STUDENT CONTEST 2016 COASTAL AREA MAPPING USING SENTINEL-2 IMAGERY CLGE Students’ contest 2015-2016 Galileo, EGNOS, Copernicus Linda Toča Riga Technical University, Faculty of Civil Engineering, Department of Geomatics, Linda.Toca@edu.rtu.lv
Abstract This paper describes the elaboration of data collected from Sentinel-2, a land monitoring Copernicus programme satellite that came online on June 23, 2015, and presents an algorithm for coastal area mapping using remotely sensed data and geographic information systems (GIS). In particular the author analyses how Sentinel-2 imagery can be accessed, pre-processed, classified and post-processed. The study area of this research is the Baltic Sea region, in particular, the coastal area from cape Akmeņrags to Ovišrags in the territory of Latvia. Data collected by Sentinel-2 outstands with respect to other satellites due to its image resolution, regularity of data collection, and free accessibility. Although Sentinel-2 images have a high potential for monitoring the coastal area of Latvia, its data has not yet been used extensively as they were released recently and there is still a lack of research, knowledge and qualified specialists. Additionally, this paper compares Sentinel-2 imagery with a high definition orthophoto images within the same coastal area and a comparison between usage of open source and commercial GIS program for this type of data. Key words: Sentinel-2, remote sensing, coastal area mapping, Latvia
Introduction Coastal areas are ones of the most dynamic natures, changing rapidly both by natural causes and human processes. Tides, sea level rise, erosion and sedimentation processes, these are just a few among many phenomena occurring in coastal areas that directly affect shoreline and change the coastal landscape over time. As a matter of fact, due to predicted sea level rise in future, the monitoring of coastal areas is becoming more and more important. Over time there have been many methods of monitoring and mapping coastal areas, and one of the most cost and time effective method is the use of optical satellite imagery and GIS tools. 1 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016 Since 1972, The United States Geological Survey (USGS) together with The National Aeronautics and Space Administration (NASA) have been running the LANDSAT program, which has provided a continuous record of earth observation and has become the longest-running program for acquisition of satellite imagery of Earth [7][1]. For many years Landsat program has provided images that were the most popular and widely used for remote sensing of Earth resources. However since the launch of the first mission of Sentiel-2 on June 23, 2015, the European Space Agency (ESA) is providing a systematic coverage of all lands. Specifically, they provide images with a regularity of 10 days with one satellite, which will later on be increased to 5 days with two satellites, with image swath of 290 km, and a resolution of 10m, 20m or 60m depending on the spectral bands and images in 13 spectral bands including 3 in the Short Wave InfraRed (SWIR) [4][3]. In general, Sentinel-2 satellite system is planned to work better than Landsat under every aspect (resolution, revisit frequency, coverage, etc.), except the presence of thermal infra-red band.
1. Materials and methods 1.1.Study area Latvia is one out of nine countries having shoreline along the Baltic Sea. The coastal line represents a significant portion of for the country as it is almost 500 km long. The selected area for the research is the coastline section from cape Akmeņrags to cape Ovišrags, on western coastline of Latvia (shown in Fig.1). This coastal region has been chosen due to the high erosion processes that have occurred in the past recent years. Additionally, the forecasts predict increased coastal erosion in the next 10 years, reaching as far as 100m in specific places. For a better coast line monitoring and to find a solution to the lack of regular data in this area and others, a fast data acquisition and processing method is required and this paper describes such a method.
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FIG.1 THE LOCATION OF THE STUDY AREA 1.2.Data The territory of Latvia is covered by at least a little amount of cloud formation by approximately 80% of the time during one year. In the past this meant very few cloud-free satellite images available per year, but now, with the commissioning of Sentinel-2 images, the situation is at a turnaround point. Sentinel-2 satellite will have 5-day geometric revisit time (for the moment - 10 days), in comparison, the previously most commonly used Landsat-7 satellite provided a 16-day geometric revisit time, while the SPOT satellite provided a 26-day revisit, and additionally neither of them provided a systematic coverage of the overall land surface. In this research two Sentinel-2 images were obtained and processed – image from August 20th, 2015 and December 25th, 2015 (Fig. 2).
FIG. 2 SENTINEL-2 SATELLITE IMAGES, WESTERN COASTLINE OF LATVIA 3 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016 Additionally, Sentinel-2 images were also compared with two high resolution orthophoto images (Fig. 3). These ortophoto images are the latest available data provided by Latvian Geospatial Information Agency (LGIA), obtained in the period 2013-2015 with an accuracy of 0.4 m. Both RBG and CIR images were processed.
FIG. 3 LGIA ORTHOPHOTO IMAGE 4112-54-1
For data processing two types of software were used: an open source, free program, QGIS 2.12.3 and a commercial one, ERDAS Imagine 2015, both being amongst the leading software for geospatial applications.
2. Methodology and findings First of all, the author looked into 3 possible ways of Sentinel-2 data acquisition: 1. Data acquisition using Copernicus “Services Data Hub” 2. Data acquisition using USGS “Earth Explorer” 3. Dara acquisition using QGIS and SCP plug-in All methods have their advantages and drawbacks and, in the end, “Earth Explorer” was chosen, due to its user-friendly interface and many options for remotely sensed data search and download. Sentinel-2 data became available on Earth Explorer only starting from March 15, 2016, when ESA came to an agreement with USGS, NASA and The National Oceanic and Atmospheric Administration (NOAA) for further cooperation and commitment to the principle of full, free and open access to the Earth observation satellite data and information [3].
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STUDENT CONTEST 2016 Data acquisition consists in indicating the area of research, specifying different criteria like date, place and type of data and checking image overlay after which, data can be downloaded in two format types: L1C Tile JPEG2000 or full Resolution Browse GeoTIFF. After data acquisition, Sentinel-2 images were processed with QGIS, thus presenting a coastal area mapping solution using both freely accessible data and software. For better image procession within QGIS a special “Semi-Automatic Classification Plugin” (SCP) is used. As shown in Fig. 4, before image classification it is necessary to implement some pre-processing procedures [2][7], like the creation of a band set, the local cumulative cut stretch of a band set and the specific area clipping.
FIG. 4 IMAGE PRE-PROCESSING PROCEDURES An additional step, prior image classification process, is creation of Band set definition using Sentinel-2 bands. As a matter of fact, different band combinations help highlighting different features like water bodies, different vegetation species or land use classes [8][5]. For this purpose four band combinations are created (shown in Fig. 5): 1. True colour (4-3-2) 2. Colour Infrared (8-4-3) 3. Land/Water (8a-11-4) 4. Vegetation analysis (11-8a-4)
FIG. 5 AREA OF VENTSPILS IN FOUR DIFFERENT BAND COMBINATIONS 5 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016 After the creation of band combinations, different sets of training shapefiles, with regions of interest (ROIs) and a signature list, are created. Then the classification process can be performed. In this research 6 most important master classes were defined: water, bare soil, forest, pasture, built-up, cropland. In this case, Spectral Angle mapping algorithm is chosen as the most suitable for classification process. This algorithm is largely used working with remotely sensed data, especially with hyperspectral data and has proven to be the most suited also for coastal area mapping within the area of research. Spectral Angle mapping algorithm is pixel-based classiďŹ cation technique that calculates the spectral angle between spectral signatures of image pixels and training spectral signatures. Using this algorithm, each spectrum is treated as a vector in n-dimensional space, where n is equal to the number of bands in the image. The calculation of spectral angle θ is defined as [6]:
∑đ?‘›đ?‘–=1 đ?‘Ľđ?‘– đ?‘Śđ?‘– đ?œƒ(đ?‘Ľ, đ?‘Ś) = đ?‘?đ?‘œđ?‘ −1 ( 1) 1 đ?‘› 2 đ?‘› 2 (∑đ?‘–=1 đ?‘Ľđ?‘– )2 ∗ (∑đ?‘–=1 đ?‘Śđ?‘– )2 Where: x = spectral signature vector of an image pixel; y = spectral signature vector of a training area; n = number of image bands
As seen in the Fig. 6, the result of the classification process is a raster image with categorized pixels into one of several land cover classes, which then can be used for thematic map production, data analysis, statistical analysis and comparison.
FIG. 6 THE RESULTING IMAGE OF CLASSIFICATION 6 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016 After image classification, several post-processing procedures are carried out in order to improve results of classification. Among them, the most important for coastal areas is the Normalized Difference Water Index (NDWI) [9]: (đ?‘‹
−đ?‘‹
)
đ?‘ đ??ˇđ?‘Šđ??ź = (đ?‘‹đ??şđ?‘&#x;đ?‘’đ?‘’đ?‘› +đ?‘‹đ?‘ đ??źđ?‘…) đ??şđ?‘&#x;đ?‘’đ?‘’đ?‘›
đ?‘ đ??źđ?‘…
(đ??ľđ?‘Žđ?‘›đ?‘‘3−đ??ľđ?‘Žđ?‘›đ?‘‘8)
→ đ?‘ đ??ˇđ?‘Šđ??źđ?‘†đ?‘’đ?‘›đ?‘Ąđ?‘–đ?‘›đ?‘’đ?‘™âˆ’2 = (đ??ľđ?‘Žđ?‘›đ?‘‘3+đ??ľđ?‘Žđ?‘›đ?‘‘8)
This remote sensing-derived index uses green and NIR wavelengths, and can be especially useful, for example, for removing of built-up land noise close to water body. Fig. 7 demonstrates an area before and after NDWI implementation and clearly shows how output appears to be cleaner with less apparent noise.
FIG. 7 NDWI IMPLEMENTATION
Finally, a classification report and an accuracy assessment are created. The report presents an error matrix with the overall accuracy, the user and the producer accuracy of each class, and the Kappa coefficient. Although the overall classification was derived as very high (98.34%) and also the Kappa coefficient (Khat=0.976), the reliability of the classification report should be better evaluated. This is due to the fact, that using SCP plug-in accuracy results depend directly on ROIs created by the user, but there are no other reference data for the comparison of results. The last part of the research compares classification results from Sentinel-2 images with the high resolution orthophoto images obtained by LGIA. Sentinel-2 images were processed with QGIS, but ortophoto images were processed with ERDAS Imagine, thus giving also a comparison between open source and commercial GIS software. As shown in Fig. 8, for the comparison mentioned above, the same area was represented with both data sources before and after image classification. By far, the biggest advantage of orthophoto is the spatial resolution (0.4m against 10m/20m/60m), which makes a substantial difference in the process of classification, especially if the area of research is small and the scale is large (as seen in the classified image close-up). On the other hand Sentinel-2 data outperform orthophoto both in spectral 7 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016
FIG. 8 COMPARISON BETWEET SENTINEL-2 AND ORTHOPHOTO IMAGERY
and temporal resolution, and the swath wide. Sentinel-2 images have 13 spectral bands (orthopohoto only 4: RGB + Near IR). In the near future Sentinel-2 constellation will provide a revisit time of 5 days at the equator in cloud-free conditions which results in 2-3 days at mid-latitudes (like the research area) whereas sets of orthophotos by LGIA are released less than once every two years (5 sets in last 12 years) with no clear timeframe about when the next set of images could be available. Therefore the temporal resolution of this data is very low and the usage of these images are only suited for long-term research. Another important advantage of Sentinel-2 data is the swath wide. The swath width of Sentinel-2 is 290 km which makes it possible to work with very large areas of multispectral imagery in a single pass. LGIA on the other hand provides orthophoto images that come in mapsheets with a scale 1:10 000, which corresponds to 5 x 5 kilometres in reality, making it much harder to work with large areas. The last aspect author looked at was not connected with scientific aspects, but rather to the economic one. Sentinel-2 data is easily and freely accessible whereas LGIA orthophotos are commercial and each mapsheet has to be purchased. Comparing two GIS software: QGIS and Erdas Imgaine, it has been proven that both programs provide the required tools for satellite image processing and image classification. In particular, Erdas 8 Address Offices in Brussels : Rue du Nord 76, BE – 1000 Bruxelles. Tel +32/2/217.39.72 Fax +32/2/219.31.47 E-mail: maurice.barbieri@clge.eu - www.clge.eu EU-Transparency Register of interest representatives - 510083513941-24
STUDENT CONTEST 2016 Imagine provides a more trustworthy classification report, accuracy assessment and friendlier interface, but QGIS, on the other hand, provides free open source program with numerous plug-ins and options for the user to freely manipulate and work with the data. It is also verified that both software are comparable and can be used equally good in terms of image processing and coastal area monitoring in the research area.
Conclusion During the last decades, western coast of Latvia has changed quite rapidly due to erosion and sedimentation processes, and most predictions show that these processes are only about to get stronger. In this study, a new approach of coastal area mapping in the territory of Latvia, leveraging Sentinel-2 satellite images and GIS tools,
has been developed and suggested, with the aim of
monitoring the land use and the shoreline changes. As a result, a series of steps, i.e., an algorithm, was developed for coastal area mapping, especially suitable for the coast of Baltic Sea, within territory of Latvia. Additionally, a comparison between high resolution data available for the research area, orhophotos by LGIA, and Senitnel-2 images was carried out. This comparison proved that although data provided by agency are with much higher spatial resolution, Sentinel-2 imagery has a higher potential for coastline monitoring due to aspects like temporal and spectral resolution, as well as data availability, and low cost of access. This paper is only a brief introduction to a wide range of applications that Sentinel-2 satellite imagery of Copernicus program can provide for monitoring of coastal areas. More research, new implementations and image processing techniques are suggested for using full potential of Sentinel-2 imagery.
References
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STUDENT CONTEST 2016
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