Assessing Potential Safety Zone Suitability Using a New Online Mapping Tool

Safety zones (SZs) are critical tools that can be used by wildland firefighters to avoid injury or fatality when engaging a fire. Effective SZs provide safe separation distance (SSD) from surrounding flames, ensuring that a fire’s heat cannot cause burn injury to firefighters within the SZ. Evaluating SSD on the ground can be challenging, and underestimating SSD can be fatal. We introduce a new online tool for mapping SSD based on vegetation height, terrain slope, wind speed, and burning condition: the Safe Separation Distance Evaluator (SSDE). It allows users to draw a potential SZ polygon and estimate SSD and the extent to which that SZ polygon may be suitable, given the local landscape, weather, and fire conditions. We begin by describing the algorithm that underlies SSDE. Given the importance of vegetation height for assessing SSD, we then describe an analysis that compares LANDFIRE Existing Vegetation Height and a recent Global Ecosystem Dynamics Investigation (GEDI) and Landsat 8 Operational Land Imager (OLI) satellite image-driven forest height dataset to vegetation heights derived from airborne lidar data in three areas of the Western US. This analysis revealed that both LANDFIRE and GEDI/Landsat tended to underestimate vegetation heights, which translates into an underestimation of SSD. To rectify this underestimation, we performed a bias-correction procedure that adjusted vegetation heights to more closely resemble those of the lidar data. SSDE is a tool that can provide valuable safety information to wildland fire personnel who are charged with the critical responsibility of protecting the public and landscapes from increasingly intense and frequent fires in a changing climate. However, as it is based on data that possess inherent uncertainty, it is essential that all SZ polygons evaluated using SSDE are validated on the ground prior to use.

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

Comparison between Topographic and Bathymetric LiDAR Terrain Models in Flood Inundation Estimations

Remotely sensed LiDAR data has allowed for more accurate flood map generation through hydraulic simulations. Topographic and bathymetric LiDARs are the two types of LiDAR used, of which the former cannot penetrate water bodies while the latter can. Usually, the topographic LiDAR is more available than bathymetric LiDAR, and it is, therefore, a very interesting data source for flood mapping. In this study, we made comparisons between flood inundation maps from several flood scenarios generated by the HEC-RAS 2D model for 11 sites in Norway using both bathymetric and topographic terrain models. The main objective is to investigate the accuracy of the flood inundations generated from the plain topographic LiDAR, the links of the inaccuracies with geomorphic features, and the potential of using corrections for missing underwater geometry in the topographic LiDAR data to improve accuracy. The results show that the difference in inundation between topographic and bathymetric LiDAR models decreases with increasing the flood size, and this trend was found to be correlated with the amount of protection embankments in the reach. In reaches where considerable embankments are constructed, the difference between the inundations increases until the embankments are overtopped and then returns to the general trend. In addition, the magnitude of the inundation error was found to correlate positively with the sinuosity and embankment coverage and negatively with the angle of the bank. Corrections were conducted by modifying the flood discharge based on the flight discharge of the topographic LiDAR or by correcting the topographic LiDAR terrain based on the volume of the flight discharge, where the latter method generally gave better improvements.

Identifying Forest Structural Types along an Aridity Gradient in Peninsular Spain: Integrating Low-Density LiDAR, Forest Inventory, and Aridity Index

Forest structure is a key driver of forest functional processes. The characterization of forest structure across spatiotemporal scales is essential for forest monitoring and management. LiDAR data have proven particularly useful for cost-effectively estimating forest structural attributes. This paper evaluates the ability of combined forest inventory data and low-density discrete return airborne LiDAR data to discriminate main forest structural types in the Mediterranean-temperate transition ecotone. Firstly, we used six structural variables from the Spanish National Forest Inventory (SNFI) and an aridity index in a k-medoids algorithm to define the forest structural types. These variables were calculated for 2770 SNFI plots. We identified the main species for each structural type using the SNFI. Secondly, we developed a Random Forest model to predict the spatial distribution of structural types and create wall-to-wall maps from LiDAR data. The k-medoids clustering algorithm enabled the identification of four clusters of forest structures. A total of six out of forty-one potential LiDAR metrics were utilized in our Random Forest, after evaluating their importance in the Random Forest model. Selected metrics were, in decreasing order of importance, the percentage of all returns above 2 m, mean height of the canopy profile, the difference between the 90th and 50th height percentiles, the area under the canopy curve, and the 5th and the 95th percentile of the return heights. The model yielded an overall accuracy of 64.18%. The producer’s accuracy ranged between 36.11% and 88.93%. Our results confirm the potential of this approximation for the continuous monitoring of forest structures, which is key to guiding forest management in this region.

Storage Method of Multi-Channel Lidar Data Based on Tree Structure

The multi-channel lidar has the characteristics of fast acquisition speed, large data volume, high dimension, and strong real-time storage, which makes it difficult to be met using the traditional lidar data storage methods. This paper presents a novel approach to store and convert the multi-channel lidar data by traversal method of the tree structure and binary code. In the proposed approach, a tree structure is constructed based on the multi-dimensional characteristics of multi-channel lidar data and the hierarchical relationship between them. The adjacency table storage structure data in the memory is used to generate the sub-tree of the multi-channel lidar data. The results show that the proposed tree structure approach can save the storage capacity and improve the retrieval speed, which can meet the needs of efficient storage and retrieval of multi-channel lidar data.