Forest inventories such as tree canopy density information require a long time and high costs, especially on extensive forest coverage. Remote sensing technology that directly captures the surface vegetation character with extensive recording coverage can be used as an alternative to carrying out such inventory activities. This research aims to determine the level of vegetation canopy cover density on rubber plants that became the location of the research and know the accuracy of the resulting data. The method used in this research is a combination of remote sensing image interpretation, geographic information system, and field measurement. Information retrieval from remote sensing data is done by using ASTER data imagery. This stage includes three parts, namely: pre-field stage, field stage, and post-field stage. The pre-field stage includes the collection of data to be used (including literature studies related to the theme of the study), image processing (geometric and radiometric correction), cropping, masking, land cover classification, vegetation index transformation, and sample determination. The final result of data processing showed that the density of the vegetation canopy in the research area ranged between 7.31 – 12.952 cm / m2 in each grade of vegetation density. These values indicate the range of low-class vegetation canopy cover density to high-class vegetation canopy cover density in the research area. In this research error rate or root mean square error obtained from the calculation of canopy cover density is equal to 1.89.
Modified Soil-Adjusted Vegetation Index In Multispectral Remote Sensing Data for Estimating Tree Canopy Cover Density at Rubber Plantation
