scholarly journals TREE SPECIES RECOGNITION IN SPECIES RICH AREA USING UAV-BORNE HYPERSPECTRAL IMAGERY AND STEREO-PHOTOGRAMMETRIC POINT CLOUD

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 185-194 ◽  
Author(s):  
S. Tuominen ◽  
R. Näsi ◽  
E. Honkavaara ◽  
A. Balazs ◽  
T. Hakala ◽  
...  
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Hyperspectral Imagery ◽  
Species Recognition ◽  
Test Area ◽  
Surface Model ◽  
Data Sets ◽  
Geospatial Information ◽  
Tree Species Composition ◽  
Test Tree

Recognition of tree species and geospatial information of tree species composition is essential for forest management. In this study we test tree species recognition using hyperspectral imagery from VNIR and SWIR camera sensors in combination with 3D photogrammetric canopy surface model based on RGB camera stereo-imagery. An arboretum forest with a high number of tree species was used as a test area. The imagery was acquired from the test area using UAV-borne cameras. Hyperspectral imagery was calibrated for providing a radiometrically corrected reflectance mosaic, which was tested along with the original uncalibrated imagery. Alternative estimators were tested for predicting tree species and genus, as well as for selecting an optimal set of remote sensing features for this task. All tested estimators gave similar trend in the results: the calibrated reflectance values performed better in predicting tree species and genus compared to uncorrected hyperspectral pixel values. Furthermore, the combination of VNIR, SWIR and 3D features performed better than any of the data sets individually, with calibrated reflectances and original pixel values alike. The highest proportion of correctly classified trees was achieved using calibrated reflectance features from VNIR and SWIR imagery together with 3D point cloud features: 0.823 for tree species and 0.869 for tree genus.

Detection of Building in Airborne Laser Scanner Data and Aerial Images

2012 ◽  
Vol 11 ◽  
pp. 7-13
Author(s):  
Dilli Raj Bhandari
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
Aerial Images ◽  
Spectral Information ◽  
Surface Model ◽  
World Population ◽  
Data Sets ◽  
Scanner Data ◽  
Airborne Laser Scanner ◽  
Airborne Laser

The automatic extraction of the objects from airborne laser scanner data and aerial images has been a topic of research for decades. Airborne laser scanner data are very efficient source for the detection of the buildings. Half of the world population lives in urban/suburban areas, so detailed, accurate and up-to-date building information is of great importance to every resident, government agencies, and private companies. The main objective of this paper is to extract the features for the detection of building using airborne laser scanner data and aerial images. To achieve this objective, a method of integration both LiDAR and aerial images has been explored: thus the advantages of both data sets are utilized to derive the buildings with high accuracy. Airborne laser scanner data contains accurate elevation information in high resolution which is very important feature to detect the elevated objects like buildings and the aerial image has spectral information and this spectral information is an appropriate feature to separate buildings from the trees. Planner region growing segmentation of LiDAR point cloud has been performed and normalized digital surface model (nDSM) is obtained by subtracting DTM from the DSM. Integration of the nDSM, aerial images and the segmented polygon features from the LiDAR point cloud has been carried out. The optimal features for the building detection have been extracted from the integration result. Mean height value of the nDSM, Normalized difference vegetation index (NDVI) and the standard deviation of the nDSM are the effective features. The accuracy assessment of the classification results obtained using the calculated attributes was done. Assessment result yielded an accuracy of almost 92 % explaining the features which are extracted by integrating the two data sets was large extent, effective for the automatic detection of the buildings.

scholarly journals COMPARISON OF UAS-BASED PHOTOGRAMMETRY SOFTWARE FOR 3D POINT CLOUD GENERATION: A SURVEY OVER A HISTORICAL SITE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 55-61 ◽  
Author(s):  
F. Alidoost ◽  
H. Arefi
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Surface Model ◽  
Geospatial Information ◽  
Software Packages ◽  
Comparison Results ◽  
3D City Modelling ◽  
Time Acquisition

Nowadays, Unmanned Aerial System (UAS)-based photogrammetry offers an affordable, fast and effective approach to real-time acquisition of high resolution geospatial information and automatic 3D modelling of objects for numerous applications such as topography mapping, 3D city modelling, orthophoto generation, and cultural heritages preservation. In this paper, the capability of four different state-of-the-art software packages as 3DSurvey, Agisoft Photoscan, Pix4Dmapper Pro and SURE is examined to generate high density point cloud as well as a Digital Surface Model (DSM) over a historical site. The main steps of this study are including: image acquisition, point cloud generation, and accuracy assessment. The overlapping images are first captured using a quadcopter and next are processed by different software to generate point clouds and DSMs. In order to evaluate the accuracy and quality of point clouds and DSMs, both visual and geometric assessments are carry out and the comparison results are reported.

scholarly journals Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1338 ◽  
Author(s):  
Camile Sothe ◽  
Michele Dalponte ◽  
Cláudia Maria de Almeida ◽  
Marcos Benedito Schimalski ◽  
Carla Luciane Lima ◽  
...  
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Species Recognition ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Kappa Index ◽  
Species Classification ◽  
Tree Species Classification

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

scholarly journals SECTION-BASED TREE SPECIES IDENTIFICATION USING AIRBORNE LIDAR POINT CLOUD

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1001-1007 ◽  
Author(s):  
C. Yao ◽  
X. Zhang ◽  
H. Liu
Keyword(s):  
Species Identification ◽  
Tree Species ◽  
Point Cloud ◽  
Airborne Lidar ◽  
Forest Community ◽  
Surface Model ◽  
Lidar Data ◽  
Palm Tree ◽  
Crown Height ◽  
Individual Tree

The application of LiDAR data in forestry initially focused on mapping forest community, particularly and primarily intended for largescale forest management and planning. Then with the smaller footprint and higher sampling density LiDAR data available, detecting individual tree overstory, estimating crowns parameters and identifying tree species are demonstrated practicable. This paper proposes a section-based protocol of tree species identification taking palm tree as an example. Section-based method is to detect objects through certain profile among different direction, basically along X-axis or Y-axis. And this method improve the utilization of spatial information to generate accurate results. Firstly, separate the tree points from manmade-object points by decision-tree-based rules, and create Crown Height Mode (CHM) by subtracting the Digital Terrain Model (DTM) from the digital surface model (DSM). Then calculate and extract key points to locate individual trees, thus estimate specific tree parameters related to species information, such as crown height, crown radius, and cross point etc. Finally, with parameters we are able to identify certain tree species. Comparing to species information measured on ground, the portion correctly identified trees on all plots could reach up to 90.65 %. The identification result in this research demonstrate the ability to distinguish palm tree using LiDAR point cloud. Furthermore, with more prior knowledge, section-based method enable the process to classify trees into different classes.

scholarly journals Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1713
Author(s):  
Linghan Gao ◽  
Xiaoli Zhang
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Above Ground Biomass ◽  
Tree Species Composition ◽  
Cloud Data ◽  
Pine Tree ◽  
Airborne Laser ◽  
Terrain Features ◽  
Forest Farm

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

scholarly journals Extraction of Spectral Information from Airborne 3D Data for Assessment of Tree Species Proportions

2021 ◽  
Vol 13 (4) ◽  
pp. 720
Author(s):  
Jonas Bohlin ◽  
Jörgen Wallerman ◽  
Johan E. S. Fransson
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Rapid Development ◽  
Aerial Images ◽  
Aerial Image ◽  
Stem Volume ◽  
Spectral Information ◽  
Surface Model ◽  
Data Set ◽  
Species Specific

With the rapid development of photogrammetric software and accessible camera technology, land surveys and other mapping organizations now provide various point cloud and digital surface model products from aerial images, often including spectral information. In this study, methods for colouring the point cloud and the importance of different metrics were compared for tree species-specific estimates at a coniferous hemi-boreal test site in southern Sweden. A total of three different data sets of aerial image-based products and one multi-spectral lidar data set were used to estimate tree species-specific proportion and stem volume using an area-based approach. Metrics were calculated for 156 field plots (10 m radius) from point cloud data and used in a Random Forest analysis. Plot level accuracy was evaluated using leave-one-out cross-validation. The results showed small differences in estimation accuracy of species-specific variables between the colouring methods. Simple averages of the spectral metrics had the highest importance and using spectral data from two seasons improved species prediction, especially deciduous proportion. Best tree species-specific proportion was estimated using multi-spectral lidar with 0.22 root mean square error (RMSE) for pine, 0.22 for spruce and 0.16 for deciduous. Corresponding RMSE for aerial images was 0.24, 0.23 and 0.20 for pine, spruce and deciduous, respectively. For the species-specific stem volume at plot level using image data, the RMSE in percent of surveyed mean was 129% for pine, 60% for spruce and 118% for deciduous.

scholarly journals TREE SPECIES CLASSIFICATION BASED ON 3D SPECTRAL POINT CLOUDS AND ORTHOMOSAICS ACQUIRED BY SNAPSHOT HYPERSPECTRAL UAS SENSOR

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 379-384 ◽  
Author(s):  
C. Iseli ◽  
A. Lucieer
Keyword(s):  
Tree Species ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Hyperspectral Imagery ◽  
Spectral Feature ◽  
Point Clouds ◽  
Sensor Calibration ◽  
Unmanned Aerial Systems ◽  
3D Point Clouds

<p><strong>Abstract.</strong> In recent years, there has been a growing number of small hyperspectral sensors suitable for deployment on unmanned aerial systems (UAS. The introduction of the hyperspectral snapshot sensor provides interesting opportunities for acquisition of three-dimensional (3D) hyperspectral point clouds based on the structure-from-motion (SfM) workflow. In this study, we describe the integration of a 25-band hyperspectral snapshot sensor (PhotonFocus camera with IMEC 600&amp;thinsp;&amp;ndash;&amp;thinsp;875&amp;thinsp;nm 5x5 mosaic chip) on a multi-rotor UAS. The sensor was integrated with a dual frequency GNSS receiver for accurate time synchronisation and geolocation. We describe the sensor calibration workflow, including dark current and flat field characterisation. An SfM workflow was implemented to derive hyperspectral 3D point clouds and orthomosaics from overlapping frames. On-board GNSS coordinates for each hyperspectral frame assisted in the SfM process and allowed for accurate direct georeferencing (&amp;lt;&amp;thinsp;10&amp;thinsp;cm absolute accuracy). We present the processing workflow to generate seamless hyperspectral orthomosaics from hundreds of raw images. Spectral reference panels and in-field spectral measurements were used to calibrate and validate the spectral signatures. This process provides a novel data type which contains both 3D, geometric structure and detailed spectral information in a single format. First, to determine the potential improvements that such a format could provide, the core aim of this study was to compare the use of 3D hyperspectral point clouds to conventional hyperspectral imagery in the classification of two Eucalyptus tree species found in Tasmania, Australia. The IMEC SM5x5 hyperspectral snapshot sensor was flown over a small native plantation plot, consisting of a mix of the <i>Eucalyptus pauciflora</i> and <i>E. tenuiramis</i> species. High overlap hyperspectral imagery was captured and then processed using SfM algorithms to generate both a hyperspectral orthomosaic and a dense hyperspectral point cloud. Additionally, to ensure the optimum spectral quality of the data, the characteristics of the hyperspectral snapshot imaging sensor were analysed utilising measurements captured in a laboratory environment. To coincide with the generated hyperspectral point cloud data, both a file format and additional processing and visualisation software were developed to provide the necessary tools for a complete classification workflow. Results based on the classification of the <i>E. pauciflora</i> and <i>E. tenuiramis</i> species revealed that the hyperspectral point cloud produced an increased classification accuracy over conventional hyperspectral imagery based on random forest classification. This was represented by an increase in classification accuracy from 67.2% to 73.8%. It was found that even when applied separately, the geometric and spectral feature sets from the point cloud both provided increased classification accuracy over the hyperspectral imagery.</p>

Langfristige Entwicklung von zwei Waldgesellschaften im Białowieża-Urwald

2008 ◽  
Vol 159 (4) ◽  
pp. 80-90 ◽  
Author(s):  
Bogdan Brzeziecki ◽  
Feliks Eugeniusz Bernadzki
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Stand Structure ◽  
Temporal Dynamics ◽  
Structural Parameters ◽  
Forest Communities ◽  
Natural Forests ◽  
Tree Species Composition ◽  
Strong Trend ◽  
Long Term Study

The results of a long-term study on the natural forest dynamics of two forest communities on one sample plot within the Białowieża National Park in Poland are presented. The two investigated forest communities consist of the Pino-Quercetum and the Tilio-Carpinetum type with the major tree species Pinus sylvestris, Picea abies, Betula sp., Quercus robur, Tilia cordata and Carpinus betulus. The results reveal strong temporal dynamics of both forest communities since 1936 in terms of tree species composition and of general stand structure. The four major tree species Scots pine, birch, English oak and Norway spruce, which were dominant until 1936, have gradually been replaced by lime and hornbeam. At the same time, the analysis of structural parameters indicates a strong trend towards a homogenization of the vertical stand structure. Possible causes for these dynamics may be changes in sylviculture, climate change and atmospheric deposition. Based on the altered tree species composition it can be concluded that a simple ≪copying≫ (mimicking) of the processes taking place in natural forests may not guarantee the conservation of the multifunctional character of the respective forests.

Site classification in a mixed hardwood forest (Hallerbos, Belgium) with a homogeneous ground vegetation dominated by Hyacinthoides non-scripta (L.) Chouard ex. Rothm.

1994 ◽  
Vol 59 ◽  
Author(s):  
D. Maddelein ◽  
B. Muys ◽  
J. Neirynck ◽  
G. Sioen
Keyword(s):  
Tree Species ◽  
Ph Value ◽  
Soil Analysis ◽  
Base Cation ◽  
Site Quality ◽  
Ground Vegetation ◽  
Site Classification ◽  
Species Classification ◽  
Tree Species Composition ◽  
Understorey Vegetation

The  forest of Halle (560 ha), situated 20 km south of Brussels is covered by a  beech (Fagus sylvatica)  forest, locally mixed with secundary species (Tilia,  Fraxinus, Acer, Quercus,... ). In almost all  stands, herbal vegetation is dominated by bluebell (Hyacinthoides  non-scripta).     The research intended to classify 36 plots of different tree species  composition according to their site quality. Three classification methods  were compared: the first one based on the indicator value of the understorey  vegetation, a second one on the humus morphology and a last one on some  quantitative soil characteristics. According to the plant sociological site  classification, the plots have the same site quality. However, humus forms  differ apparently and significant differences were found in pH value and base  cation saturation of the soil, abundance and biomass of earthworms and  biomass of the ectorganic horizon. Tree species proved to be the main cause  of these differences.     The results illustrate that the herbal vegetation is not always a reliable  indicator of site quality. In the case of a homogeneous vegetation dominated  by one or more indifferent species, classification on humus morphology or  soil analysis are more appropriate. In the forest of Halle, the tree species  is probably the main cause of the observed differences in site quality.

scholarly journals Intelligent vehicle modeling design based on image processing

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142199334
Author(s):  
Guangchao Zhang ◽  
Junrong Liu
Keyword(s):  
Image Data ◽  
Front Face ◽  
Intelligent Vehicle ◽  
Surface Model ◽  
Data Sets ◽  
Face Modeling ◽  
Shape Coefficient ◽  
Automotive Market ◽  
Modeling Analysis ◽  
Family Analysis

With the urgent demand of consumers for diversified automobile modeling, simple, efficient, and intelligent automobile modeling analysis and modeling method is an urgent problem to be solved in current automobile modeling design. The purpose of this article is to analyze the modeling preference and trend of the current automobile market in time, which can assist the modeling design of new models of automobile main engine factories and strengthen their branding family. Intelligent rapid modeling shortens the current modeling design cycle, so that the product rapid iteration is to occupy an active position in the automotive market. In this article, aiming at the family analysis of automobile front face, the image database of automobile front face modeling analysis was created. The database included two data sets of vehicle signs and no vehicle signs, and the image data of vehicle front face modeling of most models of 22 domestic mainstream brands were collected. Then, this article adopts the image classification processing method in computer vision to conduct car brand classification training on the database. Based on ResNet-8 and other model architectures, it trains and classifies the intelligent vehicle brand classification database with and without vehicle label. Finally, based on the shape coefficient, a 3D wireframe model and a curved surface model are obtained. The experimental results show that the 3D curve model can be obtained based on a single image from any angle, which greatly shortens the modeling period by 92%.

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