Study on an Agricultural Environment Monitoring Server System using Wireless Sensor Networks

This paper proposes an agricultural environment monitoring server system for monitoring information concerning an outdoors agricultural production environment utilizing Wireless Sensor Network (WSN) technology. The proposed agricultural environment monitoring server system collects environmental and soil information on the outdoors through WSN-based environmental and soil sensors, collects image information through CCTVs, and collects location information using GPS modules. This collected information is converted into a database through the agricultural environment monitoring server consisting of a sensor manager, which manages information collected from the WSN sensors, an image information manager, which manages image information collected from CCTVs, and a GPS manager, which processes location information of the agricultural environment monitoring server system, and provides it to producers. In addition, a solar cell-based power supply is implemented for the server system so that it could be used in agricultural environments with insufficient power infrastructure. This agricultural environment monitoring server system could even monitor the environmental information on the outdoors remotely, and it could be expected that the use of such a system could contribute to increasing crop yields and improving quality in the agricultural field by supporting the decision making of crop producers through analysis of the collected information

SIRIO : Integrated Forest Firesmonitoring, detection and decision supportsystem with low cost commercial sensorssuited for complex orography

Forest Fires in our society cause a lot of damage, in particular regarding the economic and environmental landscape. In order to monitor a large portion of territory automatically, with a good cost/performances trade-off, it is necessary to develop new early warning systems. We propose a ground-based system with modular architecture, equipped with low cost commercial sensor. The idea is to develop the software able to manage the forest fires monitoring. The technique is based on Static and Dynamic analysis of chromatic changes between images, tailored for our case of study in a large scale monitoring of vegetation and using different sensors to reduce or eliminate the false alarm rate. Concerning the image geo-referencing tool, the present work describes an innovative projective geo-referencing algorithm able to geo-reference complex orography regions using fixed ground station images. Besides, it does not need the collection of Ground Control Points, which is a very hard task in complex orography environments. In order to make a user oriented product and to help the operator during extinguishing activities, a decision support tool has been developed as well. This work presents the results of one year monitoring campaign conducted in cooperation with the Civil Protection Offices in Sanremo (IM), Ital

Monitoring of Urban Growth and its Related Environmental Impacts: Niamey Case Study (Niger)

AbstractThe present contribution is about a preliminary study of the evolution of Niamey city (Niger) during last decades.Recent advances in remote sensing, both in satellite hardware technology and image availability development, provide opportunities image collection and multitemporal analysis on urban form and size that can be useful for policy and planning. Some opportunities for, and limitations on, monitoring urban growth using remote sensing data are shown in the present contribution; moreover examples of environmental impacts of urban growth, as monitored with remote sensing, are provided in order to define future development of dumps and quarries and its environmental impacts on Niamey city

Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco

This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAV’s log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents

Growth limiting conditions and denitrification govern extent and frequency of volume detachment of biofilms

This study aims at evaluating the mechanisms of biofilm detachment with regard of the physical properties of the biofilm. Biofilms were developed in Couette–Taylor reactor under controlled hydrodynamic conditions and under different environmental growth conditions. Five different conditions were tested and lead to the formation of two aerobic heterotrophic biofilms (aeHB1 and aeHB2), a mixed autotrophic and heterotrophic biofilm (MAHB) and two anoxic heterotrophic biofilms (anHB1 and anHB2). Biofilm detachment was evaluated by monitoring the size of the detached particles (using light-scattering) as well as the biofilm physical properties (using CCD camera and image analysis). Results indicate that volume erosion of large biofilm particles with size ranging from 50 to 500 lm dominated the biomass loss for all biofilms. Surface erosion of small particles with size lower than 20 lm dominates biofilm detachment in number. The extent of the volume detachment events was governed by the size of the biofilm surface heterogeneities (i.e., the absolute biofilm roughness) but never impacted more than 80% of the mean biofilm thickness due to the highly cohesive basal layer. Anoxic biofilms were smoother and thinner than aerobic biofilms and thus associated with the detachment of smaller particles. Our results contradict the simplifying assumption of surface detachment that is considered in many biofilm models and suggest that discrete volume events should be considered

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

An evaluation of the effect of terrain normalization on classification accuracy of Landsat ETM+ imagery

More than 60% of land in New Zealand has been converted from native forests to residential areas, agriculture, or forest plantations. Settlers brought many species of plants and animals to New Zealand. Many native species were unable to protect themselves from these new predators, causing numerous extinctions. In light of this rapid decline in biodiversity, the New Zealand government has attempted to mitigate the destruction of endemic flora and fauna through both new environmental policies and intensive land management. Land management techniques include the restoration of developed land and the protection of remaining areas of native forest. Monitoring of restoration efforts is important to the government and organizations responsible for this work. Using remotely sensed data to perform change analysis is a powerful method for long-term monitoring of restoration areas. The accuracy of maps created from remotely sensed data may be limited by significant terrain variation within many of the restoration areas. Landcare Research New Zealand has developed a topographic suppression algorithm that reduces the effects of topography. Landsat ETM+ imagery from November 2000 was processed with this algorithm to produce two images, an orthorectified image and a terrain-flattened image of a 50-km by 60-km area near Wanganui, New Zealand. Using GLOBE reference data collected on the ground in September/October 2004 and additional reference data photointerpreted from aerial photography, thematic maps were created using unsupervised, supervised, and hybrid classification methods. The accuracy of the thematic maps was evaluated using error matrices and Kappa analysis. The different image processing techniques were statistically compared. It was determined that the topographic-flattening algorithm did not significantly improve map accuracy

Technology of crack detection in reinforced concrete structures

Some crucial signs of structural failure that are critical for repair would be cracks on the structures as well as constant exposure that can result in severe environmental damage. Being able to detect cracks on structures is becoming an essential aspect of the technology of the construction industry. Destructive Testing and Non-Destructive Testing are the two methods used for structural crack detection. This study focused on the techniques used to detect cracks. Several effective methods to detect cracks were carried out and compared to identify the most suitable method in detecting cracks on structures within the demographics of Malaysia. Image processing techniques (IPTs) through the photogrammetry method, surface crack analysis program and Convolution Neural Network (CNN) were carried out to examine crack detection through measurement and monitoring from images. The distance was determined in this study for the physical properties, using both conductibility and accuracy. The photogrammetry method was able to conduct distance at 0.1 - 40 m, with an accuracy of up to 0.005 mm. Therefore, the surface cracks analysis provided 0.10 mm accuracy, while results on CNN had an accuracy of 0.95 mm (98.22 % and 97.95 % in training and validation). Results from physical properties showed that photogrammetry had the highest accuracy, while CNN has the least accuracy. Hence, this study concluded that Photogrammetry method and Convolution Neural Network (CNN) were both the most effective methods to be used in providing clear information and effective ways to detect crack on structures

Design and Testing of a Structural Monitoring System in an Almería-Type Tensioned Structure Greenhouse

Greenhouse cultivation has gained a special importance in recent years and become the basis of the economy in south-eastern Spain. The structures used are light and, due to weather events, often collapse completely or partially, which has generated interest in the study of these unique buildings. This study presents a load and displacement monitoring system that was designed, and full scale tested, in an Almería-type greenhouse with a tensioned wire structure. The loads and displacements measured under real load conditions were recorded for multiple time periods. The traction force on the roof cables decreased up to 22% for a temperature increase of 30 °C, and the compression force decreased up to 16.1% on the columns or pillars for a temperature and wind speed increase of 25.8 °C and 1.9 m/s respectively. The results show that the structure is susceptible to daily temperature changes and, to a lesser extent, wind throughout the test. The monitoring system, which uses load cells to measure loads and machine vision techniques to measure displacements, is appropriate for use in different types of greenhouses