Keywords: Aboveground Biomass, RapidEye, Sentinel 2, PlanetScope, Mangroves 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 10 m, 20 m and 60 m 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 (r²) values were obtained using multispectral band predictors for Sentinel-2 (r² = 0.89) and Planetscope (r² = 0.80); and vegetation indices for RapidEye (r² = 0.92). Multivariate Adaptive Regression Spline (MARS) models performed better than the linear regression models with r² ranging from 0.62 to 0.92. Based on the r² 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 (r² = 0.92) and RapidEye data (r² = 0.91).

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 m average RMSE, 1.30 m average SCE). Configurations clumped to edge and distributed to edge follow suit with respective RMSE (SCE) of 2.53 m (2.13 m) and 3.11 m (2.54 m). The clumped to center configuration yielded 6.23 m RMSE and 4.66 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 m when 4 GCPs are used, there is a drastic decrease to 0.86 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 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 m³ difference, whereas the dataset processed with 23 out of 24 has a 791.12 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.

The retrieval of chlorophyll-a (Chl-a) concentrations relies on empirical or analytical analyses, which generally experience difficulties from the diversity of inland waters in statistical analyses and the complexity of radiative transfer equations in analytical analyses, respectively. Previous studies proposed the utilization of artificial neural networks (ANNs) to alleviate these problems. However, ANNs do not consider the problem of insufficient in situ samples during model training, and they do not fully utilize the spatial and spectral information of remote sensing images in neural networks. In this study, a two-stage training is introduced to address the problem regarding sample insufficiency. The neural network is pretrained using the samples derived from an existing Chl-a concentration model in the first stage, and the pretrained model is refined with in situ samples in the second stage. A novel convolutional neural network for Chl-a concentration retrieval called WaterNet is proposed which utilizes both spectral and spatial information of remote sensing images. In addition, an end-to-end structure that integrates feature extraction, band expansion, and Chl-a estimation into the neural network leads to an efficient and effective Chl-a concentration retrieval. In experiments, Sentinel-3 images with the same acquisition days of in situ measurements over Laguna Lake in the Philippines were used to train and evaluate WaterNet. The quantitative analyses show that the two-stage training is more likely than the one-stage training to reach the global optimum in the optimization, and WaterNet with two-stage training outperforms, in terms of estimation accuracy, related ANN-based and band-combination-based Chl-a concentration models.