Author : F . Naveed
Publisher : Infinite Study
Page : 6 pages
File Size : 30,38 MB
Release :
Category :
ISBN :
The inability to detect the Emerald Ash Borer (EAB) at an early stage has led to the enumerable loss of different species of ash trees. Due to the increasing risk being posed by the EAB, a robust and accurate method is needed for identifying Individual Tree Crowns (ITCs) that are at a risk of being infected or are already diseased.
Author : Faizaan Naveed
Publisher : Infinite Study
Page : 21 pages
File Size : 34,39 MB
Release :
Category : Mathematics
ISBN :
In this study, multispectral Light Detection and Ranging (LiDAR) data were utilized to improve delineation of individual tree crowns (ITC) as an important step in individual tree analysis. A framework to integrate spectral and height information for ITC delineation was proposed, and the multi-scale algorithm for treetop detection developed in one of our previous studies was improved. In addition, an advanced region-based segmentation method that used detected treetops as seeds was proposed for segmentation of individual crowns based on their spectral, contextual, and height information.
Author : Tao Liu
Publisher :
Page : 102 pages
File Size : 24,76 MB
Release : 2014
Category : Crowns (Botany)
ISBN :
Author : Jili Li
Publisher :
Page : 0 pages
File Size : 45,79 MB
Release : 2013
Category :
ISBN :
Analysis of individual trees in forests is of great value for the monitoring and sustainable management of forests. For the past decade, remote sensing has been a useful tool for individual tree analysis. However, accuracies of individual tree analysis remain insufficient because of the inadequate spatial resolution of most remote sensing data and unsophisticated methods. The improvement of individual tree analysis becomes feasible because of recent advances in LiDAR (Light Detection And Ranging) and airborne image sensing technologies. However, it is challenging to fully exploit and utilize small-footprint LiDAR data and high spatial resolution imagery for detailed tree analysis. This dissertation presents a number of effective methods on individual tree crown delineation and species classification to improve individual tree analysis with advanced remote sensing data. The individual tree crown delineation is composed of a five-step framework, which is unique in its automated determination of dominant crown sizes in a given forest scene and its determination of the number of trees in a segment based on LiDAR profiles. This framework correctly delineated 74% and 72% of the tree crowns in two plots with mixed-wood and deciduous trees, respectively. The study on individual tree species classification is focused on developing novel LiDAR and image features to characterize tree structures. First of all, coniferous and deciduous trees are classified. Features are extracted from LiDAR data to characterize crown shapes and vertical profiles of individual trees, followed by the C4.5 decision tree classification algorithm. Furthermore, groups of new LiDAR features are developed to characterize the internal structures of a tree. Important features are selected via a genetic algorithm and utilized in the multi-species classification based on linear discriminant analysis. An overall accuracy of 77 .5% is obtained for an investigation on 1, 122 sample trees in natural forests. In addition, statistical features based on gray-level co-occurrence matrix (GLCM) and structural texture-features derived from the local binary pattern (LBP) method are proved to be useful to improve the species classification using high spatial resolution aerial image. The research demonstrates that LiDAR data and high spatial resolution images can be used to effectively characterize tree structures and improve the accuracy and efficiency of individual tree species identification.
Author : H.P. Pokharel
Publisher :
Page : 70 pages
File Size : 45,94 MB
Release : 2008
Category :
ISBN :
Author : Sooyoung Kim
Publisher :
Page : 242 pages
File Size : 24,75 MB
Release : 2008
Category : Forest canopies
ISBN :
Author : Assunta Saliola
Publisher :
Page : pages
File Size : 29,35 MB
Release : 2014
Category :
ISBN :
Author : Nina Amiri
Publisher :
Page : pages
File Size : 38,40 MB
Release : 2019
Category :
ISBN :
Abstract: Knowledge about forest structures, particularly of deadwood, is fundamental for understanding, protecting, and conserving forest biodiversity. While individual tree-based approaches using single wavelength airborne laserscanning (ALS) can successfully distinguish broadleaf and coniferous trees, they still perform multiple tree species classifications with limited accuracy. Moreover, the mapping of standing dead trees is becoming increasingly important for damage calculation after pest infestation or biodiversity assessment. Recent advances in sensor technology have led to the development of new ALS systems that provide up to three different wavelengths. In this study, we present a novel method which classifies three tree species (Norway spruce, European beech, Silver fir), and dead spruce trees with crowns using full waveform ALS data acquired from three different sensors (wavelengths 532 nm, 1064 nm, 1550 nm). The ALS data were acquired in the Bavarian Forest National Park (Germany) under leaf-on conditions with a maximum point density of 200 points/m 2 . To avoid overfitting of the classifier and to find the most prominent features, we embed a forward feature selection method. We tested our classification procedure using 20 sample plots with 586 measured reference trees. Using single wavelength datasets, the highest accuracy achieved was 74% (wavelength = 1064 nm), followed by 69% (wavelength = 1550 nm) and 65% (wavelength = 532 nm). An improvement of 8-17% over single wavelength datasets was achieved when the multi wavelength data were used. Overall, the contribution of the waveform-based features to the classification accuracy was higher than that of the geometric features by approximately 10%. Our results show that the features derived from a multi wavelength ALS point cloud significantly improve the detailed mapping of tree species and standing dead trees
Author : Victor Felix Strimbu
Publisher :
Page : 162 pages
File Size : 49,17 MB
Release : 2014
Category : Atmosphere
ISBN :
Author : Shruthi Srinivasan
Publisher :
Page : pages
File Size : 15,68 MB
Release : 2014
Category :
ISBN :
Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) provides accurate understory information rapidly through non-destructive methods. This study developed algorithms to extract individual tree height, diameter at breast height (DBH), and crown width in plots at Ecosystem Science and Management (ESSM) research area and Huntsville, Texas. Further, the influence of scan settings and processing choices on the accuracy of deriving tree measurements was also investigated. The study also developed models to estimate aboveground biomass (AGB) and investigate different conceptual approaches to study tree level growth in forest structural parameters and AGB using multi-temporal TLS datasets. DBH was retrieved by cylinder fitting at different height bins. Individual trees were extracted from the TLS point cloud to determine tree heights and crown widths. The R-squared value ranged from 0.91 to 0.97 when field measured DBH was validated against TLS derived DBH using different methods. An accuracy of 92% was obtained for predicting tree heights. The R-squared value was 0.84 and RMSE was 1.08 m when TLS derived crown widths were validated using field measured crown widths. Examples of underestimations of field measured forest structural parameters due to tree shadowing have also been discussed in this study. Correction factors should be applied or multiple high resolution scans should be conducted to reduce the errors in estimation of forest structural parameters. TLS geometric and statistical parameters were derived for individual trees and used as explanatory variables to estimate AGB. An extensive literature review reveals that this is the first study to model the change in AGB using different innovative and conceptual approaches with multi-temporal TLS data. Tree level AGB growth was studied over a period of three years using three different approaches. Results showed that TLS derived geometric parameters were better correlated to field measured AGB. Promising results for AGB change were obtained using the direct modeling approach; hence forest growth could be studied independent of any field measurements when biomass models are available. However, the models could be improved by incorporating more trees with a wide range of DBH and tree heights. The results from this study will benefit foresters, planners, and other remote sensing studies from airborne and spaceborne platforms, for map upscaling, data fusion, or calibration purposes. The electronic version of this dissertation is accessible from http://hdl.handle.net/1969.1/151740
