The volume of forest stands is one of the results of the forest inventory as the basis for the preparation of a forest management plan. Tropical peat swamp forest in Central Kalimantan is very spacious with a difficult terrain to go. Inventory in all areas of peatland forests will require a long time and high cost. Remote sensing image is the solution to resolve the issue. The purpose of this study was: (1) Assess the coverage canopy peat swamp forests using transformations vegetation index digital data Landsat 7 ETM +, combined with field work and statistical analysis, (2) Predicting stand volume of peat swamp forest by using study the spectral coverage of the canopy; (3). Calculating the level of precision of the estimation of peat swamp forest stand volume obtained from the study of digital data spectral Landsat 7 ETM + imagery. The method used in this study is the technique of remote sensing digital image processing combined with field work and statistical analysis. Digital image processing is focused on aspects of the transformation of the vegetation index based on the pixel values ​​extracted from the image data of Landsat 7 ETM +. Rich tropical peat swamp forest canopy strata. In processing the strata canopy vegetation index will cause the shadow tree is a problem, because it will cause an error value of the index. This study will use the software that uses shadow correction and who do not use shadow correction. Both the software used is Forest Canopy Density (FCD) Mapper Version 1.0 and Enviromental for Visualizing Images (ENVI) version 4.2. FCD Mapper 1.0 is used to transform the Forest Canopy Density Index (FCDI) using the integration of four indices: Advances Vegetation Index (AVI), Bare Soil Index, Shadow Thermal Index and Index. ENVI 4.2 is used for the transformation of vegetation index using penisbahan between red and near infrared bands, namely Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Transformed Vegetation Index (TVI), Different Vegetation Index (DVI), Soil Adjusted Vegetation Index (SAVI) and Modified Soil Adjusted Vegetation Index (MSAVI). Forest class divisions based on digital classification results of each vegetation index transformation. Multispectral classification used is terselia classification (supervised classification) using the maximum likelihood algorithm (maximum likelihood algorithm). Using canopy coverage class divisions by class division that issued by the ITTO FCDI - JOFCA 2003 with modifications. Survey fieldwork was conducted at 32 sites each sample site sample consists of 9 pixels. Tree canopy density was measured at 9 pixels, but stand density, diameter, branch-free height, plant height and the height and other data required only measured at 2 pixels from each site selected examples. Data transformed vegetation index derived from Landsat 7 ETM + imagery and field parameters were analyzed using multiple regression models-correlation. The results of statistical calculations are used to observe the effect xvparameter field in the regression model. Regression models that qualify will be used to calculate the volume of the stand. These results indicate that it can be used to distinguish FCDI canopy coverage in tropical peat swamp forests to achieve 85.71% accuracy. What it means is that it can be used to assess FCDI peat swamp forest and tropical rain forest-rich tropical canopy layer. Instead of distinguishing canopy coverage, RVI, MSAVI, TVI, NDVI, SAVI and DVI each sequence can only provide accuracy at 65.01%, 64.86%, 61.41%, 61.15%, 59.14% and 55.92%. The case of the 543 and 453 composite image also shows the same thing. This means that both methods can not be used to assess the tropical peat swamp forests. Types of tropical peat swamp forest study area is peat swamp forest types 5 and 6, which means Whitmore classification rich canopy layer. FCDI can be inferred from the volume of forest stands within one pixel Landsat regression model Y = (X - 7.24) / 112.84 or forest stand volume = (FCDI - 7.24) / 112.84 with r = 0.90. Therefore, FCDI can be used to perform the analysis of canopy coverage and structure of peat swamp forest and tropical rain forests are rich with layers of canopy.