Today, with massive flow of the spatial and non-spatial database, enormous field data collection with enhanced electronic protocols in agricultural research has led to assemblage of massive caches of digital information on agriculture that describe the context-options for technological as well as socio-economic interventions at multiple scales and scenarios.  However such tools and web services are often limited to certain geographical regions and do not exist for drylands in developing countries. In a bid to improve digital agricultural services in the dry areas, the International Center for Agricultural Research in Dry Areas (ICARDA) recognizes that Geoinformatics in agriculture is critical in obtaining adequate data for tackling food security and improving livelihoods, particularly in dry areas of the developing world that struggle with limited natural resources.  In order to make spatial information available at the finger tips in the era of smart phones and personal computations, the ICARDA developed number of geospatial tools and web portals for data access, visualization and sharing to help researcher and decision makers at various scales to influence agricultural research, programs, and policies. In this paper we discuss latest trends and advances in an integrated web based spatial data visualization tools and technology for agro-ecological research and application. The workflow uses the hybrid protocols for data storage/access (e.g., AWS, SAN), processing (e.g., numpy, rasterio and GDAL) and map server interface (e.g., ArcGIS online, OA APIs). An interactive end-user segment consists of Django, AngularJS, Open Layers and WDSL. System renders multiple spatial data to website and web services via Open Geospatial Consortium (OGC) standards, Web Mapping Service (WMS) for map images and Web Coverage Service (WCS) for raster data. Such user friendly map interface allows researchers to take into account of different factors such as land cover dynamics, cropping patterns and intensities, water use and availability, changing demographics, infrastructure, poverty, markets, climate change, and more.  Information generated can be used to assess vulnerable areas for possible pathways to increased resilience and mitigation of risks whether biophysical (land degradation and drought) or socioeconomic (price shocks or policy changes in land tenure).

The geophysical observatory in the Antarctic Spanish Station Juan Carlos I (BAE), on Livingston Island (62.6S, 60.4W), has been monitoring the magnetic field in the Antarctic region for more than fifteen years. In 2004, a vertical incidence ionospheric sounder was incorporated to the observatory, which brings a significant added value in a region with low density of geophysical data. A High Frequency (HF) communications system was installed in 2004 in order to transmit the geomagnetic station recordings throughout the year, due to the fact that the BAE is only accessible during the austral summer. As the power supply is very limited when the station is not accessible, we had to design a low-power HF transceiver with a very simple antenna, due to environmental restrictions. Moreover, the flow of information is unidirectional, so the modulation has to be extremely robust since there is no retransmission in case of error. This led us to study the main parameters of the ionospheric channel (Signal to Noise Ratio -SNR-, delay spread, Doppler spread and availability) with narrowband and wideband soundings, and the design of modulations specially adapted to very low SNR scenarios with high levels of interference. In this poster, a review of the design of our remote geophysical observatory and associated transmission system from Antarctica to Spain (12760 km) during the last decade is presented.

At the request of the authors, the proceedings paper [1] will be retracted. Four coauthors, Maurício Zacharias Moreira, Eloisa Domingues, Fernando Peres Dias and Luiz Roberto de Campos Jacintho, do not endorse the content in the paper presented by the main author. We apologize to our readership for any inconvenience caused.

Reference

1. Macedo, R.; Santos, J.; Dias, F.; Moreira, M.; Jacintho, L.; Domingues, E. Mapping of Land Use and Land Cover on Brazil. In Proceedings of the 1st International Electronic Conference on Remote Sensing, 22 June–5 July 2015; Sciforum Electronic Conference Series, 2015, Volume 1, d007; doi:10.3390/ecrs-1-d007.

Retraction of the Proceedings Paper: https://sciforum.net/conference/ecrs-1/paper/2834

Nowadays, global warming has become more interested for scientist, because the global surface temperature has been increased since last century. More than fifty percent of people are living in cities, in this regard, urbanization has become a key factor for global warming. The urban heat island (UHI) refers to the event of higher atmospheric and surface temperatures occurring in cities than in the surrounding rural areas due to urbanization. The annual average air temperature of urban area with almost one million people can be one to three degree warmer than its surroundings. This phenomena can affect societies by increasing summertime, air pollution, air conditioning costs, heat related illness, greenhouse gas emissions and water quality.

Tehran, a capital city of Iran is case study of this research. Additionally, Tehran is one of megacities of the world. A megacity is usually defined as a metropolitan area with a total population in excess of ten million people. Due to rapid urbanization progress that has resulted in significant UHI effect in this area. Furthermore, Tehran houses to almost twenty percent of Iranian people. In this study, new launched Landsat series (Landsat 8) was used for monitoring UHI and retrieving the brightness temperatures and land use/cover types. The Landsat 8 carries two kind of sensors: The Operational Land Imager (OLI) sensor has former Landsat bands, with three new bands: a deep blue band for coastal/aerosol studies (band 1), a shortwave infrared band for cirrus detection (band 9), and a Quality Assessment band. The Thermal Infrared Sensor (TIRS) sensor provides two high resolution (near to 30 meters) thermal bands (band 10, 11). These sensors both use corrected signal-to-noise (SNR) radiometric quantized over a 12-bit. Corrected SNR performance cause better determination of land cover type. Moreover, Landsat 8 images incorporate two valuable thermal bands in 10.9 µm and 12.0 µm. These two thermal bands improve estimation of UHI by incorporating split-window methods.

Recently, quantitative models for urban thermal environment and related factors have been studied, for example, the relation between UHI and land cover structure and established corresponding regression equation. Similar works have been done and models of the relation between the surface temperature and various vegetation Indices have been established. In order to monitor the relationship between UHI and land cover indices, this paper tried to employ a quantitative approach for exploring the relationship land surface temperature and common land cover indices and select suitable indices by incorporating supervised Feature Selection (FS) procedures, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Water Index (NDWI) in two definition, Normalized Difference Bareness Index (NDBaI), Normalized Difference Build-up Index (NDBI), Modified Normalized Difference Water Index (MNDWI), Bare Soil Index (BI), Urban Index (UI), Index-based Built-Up Index (IBI) and Enhanced Built-Up and Bareness Index (EBBI). In this regards, the objectives of this research are to develop a non-linear analysis model for urban thermal environment by employing Support Vector Regression (SVR) method and Multivariate Regression (MR) algorithms. In addition, providing the hazard map for Tehran city is also one of the byproducts of proposed methods for managing and mitigating UHI effects. 

Tree biomass estimate is essential for carbon accounting, bioenergy feasibility studies, and forest sustainable management. This fact, added to the availability of airborne laser scanning (ALS) information, provided by the Spanish National Plan for Aerial Orthophotography (PNOA), and the existence of little research focusing on the use of ALS technology in Mediterranean Aleppo pine (Pinus halepensis Mill.) forest, determined the main objective of this research. Thus, this study aims to test the suitability of the low point density (0.5 points/m²), discrete, multiple-return, PNOA-ALS data, to estimate and map the total biomass (TB) and its carbon content in Pinus halepensis Mill. forest stands, located in Aragón (north-eastern Spain). TB was calculated in 45 field plots, using allometric equations, and related through a multivariate linear regression analysis with a collection of independent variables extracted from the ALS data. The predictive model was validated using a leave-one-out cross-validation (LOOCV) technique. Then, a regular grid with cell size 25 x 25 m corresponding to the sample plot size was generated by means of GIS, in order to compute TB at stand level and convert biomass to carbon by using the 0.5 conversion factor. The maximum height, kurtosis and the percentage of returns above 1 meter, were the ALS metrics included in the fitted model, which presented a R² value of 0.89. The implementation of the model in a GIS showed an average of 68633 kg/ha of TB and 34247.95 kg/ha of carbon fixed. The results indicate that despite the low point density of the ALS data, the final model is accurate enough to be used in forestry applications.