Mapping of Soil Micronutrients in Kashmir Agricultural Landscape Using Ordinary Kriging and Indicator Approach

Soil nutrient maps based on intensive soil sampling are useful to adopt site-specific management practices. Geostatistical methods have been widely used to determine the spatial correlation and the range of spatial dependence at different sampling scales. If spatial dependence is detected, the modeled semivariograms can then be used to map the interested variable by kriging, an interpolation method that produces unbiased estimates with minimal estimation variance. The objectives of this paper were to examine and map the spatial distribution of the soil micronutrients Cu, Zn, Fe and Mn on an agricultural area in Kupwara, J&K, under temperate climatic conditions. The ordinary kriging was first used to determine the values for the non-sampled locations, and then the indicator approach was used to transform the micronutrient content values into binary values having the mean values of each nutrient as the threshold content. All four elements analyzed showed spatial dependence using the indicator semivariograms. The strength of spatial dependence was assessed using the values of nugget effect and range from the semivariogram, the fitted range values decreased in the order Zn > Cu > Mn > Fe. The spatial dependence of the combination of two or more of the studied micronutrients was also examined using indicator semivariograms. In opposition to spatial analysis of individual microelements, indicator semivariograms obtained for the binary coding of the variables showed a great nugget effect value or a low proportion of sill. The maps for each nutrient obtained using indicator kriging showed some similarity in the spatial distribution, suggesting the delimitation of uniform management areas.     

半方差函数与Moran’s I在土壤微量元素空间分布研究中的应用——以寿光市为例

利用半方差函数与Moran’s I两种空间分析方法,研究了山东省寿光市土壤中微量元素的空间分布特征,以探讨不同空间分析方法的差异性与准确性。结果表明,无论表层土壤还是深层土壤,半方差函数分析得到的土壤微量元素的空间相关距离大约为20~60 km,而空间自相关图获得的正自相关距离为20~25km,负自相关距离为25~55 km。除深层土壤中的Zn元素外,其他元素均达到了显著的自相关水平。空间自相关方法对土壤微量元素空间相关性的判断要优于半方差函数法。     

Characterization of spatial and temporal variation of suspended sediments in the Yellow and East China Seas using satellite ocean color data

Total suspended sediment (TSS) data concentrations are retrieved from two sets of satellite ocean color data (the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Korean Geostationary Ocean Color Imager (GOCI)) using an existing regional model to characterize spatial and temporal variation of TSS in the Yellow and East China Seas. MODIS-derived TSS maps show that TSS concentrations are, in general, high along the Korean and Chinese coasts including the Bohai Sea and the Yangtz River estuary, and lower in the middle of the Yellow Sea and the southeastern area of the East China Sea. The monthly average of 10-year MODIS data reveals that TSS values are highest during winter (January to February) and lowest in summer (July to August). Short-term TSS concentrations retrieved from GOCI data showed the dominant influence of semi-diurnal tidal changes on sediment dynamics through temporal (hourly) and spatial distribution in coastal zones of the Yellow sea. The results presented here demonstrate that the satellite-derived TSS products can be utilized as an application tool for future studies on long- and short-term sediment dynamics of turbid coastal waters. In particular, GOCI observations provide unique important capabilities to characterize and quantify the water properties at high temporal (hourly) and spatial (0.5 km) resolutions in the turbid coastal waters of the Yellow Sea and its vicinities.     

Does plot size affect the performance of GIS-based species distribution models?

Species distribution models are used extensively in predicting the distribution of vegetation across a landscape. Accuracy of the species distribution maps produced by these models deserves attention, since low accuracy maps may lead to erroneous conservation decisions. While plot size is known to influence measures of species richness, its effect on our ability to predict species distribution ranges has not been tested. Our aim is to test whether the accuracy of the distribution maps produced depend on the size of the plot (quadrat) used to collect biological data in the field. In this study, the presences of four plant species were recorded in five sizes of circular plots, with radii ranging from 8 to 100 m. Logistic regression-based models were used to predict the distributions of the four plant species based on empirical evidence of their relationship with eight environmental predictors: distance to river, slope, aspect, altitude, and four principle component axes derived using reflectance values from Aster images. We found that plot size affected the probability of recording the four species, with reductions in plot size generally increasing the frequency of recorded absences. Plot size also significantly affected the likelihood of correctly predicting the distribution of species whenever plot size was below the minimum size required to consistently record species’ presence. Furthermore, the optimal plot size for fitting species distribution models varied among species. Finally, plot size had little impact on overall accuracy, but a strong, positive impact on Kappa accuracy (which provides a stronger measure of model accuracy by accounting for the effects of chance agreements between predictions and observations). Our results suggest that optimal plot size must be considered explicitly in the creation of species distribution models if they are to be successfully adopted into conservation efforts.     

Soil erosion modelled with USLE and PESERA using QuickBird derived vegetation parameters in an alpine catchment

The focus of soil erosion research in the Alps has been in two categories: (i) on-site measurements, which are rather small scale point measurements on selected plots often constrained to irrigation experiments or (ii) off-site quantification of sediment delivery at the outlet of the catchment. Results of both categories pointed towards the importance of an intact vegetation cover to prevent soil loss. With the recent availability of high-resolution satellites such as IKONOS and QuickBird options for detecting and monitoring vegetation parameters in heterogeneous terrain have increased. The aim of this study is to evaluate the usefulness of QuickBird derived vegetation parameters in soil erosion models for alpine sites by comparison to Cesium-137 (Cs-137) derived soil erosion estimates. The study site (67 km²) is located in the Central Swiss Alps (Urseren Valley) and is characterised by scarce forest cover and strong anthropogenic influences due to grassland farming for centuries. A fractional vegetation cover (FVC) map for grassland and detailed land-cover maps are available from linear spectral unmixing and supervised classification of QuickBird imagery. The maps were introduced to the Pan-European Soil Erosion Risk Assessment (PESERA) model as well as to the Universal Soil Loss Equation (USLE). Regarding the latter model, the FVC was indirectly incorporated by adapting the C factor. Both models show an increase in absolute soil erosion values when FVC is considered. In contrast to USLE and the Cs-137 soil erosion rates, PESERA estimates are low. For the USLE model also the spatial patterns improved and showed “hotspots” of high erosion of up to 16 t ha⁻¹ a⁻¹. In conclusion field measurements of Cs-137 confirmed the improvement of soil erosion estimates using the satellite-derived vegetation data.     

3D Geographic Reconstruction and Visualization Techniques Applied to Land Resource Management

Optimized land resource management depends on reliable and detailed information describing the spatial distribution of soils, geology, topography, and land use. Soil–landscapes are three–dimensional (3D) systems commonly represented using 2D maps utilizing geographic information systems. Addressing 3D soil–landscape reality is crucial for land resource management in terms of crop growth and transport processes (e.g. nitrate leaching) that are driving soil and water quality. Our objective was to investigate the usefulness of 3D geographic information technology (GIT) applied to land resource management. Our approach is based on 2D and 3D ordinary kriging interpolating surface and subsurface attributes to reconstruct soil–landscapes. We used Virtual Reality Modeling Language, which is a web–based 3D graphics language, to visualize objects (e.g. voxels, polyhedrons) representing soil and landscape attributes. We produced a 3D block model showing the spatial distribution of bulk densities and relief for a site in southern Wisconsin and a 3D stratigraphic model showing the spatial distribution of soil horizons and relief for a site in northern Florida. Emerging GIT was used to develop 3D soil–landscape models describing continuous changes of soil and landscape attributes. Combining multimedia elements (e.g. WWW, 3D visualization, and interactivity) can produce insight that would not arise from use of the elements alone. Three–dimensional scientific visualization is a powerful tool to help us see what is invisible from above the ground.     

Geoinformatics as a tool for the assessment of the impact of ground water quality for irrigation on soil health

Secondary salinisation is the most harmful and extended phenomenon of the unfavourable effects of irrigation on the soil and environment. An attempt was made to study the impact of poor quality ground water on soils in terms of secondary salinisation and availability of soil nutrients in Faridkot district of Punjab of northern India. Based on physiographic analysis of IRS 1C LISS-III data and semi-detailed soil survey, the soil map was finalized on a 1:50,000 scale and digitized using Arc Info GIS. Georeferenced surface soil samples (0–0.15 m) from 231 sites were collected and analyzed for available phosphorus (P) and potassium (K). Interpolation by kriging produced digital spatial maps of available P and K. Ground water quality map was generated in GIS domain on the basis of EC (electrical conductivity) and RSC (residual sodium carbonate) of ground water samples collected from 374 georeferenced tube wells. Integration of soil and ground water quality maps enabled generating a map showing degree (high, moderate and low) and type (salinity, sodicity and both) of vulnerability to secondary salinization. Fine-textured soils have been found to be highly sensitive to secondary salinisation, whereas medium-textured soils as moderately sensitive to secondary salinisation. The resultant map was integrated with available P and K maps to show the combined influence of soil texture and ground water quality on available soil nutrients. The results show that available P and K in the soils of different physiographic units were found in the order of Ap1 < Ap2 < Ap3. The soils of all physiographic units had sizeable area having high content of P (>22.5 kg / ha) and medium available K (135–335 kg ha⁻¹) in most of the test sites when irrigated with saline, sodic or poor quality water.     

类型地图的空间相关关系

针对类型地图,在介绍已有空间相关关系分析方法的基础上,给出一种计算空间相关关系的简便方法,其基本思想是用现实分布与随机分布下相对重叠度的差异来表达类型之间的相关程度;同时还讨论了位置不确定性对类型地图空间相关关系的影响.实验表明,该方法能够有效表达类型地图之间的整体空间相关和局部空间相关.     

Full Positional Accuracy Analysis of Spatial Data by Means of Circular Statistics

The positional error in spatial data is defined as a vector by comparing the coordinates between the true position and the measured position. The standard tests to assess the positional accuracy use only the magnitude of the vector and omit the azimuth. This article suggests that the use of both values allows a much more complete analysis of the positional error. A set of tests is proposed that are relevant for this purpose and demonstrate that some important features are not identified by the common procedures. The test samples come from two datasets. The first is obtained from the comparison of 100 homologous points in two conventional maps, and the second one comes from the geometric calibration of a photogrammetric scanner. The results are analyzed and discussed, showing that important issues such as error anisotropy are detected only by means of the circular statistics tests and density maps of distribution. Therefore, tests that assess the goodness of fit for uniform distribution in azimuths, such as Rayleigh and Rao tests, give low probabilities (P = 0 and P > 0.01). Moreover, density maps working with both magnitude and angle can locate the outlier candidate and offer more information about the spatial distribution of error.     

Prediction of topsoil properties at field-scale by using C-band SAR data

Designing and validating digital soil mapping (DSM) techniques can facilitate precision agriculture implementation. This study generates and validates a technique for the spatial prediction of soil properties based on C-band radar data. To this end, (i) we focused on working at farm-field scale and conditions, a fact scarcely reported; (ii) we validated the usefulness of Random Forest regression (RF) to predict soil properties based on C-band radar data; (iii) we validated the prediction accuracy of C-band radar data according to the coverage condition (for example: crop or fallow); and (iv) we aimed to find spatial relationship between soil apparent electrical conductivity and C-band radar. The experiment was conducted on two agricultural fields in the southern Argentine Pampas. Fifty one Sentinel 1 Level-1 GRD (Grid) products of C-band frequency (5.36 GHz) were processed. VH and VV polarizations and the dual polarization SAR vegetation index (DPSVI) were estimated. Soil information was obtained through regular-grid sample scheme and apparent soil electrical conductivity (ECa) measurements. Soil properties predicted were: texture, effective soil depth, ECa at 0-0.3m depth and ECa at 0-0.9m depth. The effect of water, vegetation and soil on the depolarization from SAR backscattering was analyzed. Complementary, spatial predictions of all soil properties from ordinary cokriging and Conditioned Latin hypercube sampling (cLHS) were evaluated using six different soil sample sizes: 20, 40, 60, 80, 100 and the total of the grid sampling scheme. The results demonstrate that the prediction accuracy of C-band SAR data for most of the soil properties evaluated varies considerably and is closely dependent on the coverage type and weather dynamics. The polarizations with high prediction accuracy of all soil properties showed low values of σ_VV^o and σ_VH^o, while those with low prediction accuracy showed high values of σ_VV^o and low values of σ_VH^o. The spatial patterns among maps of all soil properties using all samples and all sample sizes were similar. In conditions when summer crops demand large amount of water and there is soil water deficit backscattering showed higher prediction accuracy for most soil properties. During the fallow season, the prediction accuracy decreased and the spatial prediction accuracy was closely dependent on the number of validation samples. The findings of this study corroborates that DSM techniques at field scale can be achieved by using C-band SAR data. Extrapolation y applicability of this study to other areas remain to be tested.     

Ground Water Quality Maps of Paravanar River Sub Basin,Cuddalore District,Tamil Nadu,India

Ground water is an excellent solvent, which dissolves chemicals ions as it moves through rocks and subsurface soil. This leads to more mineralization in groundwater than surface water. The objective of the present study is to examine the groundwater quality of the Paravanar River Sub-basin, Cuddalore district, Tamil Nadu, India. The Electrical Conductivity (EC) values ranges between 160 and 2,580 μS/cm in groundwater samples. The highest value of 2,580 μS/cm was recorded in wells near the coast. pH values ranges from 7.2 to 8.6. NNE and southern part of the study area has low pH values, rest of the area represents the alkaline nature of groundwater. In south eastern part of the study area alkali values are slightly higher but it is within WHO’s tolerable limits. The spatial distribution of chloride concentration shows that Meenatchipettai, Vazhisothani palayam and Allapakkam represents maximum Cl₂ concentration of 527, 320 and 374 ppm, which is above ISI drinking standards of 250 ppm. Increase in isochlore is observed from the coast up to the Neyveli lignite mine. Nitrate concentration of groundwater samples ranges from 0.1 mg/l to 64 mg/l. As most of the study area is cultivated, fertilizers used for agriculture may be the cause for increase in concentration of nitrates in few concentrated locations.     

Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R ² value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.     

THE ROLE OF CARTOGRAPHY IN THE STUDY OF ENVIRONMENTAL POLLUTION

The authors describe a method of applying the cartographic method of research to the study of air and water pollution. More specifically, the paper outlines a program of mapping air, water, and soil pollution in the Donets Basin, which is an integral part of four different stages in the formulation of an environmental protection plan. Emphasis is placed on the visual interpretation of maps depicting the spatial distribution of physical parameters at pollution sources, of concentrations of particular pollutants over the entire study area, and of their direct and cumulative impacts on the environment. Translated from: Geograflya i prirodnyye resursy, 1986, No. 2, pp. 92–94.     

Assessing Metal-Induced Changes in the Visible and Near-Infrared Spectral Reflectance of Leaves: A Pot Study with Sunflower (<Emphasis Type="Italic">Helianthus annuus</Emphasis> L.)

The aim of this study was to monitor changes in leaf spectral reflectance due to phytoaccumulation of trace elements (Cd, Pb, and As) in sunflower mutant (M5 mutant line 38/R4-R6/15-35-190-04-M5) grown in spiked and in situ metal-contaminated potted soils. Reflectance spectra (350–2500 nm) of leaves were collected using portable ASD spectroradiometer, and respective leaves sample were analyzed for total metal contents. The spectral changes were quite noticeable and showed increased visible and decreased NIR reflectance for sunflower grown in soil spiked with 900 mg As kg^?1, and in in situ metal-contaminated soils. These changes also involved a blue-shift feature of red-edge position in the first derivatives spectra, studied vegetation indices and continuum removed absorption features at 495, 680, 970, 1165, 1435, 1780, and 1925 nm wavelength. Correlograms of leaf-metal concentration and reflectance values show highest degrees of overall correlation for visible, near-infrared, and water-sensitive wavelengths. Partial least square and multiple linear regression statistical models (cross-validated), respectively, based on Savitzky–Golay filter first-order derivative spectra and combination of spectral feature such as vegetation indices and band depths yielded good prediction of leaf-metal concentrations.     

Spatial dependency of cholera prevalence on potential cholera reservoirs in an urban area,Kumasi, Ghana

Cholera has been a public health burden in Ghana since the early 1970s. Between 1999 and 2005, a total of 25,636 cases and 620 deaths were officially reported to the WHO. In one of the worst affected urban cities, fecal contamination of surface water is extremely high, and the disease is reported to be prevalent among inhabitants living in close proximity to surface water bodies. Surface runoff from dump sites is a major source of fecal and bacterial contamination of rivers and streams in the study area. This study aims to determine (a) the impacts of surface water contamination on cholera infection and (b) detect and map arbitrary shaped clusters of cholera. A Geographic Information System (GIS) based spatial analysis is used to delineate potential reservoirs of the cholera vibrios; possibly contaminated by surface runoff from open space refuse dumps. Statistical modeling using OLS model reveals a significant negative association between (a) cholera prevalence and proximity to all the potential cholera reservoirs (R² = 0.18, p < 0.001) and (b) cholera prevalence and proximity to upstream potential cholera reservoirs (R² = 0.25, p < 0.001). The inclusion of spatial autoregressive coefficients in the OLS model reveals the dependency of the spatial distribution of cholera prevalence on the spatial neighbors of the communities. A flexible scan statistic identifies a most likely cluster with a higher relative risk (RR = 2.04, p < 0.01) compared with the cluster detected by circular scan statistic (RR = 1.60, p < 0.01). We conclude that surface water pollution through runoff from waste dump sites play a significant role in cholera infection.     

基于DEM的龙口市土地利用空间格局与时空变化研究

本文以龙口市为例,在ARCG IS软件的支持下,分别从高程、坡度与坡向三个方面,对研究区内的耕地、园地、林地和建设用地四种土地利用类型,进行了空间格局与时空变化研究。研究结果表明:从1989年到2005年的17年间,耕地总数在减少,但其分布仍主要集中于低地形等级上。园地的面积增加较多,空间分布上,有向地形高等级发展的趋势,说明园地对各地形因子的适应性较强。林地主要分布于高海拔,大坡度的区域,这有利于防止水土流失。建设用地基本上不受地形的限制,其分布主要是人类活动的结果,因此它在各地形等级的变化表现不明显。通过对土地利用空间格局指标及参数特征的定量分析,将有助于优化该区土地利用结构,实现不同地形上土地利用类型的合理布局,促进区域持续发展。     

GIS的矿区土壤重金属污染评价及空间分布

针对土壤中的重金属含量超标会对人体健康造成极大危害的问题,为检测矿区土壤重金属含量超标状况及空间分布特征,以江西省信丰县4个矿区为例,在28个点位的20~60cm处测定Hg、Cd、As、Cu、Pb、Ni的含量,采用地统计学和地理信息系统相结合的方法进行分析。结果表明,单因子污染指数显示Pb的污染程度最大,污染程度为中度污染;插值分析图显示Cu以西南方向的虎山矿区含量较高,Ni以西南方向的虎山矿区和北部的赤岗矿区含量较高,Pb的污染区域贯穿于整个分布区;重金属含量随深度的增加无明显变化。结合在污染修复方面的经验,建议通过植物修复技术、物理与化学方法进行污染治理和修复。     

Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing

Soil organic carbon (SOC) plays an important role in climate change regulation notably through release of CO2 following land use change such a deforestation, but data on stock change levels are lacking. This study aims to empirically assess SOC stocks change between 1991 and 2011 at the landscape scale using easy-to-access spatially-explicit environmental factors. The study area was located in southeast Madagascar, in a region that exhibits very high rate of deforestation and which is characterized by both humid and dry climates. We estimated SOC stock on 0.1 ha plots for 95 different locations in a 43,000 ha reference area covering both dry and humid conditions and representing different land cover including natural forest, cropland, pasture and fallows. We used the Random Forest algorithm to find out the environmental factors explaining the spatial distribution of SOC. We then predicted SOC stocks for two soil layers at 30 cm and 100 cm over a wider area of 395,000 ha. By changing the soil and vegetation indices derived from remote sensing images we were able to produce SOC maps for 1991 and 2011. Those estimates and their related uncertainties where combined in a post-processing step to map estimates of significant SOC variations and we finally compared the SOC change map with published deforestation maps. Results show that the geologic variables, precipitation, temperature, and soil-vegetation status were strong predictors of SOC distribution at regional scale. We estimated an average net loss of 10.7% and 5.2% for the 30 cm and the 100 cm layers respectively for deforested areas in the humid area. Our results also suggest that these losses occur within the first five years following deforestation. No significant variations were observed for the dry region. This study provides new solutions and knowledge for a better integration of soil threats and opportunities in land management policies.     

Automatic interpretation of digital maps

In the past, the availability and/or the acquisition of spatial data were often the main problems of the realization of spatial applications. Meanwhile this situation has changed: on one hand, comprehensive spatial datasets already exist and on the other hand, new sensor technologies have the ability to capture fast and with high quality large amounts of spatial data. More and more responsible for the increasing accessibility of spatial data are also collaborative mapping techniques which enable users to create maps by themselves and to make them available in the internet. However, the potential of this diversity of spatial data can only hardly be utilized. Especially maps in the internet are represented very often only with graphical elements and no explicit information about the map’s scale, extension and content is available. Nevertheless, humans are able to extract this information and to interpret maps. For example, it is possible for a human to distinguish between rural and industrial areas only by looking at the objects’ geometries. Furthermore, a human can easily identify and group map objects that belong together. Also the type, scale and extension of a map can be identified under certain conditions only by looking at the objects’ geometries. All these examples can be subsumed under the term “map interpretation”. In this paper it is discussed how map interpretation can be automated and how automatic map interpretation can be used in order to support other processes. The different kinds of automatic map interpretation are discussed and two approaches are shown in detail.