Yousef Ebadi; Akram Eftekhary; Hekmatollah Mohammad Khanlu; Majid Fakhri
Abstract
Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human ...
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Introduction As an important type of precipitation, snow is especially important in the hydrological cycle. This importance can be examined and analyzed from several aspects such as water supply in other seasons. The most important aspect is the possibility of creating hazards for human beings and human infrastructure (snow avalanches, floods during seasonsof snowmelt). Therefore, it is necessary to study the snow phenomenon and its covered surfaces in winter. Monitoring the changes in this important climatic phenomenon has always been considered important by researchers and planners. Remote sensing methods have revolutionized the field of natural environment monitoring since their inception. Snow depth is an example of what can be monitored and evaluated by remotely sensed data and techniques. Materials & Methods The present study seeks to evaluate the efficiency of several important remote sensing indices in monitoring snow depth, andalso to introduce and evaluate a proposed spectral index. To reach this aim, satellite images of Landsat 8 and Sentinel 2 have been used. These images were received from the relevant portal and used to calculate snow indicesafterinitial corrections. Four spectral indices were usedto extract snow covered surfaces. These indices include: NDSI - S3 - NDSII - SWI. These indices are based on reflection from snow covered surfaces in light reflection and absorption spectra of snow covered surfaces.Light reflection from snow covered surfaces in the visible spectra and absorption in the short infrared spectrum allow automatic detection and extraction of snow covered surfacesin remote sensing multispectral images. The above mentioned indices have the ability to extract snow, but they fail to differentiatebetween snow and other related phenomena such as water (in the absorption band) and light-color salt marshes (in the reflection band) and thus, similarity of the spectra occurs. This spectral mixing which occurs due to the similarity of the reflections, cannot be eliminated even when threshold limits are defined. Thus, the extracted snow cover includes not only snow, but also other similar zones. To solve this problem and extract snow covered surfaces correctly,a new index is presented in this paper based on principal component analysis (PCA) and the first component of the set, and short wave infrared (SWIR) spectrum reflection.Using the first component of the set with the highest variance makes the difference between reflectance of snow and similar phenomena visible and thus, solves the issue of spectral mixing to a very large extent. The proposed new index called PCSWIRI is also evaluated and validated along with 4 other indices in the present paper. Results & Discussion Spectral indices introduced in the previous section were examined and evaluatedusing 7 sets of images (4 Landsat images and 3 sentinel 2images) captured in different days of winter from the main study area (Lake Urmia in the northwest) and two other study areas. The results indicate efficiency of the proposed index in the extractionof snow covered surfaces. The proposed index has improved the accuracy of snow cover extractionin the whole collection of images. This increased accuracy has been confirmed withstatistical evaluation criteria, such as kappa coefficient, overall accuracy and in the visual review of indices(comparing to the composition of the original image). The main study area includes Lake Urmia, an important geographic feature containing water and salt and a mixture of the two, which makes its spectrum similar to snow. This lake is incorrectly identified by other indices as a snow covered surface. Like the main study area, the first study and assessment area contains salt covered zones (salt lake). Despite the spectral similarity between snow and salt,the proposed index has been able to distinguish between this phenomena (in both regions) and snow and to extract only realsnow covered surfaces. In addition, visual review of existing water bodies (Dam Lake) and 5 evaluated indicesindicates higher accuracy of the proposed index. In order to automate the process of calculation in the proposed spectral indices, a software was also providedbased on MatLAB. Conclusion The findings of the present study indicates higher accuracy and efficiency of the proposed index (PCSWIRI) for snow cover extraction. Snow cover maps are very useful in various hydrological, climatic, precipitation-runoff modeling studies, and etc. Therefore, increasing the accuracy of snow cover maps is of great importance and results inimprovedaccuracy and reliability of modeling processes.
Hekmatollah Mohammad Khanlu; Mahdi Modiri; Elahe Khesali; Hamid Enayati
Abstract
Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal ...
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Introduction
Hydrography is a science used for regular measurement of parameters such as depth of water, geophysical geology, tide, water flow, waves and other physical properties of seawater. It is also used for the production of maritime maps. Hydrography contributes significantly to the internal infrastructure of coastal countries. Providing proper hydrographic services ensures safe and efficient sailing. Thus, development of hydrographic services on the national level can improve safety of mariners, and protect people’s lives and belongings on the sea, while providing some facilities for the protection of marine environment. The advancement of space technologies in recent years has increased the speed of spatial information production and facilitated sea monitoring.
Materials and Methods
Different methods are used for bathymetry. Lyzanga et al (1978) used a linear combination of the logarithm of corrected radiance ratio. This method is based on the simplification of Beer's physical model in which a linear equation of five unknowns is obtained for two bands. In 2006, Lyzanga et al. presented an improved version of their model. Using Tow-Bands Reflection Ratio, Stampf et al (2003) not only reduced the number of unknown variables in Lyzenga method, but also decreased the sensitivity of depth determination to different substrates. In this method, the difference between absorption properties of green and blue bands is used. TCarta is a global supplier of geospatial products. The company generated Satellite Derived Bathymetry (SDB) dataset by accurately extracting water depth from multispectral imageries received from the European Space Agency’s Sentinel-2 Satellite. The resulting bathymetric data had a point spacing of 10 meters, while measuring up to a depth of 15 meters. Data covered a 30-square kilometer area around Preparis Island on the Bay of Bengal.
The present article used images received from Sentinel-2 in 7 different periods for depth determination, and 1: 25,000 ADMIRALTY Nautical Charts for accuracy evaluation. Following the assessment of water transparency in received images, the 12/15/2018 image was used for depth determination. Case study area contains around 130 km along the Port of Salalah, Oman.
Results and Discussion
In order to implement the model, it is necessary to separate land from water in images using NDVI, NDWI, MNDWI and AWEI indices. The NDVI index has been used in this project. NDVI is primarily used to estimate vegetation cover, but since this index exhibits a negative value in areas covered with water, this property is used to provide a mask for separating land from water. In this step, 68 control points and 68 check points were selected from the existing ADMIRALTY map. The DN values of the corresponding pixels of the selected points were extracted from four 10-meter bands of Sentinel-2 images. The control and checkpoints and the DN value of their corresponding pixels were extracted in 4 separate files, then these 4 files were logged into the Bathymetry software and the parameters of LMR and Stumpf methods were calculated. The root mean square error (RMSE) and correlation coefficient (CC) were used to assess geometric accuracy. In order to extract necessary parameters for each model, RMSE= 2.15 m and CC= 92.5% were calculated at depth distances of 0 to 20m. Results indicates higher accuracy and stronger correlation of LMR findings. Therefore, this method was used for depth determination between 0 to 20 meters. The 5 parameters extracted from the Bathymetry software and the corresponding pixel values of the four bands with 10-meter resolution extracted from the Sentinel-2 image (received from the on 12-15-2018) were used as input. Linear Regression Model was applied to transform 4 bands of Sentinel-2 image into depth. The output of the model (depth) was presented as the Substrate DEM of the coasts of Port of Salaleh, Oman.
Conclusion
Hence, it can be concluded that Remote Sensing technologies can be used for depth determination and sea monitoring at critical times (during wars or other periods of insecurity) for an acceptable time period. It also provides an appropriate context for bathymetry of inaccessible coastlines and monitoring of strategic widespread water zones. In this way, the depth of sea bed in shallow areas is extracted using spectral analysis of satellite data and different models.
