Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests

Hyyppä, Juha; Hyyppä, Hannu; Leckie, Donald G.; Gougeon, François A.; Yu, Xiaowei; Maltamo, Matti

doi:10.1080/01431160701736489

Cited by 545 publications

(364 citation statements)

References 64 publications

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“…The Swedish National Forest Inventory (NFI) carries out an inventory on about 11,000 field plots [5], annually, in Sweden. In combination with remote sensing data from, e.g., airborne laser scanning (ALS), accurate estimations of the current state of the forest can be obtained [6]. It is established that ALS can provide very accurate estimates of heights and forest related variables [7][8][9] and can be used for creating digital terrain models (DTMs) to describe ground elevation.…”

Section: Introductionmentioning

confidence: 99%

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Persson

Fransson

2014

Remote Sensing

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Abstract:The last decade has seen launches of radar satellite missions operating in X-band with the sensors acquiring images with spatial resolutions on the order of 1 m. This study uses digital surface models (DSMs) extracted from stereo synthetic aperture radar images and a reference airborne laser scanning digital terrain model to calculate the above-ground biomass and tree height. The resulting values are compared to in situ data. Analyses were undertaken at the Swedish test sites Krycklan (64°N) and Remningstorp (58°N), which have different site conditions. The results showed that, for 459 forest stands in Remningstorp, biomass estimation at the stand level could be performed with 22.9% relative root mean square error, while the height estimation showed 9.4%. Many factors influenced the results and it was found that the topography has a significant effect on the generated DSMs and should therefore be taken into consideration when the stand level mean slope is above four degrees. Different tree species did not have a major effect on the models during leaf-on conditions. Moreover, correct estimation within young forest stands was problematic. The intersection angles resulting in the best results were in the range 8-16°. Based on the results in this study, radargrammetry appears to be a promising potential remote sensing technique for future forest applications.

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Section: Introductionmentioning

confidence: 99%

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Persson

Fransson

2014

Remote Sensing

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“…Wehr and Lohr (1999) provide further details regarding physical and technical aspects of airborne lidar and also describe various scanning mechanisms. Hyyppä et al (2008) provide an overview of small-footprint airborne lidar systems and their applications in forest research. Lim et al (2003) and Naesset et al (2004) report experiences with a broader suite of airborne lidar systems for forest applications.…”

Section: Full Waveform Emitted Laser Pulsementioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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“…Likewise high resolution 3D-information is needed in many scientific fields and essential for accurate mapping of vegetation biomass [Frolking et al, 2009], habitat quality [Vierling et al, 2008] or carbon storage [Asner, 2009]. To obtain this three-dimensional information Lidar is widely and commercially used in forest inventories [Hyyppä et al, 2008]. However, aerial laser scanning (ALS) exhibits high area coverage, but relatively high costs and lower point densities, whereas the latter is limiting tree detection accuracy [Tesfamichael et al, 2009].…”

Section: Dense 3d Reconstruction For Forest Stand Parametersmentioning

confidence: 99%

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

Fritz

Kattenborn

Koch

2013

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

138

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ABSTRACT:In both ecology and forestry, there is a high demand for structural information of forest stands. Forest structures, due to their heterogeneity and density, are often difficult to assess. Hence, a variety of technologies are being applied to account for this "difficult to come by" information. Common techniques are aerial images or ground-and airborne-Lidar. In the present study we evaluate the potential use of unmanned aerial vehicles (UAVs) as a platform for tree stem detection in open stands. A flight campaign over a test site near Freiburg, Germany covering a target area of 120 × 75[m 2 ] was conducted. The dominant tree species of the site is oak (quercus robur) with almost no understory growth. Over 1000 images with a tilt angle of 45°were shot. The flight pattern applied consisted of two antipodal staggered flight routes at a height of 55[m] above the ground. We used a Panasonic G3 consumer camera equipped with a 14 − 42[mm] standard lens and a 16.6 megapixel sensor. The data collection took place in leaf-off state in April 2013. The area was prepared with artificial ground control points for transformation of the structure-from-motion (SFM) point cloud into real world coordinates. After processing, the results were compared with a terrestrial laser scanner (TLS) point cloud of the same area. In the 0.9[ha] test area, 102 individual trees above 7[cm] diameter at breast height were located on in the TLS-cloud. We chose the software CMVS/PMVS-2 since its algorithms are developed with focus on dense reconstruction. The processing chain for the UAV-acquired images consists of six steps: a. cleaning the data: removing of blurry, under-or over exposed and off-site images; b. applying the SIFT operator [Lowe, 2004]; c. image matching; d. bundle adjustment; e. clustering; and f. dense reconstruction. In total, 73 stems were considered as reconstructed and located within one meter of the reference trees. In general stems were far less accurate and complete as in the TLS-point cloud. Only few stems were considered to be fully reconstructed. From the comparison of reconstruction achievement with respect to height above ground, we can state that reconstruction accuracy decreased in the crown layer of the stand. In addition we were cutting 50[cm] slices in z-direction and applied a robust cylinder fit to the stem slices. Radii of the TLS-cloud and the SFM-cloud surprisingly correlated well with a Pearson's correlation coefficient of r = 0.696. This first study showed promising results for UAV-based forest structure modelling. Yet, there is a demand for additional research with regard to vegetation stages, flight pattern, processing setup and the utilisation of spectral information.

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Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forests

Cited by 545 publications

References 64 publications

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Forest Variable Estimation Using Radargrammetric Processing of TerraSAR-X Images in Boreal Forests

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Uav-Based Photogrammetric Point Clouds – Tree Stem Mapping in Open Stands in Comparison to Terrestrial Laser Scanner Point Clouds

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