XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/7, III/2, V/3: WAVEFORM LIDAR FOR REMOTE SENSING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: WAVEFORM ANALYSIS FOR THE EXTRACTION OF POST-FIRE VEGETATION CHARACTERISTICS F. Pirotti, A. Guarnieri, A. Vettore

Document type:: Multivolume work

Structure type:: Chapter

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia mapping (Simard et al, 2008) and above-ground biomass extraction (Lefsky et al., 2005). A high-altitude airborne sensor tested was SLICER, providing ~10 m footprints at ~5000 m altitude. Harding et al. (2001) proved that canopy height profiles can be extracted from waveform data with correlation (R?) values up to 0.75 can compared with ground truth. Lefsky et al. (2005) used SLICER data to estimate above ground biomass (AGBM) over different biomes, reaching correlation (R2) values up to 0.85. A strategy of processing waveform is to attempt for an improvement in peak detecting in the post-processing phase. Several methods have been tested and results show that the Generalized Gaussian fitting method gives best results (Chauve et al, 2007). Improved peak detection increases the number of significant returns obtained from a survey. Tests have shown that the number of returns — thus point density — can be increased by a factor of two compared to conventional discrete- return data (Reitberger et al, 2009). Point clouds with high densities (> 10 point/m2) are necessary for extracting metrics which use the distribution of 3D positions and intensities (Reitberger et al., 2009) for the identification of single trees and tree structure. Metrics which use all the information obtainable from an ALS survey have also been tested on large footprint (Drake et al, 2002) and small-footprint surveys. The objective of this paper is to extract information on low vegetation (renovation) by discrimination of terrain from low vegetation returns. 2. MATERIALS AND METHODS 2.1 Study area and ALS survey The survey was done the 20th of June 2011 with a helicopter carrying Optech’s ALTM 3100 sensor and a Rollei AIC modular P45 digital metric camera. The surveyed area is a region whose area is approximately 2.70 km?, located in the northwest part of Italy. The region of interest (ROI) for this study is a smaller portion (center at 7?29'54" longitude 45°46’18” latitude at WGSS84 datum). Part of the ROI encompasses and area which endured a severe fire event in 2005 which caused the destruction of the forest stand. Figure 1 shows the ROI with circular sample plots on five the five sectors which were tested for variability in vegetation characteristics. ctors with circular sample plots Figure 1. ROI with the five se Characteristic Value Vehicle Helicopter Sensor Optech ALTM 3100 Date of survey June 20 2011 Mean relative flight height ~525 m above ground Scan angle* z2].5? Scan frequency* 71.5 KHz Output Datum ETRS2000 (2008) — WGS84 524 *Calculated directly from the output data Table 1. Characteristics of the survey flight. 2.2 ALS data In the text the term “waveform” will be used to indicate amplitude as a function of time, specifically a vector of energy values sampled at 1 ns intervals. The length of the vector depends on the number of sampled energy values, which, in the case of Optech’s waveform file format, is a variable number for the return echo. In the case of the outgoing pulse (TO), the energy is sampled constantly every 1 ns for 40 ns thus the TO waveform is recorded in the NDF file using 40 samples. As can be seen in figure 2 the maximum value does not always correspond with the same sample time.. t9 (outgaing signal) waveform M ES E Figure 2. Plot of eight waveforms of the outgoing pulse. 2.3 Waveform recording format All laser scanner data were stored by the system in Optech's waveform data file formats, which consist in four types of files. To process the waveform the following files were necessary: - NDF file, where the recorded waveform data are stored consisting of variable length records, each of which hold the following information relative to one laser pulse: the GPS timestamp, the outgoing pulse waveform and up to 7 significant segments of the corresponding return echo waveform. The NDF data are divided into frames, each containing 16838 records. - IDX file, which is an index, relating starting and ending GPS timestamps of a specific frame in the NDF file. - CSD file holds information on trajectory, thus the position and orientation of the vehicle at each laser pulse, as well as scan angle and range of up to four returns. - DGT file holds the index between frame start time and CSD record number. 2.4 Sampling design The ROI was divided into 5 sectors with different characteristics in terms of forest cover and renovation technique; each area was sampled with circular plots of 12.6 m radius, -500 m? (figure 1). The characteristics for each area and the number of plots are reported on the table below.

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