Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Automatic Image Stitching of Agriculture Areas based on Unmanned Aerial Vehicle using SURF

Identification  of  agricultural  areas  in  remote  sensing  technology  is  needed  for  the development  of  agricultural  areas.  The  image  of  the  agricultural  area  in  this  study  uses  an Unmanned Air Vehicle (UAV). The results of images taken from a height of 100 meters on the ground will be stored and processed into one image. UAV technology that supports this research is  expected  to  help  remote  sensing  in  real  time.  For  the  current  study,  measurements  in agricultural areas are related to some fragmented images. This article creates a beautiful view of the agricultural region. The author focuses on automatic image milling methods with detection- based image matching and description of patented local features from the dataset. The features method applied is based on speeded up robust features (SURF). The method of matching images and verification results is carried out. The result will create a 2-D spatial reference that starts the panorama size. This paper shows the results of image stitching in the agriculture area

Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude

Agriculturización e impactos ambientales en un área representativa de la ecorregión de las pampas, argentina.

Estudos prévios demonstram a existência do processo de agriculturização na ecorregião das Pampas, e o partido de Tandil constitui um exemplo desse processo. Esse trabalho compara as áreas ocupadas pelos distintos usos da terra e seus impactos ambientais na Bacia Superior do Arroio Langueyú e no Partido de Tandil, na qual ela está inserta, em três cortes temporais: 1988, 2002 e 2010. Aplicou-se uma classificação supervisada sobre imagens captadas pelo sensor TM com ajustes realizados no campo. Entre 1988 e 2002, os usos agrícolas têm aumentado significativamente na Bacia (159,5%), enquanto no Partido aumentaram 39,4%. Como conseqüência, os impactos ambientais sobre o meio natural, medidos com indicadores de sustentabilidade, foram mais intensos na Bacia do que no conjunto do Partido. Os resultados obtidos permitem colaborar no desenvolvimento de propostas de gestão ambiental tendentes à sustentabilidade agroecológica.Previous studies have shown the existence of the process of agriculturization in the Ecoregion of the Pampas, and Tandil County is a representative example of the process. This paper compares the areas occupied by different land uses and their environmental impacts in the Upper Basin of the Langueyú Creek and in Tandil County, in which the basin is located, in three points of time: 1988, 2002, and 2010. Supervised classification was applied on images captured by the sensor TM with adjustments to field. Between 1988 and 2010, agricultural uses in the Basin increased significantly (159.5%) while in the County, the agricultural areas increased 39.4%. Consequently, the environmental impacts on the environment, measured by sustainability indicators, were more intense in the Basin than in the County as a whole. The results allow collaborate in the development of environmental management proposals aimed at agroecological sustainability.Des études préalables démontrent l’existence d’un processus d’agriculturization dans l’écorégion des Pampas, et le département de Tandil constitue un exemple de ce processus. Ce travail compare des zones occupées par des différents usages de la terre et ses impacts environnementaux dans le Bassin Supérieur du Ruisseau Langueyú et dans le département de Tandil, où elle s’insère, dans trois périodes de temps: 1988, 2002 et 2010. On a appliqué une classification supervisée par des images captées avec le senseur TM et des ajustements réalisé sur le terrain. Entre 1988 et 2002, des usages agricoles ont augmenté considérablement dan le Bassin (159,5%), alors que l’augmentation dans tout le département a été de 39,4%. En conséquence, les impacts environnementaux sur le milieu naturel, mesurés avec des indicateurs de durabilité, ont été plus intenses dans le Bassin que dans la totalité du département. Les résultats obtenues permettent de collaborer dans le développement de propositions de gestion environnemental tendent à durabilité agroécologique.Estudios previos demuestran la existencia del proceso de agriculturización en la Ecorregión de las Pampas y el partido de Tandil constituye un ejemplo del proceso. Este trabajo, compara las áreas ocupadas por distintos usos de la tierra y sus impactos ambientales en la Cuenca Superior del Arroyo Langueyú y en el partido de Tandil, en el cual se haya inserta, en tres cortes temporales: 1988, 2002 y 2010. Se aplicó una clasificación supervisada sobre imágenes captadas por el sensor TM con ajustes realizados a campo. Entre 1988 y 2010, los usos agrícolas aumentaron significativamente en la Cuenca (159,5%), mientras que en el Partido 39,4%. En consecuencia, los impactos ambientales sobre el medio natural, medidos con indicadores de sustentabilidad, fueron más intensos en la Cuenca que en el Partido en su conjunto. Los resultados obtenidos permiten colaborar en el desarrollo de propuestas de gestión ambiental tendientes a la sustentabilidad agroecológica.Fil: Vazquez, Patricia Susana. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ciencias Humanas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Zulaica, Maria Laura. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Agronomia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Mapping and classification of ecologically sensitive marine habitats using unmanned aerial vehicle (UAV) imagery and object-based image analysis (OBIA)

Nowadays, emerging technologies, such as long-range transmitters, increasingly miniaturized components for positioning, and enhanced imaging sensors, have led to an upsurge in the availability of new ecological applications for remote sensing based on unmanned aerial vehicles (UAVs), sometimes referred to as “drones”. In fact, structure-from-motion (SfM) photogrammetry coupled with imagery acquired by UAVs offers a rapid and inexpensive tool to produce high-resolution orthomosaics, giving ecologists a new way for responsive, timely, and cost-effective monitoring of ecological processes. Here, we adopted a lightweight quadcopter as an aerial survey tool and object-based image analysis (OBIA) workflow to demonstrate the strength of such methods in producing very high spatial resolution maps of sensitive marine habitats. Therefore, three different coastal environments were mapped using the autonomous flight capability of a lightweight UAV equipped with a fully stabilized consumer-grade RGB digital camera. In particular we investigated a Posidonia oceanica seagrass meadow, a rocky coast with nurseries for juvenile fish, and two sandy areas showing biogenic reefs of Sabelleria alveolata. We adopted, for the first time, UAV-based raster thematic maps of these key coastal habitats, produced after OBIA classification, as a new method for fine-scale, low-cost, and time saving characterization of sensitive marine environments which may lead to a more effective and efficient monitoring and management of natural resource

Parallel processing applied to image mosaic generation

The automatic construction of large mosaics obtained from high resolution digital images is an area of great importance, with applications in different areas. In agriculture, the requirements of cartographic accuracy of mosaics of annual or perennial crops are not so high, but the speed in obtaining them is the most critical factor. The efficiency in decision making is related to the obtaining faster and more accurate information, especially in the control of pests, diseases or fire control. This project proposes a methodology based on SIFT Transform and parallel processing to build mosaics automatically, using high resolution agricultural aerial images. Build mosaics with high resolution images requires high computational effort for processing them. To treat the problem of computational effort, the standard OpenMP of parallel processing was used to accelerate the process and results are presented for a computer with 2, 4 and 8 threads

Quantitative analysis of anthropogenic morphologies based on multi-temporal high-resolution topography

Human activities have reshaped the geomorphology of landscapes and created vast anthropogenic geomorphic features, which have distinct characteristics compared with landforms produced by natural processes. High-resolution topography from LiDAR has opened avenues for the analysis of anthropogenic geomorphic signatures, providing new opportunities for a better understanding of Earth surface processes and landforms. However, quantitative identification and monitoring of such anthropogenic signature still represent a challenge for the Earth science community. The purpose of this contribution is to explore a method for monitoring geomorphic changes and identifying the driving forces of such changes. The study was carried out on the Eibar watershed in Spain. The proposed method is able to quantitatively detect anthropogenic geomorphic changes based on multi-temporal LiDAR topography, and it is based on a combination of two techniques: the DEM of Difference (DoD) and the Slope Local Length of Auto-correlation (SLLAC). First, we tested the capability of the SLLAC and derived parameters to distinguish different types of anthropogenic geomorphologies in 5 study case at a small scale. Second, we calculated the DoD to quantify the geomorphic changes between 2008 and 2016. Based on the proposed approach, we classified the whole basin into three categories of geomorphic changes (natural, urban or mosaic areas). The urban area had the most clustered and largest geomorphic changes, followed by the mosaic area and the natural area. This research might help to identify and monitoring anthropogenic geomorphic changes over large areas, to schedule sustainable environmental planning, and to mitigate the consequences of anthropogenic alteration

Steep slope DEM model construction based on the unmanned aerial vehicle (UAV) images

The DEM construction of high and steep slope has great importance to slope disaster monitoring. The conventional method used to construct high and steep slope DEM model requires larger field surveying workload. First of all, the high and steep slope image was obtained through unmanned aerial vehicle (UAV) platform; Then the SIFT algorithm is used to extract the feature points which are going to be matched accurately by using RANSAC algorithm. Finally, stereo pair splicing method is used to generate orthogonal images and construct DEM model. After comparing the DEM model with actual slope measurement result collected by total station finding, it is shown that elevation error between the DEM model constructed by unmanned aerial vehicle (UAV) and actual measurement is minimal and its efficiency is proven