Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Tsyganskaya, Viktoriya; Martinis, Sandro; Marzahn, Philip; Ludwig, Ralf

doi:10.3390/rs10081286

Cited by 85 publications

(83 citation statements)

References 78 publications

(128 reference statements)

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“…Until such time as widely available L-band imagery is available, it would be beneficial to explore the use of Sentinel-1 imagery to map both open and vegetated water bodies, despite its deficiencies in terms of canopy penetration. Tsyganskaya et al [45] demonstrated a successful approach for detecting both open and vegetated water bodies in the Caprivi Strip, Namibia using Sentinel-1 imagery. This was achieved by generating metrics characterising temporal change in backscatter, enabling the delineation of temporary vegetated water bodies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

et al. 2019

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Providing timely and accurate maps of surface water is valuable for mapping malaria risk and targeting disease control interventions. Radar satellite remote sensing has the potential to provide this information but current approaches are not suitable for mapping African malarial mosquito aquatic habitats that tend to be highly dynamic, often with emergent vegetation. We present a novel approach for mapping both open and vegetated water bodies using serial Sentinel-1 imagery for Western Zambia. This region is dominated by the seasonally inundated Upper Zambezi floodplain that suffers from a number of public health challenges. The approach uses open source segmentation and machine learning (extra trees classifier), applied to training data that are automatically derived using freely available ancillary data. Refinement is implemented through a consensus approach and Otsu thresholding to eliminate false positives due to dry flat sandy areas. The results indicate a high degree of accuracy (mean overall accuracy 92% st dev 3.6) providing a tractable solution for operationally mapping water bodies in similar large river floodplain unforested environments. For the period studied, 70% of the total water extent mapped was attributed to vegetated water, highlighting the importance of mapping both open and vegetated water bodies for surface water mapping.

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Section: Introductionmentioning

confidence: 99%

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

et al. 2019

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“…Smooth open water is characterized by low SAR backscatter values due to its specular surface. In comparison, TFV shows a significant increase in backscatter, especially in the VV (vertical/vertical) polarization, which is caused by the complex double-or multi-bounce interaction between smooth open water surfaces and the structure of vegetation (e.g., tree trunks, stems) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The decisive advantage, however, is that in addition to open water surfaces, temporary flooded vegetation (TFV) can also be detected in dependency of system and environmental parameters [7]. TFV are areas where water bodies temporarily occur underneath the vegetation [8]. To avoid underestimations of the flooding, the derivation of both classes is essential to cover the entire flood extent.…”

Section: Introductionmentioning

confidence: 99%

“…However, since October 2014, the Sentinel-1 (S-1) satellite constellation has continuously and systematically captured the earths surface with C-band SAR data at short repetition time, enabling the use of SAR multi-temporal data for systematic and operational flood monitoring. Using this data source, Tsyganskaya et al, [8] recently showed a time series approach for the detection of TFV.…”

Section: Introductionmentioning

confidence: 99%

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Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features

Tsyganskaya

Martinis

Marzahn

2019

Water

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Synthetic Aperture Radar (SAR) is particularly suitable for large-scale mapping of inundations, as this tool allows data acquisition regardless of illumination and weather conditions. Precise information about the flood extent is an essential foundation for local relief workers, decision-makers from crisis management authorities or insurance companies. In order to capture the full extent of the flood, open water and especially temporary flooded vegetation (TFV) areas have to be considered. The Sentinel-1 (S-1) satellite constellation enables the continuous monitoring of the earths surface with a short revisit time. In particular, the ability of S-1 data to penetrate the vegetation provides information about water areas underneath the vegetation. Different TFV types, such as high grassland/reed and forested areas, from independent study areas were analyzed to show both the potential and limitations of a developed SAR time series classification approach using S-1 data. In particular, the time series feature that would be most suitable for the extraction of the TFV for all study areas was investigated in order to demonstrate the potential of the time series approaches for transferability and thus for operational use. It is shown that the result is strongly influenced by the TFV type and by other environmental conditions. A quantitative evaluation of the generated inundation maps for the individual study areas is carried out by optical imagery. It shows that analyzed study areas have obtained Producer’s/User’s accuracy values for TFV between 28% and 90%/77% and 97% for pixel-based classification and between 6% and 91%/74% and 92% for object-based classification depending on the time series feature used. The analysis of the transferability for the time series approach showed that the time series feature based on VV (vertical/vertical) polarization is particularly suitable for deriving TFV types for different study areas and based on pixel elements is recommended for operational use.

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“…Examples in the literature include the use of satellite data for mapping the extent of a burned area using optical and thermal sensors of different spatial resolution [21][22][23][24] and many methods are particularly developed for Sentinel data (e.g., [25,26]). Various approaches were developed for example for the automatic flood detection [27,28], and the free and open access policy of Copernicus Sentinel-1 triggered the development of more [29][30][31]. An important asset for monitoring disasters of the Sentinel-1 constellation is the frequency of data acquisition (six days) and the fact that they can be downloaded within an hour and 24 hours of reception depending on the severity of the event.…”

Section: Introductionmentioning

confidence: 99%

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

et al. 2020

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The successful implementation of the European Commission's Common Agricultural Policy (CAP) and the insurance coverage in case of a natural disaster requires precise and regular mapping of crop types and detailed delineation of the disasters' effects by frequent and accurate controls. Free and open access policy to Copernicus Sentinel data offers a big volume of data to the users on a consistent and complete basis. Today, the Sentinels are involved in an increasing number of agriculture applications, but their effective exploitation is still being investigated and the development of efficient tools, aligned to the user's needs, is yet to be realised. To this end, the DiAS (Disaster and Agriculture Sentinel Applications) project proposes methods for decision support in agriculture using Sentinel data for crop type mapping, as well as mapping of the extend of fire and flood effects in agricultural areas. The DiAS Decision Support System (DSS) is designed in consultation with potential users in participatory approach and aims to provide a prototype tool, which provides assistance to the responsible paying agencies and insurance organizations to make decisions on farmers' subsidies and compensations. The DiAS DSS prototype and its functionalities are presented in this paper and its use is demonstrated through example applications for two test sites in Greece. The DiAS DSS demonstrates the necessity for the development of similar tools, as this emerges from the user's requirements, and wishes to stimulate and inspire further research and development.Sustainability 2020, 12, 1233 2 of 20 filed inspections may determine the eligible areas for subsidy or compensation precisely, however they require a lot of effort and time and some factors like the adverse weather conditions, or remote areas, may cause large delays in the payments, which has an impact on the agricultural processes and development of the country. [1,2]. Lately, the control efforts involve the examination of satellite imagery, but this refers to very high-resolution commercial imagery and visual image interpretation or rare case image classification of single-date images [3]. These procedures improve the control efforts in some cases, but they have a high cost and their effectiveness relies mostly on the skills of the photo-interpreter and therefore cannot always offer timely, reliable, and robust results [4].There are several attempts for automating the mapping procedures in agriculture-related applications. For example, in terms of crop types mapping, the EU is coordinating the Monitoring Agricultural Resources (MARS) initiative [5] with the help of the Joint Research Center (JRC) in order to facilitate the coordination of the CAP. In the framework of MARS and following guidelines from the JRC, each EU member state is building a geographic information Land Parcel Identification System (LPIS) by interpreting very-high-resolution satellite images. Along with LPIS, the JRC is in charge of the image acquisition and it provides scientific and technical suppor...

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Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data

Cited by 85 publications

References 78 publications

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

Automatic Detection of Open and Vegetated Water Bodies Using Sentinel 1 to Map African Malaria Vector Mosquito Breeding Habitats

Flood Monitoring in Vegetated Areas Using Multitemporal Sentinel-1 Data: Impact of Time Series Features

Decision Support on Monitoring and Disaster Management in Agriculture with Copernicus Sentinel Applications

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