An Individual Tree-Based Automated Registration of Aerial Images to LiDAR Data in a Forested Area

2016 ◽  
Vol 82 (9) ◽  
pp. 699-710 ◽  
Author(s):  
Jun-Hak Lee ◽  
Gregory S. Biging ◽  
Joshua B. Fisher
Keyword(s):  
Aerial Images ◽  
Lidar Data ◽  
Individual Tree ◽  
Forested Area ◽  
Automated Registration

scholarly journals Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Forests ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 694 ◽  
Author(s):  
Selina Ganz ◽  
Yannek Käber ◽  
Petra Adler
Keyword(s):  
Remote Sensing ◽  
Tree Height ◽  
Field Measurements ◽  
Test Site ◽  
Aerial Images ◽  
Lidar Data ◽  
Individual Tree ◽  
Indirect Measurements ◽  
Remote Sensing Techniques ◽  
Remote Sensing Methods

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

scholarly journals Building Change Detection Using Old Aerial Images and New LiDAR Data

2016 ◽  
Vol 8 (12) ◽  
pp. 1030 ◽  
Author(s):  
Shouji Du ◽  
Yunsheng Zhang ◽  
Rongjun Qin ◽  
Zhihua Yang ◽  
Zhengrong Zou ◽  
...  
Keyword(s):  
Change Detection ◽  
Aerial Images ◽  
Lidar Data

scholarly journals Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Spatial Information ◽  
Feature Fusion ◽  
Aerial Images ◽  
Semantic Gap ◽  
Superior Performance ◽  
Lidar Data ◽  
Building Extraction ◽  
Hierarchical Features

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.

scholarly journals Individual Tree Position Extraction and Structural Parameter Retrieval Based on Airborne LiDAR Data: Performance Evaluation and Comparison of Four Algorithms

2020 ◽  
Vol 12 (3) ◽  
pp. 571 ◽  
Author(s):  
Chen ◽  
Xiang ◽  
Moriya
Keyword(s):  
Template Matching ◽  
Local Maximum ◽  
Airborne Lidar ◽  
Forest Monitoring ◽  
Lidar Data ◽  
Individual Tree ◽  
Matching Algorithm ◽  
Crown Width ◽  
Individual Trees ◽  
Airborne Lidar Data

Information for individual trees (e.g., position, treetop, height, crown width, and crown edge) is beneficial for forest monitoring and management. Light Detection and Ranging (LiDAR) data have been widely used to retrieve these individual tree parameters from different algorithms, with varying successes. In this study, we used an iterative Triangulated Irregular Network (TIN) algorithm to separate ground and canopy points in airborne LiDAR data, and generated Digital Elevation Models (DEM) by Inverse Distance Weighted (IDW) interpolation, thin spline interpolation, and trend surface interpolation, as well as by using the Kriging algorithm. The height of the point cloud was assigned to a Digital Surface Model (DSM), and a Canopy Height Model (CHM) was acquired. Then, four algorithms (point-cloud-based local maximum algorithm, CHM-based local maximum algorithm, watershed algorithm, and template-matching algorithm) were comparatively used to extract the structural parameters of individual trees. The results indicated that the two local maximum algorithms can effectively detect the treetop; the watershed algorithm can accurately extract individual tree height and determine the tree crown edge; and the template-matching algorithm works well to extract accurate crown width. This study provides a reference for the selection of algorithms in individual tree parameter inversion based on airborne LiDAR data and is of great significance for LiDAR-based forest monitoring and management.

scholarly journals Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1820
Author(s):  
Raoul Blackman ◽  
Fei Yuan
Keyword(s):  
Remote Sensing ◽  
Ecosystem Services ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Urban Forest ◽  
Urban Canopy ◽  
Canopy Cover ◽  
Random Point ◽  
Aerial Images ◽  
Lidar Data

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.

scholarly journals A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data

2019 ◽  
Vol 11 (24) ◽  
pp. 2948 ◽  
Author(s):  
Hoang Minh Nguyen ◽  
Begüm Demir ◽  
Michele Dalponte
Keyword(s):  
Tree Species ◽  
Remote Sensing Data ◽  
Classification Problem ◽  
Learning Phase ◽  
Support Vector ◽  
Lidar Data ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification ◽  
Training Samples

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

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