No abstract

<p>Among various stand parameters, the density of biomass volume is oftenly used as an indicator on evaluating the forest growth succes. The forest reclamation, which is intended to restore the land cover by revegetation process, the evaluation of biomass content has been a critical issue. Forest reclamation is expected to restore the land function to a proper state that might give better environment as well as productivity. In this study the authors develop a method for estimating above ground biomass (AGB), particularly in the ex open-pit coal mining area of PT. Bukit Asam Tbk using remotely-sended data taken from unmanned aerial vehicle (UAV) and developed using the least squares method. The main objective of this study is to develop a mathematical model of biomass estimation using UAV imagery having 10-cm spatial resolution. The study found that the best model of biomass estimation is: AGB(ton/ha)=0.2377Ci^1.3688 with the correlation coefficient of 0.844, mean deviation of 2.29, aggregate deviation of -0.023, bias of 0.98, and Root Mean Square Error (RMSE) of 1.784 and mean deviation (MD) &lt; 10% while Ci. This research concluded that UAV imagery could be used to estimate above ground biomass accurately.</p>

In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m.  The estimator models were established based on 65 sample plots both measured in the field and images.  The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e^0.032C with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e^16.48TNDVI with a mean deviation of 5.37%.

One of the primary sectors that contributes to green house gas emissions is land use changes. Bogor Regency is one of the districts close to the capital city and industrial areas so that the intensity of land use changes are very dynamic. This study aims to determine the dynamics of land use changes and CO2-eq emissions from land use change in 2000 to 2014 in Bogor. In the period 2000-2014 the most land undergone many changes occur in mixed garden, cropland, open land and shrub that converted turned into settlement with a total amounted to 11.12% of the total area, while the CO2-eq emissions in 2005-2009 increased approximately six times the emissions from 2000-2005 in the amount of 681 006.94 tons of CO2-eq per year.

12 managerial option site level in the in production structuring KPH Bogor.