Abstract
Materials represented by measured BRDF (Bidirectional Reflectance Distribution function) with reflectance data captured from real-world materials have become increasingly prevalent due to the development of novel measurement approaches. Nowadays, important limitations can be highlighted in the current material scanning process, mostly related to the high diversity of existing materials in the real-world and the tedious process for material scanning. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach is proposed for modelling the material appearance by sampling hyperspectral measurements on the BRDF domain. An unmanned aerial vehicle (UAV)-based hyperspectral sensor was used to capture high spatial and spectral resolution data. The generated hyperspectral data cubes were used to identify materials with a similar spectral behaviour. Then, a sparse mapping of collected samples is developed to study the appearance of natural and artificial materials in an urban scenario. © 2021 IEEE.

Abstract
The detection of invasive plant species in aquatic ecosystems is important to help in the control or to mitigate its spread and impacts. Remote sensing (RS) can be explored in this context, helping to monitor this type of plants. This study intends to present a free to use and open-source software application that, through a graphical user interface, can process remote sensed data to monitor the spread of invasive plant species in aquatic environments, enabling a multi-temporal monitoring. Both unmanned aerial vehicle and satellite-based data were used to validate the potential of the proposed application. A site containing water hyacinth (Eichhornia crassipes) was selected as case study. Both RS platforms provided effective data to detect the areas containing water hyacinth. Thus, this tool provides an alternative and user-friendly way to include RS-based data in ecological studies allowing the detection of invasive plants in water channels.

Abstract
The estimation of dendrometric parameters of tree crops is crucial to decision making support for ecological and economic reasons. However, traditional methods for its measurement are time-consuming and laborious. Remote sensing data acquired from unmanned aerial vehicles (UAVs) combined with computer vision and Structure from Motion (SfM) algorithms can provide an easier and reliable solution to estimate those parameters. Nevertheless, various UAV flight settings can influence the quality of parameters derived from these data (e.g., flight height, imagery overlap). Thus, the main goal of this study is to assess the impact of different flight configurations on the detection of olive trees and on height and crown diameter estimation. The results showed that not only the configuration of the flight affects the dendrometric results, but also the topography of the terrain. Automatic tree detection revealed to be insensitive to the different flight configurations, whereas the tree height estimation was strongly affected. Among the analysed flights, the plan in double grid at 60 m of flight altitude and 90% of frontal overlap showed the best performance.

Abstract
In the last decade Unmanned Aerial Systems (UAS) have become a reference tool for agriculture applications. The integration of multispectral sensors that can capture near infrared (NIR) and red edge spectral reflectance allows the creation of vegetation indices, which are fundamental for crop monitoring process. In this study, we propose a methodology to analyze the vegetative state of almond crops using multi-temporal data acquired by a multispectral sensor accoupled to an Unmanned Aerial Vehicle (UAV). The methodology implemented allowed individual tree parameters extraction, such as number of trees, tree height, and tree crown area. This also allowed the acquisition of Normalized Difference Vegetation Index (NDVI) information for each tree. The multi-temporal data showed significant variations in the vegetative state of almond crops.

Abstract
In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars.

Abstract
Grape moths are a significant pest in vineyards, causing damage and losses in wine production. Pheromone traps are used to monitor grape moth populations and determine their developmental status to make informed decisions regarding pest control. Smart pest monitoring systems that employ sensors, cameras, and artificial intelligence algorithms are becoming increasingly popular due to their ability to streamline the monitoring process. In this study, we investigate the effectiveness of using segmentation as a pre-processing step to improve the detection of grape moths in trap images using deep learning models. We train two segmentation models, the U-Net architecture with ResNet18 and InceptionV3 backbonesl, and utilize the segmented and non-segmented images in the YOLOv5s and YOLOv8s detectors to evaluate the impact of segmentation on detection. Our results show that segmentation preprocessing can significantly improve detection by 3% for YOLOv5 and 1.2% for YOLOv8. These findings highlight the potential of segmentation pre-processing for enhancing insect detection in smart pest monitoring systems, paving the way for further exploration of different training methods.