Inexpensive Method to Assess Mangroves Forest through the Use of Open Source Software and Data Available Freely in Public Domain

Abstract

Mapping and assessment of mangrove environment are crucial since the mangrove has an important role in the process of human-environment interaction. In Indonesia alone, 25% of South East Asia's mangroves available are under threat. Recognizing the availability and the ability of new sensor of Landsat data, this study investigates the use of Landsat ETM + 7 and Landsat 8, acquired in 2002 and 2013 respectively, for assessing the extent of mangroves along the South Sulawesi's coastline. For each year, a supervised classification of the mangrove was performed using open source GRASS GIS software. The resulting maps were then compared to quantify the change. Field work activities were conducted and confirmed with the changes that occurred in the study area. Considering the accuracy assessment, our study shows that the RGB composite color-supervised classification is better than band ratio-supervised classification methods. By linking the open source software with the Landsat data and Google Earth satellite imagery that is available in public domain, mangroves forest conversion and changes can be observed remotely. Ground truth surveys confirmed that, decreases of mangroves forest is due to the expansion of fishpond activity. This technique could potentially allow rapid, inexpensive remote monitoring of cascading, indirect effects of human activities to mangroves forest.

Assessment of the successfulness of mangrove plantation program through the use of open source software and freely available satellite

“…The first step is to convert the DN values into radiance values using the Lmin and Lmax spectral radiance scaling factors (USGS 2013). The values are specific to the individual scene and are produced in the image header file (Ramdani et al 2015). The gain and bias for TOA radiance calibration in the Landsat 4-5 TM and Landsat 7 ETM+ data could be read in the metadata(.MTL) file as "mult" and "add" values that are also similar to Landsat 8 OLI (USGS 2016).…”

“…It is considered as an independent vegetation index that was developed to delineate vegetation water content features and to enhance their presence in remotely sensed digital imagery (Alsaaideh et al 2013;Ramdani et al 2015). In the command console of GRASS GIS, the formula to calculate the NDWI is written as follows (equation 2).…”

“…The red channel in RGB composite was made by using the Blue Band in Landsat 7 ETM+ and Landsat 4-5 TM, while in Landsat 8 OLI the deep blue coastal band was used. NDVI image was used as the Green channel, and NDWI image as the Blue channel (Ramdani et al 2015). In the command console of GRASS GIS, the formula to calculate the RGB composite is written as follows (equation 3).…”

“…The gain and bias for TOA radiance calibration in the Landsat 4-5 TM and Landsat 7 ETM+ data could be read in the metadata(.MTL) file as "mult" and "add" values that are also similar to Landsat 8 OLI (USGS 2016). The "mult" is a multiplicative rescaling factor, while "add" is an additive rescaling factor for each band (Ramdani et al 2015). The second step is to convert the radiance values into Surface reflectance values using the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) algorithm.…”

“…Through this study, we want to help the evaluation and the monitoring of mangrove rehabilitation program undertaken by the government and to promote remote sensing as an inexpensive method to perform these activities with reliable results. Several studies, for example, have applied remote sensing technology for monitoring, evaluating, and conserving mangrove ecosystems by Huang et al (2009), Yeh (2009), Giri et al (2011), Kuenzer et al (2011), andRamdani et al (2015).…”