scholarly journals Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques

2018 ◽  
Vol 10 (7) ◽  
pp. 1078 ◽  
Author(s):  
Wei Chen ◽  
Xingbo Hu ◽  
Wen Chen ◽  
Yifeng Hong ◽  
Minhua Yang
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Mean Shift ◽  
Airborne Lidar ◽  
Individual Tree ◽  
Clustering Techniques ◽  
Mean Shift Clustering

scholarly journals SEGMENTATION AND CROWN PARAMETER EXTRACTION OF INDIVIDUAL TREES IN AN AIRBORNE TOMOSAR POINT CLOUD

2015 ◽  
Vol XL-3/W2 ◽  
pp. 205-209 ◽  
Author(s):  
M. Shahzad ◽  
M. Schmitt ◽  
X. X. Zhu
Keyword(s):  
Point Cloud ◽  
Mean Shift ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Critical Parameters ◽  
Individual Tree ◽  
Mean Shift Clustering ◽  
Important Field ◽  
Individual Trees

The analysis of individual trees is an important field of research in the forest remote sensing community. While the current state-of-theart mostly focuses on the exploitation of optical imagery and airborne LiDAR data, modern SAR sensors have not yet met the interest of the research community in that regard. This paper describes how several critical parameters of individual deciduous trees can be extraced from airborne multi-aspect TomoSAR point clouds: First, the point cloud is segmented by unsupervised mean shift clustering. Then ellipsoid models are fitted to the points of each cluster. Finally, from these 3D ellipsoids the geometrical tree parameters location, height and crown radius are extracted. Evaluation with respect to a manually derived reference dataset prove that almost 86% of all trees are localized, thus providing a promising perspective for further research towards individual tree recognition from SAR data.

scholarly journals Comparative Analysis of Clustering Techniques for Movie Recommendation

2018 ◽  
Vol 225 ◽  
pp. 02004
Author(s):  
T.S. Aditya ◽  
Karthik Rajaraman ◽  
M. Monica Subashini
Keyword(s):  
Neural Network ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Graphics Processors ◽  
Clustering Techniques ◽  
Advantages And Disadvantages ◽  
Mean Shift Clustering ◽  
Data Points ◽  
Energy Consuming ◽  
Movie Recommendation

Movie recommendation is a subject with immense ambiguity. A person might like a movie but not a very similar movie. The present recommending systems focus more on just few parameters such as Director, cast and genre. A lot of Power intensive methods such as Deep Convolutional Neural Network (CNN) has been used which demands the use of Graphics processors that require more energy. We try to accomplish the same task using lesser Energy consuming algorithms such as clustering techniques. In this paper, we try to create a more generalized list of similar movies in order to provide the user with more variety of movies which he/she might like, using clustering algorithms. We will compare how choosing different parameters and number of features affect the cluster's content. Also, compare how different algorithms such as K-mean, Hierarchical, Birch and mean shift clustering algorithms give a varied result and conclude which method will suit for which scenarios of movie recommendations. We also conclude on which algorithm clusters stray data points more efficiently and how different algorithms provide different advantages and disadvantages.

LiDAR – A new tool for forest measurements?

2006 ◽  
Vol 82 (2) ◽  
pp. 211-218 ◽  
Author(s):  
David L Evans ◽  
Scott D Roberts ◽  
Robert C Parker
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Measurement Tool ◽  
Mississippi State University ◽  
State University ◽  
Individual Tree ◽  
Sensing Technology ◽  
High Measurement ◽  
Application Potential ◽  
Forest Measurement

LiDAR (Light Detection and Ranging) is a remote sensing technology with strong application potential in forest resource management. It provides high measurement precision that can be used for tree and stand measurements. Although LiDAR has not been used widely as an operational measurement tool, there is a significant body of research and a number of projects at Mississippi State University (MSU) that illustrate the potential for this technology to be incorporated into operational forest assessments. This paper provides basic background on the capabilities of LiDAR in a forest measurement context that illustrates specific examples of LiDAR use including: 1) individual tree assessments, 2) a forest inventory protocol currently being operationally tested, 3) forest structure analysis, and 4) forest typing. Key words: LiDAR, remote sensing, tree identification, tree measurements, forest inventory, forest types

scholarly journals Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1263 ◽  
Author(s):  
Wen Xiao ◽  
Aleksandra Zaforemska ◽  
Magdalena Smigaj ◽  
Yunsheng Wang ◽  
Rachel Gaulton
Keyword(s):  
Mean Shift ◽  
Point Clouds ◽  
Forest Tree ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Mean Shift Algorithm ◽  
Detailed Assessment ◽  
The Mean ◽  
Airborne Lidar Data

Airborne lidar has been widely used for forest characterization to facilitate forest ecological and management studies. With the availability of increasingly higher point density, individual tree delineation (ITD) from airborne lidar point clouds has become a popular yet challenging topic, due to the complexity and diversity of forests. One important step of ITD is segmentation, for which various methodologies have been studied. Among them, a long proven image segmentation method, mean shift, has been applied directly onto 3D points, and has shown promising results. However, there are variations among those who implemented the algorithm in terms of the kernel shape, adaptiveness and weighting. This paper provides a detailed assessment of the mean shift algorithm for the segmentation of airborne lidar data, and the effect of crown top detection upon the validation of segmentation results. The results from three different datasets revealed that a crown-shaped kernel consistently generates better results (up to 7 percent) than other variants, whereas weighting and adaptiveness do not warrant improvements.

scholarly journals Predicting Forest Inventory Attributes Using Airborne Laser Scanning, Aerial Imagery, and Harvester Data

2019 ◽  
Vol 11 (7) ◽  
pp. 797 ◽  
Author(s):  
Atte Saukkola ◽  
Timo Melkas ◽  
Kirsi Riekki ◽  
Sanna Sirparanta ◽  
Jussi Peuhkurinen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Inventory ◽  
Prediction Models ◽  
Basal Area ◽  
Sample Plot ◽  
Weighted Mean ◽  
Individual Tree ◽  
Plot Size ◽  
Head Positions ◽  
Mean Square Errors

The aim of the study was to develop a new method to use tree stem information recorded by harvesters along operative logging in remote sensing-based prediction of forest inventory attributes in mature stands. The reference sample plots were formed from harvester data, using two different tree positions: harvester positions (XYH) in global satellite navigation system and computationally improved harvester head positions (XYHH). Study materials consisted of 158 mature Norway-spruce-dominated stands located in Southern Finland that were clear-cut during 2015–16. Tree attributes were derived from the stem dimensions recorded by the harvester. The forest inventory attributes were compiled for both stands and sample plots generated for stands for four different sample plot sizes (254, 509, 761, and 1018 m2). Prediction models between the harvester-based forest inventory attributes and remote sensing features of sample plots were developed. The stand-level predictions were obtained, and basal-area weighted mean diameter (Dg) and basal-area weighted mean height (Hg) were nearly constant for all model alternatives with relative root-mean-square errors (RMSE) roughly 10–11% and 6–8%, respectively, and minor biases. For basal area (G) and volume (V), using either of the position methods, resulted in roughly similar predictions at best, with approximately 25% relative RMSE and 15% bias. With XYHH positions, the predictions of G and V were nearly independent of the sample plot size within 254–761 m2. Therefore, the harvester-based data can be used as ground truth for remote sensing forest inventory methods. In predicting the forest inventory attributes, it is advisable to utilize harvester head positions (XYHH) and a smallest plot size of 254 m2. Instead, if only harvester positions (XYH) are available, expanding the sample plot size to 761 m2 reaches a similar accuracy to that obtained using XYHH positions, as the larger sample plot moderates the uncertainties when determining the individual tree position.

scholarly journals Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

2020 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Luiz Felipe Ramalho de Oliveira ◽  
H. Andrew Lassiter ◽  
Ben Wilkinson ◽  
Travis Whitley ◽  
Peter Ifju ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Three Dimensional ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Individual Tree ◽  
Tree Detection

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

Données LiDAR aériennes: pertinence de l'interprétation visuelle pour la foresterie

2017 ◽  
Vol 168 (3) ◽  
pp. 127-133
Author(s):  
Matthew Parkan
Keyword(s):  
Cross Sections ◽  
Forest Edge ◽  
Airborne Lidar ◽  
Visual Interpretation ◽  
Individual Tree ◽  
Small Areas ◽  
Interactive 3D ◽  
The Individual ◽  
Airborne Lidar Data ◽  
Stem Position

Airborne LiDAR data: relevance of visual interpretation for forestry Airborne LiDAR surveys are particularly well adapted to map, study and manage large forest extents. Products derived from this technology are increasingly used by managers to establish a general diagnosis of the condition of forests. Less common is the use of these products to conduct detailed analyses on small areas; for example creating detailed reference maps like inventories or timber marking to support field operations. In this context, the use of direct visual interpretation is interesting, because it is much easier to implement than automatic algorithms and allows a quick and reliable identification of zonal (e.g. forest edge, deciduous/persistent ratio), structural (stratification) and point (e.g. tree/stem position and height) features. This article examines three important points which determine the relevance of visual interpretation: acquisition parameters, interactive representation and identification of forest characteristics. It is shown that the use of thematic color maps within interactive 3D point cloud and/or cross-sections makes it possible to establish (for all strata) detailed and accurate maps of a parcel at the individual tree scale.

scholarly journals A Disparity Refinement Algorithm for Satellite Remote Sensing Images Based on Mean-Shift Plane Segmentation

2021 ◽  
Vol 13 (10) ◽  
pp. 1903
Author(s):  
Zhihui Li ◽  
Jiaxin Liu ◽  
Yang Yang ◽  
Jing Zhang
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Stereo Matching ◽  
Mean Shift ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Edge Matching ◽  
Refinement Method ◽  
Initial Segmentation ◽  
Refinement Algorithm

Objects in satellite remote sensing image sequences often have large deformations, and the stereo matching of this kind of image is so difficult that the matching rate generally drops. A disparity refinement method is needed to correct and fill the disparity. A method for disparity refinement based on the results of plane segmentation is proposed in this paper. The plane segmentation algorithm includes two steps: Initial segmentation based on mean-shift and alpha-expansion-based energy minimization. According to the results of plane segmentation and fitting, the disparity is refined by filling missed matching regions and removing outliers. The experimental results showed that the proposed plane segmentation method could not only accurately fit the plane in the presence of noise but also approximate the surface by plane combination. After the proposed plane segmentation method was applied to the disparity refinement of remote sensing images, many missed matches were filled, and the elevation errors were reduced. This proved that the proposed algorithm was effective. For difficult evaluations resulting from significant variations in remote sensing images of different satellites, the edge matching rate and the edge matching map are proposed as new stereo matching evaluation and analysis tools. Experiment results showed that they were easy to use, intuitive, and effective.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

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