Currently, climate change poses a global threat, which may compromise the sustainability of agriculture, forestry and other land surface systems. In a changing world scenario, the economic importance of Remote Sensing (RS) to monitor forests and agricultural resources is imperative to the development of agroforestry systems. Traditional RS technologies encompass satellite and manned aircraft platforms. These platforms are continuously improving in terms of spatial, spectral, and temporal resolutions. The high spatial and temporal resolutions, flexibility and lower operational costs make Unmanned Aerial Vehicles (UAVs) a good alternative to traditional RS platforms. In the management process of forests resources, UAVs are one of the most suitable options to consider, mainly due to: (1) low operational costs and high-intensity data collection; (2) its capacity to host a wide range of sensors that could be adapted to be task-oriented; (3) its ability to plan data acquisition campaigns, avoiding inadequate weather conditions and providing data availability on-demand; and (4) the possibility to be used in real-time operations. This review aims to present the most significant UAV applications in forestry, identifying the appropriate sensors to be used in each situation as well as the data processing techniques commonly implemented.

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.

Unmanned aerial vehicles (UAVs) have become popular in recent years and are now used in a wide variety of applications. This is the logical result of certain technological developments that occurred over the last two decades, allowing UAVs to be equipped with different types of sensors that can provide high-resolution data at relatively low prices. However, despite the success and extraordinary results achieved by the use of UAVs, traditional remote sensing platforms such as satellites continue to develop as well. Nowadays, satellites use sophisticated sensors providing data with increasingly improving spatial, temporal and radiometric resolutions. This is the case for the Sentinel-2 observation mission from the Copernicus Programme, which systematically acquires optical imagery at high spatial resolutions, with a revisiting period of five days. It therefore makes sense to think that, in some applications, satellite data may be used instead of UAV data, with all the associated benefits (extended coverage without the need to visit the area). In this study, Sentinel-2 time series data performances were evaluated in comparison with high-resolution UAV-based data, in an area affected by a fire, in 2017. Given the 10-m resolution of Sentinel-2 images, different spatial resolutions of the UAV-based data (0.25, 5 and 10 m) were used and compared to determine their similarities. The achieved results demonstrate the effectiveness of satellite data for post-fire monitoring, even at a local scale, as more cost-effective than UAV data. The Sentinel-2 results present a similar behavior to the UAV-based data for assessing burned areas.

<p><strong>Abstract.</strong> 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.</p>

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 processusing random forest algorithmclassified 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.

Rice is a historically important crop in Portugal. This crop development and production strongly depend on atmospheric conditions in the growing season. Given the strong dependence of climatic conditions, climate change may pose a significant risk for future rice production. In the present study, a high spatial resolution bioclimatic characterization over the main rice producing region in Portugal was performed for the recent past and for the future (2041-2060) under four different anthropogenic forcing scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). This zoning is performed by using eight bioclimatic indices, based on temperature and precipitation, using a very high resolution gridded dataset (Worldclim). For the future period, an 11-member global climate model ensemble was used, also taking into account model/scenario uncertainties and bias. Additionally, a new index was developed to incorporate the main features of temperature and precipitation at each rice field level. Under recent past climates, a clear north-south gradient in temperature and precipitation is apparent, with the regions of Tejo and Sado presenting higher temperatures and lower precipitation than the Mondego and Vouga regions. Additionally, there is a coastal-inland effect due to the Atlantic Ocean influence. Under anthropogenic climate change, all indices point to annual higher temperatures and lower precipitations across all rice producing regions, accompanied by increased seasonality. Furthermore, the rise of summertime temperatures may substantially increase water demands, which, when unmitigated, may bring physiological problems in the crop development. We conclude that climate change may negatively impact the viability of rice production in Portugal, particularly taking into account the national grown varieties. Thus, adequate and timely planning of suitable adaptation measures are needed to ensure the sustainability of this historically important food sector.

In this study machine learning methods were applied to RGB data obtained by an unmanned aerial vehicle (UAV) to assess this effectiveness in vineyard classification. The very high-resolution UAV-based imagery was subjected to a photogrammetric processing allowing the generation of different outcomes: orthophoto mosaic, crop surface model and five vegetation indices. The orthophoto mosaic was used in an object-based image analysis approach to group pixels with similar values into objects. Three machine learning techniques-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were applied to classify the data into four classes: grapevine, shadow, soil and other vegetation. The data were divided with 22% (n=240, 60 per class) for training purposes and 78% (n = 850) for testing purposes. The mean value of the objects from each feature were used to create a dataset for prediction. The results demonstrated that both RF and ANN models showed a good performance, yet the RF classifier achieved better results.

In the World Heritage Côa region, in northern Portugal, agriculture has crucial economic, social and cultural importance. Vineyards and olive groves are part of the economic base of this region, contributing to the regional commercial budget and the livelihood of its residents. Climate change is expected to have significant impacts on these crops, where climatic conditions are already very warm and dry, posing a key threat to the olive oil and winemaking sectors. The present study analyzes the impact of climate change on the potential yield of these two crops over the Côa region. For this purpose, two crop models were initialized and ran with state-of-the art spatial datasets for climate, soil, terrain, and plant data. As outputs of the crop models, potential yields of grapevines and olive trees were obtained for the recent-past (1981–2005) and for the future (2041–2070), under two climatic scenarios (RCP4.5 and RCP8.5). Results (potential yield) were then normalized, taking into account the recent-past maximum yields and divided into four classes (low, low-moderate, moderate-high, and high). For the recent-past, the results of the crop models present a high agreement with the current location of vineyards and olive groves. For the future, two different types of impacts (positive and negative) are found for the two crops. For olive trees, the results show promising future improvements in possible expansion areas within the Côa region. However, for grapevines, the results show a decrease in potential yields throughout the region, including a strong shift of producing moderate zones to low potentials. Nonetheless, these results also suggest that the negative impacts of climate change can be alleviated by the application of suitable adaptation measures, based on changing certain management practices, even in the more severe future scenario. Therefore, these measures should be carefully planned and implemented in a timely fashion by farmers.