Multi-resolution soil-landscape characterisation in KwaZulu Natal: Using geomorphons to classify local soilscapes for improved digital geomorphological modelling

Atkinson, Jonathan T.; Clercq, Willem de; Rozanov, Andrei

doi:10.1016/j.geodrs.2020.e00291

Cited by 22 publications

(5 citation statements)

References 65 publications

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“…The regions with high prediction uncertainty were mainly located in transition zones, and this is because topographic factors were not enabled to discriminate soil types and due to soil-landscape relationships not being clearly captured. Therefore, adding landform types better reflecting the geomorphology and landforms to the predictor variables (e.g., [35,36]) might further improve the accuracy of classification (e.g., [37][38][39]). In addition, compared to numerical variables, which are based on image pixels, LU and landform types are based on patch units, which can effectively attenuate the pretzel phenomenon (finegrained patches "noise") of soil prediction maps and maintain the spatial integrity of soil patterns.…”

Section: Discussionmentioning

confidence: 99%

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

Zhu,

et al. 2024

Remote Sensing

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Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.

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Section: Discussionmentioning

confidence: 99%

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

Zhu,

et al. 2024

Remote Sensing

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“…Topographic feature detection is a common application in hydrology (e.g., Atkinson et al., 2020; Bonetto et al., 2015; Höfle et al., 2013; Syzdykbayev et al., 2020a). Features of interest are defined by shape, size, or placement in the landscape that distinguishes them from other landscape features.…”

Section: Methodsmentioning

confidence: 99%

Automated Earthwork Detection Using Topological Persistence

Lapides,

Grindstaff,

Nichols

2024

Water Resources Research

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For thousands of years, humans have altered the movement of water through construction of earthworks. These earthworks remain in landscapes, where they continue to alter hydrology, even where structures have long since been abandoned. Management of lands containing earthworks requires an understanding of how the earthworks impact hydrology and knowledge of where the structures are located in the landscape. Various methods for detecting topographic features exist in the literature, including a set of rule and threshold‐based techniques and machine learning methods. These tools are either labor‐intensive or require special pre‐processing or a priori assumptions about structures that limit generalizability. Here, we test a topological analysis tool called “persistence” to determine if it is useful for earthwork detection in rangelands. We found that persistence can be used to detect earthworks with 83% precision and 64% accuracy. Breached berms and berms with significant upslope sedimentation are most likely not to be detected using persistence. These results indicate that persistence can be useful for terrain analysis, and it has the potential to substantially reduce manual effort in feature detection by identifying regions where berms may be found.

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“…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%

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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Multi-resolution soil-landscape characterisation in KwaZulu Natal: Using geomorphons to classify local soilscapes for improved digital geomorphological modelling

Cited by 22 publications

References 65 publications

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

Automated Earthwork Detection Using Topological Persistence

A Scoping Review of Landform Classification Using Geospatial Methods

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