2021 Fifth International Conference on Information Retrieval and Knowledge Management (CAMP) | 2021
An Approach to Mapping Deforestation in Permanent Forest Reserve Using the Convolutional Neural Network and Sentinel-1 Synthetic Aperture Radar
Abstract
Forests are essential for the protection of biodiversity and provide essential ecosystem services to humankind. Globally, 1.6 billion people depend on forests as fuel sources, construction materials, medicine, food, and freshwater sources. However, according to the monitoring service Global Forest Watch, our world lost 12 million hectares (30 million acres) of tropical tree cover in 2018, equal to 30 football pitches a minute. Surprisingly, Malaysia was among the top six countries that year with tremendous losses. Therefore, stern action is needed to increase public knowledge of ecosystem threats, improve strategic forest management decisions, and enforce land-use policies. This study aims to map deforestation in Permanent Forest Reserve (HSK) Yong in Pahang between 2017 and 2020 using satellite images of Sentinel-1 SAR. We further automate the classification of forest and non-forest by using a small and straightforward Convolutional Neural Network (CNN) model architecture. Our model used an open-source machine learning framework, namely Orfeo ToolBox TensorFlow (OTBTF). As well as machine learning, deep learning can be applied by OTBTF without image size restrictions and is computationally efficient, regardless of hardware configuration. The methodology includes data pre-processing, RGB composition, patches sampling, TensorFlow model train, TensorFlow model serve, and verification. Results show that the CNN approach with the Sentinel-1 Synthetic Aperture Radar (SAR) was 81.57 percent of the overall accuracy and the Kappa index was 0.6313. In brief, the approach mentioned in this study provides an alternative and reasonable approach for the HSK deforestation mapping in Peninsular Malaysia.