Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico

Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China

Cadmium (Cd) pollution has been widely recognized in lake ecosystems. Although the accurate prediction of the spatial distributions of Cd in lakes is important for controlling Cd pollution, the traditional monitoring methods of setting discrete and limited sampling points cannot actually reflect the continuous spatial distribution characteristics of Cd. In this study, we set up 93 sampling points in Baiyangdian Lake (BYDL), and collected surface water, overlying water and sediment samples from each sampling point. Cd contents were measured to predict their spatial distributions in different environmental components by three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), and the effects of different sampling numbers on the interpolation accuracy were also assessed to optimize the interpolation method and sampling number. The results showed that the interpolation accuracy of IDW decreased with increasing power values. The best basis function for RBF was IMQ, and the best semivariogram models for OK were the spherical model and stable model. The best interpolation method for the waters and sediments was RBF-IMQ compared with OK and IDW. Within the sampling number range of 50-93, the interpolation accuracy for Cd in surface water increased with the increase in sampling number. Comparatively, the interpolation accuracy was the highest for overlying water and sediments when the sampling number was 60. The findings of this work provide a combined sampling and spatial interpolation method for monitoring the spatial distribution and pollution levels of Cd in the waters and sediments of shallow lakes.

Combining geospatial abundance and ecological niche models to identify high-priority areas for conservation: The neglected role of broadscale interspecific competition

Ecological niche models (ENMs) have become a practical and key mechanism for filling major gaps in spatial information for targeted conservation planning, particularly when only occurrence data are available. Nonetheless, accounting for abundance patterns in the internal structure of species’ ranges, and the role of biotic interactions in such models across broadscale, remains highly challenging. Our study gathered baseline information on abundance data of two Endangered Amazonian primates (Ateles chamek and Lagothrix lagotricha cana) to create geospatial abundance models using two spatial interpolation methods: Inverse distance weight (IDW) and Ordinary kriging (OK). The main goals were to: (i) test whether geospatial abundance models are correlated with habitat suitability derived from correlative ENMs; (ii) compare the strength of the abundance-suitability relationships between original and interpolated abundances; (iii) test whether interspecific competition between the two target taxa constrained abundance over broad spatial scales; and (iv) create ensemble models incorporating both habitat suitability and abundance to identify high-priority areas for conservation. We found a significant positive relationship between habitat suitability with observed and predicted abundances of woolly (L. l. cana) and spider (A. chamek) monkeys. Abundance-suitability correlations showed no significant differences when using original relative abundances compared to using interpolated abundances. We also found that the association between L. l. cana abundance and habitat suitability depended on the abundance of its putative competitor species, A. chamek. Our final models combining geospatial abundance information with ENMs were able to provide more realistic assessments of hotspots for conservation, especially when accounting for the important, but often neglected, role of interspecific competition in shaping species’ geographic ranges at broader scales. The framework developed here, including general trends in abundance patterns and suitability information, can be used as a surrogate to identify high-priority areas for conservation of poorly known species across their entire geographic ranges.

Water quality assessment in two lakes of Panchkula, Haryana, using GIS: case study on seasonal and depth wise variations

Water is the most important commodity available on earth and exists as both surface and sub-surface sources, but increased water pollution has reduced its potability. In this context, it has become imperative to regularly monitor the water quality. In situ and laboratory experimental procedures involve point wise collection of samples for quality determination which are too elaborative and time consuming. As such, the use of methods like Geographic Information System (GIS) modelling if used in collaboration with the traditional methods can prove to be a great tool as they are less expensive and gives a complete spatial resolution of the study site. Therefore, the present study focuses on the determination of water quality using traditional methods in collaboration with GIS modelling system using the inverse distance weighing (IDW) method for two natural lakes in Haryana. The IDW technique was used to interpolate parameters like temperature, dissolved oxygen (DO), nitrates (NO₃) and total phosphorous (TP) as they represent the effects of recent and old pollution in lake waters at different depths. These parameters were interpolated for determining the overall water quality status for the lakes. The collaboration can prove to be of great practical significance in today's time by giving an elaborative view of the present water quality status, easing daily telemetric monitoring of the sites as well as give an opportunity for futuristic modelling. The technique can work for almost all the sites around the globe which have either not been evaluated from quality aspect or are inaccessible for monitoring. Parameters like temperature and DO show significant depth wise and seasonal variations for both the lakes with highest values observed at the surface levels, whereas the NO₃ and TP represented effects of point pollution sources to a smaller extent. The maximum value of temperature was determined to be of 30.7 °C and 9 mg/l for DO and was recorded at the surface of lakes 2 and 1, respectively. Further, nitrate and phosphorous concentrations were observed to have maximum values of 0.99 and 0.5 mg/l at the centre of the lake 1 for monsoon season due to influx of pollutants and settlements in the bed. The primary reason for the variation of water quality may be attributed to increased sedimentation at the bottom of lake due to agricultural activities in the vicinity which creates impacts on different hydrodynamic processes leading to increased levels of TP and NO₃ concentrations. Further, increased recreational activities lead to induced variations in the water quality as well.

Interpolation of Point Prevalence Rate of the Highly Pathogenic Avian Influenza Subtype H5N8 Second Phase Epidemic in South Korea

Humans and animals are both susceptible to highly pathogenic avian influenza (HPAI) viruses. In the future, HPAI has the potential to be a source of zoonoses and pandemic disease drivers. It is necessary to identify areas of high risk that are more vulnerable to HPAI infections. In this study, we applied unbiased predictions based on known information to find points of localities with a high probability of point prevalence rate. To carry out such predictions, we utilized the inverse distance weighting (IDW) and kriging method, with the help of the R statistical computing program. The provinces of Jeollanam-do, Gyeonggi-do, Chungcheongbuk-do and Ulsan have high anticipated risk. This research might aid in the management of avian influenza threats associated with various potential risks.

Reconstructing High-Precision Coral Reef Geomorphology from Active Remote Sensing Datasets: A Robust Spatial Variability Modified Ordinary Kriging Method

Active remote sensing technology represented by multi-beam and lidar provides an important approach for the effective acquisition of underwater coral reef geomorphological information. A spatially continuous surface model of coral reef geomorphology reconstructed from active remote sensing datasets can provide important geomorphological parameters for the research of coral reef geomorphological and ecological changes. However, the surface modeling methods commonly used in previous studies, such as ordinary kriging (OK) and natural neighborhood (NN), often represent a “smoothing effect”, which causes the strong spatial variability of coral reefs to be imprecisely reflected by the reconstructed surfaces, thus affecting the accurate calculation of subsequent geomorphological parameters. In this study, a spatial variability modified OK (OK-SVM) method is proposed to reduce the impact of the “smoothing effect” on the high-precision reconstruction of the complex geomorphology of coral reefs. The OK-SVM adopts a collaborative strategy of global parameter transformation, local residual correction, and extremum correction to modify the spatial variability of the reconstructed model, while maintaining high local accuracy. The experimental results show that the OK-SVM has strong robustness to spatial variability modification. This method was applied to the geomorphological reconstruction of the northern area of a coral atoll in the Nansha Islands, South China Sea, and the performance was compared with that of OK and NN. The results show that OK-SVM has higher numerical accuracy and attribute accuracy in detailed morphological fidelity, and is more adaptable in the geomorphological reconstruction of coral reefs with strong spatial variability. This method is relatively reliable for achieving high-precision reconstruction of complex geomorphology of coral reefs from active remote sensing datasets, and has potential to be extended to other geomorphological reconstruction applications.

Geochemistry of Sub-Depositional Environments in Estuarine Sediments: Development of an Approach to Predict Palaeo-Environments from Holocene Cores

In the quest to use modern analogues to understand clay mineral distribution patterns to better predict clay mineral occurrence in ancient and deeply buried sandstones, it has been necessary to define palaeo sub-environments from cores through modern sediment successions. Holocene cores from Ravenglass in the NW of England, United Kingdom, contained metre-thick successions of massive sand that could not be unequivocally interpreted in terms of palaeo sub-environments using conventional descriptive logging facies analysis. We have therefore explored the use of geochemical data from portable X-ray fluorescence analyses, from whole-sediment samples, to develop a tool to uniquely define the palaeo sub-environment based on geochemical data. This work was carried out through mapping and defining sub-depositional environments in the Ravenglass Estuary and collecting 497 surface samples for analysis. Using R statistical software, we produced a classification tree based on surface geochemical data from Ravenglass that can take compositional data for any sediment sample from the core or the surface and define the sub-depositional environment. The classification tree allowed us to geochemically define ten out of eleven of the sub-depositional environments from the Ravenglass Estuary surface sediments. We applied the classification tree to a core drilled through the Holocene succession at Ravenglass, which allowed us to identify the dominant paleo sub-depositional environments. A texturally featureless (massive) metre-thick succession, that had defied interpretation based on core description, was successfully related to a palaeo sub-depositional environment using the geochemical classification approach. Calibrated geochemical classification models may prove to be widely applicable to the interpretation of sub-depositional environments from other marginal marine environments and even from ancient and deeply buried estuarine sandstones.

Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data

Improving the spatiotemporal coverage of remote sensing (RS) products, such as sea surface chlorophyll concentration (SSCC), can offer a better understanding of the spatiotemporal SSCC distribution for ocean management purposes. In the first part of this work, 834 in-situ SSCC measurements of the SeaBASS-NASA (National Aeronautics and Space Administration) during 2002-2016 served as the empirical dataset. A moving window with ±3 days and ±0.5° centered at each of the in-situ SSCC measurements established a search neighborhood for Moderate Resolution Imaging Spectroradiometer Level 2 (MODIS L2) SSCC and MODIS L2 sea surface temperature (SST) data, and the matched SSCC and SST data were used for building a linear SSCC-SST relationship. The unmatched SST was introduced to the linear model for generating soft SSCC data with uniform distributions. The inherent spatiotemporal dependency of the SSCC distribution was then represented by the Bayesian maximum entropy (BME) method, which incorporated the soft SSCC data as auxiliary variable for SSCC estimation and mapping purposes. The results showed that a 75.3% accuracy improvement of remote SSCC retrieval in terms of R² can be achieved by BME-based method compared to the original MODIS L2 product. Subsequently, the BME-based method was applied to obtain daily SSCC dataset in Chesapeake Bay (USA) during the period 2010-2019. It was found that the SSCC distribution exhibited a decreasing spatial trend from the upper bay to the outer bay, whereas decreasing and increasing temporal trends were detected during the periods 2011-2014 and 2016-2019, respectively. The generalized Cauchy process was used to quantitatively describe the autocorrelation SSCC function in the Chesapeake Bay. The results showed that the outer bay exhibited the strongest long-range dependence among the four sub-regions, whereas the middle bay exhibited the weakest long-range dependence. Finally, one-point and two-point stochastic site indicators (SSIs) were employed to explore the spatiotemporal SSCC characteristics in Chesapeake Bay. The one-point SSI results showed that nearly 100% of the upper, middle and the lower bay areas experienced a high SSCC level (>5 mg/m³) during the entire study period. The area with SSCC >5 mg/m³ in the outer bay increased a lot during the winter season, but the area with SSCC >10 or 20 mg/m³ decreased significantly in the upper, middle and lower bay. Simultaneously, the SSCC dispersion in these areas was rather small during the winter season. On the other hand, the two-point SSI results showed that although the SSCC levels differ among the four sub-regions, but the SSCC connectivity structures between pairs of points also displayed some similarities in terms of their spatiotemporal dependency. In conclusion, the proposed BME-based method was shown to be a promising remote SSCC mapping technique that exhibited a powerful ability to improve both accuracy and coverage of RS products. The SSIs can be also used to explore the spatiotemporal characteristics of a variety of natural attributes in waters.

An Investigation of Takagi-Sugeno Fuzzy Modeling for Spatial Prediction with Sparsely Distributed Geospatial Data

Fuzzy set theory has shown potential for reducing uncertainty as a result of data sparsity and also provides advantages for quantifying gradational changes like those of pollutant concentrations through fuzzy clustering based approaches. The ability to lower the sampling frequency and perform laboratory analyses on fewer samples, yet still produce an adequate pollutant distribution map, would reduce the initial cost of new remediation projects. To assess the ability of fuzzy modeling to make spatial predictions using fewer sample points, its predictive ability was compared with the ordinary kriging (OK) and inverse distance weighting (IDW) methods under increasingly sparse data conditions. This research used a Takagi–Sugeno (TS) fuzzy modelling approach with fuzzy c-means (FCM) clustering to make spatial predictions of the lead concentrations in soil. The performance of the TS model was very dependent on the number of outliers in the respective validation set. For modeling under sparse data conditions, the TS fuzzy modeling approach using FCM clustering and constant width Gaussian shaped membership functions did not show any advantages over IDW and OK for the type of data tested. Therefore, it was not possible to speculate on a possible reduction in sampling frequency for delineating the extent of contamination for new remediation projects.

A webGIS Application to Assess Seawater Quality: A Case Study in a Coastal Area in the Northern Aegean Sea

The assessment of seawater quality in coastal areas is an important issue as it is related to the welfare of coastal ecosystems, a prerequisite for the provision of the related ecosystem services. During the last decades, marine eutrophication has become an important problem in coastal waters as a result of nutrient inputs increase. Consequently, there is need for appropriate methods and tools to assess the eutrophication status of seawater which should be user-friendly to coastal managers and support the adoption of effective plans for the protection and sustainable development of the coastal environment. In this framework, a user-friendly webGIS application has been developed and the Strait of Mytilene at the southeastern part of the Island of Lesvos in the NE Aegean Sea, Greece, was used as a case study. The methodology includes, as a first step, the evaluation of the accuracy of spatial interpolators widely applied in oceanographic studies for assessing the spatial distribution of relevant variables. The most appropriate interpolator revealed for each variable is subsequently applied for the production of the representative thematic layer. The second step involves the integration of the information from the optimal thematic layers representing the spatial distributions of the variables under study; as a result, a new thematic layer illustrating the eutrophication status of the study area is produced. The webGIS application is fully available via a web browser and provides a number of geoprocessing modules developed in Python which implement the user interface, the application of the interpolation analytical tasks, the statistical evaluation toolset and the integration of the optimal interpolated layers. Suggestions for further improvement of the proposed webGIS application are discussed.

Spatial benthic community analysis of shallow coral reefs to support coastal management in Culebra Island, Puerto Rico

Caribbean coral reefs provide essential ecosystem services to society, including fisheries, tourism and shoreline protection from coastal erosion. However, these reefs are also exhibiting major declining trends, leading to the evolution of novel ecosystems dominated by non-reef building taxa, with potentially altered ecological functions. In the search for effective management strategies, this study characterized coral reefs in front of a touristic beach which provides economic benefits to the surrounding coastal communities yet faces increasing anthropogenic pressures and conservation challenges. Haphazard photo-transects were used to address spatial variation patterns in the reef’s benthic community structure in eight locations. Statistically significant differences were found with increasing distance from the shoreline, reef rugosity, Diadema antillarum density, among reef locations, and as a function of recreational use. Nearshore reefs reflected higher percent macroalgal cover, likely due to increased exposure from both recreational activities and nearby unsustainable land-use practices. However, nearshore reefs still support a high abundance of the endangered reef-building coral Orbicella annularis, highlighting the need to conserve these natural shoreline protectors. There is an opportunity for local stakeholders and regulatory institutions to collaboratively implement sea-urchin propagation, restoration of endangered Acroporid coral populations, and zoning of recreational densities across reefs. Our results illustrate vulnerable reef hotspots where these management interventions are needed and recommend guidelines to address them.

Assessment of Ordinary Kriging and Inverse Distance Weighting Methods for Modeling Chromium and Cadmium Soil Pollution in E-Waste Sites in Douala, Cameroon

BACKGROUND

Several studies have demonstrated that chromium (Cr) and cadmium (Cd) have adverse impacts on the environment and human health. These elements are present in electronic waste (e-waste) recycling sites. Several interpolation methods have been used to evaluate geographical impacts on humans and the environment.

OBJECTIVES

The aim of the present paper is to compare the accuracy of inverse distance weighting (IDW) and ordinary kriging (OK) in topsoil analysis of e-waste recycling sites in Douala, Cameroon.

METHODS

Selecting the proper spatial interpolation method is crucial for carrying out surface analysis. Ordinary kriging and IDW are interpolation methods used for spatial analysis and surface mapping. Two sets of samples were used and compared. The performances of interpolation methods were evaluated and compared using cross-validation.

RESULTS

The results showed that the OK method performed better than IDW prediction for the spatial distribution of Cr, but the two interpolation methods had the same result for Cd (in the first set of samples). Results from Kolmogorov-Smirnov and Shapiro-Wilk tests showed that the data were normally distributed in the study area. The p value (0.302 and 0.773) was greater than 0.05 for Cr and for Cd (0.267 and 0.712). In the second set of samples, the OK method results (for Cd and Cr) were greatly diminished and the concentrations dropped, looking more like an average on the maps. However, the IDW interpolation gave a better representation of the concentration of Cd and Cr on the maps of the study area. For the second set of samples, OK and IDW for Cd and Cr had more similar results, especially in terms of root mean square error (RMSE).

CONCLUSIONS

Many parameters were better identified from the RMSE statistic obtained from cross-validation after exhaustive testing. Inverse distance weighting appeared more adequate in limited urban areas.

COMPETING INTERESTS

The authors declare no competing financial interests.