Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

Theobald, David M.; Stevens, Donald L.; White, Denis; Urquhart, N. Scott; Olsen, Anthony R.; Norman, John

doi:10.1007/s00267-005-0199-x

Cited by 174 publications

(152 citation statements)

References 19 publications

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“…More precisely, the mentioned probability raster was used to control sampling intensity during the generation of 2000 spatially balanced landslide initiation points (i.e. raster cells with high probabilities are more likely selected as landslide location) (Theobald et al, 2007). This comparably high number of landslide points was chosen to assure a high explanatory power of the empirical results while simultaneously assuring computational feasibility.…”

Section: Generation Of Synthetic Datamentioning

confidence: 99%

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The propagation of inventory-based positional errors into statistical landslide susceptibility models

Steger

Brenning

Bell

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. There is unanimous agreement that a precise spatial representation of past landslide occurrences is a prerequisite to produce high quality statistical landslide susceptibility models. Even though perfectly accurate landslide inventories rarely exist, investigations of how landslide inventory-based errors propagate into subsequent statistical landslide susceptibility models are scarce.The main objective of this research was to systematically examine whether and how inventory-based positional inaccuracies of different magnitudes influence modelled relationships, validation results, variable importance and the visual appearance of landslide susceptibility maps. The study was conducted for a landslide-prone site located in the districts of Amstetten and Waidhofen an der Ybbs, eastern Austria, where an earth-slide point inventory was available.The methodological approach comprised an artificial introduction of inventory-based positional errors into the present landslide data set and an in-depth evaluation of subsequent modelling results. Positional errors were introduced by artificially changing the original landslide position by a mean distance of 5, 10, 20, 50 and 120 m. The resulting differently precise response variables were separately used to train logistic regression models. Odds ratios of predictor variables provided insights into modelled relationships. Crossvalidation and spatial cross-validation enabled an assessment of predictive performances and permutation-based variable importance. All analyses were additionally carried out with synthetically generated data sets to further verify the findings under rather controlled conditions.The results revealed that an increasing positional inventory-based error was generally related to increasing distortions of modelling and validation results. However, the findings also highlighted that interdependencies between inventory-based spatial inaccuracies and statistical landslide susceptibility models are complex. The systematic comparisons of 12 models provided valuable evidence that the respective error-propagation was not only determined by the degree of positional inaccuracy inherent in the landslide data, but also by the spatial representation of landslides and the environment, landslide magnitude, the characteristics of the study area, the selected classification method and an interplay of predictors within multiple variable models. Based on the results, we deduced that a direct propagation of minor to moderate inventory-based positional errors into modelling results can be partly counteracted by adapting the modelling design (e.g. generalization of input data, opting for strongly generalizing classifiers). Since positional errors within landslide inventories are common and subsequent modelling and validation results are likely to be distorted, the potential existence of inventory-based positional inaccuracies should always be considered when assessing landslide susceptibility by means of empirical models.

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Section: Generation Of Synthetic Datamentioning

confidence: 99%

“…3f). A sampling approach developed by Theobald et al (2007) was adopted to spatially distribute landslide initiation zones (i.e. represented by points) according to the predefined relationships.…”

Section: Generation Of Synthetic Datamentioning

confidence: 99%

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Steger

Brenning

Bell

et al. 2016

Nat. Hazards Earth Syst. Sci.

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“…Spatially balanced sampling derives points by maximizing the spatial independence within the sample locations [29]. Using the spatially balanced sampling tool, in ArcGIS (Esri, Redlands, CA, USA), and the unsupervised classified map as the input, 30 sample sites were generated for each type of habitat.…”

Section: Spatially-balanced Pointsmentioning

confidence: 99%

Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery

Halls

Costin

2016

Remote Sensing

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Tidal creeks are small estuarine watersheds characterized by low freshwater input, marine to brackish salinity, and subtidal, intertidal, and supratidal habitats. Most people are familiar with large rivers and estuaries, but the smaller tidal watersheds comprise a greater percentage of the coastline. As the population along coasts rises there is growing concern about water quality and increased sedimentation rates. Therefore, these smaller tidal creek watersheds are at risk to pollution, decreased environmental health, and deterioration of protective salt marshes. The purpose of this study was to test methods for high spatial resolution mapping of benthic (submerged) and emergent habitats as well as the derivation of bathymetry using DigitalGlobe's WorldView-2 imagery. An intensive field effort was conducted to test and assess several image processing techniques. Results concluded that: (1) supervised habitat classification produced the highest map accuracy (95%); (2) sand, water, scrub/shrub, and docks/rubble were mapped the most accurately at greater than 95%; (3) saltmarsh habitats (high and low density cordgrass, Spartina alterniflora, and black needlerush, Juncus roemerianus), mud, and oyster beds were between 80 and 85% accurate; (4) pan-sharpening and atmospheric correction did not improve map accuracy; (5) LiDAR (light detection and ranging) data increased habitat map accuracy; and (6) WorldView-2 imagery was capable of deriving water depth and these data increased the map accuracy of benthic habitats. The project produced habitat maps for benthic and emergent species at high spatial resolution (4 m 2 ) which will be useful for studying the dynamic processes in this tidal environment. The data and methods developed here could be used by state and local government planning agencies to assess potential long-term changes and develop appropriate management strategies.

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“…To allow inference to be drawn on a region given data from sample sites, a randomized survey design should be used (Theobald et al 2007). Such a design allows regional abundance of species within the community to be estimated, as in our case study below.…”

Section: The Geometric Mean and Relative Abundancementioning

confidence: 99%

The geometric mean of relative abundance indices: a biodiversity measure with a difference

et al. 2011

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The 2010 Biodiversity Target of the Convention on Biological Diversity (CBD), set in 2002, which stated that there should be ‘a significant reduction of the current rate of biodiversity loss' by 2010, highlighted the need for informative and tractable metrics that can be used to evaluate change in biological diversity. While the subsequent Aichi 2020 targets are more wide‐ranging, they also seek to reduce the rate of biodiversity loss. The geometric mean of relative abundance indices, G, is increasingly being used to examine trends in biological diversity and to assess whether biodiversity targets are being met. Here, we explore the mathematical and statistical properties of G that make it useful for judging temporal change in biological diversity, and we discuss its advantages and limitations relative to other measures. We demonstrate that the index reflects trends in both abundance and evenness, and that it is not prone to bias when detectability of individuals varies by species. We note that it allows data from different surveys to be combined to generate a composite index. However, the index exhibits high variance and unstable behaviour when rarely‐recorded species are included in the analyses.

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Using GIS to Generate Spatially Balanced Random Survey Designs for Natural Resource Applications

Cited by 174 publications

References 19 publications

The propagation of inventory-based positional errors into statistical landslide susceptibility models

The propagation of inventory-based positional errors into statistical landslide susceptibility models

Submerged and Emergent Land Cover and Bathymetric Mapping of Estuarine Habitats Using WorldView-2 and LiDAR Imagery

The geometric mean of relative abundance indices: a biodiversity measure with a difference

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