Finnish Geodetic Institute

Finland

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Finland
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Liang X.,Finnish Geodetic Institute | Kankare V.,University of Helsinki | Yu X.,Finnish Geodetic Institute | Hyyppa J.,Finnish Geodetic Institute | Holopainen M.,University of Helsinki
IEEE Transactions on Geoscience and Remote Sensing | Year: 2014

This paper reports on a study of measuring stem curves of standing trees of different species and in different growth stages using terrestrial laser scanning (TLS). Pine and spruce trees are scanned using the multiscan approach in the field, and trees are felled to measure them destructively for the purpose of obtaining reference values. The stem curves are automatically retrieved from laser point clouds, resulting in an accuracy of ∼1 cm. The corresponding manual measurements yield similar accuracy but fewer measurements at the upper parts of tree stems, compared with the automated measurements. The stem volumes based on stem curve data and field measurements and the best Finnish national allometric volume equations (using tree species, height, and diameters at heights of 1.3 and 6 m as predictors) result in similar accuracy. The measurement accuracy of the stem curves and stem volumes is similar for both pine and spruce trees. The results of this paper confirm the feasibility of using TLS to produce stem curve data in an automated, accurate and noninvasive way and indicate that the point cloud provides adequate information to accurately derive stem volumes from standing trees. The stem curves and volumes retrieved from point clouds can be employed in various forest management activities, such as the calibration of national or regional allometric curve functions and the prediction of profits in preharvest inventories. © 2013 IEEE.


Hellesen T.,Copenhagen University | Matikainen L.,Finnish Geodetic Institute
Remote Sensing | Year: 2013

Due to the abandonment of former agricultural management practices such as mowing and grazing, an increasing amount of grassland is no longer being managed. This has resulted in increasing shrub encroachment, which poses a threat to a number of species. Monitoring is an important means of acquiring information about the condition of the grasslands. Though the use of traditional remote sensing is an effective means of mapping and monitoring land cover, the mapping of small shrubs and trees based only on spectral information is challenged by the fact that shrubs and trees often spectrally resemble grassland and thus cannot be safely distinguished and classified. With the aid of LiDAR-derived information, such as elevation, the classification of spectrally similar objects can be improved. In this study, we applied high point density LiDAR data and colour-infrared orthoimages for the classification of shrubs and trees in a study area in Denmark. The classification result was compared to a classification based only on colour-infrared orthoimages. The overall accuracy increased significantly with the use of LiDAR and, for shrubs and trees specifically, producer's accuracy increased from 81.2% to 93.7%, and user's accuracy from 52.9% to 89.7%. Object-based image analysis was applied in combination with a CART classifier. The potential of using the applied approach for mapping and monitoring of large areas is discussed. © 2013 by the authors.


Jiang M.,Bureau of Mineral Resources | Lin Y.,Finnish Geodetic Institute
IEEE Geoscience and Remote Sensing Letters | Year: 2013

This study proposed and tested a multistep method for the recognition of individual deciduous trees in leaf-off aerial ultrahigh spatial resolution remotely sensed (UHSRRS) imagery. This topic has received limited coverage in previous endeavors, which focused mainly on the detection and delineation of coniferous trees in remotely sensed images with relatively lower spatial resolutions. Thus, the traditional algorithms tend to fail in case of the referred scenario. In order to fill this technical gap, an algorithm that joins mathematical morphological operations and marker-controlled watershed segmentation was first assumed for the extraction of single trees in UHSRRS images. Next, a distribution-free support vector machine (SVM) classifier was applied to distinguish the extracted segments as deciduous or coniferous trees, merely in terms of two newly-derived morphological features. Experimental evaluations indicated that the integral solution plan can extract and classify the deciduous and coniferous trees in the leaf-off aerial UHSRRS images of local dense forest for test with correctness over 92% and 70%, respectively. Overall, the recognition results with >66% correctness have primarily validated the proposed technique. © 2004-2012 IEEE.


Zhu L.,Finnish Geodetic Institute | Hyyppa J.,Finnish Geodetic Institute
Remote Sensing | Year: 2014

This paper presents methods for 3D modeling of railway environments from airborne laser scanning (ALS) and mobile laser scanning (MLS). Conventionally, aerial data such as ALS and aerial images were utilized for 3D model reconstruction. However, 3D model reconstruction only from aerial-view datasets can not meet the requirement of advanced visualization (e.g., walk-through visualization). In this paper, objects in a railway environment such as the ground, railroads, buildings, high voltage powerlines, pylons and so on were reconstructed and visualized in real-life experiments in Kokemaki, Finland. Because of the complex terrain and scenes in railway environments, 3D modeling is challenging, especially for high resolution walk-through visualizations. However, MLS has flexible platforms and provides the possibility of acquiring data in a complex environment in high detail by combining with ALS data to produce complete 3D scene modeling. A procedure from point cloud classification to 3D reconstruction and 3D visualization is introduced, and new solutions are proposed for object extraction, 3D reconstruction, model simplification and final model 3D visualization. Image processing technology is used for the classification, 3D randomized Hough transformations (RHT) are used for the planar detection, and a quadtree approach is used for the ground model simplification. The results are visually analyzed by a comparison with an orthophoto at a 20 cm ground resolution. © 2014 by the authors; licensee MDPI, Basel, Switzerland.


Honkavaara E.,Finnish Geodetic Institute | Litkey P.,Finnish Geodetic Institute | Nurminen K.,Finnish Geodetic Institute
Remote Sensing | Year: 2013

Climate change has increased the occurrence of heavy storms that cause damage to forests. After a storm, it is necessary to obtain knowledge about the injured trees quickly in order to detect and aid in collecting the fallen trees and estimate the total damage. The objective in this study was to develop an automatic method for storm damage detection based on comparisons of digital surface models (DSMs), where the after-storm DSM was derived by automatic image matching using high-altitude photogrammetric imagery. This DSM was compared to a before-storm DSM, which was computed using national airborne laser scanning (ALS) data. The developed method was tested using imagery collected in extreme illumination conditions after winter storms on 8 January 2012 in Finland. The image matching yielded a high-quality surface model of the forest areas, which were mainly coniferous and mixed forests. The entire set of major damage forest test areas was correctly classified using the method. Our results showed that airborne, high-altitude photogrammetry is a promising tool for automating the detection of forest storm damage. With modern photogrammetric cameras, large areas can be collected efficiently, and the imagery also provides visual, stereoscopic support for various forest storm damage management tasks. Developing methods that work in different seasons are becoming more important, due to the increase in the number of natural disasters. © 2013 by the authors; licensee MDPI, Basel, Switzerland.


Lin Y.,Finnish Geodetic Institute | Hyyppa J.,Finnish Geodetic Institute
IEEE Transactions on Geoscience and Remote Sensing | Year: 2012

This paper presents a novel attempt at combining the mobile mapping mode and a multiecho-recording laser scanner, as well as a new methodology based on the resulting single-scan point clouds, for enhancing the integrity of individual tree crown reconstruction. The motive stemmed from the widespread but hard-to-reach demand in precision forestry, i.e., efficiently acquiring the integral 3-D structures of single crowns via single-scan light detection and ranging (LiDAR) surveys. For this task, aerospace and aerial LiDAR is generally subject to low sampling density, and static terrestrial LiDAR is restricted to high relocation cost. As a state-of-the-art mapping technology, mobile laser scanning (MLS) can somehow overcome these limitations owing to its strengths of high sampling density and moving efficiency. However, its laser emissions, even from the incorporated peculiar scanner, still suffer from leaf/branch occlusions. To address this challenge, mirroring the half crowns facing the MLS system to the other sides can be assumed as a solution strategy, in a point of view different from typically strengthening laser transmission penetrability. In the case of no multiscans available, this plan turns out to be unstable due to the shortage of reference data. For this issue, this study further expands the roles of multiechoes beyond penetration, namely, also as the self-indicators for correcting the mirrored half crowns. Quantitative evaluation about the integrity of reconstruction in terms of crown outer surface was conducted based on the sample trees, which were measured by the multiecho-recording MLS and a static terrestrial LiDAR from two opposite sides. The promising results basically validated the proposed technique. © 1980-2012 IEEE.


Hakala T.,Finnish Geodetic Institute | Suomalainen J.,Finnish Geodetic Institute | Kaasalainen S.,Finnish Geodetic Institute | Chen Y.,Finnish Geodetic Institute
Optics Express | Year: 2012

We present the design of a full waveform hyperspectral light detection and ranging (LiDAR) and the first demonstrations of its applications in remote sensing. The novel instrument produces a 3D point cloud with spectral backscattered reflectance data. This concept has a significant impact on remote sensing and other fields where target 3D detection and identification is crucial, such as civil engineering, cultural heritage, material processing, or geomorphological studies. As both the geometry and spectral information on the target are available from a single measurement, this technology will extend the scope of imaging spectroscopy into spectral 3D sensing. To demonstrate the potential of the instrument in the remote sensing of vegetation, 3D point clouds with backscattered reflectance and spectral indices are presented for a specimen of Norway spruce. © 2012 Optical Society of America.


Rosnell T.,Finnish Geodetic Institute | Honkavaara E.,Finnish Geodetic Institute
Sensors | Year: 2012

The objective of this investigation was to develop and investigate methods for point cloud generation by image matching using aerial image data collected by quadrocopter type micro unmanned aerial vehicle (UAV) imaging systems. Automatic generation of high-quality, dense point clouds from digital images by image matching is a recent, cutting-edge step forward in digital photogrammetric technology. The major components of the system for point cloud generation are a UAV imaging system, an image data collection process using high image overlaps, and post-processing with image orientation and point cloud generation. Two post-processing approaches were developed: one of the methods is based on Bae Systems' SOCET SET classical commercial photogrammetric software and another is built using Microsoft®'s Photosynth™ service available in the Internet. Empirical testing was carried out in two test areas. Photosynth processing showed that it is possible to orient the images and generate point clouds fully automatically without any a priori orientation information or interactive work. The photogrammetric processing line provided dense and accurate point clouds that followed the theoretical principles of photogrammetry, but also some artifacts were detected. The point clouds from the Photosynth processing were sparser and noisier, which is to a large extent due to the fact that the method is not optimized for dense point cloud generation. Careful photogrammetric processing with self-calibration is required to achieve the highest accuracy. Our results demonstrate the high performance potential of the approach and that with rigorous processing it is possible to reach results that are consistent with theory. We also point out several further research topics. Based on theoretical and empirical results, we give recommendations for properties of imaging sensor, data collection and processing of UAV image data to ensure accurate point cloud generation. © 2012 by the authors.


Lin Y.,Finnish Geodetic Institute | Hyyppa J.,Finnish Geodetic Institute
IEEE Geoscience and Remote Sensing Letters | Year: 2011

A novel κ-segments-based 2-D geometric modeling schematic is proposed for characterizing the scan lines of vehicle-based laser scanning (VLS) with just a few geometric primitives. VLS has been developing quickly as a new research focus recently, but the relevant data processing techniques lag behind the system establishment due to the associated huge standwise point clouds collected in a real 3-D sense. To solve this issue, the often-assumed sampling mode based on scan lines suggests an alternative frame for exploring new efficient methodologies in diverse applications. As we know, principal segments can reflect the morphological signatures of the scatter-point-represented objects, and besides, profiles comprised by various open and close outlines can be geometrically modeled by line segments and ellipses, respectively. By combining these two merits, the $k$-segments-based geometric modeling algorithm can be constructed, which segments and fits the center-clustered points, e.g., in crowns, into ellipses and the line-arranged points, e.g., on walls, into line segments. Eventually, the experiments based on real VLS data primarily validate this new algorithm. © 2010 IEEE.


Lin Y.,Finnish Geodetic Institute | Hyyppa J.,Finnish Geodetic Institute | Jaakkola A.,Finnish Geodetic Institute
IEEE Geoscience and Remote Sensing Letters | Year: 2011

Light detection and ranging (LIDAR) systems based on unmanned aerial vehicles (UAVs) recently are in rapid advancement, while mini-UAV-borne laser scanning has few reported progress, notwithstanding so extensively required. This study established a pioneered mini-UAV-borne LIDAR systemSensei, schematically with an Ibeo Lux scanner mounted on a small Align T-Rex 600E helicopter. Furthermore, the associated data processing involved in the coordinate triple, pulse intensity, and multiechoes per pulse was explored to validate its applicability for fine-scale mapping, in terms of, e.g., tree height estimation, pole detection, road extraction, and digital terrain model refinement. The feasibility and advantages of mini-UAV-borne LIDAR have been demonstrated by the promising results based on the real-measured data. © 2010 IEEE.

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