Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, Hai Phong City, Vietnam, Using GIS-Based Kernel Logistic Regression

Abstract

Abstract:The Cat Ba National Park area (Vietnam) with its tropical forest is recognized as being part of the world biodiversity conservation by the United Nations Educational, Scientific and Cultural Organization (UNESCO) and is a well-known destination for tourists, with around 500,000 travelers per year. This area has been the site for many research projects; however, no project has been carried out for forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authorities. This work aims to produce a tropical forest fire susceptibility map for the Cat Ba National Park area, which may be helpful for the local authorities in forest fire protection management. To obtain this purpose, first, historical forest fires and related factors were collected from various sources to construct a GIS database. Then, a forest fire susceptibility model was developed using Kernel logistic regression. The quality of the model was assessed using the Receiver Operating Characteristic (ROC) curve, area under the ROC curve (AUC), and five statistical evaluation measures. The usability of the resulting model is further compared with a benchmark model, the support vector machine (SVM). The results show that the Kernel logistic regression model has a high level of performance in both the training and validation dataset, with a prediction capability of 92.2%. Since the Kernel logistic regression model outperforms the benchmark model, we conclude that the proposed model is a promising alternative tool that should also be considered for forest fire susceptibility mapping in other areas. The results of this study are useful for the local authorities in forest planning and management.

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

“…Consequently, the training of the SVR model required finding the best values for the two meta-parameters, the regularization parameter (C), and the kernel width (γ). For this task, the grid search method was used, as seen in Reference [66,67]. Accordingly, the SVR model was constructed using the best C = 18 and γ = 0.102 for the SVR model with the Sentinel-2A dataset, while the best C = 17 and γ = 0.102 were found for the ALOS-2 PALSAR-2 dataset.…”

Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics

“…Given the investment of resources and time required to suppress wildfires, efficient and adaptable techniques are needed to rapidly estimate the likelihood of fire occurrence. The first extensive works on predicting wildfire probability date back to Chuvieco and Congalton [12] in Spain and De Vliegher [13] in Greece, which were significantly elaborated by recent works [14][15][16], demonstrating that the future wildfires tend to occur under similar local conditions that caused them in the past.…”

“…Wildfire predictive modeling is typically performed in the following five steps [2,7,11,[14][15][16]: (1) Detecting and documenting historical fire events; (2) identifying a set of wildfire influencing factors; (3) seeking the potential relationships between the influencing factors and the historical fires; (4) elaborating a spatially explicit distribution map of wildfire probability; and (5) assessing the reliability of the probability map and its utility for predicting the location of future fires. Over the past decades, researchers mostly focused on step three of this methodology and evaluated various models in an explicitly spatial way to fully explore the pattern of wildfire occurrences in response to different geo-environmental factors [2,7,[14][15][16]. Apart from the machine learning techniques that have emerged in recent years [14,15], bivariate and multivariate methods have always been the most commonly used modeling approaches [7,17,18].…”

A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role

“…SA supports the researcher to analyze the significance of subjectivity components and produces useful information on the importance of weighting values assigned to the corresponding parameters. SA has been widely used and proposed to validate index and rating methods in different fields of study, including FFR assessment [62][63][64][65][66]. The effective weighting was calculated using the following equation:…”