We processed an archive of more than 300 permanent Global Navigation Satellite Systems (GNSS) station with the aid of software Gamit/Globk 10.5 developed and maintained by MIT, Scripps Institution of Oceanography, and Harvard University with support from the National Science Foundation (http://www-gpsg.mit.edu/~simon/gtgk/), using our facilities based on the distributed sessions approach implemented in the package. The Global Positioning System (GPS) data are comprised in the period January 2008 – December 2012, and belongs to several different permanent GPS network in Italy like: the RING of the National Institute of Geophysics and Volcanology (INGV), ASI Italian spatial agency, EUREF – European Reference Network, IGS – International GNSS Service for geodynamics, ITALPOS of Leica Geosystems and other institution networks maintained by public and private administration and universities. The data collected daily by different sensors and archived in RINEX compressed Hatanaka format were gathered, divided in several clusters, and routinely processed using Gamit data processor, based on least square method.
Successively, our solutions were combined with SOPAC (http://garner.ucsd.edu) ones to obtain stable and high quality solutions (h_files) containing coordinates, velocities and covariance of more than 300 GPS sites. A Global Kalman filter based package (Globk) was used to compute position time series and velocities registered in the ITRF2008 reference frame. The almost rigid rotation of the Eurasian plate was removed using Altamimi’s definition of rotation pole, and residual intra-plate velocities for all the 300 stations were computed. Indications about the tectonics of Mediterranean regions can be available by studing the computed high density velocity field.

Ore Mountains (west of the Czech republic) are an example of the area that suffered from severe environmental pollution originating in coal mining and heavy industry leading to massive dieback of the local Norway spruce forests between 1970’ and 1990’. The situation became getting better at the end of 1990’ after significant decrease of pollution loads. In 1998, ASAS airborne hyperspectral data were used to study the health status of the Norway spruce forests in this area. New hyperspectral data acquisition (APEX) was organized in 2013. Therefore there is a unique opportunity to study recovery of the originally damaged forest stands and compare them with the stands that have been less affected by environmental pollution. The analysis was conducted assessing a set of 16 vegetation indices to provide complex information on vegetation foliage biochemistry, canopy biophysics and structure. Five of them (NDVI, NDVI₇₀₅, VOG₁, MSR and TCARI/OSAVI) showing the best results were employed to study their spatial gradients as well as temporal changes. The obtained results indicate that the original significant differences between the damaged and undamaged stands have been generally levelled until 2013, although it is still possible to detect some signs of the previous damages in several cases.

The monitoring of land cover and land use changes is justified by the growing need for management and monitoring of global changes, as recommended by UNCED-92 and subsequent conferences in Johannesburg (Rio + 10) and Rio de Janeiro (Rio + 20). Based on an international methodological framework, which identifies changes in terrestrial ecosystems, the main purpose of this work is to monitor changes in land cover and land use for all the brazilian territory, at regular intervals, by the systematic mapping and the use of a territorial grid for statistical purposes. The methodology basically involves four stages of work: digital processing; edition; incorporation of data to the territorial grid for statistical purposes; and validation. A preliminary analysis of 1km² cells allowed the observation of some changes in the study period. There was significant loss of forest cover. However, areas of grasslands, including savannas, scrublands and pioneer formations, suffered proportionately the greatest reduction. Some changes can be highlighted, such as the significant growth in agriculture and pastures in northern Mato Grosso, the increase of pastures in eastern Pará, east of Acre and Rondônia, and the expansion of planted forests in the south region, especially in Santa Catarina and Paraná.

Retracted on 20 January 2016, see https://sciforum.net/conference/ecrs-1/paper/3382

French Guiana is located in South America close to the equator (between 2° and 4° N), a huge asset in the production of electrical power with the use of solar panels. The yearly average solar-radiation is 5.8 kWh/m²day .The main focus of this study is to determine the solar energy potential in French Guiana using Kernel density estimation.

The knowledge of surface solar irradiance may be obtained using physical laws such as radiative transfert functions that link satellite apparent albedo to surface irradiance. However, due to the complexity of physical processes, it is difficult to develop accurate and reliable nonlinear observation law, in particular in a tropical area (our area of study). In this work we used a two dimensional Kernel density estimation with a learning dataset of clearness index data and apparent albedo data to estimate surface solar irradiance.

A time series of images every 30 min from the visible channel of the GOES-13 meteorological geostationary satellites from the year 2010 to 2013 has been selected with a spatial resolution of 1 km x 1 km. Hourly in-situ measurement data from six ground reference stations were provided by the French national meteorological agency.

The predicted solar irradiance values from the Kernel density estimation were given in the form of hourly, monthly and annual maps. The maps obtained highlighted interesting trends: the annual average maps showed that the western side of French Guiana receives the highest irradiance values and is dependent on the position of the InterTropical Convergence Zone (ITCZ).

The mean bias percentage error and mean RMSE values were respectively found to be less than 5% and 10% for the testing stations on a hourly basis. The kernel density estimation show great accuracies for evaluating solar resource potential. These results give a first overview of the solar energy potential in French Guiana.

With the development of quantitative remote sensing, regional evapotranspiration modeling based on the feature space has made substantial progresses. Among many remote sensing evapotranspiration models, accurate determination of the theoretical dry/wet lines remains a challenging task. This paper reports the development of a new method, named DDTI (Determination of dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between thermal inertia and soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value computed in the laboratory. Two evaluation methods, which are based on the comparison of the location of theoretical dry line and the comparison of Evaporative fraction, were used to assess the performance of the new method DDTI. The location of the theoretical dry line determined by DDTI is higher than the heat energy balance method, which is more reasonable in wet conditions. When compared with the in situ measurement of Evaporative fraction at YuCheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with a RMSE(Root Mean Square Error) of 0.071, which is much lower than that based on heat energy balance method with a RMSE of 0.65. Also, the bias between in situ measurements and DDTI method is 0.056，which is ower than that between in situ and the heat energy balance method with a bias of 0.645.

Although in the field of Underwater Acoustic Imaging a large number Automatic Target Detection (ATD) systems have long been developed and their bibliography has been classified, the present study aims to test the potentiality of using common and widely used feature based image classification approaches against small targets detection in SideScan Sonar (SSS) images by combining them with powerful data mining techniques. Conventional Acoustic Classification Systems (ACS) start by extracting numerous texture descriptors from distinct image neighbourhoods throughout the image and forming large Feature Vectors (FVs). In view of the FVs’ high dimensionality, prior to unsupervised classification, a component analysis technique, usually Principal Component Analysis (PCA), is performed to decompose them into a few un-correlated features that explain the majority of the image’s variance. However, small targets belong to subordinary image information and do not contribute significantly to the total information variance of the SSS image. Furthermore targets tend to be independent image characteristics rather than un-correlated ones. In this study, a newly available technique, called Independent Component Analysis (ICA), that decomposes the FVs into independent sources, is tested against its ability to separate SSS images into targets and background and lead to accurate target classification.

The proposed methodological scheme consists of the following stages: 1) windowed feature extraction, 2) ICA  decomposition, 3) selection of certain components that enhance potential targets through a maximum curtosis criterion, 4) decision of the  number of classes that the selected components need to be clustered into so that they are optimally separated in the Euclidean space through validation indices utilization, 5) unsupervised classification and 6) selection of the class or classes that most possibly correspond to areas containing potential targets via a minimum area definition. The above stages are included in the SonarClass Matlab ACS. The classification precision of the proposed system was assessed using a SSS dataset from Igoumenitsa Harbour, Greece, including more than 85 ground truthed man made targets. The classification accuracy of the proposed system was estimated as Pc=tp/(tp+fp), where tp is the number of true positive (expected) and fp the number of false positive (unexpected) predictions, and was compared to the accuracy of following conventional ACS procedures. The method exhibited unquestionable superiority indicating that ICA may worth further attention by ATD system researchers and developers.

We have devised a simple optical parameter classification system using the three apparent optical property (AOP) parameters available in the NASA Geospatial Interactive Online Visualization and Analysis Infrastructure (Giovanni). These parameters are produced by the NASA Ocean Biology Processing Group (OBPG). The three AOP parameters are: a_dg, the absorption coefficient of dissolved and detrital matter; a_ph, the absorption coefficient of phytoplankton, and b_bp, the backscatter coefficient. The Microsoft Excel ternary diagram plotting spreadsheet Tri-Plot (Graham and Midgely 2000) was used for visualization of the three-parameter ocean optical parameter classification scheme. This simple analysis method can be utilized by researchers, continuous water quality monitoring campaigns, citizen scientists, and students.

In this paper we demonstrate the use of this ternary optical classification system by applying it to an examination of the seasonal outflow of the Orinoco River into the eastern Caribbean Sea. End-member optical regimes consist of the river mouth waters during the rainy season, and high clarity Caribbean Sea waters which are not influenced by the optically active constituents in the Orinoco River plume. The variability of the optical characteristics of the surface waters between the rainy and dry seasons is clearly distinguishable in the ternary plots, particularly in the coastal region adjacent to the northern coast of South America.

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).

Agricultural productivity patterns are affected by growing season weather risks such as rainfall deficit (meteorological drought), which can develop into persistent soil moisture deficit (agricultural drought), leading to crop yield shortfall or wholesale losses. Weather index-based insurance (WII) is a financial instrument designed to assist smallholder farmers in coping with the impacts of drought through payouts when an index threshold is breached.

Critical for operational WII schemes is the skill of capturing the progression of meterological drought (rainfall deficit) to agricultural drought (crop failure), which is not fully represented in statistical correlations of historical data on rainfall deficit and crop yield. This is a non-trivial task. At the core of designing WII products is the requirement for high-quality data over the long-term and in near-real time to provide spatially explicit and internally consistent information on rainfall. Crop yield data with matching space-time characteristics are virtually non-existent and are prone to errors. Moreover, crops yield shortfalls or losses can result from factors other than rainfall such as soil properties and temperature, as well as evaporation and soil moisture dynamics, as crops differ in their sensitivities to the dynamics of water and energy fluxes during the different phonological stages within the growing season. As these factors are key in determining crop production outcomes, we evaluate new space-time volatility indicators based on land surface and crop process modelling using cotton grown in Zambia as a case study.

The results from our analysis of cotton production in Zambia suggest that combining rainfall and soil moisture information can inform WII applications at both the design and implementation stages. This is achieved through relating area-specific probabilities of rainfall deficit occurrence and severity to crop-specific water requirements and their sensitivities to agricultural drought. We discuss how this approach can be used to inform operational WII applications by capturing the physical dynamics of agro-meteorological risk along the trajectory of meteorological to agricultural drought.

The period of spontaneous development of Ho Chi Minh City in Vietnam has caused uncontrollably environmental problems occurred. Besides, the concrete surface caused increasing surface temperatures, reducing evaporation, consequently heating up urban space. This paper presents the results of the application of Landsat satellite images to study the urban thermal environment from the thermal infrared channel by capability of object surface emissivity for the northern part of Ho Chi Minh City. The Landsat satellite images was used for exploratory research to date 21-01-2014. The method to extract land surface temperature (LST) from thermal infrared bands with emissivity determined by the characteristics of Normalized Difference Vegetation Index (NDVI) values has created detailed results according to resolution of the reflectance bands. In addition, the relationship between the heating element and land cover variables (impervious surface, bare soil, vegetation and water) is also considered in order to find the relationship determining factor affecting the urban thermal environment. The study results showed that the developing urban area is where the high temperature exits. A giant heat island is formed on the central area of the city with temperature ranging from 32oC to 44oC and above. Besides, impervious surface is a major factor contributing in the warmth of the thermal environment in the city with the highest number of positive correlation (R = 0.87), whereas vegetation is factor that impact to reduce heat with the highest number of negative correlation (R = -0.84). In Ho Chi Minh City there is only one ground meteorological station, so its temperature number does not express the thermal environment in the whole city. These results are a good reference for local city authorities in urban spatial planning during climate change period nowadays.