Structure-level fuel load assessment in the wildland–urban interface: a fusion of airborne laser scanning and spectral remote-sensing methodologies

2016 ◽  
Vol 25 (5) ◽  
pp. 547 ◽  
Author(s):  
Nicholas S. Skowronski ◽  
Scott Haag ◽  
Jim Trimble ◽  
Kenneth L. Clark ◽  
Michael R. Gallagher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Large Scale ◽  
Unit Area ◽  
Airborne Laser Scanning ◽  
Fuel Load ◽  
Spatial Averaging ◽  
Fuel Reduction ◽  
Wildland Urban Interface ◽  
Airborne Laser

Large-scale fuel assessments are useful for developing policy aimed at mitigating wildfires in the wildland–urban interface (WUI), while finer-scale characterisation is necessary for maximising the effectiveness of fuel reduction treatments and directing suppression activities. We developed and tested an objective, consistent approach for characterising hazardous fuels in the WUI at the scale of individual structures by integrating aerial photography, airborne laser scanning and cadastral datasets into a hazard assessment framework. This methodology is appropriate for informing zoning policy questions, targeting presuppression planning and fuel reduction treatments, and assisting in prioritising structure defence during suppression operations. Our results show increased variability in fuel loads with decreasing analysis unit area, indicating that fine-scale differences exist that may be omitted owing to spatial averaging when using a coarser, grid-based approach. Analyses using a local parcel database indicate that approximately 75% of the structures in this study have ownership of less than 50% of the 30 m buffer around their building, illustrating the complexity of multiple ownerships when attempting to manage fuels in the WUI. Our results suggest that our remote-sensing approach could augment, and potentially improve, ground-based survey approaches in the WUI.

scholarly journals Archaeological Surveying of Subarctic and Arctic Landscapes: Comparing the Performance of Airborne Laser Scanning and Remote Sensing Image Data

2021 ◽  
Vol 13 (4) ◽  
pp. 1917
Author(s):  
Alma Elizabeth Thuestad ◽  
Ole Risbøl ◽  
Jan Ingolf Kleppe ◽  
Stine Barlindhaug ◽  
Elin Rose Myrvoll
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Digital Terrain Model ◽  
Image Data ◽  
Remote Sensing Image ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Detection Rates ◽  
Airborne Laser ◽  
Pros And Cons

What can remote sensing contribute to archaeological surveying in subarctic and arctic landscapes? The pros and cons of remote sensing data vary as do areas of utilization and methodological approaches. We assessed the applicability of remote sensing for archaeological surveying of northern landscapes using airborne laser scanning (LiDAR) and satellite and aerial images to map archaeological features as a basis for (a) assessing the pros and cons of the different approaches and (b) assessing the potential detection rate of remote sensing. Interpretation of images and a LiDAR-based bare-earth digital terrain model (DTM) was based on visual analyses aided by processing and visualizing techniques. 368 features were identified in the aerial images, 437 in the satellite images and 1186 in the DTM. LiDAR yielded the better result, especially for hunting pits. Image data proved suitable for dwellings and settlement sites. Feature characteristics proved a key factor for detectability, both in LiDAR and image data. This study has shown that LiDAR and remote sensing image data are highly applicable for archaeological surveying in northern landscapes. It showed that a multi-sensor approach contributes to high detection rates. Our results have improved the inventory of archaeological sites in a non-destructive and minimally invasive manner.

scholarly journals Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon

2021 ◽  
Vol 13 (2) ◽  
pp. 261
Author(s):  
Francisco Mauro ◽  
Andrew T. Hudak ◽  
Patrick A. Fekety ◽  
Bryce Frank ◽  
Hailemariam Temesgen ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Field Measurements ◽  
Point Clouds ◽  
Semiparametric Models ◽  
Airborne Laser Scanning ◽  
Parametric Models ◽  
Fuel Load ◽  
Forest Fuels ◽  
Airborne Laser

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning

2016 ◽  
Vol 46 (9) ◽  
pp. 1138-1144 ◽  
Author(s):  
M. Maltamo ◽  
O.M. Bollandsås ◽  
T. Gobakken ◽  
E. Næsset
Keyword(s):  
Aboveground Biomass ◽  
Laser Scanning ◽  
Large Scale ◽  
Airborne Laser Scanning ◽  
Mountain Forests ◽  
Airborne Laser ◽  
Model Predictions ◽  
Growing Conditions ◽  
Southern Norway ◽  
Forest Conditions

This study considered airborne laser scanning (ALS) based aboveground biomass (AGB) prediction in mountain forests. The study area consisted of a long transect from southern Norway to northern parts of the country with wide ranges of elevation along a long latitudinal gradient (58°N–69°N). This transect was covered by ALS data and field data from 238 plots. AGB was modeled using different types of predictor variables, namely ALS metrics, variables related to growing conditions (elevation, latitude, and climatic variables), and tree species information. Modelling of AGB in the long transect covering diverse mountainous forest conditions was challenging: the RMSE values were rather large (37%–70%). The effects of growing conditions on model predictions were minor. However, species information was essential to improve accuracy. The analysis revealed that when doing inventories of spruce-dominated areas, all plots should be pooled together when the models are developed, whereas if pine or deciduous species dominate the area in question, separate dominant species-wise models should be constructed.

Airborne Laser Scanning in Poland - Between Science and Practice

2019 ◽  
Vol 31 (1) ◽  
pp. 105-133
Author(s):  
Zdzisław Kurczyński
Keyword(s):  
Remote Sensing ◽  
Retrospective Analysis ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Production Potential ◽  
Development Trends ◽  
The Past ◽  
Airborne Laser ◽  
National Production ◽  
Research Centres

Abstract The article is a retrospective analysis of the development of airborne laser scanning technology in the country in the past twenty years, i.e. from the beginnings of this technique use in Poland to the present day. The emphasis in the text is placed on development trends and scientific and application problems in the field of technology undertaken by national research centres. The review is based on numerous publications in this field, which have been released over two decades mainly in the “Archive of Photogrammetry, Cartography and Remote Sensing”. Therefore, the article is a presentation of the progress in the area of airborne laser scanning through an attempt to systematize and review national publications in this scope. It also presents the development of the national production potential and the level of the country’s coverage with data and products derived from airborne laser scanning.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

scholarly journals EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

2021 ◽  
Author(s):  
Jakob J. Assmann ◽  
Jesper E. Moeslund ◽  
Urs A. Treier ◽  
Signe Normand
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Airborne Laser Scanning ◽  
Data Sets ◽  
Ecosystem Structure ◽  
Data Set ◽  
Vegetation Height ◽  
Airborne Laser

Abstract. Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark's publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (~ 87 GB, compressed 16.4 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

Detecting moose (Alces alces) browsing damage in young boreal forests from airborne laser scanning data

2016 ◽  
Vol 46 (1) ◽  
pp. 10-19 ◽  
Author(s):  
M. Melin ◽  
J. Matala ◽  
L. Mehtätalo ◽  
A. Suvanto ◽  
P. Packalen
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Large Scale ◽  
Airborne Laser Scanning ◽  
Large Herbivores ◽  
Clear Cut ◽  
Airborne Laser ◽  
Browsing Damage ◽  
The Difference ◽  
Moose Browsing

Large herbivores can have large impacts on their habitats through extensive browsing. Similarly, human actions can have large impacts both on habitats and on the animals utilizing the habitats. In Finland, the increase in clear-cut areas has been highly positive for moose in particular, because these areas provide an easy and abundant source of winter food. For the forest owners, moose browsing causes growth and quality losses or even the destruction of whole stand. We aimed to identify moose browsing damage from airborne laser scanning (ALS) data and to predict damaged areas. The data was used to detect the difference in forest structure caused by moose browsing (lost branches and twigs) in relation to reference areas without moose browsing. The damaged areas were located, measured, and confirmed by forestry professionals, and ALS data was collected after the damage. In the end, the structural differences that browsing caused proved to be clear enough to be detected with metrics calculated from ALS data. Many variables were significantly different between the damage and no-damage areas. With logistic regression, we were able to differentiate the areas with significant, large-scale damage from no-damage areas with a 76% accuracy. However, the model was too keen to predict false-positive cases (classifying no-damage areas as damaged). It was shown that ALS data can be used in detecting moose browsing damage in a case where the damage is extremely severe (like in here). Yet, to make the results more accurate, better field data about the damaged areas would be needed.

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