| 1. |
- Montaghi, Alessandro, et al.
(author)
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Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study
- 2013
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In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 23, s. 288-300
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Research review (peer-reviewed)abstract
- This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches such as single tree detection and area-based modeling have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems. (C) 2012 Elsevier B.V. All rights reserved.
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| 2. |
- Montaghi, Alessandro, et al.
(author)
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Analysis of effects of scanning angle on ALS-derived vegetation metrics in a nationwide airborne ALS acquisition
- 2012
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Conference paper (other academic/artistic)abstract
- In the summer of 2009, the Swedish government, under the co-ordination of Lantmäteriet (the Swedish mapping, cadastral and land registration authority), started a five-year project (2009- 2013) using airborne laser scanning for production of the New National Elevation Model (in Swedish: Ny Nationell Höjdmodell, NNH) for all of Sweden (450,000 km2). The primary aim of this project is to produce a 2 meter grid DEM (Digital Elevation Model) in which the standard error is better than 0.5 m. A total of about four hundred scanning areas, with a size of 25 km by 50 km, are being scanned with a nominal density of 0.5-1 point per square meter, and with a maximum scanning angle of ± 20 degrees (Petersen and Burman Rost, 2011). The acquisition is primarily done using Leica Geosystems ALS50-II and ALS60 sensors, with Optech ALTM Gemini as a complementary sensor. In each scanning area, 21 parallel flight lines were flown, with a nominal overlap of 20%, in addition also three perpendicular crossing lines where acquired. This flight line arrangement was designed to allow strip adjustment techniques, based on sensor parameter calibration, to be used for creation of a seamless final product (Toth, 2009). Finally, main and cross strips were merged together and delivered in blocks of 2.5 km by 2.5 km. The ALS data acquired for the NNH project is a resource of interest also for forest estimation. However, since the ALS survey is being carried out for a purpose other than measuring forest parameters, there are a number of issues that need to be considered. These include the effect of different time points for the scanning, the relatively large view angles used, and the positional accuracy for the NFI plots. In this paper, the effect of one of these issues, the view angle influence on LiDAR forest metrics, will be reported. At the conference also initial results from predictions based on LiDAR data and SPOT HRG satellite data, trained with national forest inventory sample plots, will be presented.
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| 3. |
- Montaghi, Alessandro
(author)
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Effect of scanning angle on vegetation metrics derived from a nationwide Airborne Laser Scanning acquisition
- 2013
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In: Canadian Journal of Remote Sensing. - : Informa UK Limited. - 0703-8992 .- 1712-7971. ; 39, s. S152-S173
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Journal article (peer-reviewed)abstract
- The influence of scanning angle on vegetation metrics derived from a large area Airborne Laser Scanning (ALS) data acquisition was evaluated in this study. The ALS data were derived from the ongoing acquisition for the new Swedish Nationwide Elevation Model. To make a comparison of scanning angles, a random selection of 2310 sample plots (0.01 ha in size) was taken from two large forested areas in the north and south of Sweden. Only plots that had ALS data from two different acquisitions on the same day were used: the first scanned at nadir (0 degrees scnning angle) and the second with an absolute scanning angle ranging from 0 degrees to a nominal 20 degrees. For each plot, 32 plot-level vegetation metrics were calculated from the ALS data for each pair of scanning angles. The ALS metrics for each pair were then compared using a nonparametric Wilcoxon signed-rank test. The results indicated that most metrics commonly used in area-based prediction of forest variables were relatively unaffected by high scanning angles, up to 20 degrees. However, the vegetation ratio and the understory ratio from scanning angles greater than 10 degrees were significantly different from those derived from a 0 degrees scanning angle.
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| 4. |
- Montaghi, Alessandro, et al.
(author)
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Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery
- 2013
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In: International Journal of Applied Earth Observation and Geoinformation. - : Elsevier BV. - 0303-2434 .- 1569-8432. ; 25, s. 87-97
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Journal article (peer-reviewed)abstract
- This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km(2) and 487 km(2) in Sicily (Italy), 16,761 plots of size 30-m x 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns. (C) 2013 Elsevier B.V. All rights reserved.
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