Tropical peatlands are being subjected to the consequences of rapid economic development without any consideration of the importance of sustainable management practices. Sustainable management of tropical peatlands is an important element in controlling carbon emission. However, the available information of tropical peatlands lacks of accuracy and is outdated, especially in terms of medium to high resolution. Thus, development of reliable monitoring techniques is a significant step towards the sustainable management of tropical peatlands. The remote sensing (RS) application is suitable as a tool to monitor tropical peatlands, whereas direct measurements are generally labor-intensive, time-consuming and limited to accessibility. In this study, methodology to identify degraded tropical peatland was developed by using the McFeeters Normalized Difference Water Index (McFeeters-NDWI), which was derived by Advanced Land Observing Satellite (ALOS) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) data. Additionally, a seasonal analysis was carried out to examine the characteristics of degraded tropical peatland during the rainy and dry seasons from the viewpoint of the medium to high resolution of optical RS. Overall, a relationship was discovered such that the wet shrub class was considered as the degraded tropical peatland area, and was identified as being in between -0.43 to -0.11 of the McFeeters-NDWI value. The wet-shrub class yielded a producer's accuracy of 80.6% and a user's accuracy of 91.8%. Afterwards, the seasonal change was discovered to slightly shift the threshold values (TrVs) in the identification of degraded tropical peatland by as much as -0.05. However, the interval of the TrVs for the wet shrub class was stable and remained unchanged.
- Authors: Novresiandi, D.A., Nagasawa, R.
- Author Affiliation: Tottori University Japan, Bandung Institute of Technology
- Subjects: petlands, tropics, remote sensing, monitoring
- Publication type: Journal Article
- Source: Agriculture and Agricultural Science Procedia 11: 90-94
- Year: 2016
- DOI: https://doi.org/10.1016/j.aaspro.2016.12.015