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 Sector Cover N° of samples Si Forest cover 10 S2 Test plot B 15 S3 Test plot C 15 s4 Test plot A 15 S5 Test plot D 15 Table 2. Characteristics of the four test sectors. The samples in the ROI where chosen with specific care of leaving out any surface which does not have a vegetation cover, to limit variability of the geometry and radiometry of the return echo, since, as it is described in literature (Jutsi and Stilla, 2003) there is significant variability even between different elements inside an urban environment. 2.5 Waveform extraction and analysis The process for extracting the waveform data was done by implementing custom routines developed in c++ language which have been integrated in a tool with graphical user interface. This software is in very early development stages and is work in progress; anyone interested is welcome to contact the author for testing purposes. The objective of the software is to bundle methods for processing waveforms directly and for exporting waveforms to more common formats such as ASPRS' LAS 1.3 or future open standards. The routine requires a shapefile with points representing the center of each sample plot, the radius of the plots and the folder path were all of the files are stored. It then proceeds in reading the plot coordinates, and filling a container for each sample with the pulses falling inside the sample area. Each pulse is then linked to the corresponding waveform data using the GPS timestamp to search the waveform file (see section 2.3). 5 Sector Areas in ROI | 10/15 Samples / Sectors | | ~5000 laser Pulses / Sample | 1-8 waveform Segments / Pulse | ^ 10-256 samples (1 ns) / Segment Figure 3. Plot of eight waveforms of the outgoing pulse. Figure 3 shows a scalar relationship from the ROI down to the single 1 ns sample in the waveform segment. The position in 3D space of each return sample at time # in the waveform segment can be calculated with the following: the sensor position, the sensor orientation, the pulse scan angle and the time interval (t;) between the beginning of emitted pulse (TO) and the beginning of the n™ return echo waveform segment (Tn), using the following formula: 525 Xp =X, +R, sin0 cosy —R,, singcosy Van ^ Y, + Ra) Sin 0° siny +R, sin cosy Zu Zu 7 Ra) CoOSO cos ó where, at time t, $, 0, y are respectively the pitch, roll and yaw angles of sensor, o is the scan angle of pulse, X,Y, Z are the coordinates of the sensor center, and R, is the range of the first waveform sample. The range Ry, is calculated using the speed of the laser pulse and the time interval between the maximum value of the TO pulse (see figure 2) and the first sample over the baseline of the return waveform segment Tn: 10° where P is atmospheric pressure in mbar ant T is air temperature in degrees Celsius. Pressure and temperature were considered for the sake of comprehensiveness - a difference of 30° C and 200 mbar pressure brings a difference of about 3 mm which is not significant considering the other sources of error in a airborne laser scanner survey. The method reported above was used to fill a voxel grid with 0.3 m resolution with information on the waveform data. The range was calculated for each waveform data which was significantly above the baseline of the return signal; an empirical value of 5 digitizer counts was used as threshold. This means that a surface which causes a reflection will imprint a high value in the voxel it falls. The voxel grid is then used to extract statistical information on vegetation height and density over the whole sample, and successively over the whole sectors. The Kolmogorov-Smirnov two-sample test (K-S) is a non-parametric test that is sensitive to both the shape and location of peaks within the distribution and was used to compare between samples in the same sector and between samples over different sectors to check if inter- and intra- variances are significantly different thus leading to the possibility of using the method to discriminate areas with different vegetation structure. 2.99792458-10° ee P . 2731547 Ro = T 2E T 78.7 3. RESULTS AND DISCUSSION This method sample the waveform information in 3D space, and will be the basis of further work for vegetation analysis. It is different from more common methods which use fitting of waveforms to discriminate between returns and use peak values to have a dense point cloud with width and amplitude information. In this case we base our method on statistics on the voxel grid, which overrides any signal analysis step necessary for determining the point in space which causes the reflection. In this case the retum waveform from a reflecting surface will not supply information to a single point in space or to a single voxel, but will be supplying information to many voxels along its path. The resolution of 0.3 m was chosen empirically as it is a space which can include —2 successive samples. À bigger voxel size would be less discriminate, whereas a smaller voxel size would not improve results, but increase threefold the size of the files. As can be seen in figure 4, results can visually report vegetation structure. Table 3 reports the results of the K-S test over the distribution of metrics extracted from the voxel grid, showing Q)

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