scholarly journals Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

2021 ◽  
Vol 13 (22) ◽  
pp. 4598
Author(s):  
Jeremy Arkin ◽  
Nicholas C. Coops ◽  
Lori D. Daniels ◽  
Andrew Plowright
Keyword(s):  
Forest Canopy ◽  
Lower Boundary ◽  
Point Clouds ◽  
Validation Dataset ◽  
Tree Level ◽  
Forest Fuels ◽  
Crown Length ◽  
Automated Method ◽  
Crown Base Height ◽  
Ground Based Lidar

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.

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.

Understory biomass estimation based on Structure from Motion data by Multi-layered forest drone survey

2021 ◽  
Author(s):  
Yupan Zhang ◽  
Yuichi Onda ◽  
Hiroaki Kato ◽  
Xinchao Sun ◽  
Takashi Gomi
Keyword(s):  
Structure From Motion ◽  
Forest Structure ◽  
Large Scale ◽  
Forest Canopy ◽  
Point Clouds ◽  
Understory Vegetation ◽  
Biomass Estimation ◽  
Canopy Openness ◽  
Cubic Model ◽  
Overlap Analysis

<p>Understory vegetation is an important part of evapotranspiration from forest floor. Forest management changes the forest structure and then affects the understory vegetation biomass (UVB). Quantitative measurement and estimation of  UVB is a step cannot be ignored in the study of forest ecology and forest evapotranspiration. However, large-scale biomass measurement and estimation is challenging. In this study, Structure from Motion (SfM) was adopted simultaneously at two different layers in a plantation forest made by Japanese cedar and Japanese cypress to reconstruct forest structure from understory to above canopy: i) understory drone survey in a 1.1h sub-catchment to generate canopy height model (CHM) based on dense point clouds data derived from a manual low-flying drone under the canopy; ii) Above-canopy drone survey in whole catchment (33.2 ha) to compute canopy openness data based on point clouds of canopy derived from an autonomous flying drone above the canopy. Combined with actual biomass data from field harvesting to develop regression models between the CHM and UVB, which was then used to map spatial distribution of  UVB in sub-catchment. The relationship between UVB and canopy openness data was then developed by overlap analysis. This approach yielded high resolution understory over catchment scale with a point cloud density of more than 20 points/cm<sup>2</sup>. Strong coefficients of determination (R-squared = 0.75) of the cubic model supported prediction of UVB from CHM, the average UVB was 0.82kg/m<sup>2</sup> and dominated by low ferns. The corresponding forest canopy openness in this area was 42.48% on average. Overlap analysis show no significant interactions between them in a cubic model with weak predictive power (R-squared < 0.46). Overall, we reconstructed the multi-layered structure of the forest and provided models of UVB. Understory survey has high accuracy for biomass measurement, but it’s inherently difficult to estimate UVB only based on canopy openness result.</p>

A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties

2021 ◽  
Author(s):  
Puliti Stefano ◽  
Grant D. Pears ◽  
Michael S. Watt ◽  
Edward Mitchard ◽  
Iain McNicol ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Method Development ◽  
Three Dimensional ◽  
Point Clouds ◽  
Benchmark Dataset ◽  
Validation Dataset ◽  
Tree Structures ◽  
Single Tree ◽  
Individual Trees

<p>Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m<sup>2</sup>. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.</p>

scholarly journals LiDAR-Based Wildfire Prevention in WUI: The Automatic Detection, Measurement and Evaluation of Forest Fuels

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 148 ◽  
Author(s):  
Marta Fernández-Álvarez ◽  
Julia Armesto ◽  
Juan Picos
Keyword(s):  
Point Clouds ◽  
Individual Tree ◽  
Forest Fuels ◽  
Gis Tools ◽  
Biomass Management ◽  
Measurement And Evaluation ◽  
The Individual ◽  
Individual Trees ◽  
Height Model

This paper describes a methodology using LiDAR point clouds with an ultra-high resolution in the characterization of forest fuels for further wildfire prevention and management. Biomass management strips were defined in three case studies using a particular Spanish framework. The data were acquired through a UAV platform. The proposed methodology allows for the detection, measurement and characterization of individual trees, as well as the analysis of shrubs. The individual tree segmentation process employed a canopy height model, and shrub cover LiDAR-derived models were used to characterize the vegetation in the strips. This way, the verification of the geometric legal restrictions was performed automatically and objectively using decision trees and GIS tools. As a result, priority areas, where wildfire prevention efforts should be concentrated in order to control wildfires, can be identified.

Site environment characterization of downed woody fuels in the Rincon Mountains, Arizona: regression tree approach

2004 ◽  
Vol 13 (4) ◽  
pp. 467 ◽  
Author(s):  
Erick Sánchez-Flores ◽  
Stephen R. Yool
Keyword(s):  
Remote Sensing ◽  
Fire History ◽  
Forest Canopy ◽  
Vegetation Type ◽  
Regression Tree ◽  
Successful Implementation ◽  
Forest Fuels ◽  
Sensing Technology ◽  
Rincon Mountains

Characterization of forest fuels is key to successful implementation of any fire management system. Great strides have been made in the characterization of forest canopy fuels by the use of remote sensing technology. Remote sensing of surface fuels is, however, limited by the physical intervention of the overlaying canopy. This limitation underscores the importance of exploring alternative approaches that relate site environment characteristics to the production and accumulation of understory fuels. This study predicts downed woody fuel loadings based on variables such as topography, fire history, and vegetation type in the forested area of the Rincon Mountains in southern Arizona. We used classification and regression trees (CART) to make these predictions. Results show that fine woody fuel loadings are predicted best by vegetation type and slope. Coarse woody fuels are predicted best by differences in elevation.

scholarly journals Estimation of diameter at breast height from mobile laser scanning data collected under a heavy forest canopy

2017 ◽  
Vol 63 (No. 9) ◽  
pp. 433-441 ◽  
Author(s):  
Čerňava Juraj ◽  
Tuček Ján ◽  
Koreň Milan ◽  
Mokroš Martin
Keyword(s):  
Laser Scanning ◽  
Forest Canopy ◽  
Mean Squared Error ◽  
Satellite System ◽  
Tree Level ◽  
Efficient Technology ◽  
Mobile Mapping ◽  
Mobile Mapping System ◽  
Global Navigation Satellite ◽  
Short Time

Mobile laser scanning (MLS) is time-efficient technology of geospatial data collection that proved its ability to provide accurate measurements in many fields. Mobile innovation of the terrestrial laser scanning has a potential to collect forest inventory data on a tree level from large plots in a short time. Valuable data, collected using mobile mapping system (MMS), becomes very difficult to process when Global Navigation Satellite System (GNSS) outages become too long. A heavy forest canopy blocking the GNSS signal and limited accessibility can make mobile mapping very difficult. This paper presents processing of data collected by MMS under a heavy forest canopy. DBH was estimated from MLS point cloud using three different methods. Root mean squared error varied between 2.65 and 5.57 cm. Our research resulted in verification of the influence of MLS coverage of tree stem on the accuracy of DBH data.

scholarly journals Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 117 ◽  
Author(s):  
František Chudý ◽  
Martina Slámová ◽  
Julián Tomaštík ◽  
Roberta Prokešová ◽  
Martin Mokroš
Keyword(s):  
Large Scale ◽  
Forest Canopy ◽  
Digital Elevation Models ◽  
Canopy Cover ◽  
Point Clouds ◽  
Tree Canopy ◽  
Automatic Identification ◽  
Optical Parameters ◽  
Micro Scale ◽  
Digital Elevation

An active gully-related landslide system is located in a deep valley under forest canopy cover. Generally, point clouds from forested areas have a lack of data connectivity, and optical parameters of scanning cameras lead to different densities of point clouds. Data noise or systematic errors (missing data) make the automatic identification of landforms under tree canopy problematic or impossible. We processed, analyzed, and interpreted data from a large-scale landslide survey, which were acquired by the light detection and ranging (LiDAR) technology, remotely piloted aircraft system (RPAS), and close-range photogrammetry (CRP) using the ‘Structure-from-Motion’ (SfM) method. LAStools is a highly efficient Geographic Information System (GIS) tool for point clouds pre-processing and creating precise digital elevation models (DEMs). The main landslide body and its landforms indicating the landslide activity were detected and delineated in DEM-derivatives. Identification of micro-scale landforms in precise DEMs at large scales allow the monitoring and the assessment of these active parts of landslides that are invisible in digital terrain models at smaller scales (obtained from aerial LiDAR or from RPAS) due to insufficient data density or the presence of many data gaps.

scholarly journals Detection of Very Small Tree Plantations and Tree-Level Characterization Using Open-Access Remote-Sensing Databases

2020 ◽  
Vol 12 (14) ◽  
pp. 2276
Author(s):  
Laura Alonso ◽  
Juan Picos ◽  
Guillermo Bastos ◽  
Julia Armesto
Keyword(s):  
Open Access ◽  
Point Cloud ◽  
Castanea Sativa ◽  
Point Clouds ◽  
Coefficient Of Determination ◽  
National Database ◽  
Tree Level ◽  
Tree Plantations ◽  
Support Vector ◽  
Individual Trees

Highly fragmented land property hinders the planning and management of single species tree plantations. In such situations, acquiring information about the available resources is challenging. This study aims to propose a method to locate and characterize tree plantations in these cases. Galicia (Northwest of Spain) is an area where property is extremely divided into small parcels. European chestnut (Castanea sativa) plantations are an important source of income there; however, it is often difficult to obtain information about them due to their small size and scattered distribution. Therefore, we selected a Galician region with a high presence of chestnut plantations as a case study area in order to locate and characterize small plantations using open-access data. First, we detected the location of chestnut plantations applying a supervised classification for a combination of: Sentinel-2 images and the open-access low-density Light Detection and Ranging (LiDAR) point clouds, obtained from the untapped open-access LiDAR Spanish national database. Three classification algorithms were used: Random Forest (RF), Support Vector Machine (SVM), and XGBoost. We later characterized the plots at the tree-level using the LiDAR point-cloud. We detected individual trees and obtained their height applying a local maxima algorithm to a point-cloud-derived Canopy Height Model (CHM). We also calculated the crown surface of each tree by applying a method based on two-dimensional (2D) tree shape reconstruction and canopy segmentation to a projection of the LiDAR point cloud. Chestnut plantations were detected with an overall accuracy of 81.5%. Individual trees were identified with a detection rate of 96%. The coefficient of determination R2 value for tree height estimation was 0.83, while for the crown surface calculation it was 0.74. The accuracy achieved with these open-access databases makes the proposed procedure suitable for acquiring knowledge about the location and state of chestnut plantations as well as for monitoring their evolution.

scholarly journals Processing Chain for Estimation of Tree Diameter from GNSS-IMU-Based Mobile Laser Scanning Data

2019 ◽  
Vol 11 (6) ◽  
pp. 615 ◽  
Author(s):  
Juraj Čerňava ◽  
Martin Mokroš ◽  
Ján Tuček ◽  
Michal Antal ◽  
Zuzana Slatkovská
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Forest Canopy ◽  
Point Clouds ◽  
Satellite System ◽  
Forest Road ◽  
Forest Mapping ◽  
Fitting Method ◽  
Progressive Technology ◽  
Global Navigation Satellite

Mobile laser scanning (MLS) is a progressive technology that has already demonstrated its ability to provide highly accurate measurements of road networks. Mobile innovation of the laser scanning has also found its use in forest mapping over the last decade. In most cases, existing methods for forest data acquisition using MLS result in misaligned scenes of the forest, scanned from different views appearing in one point cloud. These difficulties are caused mainly by forest canopy blocking the global navigation satellite system (GNSS) signal and limited access to the forest. In this study, we propose an approach to the processing of MLS data of forest scanned from different views with two mobile laser scanners under heavy canopy. Data from two scanners, as part of the mobile mapping system (MMS) Riegl VMX-250, were acquired by scanning from five parallel skid trails that are connected to the forest road. Misaligned scenes of the forest acquired from different views were successfully extracted from the raw MLS point cloud using GNSS time based clustering. At first, point clouds with correctly aligned sets of ground points were generated using this method. The loss of points after the clustering amounted to 33.48%. Extracted point clouds were then reduced to 1.15 m thick horizontal slices, and tree stems were detected. Point clusters from individual stems were grouped based on the diameter and mean GNSS time of the cluster acquisition. Horizontal overlap was calculated for the clusters from individual stems, and sufficiently overlapping clusters were aligned using the OPALS ICP module. An average misalignment of 7.2 mm was observed for the aligned point clusters. A 5-cm thick horizontal slice of the aligned point cloud was used for estimation of the stem diameter at breast height (DBH). DBH was estimated using a simple circle-fitting method with a root-mean-square error of 3.06 cm. The methods presented in this study have the potential to process MLS data acquired under heavy forest canopy with any commercial MMS.

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