Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery

Apostol, Bogdan; Petrila, Marius; Lorenţ, Adrian; Ciceu, Albert; Gancz, Vladimir; Badea, Ovidiu

doi:10.1016/j.scitotenv.2019.134074

Cited by 42 publications

(29 citation statements)

References 31 publications

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“…The classi cation results of the dominant species was validated by independent samples, which is a recommended way to assess accuracy (Olofsson et al, 2014), and the distribution range of seven targeted forest classes was delineated with an acceptable accuracy (kappa=0.71, OA=77.5%) across the study area, which was comparable to the existing related studies, where the object-based methods was used together with UAV or multi-temporal multi-spectral images, and an overall classi cation accuracy was achieved from 73%-82% (Apostol et al, 2020;Franklin and Ahmed, 2018;Liu et al, 2018;Michez et al, 2016). Furthermore, some of previous studies obtained better accuracy, but the number of targeted category was relatively fewer, and they were very demanding in terms of spatial resolution of the image, which is not applicable for studies on large areas (Fabian et al, 2016;Grabska et al, 2019).…”

Section: Discussionsupporting

confidence: 65%

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Xie

Cao

et al. 2021

Preprint

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Background Accurate information on tree species is much in demand for forestry management and further investigations on biodiversity and forest ecosystem services. Over regional or large areas, discriminating tree species at high resolution is deemed challenging by lack of representative features and computational power. Methods A novel methodology to delineate the explicit spatial distribution of dominated six tree species (Pinus, Quercus, Betula, Populus, Larch, and Apricot) and one residual class using the analysis-ready large volume multi-sensor imagery within Google Earth Engine (GEE) platform is demonstrated and used to map a 10 m classification with detail analysis of spatial pattern for an area covering over 90,000 km 2 between 41° N and 45° N. Random Forest (RF) algorithm built into GEE was used for tree species mapping, together with the multi-temporal features extracted from Sentinel-1/2 and topographic imagery data. The composition of tree species in natural forests and plantations in city and county-level were performed in detail afterwards. Results The proposed model achieved a reliable overall agreement (77.5%, 0.71 kappa), and the detailed analysis on the spatial distributing of targeted species indicated that the plantations (Pinus, Populus, Larch, and Apricot) outnumber natural forests (Quercus and Betula) by 6%, and they were mainly grown in the northern and southern regions, respectively. Moreover, Arhorchin had the largest total forest area of over 4,500 km 2 , while Hexingten and Aohan ranked first in natural forest and plantation area, and the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. Conclusions It is our belief that combined multi-source information of the machine learning algorithm within cloud platforms is beneficial to map a reliable spatial tree species over large areas on a fine scale. High-resolution tree species information based on online tools could be more easily considered for practical forestry management and further studies on forest ecosystems.

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Section: Discussionsupporting

confidence: 65%

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Xie

Cao

et al. 2021

Preprint

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“…Traditional forestry methods require in-depth outdoor ground-level measurements, which are time-consuming, labor-intensive, and inefficient. It is difficult to obtain continuous individual tree parameters over large areas [4], [5]. The proposal of precision The associate editor coordinating the review of this manuscript and approving it for publication was Geng-Ming Jiang .…”

Section: Introductionmentioning

confidence: 99%

Individual Tree Parameters Estimation for Plantation Forests Based on UAV Oblique Photography

Zhou

Zhang

2020

IEEE Access

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The parameters of individual trees in forests are important for accurate forestry inventory and sustainable management. Unmanned aerial vehicle (UAV) oblique photogrammetry technology plays an important role in forest surveys because of its flexibility, low cost, high spatial data resolution. Few studies have committed to extract individual tree parameters and estimate individual tree biomass using oblique photography data. In this study, images of larch(Larix gmelinii) and Chinese pine(Pinus tabuliformis) plantations with different ages were acquired by UAV oblique photogrammetry. Then, three dimension (3D) point clouds were constructed recovered by the structure from motion (SFM) algorithm and than normalized. The watershed segmentation, point cloud segmentation (PCS) and object-oriented multiresolution segmentation (MRS) methods were used to delineate individual trees and extract tree height and crown area. Finally, stepwise regression was used to fitting individual tree biomass models based on the point cloud metrics, crown area obtained from individual tree segmentation and measured biomass. The results indicated that: most suitable segmentation method are determined for different tree species at different ages; optimal prediction models of individual tree biomass can be constructed by combining the point cloud metrics with the tree crown area obtained by segmentation, accuracy of the models are all greater than 0.8. The results show that UAV oblique photography technology can be used to accurately extract the individual tree parameters of larch and Chinese pine plantation forests in northern China and can meet the requirements of large-scale and low-cost forestry inventory. INDEX TERMS UAV oblique photography, point clouds, individual tree segmentation, individual tree parameters, individual tree biomass models.

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“…Among the papers that adopted both manual and unsupervised tree segmentation, only a few research works included ground data collection [42,72,[75][76][77] with a tree sample size ranging from 109 to 2069 trees. None of those presented wood biomass in-field data.…”

Section: Introductionmentioning

confidence: 99%

“…For natural, mixed and uneven-aged forest, Mayr et al [75] gathered tree height in dry savannah and used an implementation of the watershed segmentation algorithm provided by System for Automated Geoscientific Analyses-Geographic Information System (SAGA-GIS) while Franklin and Ahmed [42] utilized the multi-resolution segmentation procedure with the ENVI software system and they collected tree height and crown dimensions in a mixed maple, aspen, and birch forest. Concerning planted, pure and even-aged forests, Ganz et al [72] used a multiresolution segmentation algorithm and measured tree height within stands of Norway spruce and common beech while Apostol et al [77] utilized the watershed algorithm and collected tree height and stem diameter in an even-aged Douglas fir stand. By taking tree height as ground-truth data in a chestnut plantation, Marques et al [76] segmented trees by combining a vegetation-index based algorithm with the Otsu method.…”

Section: Introductionmentioning

confidence: 99%

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

Gennaro

Nati

Dainelli

et al. 2020

Forests

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The agricultural and forestry sector is constantly evolving, also through the increased use of precision technologies including Remote Sensing (RS). Remotely biomass estimation (WaSfM) in wood production forests is already debated in the literature, but there is a lack of knowledge in quantifying pruning residues from canopy management. The aim of the present study was to verify the reliability of RS techniques for the estimation of pruning biomass through differences in the volume of canopy trees and to evaluate the performance of an unsupervised segmentation methodology as a feasible tool for the analysis of large areas. Remote sensed data were acquired on four uneven-aged and irregularly spaced chestnut orchards in Central Italy by an Unmanned Aerial Vehicle (UAV) equipped with a multispectral camera. Chestnut geometric features were extracted using both supervised and unsupervised crown segmentation and then applying a double filtering process based on Canopy Height Model (CHM) and vegetation index threshold. The results show that UAV monitoring provides good performance in detecting biomass reduction after pruning, despite some differences between the trees’ geometric features. The proposed unsupervised methodology for tree detection and vegetation cover evaluation purposes showed good performance, with a low undetected tree percentage value (1.7%). Comparing crown projected volume reduction extracted by means of supervised and unsupervised approach, R2 ranged from 0.76 to 0.95 among all the sites. Finally, the validation step was assessed by evaluating correlations between measured and estimated pruning wood biomass (Wpw) for single and grouped sites (0.53 < R2 < 0.83). The method described in this work could provide effective strategic support for chestnut orchard management in line with a precision agriculture approach. In the context of the Circular Economy, a fast and cost-effective tool able to estimate the amounts of wastes available as by-products such as chestnut pruning residues can be included in an alternative and virtuous supply chain.

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Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery

Cited by 42 publications

References 31 publications

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Individual Tree Parameters Estimation for Plantation Forests Based on UAV Oblique Photography

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

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Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery

Cited by 42 publications

References 31 publications

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&amp;2 Imagery within Google Earth Engine

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&amp;2 Imagery within Google Earth Engine

Individual Tree Parameters Estimation for Plantation Forests Based on UAV Oblique Photography

An Automatic UAV Based Segmentation Approach for Pruning Biomass Estimation in Irregularly Spaced Chestnut Orchards

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Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine