remote Sensing, Institute of Geography and Geology, University of Wuerzburg, Oswald-Kuelpe-Weg 86, 97074 Wuerzburg, Germany
Website 1 | Website 2 | E-Mail
Interests: remote sensing-assisted monitoring of forest structure, phenology and health; small-scale forest inventory; spatial statistics; modelling and optimization; LiDAR; UAV and their point cloud processing for forest inventory
Guest Editor
Dr. Ruben Valbuena

School of Natural Sciences, Bangor University, Bangor, Gwynedd LL57 2UW, UK
Website | E-Mail
Interests: forest ecology; remote sensing; LiDAR; forest inventory; tree size scaling theories; forest structure; competition and dominance; modelling; data fusion

Special Issue Information

Dear Colleagues,

The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties and can, thus, be crucially shaped and changed by various biotic and abiotic factors, ranging from global, continental and sub-continental climate change to macro- and micro-climatic regimes, disturbance agents and anthropogenic factors. The constant and alarming rise in the magnitude and extension of these changes during recent decades calls for enhanced cross-border and cross-continental mitigation and adaption measures, which strongly entail intensified monitoring in both space and time. In the absence or shortage of expensive logistics and field surveys, remote sensing data and methods are the main complementary sources of synoptic, up-to-date and objective information for forest ecology. Owing to the fact that forest ecosystems (and the influential factors shaping them) are inherently of a three-dimensional nature, methods based on the analysis of three-dimensional sources of remote sensing data can be considered the most appropriate tools to retrieve the forest compositional, structural and functional dynamics. Examples of these data include a broad range of methods for 3D reconstruction (stereo-photogrammetric restitution, structure from motion, interferometry, ranging, etc.) obtained using various remote sensors (digital images, LiDAR or RADAR) from a variety of platforms (ground-based, UAV-borne, airborne or spaceborne). Whereas many applications rely on the sole use of either of these data sources to answer a specific question, combined or fused applications have also received considerable attention during recent years.

In this Special Issue of Forests, we encourage state-of-the-art scientific works from all relevant fields, including experimental studies, method developments, model validations and reviews dealing with the general topic of 3D remote sensing-assisted applications in monitoring forest composition, structure and function. In particular, contributions covering the following sub-topics are welcome:

  • 3D remote sensing-assisted analysis of forest composition
    • Tree species classifications, with particular focus on bottlenecks such as broadleaves, rare tree species, and ecosystems only marginally studied with these methods, such as aquatic forest ecosystems
    • Operational, large-scale monitoring of forest composition
    • Combination and fusion of 3D and 2D sources of data form forest composition analysis
  • Advanced application of 3D sources of data for deriving forest structural attributes:
    • Algorithm development (in particular for tree object extraction, predictive and extrapolative models and automatic process chains for them)
    • Model validation and uncertainty analysis
    • Optimization of field- and remote sensing sampling schemes
    • Calibration and transferability of models across space and time
    • Remote sensing-assisted studies on community ecology with particular focus on the structure or forest understory, deadwood or herbaceous plants
  • 3D remote sensing-assisted analysis of forest function
    • Watershed-related analysis across forest landscapes using 3D sources of data
    • Erosion monitoring across forested landscapes
    • Characterization of wildlife habitats and niches using 3D sources of data
    • 3D applications for forest functional diversity monitoring, in particular across degraded, transitional or disturbed landscapes.  
    • Advancements in modeling, prediction and extrapolation of forest wood and non-wood forest products

With this Special Issue we aim at showing applications in forest ecology in a broad collection of methods/sensors/platform combinations. We therefore encourage submissions employing uncommon data fusion schemes and novel perspectives.

PD Dr. Hooman Latifi
Dr. Ruben Valbuena
Guest Editors

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. Forests is an international peer-reviewed open access monthly 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

  • Forest ecology
  • Forest structure, composition and function
  • Three-dimensional remote sensing
  • Data fusion
  • Spatiotemporal analysis
  • Causal and predictive modeling

Published Papers (9 papers)

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Research

Open AccessArticle
Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries
Forests 2019, 10(7), 540; https://doi.org/10.3390/f10070540
Received: 21 May 2019 / Revised: 20 June 2019 / Accepted: 26 June 2019 / Published: 27 June 2019
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Abstract
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial [...] Read more.
Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions. Full article
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Open AccessArticle
Relationships between Satellite-Based Spectral Burned Ratios and Terrestrial Laser Scanning
Forests 2019, 10(5), 444; https://doi.org/10.3390/f10050444
Received: 5 April 2019 / Revised: 13 May 2019 / Accepted: 18 May 2019 / Published: 23 May 2019
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Abstract
Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current [...] Read more.
Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current methods of assessing fire severity are limited to on-site visual inspection or the use of satellite and aerial images to quantify severity over larger areas. On the ground, assessment of fire severity is influenced by the observers’ knowledge of the local ecosystem and ability to accurately assess several forest structure measurements. The objective of this study is to introduce TLS to validate spectral burned ratios obtained from Landsat images. The spectral change was obtained by an image compositing technique through GEE. The 32 plots were collected using TLS in Wood Buffalo National Park, Canada. TLS-generated 3D points were converted to voxels and the counted voxels were compared in four height strata. There was a negative linear relationship between spectral indices and counted voxels in the height strata between 1 to 5 m to produce R2 value of 0.45 and 0.47 for unburned plots and a non-linear relationship in the height strata between 0 to 0.5m for burned plots to produce R2 value of 0.56 and 0.59. Shrub or stand development was related with the spectral indices at unburned plots, and vegetation recovery in the ground surface was related at burned plots. As TLS systems become more cost efficient and portable, techniques used in this study will be useful to produce objective assessments of structure measurements for fire refugia and ecological response after a fire. TLS is especially useful for the quick ground assessments which are needed for forest fire applications. Full article
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Open AccessArticle
Mapping Maximum Tree Height of the Great Khingan Mountain, Inner Mongolia Using the Allometric Scaling and Resource Limitations Model
Forests 2019, 10(5), 380; https://doi.org/10.3390/f10050380
Received: 19 March 2019 / Revised: 15 April 2019 / Accepted: 28 April 2019 / Published: 30 April 2019
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Abstract
Maximum tree height is an important indicator of forest vegetation in understanding the properties of plant communities. In this paper, we estimated regional maximum tree heights across the forest of the Great Khingan Mountain in Inner Mongolia with the allometric scaling and resource [...] Read more.
Maximum tree height is an important indicator of forest vegetation in understanding the properties of plant communities. In this paper, we estimated regional maximum tree heights across the forest of the Great Khingan Mountain in Inner Mongolia with the allometric scaling and resource limitations model. The model integrates metabolic scaling theory and the water–energy balance equation (Penman–Monteith equation) to predict maximum tree height constrained by local resource availability. Monthly climate data, including precipitation, wind speed, vapor pressure, air temperature, and solar radiation are inputs of this model. Ground measurements, such as tree heights, diameters at breast height, and crown heights, have been used to compute the parameters of the model. In addition, Geoscience Laser Altimeter System (GLAS) data is used to verify the results of model prediction. We found that the prediction of regional maximum tree heights is highly correlated with the GLAS tree heights (R2 = 0.64, RMSE = 2.87 m, MPSE = 12.45%). All trees are between 10 to 40 m in height, and trees in the north are taller than those in the south of the region of research. Furthermore, we analyzed the sensitivity of the input variables and found the model predictions are most sensitive to air temperature and vapor pressure. Full article
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Open AccessArticle
Can Field Crews Telecommute? Varied Data Quality from Citizen Science Tree Inventories Conducted Using Street-Level Imagery
Forests 2019, 10(4), 349; https://doi.org/10.3390/f10040349
Received: 18 March 2019 / Revised: 12 April 2019 / Accepted: 18 April 2019 / Published: 20 April 2019
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Abstract
Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of [...] Read more.
Street tree inventories are a critical component of urban forest management. However, inventories conducted in the field by trained professionals are expensive and time-consuming. Inventories relying on citizen scientists or virtual surveys conducted remotely using street-level photographs may greatly reduce the costs of street tree inventories, but there are fundamental uncertainties regarding the level of data quality that can be expected from these emerging approaches to data collection. We asked 16 volunteers to inventory street trees in suburban Chicago using Google Street ViewTM imagery, and we assessed data quality by comparing their virtual survey data to field data from the same locations. We also compared virtual survey data quality according to self-rated expertise by measuring agreement within expert, intermediate, and novice analyst groups. Analyst agreement was very good for the number of trees on each street segment, and agreement was markedly lower for tree diameter class and tree identification at the genus and species levels, respectively. Interrater agreement varied by expertise, such that experts agreed with one another more often than novices for all four variables assessed. Compared to the field data, we observed substantial variability in analyst performance for diameter class estimation and tree identification, and some intermediate analysts performed as well as experts. Our findings suggest that virtual surveys may be useful for documenting the locations of street trees within a city more efficiently than field crews and with a high level of accuracy. However, tree diameter and species identification data were less reliable across all expertise groups, and especially novice analysts. Based on this analysis, virtual street tree inventories are best suited to collecting very basic information such as tree locations, or updating existing inventories to determine where trees have been planted or removed. We conclude with evidence-based recommendations for effective implementation of this type of approach. Full article
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Open AccessArticle
Do High-Voltage Power Transmission Lines Affect Forest Landscape and Vegetation Growth: Evidence from a Case for Southeastern of China
Forests 2019, 10(2), 162; https://doi.org/10.3390/f10020162
Received: 22 January 2019 / Revised: 11 February 2019 / Accepted: 12 February 2019 / Published: 14 February 2019
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Abstract
The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are [...] Read more.
The rapid growth of the network of high-voltage power transmission lines (HVPTLs) is inevitably covering more forest domains. However, no direct quantitative measurements have been reported of the effects of HVPTLs on vegetation growth. Thus, the impacts of HVPTLs on vegetation growth are uncertain. Taking one of the areas with the highest forest coverage in China as an example, the upper reaches of the Minjiang River in Fujian Province, we quantitatively analyzed the effect of HVPTLs on forest landscape fragmentation and vegetation growth using Landsat imageries and forest inventory datasets. The results revealed that 0.9% of the forests became edge habitats assuming a 150 m depth-of-edge-influence by HVPTLs, and the forest plantations were the most exposed to HVPTLs among all the forest landscape types. Habitat fragmentation was the main consequence of HVPTL installation, which can be reduced by an increase in the patch density and a decrease in the mean patch area (MA), largest patch index (LPI), and effective mesh size (MESH). In all the landscape types, the forest plantation and the non-forest land were most affected by HVPTLs, with the LPI values decreasing by 44.1 and 20.8%, respectively. The values of MESH decreased by 44.2 and 32.2%, respectively. We found an obvious increasing trend in the values of the normalized difference vegetation index (NDVI) in 2016 and NDVI growth during the period of 2007 to 2016 with an increase in the distance from HVPTL. The turning points of stability were 60 to 90 meters for HVPTL corridors and 90 to 150 meters for HVPTL pylons, which indicates that the pylons have a much greater impact on NDVI and its growth than the lines. Our research provides valuable suggestions for vegetation protection, restoration, and wildfire management after the construction of HVPTLs. Full article
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Open AccessArticle
Mapping Forest Canopy Height in Mountainous Areas Using ZiYuan-3 Stereo Images and Landsat Data
Forests 2019, 10(2), 105; https://doi.org/10.3390/f10020105
Received: 11 December 2018 / Revised: 24 January 2019 / Accepted: 28 January 2019 / Published: 29 January 2019
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Abstract
Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications [...] Read more.
Forest canopy height is an important parameter for studying biodiversity and the carbon cycle. A variety of techniques for mapping forest height using remote sensing data have been successfully developed in recent years. However, the demands for forest height mapping in practical applications are often not met, due to the lack of corresponding remote sensing data. In such cases, it would be useful to exploit the latest, cheaper datasets and combine them with free datasets for the mapping of forest canopy height. In this study, we proposed a method that combined ZiYuan-3 (ZY-3) stereo images, Shuttle Radar Topography Mission global 1 arc second data (SRTMGL1), and Landsat 8 Operational Land Imager (OLI) surface reflectance data. The method consisted of three procedures: First, we extracted a digital surface model (DSM) from the ZY-3, using photogrammetry methods and subtracted the SRTMGL1 to obtain a crude canopy height model (CHM). Second, we refined the crude CHM and correlated it with the topographically corrected Landsat 8 surface reflectance data, the vegetation indices, and the forest types through a Random Forest model. Third, we extrapolated the model to the entire study area covered by the Landsat data, and obtained a wall-to-wall forest canopy height product with 30 m × 30 m spatial resolution. The performance of the model was evaluated by the Random Forest’s out-of-bag estimation, which yielded a coefficient of determination (R2) of 0.53 and a root mean square error (RMSE) of 3.28 m. We validated the predicted forest canopy height using the mean forest height measured in the field survey plots. The validation result showed an R2 of 0.62 and a RMSE of 2.64 m. Full article
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Open AccessArticle
Application of Terrestrial Laser Scanner to Evaluate the Influence of Root Collar Geometry on Stump Height after Mechanized Forest Operations
Forests 2018, 9(11), 709; https://doi.org/10.3390/f9110709
Received: 26 October 2018 / Revised: 9 November 2018 / Accepted: 14 November 2018 / Published: 15 November 2018
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Abstract
The height of tree stumps following mechanized forest operations can be influenced by machine-, tree-, terrain-, and operator-related characteristics. High stumps may pose different economic and technical disadvantages. Aside from a reduction in product recovery (often associated with sawlog potential), leaving high stumps [...] Read more.
The height of tree stumps following mechanized forest operations can be influenced by machine-, tree-, terrain-, and operator-related characteristics. High stumps may pose different economic and technical disadvantages. Aside from a reduction in product recovery (often associated with sawlog potential), leaving high stumps can complicate future entries if smaller equipment with low ground clearance is used, particularly in the case where new machine operating trails are required. The objective of this exploratory study was to examine if correlations existed between the height of tree stumps following mechanized harvesting and the shape of the above-ground root collar, stump diameter, and distance to the machine operating trail. In total, 202 sample stumps of Norway spruce (Picea abies (L.) Karst.) and the surrounding terrain were scanned with a terrestrial laser scanner. The collected data was processed into a 3D-model and then analyzed. Stump height was compared with different characteristics such as stump diameter at the cut surface, distance to the machine operating trail, number of visible root flares per stump, and the root collar. The number of root flares per stump had a positive influence on stump diameter and height, showing a general trend of increasing diameter and height with the increasing number of root flares. Root angles also had an influence on the stump diameter. The diameter of a stump and the shape of the root collar at the cut surface together had a significant effect on stump height and the model reported explained half of the variation of stump heights. Taken together, these findings suggest that other factors than the ones studied can also contribute in influencing stump height during mechanized harvesting operations. Further investigations, including pre- and post-harvest scans of trees selected for removal, are warranted. Full article
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Open AccessArticle
Sensitivity of Codispersion to Noise and Error in Ecological and Environmental Data
Forests 2018, 9(11), 679; https://doi.org/10.3390/f9110679
Received: 26 September 2018 / Revised: 19 October 2018 / Accepted: 21 October 2018 / Published: 29 October 2018
Cited by 1 | PDF Full-text (9628 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Understanding relationships among tree species, or between tree diversity, distribution, and underlying environmental gradients, is a central concern for forest ecologists, managers, and management agencies. The spatial processes underlying observed spatial patterns of trees or edaphic variables often are complex and violate two [...] Read more.
Understanding relationships among tree species, or between tree diversity, distribution, and underlying environmental gradients, is a central concern for forest ecologists, managers, and management agencies. The spatial processes underlying observed spatial patterns of trees or edaphic variables often are complex and violate two fundamental assumptions—isotropy and stationarity—of spatial statistics. Codispersion analysis is a new statistical method developed to assess spatial covariation between two spatial processes that may not be isotropic or stationary. Its application to data from large forest plots has provided new insights into mechanisms underlying observed patterns of species distributions and the relationship between individual species and underlying edaphic and topographic gradients. However, these data are not collected without error, and the performance of the codispersion coefficient when there is noise or measurement error (“contamination”) in the data heretofore has been addressed only theoretically. Here, we use Monte Carlo simulations and real datasets to investigate the sensitivity of codispersion to four types of contamination commonly seen in many forest datasets. Three of these involved comparing codispersion of a spatial dataset with a contaminated version of itself. The fourth examined differences in codispersion between tree species and soil variables, where the estimates of soil characteristics were based on complete or thinned datasets. In all cases, we found that estimates of codispersion were robust when contamination was relatively low (<15%), but were sensitive to larger percentages of contamination. We also present a useful method for imputing missing spatial data and discuss several aspects of the codispersion coefficient when applied to noisy data to gain more insight about the performance of codispersion in practice. Full article
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Open AccessArticle
Estimating Individual Tree Height and Diameter at Breast Height (DBH) from Terrestrial Laser Scanning (TLS) Data at Plot Level
Forests 2018, 9(7), 398; https://doi.org/10.3390/f9070398
Received: 12 June 2018 / Revised: 28 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
Cited by 6 | PDF Full-text (5703 KB) | HTML Full-text | XML Full-text
Abstract
Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at [...] Read more.
Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated. Full article
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