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ABSTRACT:Aboveground biomass estimation (AGB) is essential in determining the environmental and economic values of mangrove forests. Biomass prediction models can be developed through integration of remote sensing, field data and statistical models. This study aims to assess and compare the biomass predictor potential of multispectral bands, vegetation indices and biophysical variables that can be derived from three optical satellite systems: the Sentinel-2 with 10m, 20m and 60m resolution; RapidEye with 5m resolution and PlanetScope with 3m ground resolution. Field data for biomass were collected from a Rhizophoraceae-dominated mangrove forest in Masinloc, Zambales, Philippines where 30 test plots (1.2 ha) and 5 validation plots (0.2 ha) were established. Prior to the generation of indices, images from the three satellite systems were pre-processed using atmospheric correction tools in SNAP (Sentinel-2), ENVI (RapidEye) and python (PlanetScope). The major predictor bands tested are Blue, Green and Red, which are present in the three systems; and Red-edge band from Sentinel-2 and Rapideye. The tested vegetation index predictors are Normalized Differenced Vegetation Index (NDVI), Soil-adjusted Vegetation Index (SAVI), Green-NDVI (GNDVI), Simple Ratio (SR), and Red-edge Simple Ratio (SRre). The study generated prediction models through conventional linear regression and multivariate regression. Higher coefficient of determination (r 2 ) values were obtained using multispectral band predictors for Sentinel-2 (r 2 = 0.89) and Planetscope (r 2 = 0.80); and vegetation indices for RapidEye (r 2 = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r 2 ranging from 0.62 to 0.92. Based on the r 2 and root-mean-square errors (RMSE's), the best biomass prediction model per satellite were chosen and maps were generated. The accuracy of predicted biomass maps were high for both Sentinel-2 (r 2 = 0.92) and RapidEye data (r 2 = 0.91).

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<p><strong>Abstract.</strong> This research presents a method in assessing the impact of Ground Control Point (GCP) distribution, quantity, and inter-GCP distances on the output Digital Elevation Model (DEM) by utilizing SfM and GIS. The study was carried out in a quarry site to assess the impacts of these parameters on the accuracy of accurate volumetric measurements UAV derivatives. Based on GCP Root Mean Square Error (RMSE) and surface checkpoint error (SCE), results showed that the best configuration is the evenly distributed GCP set (1.58&amp;thinsp;m average RMSE, 1.30&amp;thinsp;m average SCE). Configurations clumped to edge and distributed to edge follow suit with respective RMSE (SCE) of 2.53&amp;thinsp;m (2.13&amp;thinsp;m) and 3.11&amp;thinsp;m (2.54&amp;thinsp;m). The clumped to center configuration yielded 6.23&amp;thinsp;m RMSE and 4.66&amp;thinsp;m SCE. As the number of GCPs used increase, the RMSE and SCE are observed to decrease consistently for all configurations. Further iteration of the best configuration showed that from RMSE of 4.11&amp;thinsp;m when 4 GCPs are used, there is a drastic decrease to 0.86&amp;thinsp;m once 10 GCPs are used. From that quantity, only centimeter differences can be observed until the full set of 24 GCPs have been used with a 0.012&amp;thinsp;m error. This is reflected in the stockpile measurement when the iteration results are compared to the reference data. The dataset processed with a minimum of 4 GCPs have a 606,991.43&amp;thinsp;m³ difference, whereas the dataset processed with 23 out of 24 has a 791.12&amp;thinsp;m³ difference from the reference data. The accuracy of the SfM-based DEM increases with the quantity of the GCPs used with an even distribution.</p>

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