Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images

Abstract

Sentinel-2 satellite images allow high separability for mapping burned and unburned areas. This problem has been extensively addressed using machine-learning algorithms. However, these need a suitable dataset and entail considerable training time. Recently, extreme learning machines (ELM) have presented high precision in classification and regression problems but with low computational cost. This paper proposes evaluating ELM to map burned areas and compare them with other machine-learning algorithms broadly used. Several indices, metrics and training times were used to assess the performance of the algorithms. Considering the average of datasets, the best performance was obtained by random forest (DICE = 0.93; omission and commission = 0.08) and ELM (DICE = 0.90; omission and commission = 0.07). The training time for the best model was from ELM (1.45 s) and logistic regression (1.85 s). According to results, ELM was the best burned-area classification algorithm, considering precision and training time, evidencing great potential to map burned areas at global scales with medium-high spatial resolution images. This information is essential to fire-risk systems and burned-area records used to design prevention and fire-combat strategies, and it provides valuable knowledge on the effect of fires on the landscape and atmosphere.

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

“…Based on satellite remote sensing image data and techniques, there are many studies using machine learning algorithms for burned area mapping [15,17,29,75,115]. Xulu et al [29] adopt Random Forest classifier to separate burned from unburned area using the Sentinel 2 data.…”

Comparison of convolutional neural network and support vector machine for identification of forest types and burned areas

“…Several approaches have been employed for burned area mapping, including visual interpretation, 33 spectral indices, 34 spectral unmixing, 35 radiative transfer models, 36 surface temperature inversion, 37 unstandardized principal component analysis, 38 and image classification 39 – 41 These methods have all obtained acceptable results 42 . Although many well-known spectral indices have been widely used for burned area detection, such as the normalized burned ratio, 43 burned area index, 44 and normalized difference SWIR, 45 these indices require pre-fire and post-fire satellite imagery, 46 – 48 which increases the data processing workload and errors.…”

Comparing Machine Learning and Time Series Approaches in Predictive Modeling of Urban Fire Incidents: A Case Study of Austin, Texas

“…Previous studies applied machine learning approaches to fire prediction, detection, and spread rate analysis [10][11][12][13][14]. For instance, researchers used machine learning algorithms to improve the classification of burn zones, which classifies how broadly an area may burn [15,16]. Machine learning models also can help identify environmental features that increase fire risks, such as extended drought conditions [17,18].…”