A Comparison of Two Machine Learning Classification Methods for Remote Sensing Predictive Modeling of the Forest Fire in the North-Eastern Siberia

Abstract

The problem of forest fires in Yakutia is not as well studied as in other countries. Two methods of machine learning classifications were implemented to determine the risk of fire: MaxENT and random forest. The initial materials to define fire risk factors were satellite images and their products of various spatial and spectral resolution (Landsat TM, Modis TERRA, GMTED2010, VIIRS), vector data (OSM), and bioclimatic variables (WORLDCLIM). The results of the research showed a strong human influence on the risk in this region, despite the low population density. Anthropogenic factors showed a high correlation with the occurrence of wildfires, more than climatic or topographical factors. Other factors affect the risk of fires at the macroscale and microscale, which should be considered when modeling. The random forest method showed better results in the macroscale, however, the maximum entropy model was better in the microscale. The exclusion of variables that do not show a high correlation, does not always improve the modeling results. The random forest presence prediction model is a more accurate method and significantly reduces the risk territory. The reverse is the method of maximum entropy, which is not as accurate and classifies very large areas as endangered. Further study of this topic requires a clearer and conceptually developed approach to the application of remote sensing data. Therefore, this work makes sense to lay the foundations of the future, which is a completely automated fire risk assessment application in the Republic of Sakha. The results can be used in fire prophylactics and planning fire prevention. In the future, to determine the risk well, it is necessary to combine the obtained maps with the seasonal risk determined using indices (for example, the Nesterov index 1949) and the periodic dynamics of forest fires, which Isaev and Utkin studied in 1963. Such actions can help to build an application, with which it will be possible to determine the risk of wildfire and the spread of fire during extreme events.

A Comparative Study about Vertical Accuracy of Four Freely Available Digital Elevation Models: A Case Study in the Balsas River Watershed, Brazil

“…GMTED2010 was elaborated using derived data from 11 raster-based elevation sources (Table 1), which provides global coverage from latitude 84°N to 56°S for most products at three different resolutions, 7.5, 15, and 30 arc-seconds, that correspond to nearly 250, 500, and 1000 m of pixel size, respectively [22]. In this study, we selected the GMTED2010 product available in 7.5 arc-seconds resolution, which is widely used in several scientific studies [11,[23][24][25][26][27][28][29] despite its bigger pixel size when compared with SRTM, for instance. Table 2 presents the original main characteristics of the four DEMs assessed in this study.…”

Wildfire Risk Zone Mapping in Contrasting Climatic Conditions: An Approach Employing AHP and F-AHP Models

“…However, the question of which techniques to adopt is also a matter of concern. Researchers applied models such as AHP [22], Fuzzy-AHP (F-AHP) [23], frequency ratio [24], fuzzy logic [25], logistic regression [26], artificial neural network (ANN) [26], analytical network process [25], support vector machine (SVM) [27], naïve Bayes [28], random forest [29], and decision tree [27] for mapping wildfire risk zones. AHP sorts and compares variables based on their entities and categorizes them into hierarchies or groups [30].…”

A Highly Accurate Forest Fire Prediction Model Based on an Improved Dynamic Convolutional Neural Network

“…The steps for monitoring them are (1) identifying hot spots, (2) assessing fire risks, (3) identifying areas vulnerable to forest fires, (4) recognizing manmade infrastructure, and (5) examining the meteorological conditions. These are considered to be influential parameters that play an important role when establishing a model to assess the risk of a forest fire [4][5][6][7]. Generally speaking, the current identification method of forest fire insurance is traditional and singular.…”