Introduction

Coastal zone is characterized as a wide geographic area combining two distinct ecological parameters embodied in water and land (Liquete et al. 2013). It provides an indispensable economic and ecological resource (Liquete et al. 2013), (Byrnes et al. 2004). In this rapid epoch of industrialization and urbanization, coastal areas are the most vulnerable to land-use changes (Post and Lundin 1996). Coastal areas are subject to various complex natural processes that always trigger long- and short-term changes. These changes can be summarized as: shoreline retreat, movement of sediments, degradation of water quality and coastal growth. The alteration of the coastal ecosystem has a direct impact on human life, infrastructure, property, coastal land resources, and coastal socio-economic value degradation. Sustainable development and protection of the environment in coastal regions need to be monitored permanently by collecting bathymetric data. Bathymetric data are considered a major task in the monitoring system.

Coastal erosion has always occurred, but it is now accelerated in large measure by natural and human activities. Approximately 70% of the world's coastal beaches suffer from erosion (Schwartz 2006). The effective monitoring to coastal resources is of importance particularly, shoreline position which reflects the stability of investment in such area. Shorelines are rapidly changing due to natural processes (transport of sediments, waves and rising sea levels) and anthropogenic impacts (coastal protection). Therefore, to detect changes it needs to be carefully mapped. To establish effective management strategies, it is important to assess land use / land cover and to detect changes in the shoreline (Rasuly et al. 2010). Within these techniques, remote sensing was considered to be an effective technique widely used in various fields. Managing this challenge includes collaboration between government and society levels (Clark 2018). Remote sensing is urgently needed to track changes in LULCas it plays a vital role in the sustainable development. Additionally, remote sensing helps to estimate the statistics of land use and land cover development that are critical in identifying land use variations in catchment (Selçuk et al. 2003), (Xiao et al. 2006). Recently, a powerful combination of GIS technology, remote sensing science and global positioning systems (GPS) has been allowed to detect LULC changes (Muller and Zeller 2002).

Various studies have been carried out in the identification and detection of different study areas around the world using different classification techniques:(Muller and Zeller 2002), and (Masria et al. 2015).

Different techniques are used to extract shoreline positions: conventional methods such as field surveys, and aerial photographs analysis (Lillesand et al. 1987). Data acquisition using remote sensing helps in fostering the conventional survey as it is more cheaper than traditional methods (Thieler et al. 2009). It has an essential role in gaining spatial data from an economic perspective (Alesheikh et al. 2007).

In addition, remote sensing techniques have been used to map bathymetry for decades (Green et al. 2000) from different sensors such as optical, RADAR and Light Detection and Ranging (LIDAR). Extraction of bathymetry data is mainly produced to be used in different fields: water volume computation, pollution control, mineral and fish industries, underwater engineering construction, harbor, and docks construction and maintenance.

Various studies in extracting bathymetry were carried out. (Jagalingam et al. 2015) extracted bathymetry of the southwest coast of India using ratio transform algorithm to Landsat 8 Satellite Imagery; (Setiawan et al. 2017) used Landsat 8 data to extract bathymetry information, is in Rally of the Thousand Island Jakarta bake; (Benny and Dawson 1983) used satellite Imagery to extract bathymetry in the Red Sea.

(AbuDagga 2015) used a supervised classification technique to detect LULC changes in 2004 and 2009 using SPOT 5 satellite images. (Abualtayef et al. 2012) studied the effect of Gaza's fishing port on the coastal area of Gaza through detecting changes in the 1972. They used images from MSS, TM, and ETM + Landsat. The research was conducted using techniques (ERDAS) and GIS..

(Abualtayef et al. 2013) estimated change detection for Gaza shoreline between 1972 and 2010, from Wadi Gaza, 4 km south of the Gaza Strip's fishing port, to Al-Sodania town, 3 km north of the port. (Alhin and Niemeyer 2009) analyzed the coastline dynamics at the coastal zone of Gaza during the last two decades. The shoreline extraction was conducted using Tasseled Cap Transformation, Band Ratio, and Normalized Difference Vegetation Index ' (NDVI) '.

The coastal zone is one of the most critical and most heavily populated areas in the Gaza Strip, compared with other regions (Zviely and Klein 2003). The dramatic rise in the population of the Gaza coastal region is leading to a degradation of the resources of the coastal zone and a change in coastal morphology. Anthropological influences and coastal processes, such as the dynamics of coastal shelf sediments, sea currents, waves, tides, surface circulations and coastal erosion, affect the Gaza coastal region (Alhin and Niemeye 2009).

The phenomenon of shoreline erosion is vulnerable in some parts of the southern coast of the Gaza Strip as it threatens to destroy several buildings and roads directly off the coast. In 2010, on the coast of the Egyptian side of Rafah, Egypt developed sea groins that are located about 2 km from the sea border of Gaza extending for about 1 km inside the sea. Recently, erosion in the southern regions of the Gaza Strip has become evident. (Zviely and Klein 2003).

The recent study aims to keep continuous monitoring along Gaza coastal zone which suffers from continuous erosion as an important step towards sustainable development. This will help in extracting and updating information required about shoreline position changes, LULC. Moreover, extracting bathymetric map for the coastal zone will be of importance as it will decrease the cost of field survey required to pursue different morphodynamic modelling studies.

Site description

Gaza Strip is a coastal area located along the eastern Mediterranean Sea (31° 25ʹ 0ʺ N, 34° 20ʹ 0ʺ E). Gaza Strip occupies a total area of 365 km2 with a length of 45 km and a width of 6–12 km and covers 6.1% of the total area of Palestinian territory with a population of 1,912,267 at the end of 2016. The study area covered the coastline from Beit Lahia in the north to the Swedish-village in Rafah town in the south, with the eastern part of the Gaza Sea coast as shown in Fig. 1. Over the last 15,000 years, the coastal area of Gaza Strip has been formed by sediments transported from the Nile River to the east side of the sea, usually in the north-east (MEnA 2001). The coastal zone is bordered by southern and northern sand dunes and the exposed Kurkar cliffs in the middle to the north. The land band of the coastal zone occupies 74 km2 (MOPIC 1996), the unpopulated areas, Wadi Gaza and part of Gaza City (Ali 2002).

Fig. 1
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Gaza strip location on the levantine basin of the Mediterranean sea (Source: ESRI)

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Materials & methods

Figure 2 shows the approach used in this study, which consists of many steps for achieving the goals, as the analysis focuses on three main points. (i) apping the LULC in the coastal area of Gaza between 2004 and 2016, (ii) detecting shoreline changes from 1972 to 2014 (iii) Extracting nearshore bathymetry map from Landsat 8 images. The following software and supporting tools are used: ESRI ArcGIS 10.2.2, Intergraph ERDAS Imagine 2015, Microsoft Excel 2016.

Fig. 2
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Framework of the research methodology

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Data collection and acquisition

Seven satellite images have been acquired in recent work from 1972 to 2016 without any cloud effect and in good quality as shown in Table 1.

Table 1 Detailed information about satellite images
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This study uses different satellite images from the U.S. Geological Survey (USGS) for the years 1972, 1998 and 2016 as well as some images obtained from the Environmental and Rural Research Center (ERRC) at Islamic University-Gaza (IUG) for the years (SPOT, 2004) and (QuickBird, 2009). Moreover, high-resolution images have been obtained from the Ministry of Planning (MOP) for the years 2007 and 2014. The current study is based on the 1994 bathymetric field surveys of the Gaza Sea conducted within the Dutch project.

Assessing and mapping of the shoreline changes and land cover patterns required to have a geodatabase for the coastal area. A geodatabase was developed based on digital maps for the study area according to the Ministry of Planning and Environment Quality Authority (EQA).

Image pre-processing

The image pre-processing is very important to be more suitable for various processes in remote sensing science (Akhter 2006). Preprocessing includes the correction of atmospheric issues such as darkness, cloud effect, and geometric correction.

Geometric correction

Geometric correction of the different satellite images is important since there is a potential error in image registration, leading to an overestimation of the actual change of LULC. SPOT, QuickBird and Landsat images used in the present investigation are ortho-rectified products according to the metadata documentation. To verify the geometric quality, the vector objects such as road junctions, city boundaries and Atlas base map layers of the Ministry of Planning, are superimposed on the images to notice the discrepancies between different images.

All images are ortho-rectified product. They are indeed in the orld Geodetic System (WGS 19,884) datum and the Universal Transverse Mercator (UTM) projection systems, 36 North hemisphere.. All images were rectified using a master image (aerial photo, 2007) through image-to-image registration technique.Using ArcGIS and ERDAS softwares, approximately 20 ground control points (GCP) were used to execute geometric correction for all objects in two steps: geo-reference and resampling.

Visual enhancement

A radiometric correction takes into account the atmospheric contribution, illumination, and view angles, and sensor calibration into consideration (Lillesand et al. 2014).

Before implementing any procedure, noise and other radiometric errors should be corrected. The proper use of radiometric corrections is based on various aspects: sensor optical scanners, and others. Improvement of image quality used in satellite images acquired in this study, reduction of radiometric noise, is carried out using ERDAS 2015 as shown in Fig. 3. To improve the visual overview of the image,contrast stretching was implemented, creating False Color Composites (FCC).Fig. 4 displays the 2004 satellite image before and after the enhancement. A false-color composite of 1–2-3 (Green–Red-Near infrared) bands were used in this step.

Fig. 3
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Visual enhancement through noise reduction of Landsat-5 satellite image using ERDAS

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Fig. 4
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Enhancement of SPOT-5 satellite image using ERDAS

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Image classification

Classification of images is important to distinguish between different features in the image so that changes in digital images can be detected at different dates (Dewidar and Frihy 2007).

In this research, supervised classification is used in shoreline extraction and land use/ land cover mapping. For each purpose, different classes were used as following: (the water and land) in shoreline mapping and (water, sand, vegetation, built-up area and beach sands) in LULC mapping. In this paper, the whole spectral bands in SPOT, QuickBird and Landsat images (2004, 2009 and 2016) were used in the classification process.

Land use /landcover mapping

Development of classification scheme

Six classes were assigned to identify the land use and land cover classification (agricultural land, urban/built-up area, bare land/sand, greenhouse areas, coastal sand, and water body), then supervised classification techniques were applied.

Supervised classification

The main objective of the image classification technique is to categorize all digitized pixels in an image into LULC groups automatically. The maximum likelihood method is used in this study, since (Chinea 2006) recommends that if your training sites are well defined as in the coast Area, the maximum likelihood classifier should get the best results. This classifier defined the probability that each pixel belongs to a particular category with the highest membership probability (Chinea 2006).

On the other hand, when training sites are not well specified, the Minimum Distance classifier is preferred. The classification process was applied in the SPOT, QuickBird and Landsat images (2004, 2009 and 2016) using all spectral bands. After the completion of the analysis of satellite images, the results of the analysis were converted from raster data to vector data which can be used as inputs for processing and calculations for change detection for areas. Ultimately, land use maps are produced according to Vector shapefile converted from Raster using ArcGIS software.

Shoreline extraction

Shoreline extraction using remote sensing methods depends on the different reflectance values of water and other land surfaces at the varied wavelengths. Generally, water areas absorb most of the radiation in near-infrared and mid-infrared regions. Therefore, the reflectance value of water is nearly equal to zero; while, the reflectance of the different land covers is higher than water. Based on this concept, coastline extracted from satellite images through the use of many methods such as the Manual editing method "Digitizing" and Estimating histogram threshold and image classification. A single band image can be used to extract shoreline as the water reflectance is nearly equal to zero in the reflective infrared bands. This can be implemented by selecting the histogram threshold for one of the infrared bands of the image(Alesheikh et al. 2007). Another technique is to use of the supervised classification of imagery by the selection of training sites as mentioned earlier, the shoreline is typically mapped from remote sensing data.

In this study, we used digitizing, after implementing supervised classification through Maximum Likelihood Classifier (MLC). Training sites of land and water were used to identify each class for years (1972, 1998, 2007 and 2014) in the classification process. Furthermore, the analysis results of data from supervised classification and digitized shoreline in 1972, 1998, 2007 and 2014 are displayed in vector layers after convert classified images from raster to vector in a process called “Vectorization”.

Results & discussion

This section shows the results and analysis of the changes in LULC, shoreline and bathymetric mapping in the Gaza coastal zone.

Change in land use /landcover

Table 2 shows the classification and change detection statistics based on the generated classification maps created for the years 2004, 2009 and 2016 (Fig. 5). However, the groups and changing areas are illustrated graphically (Fig. 6). Attention has been paid to the classes of urban / built-up and agriculture areas. The result of the land use/land cover change which carried out by supervised classification was summarized as following:

Table 2 LULC as derived from satellite data for the coastal area of Gaza
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Fig. 5
figure 5figure 5figure 5

Map of Land-use / Land Cover in Gaza Coastal Zone a) 2004, b) 2009, and c) 2016

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Fig. 6
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The LULC areas of the 6 classes derived from the classified maps, note that each hatching represents a class in the studied 3 years (2004, 2009 and 2016)

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Built-up areas

Based on statistical analysis, built-up areas previously occupied 7.32% in 2004 and increased to 11.54% in 2009 and 12.21% in 2016, respectively. This is a significant indication of the growth of economic and infrastructure in the coastal area of Gaza. It is significant that population growth in the study area leads to imlementing different developments to accommodate this population density.

Open / Bare Land / Sand

Bare land in 2004 was 45.15%, but in 2009 it dropped to 35.87% and in 2016 it fell again to 25.54%. It is due to the anthropogenic impact that involves building and road construction, overgrazing, land conversion, and tourism activities. I is clear that developing infrastructure in the coastal area used the sandy areas.

Agricultural lands

Agricultural land shows an increase from 34.60% to 43.47% in 2009, then increased to 54.08% in 2016. This can be attributed to the above built-up areas, which include all capacity building. This may be due to land reclaim since the 2005 occupation withdrawal from Gaza.

Water Body

Except for any sewage ponds and part of Wadi Gaza, the coastal area comprises no water bodies. In 2004, the ratios were 0.31% and 0.18% in 2016.

Greenhouses

Under this analysis, this class has reported negative change over the years. The ratios of greenhouses in 2004 were 9.02% but in 2009 they dropped to 5.41% and in 2016 they fell again to 4.14%.

Shoreline extraction

After converting the results of classified images from raster to vector as "polygons", the polygons features are converted to lines features using ArcGIS/Arc Toolbox. In the end, the same steps are applied to all years to extract shorelines. Figure 7a displays shoreline positions from 1972 to 2014 in Gaza Strip. Figure 7b focuses on Gaza Fishing Port shoreline positions.

Fig. 7
figure 7

a) Gaza shoreline change from 1972 to 2014, b) Shoreline from 1972 to 2014 in Gaza Port

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1 Change detection analysis

ArcGIS Toolbox was used to estimate the variation in (erosion/accretion patterns) across different years by transforming features into a polygon, then detecting change in areas. Accretion and erosion areas along the shoreline are highlighted as shown in Fig. 8. Areas of erosion are marked in "Green color" while "Red color" is indicated for accretion. Accretion and erosion rates are estimated along the shoreline as shown in Table 3.

Fig. 8
figure 8

Change Detection in areas between years 2007–2014

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Table 3 Accretion and erosion rates for the study area
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  • The rate of change from 1972 to 1998

The average of change rate in erosion areas was 0.88 m / year before the construction of Gaza Fishing Harbor, while the average rate of accretion was 0.58 m / year−1. The highest erosion rate was 1.88 m/year−1 as shown in Fig. 9, due to the impediment of sediments coming through Wadi Gaza to the Mediterranean Sea.

Fig. 9
figure 9

Rate of change in period 1972–1998

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  • The rate of change from 1998 to 2007

After the building of the Gaza Sea Port, the erosion rate increased dramatically, with an average rate of approximately 2.30 m / year-1 north of Gaza Fishing Harbor, while the average accretion rate is 0.97 m / year−1. The highest accretion rate in this period was 4.04 m/year−1 in the Southern Gaza harbor area as shown in Fig. 10. The reason for the increased rate of erosion is due to the construction of Gaza Fishing port. The increased rate of erosion at northern side is due to the interruption of the natural mechanism of sediment transport alongshoreline which resulted from the currents induced by prevailing wave. The direction of current is alongshore leads to transport of sedimentation in the same direction. The accretion rate increased at the southern side (updrift) due to trapping of sediments by breakwates of the port.

Fig. 10
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Rate of change in the period 1998–2007

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  • The rate of change from 2007s to 2014

With regard to increased human activity in coastal areas, the average erosion rate increased by 1.10 m / year, where the entire northern area of the Gaza Port remains severely affected by erosion as shown in Fig. 11. The southern region of Gaza Fishing harbor remains as a sediment trap with an accretion rate of 3.85 m / year.

Fig. 11
figure 11

Rate of change in the period 2007–2014

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Bathymetric mapping

Bathymetric data is one of the most important pieces of information to understand the aquatic habitat and biodiversity environment. Active remote sensing technologies, such as SONAR (Sound Navigation and Ranging), Light Detection and Ranging (LiDAR) and ALB (Airborne Laser Bathymetry), have been widely used to survey the water-covered area. While these technologies can provide high accuracy and generally have better bathymetric mapping capabilities, their costs and maintenance are high (Minghelli-Roman et al. 2009).

Almost all of the above techniques are expensive. Therefore, with the advantage of larger coverage and much lower cost, water depth estimation with satellite image is still of value. The fundamental principle to extract bathymetry from optical remote sensing images depends on the attenuation of the light when it passes through water. This attenuation occurred due to the interaction with the water column as water absorbs much of the reflected light. Recently, remote sensing satellites such as SPOT, IKONOS, QuickBird, and Worldview-2 offer high spatial and spectral resolution, but the high cost still prevent obtaining such images.

One of the satellites that can be used for mapping shallow-water bathymetry is Landsat-8. Landsat imagery has a spatial resolution of 30 m and is equipped with a visible channel required in the extraction of bathymetry information. Visible channel (blue, red and green) has the ability to penetrate the water to a certain depth, the blue channel has the ability to penetrate deeper into the water body(Setiawan et al. 2017). (Jupp 1988) concluded that Landsat imagery can be used in determining the water depth, band 2 (blue channel) has the ability to penetrate up to 25 m of water depth, band 3 (green channel) up to 15 m, band 4 (red channel) up to 5 m, while band 5 (SWIR-1 channel) is only able to penetrate 0.5 m of water depth.

In the study, free Landsat-8 images have been used to map the bathymetry of Gaza Sea. Different empirical algorithms are available in previous researches such as (Su et al. 2008); (Stumpf et al. 2003); and analytical algorithms such as(Lyzenga et al. 2006); (Philpot 1989). Bathymetry mapping using analytical methods needs a number of input parameters such as water column, atmospheric properties and geological properties of seabed.

The ratio transform algorithm developed by (Stumpf et al. 2003) was applied in the recent study to develop bathymetric map of the Gaza Sea. This algorithm can acquire the depth between 20 to 30 m in clear water in addition to the ability to predict the depth to a certain extent in the turbid water environment which relies on the sediment transport capacity, changes from location to another. The procedures of analysis are adopted using ESRI ArcGIS 10.2.2 software. All the above mentioned preprocessing steps must be implemented first.

Water separation

The separation of land from water is the main step to extract bathymetry. The water was separated by applying the Normalized Differential Water Index (NDWI) in the NIR band (Raj and Sabu 2013).

$$\mathbf{N}\mathbf{D}\mathbf{W}\mathbf{I}=\frac{\mathbf{G}\mathbf{r}\mathbf{e}\mathbf{e}\mathbf{n}-\mathbf{N}\mathbf{I}\mathbf{R}}{\mathbf{G}\mathbf{r}\mathbf{e}\mathbf{e}\mathbf{n}+\mathbf{N}\mathbf{I}\mathbf{R}}$$
(1)

Firstly, the water surface was delineated using the 'Eq. (1)'. The second band (Green) and the fifth band Near Infrared (NIR) of the Landsat-8 images were used for the calculation of NDWI using Raster Calculator Tool in ArcGIS. The value of NDWI ranges from -1 to + 1. The zero value in this step was assigned to non-water areas, while water regions take values greater than 0.

Ratio transform algorithm

The ratio transform algorithm was used to extract the bathymetry in the nearshore zone. The algorithm used two bands to reduce the number of parameters. The algorithm is capable of retrieving depths from 20—30 m in the clear water of the coastal zone.

As the radiance in the blue wavelength (400–500 nm) attenuates faster with depth than light in the green wavelength (500–600 nm)(Jerlov 1976), hence, the difference in the ratio between the bands are influenced by the depth more than bottom reflectance. To extract the seabed, a linear relationship is set up between the ratio of radiance in two bands (green and blue) and water depth (Stumpf et al. 2003).

. In this Algorithm, the reflectance value with higher absorption decreases as the depth of water becomes deeper. Hence, a linear decrease between the high and low absorption bands exists directly after the transformation of the two bands. The following equation is used to calculate the depth of the nearshore coastal zone:

$$z={m}_1\left(\frac{\mathrm{In}\left({L}_{obs}\left( Ban{d}_i\right)\right)}{\mathrm{In}\left({L}_{obs}\left( Ban{d}_j\right)\right)}\right)-{m}_o$$
(2)

where Lobs are observed radiance of bands, m1 and m0 are offset and gain which is determined empirically, and I refer to the blue band and j refers to green band, Z is depth in meter.

Linear regression analysis

The correlation coefficient R2 is estimated to judge the accuracy of the algorithm in extracting bathymetry. To calculate R2, the hydrographic chart prepared through field data conducted by single beam echo sounders for Gaza seaport in 1994 of the study area is overlaid on the satellite image. Bathymetric points in the hydrographic chart and the corresponding pixel values from the Landsat satellite image are shown in Table 4. The pixel value of the satellite-derived bathymetry image is obtained with the reference of hydrographic chart point value. Finally, the values of m1 and m0 are provided as input to the ratio transform algorithm to identify the water depths along the near-shore Gaza Sea. The procedure for estimating the bathymetry is processed using the ArcGIS 10.2.2 environment. Figure 12 shows R2 of 0.8526 between the satellites derived value versus hydrographic chart value. Where \({m}_{0}\) is 184013 and \({m}_{1}\) is 182005, further the value of m0 and m1 is adopted to plot the range of depth.

Table 4 Part of list values of band blue and green to determine m1 and m0
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Fig. 12
figure 12

Correlation between pixel values and hydrographic chart values

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Bathymetric extraction model

'Eq. (2) ' was applied in the model to extract bathymetric data in study areas. The modeling was carried out using the Arc Toolbox tools in the ArcGIS Modelling environment. The result of the analysis is a raster map containing pixel values (depths of water = Z Calculated) in the study area. This study used two bands of the Landsat 8 imagery: Band-2 "Blue" (0.452 μm—0.512 μm) and Band-3 Green (0.533 μm—0.590 μm), which has a spatial resolution of 30 m, then shallow water depth extracted using the Ratio Transform Algorithm developed by (Stumpf et al. 2003) for the Landsat 8 data.

Figure 13a illustrates the estimated water depths in the study area using the model developed by (Stumpf et al. 2003).

Fig. 13
figure 13

a) Map of calculated bathymetry “left”; b) measured bathymetry “right” of the Gaza fishing harbor in the study area.

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These results analyses were based on measured bathymetry data in 1994 as shown in Fig. 13b. It is clear that water depths estimated at Gaza sea were between datum levels to -30 m. So, it is obvious that water depths can be extracted from the depth -25 m to -30 m for Landsat-8 imagery with a spatial resolution [30 m × 30 m] (Fig. 14).

Fig. 14
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a) Map of calculated bathymetry of the study area, b) map of measured bathymetry data of Gaza Sea in 1994.

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Bathymetry accuracy assessment

Assessment of data quality was carried out using 60 checkpoints from hydrographic data as shown in Appendix Table 5. The checkpoints selected are located along the Gaza’ coastal zone study area. The identified bathymetry points vary in depth from -3,90 m to 30,82 m related to the depths obtained from the satellite. The highest and lowest accuracies are of 0.01 m and 5.82 m. The correlation coefficient (R2) between the estimated depth and endorsed data was about 0.7714 as shown in Fig. 15. The derived bathymetry is statistically correlated with the field data in a good way. This approach can therefore be an alternative method for measuring water depths for medium resolution.

Fig. 15
figure 15

Correlation between the endorsed and the calculated data

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Conclusion

Gaza’ coastal zone is a concern due to human activities resulted in instability of shoreline. To manage coastal problems at Gaza strip, a comprehensive study implemented to identify changes in LULC during the period (2014–2016), shoreline changes (1972–2014) and bathymetric mapping in Gaza Strip's coastal area using remote sensing and GIS techniques. According to the results of LULC change detection, a noticeable change has been occurred. urban / built-up area increased by more than 3.62 km2 leading to a growth in agricultural land by more than 14.42 km2 at a rate of 1.20 km2 year−1. Besides, the area of barren land / sand also shrank by -14.52km2 due to establishing the necessary infrastructure for urban development in the region. The significant change in the urban coastal area between 2004 and 2009 due to Israel's 2005 withdrawal from Gaza and the return of people to their land.

Shoreline change detection has been investigated for the period (1972–201) to identify hotspot area represented in erosion/accretion areas and its annual rate. It is clear that substantial trends of erosion and accretion have occurred due to sediment movement along the shoreline after construction of Gaza' port, in addition to the effects of human activities. Change detection was implemented at three periods: (1972–1998), (1998 + 2007), (2007–2014). The results show that almost 62.8% of shoreline has been suffered from erosion along 42-year period. In addition, northern regions are significantly eroded by a rate of 186,15 m2 which represents the highest erosion rate in the period 1998–2007. In the period 2007–2014, a large beach area was gained at a rate of 9,5103 m2/year−1 in the south of the port of Gaza, as long-shore sediment transport was trapped in front of breakwaters of the port.

Eventually, bathymetric mapping of the near-shore coastal area using Landsat-8 satellite imagery was performed by applying ratio transform algorithm. The results show a good correlation between measured and predicted bathymetry.

It is recommended to conduct more numerical studies to estimate the annual dredging works required at the southern side of the port to prevent the inlet of the port to be accreted. Also, the eastern side of the port should be addressed numerically to calculate the required sediments to overcome the erosion problem downdrift the port.