Digital terrain model on vegetated areas: Joint use of airborne lidar data and optical images (original) (raw)
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Digital Terrain Model generation using airborne LiDAR in a forested area of Galicia, Spain
Information on the shape and relief of the Earth's surface is essential for improving land management practices that promote more sustainable development. Such a need for information is even greater in regions with rough topography and a high percentage of woodland cover. In the last few years, Airborne Laser Scanning (ALS) technology has demonstrated that laser altimetry is a reliable technology for determining accurate Digital Terrain Models (DTM). This paper presents a method for filtering LiDAR data based on mathematical morphology that is capable of using point cloud data from both the first and last return to discriminate terrain points and to segment the objects in forested areas into low and high vegetation. A pilot project was conducted in a mountainous area of 4 km 2 covered by Eucalyptus globulus plantations. In the study area, 4 zones were differentiated according to land use in order to allow for better presentation and interpretation of results. To validate the results, more than 40 control plots were distributed over the study area. In general, the results obtained in the study were better than expected, considering the hilly nature of the study area, often covered by dense shrub layers. RMSE values in the range 0.12 m -0.27 m were obtained for the different zones studied, which reveals the suitability of the method for this type of data and this area. The inclusion of the first and last returns enabled an average increase of 27% in the number of terrain points, and guaranteed a final point density of 2 points/m 2 before interpolation.
Floresta e Ambiente, 2018
The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.
Algorithm for Extracting Digital Terrain Models under Forest Canopy from Airborne LiDAR Data
Remote Sensing, 2014
Extracting digital elevation models (DTMs) from LiDAR data under forest canopy is a challenging task. This is because the forest canopy tends to block a portion of the LiDAR pulses from reaching the ground, hence introducing gaps in the data. This paper presents an algorithm for DTM extraction from LiDAR data under forest canopy. The algorithm copes with the challenge of low data density by generating a series of coarse DTMs by using the few ground points available and using trend surfaces to interpolate missing elevation values in the vicinity of the available points. This process generates a cloud of ground points from which the final DTM is generated. The algorithm has been compared to two other algorithms proposed in the literature in three different test sites with varying degrees of difficulty. Results show that the algorithm presented in this paper is more tolerant to low data density compared to the other two algorithms. The results further show that with decreasing point density, the differences between the three algorithms dramatically increased from about 0.5 m to over 10 m.
Advances in Remote Sensing
Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and How to cite this paper: Asal, F.F.F. (2019) Comparative Analysis of the Digital Terrain Models Extracted from Airborne Li-DAR Point Clouds Using Different Filtering Approaches in Residential Landscapes.
Ground filtering and vegetation mapping using multi-return terrestrial laser scanning
ISPRS Journal of Photogrammetry and …, 2012
Discriminating laser scanner data points belonging to ground from points above-ground (vegetation or buildings) is a key issue in research. Methods for filtering points into ground and non-ground classes have been widely studied mostly on datasets derived from airborne laser scanners, less so for terrestrial laser scanners. Recent developments in terrestrial laser sensors (longer ranges, faster acquisition and multiple return echoes) has aroused greater interest for surface modelling applications. The downside of TLS is that a typical dataset has high variability in point density, with evident side-effects on processing methods and CPU-time. In this work we use a scan dataset from a sensor which returns multiple target echoes, in this case providing more than 70 million points on our study site. The area presents low, medium and high vegetation, undergrowth with varying density, as well as bare ground with varying morphology (i.e. very steep slopes as well as flat areas). We test an integrated work-flow for defining a terrain and surface model (DTM and DSM) and successively for extracting information on vegetation density and height distribution on such a complex environment. Attention was given to efficiency and speed of processing. The method consists on a first step which subsets the original points to define ground candidates by taking into account the ordinal return number and the amplitude. A custom progressive morphological filter (opening operation) is applied next, on ground candidate points using a multidimensional grid to account for the fallout in point density as a function of distance from scanner. Vegetation density mapping over the area is then estimated using a weighted ratio of point counts in the tri-dimensional space over each cell. The overall result is a pipeline for processing TLS points clouds with minimal user interaction, producing a Digital Terrain Model (DTM), a Digital Surface Model (DSM), a vegetation density map and a derived Canopy Height Model (CHM). These products are of high importance for many applications ranging from forestry to hydrology and geomorphology. Ó
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, 2017
Developments in airborne LiDAR data acquisition have provided better horizontal and vertical ground information in the form of 3D point clouds. This has led to satisfactory results of LiDAR-derived digital terrain models (DTMs), also across complex ecosystems like natural forest stands. However, data and site-driven factors such as spatial resolution (point density), topography (slope and aspect), and variation in forest habitat types affect the DTM accuracy. In addition, processing steps like ground filtering and interpolation of ground points may also result in differences in DTM quality. Here, a comparative study was designed by extracting DTMs from two LiDAR data sources (high-and low-density point clouds) and three ground-filtering algorithms (adaptive TIN algorithm with and without the use of mirror points as well as an interpolation-based algorithm). The accuracy of the DTMs was assessed in association with terrain parame-B Raja Ram Aryal
Delineation of forests based on airborne LIDAR data
2012
• This paper evaluates a new approach for the automatic delineation of forested areas, based on airborne laserscanning data and criterions of the forest definition of the Austrian national forest inventory.
DEM generation from lidar data in wooded mountain areas by cross-section-plane analysis
International Journal of Remote Sensing, 2014
Ground filtering for airborne lidar data is a challenging task for the generation of digital terrain models (DTMs) in wooded mountain areas. To solve this problem, this article, based on cross-section-plane (CSP) analysis, presents a CSP-based stepwise filtering strategy that can automatically separate terrain from non-terrain points. The filtering strategy consists of four main computing steps: (a) 'split'the raw lidar data are partitioned into 3D cells, in each of which multi-directional CSPs are generated at multiple directions; (b) 'filter'the potential terrain points are selected for each CSP according to lidar data characteristics, such as multi-returns, intensity, and height; (c) 'detect'the initial terrain points are detected for each CSP by exploring distances and slopes between nearby points; and (d) 'adjust-and-refine'the terrain points are extracted from all initial terrain points of all CSPs by a merging-or-intersecting strategy and a five-point refinement. The extensive experiments using three lidar data sets demonstrated that the CSP-based stepwise filtering method is capable of producing reliable DTMs in densely forested mountain areas.