Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Dalponte, Michele; Ene, Liviu Theodor; Gobakken, Terje; Næsset, Erik; Gianelle, Damiano

doi:10.3390/rs10040586

Cited by 36 publications

(27 citation statements)

References 31 publications

(42 reference statements)

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“…These studies were focused on the classification of coniferous and broadleaved trees and they reported an overall accuracy in the range of 33%-88%. Dalponte et al [47] focused directly on the prediction of Shannon's diversity index. The mean absolute deviation reached value of 9.6% when an area-based approach and a Leica ALS70 scanner was used.…”

Section: Shannon's Diversity Indexmentioning

confidence: 99%

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

2019

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Estimation of biophysical variables based on airborne laser scanning (ALS) data using tree detection methods concentrates mainly on delineation of single trees and extraction of their attributes. This study provides new insight regarding the potential and limits of two detection methods and underlines some key aspects regarding the choice of the more appropriate alternative. First, we applied the multisource-based method implemented in reFLex software (National Forest Centre, Slovakia), which uses the information contained in the point cloud and a priori information. Second, we applied the raster-based method implemented in OPALS software (Vienna University of Technology, Austria), which extracts information from several ALS-derived height models. A comparative study was conducted for a part of the university forest in Zvolen (Slovakia, Central Europe). ALS-estimated variables of both methods were compared (1) to the ground reference data within four heterogonous stands with an area size of 7.5 ha as well as (2) to each other within a comprehensive forest unit with an area size of 62 ha. We concluded that both methods can be used to evaluate forest stand and ecological variables. The overall performance of both methods achieved a matching rate within the interval of 52%–64%. The raster-based method provided faster and slightly more accurate estimate of most variables, while the total volume was more precisely estimated using the multisource-based method. Specifically, the relative root mean square errors did not exceed 7.2% for mean height, 8.6% for mean diameter, 21.4% for total volume, 29.0% for stand density index, and 7.2% for Shannon’s diversity index. Both methods provided estimations with differences that were statistically significant, relative to the ground data as well as to each other (p < 0.05).

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Section: Shannon's Diversity Indexmentioning

confidence: 99%

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

2019

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“…On the other hand, if the focus is on biodiversity studies, this represents a limitation. In this case, we can expect that the use of the new multispectral LiDAR sensors [32] could improve the results, opening up new possibilities for studies on tree species classification from LiDAR data.…”

Section: Discussionmentioning

confidence: 99%

A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019

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Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

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“…We did not use the spectral information provided by the available multispectral ALS data in this study, but we did benefit from its high point density, which was used when estimating the need for pre-harvest clearing. Similarly, Dalponte et al [44], who explored the potential of this sensor for modeling and predicting forest characteristics at the plot level (e.g., the number of trees per hectare), stated that multispectral ALS data were useful for predicting the forest characteristics considered in their work and had great potential for use in forestry and ecological applications.…”

Section: Discussionmentioning

confidence: 99%

Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning

Sanz

Malinen

Heiskanen

et al. 2020

Forests

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The methodology presented here can assist in evaluating the need for pre-harvest clearing. In the long term, similar approaches may help with managing electronic standing sales and enhance the operational environment of roundwood e-marketplaces. In cut-to-length harvesting, pre-harvest clearing is needed when the understory vegetation hinders the visibility of the stems to be harvested. It can facilitate the work of the harvester operators and thereby enhance the productivity and quality of the harvesting operation. Information about where pre-harvest clearing is required is often not available, however, or else it has to be collected during time-consuming field visits. We report here on the development and evaluation of airborne laser scanning (ALS)-based models for estimating the need for pre-harvest clearing. The reference data consisted of 99 circular field sample plots that were photographed and in which stems with diameters at breast height from one to seven centimeters were measured. An online e-questionnaire survey responded to by 66 forest professionals classified the sample plots into five categories ranging from no need for pre-harvest clearing to compulsory pre-harvest clearing. A linear discriminant analysis was used to estimate the need for pre-harvest clearing with an accuracy of 63.6%, whereas a linear model-based method that predicted the understory stem density assessed the need with an accuracy of 64.6%. Use of this method could deliver information about the understory vegetation, offer guidelines for clearing the understory, and reduce the number of field visits before harvesting, thus reducing costs.

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Predicting Selected Forest Stand Characteristics with Multispectral ALS Data

Cited by 36 publications

References 31 publications

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

Need for Pre-Harvest Clearing of Understory Vegetation Determined by Airborne Laser Scanning

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