Spectral Mixture Analysis (SMA) of Landsat Imagery for Land Cover Change Study of Highly Degraded Peatland in Indonesia

Abstract

Commission VI, WG VI/4KEY WORDS: Tropical Peatland Degradation, Spectral Mixture Analysis, Endmember Analysis, Burned Peat Fraction, Pelalawan District. ABSTRACT:Indonesian peatland, one of the world's largest tropical peatlands, is facing immense anthropogenic pressures such as illegal logging, degradation and also peat fires, especially in fertile peatlands. However, there still is a lack of appropriate tools to assess peatland land cover change. By taking Pelalawan district located in Sumatra Island, this study determines number of land cover endmembers that can be detected and mapped using new generation of Landsat 8 OLI in order to develop highquality burned peat fraction images. Two different image transformations, i.e. Principle Component Analysis (PCA), Minimum Noise Fraction (MNF) and two different scatterplot analyses, i.e. global and local, were tested and their accuracy results were compared. Analysis of image dimensionality was reduced by using PCA. Pixel Purity Index (PPI), formed by using MNF, was used to identify pure pixel. Four endmembers consisting of two types of soil (peat soil and dry soil) and two types of vegetation (peat vegetation and dry vegetation) were identified according to the scatterplot and their associated interpretations were obtained from the Pelalawan Fraction model. The results showed that local scatterplot analysis without PPI masking can detect high accuracy burned peat endmember and reduces RMSE value of fraction image to improve classification accuracy.

Spatial Prioritization for Wildfire Mitigation by Integrating Heterogeneous Spatial Data: A New Multi-Dimensional Approach for Tropical Rainforests

“…In the context of tropical rainforests management, a multi-sensor data-intensive approach could be explored to study long-term forests dynamics in the priority areas [69,70]. In addition, land-suitability analysis could be conducted to support wildfire mitigation through rehabilitation and agroforestry programs [71][72][73][74]. New trends in image processing, such as artificial intelligence methods, especially machine learning [75] and deep learning, can improve forest fire susceptibility analysis in the future [76][77][78].…”

School Location Analysis by Integrating the Accessibility, Natural and Biological Hazards to Support Equal Access to Education

“…For instance, buildings in West Java Province are also susceptible to strong wind incidents [68]. Other disasters such as land subsidence [69,70], volcanic eruption [71,72], tsunami [73,74], and wildfire [75,76] should be considered in Indonesia. This multi-hazard analysis and comfort index could be applied in other potential sectors such as transportation [77] or other infrastructure developments such as power plants [78] and river networks [79].…”

A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

“…Improved cropping intensity and sowing month would enhance our ability to measure crop production and improve crop management [82]. The impact of this change on environmental health, including in forests [83][84][85], urban areas [86,87], and surface water or rivers [88], could also be explored. Further investigations are required to elucidate the reason for this sowing month change, the impact of this change on the food security situation, and future projections.…”