Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.

This study explores the use of structure from motion (SfM), a computer vision technique, to model vine canopy structure at a study vineyard in the Texas Hill Country. Using an unmanned aerial vehicle (UAV) and a digital camera, 201 aerial images (nadir and oblique) were collected and used to create a SfM point cloud. All points were classified as ground or non-ground points. Non-ground points, presumably representing vegetation and other above ground objects, were used to create visualizations of the study vineyard blocks. Further, the relationship between non-ground points in close proximity to 67 sample vines and collected leaf area index (LAI) measurements for those same vines was also explored. Points near sampled vines were extracted from which several metrics were calculated and input into a stepwise regression model to attempt to predict LAI. This analysis resulted in a moderate R² value of 0.567, accounting for 57 percent of the variation of LAI_SQRT using six predictor variables. These results provide further justification for SfM datasets to provide three-dimensional datasets necessary for vegetation structure visualization and biophysical modeling over areas of smaller extent. Additionally, SfM datasets can provide an increased temporal resolution compared to traditional three-dimensional datasets like those captured by light detection and ranging (lidar).

This study investigated how underlying biophysical attributes affect the characterization of the Surface Urban Heat Island (SUHI) phenomenon using (and comparing) two statistical techniques: global regression and geographically weighted regression (GWR). Land surface temperature (LST) was calculated from Landsat 8 imagery for 20 July 2015 for the metropolitan areas of Austin and San Antonio, Texas. We sought to examine SUHI by relating LST to Lidar-derived terrain factors, land cover composition, and landscape pattern metrics developed using the National Land Cover Database (NLCD) 2011. The results indicate that (1) land cover composition is closely related to the SUHI effect for both metropolitan areas, as indicated by the global regression coefficients of building fraction and NDVI, with values of 0.29 and −0.74 for Austin, and 0.19 and −0.38 for San Antonio, respectively. The terrain morphology was also an indicator of the SUHI phenomenon, implied by the elevation (0.20 for Austin and 0.09 for San Antonio) and northness (0.20 for Austin and 0.09 for San Antonio); (2) the SUHI phenomenon of Austin on 20 July 2015 was affected by the spatial pattern of the land use and land cover (LULC), which was not detected for San Antonio; and (3) with a local determination coefficient higher than 0.8, GWR had higher explanatory power of the underlying factors compared to global regression. By accommodating spatial non-stationarity and allowing the model parameters to vary in space, GWR illustrated the spatial heterogeneity of the relationships between different land surface properties and the LST. The GWR analysis of SUHI phenomenon can provide unique information for site-specific land planning and policy implementation for SUHI mitigation.

We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R² values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R² values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area.

Hot drought is a climate phenomenon that has received much attention lately for its potential to disrupt forest function worldwide. A sharp increase in tree mortality associated with this climate pattern is often cast as a disturbance, in which high temperature is responsible for causing exceptional rates of mortality across species. The alternative interpretation is simply that drought kills trees species in a density-dependent manner, related to the water deficit experienced by individual trees. To evaluate the evidence for density-dependent vs. -independent dynamics, we conducted censuses on 30 m × 30 m plots across 30 sites in Ashe juniper (Juniperus ashei) woodlands on the Edwards Plateau of central Texas, USA, 4 years after the hot drought of 2011. We hypothesized that variation in crown mortality of the two most dominant species, Ashe juniper and live oak (Quercus virginiana and Q. fusiformis), could be attributed to tree size, stand density, precipitation amount and/or bedrock depth; factors we considered indicative of density (resource)-dependent dynamics. We further hypothesized that crown mortality in subordinate species and the occurrence of resprouting in trees whose crowns had died would be more strongly controlled by air temperature, vapor pressure deficit, or aspect, acting independently of density. Through model selection analysis, we identified the most parsimonious binomial regression models for crown death in Ashe juniper, live oak and the understory shrub Texas persimmon (Diospyros texana). In Ashe juniper populations, overall crown mortality was 20% and largely followed hypothesized patterns. Live oak had an overall crown mortality rate of 23%, which declined rather than increased with tree density, and no evidence that crown death was linked to atmospheric conditions. Further in support of hypotheses, crown mortality in Texas persimmon and resprouting in Ashe juniper and live oak were significantly affected by exposure to hot and dry conditions and not by density-dependent factors. A surprise finding was the positive effect of bedrock depth on crown mortality. Based on data analysis, we hypothesized that excess precipitation in 2010 was stored in the fractured bedrock below the soil horizon, especially on sites where the soil layer was thin. In the following drought year, this “rock-stored” water rescued larger Ashe juniper trees and live oak trees from crown death, presumably because they had roots in the fractured rock zone.