Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa

Gxokwe, Siyamthanda; Dube, Timothy; Mazvimavi, Dominic; Grenfell, Michael

doi:10.1016/j.jhydrol.2022.128080

Cited by 15 publications

(7 citation statements)

References 35 publications

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“…The present framework can be of special interest for a range of research and application fields in semiarid environments. It can contribute to the restoration/conservation of small wetland areas, increasingly encouraged by initiatives such as the Ramsar Convention (De Meester et al, 2005; Gxokwe et al, 2022; Slagter et al, 2020). The predominance of Sentinel-2 data in explaining water surface occurrence revealed that the inclusion of Sentinel-1 data may provide some gains in open water scenarios, for instance when targeting specific ponds (i.e., temporary ponds).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the high intra-and inter-annual variability of these ecosystems, resource management programs require estimates of surface water and systematic site monitoring at an appreciable level of detail (Scholes 2020;Wickens 1998). Because water-related catalogues are often insufficient, understanding ecohydrological processes is traditionally achieved via remote sensing information, even though the selection of a sensor and type of application are typically context-specific (Bijeesh and Narasimhamurthy, 2020;Gxokwe et al, 2022). Most research has been devoted to inferring water characteristics and dynamics over the long term through quantitative remote sensing methods.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1&2 Data Applied to Small Seasonal Ponds in Semiarid Environments

Valerio,

Godinho,

Ferraz

et al. 2024

Preprint

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Inland freshwater resources in semiarid environments play a key role in maintaining ecological systems and supporting human development. Space-based remote sensing spatiotemporal data have emerged as a new paradigm for understanding ecohydrological processes and trends, particularly in water-stressed areas. However, comprehensive cataloging is still lacking, especially in semi-arid regions and for small-sized water bodies (i.e., ponds), which are often overlooked despite their ecological relevance. In this study, high-resolution optical and radar Sentinel data (Sentinel-1 and Sentinel-2) were used to construct Sentinel-1&2-based local surface water (SLSW) models, to infer surface water occurrence and extent. To assess the reliability of this model, the results were compared with verification data, and separately with Landsat-based global water (LGSW) models. Three distinct semiarid regions were selected in SW Iberia, within a Mediterranean climate, each encompassing special protection areas for conservation and subjected to marked seasonality and bioclimatic changes. Surface water attributes were modeled using Random Forests for SLSW time series forecasting, which included the period from January 1, 2020, to December 31, 2021. During this period, the completeness of the archived information was compared between SLSW and LGSW, considering both intra-annual and inter-annual variations. The predictive performance of these models was then compared for specific periods (dry and wet), and each was independently validated with verification data. The results showed that SLWM achieved satisfactory predictive performances in detecting surface water occurrence (μ≈72%), with far greater completeness and reconstructed seasonality patterns compared to LGSW. The relatedness between SLSW and LGSW was stronger during wet periods (R2=0.38) than dry periods (R2=0.05), and SLSW related much better with the verification data (R2=0.66) than when compared to LGSW (R2=0.24). The proposed SLSW approach may therefore provide advantages in the delineation of dynamic surface water characteristics (occurrence and extent) in very small-sized water bodies (i.e., <0.5 ha), allowing for uninterrupted surface water time series forecasting at high spatiotemporal detail, and over extensive areas. Given the water constraints in semiarid regions and water resources vulnerability to climate change, our results show high potential for supporting a variety of activities underlying rural development and biodiversity conservation. Additionally, the socio-ecological applications of this research may help identify surface water anomalies (e.g., drought events) and enhance sustainable water supply governance, a particular priority in climate change hotspots.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1&2 Data Applied to Small Seasonal Ponds in Semiarid Environments

Valerio,

Godinho,

Ferraz

et al. 2024

Preprint

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“…In recent years, research on SMWs has gradually attracted global attention [2,[11][12][13][14][15][16][17][18]. For instance, Cheng et al [19] discovered the significant role of independent small wetlands in nutrient retention in the USA.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020

Zhai,

Du,

et al. 2024

Remote Sensing

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Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors.

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“…Multi-temporal remote sensing images are utilized for identification of alterations in urban areas, forestation, and flooding [2][3][4] . Pre and post Landsat 7 ETM satellite images of flood affected area were used to classify these bi-temporal images to identify flooded area [5] . The difference of backscattering coefficients from the bi-temporal TerraSAR-X intensity images of the 2011 Japan tsunami were used for identifying affected flooded areas and collapsed and damaged buildings [6] .…”

Section: Introductionmentioning

confidence: 99%

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

Kumar

Mandal

Singh

et al. 2023

Big Data Min. Anal.

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A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods.

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Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa

Cited by 15 publications

References 35 publications

Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1&2 Data Applied to Small Seasonal Ponds in Semiarid Environments

Multi-Temporal Remote Sensing of Inland Surface Waters: A Fusion of Sentinel-1&2 Data Applied to Small Seasonal Ponds in Semiarid Environments

Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

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