Halimun Salak Corridor (HSC) is an important area that connects the Mount Halimun and Mount Salak, and has important role of animals movements. As the corridor have become degraded over the last ten years, ecosystem restoration action is required. In order to monitor that restoration program, then, it is necessary to mapping the vegetation cover in the corridor. Unmanned Aerial Vehicle (UAV) technology is an alternative technology that can be used to provide a detail vegetation cover map based on a high resolution image. This research aim to mapping vegetation cover based on a combination of structural characteristics of height and vegetation indices by using Object Based Image Analysis (OBIA) method. Structural characteristics was defined from the canopy height model (CHM) using the Structure from Motion (SfM) method, meanwhile, several spectral indices (NDVI, NDWI, and SAVI) were produced from multispectral images. We applied Object Based Image Analysis (OBIA) to classify vegetation cover based on their structure and spectral characteristics. The results shown that the most dominant vegetation cover is the tree class, which is 70.74 ha (77.31 % of the 91.5 ha mapped area) and accuracy test revealed 73.11% of overall accuracy.
Agroforestry/mixed gardens is a land management system that combines agricultural, livestock production with tree to obtain various products in a sustainable manner so as to increase social, economic and environmental benefits This system can be a form of mitigation and adaptation to global climate change, especially in areas with high population densities, but with less agricultural labor, such as in urban fringe area. Based on the formal definition of forests from the Indonesian Ministry of Environment and Forestry of Indonesia based on canopy cover, agroforestry might be considered as forest, whereas the canopy cover >30%. The research aim to estimate canopy cover base on integration of Lidar and Landsat 8 OLI of agroforestry in the Cidanau watershed. The most suitable equation model is an exponential equation (FRCI = 22.928e (-80.439 * <sup>'RED</sup>')), however, some underestimation in high canopy cover ( >70%) and underestimation in low canopy cover (< 60%) should be anticipated. The result showed that agroforestry in some location have canopy cover greater than 30% and therefore it can be considered as a forest.
Agroforestry is a land use management-system represents unique vegetation characteristics among tree vegetation types. Tree height is a vegetation variable used to characterize vertical structure, including mixed vegetation structure in agroforestry. Estimation of tree heights with multispectral imagery is a relatively new application and is dependent on integrating synoptic coverage optical data with samples of height data, often from LiDAR-derived reference data. In this study, multispectral Landsat 8 data, Unmanned Aerial Vehicle (UAV)-based LiDAR height data and a log-linear regression model were used to estimate tree height for agroforestry land use in western part of Java Island, Indonesia. We generated a Canopy Height Model (CHM) directly from height-normalized LiDAR points and used as reference data in modeling the key height variable in the multispectral bands of Landsat 8. The analysis showed that red band was the best band to estimate tree height in agroforestry land use, followed by swir band. The log-linear regression algorithm of red band accurately reproduced the LiDAR-derived height training data using Landsat 8 data with overestimate 1.46 m in estimating tree height < 5 m and underestimate 7.79 m for tree height > 20 m.
Precise digital classification for Landsat 8 data of remote sensing images require pre-processing steps. The preprocessing consist of conversion from digital numbers (DN) to top of atmosphere (TOA) reflectance, cloud and cloud shadow masking, topographic correction and image normalization. In general, pre-processing steps were implemented to National scale (Indonesia) excluding topographic correction. The topographic correction algorithm is required to avoid reflectance bias from terrain effects due to shading. The highest mountains in Indonesia were selected as window areas, considering the reflectance bias is produced due to terrain effects. The results showed that algorithm is able to solve overcorrection problems and will be implemented into LAPAN’s system of image pre-processing for National scale. This research is a collaboration between Bogor Agricultural University (IPB) with National Institute of Aeronautics and Space (LAPAN) under Forests2020 Programme, in order to produce Landsat 8 data with the minimal cloud over Indonesia annually and then to automatically digital classification for forest monitoring. The automated system of preprocessing was developed with Perl and Python programming languages.