Abstract
Today's climatic proneness to extreme conditions together with human activity have been triggering a series of wildfire-related events that put at risk ecosystems, as well as animal and vegetal patrimony, while threatening dwellers nearby rural or urban areas. When intervention teams-firefighters, civil protection, police-acknowledge these events, usually they have already escalated to proportions hardly controllable mainly due wind gusts, fuel-like solo conditions, among other conditions that propitiate fire spreading. Currently, there is a wide range of camera-capable sensing systems that can be complemented with useful location data-for example, unmanned aerial systems (UAS) integrated cameras and IMU/GPS sensors, stationary surveillance systems-and processing components capable of fostering wildfire events detection and monitoring, thus providing accurate and faithful data for decision support. Precisely in what concerns to detection and monitoring, Deep Learning (DL) has been successfully applied to perform tasks involving classification and/or segmentation of objects of interest in several fields, such as Agriculture, Forestry and other similar areas. Usually, for an effective DL application, more specifically, based on imagery, datasets must rely on heavy and burdensome logistics to gather a representative problem formulation. What if putting together a dataset could be supported in customizable virtual environments, representing faithful situations to train machines, as it already occurs for human training in what regards some particular tasks (rescue operations, surgeries, industry assembling, etc.)? This work intends to propose not only a system to produce faithful virtual environments to complement and/or even supplant the need for dataset gathering logistics while eventually dealing with hypothetical proposals considering climate change events, but also to create tools for synthesizing wildfire environments for DL application. It will therefore enable to extend existing fire datasets with new data generated by human interaction and supervision, viable for training a computational entity. To that end, a study is presented to assess at which extent data virtually generated data can contribute to an effective DL system aiming to identify and segment fire, bearing in mind future developments of active monitoring systems to timely detect fire events and hopefully provide decision support systems to operational teams. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
In the early 1980's, the European chestnut tree (Castanea sativa, Mill.) assumed an important role in the Portuguese economy. Currently, the Trás-os-Montes region (Northeast of Portugal) concentrates the highest chestnuts production in Portugal, representing the major source of income in the region (€50M-€60M). The recognition of the quality of the Portuguese chestnut varieties has increasing the international demand for both industry and consumer-grade segments. As result, chestnut cultivation intensification has been witnessed, in such a way that widely disseminated monoculture practices are currently increasing environmental disaster risks. Depending on the dynamics of the location of interest, monocultures may lead to desertification and soil degradation even if it encompasses multiple causes and a whole range of consequences or impacts. In Trás-os-Montes, despite the strong increase in the cultivation area, phytosanitary problems, such as the chestnut ink disease (Phytophthora cinnamomi) and the chestnut blight (Cryphonectria parasitica), along with other threats, e.g. chestnut gall wasp (Dryocosmus kuriphilus) and nutritional deficiencies, are responsible for a significant decline of chestnut trees, with a real impact on production. The intensification of inappropriate agricultural practices also favours the onset of phytosanitary problems. Moreover, chestnut trees management and monitoring generally rely on in-field time-consuming and laborious observation campaigns. To mitigate the associated risks, it is crucial to establish an effective management and monitoring process to ensure crop cultivation sustainability, preventing at the same time risks of desertification and land degradation. Therefore, this study presents an automatic method that allows to perform chestnut clusters identification, a key-enabling task towards the achievement of important goals such as production estimation and multi-temporal crop evaluation. The proposed methodology consists in the use of Convolutional Neural Networks (CNNs) to classify and segment the chestnut fruits, considering a small dataset acquired based on digital terrestrial camera. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
In recent years unmanned aerial vehicles (UAVs) have been used in several applications and research studies related to environmental monitoring. The works performed have demonstrated the suitability of UAVs to be employed in different scenarios, taking advantage of its capacity to acquire high-resolution data from different sensing payloads, in a timely and flexible manner. In forestry ecosystems, UAVs can be used with accuracies comparable with traditional methods to retrieve different forest properties, to monitor forest disturbances and to support disaster monitoring in fire and post-fire scenarios. In this study an area recently affected by a wildfire was surveyed using two UAVs to acquire multi-spectral data and RGB imagery at different resolutions. By analysing the surveyed area, it was possible to detect trees, that were able to survive to the fire. By comparing the ground-truth data and the measurements estimated from the UAV-imagery, it was found a positive correlation between burned height and a high correlation for tree height. The mean NDVI value was extracted used to create a three classes map. Higher NDVI values were mostly located in trees that survived that were not/barely affected by the fire. The results achieved by this study reiterate the effectiveness of UAVs to be used as a timely, efficient and cost-effective data acquisition tool, helping for forestry management planning and for monitoring forest rehabilitation in post-fire scenarios. © 2019 International Society for Photogrammetry and Remote Sensing.

Abstract
This paper explores the usage of unmanned aerial vehicles (UAVs) to acquire remotely sensed very high-resolution imagery for classification of an agrosilvopastoral system in a rural region of Portugal. Aerial data was obtained using a low-cost UAV, equipped with an RGB sensor. Acquired imagery undergone a photogrammetric processing pipeline to obtain different data products: an orthophoto mosaic, a canopy height model (CHM) and vegetation indices (VIs). A superpixel algorithm was then applied to the orthophoto mosaic, dividing the images into different objects. From each object, different features were obtained based in its maximum, mean, minimum and standard deviation. These features were extracted from the different data products: CHM, VIs, and color bands. Classification process – using random forest algorithm – classified objects into five different classes: trees, low vegetation, shrubland, bare soil and infrastructures. Feature importance obtained from the training model showed that CHM-driven features have more importance when comparing to those obtained from VIs or color bands. An overall classification accuracy of 86.4% was obtained. © Springer Nature Switzerland AG 2019.

Abstract
Climate change is projected to be a key influence on crop yields across the globe. Regarding viticulture, primary climate vectors with a significant impact include temperature, moisture stress, and radiation. Within this context, it is of foremost importance to monitor soils' moisture levels, as well as to detect pests, diseases, and possible problems with irrigation equipment. Regular monitoring activities will enable timely measures that may trigger field interventions that are used to preserve grapevines' phytosanitary state, saving both time and money, while assuring a more sustainable activity. This study employs unmanned aerial vehicles (UAVs) to acquire aerial imagery, using RGB, multispectral and thermal infrared sensors in a vineyard located in the Portuguese Douro wine region. Data acquired enabled the multi-temporal characterization of the vineyard development throughout a season through the computation of the normalized difference vegetation index, crop surface models, and the crop water stress index. Moreover, vigour maps were computed in three classes (high, medium, and low) with different approaches: (1) considering the whole vineyard, including inter-row vegetation and bare soil; (2) considering only automatically detected grapevine vegetation; and (3) also considering grapevine vegetation by only applying a normalization process before creating the vigour maps. Results showed that vigour maps considering only grapevine vegetation provided an accurate representation of the vineyard variability. Furthermore, significant spatial associations can be gathered through (i) a multi-temporal analysis of vigour maps, and (ii) by comparing vigour maps with both height and water stress estimation. This type of analysis can assist, in a significant way, the decision-making processes in viticulture.

Abstract
The feasibility of a compact, modular sensing system able to quantify the presence of nitrogen, phosphorus and potassium (NPK) in nutrient-containing fertilizer water was investigated. Direct UV-Vis spectroscopy combined with optical fibers were employed to design modular compact sensing systems able to record absorption spectra of nutrient solutions resulting from local producer samples. N, P, and K spectral interference was studied by mixtures of commercial fertilizer solutions to simulate real conditions in hydroponic productions. This study demonstrates that the use of bands for the quantification of nitrogen with linear or logarithmic regression models does not produce analytical grade calibrations. Furthermore, multivariate regression models, i.e., Partial Least Squares (PLS), which consider specimens interference, perform poorly for low absorbance nutrients. The high interference present in the spectra has proven to be solved by an innovative self-learning artificial intelligence algorithm that is able to find interference modes among a spectral database to produce consistent predictions. By correctly modeling the existing interferences, analytical grade quantification of N, P, and K has proven feasible. The results of this work open the possibility of real-time NPK monitoring in Micro-Irrigation Systems.