Abstract
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.

Abstract
Phytosanitary conditions can hamper the normal development of trees and significantly impact their yield. The phytosanitary condition of chestnut stands is usually evaluated by sampling trees followed by a statistical extrapolation process, making it a challenging task, as it is labor-intensive and requires skill. In this study, a novel methodology that enables multi-temporal analysis of chestnut stands using multispectral imagery acquired from unmanned aerial vehicles is presented. Data were collected in different flight campaigns along with field surveys to identify the phytosanitary issues affecting each tree. A random forest classifier was trained with sections of each tree crown using vegetation indices and spectral bands. These were first categorized into two classes: (i) absence or (ii) presence of phytosanitary issues. Subsequently, the class with phytosanitary issues was used to identify and classify either biotic or abiotic factors. The comparison between the classification results, obtained by the presented methodology, with ground-truth data, allowed us to conclude that phytosanitary problems were detected with an accuracy rate between 86% and 91%. As for determining the specific phytosanitary issue, rates between 80% and 85% were achieved. Higher accuracy rates were attained in the last flight campaigns, the stage when symptoms are more prevalent. The proposed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Moreover, it is also able to identify decline or expansion situations. It may be of help as part of decision support systems that further improve on the efficient and sustainable management practices of chestnut stands.

Abstract
Individual tree segmentation is a challenging task due to the labour-intensive and time-consuming work required. Remote sensing data acquired from sensors coupled in unmanned aerial vehicles (UAV) constitutes a viable alternative to provide a quicker data acquisition, covering broader areas in a shorter period of time. This study aims to use UAV-based multispectral imagery to automatically identify individual trees in a chestnut stand. Tree parameters were estimated allowing its characterization. The leaf area index (LAI) was measured and was correlated with the estimated parameters. A good correlation was found for NDVI (R-2 = 0.76), while this relationship was less evident in the tree crown area and tree height. This way, our results indicate that the use of UAV-based multispectral imagery is a quick and reliable way to determine canopy structural parameters and LAI of chestnut trees.

Abstract
Alto Douro Wine Region is located in the Northeast of Portugal and is classified by UNESCO as a World Heritage Site. Snaked by the Douro River, the region has been producing wines for over 2000 years, with the world-famous Porto wine standing out. The vineyards, in that region, are built in a territory marked by steep slopes and the almost inexistence of flat land and water. The vineyards that cover the great slopes rise from the Douro River and form an immense terraced staircase. All these ingredients combined make the right key for exploring precision agriculture techniques. In this study, a preliminary approach allowing to perform terrace vineyards identification is presented. This is 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 terrace vineyards, considering a high-resolution dataset acquired with remote sensing sensors mounted in unmanned aerial vehicles (UAVs).

Abstract

Abstract
A digital image correlation (DIC) algorithm for displacement measurements combining cross-correlation and a differential technique was validated through a set of experimental tests. These tests consisted of in-plane rigid-body translation and rotation tests, a tensile mechanical test, and a mode I fracture test. The fracture mechanical test, in particular, was intended to assess the accuracy of the method when dealing with discontinuous displacement fields, for which subset-based image correlation methods usually give unreliable results. The proposed algorithm was systematically compared with the Aramis (R) DIC-2D commercial code by processing the same set of images. When processing images from rigid-body and tensile tests (associated with continuous displacement fields), the two methods provided equivalent results. When processing images from the fracture mechanical test, however, the proposed method obtained a better qualitative description of the discontinuous displacements. Moreover, the proposed method gave a more reliable estimation of both crack length and crack opening displacement of the fractured specimen.(C) (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.OE.51.4.043602]