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 17 publications

(4 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to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy.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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“…In addition, established soilgeomorphic associations such as simple and complex catenas (169), soil associations, and other aggregations of landscape scale soil patterns, can be configured into elements with embedded soil covariate properties. An example of this approach is illustrated by Atkinson et al (170) where geomorphon (a geomorphological phonotype) is used for digital geomorphological mapping. They point out that geomorphon feature relevance for defining landscape structure and terrain spatial heterogeneity must be framed in the context of landscape or terrain detail, soil covariate membership, DEM pixel resolution, and user preference.…”

Section: Predictors Adopted By Large Spatial Extent Modeling Effortsmentioning

confidence: 99%

Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales

Xia

McSweeney²,

Wander³

2022

Front. Soil Sci.

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To explore how well large spatial scale digital soil mapping can contribute to efforts to monitor soil organic carbon (SOC) stocks and changes, we reviewed regional and national studies quantifying SOC within lands dominated by agriculture using SCORPAN approaches that rely on soil (S), climate (C), organisms (O), relief (R), parent material (P), age (A), and space (N) covariates representing soil forming factors. After identifying 79 regional (> 10,000 km2) and national studies that attempted to estimate SOC, we evaluated model performances with reference to soil sampling depth, number of predictors, grid-distance, and spatial extent. SCORPAN covariates were then investigated in terms of their frequency of use and data sources. Lastly, we used 67 studies encompassing a variety of spatial scales to determine which covariates most influenced SOC in agricultural lands using a subjective ranking system. Topography (used in 94% of the cases), climate (87%), and organisms (86%) covariates that were the most frequently used SCORPAN predictors, aligned with the factors (precipitation, temperature, elevation, slope, vegetation indices, and land use) currently identified to be most influential for model estimate at the large spatial extent. Models generally succeeded in estimating SOC with fits represented by R2 with a median value of 0.47 but, performance varied widely (R2 between 0.02 and 0.86) among studies. Predictive success declined significantly with increased soil sampling depth (p < 0.001) and spatial extent (p < 0.001) due to increased variability. While studies have extensively drawn on large-scale surveys and remote sensing databases to estimate environmental covariates, the absence of soils data needed to understand the influence of management or temporal change limits our ability to make useful inferences about changes in SOC stocks at this scale. This review suggests digital soil mapping efforts can be improved through greater use of data representing soil type and parent material and consideration of spatio-temporal dynamics of SOC occurring within different depths and land use or management systems.

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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 17 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

Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales

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