Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Pirotti, Francesco; Travaglini, Davide; Giannetti, Francesca; Kutchartt, E.; Bottalico, Francesca; Chirici, Gherardo

doi:10.5194/isprs-archives-xli-b7-17-2016

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The forest is characterised by an even-aged silver fir (Abies alba Mill.) plantation that was completely destroyed by a windstorm on the 5 th of March, 2015 (Pirotti et al 2016, Chirici et al 2017). The climate is temperate-humid with Mediterranean-type rainfall (summer minimum) and a mean annual temperature of 9.7 °C.…”

Section: Study Areamentioning

confidence: 99%

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

Cambi¹,

Giannetti²,

Bottalico³

et al. 2018

iForest

View full text Add to dashboard Cite

Several studies have been carried out to investigate soil compaction and rutting after logging vehicle traffic, based on time consuming and punctual field measurements. The objective of this study was to measure soil disturbances with two methods: (i) a new, image-based models derived by a structure-frommotion (SfM) photogrammetry approach; and (ii) a traditional soil sampling (bulk density and shear strength). Two trails were selected in a logging area (central Italy), one trafficked by a forwarder (FT) and one trafficked by a skidder (ST). Data collection was conducted before, during and after timber extraction. Image-based models derived by SfM photogrammetry was used to highlight the differences in the shape and distribution of the disturbances along ST and FT. Results showed that the physical parameters of soil significantly changed due to both FT and ST traffic. Machine passes increased bulk density (111% and 31% for FT and ST, respectively), penetration resistance (29% and 24% for FT and ST, respectively) and shear resistance (14% and 6% for FT and ST, respectively), whereas porosity decreased (46% and 9% for FT and ST, respectively). Significant differences between FT and ST were found when comparing ruts removal and bulges with SfM photogrammetry. After logging, FT clearly showed ruts and bulges, whereas in ST ruts and bulges were not visible, but soil displacement in the direction of extraction was evident and measurable. Nevertheless, although our result shows a larger soil disturbance caused by forwarders than skidders, it is not possible to draw any general conclusions about differences between the two machines. Data about the machine passes, or the wood volumes transported over each trial area were not available; therefore, any general conclusion is misleading. SfM photogrammetry give information not available via traditional methods, thus improving impact assessment.

show abstract

Section: Study Areamentioning

confidence: 99%

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

Cambi¹,

Giannetti²,

Bottalico³

et al. 2018

iForest

View full text Add to dashboard Cite

show abstract

“…Many methods require a canopy height model, which is segmented using watershed and region-growing algorithms. Others use templates and similarity measures to detect best possibilities of tree positions (Pirotti et al, 2016). A multi-scale template matching approach for tree detection and measurement in (Korpela et al, 2007); elliptical and other templates are used in this study to represent tree models.…”

Section: Lidar For Treesmentioning

confidence: 99%

A Comparison of Tree Segmentation Methods Using Very High Density Airborne Laser Scanner Data

Pirotti

Kobal

Roussel³

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

ABSTRACT:Developments of LiDAR technology are decreasing the unit cost per single point (e.g. single-photo counting). This brings to the possibility of future LiDAR datasets having very dense point clouds. In this work, we process a very dense point cloud (~200 points per square meter), using three different methods for segmenting single trees and extracting tree positions and other metrics of interest in forestry, such as tree height distribution and canopy area distribution. The three algorithms are tested at decreasing densities, up to a lowest density of ~5 point per square meter. Accuracy assessment is done using Kappa, recall, precision and F-Score metrics comparing results with tree positions from groundtruth measurements in six ground plots where tree positions and heights were surveyed manually. Results show that one method provides better Kappa and recall accuracy results for all cases, and that different point densities, in the range used in this study, do not affect accuracy significantly. Processing time is also considered; the method with better accuracy is several times slower than the other two methods and increases exponentially with point density. Best performer gave Kappa = 0.7. The implications of metrics for determining the accuracy of results of point positions' detection is reported. Motives for the different performances of the three methods is discussed and further research direction is proposed.

show abstract

“…Estimation of DBH from airborne laser scanner data is only possible through allometric equations. Accurate canopy height models from laser scanning surveys allow area-based and single-tree based methods (Pirotti et al, 2017), and derived informative layers such as damage assessment (Pirotti et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Application of Tree Detection Methods Over Lidar Data for Forest Volume Estimation

Pirotti

Paterno²,

Pividori³

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

Abstract. Lidar (light detection and ranging) data are becoming more and more important in the analysis of the most relevant forest parameters. This study aims to compare the most recent segmentation methods for single trees using the ALS (Airborne Laser Scanning) point cloud and the CHM (Canopy Height Model). The methods used were the Li et al., method developed in 2012 and the Multi CHM method developed in 2015. The parameters analysed were the height and diameter for the individual trees and the volume and density for the entire forest. The efficiency of each method was verified by comparing the estimated parameters with those measured through 30 test areas. To better identify the useful parameters for the correct calibration of the algorithms, the population was divided into three layers according to the vertical structure and chronological class. From the comparison of the volumes obtained with the above methods and those calculated for the test areas, it emerges a tendency to over-segment for the Multi CHM method, while for the appropriately calibrated Li method there is a better correspondence to reality. The F-score values for the volumes obtained for the Li method are between 0.52 and 0.69 while for those obtained for the Multi CHM method are between 0.47 and 0.55. When compared with relascopic measures for each of the 48 parcels, a mean absolute difference ∼127 m3/ha and ∼141 m3/ha were found for Li2012 and MultiCHM respectively.

show abstract

Kernel Feature Cross-Correlation for Unsupervised Quantification of Damage From Windthrow in Forests

Cited by 5 publications

References 14 publications

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

Estimating machine impact on strip roads via close-range photogrammetry and soil parameters: a case study in central Italy

A Comparison of Tree Segmentation Methods Using Very High Density Airborne Laser Scanner Data

Application of Tree Detection Methods Over Lidar Data for Forest Volume Estimation

Contact Info

Product

Resources

About