Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Luis A Pérez-Rodríguez,Leonor Calvo,Elena Marcos,Alfonso Fernández-Manso,Carmen Quintano,Susana Suarez-Seoane

doi:10.3390/rs12081295

Luis A Pérez-Rodríguez, Leonor Calvo + Show 4 more

Open Access

https://doi.org/10.3390/rs12081295

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Abstract

Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.

Highlights

Wildfires are a natural phenomenon that have dramatically increased in number, extent and severity in the Mediterranean Basin due to huge territorial changes and global warming in recent decades, amongst other causes [1,2]
Vegetation burn severity refers to the effect on vegetation including short- and long-term impacts [5], whereas soil burn severity mainly refers to the loss of organic matter in soil [6]
Our goal is to evaluate the viability of images obtained by a multispectral sensor on board a Unmanned Aerial Vehicles (UAV) to estimate vegetation and soil burn severity after prescribed burning using an Artificial Neural Networks (ANNs)-based classifier

Summary

Introduction

Wildfires are a natural phenomenon that have dramatically increased in number, extent and severity in the Mediterranean Basin due to huge territorial changes and global warming in recent decades, amongst other causes [1,2]. Vegetation burn severity refers to the effect on vegetation including short- and long-term impacts [5], whereas soil burn severity mainly refers to the loss of organic matter in soil [6] In this context, technical advances in both in situ evaluation of fire damage and post-fire regeneration monitoring must be a priority for management purposes in fire prone areas [7]. Satellite images have been widely applied in this field, they might show certain weaknesses, such as low temporal resolution not controlled by the user, cloud cover or multispectral spatial resolution >1 m This could limit their use in post-fire monitoring studies requiring very high spatial resolution, such as those evaluating changes in soil organic carbon or soil structure [9,10]. Other relevant advantages are the possibility of user-programing flights for data collection in target areas and flexibility of type of sensor installed on board (for example, RedGreenBlue (RGB), multispectral or Laser Imaging Detection and Ranging (LiDAR)) [12]

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