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: THEORY AND EXPERIMENTS IN RADAR AND LIDAR]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: FULL WAVEFORM ACTIVE HYPERSPECTRAL LIDAR T. Hakala, J. Suomalainen, S. Kaasalainen

Document type:: Multivolume work

Structure type:: Chapter

7/ 4000 900 (7 96 “ny Ot rocessing. measured et spruce | canvas. lying the alibration »pectralon intensities cho at the ". As the with the scanner R(4)) is itting was ed from a e and the dividually deviation) kscattered r than 2% and better ffected by | expected istrument. 1umber of ance, the sision due ium range orm echo nsity. The 12-meter lity point range and s. asured in JAR. The ym lack of while the 1e LIDAR a passive -tungsten- Fig. 3. The Norway spruce. Spectrometer 0.37 MNNYL 3341 9 N a T © N ^, "Bpactometor e Lx C1 Backscattered Reflectance e = 0 1 À. L 1 J 500 600 700 800 900 1000 Wavelength / nm Fig. 4. Passive spectrometer measurement (solid line) and hyperspectral LiDAR (dashed line) of same areas of the tree are shown. Spectra of the passive spectrometer and LiDAR measurements of selected regions of interest are presented in Fig. 4. A clear distinction between the tree trunk and the top can be observed in the shape of the spectra. The LiDAR and passive spectrometer spectral shapes are clearly similar. In case of the tree top, the LiDAR observes less light than the passive measurement in near-infrared. This difference is caused by multiple scattering in a medium with a low optical density and a high single scattering albedo. In an active LIDAR measurement, only a small spot on the target is illuminated and observed. A significant part of the pulse energy is lost outside the sensor field of view, if multiple scattering plays a major role in reflectance and the scattering mean free path is long in the medium. This is not experienced in passive measurement as the same amount of light is scattered both in and out of the sensor field of view. The backscattered reflectance from Spectralon is 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 not significantly affected by this effect, as Spectralon has a high single scattering albedo but only a short mean free path. As the LiDAR backscattered reflectance is calibrated with that of the Spectralon panel, the backscattered reflectance values are decreased for bright and low optical density targets such as needles. The backscattered reflectance values produced by the LiDAR do not strictly follow the definition of reflectance factor for three reasons: First, due to hot spot effect (Hapke, 1993), the 99% Spectralon is not a Lambertian surface in backscattering direction causing systematic error in the reflectance values. Second, the illuminated surface area of the target is not constant (as in the definition of reflectance factor) and this results in uncertainty in the returned intensity. Third, part of the transmitted light is lost outside the sensor field of view due to multiple scattering, as described above. Despite these limitations, the backscattered reflectance is a practical quantity providing intensity readings independent of measurement distance. For most applications, the backscattered reflectance spectra can be exploited similarly to traditional reflectance factors (e.g., in the computation and comparison of spectral indices), but caution should be used when accurate absolute values are needed. Different vegetation indices can be obtained from the measured dataset. For this study we selected Normalized Difference Vegetation Index (NDVI) (Tucker, 1979), water concentration index (Penuelas et al, 1993) and Modified Chlorophyll Absorption Ratio Index (MCARII) (Haboudane et al., 2004). In Fig. 5, these indices have been applied to the measured dataset of the spruce. Fig. 5. Different spectral indices are calculated for 5 cm voxels and the full point cloud is colored according to the result.

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