Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species

Moffiet, Trevor; Mengersen, Kerrie; Witte, Christian; King, Robert; Denham, Robert

doi:10.1016/j.isprsjprs.2005.05.002

Cited by 106 publications

(83 citation statements)

References 14 publications

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“…Recently, there have been a few attempts to use LiDAR intensities in glacier monitoring, land cover classification, and forest type identification [9], [13], [36], [40], [47]. However, it should be noted that the backscattered LiDAR intensity values are a function of many other variables in additional to the material characteristics of the targets hit by the laser beam.…”

Section: Lidar Systemsmentioning

confidence: 99%

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

et al. 2007

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Orthophotos (or orthoimages if in digital form) have long been recognised as a supplement or alternative to standard maps. The increasing applications of orthoimages require efforts to ensure the accuracy of produced orthoimages. As digital photogrammetry technology has reached a stage of relative maturity and stability, the availability of high quality ground control points (GCPs) and digital elevation models (DEMs) becomes the central issue for successfully implementing an image orthorectification project. Concerns with the impacts of the quality of GCPs and DEMs on the quality of orthoimages inspire researchers to look for more reliable approaches to acquire high quality GCPs and DEMs for orthorectification. Light Detection and Ranging (LiDAR), an emerging technology, offers capability of capturing high density three dimensional points and generating high accuracy DEMs in a fast and cost-effective way. Nowadays, highly developed computer technologies enable rapid processing of huge volumes of LiDAR data. This leads to a great potential to use LiDAR data to get high quality GCPs and DEMs to improve the accuracy of orthoimages. This paper presents methods for utilizing LiDAR intensity images to collect high accuracy ground coordinates of GCPs and for utilizing LiDAR data to generate a high quality DEM for digital photogrammetry and orthorectification processes. A comparative analysis is also presented to assess the performance of proposed methods. The results demonstrated the feasibility of using LiDAR intensity image-based GCPs and the LiDAR-derived DEM to produce high quality orthoimages.

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Section: Lidar Systemsmentioning

confidence: 99%

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

et al. 2007

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“…The 3D attributes of discrete-return single frequency Li-DAR have been used to classify open water and wetland surfaces (Hogg and Todd 2007;Goodale et al 2007;Crasto et al 2015); forested land cover types and species (Moffiet et al 2005;Antonarakis et al 2008;Vaukonen et al 2010;Korpela et al 2010). LiDAR 3D derivatives have also been combined with passive multispectral data in forest environment classifications (Koukoulas and Blackburn 2005;Holmgren et al 2008;Ke et al 2010;Millard and Richardson 2013;Yang et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…LiDAR 3D derivatives have also been combined with passive multispectral data in forest environment classifications (Koukoulas and Blackburn 2005;Holmgren et al 2008;Ke et al 2010;Millard and Richardson 2013;Yang et al 2014). Traditionally, however, single-frequency LiDAR signal intensity alone (i.e., through intensity thresholding) has not been considered of high value for species classification due to the many controls, in addition to surface reflectance, that influence intensity values (Moffiet et al 2005;Hopkinson 2007). When 3D and intensity responses are considered together, forest species classification improves .…”

Section: Introductionmentioning

confidence: 99%

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Hopkinson

Chasmer

Gynan³

et al. 2016

Canadian Journal of Remote Sensing

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Abstract. Airborne LiDAR is increasingly used in forest carbon, ecosystem, and resource monitoring. For practical design and manufacture reasons, the 1064 nm near-infrared (NIR) wavelength has been the most commonly adopted, and most literature in this field represents sampling characteristics in this wavelength. However, due to eye-safety and application-specific needs, other common wavelengths are 1550 nm and 532 nm. All provide canopy structure reconstructions that can be integrated or compared through space and time but the consistency or complementarity of 3D airborne LiDAR data sampled at multiple wavelengths is poorly understood. Here, we report on multispectral LiDAR missions carried out in 2013 and 2015 over a managed forest research site. The 1st used 3 independent sensors, and the 2nd used a single sensor carrying 3 lasers. The experiment revealed differences in proportions of returns at ground level, vertical foliage distributions, and gap probability across wavelengths. Canopy attenuation was greatest at 532 nm, presumably due to leaf tissue absorption. Relative to 1064 nm, foliage was undersampled at midheight percentiles at 1550 nm and 532 nm. Multisensor data demonstrated differences in foliage characterization due to combined influences of wavelength and acquisition configuration. Single-sensor multispectral data were more stable but demonstrated clear wavelength-dependent variation that could be exploited in intensity-based land cover classification without the aid of 3D derivatives. This work sets the stage for improvements in land surface classification and vertical foliage partitioning through the integration of active spectral and structural laser return information.

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“…Vrhove posameznih dreves v gozdnem sestoju lahko ločimo med seboj z različnimi algoritmi, ki iščejo lokalne maksimume v surovih podatkih ali na digitalnih modelih krošenj (Reitberger et al, 2008;Kim et al, 2009;Li et al, 2012;Mongus in Žalik, 2015). Če k preučevanju geometrijskih odvisnosti med točkami dodamo še preučevanje intenzitete odbitih laserskih točk, lahko raziskave razširimo na preučevanje zmožnosti določitve posameznih drevesih vrst v laserskih podatkih (Holmgren in Persson, 2004;Moffiet et al, 2005;Kim et al, 2009;Ørka et al, 2009;Korpela et al, 2010). Ker je intenziteta laserskih točk odvisna od različnih parametrov, ta naloga še zdaleč ni enostavna in rešena.…”

Section: Uvodunclassified

Deciduous and coniferous tree separation based on the raw intensity of laser points

Čekada¹,

Lavrič²,

Fras³

2017

geod. vestn.

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Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees or forest stands according to species

Cited by 106 publications

References 14 publications

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

LiDAR-Derived High Quality Ground Control Information and DEM for Image Orthorectification

Multisensor and Multispectral LiDAR Characterization and Classification of a Forest Environment

Deciduous and coniferous tree separation based on the raw intensity of laser points

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