Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Fichtner, Florian; Mandery, Nico; Wieland, Marc; Groth, Sandro; Martinis, Sandro; Riedlinger, Torsten

doi:10.1016/j.jag.2023.103329

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Over recent decades, the consequences of floods in Mozambique include the destruction of landscape structures [82] and crops during the flood period [83]. Moreover, this increased landscape fragmentation resulted in the continuous physical disintegration of habitats into smaller patches with decreased size and isolation between habitat clusters.…”

Section: Study Areamentioning

confidence: 99%

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Lemenkova

2024

Coasts

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Mapping coastal regions is important for environmental assessment and for monitoring spatio-temporal changes. Although traditional cartographic methods using a geographic information system (GIS) are applicable in image classification, machine learning (ML) methods present more advantageous solutions for pattern-finding tasks such as the automated detection of landscape patches in heterogeneous landscapes. This study aimed to discriminate landscape patterns along the eastern coasts of Mozambique using the ML modules of a Geographic Resources Analysis Support System (GRASS) GIS. The random forest (RF) algorithm of the module ‘r.learn.train’ was used to map the coastal landscapes of the eastern shoreline of the Bight of Sofala, using remote sensing (RS) data at multiple temporal scales. The dataset included Landsat 8-9 OLI/TIRS imagery collected in the dry period during 2015, 2018, and 2023, which enabled the evaluation of temporal dynamics. The supervised classification of RS rasters was supported by the Scikit-Learn ML package of Python embedded in the GRASS GIS. The Bight of Sofala is characterized by diverse marine ecosystems dominated by swamp wetlands and mangrove forests located in the mixed saline–fresh waters along the eastern coast of Mozambique. This paper demonstrates the advantages of using ML for RS data classification in the environmental monitoring of coastal areas. The integration of Earth Observation data, processed using a decision tree classifier by ML methods and land cover characteristics enabled the detection of recent changes in the coastal ecosystem of Mozambique, East Africa.

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Section: Study Areamentioning

confidence: 99%

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Lemenkova

2024

Coasts

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“…In order to analyze changes in ξ, we introduce a method that decomposes remotely sensed ξ time series into trend, seasonal and residual components [52]. The seasonaltrend decomposition uses LOESS (STL), a robust method for time-series decomposition in environmental analysis.…”

Section: Time Series Analysis Of the Psd Slope ξmentioning

confidence: 99%

Particle Size Distribution Slope Changes along the Yellow River Delta Observed from Sentinel 3A/B OLCI Images

Jin,

Zou,

Xing

et al. 2024

Remote Sensing

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Quantitative estimates of particle size in estuaries and shelf areas are important to understand ocean ecology and biogeochemistry. Particle size can be characterized qualitatively from satellite observations of ocean color. As a typical marginal sea, the Yellow River Delta (YRD) with the Bohai Sea experiences a complex hydrodynamic environment. Here, we attempt to quantify the particle size distribution (PSD) slope (ξ) based on its relationship with the particle backscattering exponent from Sentinel-3A/B OLCI. The PSD slope, ξ displays temporal and spatial variability in the YRD with the Bohai Sea. Its value varies between 3 and 4, and typically exceeds 5 in offshore areas. The lowest value of ξ occurs in the winter, indicating the presence of fine inorganic particles in the water, while high values are attained in the spring, when phytoplankton blooms increase the particle size. ξ decreases near the river mouth because of the large sediment-laden discharge debouching into the sea. We detected a slight increase in ξ when turbid waters were present in the period 2016–2022. Environmental factors, such as sea surface temperature, sea surface wave height, and wind, may control particle size and ξ in the long term. Inorganic suspended particle matter is derived along the YRD using the magnitude of ξ. The mean inorganic suspended particle matter area in winter approaches 23,900 km2 when ξ < 4.6. This study thoroughly characterizes variations in ξ in the YRD with the Bohai Sea and clarifies the contributions of driving factors from human activities and climate change.

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“…In this study, the multispectral data of the Sentinel-2 satellite were selected as the main data. This choice was made as multispectral imagery has been successfully used to monitor water bodies and rivers, detect changes, and extract water characteristics [22,40,48,49,[66][67][68][69][70]. Furthermore, when cloud cover is not a major issue, the application of optical remote sensing to floodplain maps offers the possibility of reliably but also quickly identifying hazardous areas and supporting the implementation of response activities and flood coping strategies.…”

Section: Datasetsmentioning

confidence: 99%

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Petropoulos,

Georgiadi,

Kalogeropoulos

2024

GeoHazards

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Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

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Time-series analysis of Sentinel-1/2 data for flood detection using a discrete global grid system and seasonal decomposition

Cited by 11 publications

References 11 publications

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Random Forest Classifier Algorithm of Geographic Resources Analysis Support System Geographic Information System for Satellite Image Processing: Case Study of Bight of Sofala, Mozambique

Particle Size Distribution Slope Changes along the Yellow River Delta Observed from Sentinel 3A/B OLCI Images

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

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