scholarly journals Comparing Filtering Techniques for Removing Vegetation from UAV-Based Photogrammetric Point Clouds

Drones ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 61 ◽  
Author(s):  
Niels Anders ◽  
João Valente ◽  
Rens Masselink ◽  
Saskia Keesstra
Keyword(s):  
Vegetation Index ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Mediterranean Area ◽  
Alternative Methods ◽  
Terrain Model ◽  
Digital Terrain ◽  
Filter Methods ◽  
Digital Elevation ◽  
Filtering Techniques

Digital Elevation Models (DEMs) are 3D representations of the Earth’s surface and have numerous applications in geomorphology, hydrology and ecology. Structure-from-Motion (SfM) photogrammetry using photographs obtained by unmanned aerial vehicles (UAVs) have been increasingly used for obtaining high resolution DEMs. These DEMs are interpolated from point clouds representing entire landscapes, including points of terrain, vegetation and infrastructure. Up to date, there has not been any study clearly comparing different algorithms for filtering of vegetation. The objective in this study was, therefore, to assess the performance of various vegetation filter algorithms for SfM-obtained point clouds. The comparison was done for a Mediterranean area in Murcia, Spain with heterogeneous vegetation cover. The filter methods that were compared were: color-based filtering using an excessive greenness vegetation index (VI), Triangulated Irregular Networks (TIN) densification from LAStools, the standard method in Agisoft Photoscan (PS), iterative surface lowering (ISL), and a combination of iterative surface lowering and the VI method (ISL_VI). Results showed that for bare areas there was little to no difference between the filtering methods, which is to be expected because there is little to no vegetation present to filter. For areas with shrubs and trees, the ISL_VI and TIN method performed best. These results show that different filtering techniques have various degrees of success in different use cases. A default filter in commercial software such as Photoscan may not always be the best way to remove unwanted vegetation from a point cloud, but instead alternative methods such as a TIN densification algorithm should be used to obtain a vegetation-less Digital Terrain Model (DTM).

scholarly journals Detection of Levee Damage Based on UAS Data—Optical Imagery and LiDAR Point Clouds

2020 ◽  
Vol 9 (4) ◽  
pp. 248
Author(s):  
Krzysztof Bakuła ◽  
Magdalena Pilarska ◽  
Adam Salach ◽  
Zdzisław Kurczyński
Keyword(s):  
Laser Scanning ◽  
Vegetation Index ◽  
Laser Scanner ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Optical Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Levee Failure ◽  
Multi Temporal

This paper presents a methodology for levee damage detection based on Unmanned Aerial System (UAS) data. In this experiment, the data were acquired from the UAS platform, which was equipped with a laser scanner and a digital RGB (Red, Green, Blue) camera. Airborne laser scanning (ALS) point clouds were used for the generation of the Digital Terrain Model (DTM), and images were used to produce the RGB orthophoto. The main aim of the paper was to present a methodology based on ALS and vegetation index from RGB orthophoto which helps in finding potential places of levee failure. Both types of multi-temporal data collected from the UAS platform are applied separately: elevation and optical data. Two DTM models from different time periods were compared: the first one was generated from the ALS point cloud and the second DTM was delivered from the UAS Laser Scanning (ULS) data. Archival and new orthophotos were converted to Green-Red Vegetation Index (GRVI) raster datasets. From the GRVI raster, change detection for unvegetation ground areas was analysed using a dynamically indicated threshold. The result of this approach is the localisation of places, for which the change in height correlates with the appearance of unvegetation ground. This simple, automatic method provides a tool for specialist monitoring of levees, the critical objects protecting against floods.

scholarly journals Automated Generation of Digital Terrain Model using Point Clouds of Digital Surface Model in Forest Area

2011 ◽  
Vol 3 (5) ◽  
pp. 845-858 ◽  
Author(s):  
Kande R.M.U. Bandara ◽  
Lal Samarakoon ◽  
Rajendra P. Shrestha ◽  
Yoshikazu Kamiya
Keyword(s):  
Digital Terrain Model ◽  
Point Clouds ◽  
Forest Area ◽  
Surface Model ◽  
Digital Surface Model ◽  
Automated Generation ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals Photogrammetric surveying forests and woodlands with UAVs: techniques for automatic removal of vegetation and digital terrain model production for hydrological applications

2019 ◽  
Vol 7 (1) ◽  
pp. 1-20
Author(s):  
Fotis Giagkas ◽  
Petros Patias ◽  
Charalampos Georgiadis
Keyword(s):  
Vegetation Type ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Aerial Images ◽  
Surface Model ◽  
Terrain Model ◽  
Vegetation Height ◽  
Digital Terrain ◽  
Dense Point ◽  
Height Model

The purpose of this study is the photogrammetric survey of a forested area using unmanned aerial vehicles (UAV), and the estimation of the digital terrain model (DTM) of the area, based on the photogrammetrically produced digital surface model (DSM). Furthermore, through the classification of the height difference between a DSM and a DTM, a vegetation height model is estimated, and a vegetation type map is produced. Finally, the generated DTM was used in a hydrological analysis study to determine its suitability compared to the usage of the DSM. The selected study area was the forest of Seih-Sou (Thessaloniki). The DTM extraction methodology applies classification and filtering of point clouds, and aims to produce a surface model including only terrain points (DTM). The method yielded a DTM that functioned satisfactorily as a basis for the hydrological analysis. Also, by classifying the DSM–DTM difference, a vegetation height model was generated. For the photogrammetric survey, 495 aerial images were used, taken by a UAV from a height of ∼200 m. A total of 44 ground control points were measured with an accuracy of 5 cm. The accuracy of the aerial triangulation was approximately 13 cm. The produced dense point cloud, counted 146 593 725 points.

The Coupling Relationship between Forest Vegetation and Terrain of Daliuta Mine

2014 ◽  
Vol 641-642 ◽  
pp. 1191-1194 ◽  
Author(s):  
Dong Wen Liu ◽  
Zhi Yong Qiao ◽  
Ting Ting Wei ◽  
Shu Jiang ◽  
Ya Kai Chen ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Vegetation Coverage ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Object Use ◽  
Trend Data ◽  
Elevation Model ◽  
Digital Elevation Model Data ◽  
Evolution Trend

Taking Daliuta mine as research object, use its 2002, 2011 two same period Landsat TM/ ETM and remote sensing image as the data source, use pixel dichotomy to get its vegetation coverage evolution trend data; Use DEM digital elevation model data in the region to generate digital terrain model based on ArcGIS, and make overlay analysis with the vegetation coverage evolution trend data to study the relationship between the vegetation coverage and terrain factor of the mine area. The results showed that: From 2002 to 2011, the vegetation coverage evolution trend of Daliuta mining mainly moderate improvement and significantly improvement, and concentrated in middle altitude, low slope, sunny area.

scholarly journals Comparison of Digital Elevation Models by Visibility Analysis in Landscape

2016 ◽  
Vol 19 (2) ◽  
pp. 28-31
Author(s):  
Jozef Sedláček ◽  
Ondřej Šesták ◽  
Miroslava Sliacka
Keyword(s):  
Field Survey ◽  
Reference Model ◽  
Digital Terrain Model ◽  
Surface Model ◽  
Digital Surface Model ◽  
Visibility Analysis ◽  
Terrain Model ◽  
Digital Terrain ◽  
Model Average ◽  
Digital Elevation

Abstract The paper investigates suitability of digital surface model for visibility analysis in GIS. In experiment there were analysed viewsheds from 14 observer points calculated on digital surface model, digital terrain model and its comparison to field survey. Data sources for the investigated models were LiDAR digital terrain model and LiDAR digital surface model with vegetation distributed by the Czech Administration for Land Surveying and Cadastre. The overlay method was used for comparing accuracy of models and the reference model was LiDAR digital surface model. Average equalities in comparison with LiDAR digital terrain model, ZABAGED model and field survey were 15.5 %, 17.3% and 20.9%, respectively.

scholarly journals Application of the global SRTM and AW3D30 digital elevation models to mapping folds at cave sites

2021 ◽  
Vol 50 (1) ◽  
pp. 75-89
Author(s):  
Mark Abolins ◽  
Albert Ogden
Keyword(s):  
Cell Size ◽  
Forest Canopy ◽  
Digital Elevation Models ◽  
Digital Terrain Model ◽  
Surface Model ◽  
M Cell ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
The Mean

A novel method to map and quantitatively describe very gentle folds (limb dip <5°) at cratonic cave sites was evaluated at Snail Shell and Nanna caves, central Tennessee, USA. Elevations from the global SRTM digital terrain model (DTM) were assigned to points on late Ordovician geologic contacts, and the elevations of the points were used to interpolate 28 m cell size natural neighbor digital elevation models (DEM’s) of the contacts. The global Forest Canopy Height Dataset was subtracted from the global 28 m cell size AW3D30 digital surface model (DSM) to create a DTM, and that DTM was applied in the same way. Comparison of mean and modal strikes of the interpolated surfaces with mean and modal cave passage trend shows that many passages are sub-parallel to the trend of an anticline. WithiSn 500 m of the caves, the SRTM- and AW3D30-based interpolated surfaces have mean strikes within 8° of the mean strike of an interpolated reference surface created with a high resolution (~0.76 m cell size and 10 cm RMSE) Tennessee, USA LiDAR DTM. This evaluation shows that the SRTM- and AW3D30-based method has the potential to reveal a relationship between the trend of a fold, on one hand, and cave passages, on the other, at sites where a geologic contact varies in elevation by >35 m within an area of <12.4 km2 and the mean dip of bedding is >0.9°.

scholarly journals EVALUATING ERROR OF LIDAR DERIVED DEM INTERPOLATION FOR VEGETATION AREA

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 141-150 ◽  
Author(s):  
Z. Ismail ◽  
M. F. Abdul Khanan ◽  
F. Z. Omar ◽  
M. Z. Abdul Rahman ◽  
M. R. Mohd Salleh
Keyword(s):  
Spatial Resolution ◽  
Accuracy Assessment ◽  
Digital Terrain Model ◽  
Geographical Information ◽  
Interpolation Methods ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Slope Class

Light Detection and Ranging or LiDAR data is a data source for deriving digital terrain model while Digital Elevation Model or DEM is usable within Geographical Information System or GIS. The aim of this study is to evaluate the accuracy of LiDAR derived DEM generated based on different interpolation methods and slope classes. Initially, the study area is divided into three slope classes: (a) slope class one (0° – 5°), (b) slope class two (6° – 10°) and (c) slope class three (11° – 15°). Secondly, each slope class is tested using three distinctive interpolation methods: (a) Kriging, (b) Inverse Distance Weighting (IDW) and (c) Spline. Next, accuracy assessment is done based on field survey tachymetry data. The finding reveals that the overall Root Mean Square Error or RMSE for Kriging provided the lowest value of 0.727 m for both 0.5 m and 1 m spatial resolutions of oil palm area, followed by Spline with values of 0.734 m for 0.5 m spatial resolution and 0.747 m for spatial resolution of 1 m. Concurrently, IDW provided the highest RMSE value of 0.784 m for both spatial resolutions of 0.5 and 1 m. For rubber area, Spline provided the lowest RMSE value of 0.746 m for 0.5 m spatial resolution and 0.760 m for 1 m spatial resolution. The highest value of RMSE for rubber area is IDW with the value of 1.061 m for both spatial resolutions. Finally, Kriging gave the RMSE value of 0.790m for both spatial resolutions.

scholarly journals Geoinformation technologies in the process of terrain slope detection in the area of Fruska Gora with reference to potential development of slope processes

2012 ◽  
Vol 92 (4) ◽  
pp. 51-62
Author(s):  
Ivana Badnjarevic ◽  
Miro Govedarica ◽  
Dusan Jovanovic ◽  
Vladimir Pajic ◽  
Aleksandar Ristic
Keyword(s):  
High Risk ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Digital Terrain Model ◽  
Terrain Model ◽  
Digital Terrain ◽  
Geoinformation Technologies ◽  
Terrain Slope ◽  
Sensing Technology

This research aims to describe the analysis of geoinformation technologies and systems and its usage in detection of terrain slope with reference to timely detection and mapping sites with a high risk of slope movement and activation of landslides. Special attention is referred to the remote sensing technology and data acquisition. In addition to acquisition, data processing is performed: the production of digital terrain model, calculating of the vegetation index NDVI (Normalized Difference Vegetation Index) based on satellite image and analyses of pedology maps. The procedures of processing the satellite images in order to identify locations of high risk of slope processes are described. Several factors and identifiers are analyzed and used as input values in automatic processing which is performed through a unique algorithm. Research results are presented in raster format. The direction of further research is briefly defined.

scholarly journals Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2063 ◽  
Author(s):  
Zsuzsanna Szabó ◽  
Csaba Albert Tóth ◽  
Imre Holb ◽  
Szilárd Szabó
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Point Clouds ◽  
Threshold Value ◽  
Noise Filtering ◽  
Terrain Model ◽  
Digital Terrain ◽  
Natural Neighbor ◽  
Wide Range ◽  
Ground Point

Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

scholarly journals Horizon v0.11b

2018 ◽  
Vol 3 (2) ◽  
pp. 269-270 ◽  
Author(s):  
Andrew G. K. Smith
Keyword(s):  
Digital Terrain Model ◽  
Landscape Archaeology ◽  
The Sun ◽  
Mapping Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Model Mapping ◽  
Elevation Model ◽  
Rendering Techniques

Horizon is a Geographic Information System (GIS) tool designed for archaeoastronomers investigating alignments of prehistoric monuments with astronomical phenomena (for example, rising and setting of the Sun, Moon and stars). It gets its name from its primary function, calculating accurate horizon profiles using Digital Terrain Model/Digital Elevation Model mapping data. More generally, it is a landscape visualisation tool which can generate full 360° panoramic scenes using 3D rendering techniques which may have some applications in the field of landscape archaeology.

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