Digital terrain modeling

Wilson, John P.

doi:10.1016/j.geomorph.2011.03.012

Cited by 267 publications

(158 citation statements)

References 163 publications

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“…The algorithms and modeling techniques themselves, through their structure and parameters, can also introduce uncertainty in the final outputs (Heuvelink, 1998). While the concepts associated with error and uncertainty propagation have been studied in the geospatial literature (Fisher and Tate, 2006;Wilson, 2012), the attempts to raise end-users' awareness in fields like ecology and environmental modeling have failed (Brown and Heuvelink, 2007;. With a few exceptions from the terrestrial literature (e.g., van Niel and Austin, 2007;Livne and Svoray, 2011), and despite repeated calls for the appropriate consideration of error and uncertainty propagation in environmental modeling and mapping (Rocchini et al, 2011;Beale and Lennon, 2012;Lechner et al, 2012), these concepts have yet to be better implemented, especially in a marine context .…”

Section: Data Qualitymentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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Conservation planning and management typically require accurate and spatially explicit data at scales that are relevant for conservation objectives. In marine conservation, these data are often combined with spatial analytical techniques to produce marine habitat maps. While marine habitat mapping is increasingly used to inform conservation efforts, this field is still relatively young and its methods are rapidly evolving. Because conservation efforts do not always specify standards or guidelines for the production of habitat maps, results can vary dramatically. As representations of real environmental characteristics, habitat maps are highly sensitive to how they are produced. In this review paper, I present four concepts that are known to cause variation in spatial representation and prediction of habitats: the methodology used, the quality and scale of the data, and the choice of variables in regards to fitness for use. I then discuss the potential antinomy associated with the use of habitat maps in conservation: while habitat maps have become an invaluable tool to inform and assist decision-making, maps of the same area built using different methods and data may provide dissimilar representations, thus providing different information and possibly leading to different decisions. Exploring the theories and methods that have proved effective in terrestrial conservation and the spatial sciences, and how they can be integrated in marine habitat mapping practices, could help improve maps used to support marine conservation efforts and result in more reliable products to inform conservation decisions. Having a strong, consistent, transparent, repeatable, and science-based protocol for data collection and mapping is essential for effectively supporting decision-makers in developing conservation and management plans. The development of user-friendly tools to assist in the application of such protocol is crucial to a widespread improvement in practices. I discuss the potential to use interactive and collaborative Geographic Information Systems (GIS) to encourage the conservation and management community, from data collectors and mapmakers to decision-makers, to move toward a digital resilience and to develop such science-based protocol. Until standards and protocols are developed, habitat maps should always be interpreted with care, and the methods and metadata associated with their production should always be explicitly stated.

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Section: Data Qualitymentioning

confidence: 99%

On the Use of Maps and Models in Conservation and Resource Management (Warning: Results May Vary)

Lecours

2017

Front. Mar. Sci.

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“…The covariates were generated according to Wilson & Gallant (2000), based on a digital elevation model (DEM). The DEM (spatial resolution 30 m) was derived from topographic map contour lines at a 1:50,000 scale.…”

Section: Predictive Covariatesmentioning

confidence: 99%

Digital soil mapping: strategy for data pre-processing

Caten

Dalmolin

Ruiz

2012

Rev. Bras. Ciênc. Solo

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SUMMARYThe region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

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“…The DEM-associated errors such as peaks and sinks were deleted to minimize discontinuities. The fi lled DEM approach, also known as eight-direction pour point model, was used to calculate the fl ow direction for each pixel in which fl ow of the water at each pixel is infl uenced by one of the eight surrounding pixels [12][13]. The extraction of drainage network was performed using a raster calculator along with contour crenulation [14].…”

Section: Methodsmentioning

confidence: 99%