Pilot study of the application of Tellus airborne radiometric and soil geochemical data for radon mapping

The scope for using Tellus Project airborne gamma-ray spectrometer and soil geochemical data to predict the probability of houses in Northern Ireland having high indoor radon concentrations is evaluated, in a pilot study in the southeast of the province, by comparing these data statistically with in-house radon measurements. There is generally good agreement between radon maps modelled from the airborne radiometric and soil geochemical data using multivariate linear regression analysis and conventional radon maps which depend solely on geological and indoor radon data. The radon maps based on the Tellus Project data identify some additional areas where the radon risk appears to be relatively high compared with the conventional radon maps. One of the ways of validating radon maps modelled on the Tellus Project data will be to carry out additional indoor measurements in these areas.     

Uncertainty in the difference between maps of future land change scenarios

It is essential to measure whether maps of various scenarios of future land change are meaningfully different, because differences among such maps serve to inform land management. This paper compares the output maps of different scenarios of future land change in a manner that contrasts two different approaches to account for the uncertainty of the simulated projections. The simpler approach interprets the scenario storyline concerning the quantity of each land change transition as assumption, and then considers the range of possibilities concerning the value added by a simulation model that specifies the spatial allocation of land change. The more complex approach estimates the uncertainty of future land maps based on a validation measurement with historic data. The technique is illustrated by a case study that compares two scenarios of future land change in the Plum Island Ecosystems of northeastern Massachusetts, in the United States. Results show that if the model simulates only the spatial allocation of the land changes given the assumed quantity of each transition, then there is a clearly bounded range for the difference between the raw scenario maps; but if the uncertainties are estimated by validation, then the uncertainties can be so great that the output maps do not show meaningful differences. We discuss the implications of these results for a future research agenda of land change modeling. We conclude that a productive approach is to use the simpler method to distinguish clearly between variations in the scenario maps that are due to scenario assumptions versus variations due to the simulation model.     

Prognosis of the spatiotemporal dynamics of ecosystems by means of universal kriging

The use of quantitative data for constructing prognostic maps of the dynamics of ecosystem degradation and restoration by nonlinear simulation methods is a topical field of landscape ecology. This method of dynamic cartography is based on fiberwise comparison of data on the state of Chernye Zemli (the Kalmyk Republic, Russia) in different years and the detailed analysis of the period on which the prognosis was based. For this purpose, materials of repeated aerial and satellite photography obtained during a long period (1954–1993) were used. Comparison of maps characterizing the dynamics of Chernye Zemli between 1958 and 1993 allows prognostic electronic maps for the next 10–15 years (with a five-year interval) to be drawn and land prognosis for the next 20–30 years (1998–2023) to be obtained. Deceased     

江汉平原土地资源数据库的建立与功能

根据所收集的江汉平原土地资源数据及相关图件（包括１９８３年版１：５万地形图,１：５万各县市土壤图,利用多时相卫片编制的１：２５万专题图）,以ＰＣ－５８６微机为主机,以ＰＣ ＡＲＣ／ＩＮＦＯ地理信息系统工具软件为支持,建立了江汉平原土地资源数据库,其内容包括行政区划空间数据库（各县市行政区划图１：５万）,水系空间数据库（各县市河流,沟渠及水面图,１：５万）,土壤空间数据库（各县市１：５万土壤图）,土     

Spatial assessment of climate change effects on crop suitability for major plantation crops in Sri Lanka

Climate change is the main global challenge of this century; it is therefore imperative to identify its effects on agriculture in developing countries. This research makes spatial assessment of climate change effect on major plantation crops in Sri Lanka, with emphasis on crop suitability of tea, rubber, and coconut. Geo-referenced maps of spatial and temporal changes in crop suitability and production potentials are generated and compared. Data pertaining to six agro-ecological zones under the study area are analyzed for a period of 1980–2007. Crop suitability maps are generated amalgamating yield maps and climatic factors maps using AHP in multi-criteria analysis under two time frames of 1980–1992 and 1993–2007. Percent change in crop suitability and crop yield classes is calculated based on five crop suitability and five crop yield classes during two time frames. Dynamics of climatic parameters and crop yield are recognized using geo-referenced maps. The suitability maps of the two time frames are compared to identify the changes with each crop in conjunction with changes in the prevailing climate and yield. Geographic shift of suitability, yield, and climate classes are examined. Net gain or loss in crop production is quantified. Long-term annual rainfall significantly decreased in mid-country wet zone, whereas the mean temperature of the study area increased by 1.4°C. Results clearly showed that the climate and yield can be meaningfully related to the crop suitability and management.     

Expansion of Tree Vegetation in the Forest–Mountain Steppe Ecotone on the Southern Urals in Relation to Changes in Climate and Habitat Moisture

Russian Journal of Ecology - As found by comparing historical topographic maps and present-day satellite images, steppificated areas in the southern part of the eastern macroslope of the Southern...     

Geographical mapping and associated fractal analysis of the long-lived Chernobyl fallout radionuclides in Greece

Immediately after the Chernobyl accident, a soil sampling programme was undertaken in order to detect and quantitatively analyse the long-lived radionuclides in the Chernobyl fallout. Soil samples (1242 in number) of 1 cm thick surface soil were collected in Greece during the period from May-November 1986. The samples were counted and analysed using Ge detector set-ups. The 137Cs fallout data have already been analysed, mapped and published. In an attempt to improve this analysis and also to extent it to other fallout radionuclides, an in-house unix-based data base/geographical information system (DBGIS) was developed. Multifractal analyses of the deposition patterns have also been performed. In the present work, an analysis of the results of the deposition of 137Cs, 134Cs, 144Ce, 141Ce, 125Sb, 110mAg, 106Ru, 103Ru, 95Zr and 54Mn are presented together with relevant fractal analysis and three characteristic contour maps. The maximum detected values of the above-mentioned radionuclides were 149.5 +/- 0.1, 76.1 +/- 0.1, 32.9 +/- 0.2, 46 +/- 2, 4.56 +/- 0.02, 7.98 +/- 0.02, 79.1 +/- 0.4, 337 +/- 2, 20.1 +/- 0.2 and 3.02 +/- 0.02 kBq m-2, respectively. Furthermore, a statistical technique to compare contour maps was introduced and applied to explain the differences which appeared in the maps of the above-mentioned radionuclides.     

Multivariate statistical methods as a tool for model-based prediction of vegetation types

The current research was carried out to find the most effective environmental factors in plant species occurrence and providing their predictive habitat models. For this purpose, study was conducted in Poshtkouh rangelands of Yazd province in the central of Iran. For modeling, vegetation data in addition to site condition information including topography, climate, geology and soil were prepared. CCA and Logistic regression (LR) techniques were implemented for plant species predictive modeling. To plants predictive mapping, it is necessary to prepare the maps of all affective factors of models. To mapping soil characteristics, geoestatistical method including variogram analysis and Kriging interpolation were used. Based on obtained predictive models for each species (through LR method) and for whole species (through CCA method) related predictive maps were prepared in GIS. The accuracy of predictive maps were tested with actual vegetation maps. Vegetation modeling results with CCA indicates that predictive map of vegetation corresponds with actual map (with high accuracy). Predictive maps of Cornulaca monachantha, Ephedra strobilacea-Zygophyllum eurypterum, Seidlitzia rosmarinus, and Tamarix ramosissima, which have narrow amplitude, has high accordance with actual vegetation map prepared for the study area. Among species of study area, predictive model of Artemisia sieberi, due to its ability to grow in most parts of Poshtkouh rangelands with relatively different habitat conditions, is not possible. Comparing CCA and LR methods showed that each technique has its advantages and drawbacks. In general, LR will provide better specific-model, but CCA will provide a broader overview of multiple species.     

Land use and cover dynamics in Africa since the nineteenth century: warped terrestrial photographs of North Ethiopia

Quantitative research on land use and land cover (LUC) in Africa usually addresses the second half of the twentieth century, by using remote sensing data. Terrestrial photographs, which are available since 1868 in Ethiopia, are seldom used in a quantitative way. This paper presents a methodology that allows to produce land use and land cover (LUC) maps on the basis of old terrestrial photographs. Therefore, land use and land cover was investigated on historical and present-day photographs, and these interpretations were warped to the horizontal plane of the map. The resulting maps allow to gain better insights into LUC changes over a period of 140 years. The results show that woody vegetation increased strongly, together with an increase in built-up area. This occurred especially at the expense of bushland. The study validates pervious findings and shows that improved land management strategies in one of the world’s most degraded areas can lead to environmental rehabilitation.     

A model of vegetation pattern at the Verkhneussuriysky Biogeocenotic Station

A model of relationships between forest communities and basic parameters of environmental conditions, which is suitable for making prognostic maps of forest types, has been developed and tested on the territory of Verkhneussuriysky Biogeocenotic Station of the Institute of Biology and Soil Science. A comparative analysis of the spatial distribution of forest communities has been performed according to this model and to the large-scale map of recent forest vegetation on the territory of the station.     

Mapping large areas of radioactively contaminated land with a self adapted, handheld, GPS coupled, scintillation detector

In Belgium, during several decennia, a phosphate plant discharged radium chloride containing waste water into two small rivers. One of those is part of a hydrographically very complex ecosystem with lots of small tributaries and hundreds of hectares of flooding zones. Hence, the river banks and large parts of these flooding zones have become contaminated with radium, heavy metals and chlorides. During a foot campaign, using a home made portable data logging system, consisting of a commercial 2.5 kg NaI detector, a computer mouse sized GPS, and a small pocket PC, the radioactive contamination of about 600 ha of sometimes very rough terrain was measured and mapped. The resulting very detailed radium contamination maps shed a whole new light on the water flow patterns of the ecosystem. The apparatus can also be used for efficiently guiding sampling campaigns for investigating other types of contamination. The ground maps are also compared to existing maps from helicopter measurements, evaluating strengths and weaknesses from both methods.     

An approach to improve the Austrian Radon Potential Map by Bayesian statistics

A Radon Potential Map as well as a mean indoor Radon Concentration Map is available from the Austrian National Radon Project (1992-2002). These maps are based on the average Radon Potential/Concentration within every municipality and they sort municipalities into three radon ‘risk’ classes. This is a convenient way for the administration, but it does not describe the real radon risk distribution within a municipality because of the often inhomogeneous geological situation. Therefore, a combination of indoor radon data with all relevant parameters such as house type, storey and ventilation rates along with geological information should be used to improve the existing radon maps. The method, described here, uses Bayes' theory to combine the Radon Potential derived from indoor radon measurements with information from geology. The existing Radon Potential Map was improved by using available soil gas radon data at certain geological units and extrapolated transfer factors. The modifications of the map are shown and several problems arising with the application of this technique are discussed.     

Spatial distribution of soil radon as a tool to recognize active faulting on an active volcano: the example of Mt. Etna (Italy)

This study concerns measurements of radon and thoron emissions from soil carried out in 2004 on the eastern flank of Mt. Etna, in a zone characterized by the presence of numerous seismogenic and aseismic faults. The statistical treatment of the geochemical data allowed recognizing anomaly thresholds for both parameters and producing distribution maps that highlighted a significant spatial correlation between soil gas anomalies and tectonic lineaments. The seismic activity occurring in and around the study area during 2004 was analyzed, producing maps of hypocentral depth and released seismic energy. Both radon and thoron anomalies were located in areas affected by relatively deep (5-10 km depth) seismic activity, while less evident correlation was found between soil gas anomalies and the released seismic energy. This study confirms that mapping the distribution of radon and thoron in soil gas can reveal hidden faults buried by recent soil cover or faults that are not clearly visible at the surface. The correlation between soil gas data and earthquakes depth and intensity can give some hints on the source of gas and/or on fault dynamics.     

Climate change impacts on irrigation water requirements in the Guadalquivir river basin in Spain

Irrigated production in the Guadalquivir river basin in Spain has grown significantly over the last decade. As a consequence, water resources are under severe pressure, with an increasing deficit between available supplies and water demand. To conserve supplies, the water authority has reduced the volume of water assigned to each irrigation district. Major infrastructural investments have also been made to improve irrigation efficiency, including the adoption of high technology micro-irrigation systems. Within a context of increasing water scarcity, climate change threatens to exacerbate the current supply-demand imbalance. In this study, the impacts of climate change on irrigation water demand have been modelled and mapped. Using a combination of crop and geographic information systems, maps showing the predicted spatial impacts of changes in agroclimate (climate variables that determine the irrigation requirements) and irrigation need have been produced. The maps highlight a significant predicted increase in aridity and irrigation need. Modelling of irrigation water requirements shows a typical increase of between 15 and 20% in seasonal irrigation need by the 2050s, depending on location and cropping pattern, coupled with changes in seasonal timing of demand.     

ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran

The main goal of this study was to evaluate the different feed-forward back-propagation artificial neural networks’ (ANNs) potential to estimate and interpolate the reference crop evapotranspiration (ET₀) in Fars province of Iran. ET₀ was calculated using the FAO-56 Penman–Monteith method over 24 synoptic stations. Then, altitude, latitude, longitude and the month’s number as inputs and the monthly ET₀ as output (target) were used to train the ANNs. In addition, the three-layered ANNs optimized with different training algorithms including gradient descent back-propagation (gd), gradient descent with adaptive learning rate back-propagation (gda), gradient descent with momentum and adaptive learning rate back-propagation (gdx) and scaled conjugate gradient back-propagation (scg). The results indicated that scg algorithm with architecture (4 2 1) had more satisfactory results with the RMSE and R correlation coefficient equal to 18.538 mm and 0.967 in validation phase, respectively. Based on the mentioned architecture of scg algorithm, and input data form different parts of Fars province and surrounding areas, monthly ET₀ maps were produced and annual one achieved by summation of monthly maps. The maps particularly annual one showed that highest values of ET₀ could be found in the southern and especially southeastern regions, while the lowest values of ET₀ could be seen in the northern parts. Contribution of geographic and topographic variables improved the accuracy and spatial details of the resulting maps. It is interesting to note that the fundamental capability of this model is the usage of just a few parameters for ET₀ mapping. Since ET₀ is a key parameter in water demand planning, therefore, the derived maps could be useful and applicable for many purposes mainly irrigation scheduling in Fars province, Iran.     

Experience in mapping the ecological state of the plant cover

Objectives and methods of mapping the ecological state of the plant cover at different spatial levels—federal, regional, and local—are analyzed. The maps used as examples are as follows: at the federal level, “Ecological State of Rangelands in Russia” (1:10 000 000); at the regional level, “Vegetation of Moscow Oblast” (1:200 000); and at the local level, “Vegetation of Narofominskii Raion” and “Current State of the Plant Cover in Narofominskii Raion” (1:50 000).     

Ecological footprint: an indicator of environmental sustainability of a surface coal mine

Ecological footprint of mining can be used as an indicator to monitor and regulate mining operations and ensure long-term environmental sustainability. It can be viewed as a mining footprint, which is surrogate of the environmental impacts of mining. In this paper, a methodological framework is developed to demonstrate how ecological footprint can be used as an indicator of environmental degradation. Nine air quality and 26 soil quality samples are collected from the adjoining area of a mechanized coal mine, located in Raniganj coal mining belt of Burdwan district, West Bengal. Geographical information system is used for data interpolation and preparation of air and soil quality maps. The weights of different air and soil quality parameters are calculated by running principal component analysis. These derived weights are used for preparation of final composite air and soil quality maps. The composite maps show the mining footprints, expressed as land equivalent, around the active mine sites. The impact zones reveal the extent of degradation of the soil and air qualities in the areas near a mine. It is found that the impact zones, with respect to air and soil qualities, extend over areas which are 7.7 and 7.8 times the actual mining areas, that is, the area covered under mining operations, respectively. The results show the extent of degradation of air and soil qualities of the area. At different stages of mining, these footprints can be used as indicators to reveal the areas where soil and air qualities are adversely impacted.     

A spatial risk assessment methodology to support the remediation of contaminated land

When soil and groundwater contaminations occur over large areas, remediation measures should be spatially prioritized on the basis of the risk posed to human health and in compliance with technological and budget constraints. Within this scope, the application of human health risk assessment algorithms in a spatially resolved environment raises a number of methodological and technical complexities. In this paper, a methodology is proposed and applied in a case study to support the entire formulation process of remediation plans, encompassing hazard assessment, exposure assessment, risk characterisation, uncertainty assessment and allocation of risk reduction measures. In the hazard assessment, it supports the selection of Contaminants of Concern (CoC) with regard to both their average concentrations and peak concentrations, i.e. hot spots. In the exposure assessment, it provides a zoning of the site based on the geostatistical mapping of contaminant. In the risk characterisation, it generates vector maps of Risk Factors on the basis of the risk posed by multiple substances and allows the interrogation of most relevant CoC and exposure pathways for each zone of the site. It also supports the Monte Carlo based probabilistic estimation of the Risk Factors and generates maps of the associated uncertainty. In the risk reduction phase, it supports the formulation of remediation plans based on the stepwise spatial allocation of remediation interventions and the on-time simulation of risk reduction performances. The application of this methodology is fully supported by an easy-to-use and customized Geographical Information System and does not require high expertise for interpretation. The proposed methodology is the core module of a Decision Support System (DSS) that was implemented in the DESYRE software aimed at supporting the risk-based remediation of megasites.     

Monitoring environmental pollution in Erzurum by chemical analysis of Scots pine (Pinus sylvestris L.) needles

Pollution distribution maps of copper (Cu), zinc (Zn) and lead (Pb) for Erzurum province were developed on the basis of chemical analysis of Scots pine (Pinus sylvestris L.) needles collected from randomly selected sampling points during 2 years. The maps show deposition zones for the studied elements and could help in identification of sources and directions of air pollution dispersion. This study indicated that vegetation in Erzurum was greatly endangered by sulphur dioxide (SO(2)), whereas Zn does not pose an immediate threat to vegetation in most of the country's territory. However, in the city center, main and secondary road agglomeration, very high pollution with Pb and Cu could limit growth. Higher levels of SO(2) measured in some areas of Erzurum might be harmful for human and animal health. Results of this study indicated that Scots pine needles were not contaminated by Zn.