This thesis looks at the efficiency of large-footprint, waveform-digitizing lidar for the inventory and mapping of canopy fuels for use in fire behavior simulation systems. Due to its capability to measure the vertical structure of forest canopies lidar is distinctively matched with remote sensing devices to notice the canopy structure qualities related to fuels characterization and could assist tackle the absence of high-quality fuels data for several regions, particularly in more remote areas. Lidar data had been accumulated by the Laser Vegetation Imaging Sensor (LVIS) over the Sierra National Forest in California. Numerous waveform metrics had been computed from the waveforms. Field data was gathered at 135 plots co-located with a part of the lidar footprints. The field data had been utilised to estimate ground-based observations of canopy bulk density (CBD) and canopy base height (CBH). These found values of CBD and CBH were utilised as dependent variables in a series of regressi…
Contents: Canopy Fuels Inventory and Mapping Using Large-Footprint Lidar
List of Abbreviations
Chapter 1: Introduction
1.1 Canopy Fuels: The Evolving Need for Data
1.2 Background to Fire Behavior Modeling
1.3 Collecting Fuels Data: Field Sampling and Remote Sensing Methods
1.3.1 Ground-Based Measurement
1.3.2 Remote Sensing
1.4 Use of Remote Sensing in Fire Behavior Studies
1.5 Exploring the Use of Large-Footprint Lidar for Fuels Monitoring
Chapter 2: Study Area and Data Description
2.1 Study Site
2.2 Lidar Data
2.3 Field Data
Chapter 3: Predicting CBD from Lidar Metrics
3.1 Chapter Summary
3.2 Introduction
3.3 Objective
3.4 Methods
3.4.1 Comparison of Lidar Waveforms and Crown Volume Profiles
3.4.2 Derivation of CBD from Lidar
3.5 Results
3.5.1 Comparison of Lidar Waveforms and Crown Volume Profiles
3.5.2 Validation of CBD Derivation from Lidar
3.5.3 PCA
3.6 Discussion
3.6.1 Comparison of Lidar Waveform to Crown Volume Profiles
3.6.2 Derivation of CBD from Lidar
3.7 Conclusion
Chapter 4: Predicting CBH from Lidar Metrics
4.1 Chapter Summary
4.2 Introduction
4.3 Objective
4.4 Methods
4.4.1 Field-Based Canopy Base Height
4.4.2 Lidar Metrics
4.5 Results
4.5.1 Single Linear Regression Approach
4.5.2 Multiple Linear Regression Approach
4.5.3 PCA
4.6 Discussion
4.7 Conclusion
Chapter 5: FARSITE Simulations using Lidar-Derived Inputs
5.1 Chapter Summary
5.2 Introduction
5.3 Objectives
5.4 Methods
5.4.1 Generation and Comparison of FARSITE Input Data Layers
5.4.2 FARSITE Simulation and Output Comparison
5.4.3 Spatial Variability Analysis
5.4.4 Sensitivity Analysis
5.5 Results
5.5.1 Comparison of Input Data
5.5.2 FARSITE Output Using USFS Data
5.5.3 Output Using LVIS25 Data
5.5.4 Spatial Variability Analysis
5.5.5 Differences Between USFS and LVIS Output
5.5.6 Sensitivity Analysis…
Source: University of Maryland