Abstract
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.
Highlights
Wildfires significantly impact environments and communities around the world by changing vegetation composition [1], altering soil characteristics lasting years after the fire [2,3], and modifying hydrologic regimes by increasing runoff and decreasing soil infiltration [4,5]
The goal of this paper is to provide the reader with a snapshot of the current applications of remote sensing technologies and techniques in the field of fire ecology
Veraverbeke and Hook [159] compared spectral mixture analysis (SMA) to spectral indices (NBR, dNBR, relative difference NBR (RdNBR)) for burn severity detection. They found that the dNBR performed best but noted that both approaches performed well and that SMA has the advantage of providing transferable quantitative data which does not require field data for calibration
Summary
Wildfires significantly impact environments and communities around the world by changing vegetation composition [1], altering soil characteristics lasting years after the fire [2,3], and modifying hydrologic regimes by increasing runoff and decreasing soil infiltration [4,5]. Remote sensing systems provide biophysical measurements of the ground conditions prior to and post-fire These measurements have been used to assist in fire risk mapping [13,14,15], fuel mapping [16,17,18,19,20], active fire detection [21,22,23,24,25], burned area estimates [26,27,28], assessing burn severity [29,30,31,32], and monitoring vegetation recovery [33,34,35]. 2019, 11, 2638 so for the purpose of this review, remote sensing is limited to imagery acquired by orbital sensors, lidar systems, and UASs. The paper examines research in several broad categories, which include fire risk modeling, fuel mapping, active fire detection, burned area estimates, burn severity assessments, and monitoring vegetation recovery. The goal of this paper is to provide the reader with a snapshot of the current applications of remote sensing technologies and techniques in the field of fire ecology
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