scholarly journals Methods for separating orchards from forest using airborne LiDAR

2021 ◽  
Vol 78 (4) ◽  
Author(s):  
Tomasz Hycza ◽  
Przemysław Kupidura
Keyword(s):  
Climate Change ◽  
International Organizations ◽  
Laser Scanning ◽  
Pattern Analysis ◽  
Data Bank ◽  
Forest Vegetation ◽  
Forest Area ◽  
Airborne Laser ◽  
Granulometric Analysis

Abstract • Key message The aim of the study was to distinguish orchards from other lands with forest vegetation based on the data from airborne laser scanning. The methods based on granulometry provided better results than the pattern analysis. The analysis based on the Forest Data Bank/Cadastre polygons provided better results than the analysis based on the segmentation polygons. Classification of orchards and other areas with forest vegetation is important in the context of reporting forest area to international organizations, forest management, and mitigating effects of climate change. • Context Agricultural lands with forest vegetation, e.g., orchards, do not constitute forests according to the forest definition formulated by the national and international definitions, but contrary to the one formulated in the Kyoto Protocol. It is a reason for the inconsistency in the forest area reported by individual countries. • Aims The aim of the study was to distinguish orchards from other lands with forest vegetation based on the data from airborne laser scanning. • Methods The study analyzed the usefulness of various laser scanning products and the various features of pattern and granulometric analysis in the Milicz forest district in Poland. • Results The methods based on granulometry provided better results than the pattern analysis. The analysis based on the Forest Data Bank/Cadastre polygons provided better results than the analysis based on the segmentation polygons. • Conclusion Granulometric analysis has proved to be a useful tool in the classification of orchards and other areas with forest vegetation. It is important in the context of reporting forest area to international organizations, forest management, and mitigating effects of climate change.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

2020 ◽  
Vol 9 (4) ◽  
pp. 224
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Thin Plate ◽  
Laser Scanning ◽  
Ground Surface ◽  
Forest Vegetation ◽  
Airborne Laser Scanning ◽  
Canopy Density ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Dense Forest

A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.

scholarly journals Classification of Needle Loss of Individual Scots Pine Trees by Means of Airborne Laser Scanning

Forests ◽  
2013 ◽  
Vol 4 (2) ◽  
pp. 386-403 ◽  
Author(s):  
Tuula Kantola ◽  
Mikko Vastaranta ◽  
Päivi Lyytikäinen-Saarenmaa ◽  
Markus Holopainen ◽  
Ville Kankare ◽  
...  
Keyword(s):  
Scots Pine ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser ◽  
Pine Trees ◽  
Needle Loss

scholarly journals Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data

2014 ◽  
Vol 6 (2) ◽  
pp. 1347-1366 ◽  
Author(s):  
Mariana Belgiu ◽  
Ivan Tomljenovic ◽  
Thomas Lampoltshammer ◽  
Thomas Blaschke ◽  
Bernhard Höfle
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser

An approach to classification of airborne laser scanning point cloud data in an urban environment

2011 ◽  
Vol 32 (24) ◽  
pp. 9151-9169 ◽  
Author(s):  
Cici Alexander ◽  
Kevin Tansey ◽  
Jörg Kaduk ◽  
David Holland ◽  
Nicholas J. Tate
Keyword(s):  
Urban Environment ◽  
Point Cloud ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Airborne Laser

scholarly journals Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

2015 ◽  
Vol 7 (12) ◽  
pp. 17051-17076 ◽  
Author(s):  
Sudan Xu ◽  
George Vosselman ◽  
Sander Oude Elberink
Keyword(s):  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Airborne Laser

scholarly journals Možnosti srovnávací analýzy plužiny zaniklých středověkých vsí. Vypovídací hodnota vybraných lokalit a role digitálního modelu reliéfu z dat leteckého laserového skenování

2021 ◽  
Vol 6 (1-2) ◽  
pp. 177-196
Author(s):  
Ondřej Malina ◽  
Lukáš Holata ◽  
Jindřich Plzák
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Specific Data ◽  
Archaeological Heritage ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser ◽  
Data Source ◽  
Definition Of

The paper deals with the plowlands of deserted medieval villages (DMVs) representing a specific data source of medieval settlement research. Its basic priorities are based on the needs of archaeological heritage protection for a better definition of DMVs’ hinterlands, which are significantly less distinguishable in comparison with villages’ intravilans. At the same time, not much attention was paid to this area, even in known or well-surveyed sites. These issues are important especially in the context of what exactly we are looking for within the DMVs, how we define it and where we can find the best examples worthy of protection or further study. The basis of the presented work is the processing of a digital terrain model derived from airborne laser scanning data. The primary procedure consists of the ALS data processing into a DEM, its subsequent visualization, and classification of objects in DMVs’ hinterlands, which is further supplemented by selected examples of field verification. The informative value of the hinterlands is also discussed on the example of several differently preserved sites.

scholarly journals Drone data for decision making in regeneration forests: from raw data to actionable insights

2020 ◽  
Author(s):  
Stefano Puliti ◽  
Aksel Granhus
Keyword(s):  
Data Analytics ◽  
Logistic Model ◽  
Laser Scanning ◽  
Binary Classification ◽  
Multiple Linear Regression Model ◽  
Cost Models ◽  
Airborne Laser ◽  
Aerial Vehicle ◽  
The Cost

Unmanned aerial vehicle (UAV) photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from UAV data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior inventory information (Prior), and the combination of UAV and Prior and ALS and Prior. The use of UAV and Prior data outperformed the remaining alternatives in terms of classification of tending needs, while UAV alone produced the most accurate cost models. Our results are encouraging for further use of UAVs in the operational management of regeneration forests and show that UAV data and data analytics are useful for deriving actionable insights.

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