#TITLE_ALTERNATIVE#

• Main Author: ILHAM (NIM 15107004); Pembimbing: Prof. Ketut Wikantika, Ph.D. dan Dr. Firman Hadi, MUHAMMAD
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/14334
• Institution: Institut Teknologi Bandung

In the process of suppressing the increase of Global Warming, there are several solutions that can be considered. One of them is to optimize forest ecosystem as CO2 storage. It has been understood that 80% of Carbon (C) terrestrial stored in the forest ecosystem, and the other 40% stored in the ground. Activities such as exploration and exploitation against nature (deforestation, degradation, and reforestation), potentially change the biomass composition above and below the ground. Therefore, we need accurate information which can be trusted to estimate the amount of carbon stock stored at above ground biomass. The purpose of this research is to create mathematical model to estimate the amount of carbon stock. The estimation of carbon stock has been done with regression method which involves vegetation indices (as independent variable) using LANDSAT 5 TM images as raw satellite data located in a part of West Java, Indonesia. The satellite data were taken on July, 2, 2005 and the field data of carbon stock (as sample) were collected on July 2008 – August 2008. The amount of Carbon stock was approached by two ways of multiple regressions which are linearly and exponentially. <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> On linearly regression, the output is Y=102.135*WI-295.647*NDVI-23 with R-square value of 0.78, and exponential regression, the output is Y= 0.192 * (&#119890;&#119890;&#119890;&#119890;&#119890;&#119890;(0.394 &#119909; &#119878;&#119878;&#119878;&#119878;)+ &#119890;&#119890;&#119890;&#119890;&#119890;&#119890;(1.114 &#119909; &#119882;&#119882;&#119882;&#119882;)+ &#119890;&#119890;&#119890;&#119890;&#119890;&#119890;(5.405 &#119909; &#119873;&#119873;&#119873;&#119873;&#119882;&#119882;&#119882;&#119882;)) with R2 value of 0.865. Study result, showed that the combination of using more than one independent variable (multiple regressions) can create a better mathematic model compared to just using only one independent variable (single regression). The increasing of R2 value, indicate this result.