Spatial pattern of urban functional landscapes along an urban–rural gradient: A case study in Xiamen City,China

Since there is an increasing demand for integrating landscape ecology and urban planning theories to study complex urban ecosystems and establish rational and ecological urban planning, we introduced a new concept-urban functional landscapes which can be reclassified based on detailed land use data to fulfill the various urban functions, such as residential, commercial, industrial, and infrastructure purposes. In this paper, urban functional landscapes were defined based on urban land use data produced from Pleiades images, and then landscape metrics and population density were combined to identify the urban functional zones along an urban–rural gradient. The features of urban functional landscape patterns and population density were also analyzed, and their relationship has been explored. The results showed that the pattern of urban functional landscapes and population density in the urban functional zones (Urban center, Urban peripheral area, Landscape barrier, Satellite city and Far-suburb) along the urban–rural gradient in Xiamen doesn't totally conform to the classical theories in spatial and social aspects. Urban functional landscapes is potential of acting as bridges between the landscape ecology and urban planning theories, providing scientific support for rational urban landscape planning and urban land use policy making.     

Variability analysis of groundwater levels — AGIS-based case study

The present study has analyzed the variability in depth to water level below ground level (bgl) vis-à-vis groundwater development and rainfall from 1987 to 2007 in agriculture dominated Kaithal district of Haryana state in India. Spatial distribution of groundwater depth was mapped and classified into different zones using ILWIS 3.6 GIS tools. Change detection maps were prepared for 1987–1997 and 1997–2007. Groundwater depletion rates during successive decades were compared and critical areas with substantial fall in groundwater levels were identified. Further, block wise trends of change in groundwater levels were also analyzed. The water table in fresh belt areas of the district (Gulha, Pundri and Kaithal blocks) was observed to decline by a magnitude ranging from 10 m to 23 m. In Kalayat and Rajaund blocks, the levels were found fluctuating in a relatively narrow range of 4–9 m. During 1997–2007, the depletion has been faster compared to the preceding decade. Excessive groundwater depletion in major part of the district may be attributed to indiscriminate abstraction for irrigation and decrease in rainfall experienced since 1998. Changes in cropping pattern and irrigation methods are needed in the study area for sustainable management of the resource.     

Mapping irrigated agriculture in complex landscapes using SPOT6 imagery and object-based image analysis – A case study in the Central Rift Valley,Ethiopia –

Irrigation infrastructure development for smallholder farmers in developing countries increasingly gains attention in the light of domestic food security and poverty alleviation. However, these complex landscapes with small cultivated plots pose a challenge with regard to mapping and monitoring irrigated agriculture. This study presents an object-based approach to map irrigated agriculture in an area in the Central Rift Valley in Ethiopia using SPOT6 imagery. The study is a proof-of-concept that the use of shape, texture, neighbour and location information next to spectral information is beneficial for the classification of irrigated agriculture. The underlying assumption is that the application of irrigation has a positive effect on crop growth throughout the field, following the field's borders, which is detectable in an object-based approach. The type of agricultural system was also mapped, distinguishing smallholder farming and modern large-scale agriculture. Irrigated agriculture was mapped with an overall accuracy of 94% and a kappa coefficient of 0.85. Producer's and user's accuracies were on average 90.6% and 84.2% respectively. The distinction between smallholder farming and large-scale agriculture was identified with an overall accuracy of 95% and a kappa coefficient of 0.88. The classifications were performed at the field level, since the segmentation was able to adequately delineate individual fields. The additional use of object features proved essential for the identification of cropland plots, irrigation period and type of agricultural system. This method is independent of expert knowledge on crop phenology and absolute spectral values. The proposed method is useful for the assessment of spatio-temporal dynamics of irrigated (smallholder) agriculture in complex landscapes and yields a basis for land and water managers on agricultural water use.     

Assessing gaps in irrigated agricultural productivity through satellite earth observations—A case study of the Fergana Valley,Central Asia

Improving crop area and/or crop yields in agricultural regions is one of the foremost scientific challenges for the next decades. This is especially true in irrigated areas because sustainable intensification of irrigated crop production is virtually the sole means to enhance food supply and contribute to meeting food demands of a growing population. Yet, irrigated crop production worldwide is suffering from soil degradation and salinity, reduced soil fertility, and water scarcity rendering the performance of irrigation schemes often below potential. On the other hand, the scope for improving irrigated agricultural productivity remains obscure also due to the lack of spatial data on agricultural production (e.g. crop acreage and yield). To fill this gap, satellite earth observations and a replicable methodology were used to estimate crop yields at the field level for the period 2010/2014 in the Fergana Valley, Central Asia, to understand the response of agricultural productivity to factors related to the irrigation and drainage infrastructure and environment. The results showed that cropping pattern, i.e. the presence or absence of multi-annual crop rotations, and spatial diversity of crops had the most persistent effects on crop yields across observation years suggesting the need for introducing sustainable cropping systems. On the other hand, areas with a lower crop diversity or abundance of crop rotation tended to have lower crop yields, with differences of partly more than one t/ha yield. It is argued that factors related to the infrastructure, for example, the distance of farms to the next settlement or the density of roads, had a persistent effect on crop yield dynamics over time. The improvement potential of cotton and wheat yields were estimated at 5%, compared to crop yields of farms in the direct vicinity of settlements or roads. In this study it is highlighted how remotely sensed estimates of crop production in combination with geospatial technologies provide a unique perspective that, when combined with field surveys, can support planners to identify management priorities for improving regional production and/or reducing environmental impacts.     

Spatial and temporal variations of spatial population accessibility to public hospitals: a case study of rural–urban comparison

Quantification and assessment of nationwide population access to health-care services is a critical undertaking for improving population health and optimizing the performance of national health systems. Rural–urban unbalance of population access to health-care services is widely involved in most of the nations. This unbalance is also potentially affected by varied weather and road conditions. This study investigates the rural and urban performances of public health system by quantifying the spatiotemporal variations of accessibility and assessing the impacts of potential factors. Australian health-care system is used as a case study for the rural–urban comparison of population accessibility. A nationwide travel time-based modified kernel density two-step floating catchment area (MKD2SFCA) model is utilized to compute accessibility of travel time within 30, 60, 120, and 240 min to all public hospitals, hospitals that provide emergency care, and hospitals that provide surgery service, respectively. Results show that accessibility is varied both temporally and spatially, and the rural–urban unbalance is distinct for different types of hospitals. In Australia, from the perspective of spatial distributions of health-care resources, spatial accessibility to all public hospitals in remote and very remote areas is not lower (and may even higher) than that in major cities, but the accessibility to hospitals that provide emergency and surgery services is much higher in major cities than other areas. From the angle of temporal variation of accessibility to public hospitals, reduction of traffic speed is 1.00–3.57% due to precipitation and heavy rain, but it leads to 18–23% and 31–50% of reduction of accessibility in hot-spot and cold-spot regions, respectively, and the impact is severe in New South Wales, Queensland, and Northern Territory during wet seasons. Spatiotemporal analysis for the variations of accessibility can provide quantitative and accurate evidence for geographically local and dynamic strategies of allocation decision-making of medical resources and optimizing health-care systems both locally and nationally.     

Channel pattern transformation of the Burhi‐Gandak river,Bihar, India: A study based on multidata sets

The Burhi‐Gandak river is one of the major tributaries of the Ganga River in the middle Ganga basin. The river is i known to have an oscillatory character and has changed its course in Recent times. In the present study, a variety of remote sensing data combined with surface and subsurface data have been used to delineate the fluvial palaeofeatures of this river. The palaeofeatures of the Burhi‐Gandak indicate that the river has shifted from N to S over a distance of 30 km. Further, its is observed that Burhi‐Gandak river had a larger channel width and higher discharge on earlier times and was very active. On detailed examination, a past link between the now northerly flowing Baghmati and Burhi‐Gandak rivers is detected. Based on the above features, three distinct stages of migration of the river have been identified and a summary of fluvial palaeohistory is presented.     

Geographic information technology usage in developing countries – A case study in Mozambique

ABSTRACT

Geographic information technologies (GIT) have matured, have become pervasive throughout many types of organizations over recent decades and gained a wide range of attention in diverse sectors of society. The goal of this paper is to identify the main drivers and uses of GIT in organizations and to give an overview of GIT uses in developing countries with a focus on a case study developed for Mozambique by analyzing the results of a survey of 123 Mozambican organizations that are current GIT users. The results show that the main drivers behind are compatibility, complexity, competitive pressure, donor pressure, government policy, intention to adopt, innovation, relative advantage, security, and technology competence. Organizations of the public and private sectors use GIT for customer/resource/risk management, decision support, education, research, monitoring, prediction, simulation, suitability analysis, sustainability, use and access.     

Changing water resources study using GIS and spatial model — A case study of Bhatar Block,district Burdwan,West Bengal,India

Markov chains have been used to model spatial changes in a variety of spheres. Changes in social situations, economic standards, natural resource availability, and even weather conditions have been explored and predicted using Markov Random Function (MRF) and Markov Random Chains (MRC). In this section, we try to use data of Mahata village of Bhatar Block, extracted from GIS based maps/images in a MRC to obtain present transition probabilities and predict future changes. The village is facing the problem of decreasing the water table and at the same time the number of surface water bodies is also decreasing. This is a serious situation for the development of the agricultural activities in general and at the same time it poses threat to the human habitation of the village in the long run. The average depth of the ground water table from ground level increased from 8 meter to 15 meter within the last 10 years. The threat is coming from the changes in land use and land cover, especially due to substantial extension of agricultural activities which is expanding at a very fast rate. Increasing population is also demanding more lands for settlement and industrial uses. The surface water bodies i.e. the ponds etc. are used for such intensive irrigation purposes. As a result the surface water bodies depletes before the onset of summer. The cultivators use those dried up ponds or surface water bodies for agricultural purposes also. There is thus a serious trend to convert the surface water bodies into the agricultural land. It is estimated using MRC, that in next 25 years, the number of surface water bodies will deplete by 50% in the agriculturally active Bhatar PS at the current rates of depletion. Shifting to less water needy crops, prevention of LULC conversion, and water harvesting would provide some solace to the situation.     

Spatial analysis in epidemiology: Nascent science or a failure of GIS?

This paper summarizes contributions of GIS in epidemiology, and identifies needs required to support spatial epidemiology as science. The objective of spatial epidemiology is to identify disease causes and correlates by relating spatial disease patterns to geographic variation in health risks. GIS supports disease mapping, location analysis, the characterization of populations, and spatial statistics and modeling. Although laudable, these accomplishments are not sufficient to fully identify disease causes and correlates. One reason is the failure of present-day GIS to provide tools appropriate for epidemiology. Two needs are most pressing. First, we must reject the static view: meaningful inference about the causes of disease is impossible without both spatial and temporal information. Second, we need models that translate space-time data on health outcomes and putative exposures into epidemiologically meaningful measures. The first need will be met by the design and implementation of space-time information systems for epidemiology; the second by process-based disease models.     

The pyrogeography of sub-Saharan Africa: a study of the spatial non-stationarity of fire–environment relationships using GWR

This study analyses the relationship between fire incidence and some environmental factors, exploring the spatial non-stationarity of the phenomenon in sub-Saharan Africa. Geographically weighted regression (GWR) was used to study the above relationship. Environment covariates comprise land cover, anthropogenic and climatic variables. GWR was compared to ordinary least squares, and the hypothesis that GWR represents no improvement over the global model was tested. Local regression coefficients were mapped, interpreted and related with fire incidence. GWR revealed local patterns in parameter estimates and also reduced the spatial autocorrelation of model residuals. All the covariates were non-stationary and in terms of goodness of fit, the model replicates the data very well (R ² = 87%). Vegetation has the most significant relationship with fire incidence, with climate variables being more important than anthropogenic variables in explaining variability of the response. Some coefficient estimates exhibit locally different signs, which would have gone undetected by a global approach. This study provides an improved understanding of spatial fire–environment relationships and shows that GWR is a valuable complement to global spatial analysis methods. When studying fire regimes, effects of spatial non-stationarity need to be incorporated in vegetation-fire modules to have better estimates of burned areas and to improve continental estimates of biomass burning and atmospheric emissions derived from vegetation fires.     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.     

Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25％ compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.     

Analyzing the impact of thermal stress on vegetation health and agricultural drought – a case study from Gujarat,India

Although poor precipitation due to delayed arrival and/or early retreat of the southwest monsoon is considered the chief architect of drought in India, heat waves may also play a crucial role in the intensification of droughts. In the Indian subcontinent, occurrence of heat waves during the pre-monsoon and high air-temperature in the subsequent monsoon season imparts thermal stress on vegetation causing degradation of vegetation health (VH). In the present study, various vegetation indices and land-use/land-cover data derived from multi-sensor satellite have been used to assess VH and agricultural drought in Gujarat during 1981–2010. This Geographical Information Systems-based study has also used heat wave and temperature data to analyze the adverse effects of high temperature on VH. The time series of Vegetation Condition Index and Temperature Condition Index (TCI) has shown that the combined influence of moisture-stress and thermal stress determines the occurrence and severity of drought, which is reflected in the Vegetation Health Index (VHI). A strong correlation among aboveground air-temperature, the TCI and the VHI indicates definite influence of thermal stress on VH. Further, a systematic variation and strong resemblance between temperature, crop yield, TCI and VHI has established the impact of thermal stress on agricultural productivity.     

Variability of SeaWiFS-derived chlorophyll-a concentrations in waters off central east coast of India, 1998–2003

Seasonal and inter-annual variability in satellite-derived estimates of near-surface chlorophyll-a concentration off the central east coast of India from 1998 to 2003 is examined. Wind-induced upwelling predominates in late spring and winter, coinciding with the maximum in solar radiation, leading to increased accumulations of phytoplankton biomass. Chlorophyll concentrations varied from 2 to 10 mg/m³ over the central east coast of India and were generally lower in June and maximal in March. Chlorophyll concentrations along the coast followed a similar seasonal pattern (ranging from 0.5 to 6 mg/m³); however, concentrations were always greater on the Machilipatnam and Nellore compared with the Visakhapatnam and Chennai. The lack of upwelling favorable conditions results in the majority of the southern side of the central east coast of India waters being insufficient, which is reflected in low or moderate productivity. The possible reasons and observed correlations between chlorophyll-a and upwelling index during the study period was discussed.     

A quantitative method for storm surge vulnerability assessment – a case study of Weihai city

ABSTRACT

In traditional vulnerability assessments, a synthetic index method is usually used to select all types of social and economic indexes so that more aspects can be covered; however, the requisite social and economic data are not always available or are not highly relevant to the studied geographical space, which makes it difficult to conduct quantitative calculations. In this paper, a spatial value density assessment method was developed to improve the hazard of place model. First, a three-dimensional (3D) model of a coastal city was obtained using oblique airborne photogrammetry and image-based 3D reconstruction and then, architecture footprints were employed to extract the geometric information of each individual building. Additionally, a vulnerability assessment system was established to quantitatively account for the aggregate economic value of a selected set of urban surface features. Using geographic information system (GIS) techniques, the aggregate value of these urban features within each geographic unit can be accurately calculated to quantify the exposure and vulnerability of coastal cities to storm surge. A vulnerability assessment was conducted using Weihai city as an example. The study shows that vulnerability assessment accuracy was greatly improved by downscaling the assessment granularity from county-level administrative districts to a 1-km grid.     

GIS-based approach for flood analysis: case study of Keçidere flash flood event (Turkey)

The purpose of this study is to explain the formation mechanism of the floods which occurred in the Keçidere basin in 2009. In this study, discharge data in between 1981 and 2009, digital elevation model (DEM), satellite images and field works were used as a main data sources. LPT3 was applied to 29-year maximum flow data to produce different flood return periods such as 2, 5, 50, 100, 200, 500 and 1000-year flood. The DEM was created using 1:25,000 topographic contours with Topo to Raster interpolation techniques in geographical information systems (GIS). Land use and some geometric data were digitized using high resolution satellite images for hydraulic modelling purposes. Simulation of the 2009 flash flood event and different return periods flow data was done using one-dimensional hydraulic modelling with HEC-RAS. In the last phase, results obtained from the simulations and field works were compared based on fits statistics and mean absolute error in terms of extent and depth. An analysis of water extent and depth features observed during the highest flow ever measured in the basin revealed that the result overlapped with 500-year inundation extent. Overall, the results of the research indicate that GIS is an effective environment for floodplain mapping and analysis.     

A priori fully populated covariance matrices in least-squares adjustment—case study: GPS relative positioning

In this contribution, using the example of the Mátern covariance matrices, we study systematically the effect of apriori fully populated variance covariance matrices (VCM) in the Gauss–Markov model, by varying both the smoothness and the correlation length of the covariance function. Based on simulations where we consider a GPS relative positioning scenario with double differences, the true VCM is exactly known. Thus, an accurate study of parameters deviations with respect to the correlation structure is possible. By means of the mean-square error difference of the estimates obtained with the correct and the assumed VCM, the loss of efficiency when the correlation structure is missspecified is considered. The bias of the variance of unit weight is moreover analysed. By acting independently on the correlation length, the smoothness, the batch length, the noise level, or the design matrix, simulations allow to draw conclusions on the influence of these different factors on the least-squares results. Thanks to an adapted version of the Kermarrec–Schön model, fully populated VCM for GPS phase observations are computed where different correlation factors are resumed in a global covariance model with an elevation dependent weighting. Based on the data of the EPN network, two studies for different baseline lengths validate the conclusions of the simulations on the influence of the Mátern covariance parameters. A precise insight into the impact of apriori correlation structures when the VCM is entirely unknown highlights that both the correlation length and the smoothness defined in the Mátern model are important to get a lower loss of efficiency as well as a better estimation of the variance of unit weight. Consecutively, correlations, if present, should not be neglected for accurate test statistics. Therefore, a proposal is made to determine a mean value of the correlation structure based on a rough estimation of the Mátern parameters via maximum likelihood estimation for some chosen time series of observations. Variations around these mean values show to have little impact on the least-squares results. At the estimates level, the effect of varying the parameters of the fully populated VCM around these approximated values was confirmed to be nearly negligible (i.e. a mm level for strong correlations and a submm level otherwise).     

‘Lineaments’ the Potential Groundwater Zones in Hard Rock Area: A Case Study of Basaltic Terrain of WGKKC-2 Watershed from Kalmeswar Tehsil of Nagpur District,Central India

Hard rock aquifer system, as it lacks in primary porosity, is complex because of heterogeneity, and hence its performance solely depends on secondary factors such as weathering, fractures, joints and lineaments, etc. As a result, there is no uniformity in behavior of such aquifer system which varies with the intensity of factors causing secondary porosity. In view of this aquifer system of hard rock, particularly multilayered aquifer system such as basalt, the targeting of deeper aquifer system becomes uncertain. Under this situation, mapping of lineament assumes great importance particularly for targeting the deeper aquifer. In view of this, a small watershed WGKKC-2 falling in Nagpur District of Central India has been studied in detail to understand the significance of lineaments on governing deeper aquifer system. The study has been carried out by deploying remote sensing technique for delineation of hydro geomorphology and lineaments vis-à-vis performance of bore wells. The results are encouraging which mainly emphasize the role of lineament mapping in hard rock aquifer system for identification of groundwater potential zones.     

Spatial mashup technology and real time data integration in geo-web application using open source GIS – a case study for disaster management

The Web 2.0 technologies and standards enable web as a platform by allowing the user participation in web application. In the realization of Web 2.0, new knowledge and services are created by combining information and services from different sources which are known as ‘mashups'. The present study focused on spatial mashup solution for disaster management using open source GIS, mobile applications, web services in web 2.0, Geo-RDBMS and XML which are in the central of intelligent geo web services. The geo-web application is developed to generate the actionable GIS products at user end during disaster event by consuming various data and information services from web and central server system and also real time ground observation data collected through a mobile device. The technological solution developed in this study is successfully demonstrated for disaster management in the Assam State of India during the floods in 2010.     

Application of Spatial Database in Quantitative Analysis of Litho-Paleogeography—A Case Study of a Middle Ordovician Sequence Interval in the Ordos Basin

In the study of sequence stratigraphy and litho-paleogeography,quantitative analysis,precise calculation and detailed comparison of tremendous geological data,such as field profiles,logging records and seismic curves from different areas,are the basic requirements.In order to obtain a more reliable and precise result,this paper presents a novel method that combines spatial database analysis with the single-factor mapping technology to establish sequence stratigraphical succession and to map the Ordo-vician litho-paleogeography of the Ordos Basin,one of the largest oil-gas bearing basins in North China Platform.By using this method,all of the related basic geological data can be quantitatively analyzed and effectively managed.Various attributes of the ba-sic stratigraphic units and their characters,such as sequence thickness,penecontemporaneous dolostone content,shallow water parget content,and terrigenous material content,can be fully utilized statistically in facies analysis and in mapping.Based on this analysis,this paper has be exerted single-factor isopachous mapping quantitatively for each of the Ordovician sequences in the ba-sin,and finally synthesized multiple factors to reconstruct the litho-paleogeography for each of the sequence intervals.The study shows that the proposed method is quite effective and has a much higher resolution in recognizing litho-paleogeographic subunits compared with traditional ways.For example,in one of the Middle Ordovician sequence intervals(SQ19 in the Lower Majiagou Formation) of the Ordos Basin,the authors have successfully developed a mathematical formula to divide the distribution of various facies units substantially,such as old lands,submarine uplifts,supratidal zones,intertidal zones and subtidal zones.