scholarly journals Synthesis of Leaf-on and Leaf-off Unmanned Aerial Vehicle (UAV) Stereo Imagery for the Inventory of Aboveground Biomass of Deciduous Forests

2019 ◽  
Vol 11 (7) ◽  
pp. 889 ◽  
Author(s):  
Wenjian Ni ◽  
Jiachen Dong ◽  
Guoqing Sun ◽  
Zhiyu Zhang ◽  
Yong Pang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Point Cloud ◽  
Forest Canopy ◽  
Deciduous Forest ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Canopy Height ◽  
Surface Model ◽  
Lidar Data ◽  
Stereo Imagery

Applications of stereo imagery acquired by cameras onboard unmanned aerial vehicles (UAVs) as practical forest inventory tools are hindered by the unavailability of ground surface elevation. It is still a challenging issue to remove the elevation of ground surface in leaf-on stereo imagery to extract forest canopy height without the help of lidar data. This study proposed a method for the extraction of forest canopy height through the synthesis of UAV stereo imagery of leaf-on and leaf-off, and further demonstrated that the extracted forest canopy height could be used for the inventory of deciduous forest aboveground biomass (AGB). The points cloud of the leaf-on and leaf-off stereo imagery was firstly extracted by an algorithm of structure from motion (SFM) using the same ground control points (GCP). The digital surface model (DSM) was produced by rasterizing the point cloud of UAV leaf-on. The point cloud of UAV leaf-off was processed by iterative median filtering to remove vegetation points, and the digital terrain model (DTM) was generated by the rasterization of the filtered point cloud. The mean canopy height model (MCHM) was derived from the DSM subtracted by the DTM (i.e., DSM-DTM). Forest AGB maps were generated using models developed based on the MCHM and sampling plots of forest AGB and were evaluated by those of lidar. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from lidar data with R2 higher than 0.94 and RMSE lower than 10.0 Mg/ha (i.e., relative RMSE 18.8%). These results demonstrated that UAV stereo imagery could be used as a practical inventory tool for deciduous forest AGB.

scholarly journals ABOVEGROUND BIOMASS ESTIMATION USING RECONSTRUCTED FEATURE OF AIRBORNE DISCRETE-RETURN LIDAR BY AUTO-ENCODER NEURAL NETWORK

2018 ◽  
Vol XLII-3 ◽  
pp. 943-948
Author(s):  
T. Li ◽  
Z. Wang ◽  
J. Peng
Keyword(s):  
Neural Network ◽  
Aboveground Biomass ◽  
Point Cloud ◽  
Forest Canopy ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Height Distribution ◽  
Prediction Ability ◽  
Great Ability ◽  
Canopy Area

Aboveground biomass (AGB) estimation is critical for quantifying carbon stocks and essential for evaluating carbon cycle. In recent years, airborne LiDAR shows its great ability for highly-precision AGB estimation. Most of the researches estimate AGB by the feature metrics extracted from the canopy height distribution of the point cloud which calculated based on precise digital terrain model (DTM). However, if forest canopy density is high, the probability of the LiDAR signal penetrating the canopy is lower, resulting in ground points is not enough to establish DTM. Then the distribution of forest canopy height is imprecise and some critical feature metrics which have a strong correlation with biomass such as percentiles, maximums, means and standard deviations of canopy point cloud can hardly be extracted correctly. In order to address this issue, we propose a strategy of first reconstructing LiDAR feature metrics through Auto-Encoder neural network and then using the reconstructed feature metrics to estimate AGB. To assess the prediction ability of the reconstructed feature metrics, both original and reconstructed feature metrics were regressed against field-observed AGB using the multiple stepwise regression (MS) and the partial least squares regression (PLS) respectively. The results showed that the estimation model using reconstructed feature metrics improved R<sup>2</sup> by 5.44&amp;thinsp;%, 18.09&amp;thinsp;%, decreased RMSE value by 10.06&amp;thinsp;%, 22.13&amp;thinsp;% and reduced RMSE<sub>cv</sub> by 10.00&amp;thinsp;%, 21.70&amp;thinsp;% for AGB, respectively. Therefore, reconstructing LiDAR point feature metrics has potential for addressing AGB estimation challenge in dense canopy area.

scholarly journals AUTOMATIC ROOFTOP EXTRACTION IN STEREO IMAGERY USING DISTANCE AND BUILDING SHAPE REGULARIZED LEVEL SET EVOLUTION

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 393-397 ◽  
Author(s):  
J. Tian ◽  
T. Krauß ◽  
P. d’Angelo
Keyword(s):  
Level Set ◽  
Active Contour Model ◽  
Digital Terrain Model ◽  
Surface Model ◽  
Building Shape ◽  
Fitting Procedure ◽  
Level Set Function ◽  
Stereo Imagery ◽  
3D Information ◽  
High Level

Automatic rooftop extraction is one of the most challenging problems in remote sensing image analysis. Classical 2D image processing techniques are expensive due to the high amount of features required to locate buildings. This problem can be avoided when 3D information is available. In this paper, we show how to fuse the spectral and height information of stereo imagery to achieve an efficient and robust rooftop extraction. In the first step, the digital terrain model (DTM) and in turn the normalized digital surface model (nDSM) is generated by using a newly step-edge approach. In the second step, the initial building locations and rooftop boundaries are derived by removing the low-level pixels and high-level pixels with higher probability to be trees and shadows. This boundary is then served as the initial level set function, which is further refined to fit the best possible boundaries through distance regularized level-set curve evolution. During the fitting procedure, the edge-based active contour model is adopted and implemented by using the edges indicators extracted from panchromatic image. The performance of the proposed approach is tested by using the WorldView-2 satellite data captured over Munich.

scholarly journals Estimating Pasture Biomass and Canopy Height in Brazilian Savanna Using UAV Photogrammetry

2019 ◽  
Vol 11 (20) ◽  
pp. 2447 ◽  
Author(s):  
Juliana Batistoti ◽  
José Marcato Junior ◽  
Luís Ítavo ◽  
Edson Matsubara ◽  
Eva Gomes ◽  
...  
Keyword(s):  
Digital Terrain Model ◽  
Technical Note ◽  
Canopy Height ◽  
Brazilian Savanna ◽  
Surface Model ◽  
Panicum Maximum ◽  
Ground Sample ◽  
Terrain Model ◽  
Sampling Height ◽  
The Difference

The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome.

scholarly journals DTM generation in forest regions from satellite stereo imagery

2014 ◽  
Vol XL-1 ◽  
pp. 401-405 ◽  
Author(s):  
J. Tian ◽  
T. Krauss ◽  
P. Reinartz
Keyword(s):  
Extraction Method ◽  
Laser Scanning ◽  
Single Channel ◽  
Median Filter ◽  
Digital Terrain Model ◽  
Classification Method ◽  
Surface Model ◽  
Probability Maps ◽  
Forest Regions ◽  
Stereo Imagery

Satellite stereo imagery is becoming a popular data source for derivation of height information. Many new Digital Surface Model (DSM) generation and evaluation methods have been proposed based on these data. A novel Digital Terrain Model (DTM) extraction method based on the DSM from satellite stereo imagery is proposed in this paper. Instead of directly filtering the DSM, firstly a single channel based classification method is proposed. In this step, no multi-spectral information is used, because for some stereo sensors, like Cartosat-1, only panchromatic channels are available. The proposed classification method adopts the random forests method to get initial probability maps of the four main classes in forest regions (high-forest, low-forest, ground, and buildings). To cover the pepper and salt effect of this pixel based classification method, the probability maps are further filtered based on the adaptive Wiener filtering. Then a cube-based greedy strategy is applied in generating the final classification map from these refined probability maps. Secondly, the height distances between neighboring regions are calculated along the boundary regions. These height distances can be used to estimate the relative region heights. Thirdly, the DTM is extracted by subtracting these relative region heights from the DSM in the order of: buildings &ndash; low forest &ndash; high forest. In the end, the extracted DTM is further smoothed using median filter. <br><br> The proposed DTM extraction method is finally tested on satellite stereo imagery captured by Cartosat-1. Quality evaluation is performed by comparing the extracted DTMs to a reference DTM, which is generated from the last return airborne laser scanning point cloud.

scholarly journals ROAD NETWORK MAPPING USING AIRBORNE LiDAR DATA

2018 ◽  
Vol XLII-5 ◽  
pp. 707-711 ◽  
Author(s):  
S. Upadhayay ◽  
M. Yadav ◽  
D. P. Singh
Keyword(s):  
Road Network ◽  
Spatial Databases ◽  
Digital Terrain Model ◽  
Airborne Lidar ◽  
Surface Model ◽  
Lidar Data ◽  
Classification Approach ◽  
Modified Maximum Likelihood ◽  
Road Network Extraction ◽  
Airborne Lidar Data

<p><strong>Abstract.</strong> The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this research is to develop an efficient algorithm for road network extraction from airborne LiDAR data using supervised classification approach. The proposed algorithm first classifies the input data into the road and non-road features using modified maximum likelihood classification approach. Then Digital Terrain Model (DTM) mask is generated by removing non-ground features from Digital Surface Model using hierarchical morphology and road candidate image if obtained. The parking lots are removed and road network is extracted successfully.</p>

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 METHODS FOR THE UPDATE AND VERIFICATION OF FOREST SURFACE MODEL

2016 ◽  
Vol XLI-B4 ◽  
pp. 51-54
Author(s):  
M. Rybansky ◽  
M. Brenova ◽  
P. Zerzan ◽  
J. Simon ◽  
T. Mikita
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Geographic Location ◽  
Canopy Height ◽  
Aerial Photographs ◽  
Surface Model ◽  
Vegetation Growth ◽  
Terrain Model ◽  
Digital Terrain ◽  
Pulse Echo

The digital terrain model (DTM) represents the bare ground earth's surface without any objects like vegetation and buildings. In contrast to a DTM, Digital surface model (DSM) represents the earth's surface including all objects on it. The DTM mostly does not change as frequently as the DSM. The most important changes of the DSM are in the forest areas due to the vegetation growth. Using the LIDAR technology the canopy height model (CHM) is obtained by subtracting the DTM and the corresponding DSM. The DSM is calculated from the first pulse echo and DTM from the last pulse echo data. The main problem of the DSM and CHM data using is the actuality of the airborne laser scanning. <br><br> This paper describes the method of calculating the CHM and DSM data changes using the relations between the canopy height and age of trees. To get a present basic reference data model of the canopy height, the photogrammetric and trigonometric measurements of single trees were used. Comparing the heights of corresponding trees on the aerial photographs of various ages, the statistical sets of the tree growth rate were obtained. These statistical data and LIDAR data were compared with the growth curve of the spruce forest, which corresponds to a similar natural environment (soil quality, climate characteristics, geographic location, etc.) to get the updating characteristics.

scholarly journals Deteksi Tinggi Vegetasi di Delta Mahakam dengan Penginderaan Jauh

2019 ◽  
Vol 4 (3) ◽  
pp. 175
Author(s):  
Nanin Anggraini ◽  
Atriyon Julzarika
Keyword(s):  
Remote Sensing ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Geoid Model ◽  
Alos Palsar ◽  
Vegetation Height ◽  
Vertical Accuracy ◽  
Mahakam Delta

<strong>Detection of Vegetation Height in Mahakam Delta Using Remote Sensing. </strong>The vegetation height is a vertical distance between top of the vegetation to ground surface. Vegetation height is one of the parameters for vegetation growth. There are various methods to measure vegetation height; one of them is the use of remote sensing technology. This study aims to map vegetation height in Mahakam Delta by using height models derived from remote sensing data. Such models are Digital Surface Model (DSM) and Digital Terrain Model (DTM). DSM was generated using a combination of interferometric processing of ALOS PALSAR interferometry, X-SAR, Shuttle Radar Topography Mission (SRTM), and geodetic height of Icesat/GLAS satellite imagery. This integration technique incorporated the Digital Elevation Model (DEM) method. The geoid model used in this study was EGM 2008. The following step was the correction of height errors of DSM. Terrain correction was undertaken to convert DSM into DTM, while vegetation heights were obtained from subtraction of DSM and DTM. Vertical accuracy verification refers to a tolerance of 1.96σ (95%) or ~80 cm. In DSM, a vertical accuracy value of 60.4 cm was obtained so that the DSM is feasible for mapping with scale of 1: 10,000, while the DTM was 37 cm so it is also applicable for mapping with such scale. Based on the subtraction of DSM and DTM, the vegetation heights in Mahakam Delta varied between 0 and 64 m.

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