Analysis of Digital Data Obtained from Raster and Vector Maps

Ayhan, Eminnur; Akar, Özlem; Uzun, Süleyman; Dilaver, A.; Kansu, O.

doi:10.1061/(asce)su.1943-5428.0000040

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The presented values for EEG are based on the original data, while the remaining maps are the back-transformations of the modeled predictions. Further visualization and analysis were made possible by classifying the maps from raster to vector format [54]. The spatial distribution map of TG was classified into three categories as follows: (a) Class 1: less than 0.271 mg/g; (b) Class 2: 0.271-0.318 mg/g; and (c) Class 3: more than 0.318 mg/g.…”

Section: Digital Mapsmentioning

confidence: 99%

Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques

et al. 2022

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Agricultural practices affect arbuscular mycorrhizal fungal (AMF) hyphae growth and glomalin production, which is a recalcitrant carbon (C) source in soil. Since the spatial distribution of glomalin is an interesting issue for agronomists in terms of carbon sequestration, digital maps are a cost-free and useful approach. For this study, a set of 120 soil samples was collected from an experimental area of 310 km2 from the Sarab region of Iran. Soil total glomalin (TG) and easily extractable glomalin (EEG) were determined via ELISA using the monoclonal antibody 32B11. Soil organic carbon (OC) was also measured. The ratios of TG/OC and EEG/OC as the glomalin–C quotes of OC were calculated. A total of 17 terrain-related attributes were also derived from the digital elevation model (DEM) and used as static environmental covariates in digital soil mapping (DSM) using three predictive models, including multiple linear regression (MLR), random forests (RF), and Cubist (CU). The major findings were as follows: (a) DSM facilitated the interpretation of recalcitrant C source variation; (b) RF outperformed MLR and CU as models in predicting and mapping the spatial distribution of glomalin using available covariates; (c) the best accuracy in predictions was for EEG, followed by EEG/OC, TG, and TG/OC.

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Section: Digital Mapsmentioning

confidence: 99%

Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques

et al. 2022

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“…Spatial coordinate-based positions often need to be transformed from their own reference frame to another reference frame by an analytic parametric model. This is a standard problem in several fields of geosciences and engineering, including geodesy [11,18,19,30,33], land surveying and cadastral planning [2,8,16,32], cartography and digital mapping [1,5,28,38], photogrammetry and remote sensing [22,25,39], robotics and computer vision [7,13,31], among others.…”

Section: Introductionmentioning

confidence: 99%

Spatial Coordinate Transformations with Noisy Data

Kotsakis¹

2019

Geospatial Analyses of Earth Observation (EO) Data [Working Title]

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The parametric transformation of spatial coordinates between different reference frames is traditionally dealt with a stepwise approach which imposes a suboptimal treatment in the presence of noisy data. The chapter explains briefly the drawbacks of this approach and then presents an alternative scheme for spatial coordinate transformations that improves the classic stepwise solution when using noisy coordinates of known stochastic structure. The proposed methodology is simple in principle, although its numerical implementation with nonlinear parametric models is a bit more involved and it relies on the joint least squares adjustment of the observed coordinates using their full stochastic model over all points of interest. The mathematical framework and the related properties of this "stacking" approach are presented in detail, along with a numerical example that demonstrates its feasibility for practical problems in geospatial applications. and/or vector-type data in multiple frames, the present chapter is confined to the vectorized version of SCT problems between two different frames. Problem statement and research motivationThe transformation of spatial coordinates between different reference frames using a parametric model is traditionally resolved via a two-step procedure. Firstly, the parameters of the adopted model are estimated by least squares adjustment (LSA) techniques on the basis of control points with known coordinates in both frames of interest. The estimated parameters are then used to transform any set of known coordinates from the original frame (also called source or initial frame) to the desired target frame. The second step is applied not only to control points but also to additional points whose spatial coordinates are originally known only in the initial frame. Their transformed coordinates are often the primary objective in many practical applications, whereas the post-fit residuals at control points are commonly used as quality metrics of the transformation process.Despite its rational character, the stepwise methodology imposes a suboptimal treatment in the presence of noisy data. The reason is that the accuracy of the original coordinates in the initial frame is ignored during their transformation to the target frame. Indeed, the second step entails only the forward implementation of the parametric model without attempting to minimize or, at least, reduce the propagated random errors of the original coordinates. As shown in [18], the stepwise procedure may actually enhance the data noise into the final results, in the sense that the transformed coordinates in the target frame could become less accurate than the original coordinates in the initial frame.An additional weakness of the traditional stepwise procedure is that the control and non-control points (abbreviated hereafter as CPs and NPs) are handled independently throughout the transformation process. Yet, the initial coordinates of these points are usually acquired from the same pre-analysis phase or observational procedure (e.g.,...

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Nonlinear Geospatial Frame Transformations in the Presence of Noisy Data

Kotsakis

2018

Math Geosci

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Analysis of Digital Data Obtained from Raster and Vector Maps

Cited by 6 publications

References 4 publications

Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques

Recalcitrant C Source Mapping Utilizing Solely Terrain-Related Attributes and Data Mining Techniques

Spatial Coordinate Transformations with Noisy Data

Nonlinear Geospatial Frame Transformations in the Presence of Noisy Data

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