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.
Highlights
Forests provide resources for millions of people and make a high contribution to employment, economic development, and terrestrial biodiversity in many countries [1,2]
According to the Department of Forest Protection of Vietnam (DoFP), there were around 704 forest fires yearly during the period of 2002 to 2010, which resulted in a loss of 5081.9 ha forest annually [8]
Kernel logistic regression (KLR) is a powerful machine learning classification method where probabilistic outcomes are estimated based on minimizing the negative log-likelihood function using the Broyden–Fletcher–Goldfarb–Shanno (BFGS) optimization [40]
Summary
Forests provide resources for millions of people and make a high contribution to employment, economic development, and terrestrial biodiversity in many countries [1,2]. For the case of Vietnam, forests occupy around 42.1% of the total land area in which forest plantations cover 3.5 million ha and 10.4 million ha are natural forests [5]. Together with tropical storms and floods, forest fires are the most common disasters that recurrently occur in the country, causing huge economic losses and devastating natural ecological systems and the environment [6,7]. According to the Department of Forest Protection of Vietnam (DoFP), there were around 704 forest fires yearly during the period of 2002 to 2010, which resulted in a loss of 5081.9 ha forest annually [8]
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