Object-based feature extraction method using airborne laser Scanning data and aerial Photographs

2011 ◽  
Author(s):  
Jiqiang Tan ◽  
Qingming Zhan
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Feature Extraction Method ◽  
Airborne Laser ◽  
Object Based

Inoptimality losses in forest management decisions caused by errors in an inventory based on airborne laser scanning and aerial photographs

2010 ◽  
Vol 40 (12) ◽  
pp. 2427-2438 ◽  
Author(s):  
Md. Nurul Islam ◽  
Mikko Kurttila ◽  
Lauri Mehtätalo ◽  
Timo Pukkala
Keyword(s):  
Input Data ◽  
Laser Scanning ◽  
Basal Area ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Stand Age ◽  
Inventory Data ◽  
Airborne Laser ◽  
Mean Diameter ◽  
The Mean

Errors in inventory data may lead to inoptimal decisions that ultimately result in financial losses for forest owners. We estimated the expected monetary losses resulting from data errors that are similar to errors in laser-based forest inventory. The mean loss was estimated for 67 stands by simulating 100 realizations of inventory data for each stand with errors that mimic those in airborne laser scanning (ALS) based inventory. These realizations were used as input data in stand management optimization, which maximized the present value of all future net incomes (NPV). The inoptimality loss was calculated as the difference between the NPV of the optimal solution and the true NPV of the solution obtained with erroneous input data. The results showed that the mean loss exceeded €300·ha–1 (US$425·ha–1) in 84% of the stands. On average, the losses increased with decreasing stand age and mean diameter. Furthermore, increasing errors in the basal area weighted mean diameter and basal area of spruce were found to significantly increase the loss. It has been discussed that improvements in the accuracy of ALS-based inventory could be financially justified.

scholarly journals CLASSIFICATION OF AIRBORNE LASER SCANNING DATA USING GEOMETRIC MULTI-SCALE FEATURES AND DIFFERENT NEIGHBOURHOOD TYPES

2016 ◽  
Vol III-3 ◽  
pp. 169-176 ◽  
Author(s):  
R. Blomley ◽  
B. Jutzi ◽  
M. Weinmann
Keyword(s):  
Feature Extraction ◽  
Laser Scanning ◽  
Benchmark Dataset ◽  
Airborne Laser Scanning ◽  
Geometric Features ◽  
Multi Scale ◽  
Airborne Laser ◽  
Single Scale ◽  
Detailed Evaluation

In this paper, we address the classification of airborne laser scanning data. We present a novel methodology relying on the use of complementary types of geometric features extracted from multiple local neighbourhoods of different scale and type. To demonstrate the performance of our methodology, we present results of a detailed evaluation on a standard benchmark dataset and we show that the consideration of multi-scale, multi-type neighbourhoods as the basis for feature extraction leads to improved classification results in comparison to single-scale neighbourhoods as well as in comparison to multi-scale neighbourhoods of the same type.

scholarly journals Potential of Modern Photogrammetry Versus Airborne Laser Scanning for Estimating Forest Variables in a Mountain Environment

2019 ◽  
Vol 11 (6) ◽  
pp. 661 ◽  
Author(s):  
Sami Ullah ◽  
Matthias Dees ◽  
Pawan Datta ◽  
Petra Adler ◽  
Mathias Schardt ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Basal Area ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Aerial Photographs ◽  
Data Types ◽  
Viable Option ◽  
Airborne Laser ◽  
Mountain Environment

Digital stereo aerial photographs are periodically updated in many countries and offer a viable option for the regular update of information on forest variables. We compared the potential of image-based point clouds derived from three different sets of aerial photographs with airborne laser scanning (ALS) to assess plot-level forest attributes in a mountain environment. The three data types used were (A) high overlapping pan-sharpened (80/60%); (B) high overlapping panchromatic band (80/60%); and (C) standard overlapping pan-sharpened stereo aerial photographs (60/30%). We used height and density metrics at the plot level derived from image-based and ALS point clouds as the explanatory variables and Lorey’s mean height, timber volume, and mean basal area as the response variables. We obtained a RMSE = 8.83%, 29.24% and 35.12% for Lorey’s mean height, volume, and basal area using ALS data, respectively. Similarly, we obtained a RMSE = 9.96%, 31.13%, and 35.99% and RMSE = 11.28%, 31.01%, and 35.66% for Lorey’s mean height, volume and basal area using image-based point clouds derived from pan-sharpened stereo aerial photographs with 80/60% and 60/30% overlapping, respectively. For image-based point clouds derived from a panchromatic band of stereo aerial photographs (80%/60%), we obtained an RMSE = 10.04%, 31.19% and 35.86% for Lorey’s mean height, volume, and basal area, respectively. The overall findings indicated that the performance of image-based point clouds in all cases were as good as ALS. This highlights that in the presence of a highly accurate digital terrain model (DTM) from ALS, image-based point clouds offer a viable option for operational forest management in all countries where stereo aerial photographs are updated on a routine basis.

scholarly journals Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

2017 ◽  
Vol 63 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Maroš Sedliak ◽  
Ivan Sačkov ◽  
Ladislav Kulla
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

Feature Extraction Method of Laser Scanning Point Cloud Based on Morphological Gradient

2018 ◽  
Vol 55 (5) ◽  
pp. 051203
Author(s):  
邓博文 Deng Bowen ◽  
王召巴 Wang Zhaoba ◽  
金永 Jin Yong ◽  
陈友兴 Chen Youxing ◽  
吴其洲 Wu Qizhou ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Point Cloud ◽  
Laser Scanning ◽  
Feature Extraction Method ◽  
Morphological Gradient
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