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Carbon stock in tree biomass can be quantified directly by cutting and weighing trees. It is assumed that 50% of the dry weight of biomass consists of carbon. This direct measurement is the most accurate method, however for large areas it is considered time consuming and costly. Remote sensing has been proven to be an important tool for mapping and monitoring carbon stock from landscape to global scale in order to support forest management and policy practices. The study aimed to (1) develop regression models for estimating carbon stock of pine forests using field measurement and remotely sensed data; and (2) quantify soil carbon stock under pine forests using field measurement. The study was conducted in Kedung Bulus sub-watershed, Gombong - Central Java. The derived data from Satellite Probatoire d'Observation de la Terre (SPOT) included spectral band 1, 2, 3, and 4, Normalized Differences Vegetation Index (NDVI), and Principle Component Analysis (PCA) images. These data were integrated with field measurement to develop models. Soil samples were collected by augering for every 20 cm until a depth of  100 cm. The potential of  remote sensing to estimate carbon stock was shown by the strong correlation between multiple bands of SPOT (band 2 , 3; band 1, 2, 3; band 1, 3, 4; and band 1, 2, 3, 4) and carbon stock with r = 0.76, PCA (PC1, PC2, PC3) and carbon stock with r = 0.73. The role of pine forest to reduce CO2 in the atmosphere was demonstrated by the amount of carbon in the tree and the soil. Carbon stock in the tree biomass varied from 26 to 206 Mg C ha-1 and in the soil under pine forest ranged from 85 to 194 Mg C ha-1.

Carbon Stock Assessment in Pine Forest of Kedung Bulus Sub-Watershed (Gombong District) Using Remote Sensing and Forest Inventory Data