Tropical Forest Fire Susceptibility Mapping at the Cat Ba National Park Area, the Hai Phong city (Vietnam) using GIS-Based Kernel Logistic Regression

Abstract

The Cat Ba National Park area (Vietnam) with the tropical forest is recognized to be part of the world biodiversity conservation by United Nations Educational, Scientific and Cultural Oranization (UNESCO) and is a well-known destination for tourist with around 500,000 travellers per year. This area has been the site for many research projects; however no project has been carried out for the forest fire susceptibility assessment. Thus, protection of the forest including fire prevention is one of the main concerns of the local authority. 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 authority in the 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. The results show that the Kernel logistic regression model has high performance on both the training and validation dataset with a prediction capability of 92.2%. Since the Kernel logistic regression model outperform the benchmark model, we conclude that the proposed model is a promising alternative tool that should be considered for forest fire susceptibility mapping also for other areas. The result in this study is useful for the local authority in forest planning and management.