Developed tools of Remote Sensing and Geographic Information System are rapidly spread in recent years in order to manage natural resources and to monitor environmental changes. This research aims to study the spatial behavior of the Dead Sea through time. To achieve this aim, time series analysis has been performed to track this behavior. For this purpose, fifteen satellite imageries are collected from 1972 to 2013 in addition to 2011-ASTGTM-DEM. Then, the satellite imageries are radiometrically and atmospherically corrected. Geographic Information system and Remote Sensing techniques are used for the spatio-temporal analysis in order to detect changes in the Dead Sea area, shape, water level, and volume. The study shows that the Dead Sea shrinks by 2.9 km 2/year while the water level decreases by 0.65 m/year. Consequently, the volume changes by –0.42 km 3/year. The study has also concluded that the direction of this shrinkage is from the north, northwest and from the south direction of the northern part due to the nature of the bathymetric slopes. In contrast, no shrinkage is detected from the east direction due to the same reason since the bathymetric slope is so sharp. The use of the Dead Sea water for industrial purposes by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. The intensive human water consumption from the Jordan and Yarmouk Rivers for other usages is another main reason of this shrinkage in the area as well.
Understanding changes in wetlands, land-uses, seashores and vegetation areas over time is essential to many aspects of engineering, geographic and planning researches. Interpretation and analysis of remotely sensed imagery require an understanding of the processes that determine the relationships between the property the sensor actually measures and the surface properties we are interested in identifying and studying [
Changes of the earth’s surface are becoming more and more important in monitoring the local, regional and global resources. Large collection of past and present remote sensing imagery makes it possible to analyze the spatio-temporal pattern of environmental elements and impact of human activities in past decades [
Change detection algorithms analyze multiple images of the same scene―taken at different times―to identify regions of change [
Reference [
According to [
The Dead Sea is located on 31˚30'N, 35˚30'E, WGS84 reference datum, bordering Jordan to the east, historical Palestine and the West Bank to the west. The Dead Sea is considered as the lowest point on the Earth’s surface at about −400 m [
The following points describe the methodology followed by the researchers:
1) Satellite imageries are collected based on the criteria shown in the next section;
2) All the imageries are pre-processed and normalized by converting Digital Number (DN) to spectral radiance. Then, atmospheric effects are removed. After that, the resulted image is converted to reflectance. Finally, the black gaps are removed if exist;
3) Supervised classification is implemented;
4) Change detection techniques are conducted to study the changes in the area, shape, water level, and volume of the Dead Sea;
5) Results and discussion.
In this research, ASTGTM-DEM for 2011 and the Landsat imageries are downloaded from USGS website. The first criterion in data collection is to download even-year imageries including the oldest and newest Landsat archived imageries, 1972 and 2013. The second criterion is to give TM imagery a priority over ETM+ and/or MSS since TM sensor life span is longer than ETM+ (approx. 20 years) so consistent data will be collected. In contrast, Landsat ETM+ imageries have the problem of the Scan Line Corrector Failure (SLC-off) which presents in black gaps. Moreover, Landsat ETM+ has the only advantage of the panchromatic band existence. However, the resolution in the multispectral bands in ETM+ is the same of TM, 30 m. The third criterion is reducing the pre- processing by downloading all imageries in the same date; the month of October is chosen to avoid clouds in the scene. In the case of the October imagery did not match the listed criteria, the closest imagery to October match- ing the criteria is downloaded. The fourth criterion is downloading full-bands imagery in a Geostationary Earth Orbit Tagged Image File Format (Geo TIFF). The fifth criterion is downloading free clouds scenes, at least above the water-body. Based on these criteria, fifteen imageries were downloaded as illustrated in
Since digital sensors record the intensity of electromagnetic radiation from each spot viewed on the Earth’s surface as a Digital Number (DN) for each spectral band, the exact range of DN that a sensor utilizes depends on its radiometric resolution. For example, a sensor such as Landsat MSS measures radiation on a 0 - 63 DN scale even as Landsat TM and ETM+ measure it on a 0 - 255 scale [
Using the Cosine of the Solar Zenith Angle (COST) method, the DN is transformed to reflectance as discuss- ed above. Atmospheric correction using Dark Object Subtraction (DOS) is also included in this method [
In this research, supervised classification was used since the Areas of Interest (AOI) are known and clear to be
Year | Imagery Type | Year | Imagery Type |
---|---|---|---|
Sep 15, 1972 | MSS | Oct 04, 2000 | TM |
Jun 29, 1975 | MSS | Oct 18, 2002 | TM |
Sep 06, 1984 | TM | Jun 17, 2004 | ETM+ |
Sep 28, 1986 | TM | Oct 13, 2006 | ETM+ |
Dec 30, 1988 | TM | Oct 18, 2008 | ETM+ |
Aug 30, 1990 | TM | Dec 03, 2010 | TM |
Aug 3, 1992 | TM | Jun 03, 2013 | OLI |
Oct 15, 1998 | TM |
distinguished (the water-body). The spectral signatures of the sea body and the land around are developed and then the software assigned each pixel in the image to the type to which its signature is most similar. The steps performed for supervised classification area are as follows:
§ Identifying Training sites;
§ Creating spectral signatures for each of the cover types;
§ Classifying the entire image pixel by pixel, according to identified signatures.
In term of evaluation how similar signatures are to each other, there are several different statistical techniques that can be used; minimum distance, maximum likelihood classifier and parallelepiped classifier. In this research the maximum likelihood method is used since Reference [
In this stage, area, shape, water level and volume change detection analysis are done using ArcGIS tools. Reference [
Historical studies tell that the Dead Sea was separated into two basins in 1978, unfortunately, the earliest im-
agery which is convenient to the data collection criteria is in 1984, so from this date, it was preferred to distinguish between the two parts with focusing on the northern part since southern part was turned to manmade basins. The general trend of the area of the northern part is decreasing as shown in
The behavior of this trend is nonlinear and it is noticeable that in 2000, 2004 and 2010 the level increases a little bit if compared with previous and later years. This increasing has been making such a cycle, however this cycle can’t be identified accurately due to the lack of imageries from 1992 to 1998. Worthy to say, that the decrease between 1984 and 1992 (8 years) reaches 7.98% while this percentage decreases to 5% from 1992 to 2013 (21 years). This variation is due the amount used in the industry and the amount of inflow released from Jordan River. Perhaps this variation is also due to the period of time consumed to use the water from the southern part in industrial use. In other words, from 1978 to 1984 is the period needed to use the southern part water and to start compensating by northern part water. In terms of defining the minimum values, it’s clear that the area reached the deck in 2013, 611.23 km2. In general, the annual average area change is −2.98 km2.
Year | Area (km2) | Northern part (km2) | Southern part (km2) |
---|---|---|---|
1972 | 986.21 | 986.21 | |
1975 | 926.01 | 926.01 | |
1984 | 968.37 | 697.94 | 270.43 |
1986 | 911.65 | 672.08 | 239.57 |
1988 | 898.30 | 667.70 | 230.60 |
1990 | 892.10 | 661.50 | 230.60 |
1992 | 904.62 | 642.87 | 261.75 |
1998 | 887.99 | 639.35 | 248.63 |
2000 | 901.82 | 642.04 | 259.78 |
2002 | 859.80 | 639.57 | 220.23 |
2004 | 865.80 | 637.43 | 228.37 |
2006 | 837.99 | 624.69 | 213.30 |
2008 | 832.89 | 618.34 | 214.55 |
2010 | 844.47 | 631.27 | 213.21 |
2013 | 812.18 | 611.32 | 200.86 |
percentage is too hard and need lots of researches and a closed sea such as the Dead Sea reflects more than one factor. The use of Dead Sea water for the industry by both Israel and Jordan is one of the essential factors that affect the area of the Dead Sea. Moreover, these quantities are roughly estimated even in governmental reports. Climatic conditions play an important part of this behavior of the water body since this behavior is a result of the balance between water running into the sea from the tributary area and direct precipitation, minus water evaporation. In any case, the main reason causing this dramatic recession in the Dead Sea area is the intensive human water consumption from the Jordan and Yarmouk Rivers for other usages. Israel transfers huge quantities of
surface water through the National Water Carrier from Upper Jordan River to the Negev, where these quantities equal 420 MCM/year in addition to local consumption in the Tiberius Basin and the Huleh Valley. By studying the trend behavior of this decrease, a third degree polynomial could be derived from regression analysis as shown in
The derivation of the Dead Sea level is not a straight forward process even though the results are consistent with area changing as well as with previous studies, as will be discussed later.
Year | Area (km2) | Standard Deviation |
---|---|---|
1972 | −404.30 | 7.2 |
1975 | −404.80 | 6 |
1984 | −411.40 | 8 |
1986 | −420.00 | 8 |
1988 | −419.5 | 7.5 |
1990 | −421.32 | 7.1 |
1992 | −424.30 | 14 |
1998 | −427.90 | 6 |
2000 | −427.30 | 5 |
2002 | −428.20 | 5.1 |
2004 | −428.90 | 4.7 |
2006 | −431.10 | 2.5 |
2008 | −431.00 | 2.6 |
2010 | −429.90 | 4.5 |
2013 | −430.13 | 5.36 |
level change is −0.65 m. Reference [
In order to find the relation between the Dead Sea area and its water level, regression analysis is used putting the areas on x-axis and corresponding water level on y-axis to get a linear equation with R2 = 0.9672 which is considered a strong relation between both, see
Based on the area and water level derivations, the volume change could be calculated.
The area of the Dead Sea surface (at the end of the fifties) is about 1000 km2. The altitude of the surface is around 350 m below sea level. Since 1978, the Dead Sea has retreated, and the sea body turned into two basins: the principal northern one that was about 631.27 km2 with a water level of −430.13 m (in 2013), and the shallow southern one with the Lisan Peninsula and the Lynch Straits in between, which has a sill elevation of about 400 m below the sea level. The Dead Sea shrinks by 2.9 km2/year while the water level decreases by 0.65 m/year. Consequently, the volume changes by −0.42 km3/year. The direction of this shrinkage is from the north, north-
west and from the south direction of the northern part due to slopes of bathymetry. No shrinkage was considered from the east direction due to the same reason since the bathymetric slope is so sharp.