Decision Agriculture

In this chapter, the latest developments in the field of decision agriculture are discussed. The practice of management zones in digital agriculture is described for efficient and smart faming. Accordingly, the methodology for delineating management zones is presented. Modeling of decision support systems is explained along with discussion of the issues and challenges in this area. Moreover, the precision agriculture technology is also considered. Moreover, the chapter surveys the state of the decision agriculture technologies in the countries such as Bulgaria, Denmark, France, Israel, Malaysia, Pakistan, United Kingdom, Ukraine, and Sweden. Finally, different field factors such as GPS accuracy and crop growth are also analyzed

Trasformazioni di datum e di coordinate in alcuni software gis: applicazioni concernenti il territorio della Campania

In this paper results obtained with automatic executions of datum and coordinates transformations using some GIS softwares are described: 43 points of IGM95 network within Campania Region, which are known in reference to UTM-WGS84 and Gauss-Boaga Roma40, have been considered; two vector files, one in UTM-WGS84 plane coordinates, the other in UTM-WGS84 geographic coordinates, have been produced. Using softwares ArcGIS 8.3 (ESRI, 2002), Autodesk Map 3D 2005 (Autodesk, 2005), Idrisi 32 Release 2 (Clark Labs, 2002) and MapInfo Professional 7.0 (MapInfo Corporation, 2002), datum transformations (from WGS84 to Gauss-Boaga Roma 40) and coordinates transformations (from UTM-WGS84 plane coordinates to UTM-WGS84 geographic ones) have been applied; the results have been compared with values supplied by IGM. To conduct additional analyses, for 43 considered vertices also UTM-ED50 coordinates have been calculated with Verto1 (IGM, 2002) and used as comparison terms for analogous ones derived from applications of above mentioned GIS softwares. Datum transformations concerning plane coordinates have been applied using similitude (4 parameters: 1 rotation, 2 translations, 1 factor scale) in CAD MicrostastionV8 XM Edition (Bentley, 2005); in this case 4 points known in the different systems have been considered

A Proposal for Automatic Coastline Extraction from Landsat 8 OLI Images Combining Modified Optimum Index Factor (MOIF) and K-Means

The coastal environment is a natural and economic resource of extraordinary value, but it is constantly modifying and susceptible to climate change, human activities and natural hazards. Remote sensing techniques have proved to be excellent for coastal area monitoring, but the main issue is to detect the borderline between water bodies (ocean, sea, lake or river) and land. This research aims to define a rapid and accurate methodological approach, based on the k-means algorithm, to classify the remotely sensed images in an unsupervised way to distinguish water body pixels and detect coastline. Landsat 8 Operational Land Imager (OLI) multispectral satellite images were considered. The proposal requires applying the k-means algorithm only to the most appropriate multispectral bands, rather than using the entire dataset. In fact, by using only suitable bands to detect the differences between water and no-water (vegetation and bare soil), more accurate results were obtained. For this scope, a new index based on the optimum index factor (OIF) was applied to identify the three best-performing bands for the purpose. The direct comparison between the automatically extracted coastline and the manually digitized one was used to evaluate the product accuracy. The results were very satisfactory and the combination involving bands B2 (blue), B5 (near infrared), and B6 (short-wave infrared-1) provided the best performance

Estrazione in automatico della linea di costa da immagini satellitari IKONOS,

RIASSUNTO In molteplici studi ed applicazioni concernenti, ad esempio, il monitoraggio dell’erosione dei litorali e la sicurezza della navigazione, è di fondamentale importanza conoscere la posizione della linea di costa che, qualora non disponibile da rilievo diretto o da cartografia tecnica, può essere ricavata da foto aeree o da immagini satellitari. L’individuazione risulta facilitata qualora si disponga di dati multispettrali che evidenziano le differenti risposte alla radiazione elettromagnetica da parte del mare e della spiaggia. Anche in considerazione della tipologia dei dati telerilevati possono essere utilizzati differenti approcci metodologici: l’estrazione in automatico può avvenire, ad esempio, utilizzando l’algoritmo di Canny o quello denominato mean shift segmentation o altri metodi di segmentazione dell’immagine. In questo lavoro si illustra la procedura adottata per l’estrazione in automatico della linea di costa da immagini satellitari IKONOS relative al litorale campano. Al fine di migliorare il risultato dell’applicazione che prevede l’impiego dei dati non solo pancromatici (risoluzione geometrica: 1 m x 1 m), ma anche multispettrali (risoluzione geometrica: 4 m x 4 m), si propone un approccio metodologico basato su tecniche di pansharpening per il ricampionamento delle immagini multispettrali e di segmentazione delle stesse mediante mean shift. ABSTRACT In many studies and applications concerning, for example, monitoring of coastal erosion and navigation safety, it is very important to know shoreline position that, if it is not supplied by topographic survey or technical maps, can be derived from air photos or satellite images. The identification is facilitated by multispectral data that differentiate sea and land in base to reflectance. Also in consideration of the typology of remote sensed data different methodological approaches can be adopted: automatic extraction can be done using algorithms such as Canny, mean shift or others for image segmentation. In this paper procedure followed for automatic extraction of coastline from IKONOS satellite images concerning coasts of Campania Region is illustrated. To increase the level of the results of the application that is based on the use of panchromatic images (geometric resolution: 1 m x 1 m) as well as multispectral ones (4 m x 4 m), a methodological approach based on integration of pansharpening techniques and mean shift segmentation algorithm is proposed

Intervisibility analysis of an offshore wind farm using GIS tools.

One of Kyoto’s protocol targets is to limit greenhouse gas emissions produced by human activities through renewable energy and the conversion of wind energy into a useful form of energy (for example electricity) which is a kind of renewable source undergoing strong development. In this work, the study is focused on offshore aeolic parks. The term “offshore aeolic” indicates wind turbines installed a few miles from the coasts of seas or lakes, exploiting the exposure of strong currents. The offshore aeolic farms have some disadvantages including the impact of these generators on the landscape, but they produce a minimum visual disturbance compared to onshore aeolic farms. Environmental Impact Assessment (known as the EIA), introduced in Europe by the 377/85/EEC Directive and amended three times, in 1997 (Directive 97/11/EC), in 2003 (Directive 2003/35/EC) and in 2009 (Directive 009/31/EC), ensures that natural resources, usability and identity of landscapes are not compromised by engineering operations. Planning applications must be accompanied by an Environmental Statement (ES), with the purpose of giving an idea about the effect that the operations will have on the local environment. One of the main aims of the ES is the understanding of the likely visual intrusion of he proposed wind farm, which necessitates the use of visual techniques. A visual technique commonly used is the intervisibility map, normally represented by a two-dimensional map, centered on the location of the wind farm. Digital Terrain Models (DTMs) are used to achieve Thematic Maps obtained with GIS software. In this paper, GIS tools are used to identify the effects of wind farms on the landscape; in particular, attention is focused on Map Algebra functions and the study of the analysis of the intervisibility of an offshore aeolic farm is tested in a zone in Italy, localized in the Adriatic sea, along the coast of the Apulia and Molise Regions

Using Images Generated by Sentinel-2 Satellite Optical Sensor for Burned Area Mapping

Remote Sensing is identified as an effective and efficient tool for monitoring fire events, and for quantifying fire effects on environment. Satellite images are used both to identify active fires and to analyse their effects, as well as to define burnt areas and map the severity of fires. Fires modify the structure and the reflectance of vegetation as well as the soil properties within the burned area; the produced changes are detectable in the visible, infrared and microwave parts of the electromagnetic spectrum. One of the most useful approach is based on classification of images using the spectral properties of burnt residues. This paper aims to use data obtained from optical sensor mounted on Sentinel-2 platform for mapping areas damaged by fire in a precise and rapid way. Sentinel-2 offers multispectral medium and high spatial resolution images with 13 spectral bands and about 5-day temporal resolution. Two images concerning the same scene in Campania Region but acquired on different dates are considered: pre-fire and post-fire. For each image, the Normalized Burned Ratio Index (NBR) is calculated, which allows to identify the areas affected by the fire and the relative degree of severity. Using change detection techniques, burned map can be identified. The evaluation of the accuracy is carried out using some indexes widespread in remote sensing literature, such as User’s Accuracy, Producer’s Accuracy, Overall Accuracy and Kappa coefficient. The values obtained in the confusion matrix showed the high quality of the developed method based on the use of the NBR index