Browsing by Author "Britto, Raphael Duarte"
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- Assessment of carbon sequestration in forest areas using deep learningPublication . Britto, Raphael Duarte; Lima, José; Pereira, Ana I.; Santos, Murillo Ferreira dosGrowing awareness of environmental impacts is making it more important than ever to explore regions with dense vegetation. Remote monitoring is a viable solution for the surveillance of large areas, such as forests. Based in intelligent systems, this work aims to develop a methodology for assessing carbon sequestration in forest areas. Deep learning (DL) structures were used to predict the heights and stand densities in tree colonies. Light Detection and Ranging (LiDAR) sensor scans obtained by Unmanned Aerial Vehicle (UAV) overflight were processed to extract elevation values and images. Point clouds were processed using QGIS software. The LAStools extension was employed to manipulate Digital Elevation Model (DEM) and rasters, obtaining relevant information. This data was then used to create a dataset for implementation in Convolutional Neural Network (CNN) models. Specific biometric relationships were implemented to estimate additional data such as Above Ground Biomass (AGB) and phytovolume. After evaluating different architectures, the VGG19 CNN model was highlighted as the most promising. An area of 46.6 hectares was covered, with an estimated total value of 4225.81 tons of carbon. This value provided an accuracy of 91%, based on forest inventories carried out in the same region. The study was conducted in the northern region of mainland Portugal, encompassing two distinct Pinus pinaster Ait. forests.
- A deep learning approach for average height estimation in oak colony using rgb imagesPublication . Britto, Raphael Duarte; Mendes, João; Grilo, Vinicius; Castro, João Paulo; Santos, Murillo Ferreira dos; Castro, Marina; Pereira, Ana I.; Lima, JoséMany strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.