Farm-scale soil patterns derived from automated terrain classification

Flynn, Trevan; Rozanov, Andrei; Ellis, F.; Clercq, Willem de; Clarke, Catherine E.

doi:10.1016/j.catena.2019.104311

Cited by 15 publications

(5 citation statements)

References 41 publications

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“…[3,6] Soil mapping Studies that have employed landforms for digital soil mapping. [20] Radiative applications Use of landforms for computing viewsheds and visibility analysis [3] Table 2. Cont.…”

Section: Geomorphologicalmentioning

confidence: 99%

“…Very high-resolution DEMs and high-resolution DEMs are commonly used to delineate landforms at local, regional and national scales, and their frequency of use appears to be comparable (Figure 3). The common use of high-resolution DEMs is most likely due to the recent availability of freely available DEMs datasets covering the entire globe [20,27,28]. Additionally, there appears to be wide use of very high-resolution DEMs available at local scales that are either generated for purposes of the studies or sourced commercially [29][30][31].…”

Section: Datasets Used For Classifying Landforms (Rq2)mentioning

confidence: 99%

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A Scoping Review of Landform Classification Using Geospatial Methods

Mashimbye

Loggenberg

2023

Geomatics

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Landform classification is crucial for a host of applications that include geomorphological, soil mapping, radiative and gravity-controlled processes. Due to the complexity and rapid developments in the field of landform delineation, this study provides a scoping review to identify trends in the field. The review is premised on the PRISMA standard and is aimed to respond to the research questions pertaining to the global distribution of landform studies, methods used, datasets, analysis units and validation techniques. The articles were screened based on relevance and subject matter of which a total of 59 articles were selected for a full review. The parameters relating to where studies were conducted, datasets, methods of analysis, units of analysis, scale and validation approaches were collated and summarized. The study found that studies were predominantly conducted in Europe, South and East Asia and North America. Not many studies were found that were conducted in South America and the African region. The review revealed that locally sourced, very high-resolution digital elevation model ( DEM) products were becoming more readily available and employed for landform classification research. Of the globally available DEM sources, the SRTM still remains the most commonly used dataset in the field. Most landform delineation studies are based on expert knowledge. While object-based analysis is gaining momentum recently, pixel-based analysis is common and is also growing. Whereas validation techniques appeared to be mainly based on expert knowledge, most studies did not report on validation techniques. These results suggest that a systematic review of landform delineation may be necessary. Other aspects that may require investigation include a comparison of different DEMs for landform delineation, exploring more object-based studies, probing the value of quantitative validation approaches and data-driven analysis methods.

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Section: Geomorphologicalmentioning

confidence: 99%

Section: Datasets Used For Classifying Landforms (Rq2)mentioning

confidence: 99%

A Scoping Review of Landform Classification Using Geospatial Methods

Mashimbye

Loggenberg

2023

Geomatics

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“…Regarding the original scale geomorphons, the results show consistent valley-slope-top landforms sequences in the most of mapping site. However, at same scale of soil mapping in South Africa, the geomorphons were compared with expert manual delineation with a high degree of mismatch (57% of the area), despite reasonable prediction results [51]. Alternatively, considering the low importance of geomorphons in decision trees, as we will see in the next section, it is possible to test other approaches to generalization, including the variation of search parameters in the definition of relief units.…”

Section: Generalized Multiscale Geomorphometricsmentioning

confidence: 99%

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Araújo¹,

Oliveira²,

Beldini³

2021

JGIS

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Multiscalar topography influence on soil distribution has a complex pattern that is related to overlay of pedological processes which occurred at different times, and these driving forces are correlated with many geomorphologic scales. In this sense, the present study tested the hypothesis whether multiscale geomorphometric generalized covariables can improve pedometric modeling. To achieve this goal, this case study applied the Random Forest algorithm to a multiscale geomorphometric database to predict soil surface attributes. The study area is in phanerozoic sedimentary basins, in the Alter do Chão geological formation, Eastern Amazon, Brazil. The multiscale geomorphometric generalization was applied at general and specific geomorphometric covariables, producing groups for each scale combination. The modeling was run using Random Forest for A-horizon thickness, pH, silt and sand content. For model evaluation, visual analysis of digital maps, metrics of forest structures and effect of variables on prediction were used. For evaluation of soil textural classifications, the confusion matrix with a Kappa index, and the user's and producer's accuracies were employed. The geomorphometry generalization tends to smooth curvatures and produces identifiable geomorphic representations at sub-watershed and watershed levels. The forest structures and effect of variables on prediction are in agreement with pedological knowledge. The multiscale geomorphometric generalized covariables improved accuracy metrics of soil surface texture classification, with the Kappa Index going from 43% to 62%. Therefore, it can be argued that topography influences soil distribution at combined coarser spatial scales and is able to predict soil particle size contents in the studied watershed. Future development of the multiscale geomorphometric generalization framework could include generalization methods concerning preservation of features,

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“…Accordingly, Kornblau et al [18] used multi-spectral satellite images to generate soil classification maps, and Dobos et al [19] supplied a normalized difference vegetation index (NDVI) (calculated from satellite images) in DSM to characterize vegetation phenology [20]. With topsoil information being the primary DSM target, Flynn et al [21] developed a soil adjusted vegetative index (SAVI) for predicting soil properties at a farm-scale to intensify topsoil information.…”

Section: Introductionmentioning

confidence: 99%

Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion

Yang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multi-temporal Landsat-8 satellite images with fine spatial resolution (i.e., 30 m) are crucial for modern digital soil mapping (DSM). Generally, cloud-free images covering bare topsoil are common choices for DSM. However, the number of effective Landsat-8 data is greatly limited due to cloud contamination coupled with the coarse temporal resolution, and interference of material covering topsoil in most of the months, hindering the development of accurate DSM. To address this issue, temporally dense Landsat images were predicted using a spatio-temporal fusion method to improve DSM. Specifically, the recently developed virtual image pair-based spatio-temporal fusion (VIPSTF) method was adopted to produce simulated Landsat-8 time-series, by fusing with 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) time-series with frequent observations. Subsequently, the simulated Landsat-8 data were used for distinguishing different soil classes via a random forest (RF) model. Training and validation samples of soil classes were collected from legacy soil data. Our results indicate that the simulated data were beneficial for improving DSM owing to the increase in class separability. More precisely, after combining the observed and simulated data, the overall accuracy (OA) and kappa coefficient (Kappa) were increased by 3.099% and 0.047, respectively. This research explored the potential of the spatio-temporal fusion method for DSM, providing a new solution for remote sensing-based DSM.  Index Terms-Soil classes, Digital soil mapping (DSM), Spatio-temporal fusion, Landsat-8.

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Farm-scale soil patterns derived from automated terrain classification

Cited by 15 publications

References 41 publications

A Scoping Review of Landform Classification Using Geospatial Methods

A Scoping Review of Landform Classification Using Geospatial Methods

Multiscalar Geomorphometric Generalization for Soil-Landscape Modeling by Random Forest: A Case Study in the Eastern Amazon

Digital Soil Mapping Based on Fine Temporal Resolution Landsat Data Produced by Spatiotemporal Fusion

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