Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery

Abstract

Forest fires threaten the population's health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km2 fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results.

Machine learning algorithm for estimating karst rocky desertification in a peak-cluster depression basin in southwest Guangxi, China

“…However, because of its high stability and its ability to perform efficient processing of large-scale data 64 , the random forest classifier is a more practical integrated learning method, and it can effectively reduce the error of a single classifier and improve the classification accuracy using multiple classifiers for voting classification. Random forest algorithms have randomness in sample and feature selection, which makes it difficult for random forest to fall into overfitting and gives it a good antinoise ability 65 , 66 . In this study, a bagging integrated random forest classification algorithm was used to predict the degree of karst rocky desertification.…”

A Method for SMS Spam Message Detection Using Machine Learning

“…As the primary KNN adjustment parameter, the parameter k is critically important to the operation of the KNN and plays a significant role in its performance. In this work, we investigated a variety of k values, ranging from five to twenty, in order to identify the best parameter for the KNN classifier based on the lowest estimate of the root mean square error (RMSE) (Pacheco et al, 2021). We used a variety of data subsets to conduct these tests.…”

Vegetation Mapping with Random Forest using Sentinel 2- A case study for Lousã region, Portugal

“…Sentinel-2 includes three red-edged vegetation and the SWIR bands that are highly susceptible to chlorophyll content and amend distinguishing different vegetation types and LCLU classification accuracy (Chaves et al 2020). Several studies found Sentinel-2 data with high potential in different applications such as crop classification (Hernandez et al 2020), tree species classification (Costa et al 2022, Wessel et al 2018, Persson et al 2018, mapping burned area (Pacheco et al 2021), and forest type classification (Kaplan 2021). Most of the recent studies have shown that non-parametric machine learning approaches such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) have a great potential to classify heterogeneous land covers (Wessel et al 2018, Pacheco et al 2021, Sheykhmousa et al 2020.…”

“…Several studies found Sentinel-2 data with high potential in different applications such as crop classification (Hernandez et al 2020), tree species classification (Costa et al 2022, Wessel et al 2018, Persson et al 2018, mapping burned area (Pacheco et al 2021), and forest type classification (Kaplan 2021). Most of the recent studies have shown that non-parametric machine learning approaches such as Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF) have a great potential to classify heterogeneous land covers (Wessel et al 2018, Pacheco et al 2021, Sheykhmousa et al 2020. found RF as the mostly used supervised classifier and more stable object-based image analysis (OBIA) with the highest mean accuracy of 85.81%, followed by SVM through reviewing 173 publications on supervised object-based classification.…”