Horizontal accuracy assessment of very high resolution Google Earth images in the city of Rome, Italy

Google Earth (GE) has recently become the focus of increasing interest and popularity among available online virtual globes used in scientific research projects, due to the free and easily accessed satellite imagery provided with global coverage. Nevertheless, the uses of this service raises several research questions on the quality and uncertainty of spatial data (e.g. positional accuracy, precision, consistency), with implications for potential uses like data collection and validation. This paper aims to analyze the horizontal accuracy of very high resolution (VHR) GE images in the city of Rome (Italy) for the years 2007, 2011, and 2013. The evaluation was conducted by using both Global Positioning System ground truth data and cadastral photogrammetric vertex as independent check points. The validation process includes the comparison of histograms, graph plots, tests of normality, azimuthal direction errors, and the calculation of standard statistical parameters. The results show that GE VHR imageries of Rome have an overall positional accuracy close to 1 m, sufficient for deriving ground truth samples, measurements, and large-scale planimetric maps

Water Use and Urban Agriculture: Estimation and Water Saving Scenarios for Residential Kitchen Gardens

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Spatial Exploration of the Relationships between Agricultural Land Use and Water Quality Measures

Agricultural land-use effect on water quality (surface and groundwater) is a well-known issue, and actions are needed to reduce the impacts of farm inputs management. Direct and indirect links can also be found with some of the SDGs (i.e., SDG 6—Clean water and sanitation). Assessing these impacts can support the definition of sustainable management practices for agricultural production as well as evaluating the performance of policies and directives (e.g., European Common Agricultural Policy). In this work, we performed an analysis of the relationship between agricultural land use and pesticides found in surface waters located in South Italy (Foggia province—Apulia). Land-use data were produced with a complex data integration process using administrative geospatial data from the Italian agricultural paying agency. Land-use data were jointly analyzed with water quality measures on surface waters within a large watershed in the study area. A statistical analysis was carried out in order to assess the relationships between specific types of chemicals in water and land uses within a circular buffer of 5 km around each monitoring station

Assessing multiple years' spatial variability of crop yields using satellite vegetation indices

Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Dierence Vegetation Index (NDVI), EnhancedVegetation Index (EVI), Soil AdjustedVegetation Index (SAVI), Green Normalized Dierence Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2\u20134 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over dierent crops in a rotation, at a relatively small field scale

Perspectives on “Earth Observation and GIScience for Agricultural Applications”

Current and future scenarios for global agricultural systems under a changing climate require innovative approaches, novel datasets, and methods for improving environmental resource management and better data-driven decision-making [...

Collaborative mapping response to disasters through OpenStreetMap: the case of the 2016 Italian earthquake

Digital humanitarians represent the current generation of volunteers providing timely contributions in the form of digital map data in the aftermath of natural disasters. Starting from the tragic 2010 earthquake in Haiti and thanks to the success of the OpenStreetMap (OSM) project, the presence and coordination of these volunteers have grown incredibly over the past years. This work investigates the dynamics of the mapping process and the nature of the OSM volunteers who contributed map data after the 2016 earthquake in Central Italy. The analyses show that the existing OSM users were the majority of those contributing to the mapping activity, with less edits performed by new users. The collaborative mapping process was efficiently coordinated through a dedicated platform and the area hit by the earthquake was significantly edited in OSM after the disaster

Investigating the feasibility of geo-tagged photographs as sources of land cover input data

Geo-tagged photographs are used increasingly as a source of Volunteered Geographic Information (VGI), which could potentially be used for land use and land cover applications. The purpose of this paper is to analyze the feasibility of using this source of spatial information for three use cases related to land cover: Calibration, validation and verification. We first provide an inventory of the metadata that are collected with geo-tagged photographs and then consider what elements would be essential, desirable, or unnecessary for the aforementioned use cases. Geo-tagged photographs were then extracted from Flickr, Panoramio and Geograph for an area of London, UK, and classified based on their usefulness for land cover mapping including an analysis of the accompanying metadata. Finally, we discuss protocols for geo-tagged photographs for use of VGI in relation to land cover applications

Usability of VGI for validation of land cover maps

Volunteered Geographic Information (VGI) represents a growing source of potentially valuable data for many applications, including land cover map validation. It is still an emerging field and many different approaches can be used to take value from VGI, but also many pros and cons are related to its use. Therefore, since it is timely to get an overview of the subject, the aim of this article is to review the use of VGI as reference data for land cover map validation. The main platforms and types of VGI that are used and that are potentially useful are analysed. Since quality is a fundamental issue in map validation, the quality procedures used by the platforms that collect VGI to increase and control data quality are reviewed and a framework for addressing VGI quality assessment is proposed. A review of cases where VGI was used as an additional data source to assist in map validation is made, as well as cases where only VGI was used, indicating the procedures used to assess VGI quality and fitness for use. A discussion and some conclusions are drawn on best practices, future potential and the challenges of the use of VGI for land cover map validation

Nuovi Metodi di visualizzazione geografica: l'approccio Focus+Glue+Context

New cartographic visualization methods: the Focus+Glue+Context approach Focus+Glue+Context is a new cartographic visualization method specifically designed to solve the fruition problems connected with the use of mobile devices and web mapping services. The objective of the F+G+C approach is to reduce users cognitive efforts when reading a map: to do so, the area of interest is ‘highlighted’ in a lower and more detailed scale through a fisheye lens effect, while the sorrouding context, useful to the user to determine the items relationships in a map, is maintained on a higher scale