Andrew D. Heap

A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors

Spatial interpolation methods have been applied to many disciplines. Many factors affect the performance of the methods, but there are no consistent findings about their effects. In this study, we use comparative studies in environmental sciences to assess the performance and to quantify the impacts of data properties on the performance. Two new measures are proposed to compare the performance of the methods applied to variables with different units/scales. A total of 53 comparative studies were assessed and the performance of 72 methods/sub-methods compared is analysed. The impacts of sample density, data variation and sampling design on the estimations of 32 methods are quantified using data derived from their application to 80 variables. Inverse distance weighting (IDW), ordinary kriging (OK), and ordinary co-kriging (OCK) are the most frequently used methods. Data variation is a dominant impact factor and has significant effects on the performance of the methods. As the variation increases, the accuracy of all methods decreases and the magnitude of decrease is method dependent. Irregular-spaced sampling design might improve the accuracy of estimation. The effect of sampling density on the performance of the methods is found not to be significant. The implications of these findings are discussed.

Spatial interpolation methods applied in the environmental sciences: A review

Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided.

Application of machine learning methods to spatial interpolation of environmental variables

Machine learning methods, like random forest (RF), have shown their superior performance in various disciplines, but have not been previously applied to the spatial interpolation of environmental variables. In this study, we compared the performance of 23 methods, including RF, support vector machine (SVM), ordinary kriging (OK), inverse distance squared (IDS), and their combinations (i.e., RFOK, RFIDS, SVMOK and SVMIDS), using mud content samples in the southwest Australian margin. We also tested the sensitivity of the combined methods to input variables and the accuracy of averaging predictions of the most accurate methods. The accuracy of the methods was assessed using a 10-fold cross-validation. The spatial patterns of the predictions of the most accurate methods were also visually examined for their validity. This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables. Averaging the predictions of the most accurate methods showed no significant improvement in the predictive accuracy. Visual examination proved to be an essential step in assessing the spatial predictions. This study has opened an alternative source of methods for spatial interpolation of environmental properties.

Environmental management of clastic coastal depositional environments: inferences from an Australian geomorphic database

Simple, conceptual geomorphic models can assist environmental managers in making informed decisions regarding management of the coast at continental and regional scales. This basic information, detected from aerial photographs and/or satellite images, can be used to ascertain the relative significance of several common environmental issues, including: sediment trapping efficiency, turbidity, water circulation, and habitat change due to sedimentation for different types of clastic coastal depositional environments. The classification of 780 Australian clastic coastal depositional environments based on their geomorphology is used to derive a coastal regionalisation, comprised of a distinctive suite of environments for each region. Because of the close link between the relative influence of waves and tides and the geomorphology of clastic coastal depositional environments, a basic understanding of the broad geomorphic and sedimentary characteristics by environmental managers will assist them in ascertaining the relative significance of environmental issues in each region. The benefit of this approach is that it provides guidance in tailoring management schemes differently for each region, resulting in more effective and efficient treatment of these issues.

Spatial interpolation methods applied in the environmental sciences

Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided.

Origin and Formation of an Estuarine Barrier Island, Tapora Island, New Zealand

Abstract Barrier islands in sheltered settings are rare coastal geomorphic features. Here we present a case study of controls on the evolution of Tapora Island, North Island, New Zealand. Tapora Island is an active barrier island located opposite the entrance to the Kaipara Harbour on a high-energy coast. Subsurface facies form an aggradational barrier island succession from subtidal to subaerial elevations. This facies succession, combined with surface samples and geomorphic and geologic relationships, indicates that Tapora Island is the most recent barrier island at this location in the estuary and forms part of a prograded coast opposite the entrance. Wave data indicate that ocean swell waves penetrate the inlet for approximately 2 hours either side of high tide and are capable of transporting sand onto the island. The combined effects of swell waves, abundant sediment supply, and exposed aspect are the critical factors that have formed the barrier island. Despite the “sheltered’ estuarine setting, Tapora Island has formed under conditions that are more akin to open ocean coasts. The origin and development of Tapora Island broadly conforms to the accumulating barrier island model.

Predicting Seabed Hardness Using Random Forest in R

The spatial information of the seabed biodiversity is important for marine zone management in Australia. The biodiversity is often predicted using spatially continuous data of seabed biophysical properties. Seabed hardness is an important property for predicting the biodiversity and is often inferred from multibeam backscatter data. Seabed hardness can also be inferred based on underwater video footage that is, however, only available at a limited number of sampled locations. In this study, we predict the spatial distribution of seabed hardness using random forest (RF) based on video classification and seabed properties. We illustrate the effects of cross-validation methods including a new cross-validation function ( rf.cv ) on selecting the most optimal predictive model. We also test the effects of various predictor sets on the accuracy of predictive models. This study provides an example of predicting the spatial distribution of environmental properties using RF in R.

Geology and Sedimentary History of Modern Estuaries

Modern estuaries are part of a continuum of coastal depositional environments within which the variation in geomorphology is closely related to the dominant one of three main processes affecting sedimentation, viz waves, tides or rivers. The present location of the coast is controlled by sea-level rise brought about by the release of water from continental ice sheets following the glacial maximum around 20,000 years ago. The current form of the coast is partly inherited from the shape of the precedent land surface flooded by the rising sea, which is then modified by a combination of ongoing local erosion and/or deposition of sediment transported by rivers from the adjacent land mass or submarine erosion, and then redistributed by the locally dominant marine processes. Once eustatic sea level stabilised around 6–7000 years ago, sediment was able to progressively infill the topographically lower areas, except in areas where glacial rebound is ongoing. In some cases, where the rate of sedimentation is relatively high, infill of coastal indentations may have been completed, and the coast is now prograding seaward. Elsewhere, where sedimentation rates are lower, or waves and tides are able to effectively move sediment away from the point of river entry, infill may have only partially proceeded, and the coast has been modified into characteristic forms. Where waves dominate over tides, features made from coarse-grained sediments such as barriers, beaches and bars, form parallel to the general trend of the coast. These establish less-energetic environments isolated from the full force of the ocean, where fine-grained sediments can accumulate. Where tidal forces are relatively dominant, the coarser-grained bars tend to orient at right angles to the coast, and fine-grained sediments accumulate in the intertidal areas as mud flats, and marshes.

Habitats and Benthos of a Deep-Sea Marginal Plateau, Lord Howe Rise, Australia

Publisher Summary Lord Howe Rise is a marginal plateau located in the Coral Sea and Tasman Sea, composed mainly of continental fragments that detached from the eastern margin of continental Australia during the late Jurassic and Cretaceous. Lord Howe Rise is an extensive feature of the South Pacific Ocean, spanning ∼2,800 km in latitude (19°S to 43°S) and 450–650 km wide. Geomorphic features in the survey area include ridges, valleys, plateaus, and basins. Smaller superimposed features include peaks, moats, holes, polygonal furrows, scarps, and aprons. The physical structure and biological composition of the seabed were characterized using towed video and sampling of epifaunal and infaunal organisms. These deep-sea environments are dominated by thick, depositional, soft sediments (sandy mud), with local outcrops of volcanic rock and mixed gravel–boulders. Ridge, valley, and plateau environments were moderately bioturbated, but few organisms were directly observed or collected. Volcanic peaks were bathymetrically complex hard-rock structures that supported sparse distributions of suspension feeders (e.g., cold-water corals and glass sponges) and associated epifauna (e.g., crinoids and brittle stars). Isolated outcrops along the sloping edge of one ridge also supported similar assemblages, some with high localized densities of coral-dominated assemblages.

Review: Spatial interpolation methods applied in the environmental sciences: A review

Spatially continuous data of environmental variables are often required for environmental sciences and management. However, information for environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided.