The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

Wildfire shapes vegetation assemblages in Mediterranean ecosystems, such as those in the state of California, United States. Successful restorative management of forests in-line with ecologically beneficial fire regimes relies on a thorough understanding of wildfire impacts on forest structure and fuel loads. As these data are often difficult to comprehensively measure on the ground, remote sensing approaches can be used to estimate forest structure and fuel load parameters over large spatial extents. Here, we analyze the capabilities of one such methodology, unoccupied aerial system structure from motion (UAS-SfM) from digital aerial photogrammetry, for mapping forest structure and wildfire impacts in the Mediterranean forests of northern California. To determine the ability of UAS-SfM to map the structure of mixed oak and conifer woodlands and to detect persistent changes caused by fire, we compared UAS-SfM derived metrics of terrain height and canopy structure to pre-fire airborne laser scanning (ALS) measurements. We found that UAS-SfM was able to accurately capture the forest’s upper-canopy structure, but was unable to resolve mid- and below-canopy structure. The addition of a normalized difference vegetation index (NDVI) ground point filter to the DTM generation process improved DTM root-mean-square error (RMSE) by ~1 m with an overall DTM RMSE of 2.12 m. Upper-canopy metrics (max height, 95th percentile height, and 75th percentile height) were highly correlated between ALS and UAS-SfM (r > +0.9), while lower-canopy metrics and metrics of density and vertical variation had little to no similarity. Two years after the 2017 Sonoma County Tubbs fire, we found significant decreases in UAS-SfM metrics of bulk canopy height and NDVI with increasing burn severity, indicating the lasting impact of the fire on vegetation health and structure. These results point to the utility of UAS-SfM as a monitoring tool in Mediterranean forests, especially for post-fire canopy changes and subsequent recovery.

[1]  Jonathan P. Dash,et al.  UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health , 2018, Remote. Sens..

[2]  David Saah,et al.  Modeling hazardous fire potential within a completed fuel treatment network in the northern Sierra Nevada , 2013 .

[3]  Joanne C. White,et al.  Airborne laser scanning and digital stereo imagery measures of forest structure: comparative results and implications to forest mapping and inventory update , 2013 .

[4]  Douglas C. Morton,et al.  Mapping tall shrub biomass in Alaska at landscape scale using structure-from-motion photogrammetry and lidar , 2020 .

[5]  Lara A. Arroyo,et al.  Fire models and methods to map fuel types: The role of remote sensing , 2008 .

[6]  Fernando Carvajal-Ramírez,et al.  A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest , 2019, Remote. Sens..

[7]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

[8]  Rudolf Urban,et al.  Sensitivity analysis of parameters and contrasting performance of ground filtering algorithms with UAV photogrammetry-based and LiDAR point clouds , 2020, Int. J. Digit. Earth.

[9]  Jon E. Keeley,et al.  Mediterranean Biomes: Evolution of Their Vegetation, Floras, and Climate , 2016 .

[10]  B. Quayle,et al.  A Project for Monitoring Trends in Burn Severity , 2007 .

[11]  Wuming Zhang,et al.  An Easy-to-Use Airborne LiDAR Data Filtering Method Based on Cloth Simulation , 2016, Remote. Sens..

[12]  A. Taylor,et al.  Fire history and the structure and dynamics of a mixed conifer forest landscape in the northern Sierra Nevada, Lake Tahoe Basin, California, USA , 2008 .

[13]  D. Morton,et al.  Quantifying Boreal Forest Structure and Composition Using UAV Structure from Motion , 2018 .

[14]  M. Moritz,et al.  Alternative community states maintained by fire in the Klamath Mountains, USA , 2010 .

[15]  S. Barr,et al.  Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations , 2019, Remote Sensing of Environment.

[16]  M. Clark,et al.  Topoclimates, refugia, and biotic responses to climate change , 2020, Frontiers in Ecology and the Environment.

[17]  Behnaz Bigdeli,et al.  DTM extraction under forest canopy using LiDAR data and a modified invasive weed optimization algorithm , 2018, Remote Sensing of Environment.

[18]  R. Cowling,et al.  Plant diversity in mediterranean-climate regions. , 1996, Trends in ecology & evolution.

[19]  Tristan R. H. Goodbody,et al.  Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems , 2018 .

[20]  Fernando Montes-Gonzalez,et al.  Vertical forest structure analysis for wildfire prevention: Comparing airborne laser scanning data and stereoscopic hemispherical images , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Joanne C. White,et al.  Quantifying the contribution of spectral metrics derived from digital aerial photogrammetry to area-based models of forest inventory attributes , 2019 .

[22]  Joaquim J. Sousa,et al.  Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery , 2020, ISPRS Int. J. Geo Inf..

[23]  M. Lefsky,et al.  Comparison and integration of lidar and photogrammetric point clouds for mapping pre-fire forest structure , 2019, Remote Sensing of Environment.

[24]  Fernando Carvajal-Ramírez,et al.  Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV , 2019, Remote. Sens..

[25]  Satoshi Tsuyuki,et al.  Evaluating the Performance of Photogrammetric Products Using Fixed-Wing UAV Imagery over a Mixed Conifer-Broadleaf Forest: Comparison with Airborne Laser Scanning , 2018, Remote. Sens..

[26]  Joseph M. Burgett,et al.  Assessing the Accuracy of Unmanned Aerial Vehicles Photogrammetric Survey , 2020 .

[27]  Nicholas C. Coops,et al.  Evaluation of Ground Surface Models Derived from Unmanned Aerial Systems with Digital Aerial Photogrammetry in a Disturbed Conifer Forest , 2019, Remote. Sens..

[28]  Eben N. Broadbent,et al.  Single-Pass UAV-Borne GatorEye LiDAR Sampling as a Rapid Assessment Method for Surveying Forest Structure , 2020, Remote. Sens..

[29]  F. Lloret,et al.  Influence of fire severity on plant regeneration by means of remote sensing imagery , 2003 .

[30]  Nicholas C. Coops,et al.  Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models , 2018, Remote. Sens..

[31]  Jay D. Miller,et al.  Quantitative Evidence for Increasing Forest Fire Severity in the Sierra Nevada and Southern Cascade Mountains, California and Nevada, USA , 2009, Ecosystems.

[32]  Simon D. Jones,et al.  A comparison of terrestrial and UAS sensors for measuring fuel hazard in a dry sclerophyll forest , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Mariano García,et al.  Satellite Remote Sensing Contributions to Wildland Fire Science and Management , 2020, Current Forestry Reports.

[34]  Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires , 2019, Fire Ecology.

[35]  Terje Gobakken,et al.  A new approach with DTM-independent metrics for forest growing stock prediction using UAV photogrammetric data , 2018, Remote Sensing of Environment.

[36]  Marc Olano,et al.  Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure , 2015, Remote. Sens..

[37]  Lluís Brotons,et al.  Greenness Indices from a Low-Cost UAV Imagery as Tools for Monitoring Post-Fire Forest Recovery , 2019, Drones.

[38]  I. Gitas,et al.  A national fuel type mapping method improvement using sentinel-2 satellite data , 2020, Geocarto International.

[39]  Simon D. Jones,et al.  Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover , 2019, Forests.

[40]  Scott L. Stephens,et al.  Fire Climbing in the Forest: A Semiqualitative, Semiquantitative Approach to Assessing Ladder Fuel Hazards , 2007 .

[41]  Guangjian Yan,et al.  Filtering Airborne LiDAR Data Through Complementary Cloth Simulation and Progressive TIN Densification Filters , 2019, Remote. Sens..

[42]  Benjamin P. Bryant,et al.  Climate change and wildfire in California , 2008 .

[43]  Maggi Kelly,et al.  Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications , 2020, Remote. Sens..

[44]  B. Quayle,et al.  Changes to the Monitoring Trends in Burn Severity program mapping production procedures and data products , 2020 .

[45]  Andrew M. Cunliffe,et al.  Ultra-fine grain landscape-scale quantification of dryland vegetation structure with drone-acquired structure-from-motion photogrammetry , 2016 .

[46]  Maggi Kelly,et al.  Quantifying Ladder Fuels: A New Approach Using LiDAR , 2014 .

[47]  S. Reutebuch,et al.  Estimating forest canopy fuel parameters using LIDAR data , 2005 .

[48]  Erle C. Ellis,et al.  High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision , 2013 .

[49]  Nicholas J. Nauslar,et al.  The 2017 North Bay and Southern California Fires: A Case Study , 2018, Fire.

[50]  P. Dennison,et al.  A multi-sensor, multi-scale approach to mapping tree mortality in woodland ecosystems , 2020 .

[51]  Joanne C. White,et al.  Comparison of airborne laser scanning and digital stereo imagery for characterizing forest canopy gaps in coastal temperate rainforests , 2018 .