This study was attempted to understand the impact of selected ports on the coastal and nearshore dynamics. Dwelling activities along the coastal zone were particularly high in the last two decades. Hence ports constructed between 2000 and 2022 such as Mundra, Hazira, Karaikal, Krishnapatnam, Gangavaram and Gopalpur were considered for the study. The land and shore dynamics were assessed prior and after their construction. The landward impact was identified by the changes in LULC and coastal sediment dynamics were estimated from numerical model before and after the ports construction. The highly impacted land and shore features were used in Analytical Hierarchy Process (AHP) to assess the extent of their impact due to port construction. The extent of impacts was indicated as ‘Criteria Weight (CW)’ expressed as percentage. Village-wise Local Sensitivity Analysis (LSA) of the port environs due to port activities were also assessed with six parameters such as changes in LULC, cyclone, population, Road network, cultural heritage site and shore stability based on their influence on the coast on case- by-case basis using AHP. The obtained influence of the impact of these parameters was used to determine the category of local sensitivity of the adjacent villages around the ports. For effective management planning the LSA was classified into five classes namely Very Low, Low, Moderate, High and Very High. Villages categorized with different sensitivities can be attempted for suitable management plans and similar studies can be attempted for all villages impacted by port interventions.
Research Article
Impact assessment of selected ports intervention to coastal and nearshore dynamics:A GIS and AHP approach
https://doi.org/10.21203/rs.3.rs-3234089/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 29 Feb, 2024
Read the published version in Environmental Monitoring and Assessment →
You are reading this latest preprint version
Port impact
MLC
AHP
LSA
India is one of the fastest growing large economies in the world. Ports and shipping are playing crucial roles in the overall economic development (Balasubramanian, 2018). The Port being a vital infrastructure for both international and domestic trade, about 95% of India’s trade is transported through seaports (IBEF, 2018). Ports support local and regional industries for goods movement which directly and indirectly influences developmental activities and socio-economic growth. However, port construction, expansion, operation, and other related activities impart numerous risks to the coastal environment and the community (Peris-Mora et al., 2005). India has 12 major ports and 200 minor ports, and the Indian government has initiated a project named ‘Sagarmala’, under which establishment of new ports and modernization of existing ports are planned and commenced (GOI, 2018).
A Port function has both positive and negative impacts on the coastal regions. Positive impacts are related to economic benefits whereas the negative impacts are related to changes in environment, land uses and coastal processes. Human activities like port construction and dredging activities for the development interfere in the coastal processes (Wang & Andutta, 2013; Kudale, 2010).
The Rate of land use changes in port areas differs from that in cities and rural areas. Generally, establishment of a port changes an area from traditional activities to economic and commercial activities along with substantial urbanization. These short term changes highlight environment degradation and coastal instability in and around the port areas. Mundra, Hazira, Karaikal, Krishnapatnam, Gangavaram and Gopalpur ports along the Indian coast have been selected to understand the impact of port intervention on their coastal environment. This paper utilized Remote Sensing and GIS techniques to assess historic and periodic Land use/ Land cover changes (Yan et al., 2021). Also, the manuscript discusses sediment quantity dynamics along the port area using numerical model simulations. Identification of features with significant impact and the extent of impacts were also assessed in the study and the Local Sensitivity Analysis (LSA) of the adjacent villages for the selected ports was indicated using AHP technique. Research studies focussed on both land and shore dynamics due to port constructions were found meagre. Hence the present study would provide comprehensive information about the changes and extent of influence of ports to its surrounding areas. On this basis, the outcome of the study indicated the different nature of influence of the adjacent features and the environmental changes imposed by ports which can be addressed by coastal policy makers to ensure sustainable port environs.
Study area
In the present study six ports (2 from west coast and 4 from east coast) located along the Indian coast have been chosen to analyse the impact of port construction on coastal dynamics (Fig. 1). Owing to remarkable human intervened coastal activities and other infrastructure developments noticed along the coast in the last two decades, Mundra and Hazira ports in Gujarat; Karaikal port in Union Territory of Puducherry, Krishnapatnam and Gangavaram ports in Andhra Pradesh and Gopalpur port in Odisha have been selected for the study which were constructed/operational between 2000 and 2022. Villages surrounding the ports were taken to understand the impact of port on the local environment. Description of each selected port location has been given below:
Mundra Port located on the north shores of the Gulf of Kachchh in Kachchh district in Gujarat is the largest commercial port in India. The port is merged with Adani port and designated as Special Economic Zone (SEZ) in 2003 and it is the largest private SEZ in the country. It is also known as the “All weather port” as it functions across all seasons. Mundra, Dhrab, Jarpara, Navinal, shiracha, Tunda, and Kandagara Mota are the adjacent villages which were selected to understand the impact of the port on coastal features.
Hazira port is located on the mouth of Hazira creek in Surat district of Gujarat. The port was operational since 2005. It is a deep water port with direct berthing facilities and to function across all weather. The port location is planned as to function as a Gateway port to facilitate movement of cargo from hinderlands of northern, western and central India. The port is surrounded by Hazira, Sunvali, Kidiabet, Mora, Dumas and Sultanabad villages.
Karaikal port is an all-weather deep water port located between Vettar River on the south and Paravanar River on the north in Karaikal town within the Union Territory of Puducherry. It is the only major port between Chennai and Thoothukudi utilised mostly for serving industrial cargo movements to central Tamil Nadu. The periodic expansion of the port since its operation in 2009 has made Karaikal an industrialised smart city over the years. The coastal stretch has narrow beaches. Keezhaiyur south, and Vanjoor in Karaikal, Panangudi, Nagore and Nagapattinam in Tamil Nadu are the adjacent villages situated around Karaikal port and are considered for the present study.
Krishnapatnam port is situated on the north of Kandaleru River mouth in Nellore district of Andhra Pradesh. The port is a deep water port established in 2008. The port terminals have vast coastal infrastructures to facilitate easy bulk cargo handling. Among the top 100 world’s high efficient large Cargo handling terminals Krishnapatnam is ranked 95 in the list. The port is surrounded by Krishnapatnam (rural), Krishnapatnam, Muthukur, Narikellapalle, Thamminapatnam, Epuru Bit-IA, Epuru Bit-IB, Epuru Bit-II, and Momidi villages.
Gangavaram Port located on the south of Visakhapatnam port in Visakhapatnam district of Andhra Pradesh.It is the deepest multipurpose all weather port in India with a water depth of about 20.2m established in 2009. It is one of the Greenfield ports in India. Nellimukku, Pedagantyada, Ukkunagaram, and Nadupuru villages around the port location are selected for the study.
Gopalpur Port is a natural, deep sea port on the east coast of India situated in Ganjam district of Odisha state. The port was established in 2013 so as to increase sea trade of Odisha and to promote industries and employment in the state. It is a natural harbour with a water depth of 18.5m. The port is strategically located between two major ports Paradip and Vishakapatnam along the east coast to support vast mineral and industrial cargo to the hinderlands across Odisha, Chattisgarh and Jharkhand. The port is located amidst sand dunes. Five villages namely Arjyapalli, Matikhala, Badaputi, Basanaputi, Uppalaputi neighbouring the port are selected for the analysis in the present study.
Data source
The Landsat 7, LISS III and Sentinel images (Table 1) were georeferenced using ERDAS imagine software and transformed to UTM projection, WGS 84 in zone - 42N, 43N, 44N, 45N based on the study areas.
Table: 1 Specification of satellite images used for the study
|
Satellite |
Sensor |
Year |
Spatial Resolution (m) |
|
Landsat - 7 |
ETM+ |
2000 |
30 |
|
LISS III |
MSI |
2010 |
23.5 |
|
Sentinel 2A |
MSI |
2022 |
10 |
Pre-processing of the raw temporal satellite images considered for the study includes radiometric corrections using ‘Radiometric enhancement’ tool to minimize haze and noise distortions. Histogram equalization was applied to improve the contrast of the images for better interpretability. The pre-processed images were interpreted for different Land use/cover features for the periods 2000, 2010 and 2022 using the Maximum Likelihood Classifier (MLC), as this technique provides good accuracy in supervised classification (Kaliraj et al., 2017). The MLC supervised classification method is one of the popularly used land features classifier method. In this method, each pixel is assigned to the class that has the highest probability i.e. the maximum likelihood. The significant temporal LULC changes were utilised to delineate the “Extent of Impact” around the selected ports.
Temporal sediment flow simulations using numerical model
In the present study, Mike 21/3 integrated Coupled Flow Model was used to assess the sediment dynamics along the shore prior and after ports construction. Mike 21/3 is a dynamic modelling system widely used for the study of coastal and estuarine environments especially to study the flow and transport phenomena of sediments (DHI Report, 2017). The following modules of Mike 21/3 Coupled FM are used for the study:
Hydrodynamic module to determine the water level variations in response to various forcing functions in the coastal areas. The specific input of parameters to the module includes, Coriolis force, bottom shear stress, flooding and drying extents, momentum dispersion, wave radiation stresses, wind shear stress, barometric pressure gradients, sources and sinks, tidal potential, precipitation/evaporation, structures and gridded domain of the study area. The output includes current speed, current velocity, wind velocity and surface elevation. The model algorithm is based on the empirical depth-integrated incompressible Reynolds averaged Navier–Stokes equations for coastal waters.
Spectral Wave module to determine the growth, decay and transformation of wind generated waves in the coastal areas. Parameters such as water level conditions, wave breaking angle, current conditions (from HD model), wind forcing parameters, energy transfer rate, diffraction of waves, structures, bottom friction, white capping, gridded domain of the study area would simulate parameters such as significant wave height, peak wave height, significant wave period, and significant wave direction.
Sand /Mud Transport module to determine the current-wave influenced total sediment transport and rate of transport within the specified coastal area. The Sand/Mud Transport Module (ST/MD) calculated the transport of cohesive/non-cohesive materials based on the mean horizontal flow conditions simulated in the hydrodynamic and wave dynamic calculations (Wibowo et al., 2020). The physical parameters for the model included wave-current conditions from HD and SW models, grain size and density of sediments, sediment properties including porosity, Bed Resistance, Forcing parameters, Dispersion capabilities of the coastal areas due to non-resolved components such as eddies and turbulence, gridded domain of the coastal area and change in water depth. Quantitative information on total sediment load (magnitude and direction), quantity of alongshore sediments and quantity of sediments flow normal to the coast, rate and change of bed level are computed from the module.
Analytical hierarchical process (AHP) method
Analytical Hierarchy Process (AHP) is a structured mathematical decision making method over a set of complex interrelated parameters. The AHP process involves 3 steps where step 1: assigns ranks by pairwise comparison of parameters step 2: calculate criteria weights (CW) based on normalized matrix format of the compared parameters and step 3: determines the reliability of the assigned weightages to parameters by computing Consistency Ratio (CR). As formulated by Saaty (1977) the CR less than 0.1 indicate reliability in derived CW among the parameters for further analysis of the study. On the other hand, if the CR > 0.1, the assumed weightages for the parameters need to be refined for best fit for the application of the study. The expressions used for assigning criteria weights and consistency ratio calculation suggested by Saaty (1977) was explained in detail with the spatial data obtained in the study in ‘result and discussion’ section.
Local sensitivity analysis (LSA)
Sensitivity analysis determines how different features are affected or impacted to a dependent intervention within a given set of period. Local Sensitivity Analysis determines the local impact of set of input factors (Table 2) in response to the intervention as a function of vicinity to the intervened activity. The impact factors around selected ports are further assessed village wise and the variations and extent of impacts are classified into five categories of sensitivity as shown in Table 5 using AHP method discussed in Table 6. The Villages around the six selected ports are classified into different sensitivity classes based on the extent of impacts and their influence on the environment degradation and shore instability. The global sensitivity analysis discussed about port impacts on land and shore changes globally.
| Parameters | Data | Year |
|---|---|---|
| Land use/Land cover | Satellite images | 2022 |
| Population | http://censusindia.gov.in | 2011 |
| Cyclone | India Meteorological Department (https://mausam.imd.gov.in) | 2000 to 2022 |
| Road Network | GIS data | 2022 |
| Cultural heritage | GIS data | 2022 |
| Shore stability | Model simulated sediment quantity | 2000,2010,2022 |
The landward impact of ports was assessed using changes in the LULC features and in the nearshore using changes in the coastal sediment quantity calculated from numerical model prior and after the ports construction. Significant findings from the study for all the selected port sites are discussed in detail.
Mundra port
Result of LULC change analysis indicated spatial changes of the coastal area near the port. The port area was mostly surrounded by extensive mudflats and mangroves. The LULC change analysis indicated a simultaneous increase in mangroves (2051ha, 2422.6ha, 2800ha) and decrease in mudflats (7307ha, 5120ha, 1574.2ha) during the years 2000, 2010 and 2022. The significant loss of mudflats was mostly influenced by temporal port expansions noted from the study. Growth of salt pans and boomed industrial zones around the ports were noticed after port construction. Agricultural lands and habitation areas were found beyond 8 kms landside of the port. However, no significant change was observed in the spatial extent of the cultivation lands. Gradual increase of habitation areas especially expansion of Mundra city was noted between 2000 and 2022. The impact extent ranged from 4 to 10km from the vicinity of the port (Fig. 2).
The nearshore sediment quantity (predominately mud) changes prior and after the port indicated erosion around the port infrastructures. The decrease in sediment quantity varied from 34249.o2 Mcum/year to 22277.47 Mcum/year between 2000 and 2022 indicating loss of mudflats and sandy areas along the coast.
Hazira port
The Hazira port was found on the western bank of Hazira creek. Periodic extension of the port from 271ha to 596 ha was noted between 2010 and 2022. Significant impact of the port included conversion of 22% of agriculture lands, 1% of mudflats and 2% of scrub lands into industries around the port area. Conversion of 194ha of mudflats and 231ha of mangroves into aquaculture farms were yet another significant impact of the port. Though not within the direct impact extent of the port, significant changes were noted in the Kadiabet island located in the Hazira creek (Fig. 3). The changes included improved mangrove areas from 419 ha in 2000 to 597 ha in 2022. Most of the mudflats in the island were converted to aquaculture farms between 2000 and 2022.
The nearshore sediment transport flow indicated a significant accretion on the western bank of the Hazira creek between 2000 and 2022. The port is located on the accreted western bank of the hazira creek. Almost 3km accretion was noted between 2000 to 2022. However, a decrease in the total sediment quantity was observed from the model prior and after the port which influenced a minor change in the rate of accretion from 130m/year between 2000 and 2010 to 108m/year between 2010 and 2022.
Karaikal port
The LULC changes indicated a gradual expansion of the port area from 88.3 ha to 106.9 ha between the years 2010 to 2022. The land features converted into the port area includes unequal proportions of agriculture, aquaculture, scrub land, mudflat and mangrove areas. The impact of the port was found significant on the northern bank of Vettar river where the port is located compared to expansion of Karaikal town area located on the southern bank of the river. The port expansion has decreased the spatial spread of mangroves from 21 ha in 2000 to 13 ha in 2022. The LULC impacts included increased industrial area from 135 ha to 199 ha and built up area by 184 ha between 2000 and 2022 associated with loss of agriculture lands from 1304.8 ha to 746 ha during the assessed period.
Periodic development of port infrastructures such as breakwaters, jetties and groynes had interrupted the flow of sediment along the Karaikal coast. The model simulations indicated a gradual decrease in the total sediment load along the coastal stretch from 2585.5 Mcum/year in 2000 to 1131.3Mcum/year in 2022 which was evident from the beach erosion noted from the temporal LULC (Fig. 4). The spatial extent of beaches along the Karaikal port varied from 97.6 ha in 2000 to about 57.9 ha in 2022 with significant erosion on the northern side of the port as evident from the model simulations as well.
Krishnapatnam port
This port was established in 2008 with an area of about 389.19 ha and increased to 588.4 ha in 2022. The study indicated a 200 ha expansion of port area mostly utilised from the mangrove and mudflats found in close vicinity to the port location. The conversion of these coastal ecosystems to port expansions has resulted in a decrease in the mangrove area from 727.7 ha in 2000 to 632.6 ha in 2010 and 679.1 ha in 2022 due to natural colonization in mudflats. Conversion of 73.2 ha of agriculture lands into other revenue generating activities such as industries and aquaculture farms were noted after port construction. The availability of sediments along the coast were found to be decreased by 31% (11315.6 M cum/yr to 7797.6 M cum/yr) after the establishment of the port. The model simulations indicated a significant accretion on the southern side of the port with noticeable erosion on the northern side of the port. The LULC change analysis has indicated a change in the beach area from 70ha to 37.5 ha in the northern side of the port with an improved beach area from 41ha to 63.4 ha on the southern side of the port between the years 2000 and 2022 (Table 3 and Fig. 5).
Gangavaram port
The port was initially constructed by converting unequal proportion of mangroves, mudflats, scrub lands and marshy area of about 288.9 ha. Gradual expansion of port was noted between 2010 and 2022 utilizing scrub lands and marsh lands around the port. The area of the port was found to be 425.1 ha in 2022. Significant conversions after port construction included growth of industrial areas by 49% (ie 1590.3 ha to 2376.7 ha) and urban areas by 62% (ie 495.4 ha to 803.5 ha). The study indicated existence of about 86 ha of mangroves close to the port location in 2000 whereas 2022 indicated no evidence for mangrove areas near the port. The existing marsh lands found close to the port experiences stress due to various port activities. Prior port construction, an uninterrupted sediment flow was showcased by the model simulation along the Gangavaram coast. The total sediment load obtained from the model was about 4435 Mcum/yr in 2000. Development of coastal port structures has restricted the steady flow of sediments which resulted in marginal accretion on the southern coast of the port and erosion on the northern coast of the port.
The sandy beach on the north which was about 22.5 ha near the port area during 2000 was eroded to 5.2ha during 2022. The beach area on the southern end of the port indicated gradual decrease in area from 41.5 ha in 2000 to about 22.7 ha in 2010 and a marginal accretion of about 28.9 ha in 2022. The model estimated a 58% decrease in the sediment quantity along the coast between 2000 and 2022 with moderate to severe erosion on the north of the port as indicated by the LULC change analysis (Table. 3 and Fig. 6).
Gopalpur port
Scrub lands, mining area and sandy beach of about 104.4ha were converted to Gopalpur port in 2013. Significant expansion of mineral mining areas (from 99.8 ha to 251.6 ha) around the port was noted from the study during 2000 to 2022. 68% increase of settlement areas (34.2 ha to 57.6 ha) around Arjyapalli was noted during the assessed period.
|
Mundra |
Landuse/ Land cover |
Area (in ha) |
Changes area (in ha) |
Percentage (%) |
||||||
|
2000 |
2010 |
2022 |
2000–2010 |
2010–2022 |
2000–2022 |
2000 |
2010 |
2022 |
||
|
Agriculture Land |
6132.4 |
6134.3 |
5655.9 |
1.9 |
-478.4 |
-476.5 |
16.5 |
16.5 |
15.3 |
|
|
Industial Area |
29.7 |
1372.8 |
1957.7 |
1343.1 |
584.9 |
1928.0 |
0.1 |
3.7 |
5.3 |
|
|
Scrub land |
9218.9 |
9165.7 |
9754.6 |
-53.2 |
588.9 |
535.7 |
24.9 |
24.7 |
26.3 |
|
|
Mangroves |
2051.2 |
2422.6 |
2800.3 |
371.3 |
377.7 |
749.1 |
5.5 |
6.5 |
7.6 |
|
|
Mudflat |
7307.9 |
5120.2 |
1574.7 |
-2187.7 |
-3545.4 |
-5733.2 |
19.7 |
13.8 |
4.2 |
|
|
Port |
192.4 |
845.6 |
1198.0 |
653.2 |
352.4 |
1005.6 |
0.5 |
2.3 |
3.2 |
|
|
Saltpan |
666.5 |
258.4 |
907.7 |
-408.0 |
649.3 |
241.3 |
1.8 |
0.7 |
2.4 |
|
|
Sandy area |
161.9 |
117.9 |
156.0 |
-44.1 |
38.2 |
-5.9 |
0.4 |
0.3 |
0.4 |
|
|
Settlement |
201.4 |
360.1 |
415.8 |
158.7 |
55.7 |
214.5 |
0.5 |
1.0 |
1.1 |
|
|
Water body |
11123.9 |
11288.6 |
12665.2 |
164.7 |
1376.6 |
1541.3 |
30.0 |
30.4 |
34.2 |
|
|
Total |
37086.1 |
37086.1 |
37086.1 |
100.0 |
100.0 |
100.0 |
||||
|
Hazira |
Agriculture Land |
2178.6 |
1611.0 |
1382.0 |
-567.5 |
-229.0 |
-796.6 |
13.7 |
10.1 |
8.7 |
|
Aquaculture |
81.9 |
105.4 |
1039.8 |
23.5 |
934.4 |
957.9 |
0.5 |
0.7 |
6.5 |
|
|
Port |
0.0 |
271.0 |
595.7 |
271.0 |
324.7 |
595.7 |
0.0 |
1.7 |
3.7 |
|
|
Industries |
1016.1 |
1725.2 |
1989.2 |
709.0 |
264.1 |
973.1 |
6.4 |
10.8 |
12.5 |
|
|
Scrub land |
3033.2 |
2879.0 |
2100.3 |
-154.1 |
-778.8 |
-932.9 |
19.1 |
18.1 |
13.2 |
|
|
Mangroves |
550.0 |
528.6 |
762.4 |
-21.3 |
233.7 |
212.4 |
3.5 |
3.3 |
4.8 |
|
|
Marsh land |
0.0 |
47.4 |
47.4 |
47.4 |
0.0 |
47.4 |
0.0 |
0.3 |
0.3 |
|
|
Mudflat |
1303.9 |
1083.9 |
585.3 |
-220.0 |
-498.6 |
-718.6 |
8.2 |
6.8 |
3.7 |
|
|
Sandy area |
52.4 |
11.3 |
10.1 |
-41.1 |
-1.1 |
-42.3 |
0.3 |
0.1 |
0.1 |
|
|
Settlement |
494.9 |
775.8 |
949.0 |
281.0 |
173.2 |
454.2 |
3.1 |
4.9 |
6.0 |
|
|
Vegetation |
404.7 |
327.6 |
263.0 |
-77.1 |
-64.6 |
-141.7 |
2.5 |
2.1 |
1.7 |
|
|
Water body |
6786.7 |
6535.9 |
6177.9 |
-250.7 |
-358.1 |
-608.8 |
42.7 |
41.1 |
38.8 |
|
|
Total |
15902.2 |
15902.2 |
15902.2 |
100 |
100 |
100 |
||||
|
Karaikal |
Agriculture Land |
1304.8 |
997.2 |
746.0 |
-307.7 |
-251.1 |
-558.8 |
15.5 |
11.8 |
8.8 |
|
Aquaculture |
158.4 |
144.1 |
113.8 |
-14.4 |
-30.3 |
-44.7 |
1.9 |
1.7 |
1.3 |
|
|
Coastal Structures |
0.0 |
6.9 |
9.8 |
6.9 |
2.9 |
9.8 |
0.0 |
0.1 |
0.1 |
|
|
Industries |
135.2 |
156.8 |
199.3 |
21.6 |
42.5 |
64.1 |
1.6 |
1.9 |
2.4 |
|
|
Scrub land |
1070.8 |
1163.5 |
1355.6 |
92.7 |
192.0 |
284.8 |
12.7 |
13.8 |
16.1 |
|
|
Mangroves |
21.4 |
8.8 |
13.7 |
-12.5 |
4.8 |
-7.7 |
0.3 |
0.1 |
0.2 |
|
|
Mudflat |
12.1 |
10.3 |
0.8 |
-1.8 |
-9.5 |
-11.3 |
0.1 |
0.1 |
0.0 |
|
|
Port |
0.0 |
88.3 |
106.9 |
88.3 |
18.6 |
106.9 |
0.0 |
1.0 |
1.3 |
|
|
Sandy area |
97.6 |
79.8 |
57.9 |
-17.8 |
-21.9 |
-39.7 |
1.2 |
0.9 |
0.7 |
|
|
Settlement |
1120.6 |
1228.3 |
1304.9 |
107.7 |
76.7 |
184.4 |
13.3 |
14.6 |
15.5 |
|
|
Water body |
4514.2 |
4551.0 |
4526.3 |
36.8 |
-24.7 |
12.1 |
53.5 |
54.0 |
53.7 |
|
|
Total |
8435.1 |
8435.1 |
8435.1 |
100 |
100 |
100 |
||||
|
Krishnapatnam |
Agriculture Land |
5347.3 |
5436.4 |
6235.1 |
89.0 |
798.7 |
887.8 |
25.1 |
25.5 |
29.2 |
|
Aquaculture |
2226.3 |
1058.5 |
1310.1 |
-1167.8 |
251.6 |
-916.2 |
10.4 |
5.0 |
6.1 |
|
|
Sand Dune |
490.4 |
512.7 |
477.3 |
22.3 |
-35.4 |
-13.2 |
2.3 |
2.4 |
2.2 |
|
|
Industries |
0.0 |
503.2 |
669.9 |
503.2 |
166.7 |
669.9 |
0.0 |
2.4 |
3.1 |
|
|
Scrub land |
4582.9 |
4808.9 |
3417.1 |
226.0 |
-1391.8 |
-1165.8 |
21.5 |
22.6 |
16.0 |
|
|
Mangroves |
727.7 |
632.6 |
686.2 |
-95.1 |
53.6 |
-41.5 |
3.4 |
3.0 |
3.2 |
|
|
Marsh land |
347.3 |
285.3 |
269.5 |
-62.0 |
-15.8 |
-77.8 |
1.6 |
1.3 |
1.3 |
|
|
Mine area |
297.4 |
297.4 |
297.4 |
0.0 |
0.0 |
0.0 |
1.4 |
1.4 |
1.4 |
|
|
Mudflat |
178.3 |
193.6 |
111.1 |
15.3 |
-82.5 |
-67.2 |
0.8 |
0.9 |
0.5 |
|
|
Port |
0.0 |
389.2 |
588.4 |
389.2 |
199.2 |
588.4 |
0.0 |
1.8 |
2.8 |
|
|
sand beach |
110.5 |
110.8 |
101.9 |
0.3 |
-8.9 |
-8.6 |
0.5 |
0.5 |
0.5 |
|
|
Settlement |
588.6 |
746.0 |
834.0 |
157.4 |
88.0 |
245.4 |
2.8 |
3.5 |
3.9 |
|
|
Vegetation |
2256.5 |
2249.8 |
2122.5 |
-6.6 |
-127.4 |
-134.0 |
10.6 |
10.6 |
10.0 |
|
|
Water body |
4167.7 |
4096.7 |
4200.6 |
-71.0 |
103.9 |
32.9 |
19.5 |
19.2 |
19.7 |
|
|
Total |
21321.0 |
21321.0 |
21321.0 |
100 |
100 |
100 |
||||
|
Gangavaram |
Agriculture Land |
407.2 |
260.2 |
0.0 |
-147.0 |
-260.2 |
-407.2 |
3.6 |
2.3 |
0.0 |
|
Forest |
3218.2 |
3046.8 |
2956.7 |
-171.4 |
-90.1 |
-261.4 |
28.1 |
26.7 |
25.9 |
|
|
Industries |
1590.3 |
1698.2 |
2376.7 |
107.9 |
678.5 |
786.4 |
13.9 |
14.9 |
20.8 |
|
|
Scrub land |
1870.9 |
1873.9 |
1329.9 |
3.0 |
-544.1 |
-541.1 |
16.4 |
16.4 |
11.6 |
|
|
Mangroves |
86.4 |
0.0 |
0.0 |
-86.4 |
0.0 |
-86.4 |
0.8 |
0.0 |
0.0 |
|
|
Marsh land |
407.3 |
238.6 |
243.2 |
-168.7 |
4.6 |
-164.1 |
3.6 |
2.1 |
2.1 |
|
|
Mudflat |
24.4 |
0.0 |
0.0 |
-24.4 |
0.0 |
-24.4 |
0.2 |
0.0 |
0.0 |
|
|
Port |
0.0 |
288.9 |
425.1 |
288.9 |
136.2 |
425.1 |
0.0 |
2.5 |
3.7 |
|
|
Sandy area |
66.3 |
36.6 |
34.1 |
-29.7 |
-2.5 |
-32.2 |
0.6 |
0.3 |
0.3 |
|
|
Settlement |
495.5 |
622.0 |
803.5 |
126.5 |
181.5 |
308.0 |
4.3 |
5.4 |
7.0 |
|
|
Water body |
3265.9 |
3367.1 |
3263.1 |
101.2 |
-104.0 |
-2.9 |
28.6 |
29.5 |
28.5 |
|
|
Total |
11432.3 |
11432.3 |
11432.3 |
100 |
100 |
100 |
||||
|
Gopalpur |
Agriculture Land |
231.6 |
197.3 |
120.6 |
-34.3 |
-76.7 |
-111.0 |
4.2 |
3.6 |
2.2 |
|
Aquaculture |
5.2 |
5.2 |
20.2 |
0.0 |
15.0 |
15.0 |
0.1 |
0.1 |
0.4 |
|
|
Coastal Structures |
1.5 |
2.6 |
3.1 |
1.1 |
0.5 |
1.6 |
0.0 |
0.0 |
0.1 |
|
|
Sand Dune |
215.2 |
181.4 |
190.9 |
-33.9 |
9.6 |
-24.3 |
3.9 |
3.3 |
3.5 |
|
|
Fishing Harbour |
10.7 |
10.7 |
10.7 |
0.0 |
0.0 |
0.0 |
0.2 |
0.2 |
0.2 |
|
|
Industries |
50.2 |
71.8 |
87.8 |
21.5 |
16.1 |
37.6 |
0.9 |
1.3 |
1.6 |
|
|
Scrub Land |
1051.1 |
900.9 |
790.2 |
-150.1 |
-110.7 |
-260.9 |
19.3 |
16.5 |
14.5 |
|
|
Marshy Land |
26.6 |
26.6 |
28.7 |
0.0 |
2.0 |
2.0 |
0.5 |
0.5 |
0.5 |
|
|
Mining |
99.9 |
215.0 |
251.7 |
115.1 |
36.7 |
151.8 |
1.8 |
3.9 |
4.6 |
|
|
Port |
0.0 |
0.0 |
104.5 |
0.0 |
104.5 |
104.5 |
0.0 |
0.0 |
1.9 |
|
|
Sandy area |
120.9 |
144.6 |
182.7 |
144.6 |
38.1 |
61.8 |
2.2 |
2.7 |
3.4 |
|
|
Settlement |
64.8 |
76.3 |
130.1 |
11.5 |
53.9 |
65.4 |
1.2 |
1.4 |
2.4 |
|
|
Vegetation |
329.5 |
393.8 |
391.9 |
64.3 |
-2.0 |
62.3 |
6.0 |
7.2 |
7.2 |
|
|
Water body |
3242.7 |
3223.6 |
3136.9 |
-19.0 |
-86.8 |
-105.8 |
59.5 |
59.2 |
57.6 |
|
|
Total |
5449.8 |
5449.8 |
5449.8 |
100 |
100 |
100 |
||||
The impact zone of the port ranged from 300m to 1.3km landward from the coast. A wide beach south of the port was noted from the study in 2022. The model simulation of sediment flow pattern prior and after the port construction exhibited significant flow of sediment along the south than on the north of the port.
The southern beach which was about 178m wide in 2000 was accreted to about 698m in width during 2022. However due to disturbed sediment flow to the north of the port the total sediment quantity along the Gopalpur coast was found to decrease from 11343 Mcum/yr in 2000 to 8467.4Mcum/yr in 2022 (Fig. 7).
The present study mapped and quantified the LULC changes around port areas and their influence on the nearshore sediment flow (NSF) from 2000 to 2022. The study indicated significant dynamics in both LULC and NSF between 2010 and 2022 compared to changes between 2000 to 2010. The LULC changes post port construction was mostly associated with commercial and economic infrastructure developments and built-up area expansions, around all the port areas considered for the study (EIA Report, 2016). The Mundra port in Gujarat was constructed on scrub, barren and sandy area surrounded by extensive mudflats and sparse mangroves. However, no significant habitation or cultivation areas were found near the port location. Post impact of the port includes loss of mudflats and mangrove areas especially in between the west and east and on the eastern side of the Mundra port. On comparison between the coastal villages around the port, rapid growth of habitation areas, economic (ie saltpans) and commercial (industries) activities were found in Mundra village located on the east of Mundra port (Fig. 8). Site of Hazira port area was an accreting bank of Hazira creek located on the mouth of Gulf of Khambhat in Gujarat. However, the study indicated a decreased rate of accretion after port construction. The impact of port included gradual lateral spread of economic, commercial and tourism related activities especially along the eastern side of Hazira village. The riverine island Kadiabet which was an extensive mudflat with mangrove species in 2000 were significantly utilised for aquaculture activities in 2022. Both the encroachment of industries and settlement areas in agriculture lands and vegetation in Hazira village was noted as a significant impact of Hazira port. Gujarat ports indicated severe loss of mudflats due to port interventions.
There was no entrenched mangrove vegetation in the Karaikal port area. However, small patches of mangroves were found along the estuary of the Vettar river mouth. The accreting nature of the coast in 2000 was significantly altered to erosion in the north of the port infrastructure in 2022 (EIA Report, 2015). Conversion of traditional agriculture lands and scrub lands to industries and settlement areas were considered as prime impacts of the port. Coastal villages with most impacts were Keezhayur south and Vanjoor in Karaikal UT and Nagore (kottagam) in Nagapattinam district.
The flat terrain at the mouth of Kandaleru river was converted to Krishnapatnam port in 2008.The significant landward impact of the port included loss of ecologically sensitive areas such as mudflats and mangroves in close vicinity to the port. Periodic removal of accumulated sand and silt on the southern side was noted in LULC change analysis for ease movement of vessels to and fro the port.
Gangavaram port was constructed by converting mudflats, mangroves and saltpans between the coastal headlands in Mettapalem and Muduchu Konda. LULC analysis of pre and post port construction indicated 100% loss of ecological systems such as mudflats and mangroves in 2022. Expansion of settlement area at the foot hills of Nadupuru, Yaradakonda and Visakhapatnam steel plant including encroachment in the fringe of Peddakonda forest area. No agricultural activities were noted after 2010 around the port area. Significant development impacts were noted in the Pedagantyada, Ukkunagaram. Nellimukku and Nadupuru villages.
The Gopalpur port was established adjacent to the mineral mining area south of Arjapalli village. Mostly scrub lands and coastal vegetation and sand dunes were utilised for port construction. Significant impact of port included growth of industries and settlement areas. Expanded mining activities south of the port were noted in the LULC change analysis. Nearshore sediment flow dynamics indicated an accreting trend towards the south of the port with a relatively low sediment distribution pattern on the north. (Pratap et al., 2015). High impact villages included Uppalaputi, Basanaputi, Badaputi and Arjyapalli in Ganjam district.
Determination of impact influence of LULC features using AHP
The high impact features identified by LULC change analysis were assessed using AHP to determine the landward extent of impact due to port construction. Different features stressed around the selected ports were assessed and their extent of impact was indicated as criteria weight (CW) percentage for the selected ports (Table 4). Impact analysis for Mundra port revealed that among the affected features loss of mudflats was highly influenced by 47% followed by moderate affected mangroves while agriculture land, saltpans and sandy areas were among the very low impact features. Optimum industrial growth was noted around the port during the assessed period. Hazira port influenced high growth of industrial areas and moderate improvement of aquaculture farms with significant loss of scrub and cultivation lands. Conversion of high proportion of agriculture lands and scrub areas to industries and settlements were noted around Karaikal port. Significant proportion of green areas including scrubs and croplands were converted to industries, settlements and aquaculture farms around Krishnapatnam port. High growth of industrialization was noted around Gangavaram port with substantial loss of crop lands and scrub areas. Scrub lands were the mostly affected feature due to Gopalpur port construction with expanded mining and settlement areas in close vicinity to the port.
Table: 4 Derived “Influence of Impacts” of features around selected ports using AHP
|
M u n d r a
|
|
Mudflat |
Industries |
Mangroves |
Scrub |
Agriculture Land |
Saltpan |
Sandy area |
|
|
CW % |
|
|
Mudflat |
1.0 |
9.0 |
5.0 |
5.0 |
7.0 |
8.0 |
9.0 |
|
|
46.99 |
||
|
Industries |
0.1 |
1.0 |
3.0 |
3.0 |
4.0 |
5.0 |
7.0 |
|
|
19.62 |
||
|
Mangroves |
0.2 |
0.3 |
1.0 |
2.0 |
2.0 |
3.0 |
5.0 |
|
|
11.62 |
||
|
Scrub |
0.2 |
0.3 |
0.5 |
1.0 |
2.0 |
2.0 |
4.0 |
|
|
8.65 |
||
|
Agriculture Land |
0.1 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
3.0 |
|
|
6.22 |
||
|
Saltpan |
0.1 |
0.2 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
|
|
4.24 |
||
|
Sandy area |
0.1 |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
1.0 |
|
|
2.66 |
||
|
|
1.9 |
11.3 |
10.5 |
12.3 |
16.8 |
21.5 |
31.0 |
|
|
CR=CI/RI |
0.067 |
|
|
H a z i r a |
|
Industries |
Aquaculture |
Scrub |
Agriculture Land |
Mudflat |
Settlement |
Mangroves |
Vegetation |
Sandy area |
CW % |
|
|
Industries |
1.0 |
2.0 |
5.0 |
5.0 |
5.0 |
7.0 |
7.0 |
5.0 |
9.0 |
32.69 |
||
|
Aquaculture |
0.5 |
1.0 |
2.0 |
3.0 |
2.0 |
6.0 |
5.0 |
7.0 |
9.0 |
20.20 |
||
|
Scrub |
0.2 |
0.5 |
1.0 |
2.0 |
2.0 |
5.0 |
4.0 |
5.0 |
7.0 |
13.77 |
||
|
Agriculture Land |
0.2 |
0.3 |
0.5 |
1.0 |
2.0 |
4.0 |
3.0 |
4.0 |
5.0 |
10.27 |
||
|
Mudflat |
0.2 |
0.5 |
0.5 |
0.5 |
1.0 |
3.0 |
2.0 |
3.0 |
4.0 |
7.92 |
||
|
Settlement |
0.1 |
0.2 |
0.2 |
0.3 |
0.3 |
1.0 |
3.0 |
2.0 |
5.0 |
5.29 |
||
|
Mangroves |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
0.3 |
1.0 |
2.0 |
7.0 |
4.94 |
||
|
Vegetation |
0.2 |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
3.12 |
||
|
Sandy area |
0.1 |
0.1 |
0.1 |
0.2 |
0.3 |
0.2 |
0.1 |
0.5 |
1.0 |
1.81 |
||
|
2.7 |
5.0 |
9.8 |
12.5 |
13.4 |
27.0 |
25.6 |
29.5 |
49.0 |
CR=CI/RI |
0.071 |
||
|
K a r a i k a l
|
|
Agriculture Land |
Scrub land |
Settlement |
Industries |
Aquaculture |
Sandy area |
Mudflat |
Mangroves |
|
CW% |
|
|
Agriculture Land |
1.0 |
3.0 |
2.0 |
4.0 |
5.0 |
2.0 |
7.0 |
9.0 |
|
30.19 |
||
|
Scrub land |
0.3 |
1.0 |
3.0 |
3.0 |
4.0 |
5.0 |
5.0 |
7.0 |
|
23.25 |
||
|
Settlement |
0.5 |
0.3 |
1.0 |
2.0 |
3.0 |
3.0 |
4.0 |
5.0 |
|
14.65 |
||
|
Industries |
0.3 |
0.3 |
0.5 |
1.0 |
4.0 |
3.0 |
5.0 |
3.0 |
|
12.09 |
||
|
Aquaculture |
0.2 |
0.3 |
0.3 |
0.3 |
1.0 |
2.0 |
3.0 |
5.0 |
|
7.59 |
||
|
Sandy area |
0.5 |
0.2 |
0.3 |
0.3 |
0.5 |
1.0 |
2.0 |
2.0 |
|
6.16 |
||
|
Mudflat |
0.1 |
0.2 |
0.3 |
0.2 |
0.3 |
0.5 |
1.0 |
2.0 |
|
3.47 |
||
|
Mangroves |
0.1 |
0.1 |
0.2 |
0.3 |
0.2 |
0.5 |
0.5 |
1.0 |
|
2.59 |
||
|
3.0 |
5.5 |
7.6 |
11.1 |
18.0 |
17.0 |
27.5 |
34.0 |
|
CR=CI/RI |
0.072 |
||
|
K r i s h n a p a t n a m |
|
Scrub land |
Aquaculture |
Agriculture Land |
Industries |
Settlement |
Vegetation |
Mangroves |
Sand dunes |
Sandy area |
CW% |
|
|
Scrub land |
1.0 |
2.0 |
5.0 |
4.0 |
5.0 |
5.0 |
7.0 |
9.0 |
9.0 |
32.70 |
||
|
Aquaculture |
0.5 |
1.0 |
3.0 |
3.0 |
3.0 |
4.0 |
5.0 |
7.0 |
7.0 |
21.25 |
||
|
Agriculture Land |
0.2 |
0.3 |
1.0 |
2.0 |
2.0 |
3.0 |
4.0 |
5.0 |
5.0 |
12.52 |
||
|
Industries |
0.3 |
0.3 |
0.5 |
1.0 |
4.0 |
4.0 |
3.0 |
4.0 |
5.0 |
12.33 |
||
|
Settlement |
0.2 |
0.3 |
0.5 |
0.3 |
1.0 |
2.0 |
2.0 |
3.0 |
3.0 |
6.91 |
||
|
Vegetation |
0.2 |
0.3 |
0.3 |
0.3 |
0.5 |
1.0 |
2.0 |
2.0 |
2.0 |
5.03 |
||
|
Mangroves |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
2.0 |
3.96 |
||
|
Sand dunes |
0.1 |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
2.90 |
||
|
Sandy area |
0.1 |
0.1 |
0.2 |
0.2 |
0.3 |
0.5 |
0.5 |
0.5 |
1.0 |
2.38 |
||
|
2.7 |
4.7 |
11.0 |
11.3 |
16.7 |
20.5 |
25.0 |
33.5 |
36.0 |
CR=CI/RI |
0.040 |
||
|
G a n g a v a r a m |
|
Industries |
Scrub |
Agriculture Land |
Forest |
Settlement |
Mangroves |
Sandy area |
|
|
CW% |
|
|
Industries |
1.0 |
2.0 |
5.0 |
5.0 |
5.0 |
7.0 |
9.0 |
|
|
39.85 |
||
|
Scrub |
0.5 |
1.0 |
2.0 |
3.0 |
4.0 |
5.0 |
7.0 |
|
|
23.47 |
||
|
Agriculture Land |
0.2 |
0.5 |
1.0 |
2.0 |
2.0 |
3.0 |
4.0 |
|
|
12.61 |
||
|
Forest |
0.2 |
0.3 |
0.5 |
1.0 |
2.0 |
3.0 |
3.0 |
|
|
9.64 |
||
|
Settlement |
0.2 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
2.0 |
|
|
6.65 |
||
|
Mangroves |
0.1 |
0.2 |
0.3 |
0.3 |
0.5 |
1.0 |
2.0 |
|
|
4.56 |
||
|
Sandy area |
0.1 |
0.1 |
0.3 |
0.3 |
0.5 |
0.5 |
1.0 |
|
|
3.22 |
||
|
|
2.4 |
4.4 |
9.6 |
12.2 |
15.0 |
21.5 |
28.0 |
|
|
CR=CI/RI |
0.021 |
|
|
G o p a l p u r |
|
Scrub |
Mining |
Settlement |
Sandy area |
Industries |
Sand Dune |
|
|
|
CW% |
|
|
Scrub |
1.0 |
2.0 |
5.0 |
7.0 |
5.0 |
9.0 |
|
|
|
43.24 |
||
|
Mining |
0.5 |
1.0 |
3.0 |
5.0 |
4.0 |
7.0 |
|
|
|
27.34 |
||
|
Settlement |
0.2 |
0.3 |
1.0 |
2.0 |
2.0 |
4.0 |
|
|
|
11.56 |
||
|
Sandy area |
0.1 |
0.2 |
0.5 |
1.0 |
2.0 |
3.0 |
|
|
|
8.04 |
||
|
Industries |
0.2 |
0.3 |
0.5 |
0.5 |
1.0 |
2.0 |
|
|
|
6.38 |
||
|
Sand Dune |
0.1 |
0.1 |
0.3 |
0.3 |
0.5 |
1.0 |
|
|
|
3.44 |
||
|
|
2.2 |
3.9 |
10.3 |
15.8 |
14.5 |
26.0 |
|
|
|
CR=CI/RI |
0.029 |
Local sensitivity analysis(LSA)
To assess the overall impact of port environs due to port construction, six variables such as LULC, population, hazard (ie cyclone), existence of cultural heritage site, improved road infrastructure network and shore stability were considered. The extent of impact of the variables in the adjacent villages of the selected ports were studied and the local sensitivity of the villages around the ports were classified based on the range of impacts (Table 6) as shown in Fig. 8.
Table: 5 Assigned classifications for ‘Impact Influence’ and ‘Local Sensitivity’ analysis
Table: 6 Determination of Local Sensitivity of impact variables using AHP
|
Mundra |
Parameters |
LULC |
Population |
Cyclone |
Road network |
Shore Stability |
CW (%) |
|
|
LULC |
1 |
3 |
2 |
0.25 |
0.125 |
10 |
||
|
Population |
0.33 |
1 |
0.33 |
0.33 |
0.11 |
5 |
||
|
Cyclone |
0.5 |
3 |
1 |
0.33 |
0.11 |
8 |
||
|
Road network |
4 |
3 |
3 |
1 |
0.33 |
21 |
||
|
Shore Stability |
8 |
9 |
9 |
3 |
1 |
56 |
||
|
|
|
|
|
|
CR |
0.06 |
||
|
Hazira |
Parameters |
LULC |
Population |
Heritage Site |
Road network |
Shore Stability |
CW (%) |
|
|
LULC |
1 |
3 |
7 |
3 |
4 |
44 |
||
|
Population |
0.33 |
1 |
5 |
3 |
2 |
24 |
||
|
Heritage Site |
0.14 |
0.2 |
1 |
0.33 |
0.2 |
4 |
||
|
Road network |
0.33 |
0.33 |
3 |
1 |
3 |
16 |
||
|
Shore Stability |
0.25 |
0.5 |
5 |
0.33 |
1 |
12 |
||
|
|
|
|
|
|
CR |
0.08 |
||
|
Karaikal |
Parameters |
LULC |
Population |
Heritage site |
Road network |
Cyclone |
Shore stability |
CW (%) |
|
LULC |
1 |
3 |
4 |
2 |
3 |
0.5 |
27 |
|
|
Population |
0.33 |
1 |
3 |
0.33 |
0.5 |
0.25 |
9 |
|
|
Heritage site |
0.25 |
0.33 |
1 |
0.5 |
0.25 |
0.11 |
5 |
|
|
Road network |
0.5 |
3 |
2 |
1 |
0.25 |
0.11 |
11 |
|
|
Cyclone |
0.33 |
2 |
4 |
4 |
1 |
0.33 |
19 |
|
|
Shore stability |
2 |
3 |
9 |
3 |
0.11 |
1 |
29 |
|
|
|
|
|
|
|
CR |
0.07 |
||
|
Krishnapatnam |
Parameters |
LULC |
Population |
Cyclone |
Road network |
Shore stability |
CW (%) |
|
|
LULC |
1 |
8 |
6 |
6 |
5 |
56 |
||
|
Population |
0.125 |
1 |
2 |
0.33 |
2 |
11 |
||
|
Cyclone |
0.17 |
0.5 |
1 |
2 |
3 |
13 |
||
|
Road network |
0.17 |
3 |
0.5 |
1 |
3 |
14 |
||
|
Shore stability |
0.2 |
0.5 |
0.33 |
0.33 |
1 |
6 |
||
|
|
|
|
|
|
CR |
0.05 |
||
|
Gangavaram |
Parameters |
LULC |
Population |
Cyclone |
Road network |
Shore stability |
CW (%) |
|
|
LULC |
1 |
5 |
6 |
6 |
5 |
52 |
||
|
Population |
0.2 |
1 |
4 |
3 |
5 |
23 |
||
|
Cyclone |
0.17 |
0.25 |
1 |
2 |
3 |
11 |
||
|
Road network |
0.17 |
0.33 |
0.5 |
1 |
3 |
9 |
||
|
Shore stability |
0.2 |
0.2 |
0.33 |
0.33 |
1 |
5 |
||
|
|
|
|
|
|
CR |
0.03 |
||
|
Gopalpur |
Parameters |
LULC |
Population |
Cyclone |
Road network |
Shore stability |
CW (%) |
|
|
LULC |
1 |
3 |
2 |
2 |
2 |
31 |
||
|
Population |
0.33 |
1 |
0.25 |
3 |
0.33 |
10 |
||
|
Cyclone |
0.5 |
4 |
1 |
2 |
0.33 |
18 |
||
|
Road network |
0.5 |
0.33 |
0.5 |
1 |
0.125 |
8 |
||
|
Shore stability |
0.5 |
3 |
3 |
8 |
1 |
33 |
||
|
|
|
|
|
|
CR |
0.06 |
||
The study attempted to understand the impact of ports to its environs taking into consideration some significant ports globally. The sensitivity analyses from certain selected ports worldwide clearly indicated that the growth of ports unerringly boost the country’s economy with significant negative impact to the natural environ post their construction. For instance, Qingdao and Yantai ports in China showed pre and post port development from sea to land with enhanced constructions and replacement of cultivation and forest areas to development infrastructures (Yan et al., 2021). Sydney harbour in Australia was reported for prominent coastal area developments and infrastructures (Mayer-Pinto et al., 2015). Coastal area modification and quick urbanization were reported as the impact of Singapore’s port (Loke et al., 2019).
Nowadays Remote sensing and GIS are inevitable tools for mapping, evaluating and monitoring the changes in the coastal features. This study revealed the impacts of some of the ports established between 2000 and 2022 on their adjacent land and shore features. General impacts of the ports irrespective of their location includes growth of industries, improved road networks economic activities and expansion of human settlements upon conversion of green areas such as scrub lands, agriculture areas and mangroves. Mudflats and sand dunes were among the few significantly converted ecosystems for port related activities. Development of coastal structures for port functioning played a dominant role in altering the flow of sediment quantity along their coasts. The model simulation for temporal sediment quantity indicated remarkable decrease in the sediment load available along the shore prior and after the port constructions. Improving green coastal infrastructure near the port areas can be attempted to restore these affected ESAs and other coastal features around port environs. The study assessed the impact of port on their adjacent villages and found that most of the adjacent villages had high to very high impacts due to port. The outcome of the study can be further used to plan strategies to manage the coastal stretch of the villages indicating negative impacts. The study can be further dealt with climate change parameters which was not attempted in this study due to lack of primary in situ data.
The authors have no conflict of interests to declare.
Ethics approval and consent to participate
I declare that this manuscript is original and has not been published or submitted simultaneously for publication elsewhere. I also confirm that I have obtained all necessary permissions and ethical approvals for this study.
Availability of data and material
The datasets generated during and / or analysed during the current study are available from the corresponding author on reasonable request.
Competing interests
The authors have no financial or proprietary interests in any material discussed in this article.
Funding
The authors did not receive support from any organization for the submitted work.
Auhors contributions
Madhumitha, R: Concept development for the manuscript, Temporal LULC preparation for selected ports, Change analysis of LULC data and interpretation, Determination of impact extents of ports, AHP and Local sensitivity analysis, Preparation of maps and data and Portion of manuscript preparation.
Rajakumari, S: Concept discussion and finalization for the manuscript, Quality check of all data, Sediment flow pattern simulations and interpretation, Portion of manuscript preparation, correlation, modification and finalization.
Deepika, B: Assisted in manuscript concept, Assisted in AHP and local sensitivity analysis and assisted in manuscript preparation and modification.
Acknowledgement
The paper was a part of the in-house study undertaken by the Geo Division of NCSCM. This project is funded by The Ministry of Environment, Forest and Climate Change (MoEF&CC), New Delhi and the authors express their gratitude for the continued support of MoEF&CC in various capacity building activities of NCSCM. The authors also wish to express their thanks to their team members of this study in Geo division for their technical support. The opinions expressed in this paper are those of the authors based on the analysis and do not necessarily indicate the opinion of their organization.
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No competing interests reported.