Sentinel-1 and Sentinel-2 Data Fusion for Mapping and Monitoring Wetlands

Abstract

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

Reviewing the Potential of Sentinel-2 in Assessing the Drought

“…The most common methods for mapping wetlands and surface waters that use Sentinel-2 mission data, or use Sentinel-1 and Sentinel-2 mission data fusion, are unsupervised classification [47,[208][209][210], supervised classification [47,209,211], change detection using vegetation indices [212,213], object-based classification [47,209,214], index-based classification [209], tile-based image thresholding [215], OTSU algorithm [200,203,216], and the rule-based super pixel (RBSP) approach [217]. In addition to these approaches, machine learning algorithms are being used more and more often.…”

Técnicas De Processamento De Imagens Para Treinamento De Fotointerpretes Na Avaliação Visual De Áreas Úmidas E De Manchas E Cicatrizes De Queimadas Ou Incêndios

“…Embora diferentes métodos tenham sido utilizados para o mapeamento de áreas úmidas, o uso de dados de sensoriamento remoto através de imagens de satélite, devido à resolução espacial média do legado Landsat, ASTER ou outros satélites, ainda é difícil, para análise em escala refinada, separar áreas úmidas de outros tipos de cobertura do solo, sem o uso de dados adicionais, provenientes de medidas de campo, modelos digitais de elevação, LIDAR, etc. (Tiner et al, 2015;Kaplan & Avdan, 2018). A técnica de reamostragem por pixels no realce de imagens orbitais, para identificação de alvos e suporte ao monitoramento de áreas úmidas a partir de dados do Sentinel-2 com resolução espacial de 10 m tem sido utilizada em vários estudos (Wang et al, 2016;Kaplan, 2018;Wald, 2000).…”

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

“…Data fusion techniques, including many possible combinations of data integration as illustrated in Unlike single source data, multi-source, multi-sensor data integration offers advanced and better potential for interpretation and discrimination between different features of land cover types easily and effectively (Chatziantoniou et al 2017;Chen B et al 2017). There are several studies based on data integration and its potential to discriminate features with good results as compared to individual data (Pohl and Van Genderen 1998;Amarsaikhan et al 2007;Kaplan and Avdan 2018). Data integration generates new composite image which delivers better-enhanced spatial and spectral information (Shen 1990;Pohl and Van Genderen 1998;Karathanassi et al 2007;Dong et al 2009), hence provide more information and achieves improved results for decision making (Hall and McMullen 2004).…”

“…RADAR + Multispectral/RADAR/digital data (SRTM) (Evans et al 2010;Balzter et al 2015;Chatziantoniou et al 2017;Clerici et al 2017;Gibril et al 2017;Brown et al 2018;Colson et al 2018;Kaplan and Avdan 2018) 7 Different data borne fusion Aerial photographs + RS data like aerial colour photographs (Park et al 2001)…”