Machine learning digital soil mapping to inform gully erosion mitigation measures in the Eastern Cape, South Africa

Plessis, Casper du; Zijl, George van; Tol, Johan van; Manyevere, Alen

doi:10.1016/j.geoderma.2020.114287

Cited by 31 publications

(17 citation statements)

References 18 publications

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“…These soils limit intrinsic permeability since water does not move readily into the subsurface matrix, which leads to increased subsurface flow causing soil pipes (Beckedahl and De Villiers, 2000). In addition, the subsurface matrix of duplex soils is dispersive as a result of high sodium absorption (Du Plessis et al, 2020). Due to the dispersive nature of these soils, they easily lose aggregation and are transported mainly as fine suspended load that is generally not deposited along the channel (Le Roux, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Formation of such a large gully in relation to the relatively small drainage area and gentle slope is very unusual. Although, soils derived from mudstones in the catchment are associated with duplex and dispersive soils that are highly susceptible to gully development (Du Plessis et al, 2020; Le Roux and Van der Waal, 2020).…”

Section: Conclusion and Recommendationsmentioning

confidence: 99%

“…Karoo dolerite dykes are also present (where barriers or walls of resistant dolerite intrude the soft mudstones). Soils derived from mudstone formations are typically duplex soils that are highly erodible with widespread gully erosion (Du Plessis et al, 2020; Parwada and Van Tol, 2017). Duplex soils are classified as Planosols by the FAO International Soil Classification System.…”

Section: Introductionmentioning

confidence: 99%

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Intra-gully mapping of the largest documented gully network in South Africa using UAV photogrammetry: Implications for restoration strategies

Roux

Morake

Waal

et al. 2022

Progress in Physical Geography: Earth and Environment

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Gully erosion can reach alarming dimensions, and in several cases contributes significantly to soil loss and sediment yield in catchments. The studied example is one of the largest known gully networks in the world (its surface area is approximately 0.5 million m2 and volume 5 million m3), more than twice as large compared to the largest gullies described in peer-reviewed literature. To improve gully management strategies, it is not only critical to understand the factors that lead to erosion, but also to identify the extent of in-channel erosional and depositional features. Few studies consider in-channel patterns of erosion and deposition within gully systems. This study utilises a fixed-wing Unmanned Aerial Vehicle (UAV) and Structure-from-Motion (SfM) photogrammetry to create a high resolution orthomosaic and Digital Surface Model (DSM) to determine the position and spatial extent of erosional and depositional features within a large gully network in South Africa. Formation of such a large gully in relation to a relatively small drainage area is unusual but can be explained by the presence of dispersive soils and regional landscape incision. Longitudinal and cross-sectional profiles indicate that the gully network consists of three channel types (i.e. V-shaped, U-shaped and trapezoidal), each with a unique combination of erosional and depositional features. This study demonstrates the value of identifying and mapping of intra-gully features to better understand sediment transfer within the gully network to implement appropriate restoration strategies, which in this instance should focus on depositional zones to enhance channel decoupling.

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Section: Discussionmentioning

confidence: 99%

Section: Conclusion and Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Intra-gully mapping of the largest documented gully network in South Africa using UAV photogrammetry: Implications for restoration strategies

Roux

Morake

Waal

et al. 2022

Progress in Physical Geography: Earth and Environment

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“…Especially in South Africa, several elements contribute to the development of gully erosion, including soil type, bedrock lithology and structure, precipitation, slope angle, vegetation and land use [1,23,[35][36][37][38][39]. Moreover, human activity and climate change can increase this phenomenon in many regions [16,40,41].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Bernini

Bosino

Botha

et al. 2021

IJGI

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Soil erosion is one of the most challenging environmental issues in the world, causing unsustainable soil loss every year. In South Africa, several episodes of gully erosion have been documented and clearly linked to the presence of Quaternary colluvial deposits on the Drakensberg Mountain footslopes. The aim of this study was to identify and assess the triggering factors of gully erosion in the upper Mkhomazi River basin in KwaZulu-Natal, South Africa. We compiled a gully inventory map and applied remote sensing techniques as well as field surveys to validate the gully inventory. The gullies were subdivided into slope gullies and fluvial gullies. We derived susceptibility maps based on the spatial distribution of gully types to assess the most important driving factors. A stochastic modeling approach (MaxEnt) was applied, and the results showed two susceptibility maps within the spatial distribution of the gully erosion probability. To validate the MaxEnt model results, a subset of the existing inventory map was used. Additionally, by using areas with high susceptibilities, we were able to delineate previously unmapped colluvial deposits in the region. This predictive mapping tool can be applied to provide a theoretical basis for highlighting erosion-sensitive substrates to reduce the risk of expanding gully erosion.

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“…In recent years, remote sensing technology and processing of satellite images have been increasingly applied to tackle various challenging problems in a wide span of domains including agriculture [11][12][13], natural hazard prevention [14][15][16][17], civil engineering [18][19][20], and environmental engineering [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

Hoang

2021

Computational Intelligence and Neuroscience

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Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.

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Machine learning digital soil mapping to inform gully erosion mitigation measures in the Eastern Cape, South Africa

Cited by 31 publications

References 18 publications

Intra-gully mapping of the largest documented gully network in South Africa using UAV photogrammetry: Implications for restoration strategies

Intra-gully mapping of the largest documented gully network in South Africa using UAV photogrammetry: Implications for restoration strategies

Evaluation of Gully Erosion Susceptibility Using a Maximum Entropy Model in the Upper Mkhomazi River Basin in South Africa

Automatic Impervious Surface Area Detection Using Image Texture Analysis and Neural Computing Models with Advanced Optimizers

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