Fuzzy logic and differences between interpretive soil maps

Hodza, Paddington

doi:10.1016/j.geoderma.2010.02.016

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…This scenario would involve using the proportion of tree predictions as membership functions where multiple soils are predicted for the same pixel in different trees within the ensemble model. This should be investigated in future disaggregation attempts as it would allow for continuous fuzzy thematic representations of soil classes (e.g., Burrough, 1989;Burrough et al, 1997;De Gruijter et al, 1997;Hodza, 2010;Lagacherie et al, 1997;McBratney and Odeh, 1997;Qi et al, 2006;Zhu, 1997;Zhu et al, 1996Zhu et al, , 2001, and Author's Copy 2010) that could be translated into soil property maps (e.g., Malone et al, 2011;Nauman et al, 2012;Qi et al, 2006;Zhu et al, 1997Zhu et al, , 2001Zhu et al, , and 2010.…”

Section: Discussionmentioning

confidence: 99%

Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Mining and Random Forests in the Sonoran Desert, USA

Nauman¹,

Thompson²,

Rasmussen³

2014

photogramm eng remote sensing

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Conventional soil maps (CSM) have provided baseline soil information for land use planning for over 100 years. Although CSM have been widely used, they are not suitable to meet growing demands for high resolution soil information at field scales. We present a repeatable method to disaggregate CSM data into ~30-meter resolution rasterized soil class maps that include continuous representation of probabilistic map uncertainty. Methods include training set creation for original CSM component soil classes from soil-landscape descriptions within the original survey database. Training sets are used to build a random forest predictive model utilizing environmental covariate maps derived from ASTER satellite imagery and the USGS National Elevation Dataset. Results showed agreement at 70 percent of independent field validation sites and equivalent accuracy between original CSM map units and the disaggregated map. Uncertainty of predictions was mapped by relating prediction frequencies of the random forest model and success rates at validation sites.

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Section: Discussionmentioning

confidence: 99%

Semi-Automated Disaggregation of a Conventional Soil Map Using Knowledge Driven Data Mining and Random Forests in the Sonoran Desert, USA

Nauman¹,

Thompson²,

Rasmussen³

2014

photogramm eng remote sensing

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“…As maps are visual displays, visual methods are naturally part and parcel of juxtapositions of these products. These methods engage our visual senses, allowing us to 'see' map inconsistencies sometimes much faster than computational techniques [43,44]. In previous studies, they brought to light perceptible differences between equivalent roads mapped by different experts [17], various tools and techniques [31], and similar methods [16].…”

Section: Road Map Comparison Criteria and Methodsmentioning

confidence: 99%

Juxtaposing GIS and Archaeologically Mapped Ancient Road Routes

Hodza

Butler

2022

Geographies

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Mapping ancient roads is crucial to tell credible geospatial stories about where, how, or why different people might have travelled or transported materials within and between places in the distant past. Achieving this process is challenging and commonly accomplished by means of archaeological and GIS methods and materials. It is not uncommon for different experts employing these methods to generate inconsistent delineations of the same ancient roads, creating confusion about how to produce knowledge and decisions based on multiple geospatial perspectives. This yet to be adequately addressed problem motivates our desire to enrich existing literature on the nature and extents of these differences. We juxtapose GIS and archaeologically generated road maps for northern Etruria, a region of ancient Italy with a well-developed road network built by the Etruscans and Romans. We reveal map differences through a map comparison approach that integrates a broad set of qualitative and quantitative measures plus geospatial concepts and strategies. The differences are evident in route locations, sinuosities, lengths, and complexities of the terrains on which the routes were set as defined by subtle variations in elevation, slope, and ruggedness. They ranged from 11.2–34.4 km in road length, 0–65.7 m in road relief, 1.0–13.5% in mean road grade, 0.07–0.79 in detour indices and 0.19–3.08 for mean terrain roughness indices, all of which can be considerable depending on application. Taken together, the measures proved effective in furthering our understanding of the range of possible disagreements between ancient linear features mapped by different experts and methods and are extensible for other application areas. They point to the importance of explicitly acknowledging and maintaining all usable perspectives in geospatial databases as well as visualization and analysis processes, regardless of levels of disagreement, and especially where ground-truth informed assessments cannot be reliably performed.

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“…Despite using sharp boundaries to represent soil map units, soil scientists, indeed appreciate the fuzziness of transitions between different soil types within a map unit (Burrough et al, 1997;Hodza, 2010). Despite using sharp boundaries to represent soil map units, soil scientists, indeed appreciate the fuzziness of transitions between different soil types within a map unit (Burrough et al, 1997;Hodza, 2010).…”

Section: Accepted Manuscriptmentioning

confidence: 99%