Combining unmanned aerial vehicle and multispectral Pleiades data for tree species identification, a prerequisite for accurate carbon estimation

Forest carbon estimation currently largely relies on remote sensing techniques in combination with field measurement. High-resolution images, which are commonly utilized for carbon estimation, are not readily available, and their cost prohibits communities from reaping the benefits of maintaining their forest under the UN reducing emissions from deforestation and forest degradation program. Our study explores the combination of readily available and relatively cheaper unmanned aerial vehicle (UAV) (4-cm resolution) and multispectral Pleiades (50-cm resolution) images for species classification robustness in view for carbon estimation through object-based image analysis. The images are resampled and used to evaluate the effect of combining multispectral Pleiades image on the accuracies of segmenting UAV images for tree crown projection area (CPA) estimation and species classification. RGB images from a UAV platform are processed in a photogrametric software and combined with the near-infrared band of a Pleiades image to get a UAV-Pleiades image composite. The images are segmented using the ESP 2 tool and the segmentation accuracy compared using a paired t-test. The segmented tree crowns are classified using random trees (RT), support vector machines (SVM), and maximum likelihood (ML) classifiers, and the classification accuracies of the three classifiers are compared using the McNemar's chi-squared test. Our study demonstrates a 93.5% accuracy of segmenting UAV-Pleiades image composite, which is significantly higher than the 84.8% accuracy of segmenting UAV images (p < 0.05). Also an 84% classification accuracy of UAV-Pleiades image composite is significantly higher than the 54% classification accuracy of the UAV images (p < 0.05). Of the three classifiers used, the classification accuracies of SVM and RT are significantly higher (p < 0.05) than that of the ML classifier. Given the significantly high accuracies observed from this study for tree CPA extraction and tree species classification, carbon/above ground biomass modeling is possible with significantly high accuracy using the combination of multispectral Pleiades and UAV images

Analysis of aerosol absorption properties and transport over North Africa and the Middle East using AERONET data

In this paper particle categorization and absorption properties were discussed to understand transport mechanisms at different geographic locations and possible radiative impacts on climate. The long-term Aerosol Robotic Network (AERONET) data set (1999&ndash;2015) is used to estimate aerosol optical depth (AOD), single scattering albedo (SSA), and the absorption Ångström exponent (αabs) at eight locations in North Africa and the Middle East. Average variation in SSA is calculated at four wavelengths (440, 675, 870, and 1020 nm), and the relationship between aerosol absorption and physical properties is used to infer dominant aerosol types at different locations. It was found that seasonality and geographic location play a major role in identifying dominant aerosol types at each location. Analyzing aerosol characteristics among different sites using AERONET Version 2, Level 2.0 data retrievals and the Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectories shows possible aerosol particle transport among different locations indicating the importance of understanding transport mechanisms in identifying aerosol sources

A prediction of time series driving motion scenarios using LSTM and ESN

Abstract The motion signals are generated for a simulator user based on the visual understanding of the environment using virtual reality. In this respect, a motion cueing algorithm (MCA) is employed to reproduce the motion signals based on the real driving motion scenarios. Advanced MCAs are required to predict precise driving motion scenarios. Nonetheless, investigations on effective methods for predicting the driving motion scenarios accurately are limited. Current state-of-the-art studies mainly focus on the averaged motion signals from several simulator users pertaining to a specific map or from feedforward neural network and non-linear autoregressive. The existing methods are unable to yield precise predictions of the driving scenarios. In this research, the echo state network and long short-term memory models are employed for the first time in MCA to forecast the driving motion signals. Our evaluation proves the efficiency of our proposed methods in comparison with existing methods