Chapters authored
Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests
Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.
Part of the book: Recent Remote Sensing Sensor Applications