Geo-informatics is the science and the technology which develops and uses information science, infrastructure to address the problems of geography, geosciences and related branches of engineering. “The art, science or technology dealing with the acquisition, storage, processing, production, presentation & dissemination of geo-information“. Perhaps the most important concern for all of us today is protecting the environment we live and breathe in. Climate change issues are creating havoc with erratic weather patterns affecting everything from crop production to untimely melting of ice glaciers.
There is a lot to worry about and immediate action is definitely required. It’s not that the world has not geared up to take corrective actions, but we need to do more, and Geo-informatics can help us achieve that. Geo-informatics is a powerful platform which enables every sector to perform better and the environment is no exception! Coupled with a digital map, GIS allows a user to see locations, events, features, and environmental changes with unprecedented clarity, showing layer upon layer of information such as environmental trends, soil stability, pesticide use, migration corridors, hazardous waste generators, dust source points, lake remediation efforts, and at-risk water wells. Effective environmental practice considers the whole spectrum of the environment. ArcGIS® & other GIS technologies offers a wide variety of analytical tools to meet the needs of many people, helping them make better decisions about the environment. People in the environmental management community use GIS to organize existing information and communicate that information throughout their organizations. GIS can be used as a strategic tool to automate processes, transform environmental management operations by garnering new knowledge, and support decisions that make a profound difference on our environment.
2. What Is Geo-informatics
• Geo-informatics is the science and the technology which
develops and uses information science, infrastructure to
address the problems of geography, geosciences and related
branches of engineering.
• “The art, science or technology dealing with the acquisition,
storage, processing, production, presentation & dissemination
of geo-information“
3. Branches of geo-informatics include
1. Remote Sensing
2. Geographic Information Systems (GIS)
3. Cartography
4. Global Navigation Satellite Systems
5. Photogrammetry
6. DBMS- Data Base Management System
4. Environmental Management
• As Barrow (2005) has acknowledged, environmental management can
be referred to a goal or vision, to attempts to steer a process, to the
application of a set of tools, to a philosophical exercise seeking to establish
new perspectives towards the environment and human societies, and to
much more besides.
• Environmental managers are a diverse group of people including
academics, policy-makers, non-governmental organization (NGO)
workers, company employees, civil servants and a wide range of
individuals or groups who make decisions about the use of natural
resources (such as fishers, farmers and pastoralists).
5. Introduction
• Perhaps the most important concern for all of us today is protecting the
environment we live and breathe in.
• Climate change issues are creating havoc with erratic weather patterns
affecting everything from crop production to untimely melting of ice
glaciers.
• There is a lot to worry about and immediate action is definitely required.
It’s not that the world has not geared up to take corrective actions, but we
need to do more, and Geo-informatics can help us achieve that.
• Geo-informatics is a powerful platform which enables every sector to
perform better and the environment is no exception!
6. • Coupled with a digital map, GIS allows a user to see locations, events, features, and
environmental changes with unprecedented clarity, showing layer upon layer of
information such as environmental trends, soil stability, pesticide use, migration
corridors, hazardous waste generators, dust source points, lake remediation efforts,
and at-risk water wells.
• Effective environmental practice considers the whole spectrum of the environment.
• ArcGIS® & other GIS technologies offers a wide variety of analytical tools to meet
the needs of many people, helping them make better decisions about the
environment.
• People in the environmental management community use GIS to organize existing
information and communicate that information throughout their organizations.
7. • GIS can be used as a strategic tool to automate processes, transform environmental
management operations by garnering new knowledge, and support decisions that
make a profound difference on our environment.
10. Role of Geo-Informatics in Environmental Management
• Ensure accurate reporting with improved data collection.
• Improve decision making.
• Increase productivity with streamlined work processes.
• Provide better data analysis and presentation options.
• Model dynamic environmental phenomena.
• Create predictive scenarios for environmental impact studies.
• Automate regulatory compliance processes.
• Disseminate maps and share map data across the Internet.
11. Environmental Impact Analysis (EIA)
• EIA is an important policy initiative to conserve natural resources and
environment.
• Many human activities produce potential adverse environmental effects
which include the construction and operation of highways, rail roads,
pipelines, airports, radioactive waste disposal and more.
• Environmental impact statements are usually required to contain specific
information on the magnitude and characteristics of environmental impact.
• The EIA can be carried out efficiently by the help of GIS, by integrating
various GIS layers, assessment of natural features can be performed.
12. Disaster Management
• Today a well-developed Geo-informatic systems are used to protect
the environment.
• It has become an integrated, well developed and successful tool in
disaster management and mitigation. GIS can help with risk
management and analysis by displaying which areas are likely to be
prone to natural or man-made disasters.
• When such disasters are identified, preventive measures can be
developed.
13.
14. Management of Natural Resources
• GIS helps in identifying the impact of human behavior on natural resources and
leads to more effective utilization of the same.
• Data about natural resources could be collected through remote sensing, aerial
photography or satellite imagery and then they are mapped using GIS technology.
• The major application of GIS in natural resource management is in confronting
environmental issues like a flood, landslide, soil erosions, drought, earthquake
Habitat analysis, Environmental assessment, Pest/disease outbreaks, Impervious
surface mapping, Lake monitoring, Hydrology, Landuse-Landcover monitoring,
Mineral province, Geomorphology, Geology
• It also addresses the current problems of climate change, habitat loss, population
growth, pollution etc. and provides information about land area change between
time periods.
15. • The information obtained from GIS help to study specific areas and monitoring can be
done in and around those areas. It provides relevant information about the
environmental condition and policy, including conservation programs. Maps in GIS
provide the information of location and current resources.
• This technology can also help in the well management and maintenance of the
agricultural, water and forest resources
• Foresters can easily monitor forest condition. Agricultural land includes managing
crop yield, monitoring crop rotation, and more.
• Water is one of the most essential constituents of the environment. GIS is used to
analyze geographic distribution of water resources.
• They are interrelated, i.e. forest cover reduces the storm water runoff and tree canopy
stores approximately 215,000 tons carbon.
• GIS is also used in aforestation.
16. Determination of land cover and land use
• Land cover means the feature that is covering the barren surface .
• Land use means the area in the surface utilized for particular use.
• The role of GIS technology in land use and land cover applications is that
we can determine land use/land cover changes in the different areas.
• Also it can detect and estimate the changes in the land use/ land cover
pattern within time. It enables to find out sudden changes in land use and
land cover either by natural forces or by other activities like deforestation.
17. Soil Mapping
• Soil mapping provides resource information about an area.
• It helps in understanding soil suitability for various land use
activities. It is essential for preventing environmental deterioration
associated with misuse of land.
• GIS Helps to identify soil types in an area and to delineate soil
boundaries. It is used for the identification and classification of soil.
• Soil map is widely used by the farmers in developed countries to
retain soil nutrients and earn maximum yield.
18. Irrigation management
• Water availability for irrigation purposes for
any area is vital for crop production in that
region. It needs to be properly and efficiently
managed for the proper utilization of water
19. Air quality monitoring
• Air quality monitoring has become an important part of healthy living, and
GIS can play a very important role here as well. A GIS integrated platform
by leveraging sensors and IoT for air quality monitoring, analytics, and
planning, can accurately predict the PM levels in varied areas within a city.
• It can also tell you which areas are the most hazardous or most dangerous
for everyone, more specifically for asthma patients. This analysis can help
the field officers to take corrective action on time to improve the air quality.
• Citizen engagement is also becoming an important part of such
applications. Using mobile apps, the citizens can also make the authorities
aware which areas need immediate attention.
20. Wetland Mapping
• Wetlands contribute to a healthy environment and retain water during dry
periods, thus keeping the water table high and relatively stable.
• During the flooding they act to reduce flood levels and to trap suspended
solids and attached nutrients.
• GIS provide options for wetland mapping and design projects for wetland
conservation quickly with the help of GIS.
• Integration with Remote Sensing data helps to complete wetland mapping
on various scale. We can create a wetland digital data bank with species
information using GIS
21. Application in Watershed Management
• Water as a resource has been diminishing over the years. In Africa and other
developing nations, the availability of clean water has been always scarce.
• Water management has therefore been a challenge in developing nations. However,
with the use of satellite data, water bodies such as rivers, lakes, dams and reservoirs
can be mapped in 3D with the help of GIS technology.
• This data can be used in the sustainable management of water bodies since
respective authorities can decide which regions need effective protection and
management.
• At the same time, decisions regarding the most effective means of utilization of
these regions can always be arrived at.
22. Application of GIS Data in Forest Management
• Over the last century, the forest cover of the world has declined at an alarming rate.
Being a renewable resource, forest cover can be regenerated through sustainable
management.
• With the help of remote sensing and GIS data, a forest manager can generate
information with regards to forest cover, types of forest present within the area of
the study, human encroachment into forest land/protected areas, encroachment of
desert like conditions and so on.
• This information is critical in the development of forest management plans and in
the process of decision making to ensure that effective policies have been put in
place to control and govern the manner in which forest resources is utilized.
23.
24. Application of GIS Data to Combat Desertification
• Geospatial data can be used to determine the soil types present in a given area and
nutrient availability.
• Negative change can always be identified once this data is collected over a long
period of time.
• GIS data can also be used to determine the land use practices within a given area
and vegetation constitution and the impact that they have on the environment.
• Consequently, slope information of a region can also be determined with the use of
GIS data.
• With all this information, an individual can easily determine whether desert like
conditions are encroaching in an area. If desert like conditions have been identified,
its impacts and intensity shall be analysed in order to decide on whether artificial or
natural methods shall be used to combat the situation.
25.
26. Agriculture Mapping
• Technological innovations & geospatial technology help in creating a dynamic
and competitive agriculture which is protective of the environment and
capable of providing excellent nutrition to the people.
• GIS tools and online web resources are helping farmers to conduct crop
forecasting and manage their agriculture production by utilizing multispectral
imagery collected by satellites. The ability of GIS to analyze and visualize
agricultural environments and workflows has proven to be very beneficial to
those involved in the farming industry. GIS has the capability to analyze soil
data and determine which crops should be planted where and how to maintain
soil nutrition so that the plants are best benefitted.
27. • GIS in agriculture helps farmers to achieve increased production and reduced costs
by enabling better management of land resources. The risk of marginalization and
vulnerability of small and marginal farmers, who constitute about 85% of farmers
globally, also gets reduced.
• Agricultural Geographic Information Systems using Geo-informatics Technology
enable the farmers to map and project current and future fluctuations in
precipitation, temperature, crop output etc.
• Farming is getting smarter with the availability of advanced technologies like
precision equipment, the Internet of Things (IoT), sensors and actuators, geo-
positioning systems, Big Data, Unmanned Aerial Vehicles, robotics etc.
28. Application of GIS Data in Biodiversity Management
• Geospatial data can also be used in the management of flora and fauna within
protected areas. Ground and aerial photographs, for instance, are essential in this
practice. A
• Aerial and satellite photographs can be used to determine the presence and
distribution of vegetation within a protected area. These photos can also be used to
determine the presence and distribution of invasive species within an ecosystem.
• This information is essential as it determines the amount of cover and food that is
present, particularly for herbivores during various seasons of the year.
• Aerial photographs can be used to ease the process of counting during animal
census activities. The stop capability of photographs eases this process.
29. • It is always essential for protected area managers to determine the population and
distribution of various animal species within a protected area to ensure that they
have enough food and water, to eliminate the chances of overstocking that might
lead to soil erosion and to ensure that a balance within the ecosystem is arrived at.
• Geospatial data can also be used to show human encroachment into protected areas
as well as animal activities outside protected areas. This data critical especially in
the process of resolving human/wildlife conflicts.
• Finally, the use of GPS technology can be applied to monitor the movement of
endangered species as well as newly introduced species to determine their progress
as well as protecting them from poachers.
30. • Finally, geospatial data can be used to carry out environmental impact assessment
(EIA) of various projects carried out within protected areas
• Projects such as building of roads, buildings, pipe ways, dams, and so on might
have various effects on the flora and fauna of the ecosystem. Thus, geospatial data
has become essential in biodiversity management.
31. Zoning of Landslides hazard
• Landslide hazard zonation is the process of ranking different parts of
an area according to the degrees of actual or potential hazard from
landslides.
• The evaluation of landslide hazard is a complex task.
• It has become possible to efficiently collect, manipulate and
integrate a variety of spatial data such as geological, structural,
surface cover and slope characteristics of an area, which can be used
for hazard zonation.
32. Estimation of flood damage
• GIS helps to document the need for federal disaster relief funds,
when appropriate and can be utilized by insurance agencies to assist
in assessing monetary value of property loss.
• A local government need to map flooding risk areas for evaluate the
flood potential level in the surrounding area.
• The damage can be well estimated and can be shown using digital
maps .
33. Identification of Volcanic Hazard
• Volcanic hazard to human life and environment include hot
avalanches, hot particles gas clouds, lava flows and flooding.
• Potential volcanic hazard zone can be recognized by the
characteristic historical records of volcanic activities, it can
incorporate with GIS.
• Thus an impact assessment study on volcanic hazards deals with
economic loss and loss of lives and property in densely populated
areas.
34.
35. Wild Fire Mitigation
• Wildfire causes huge loss to flora and fauna. The very first strategy to
defend the forests against wildfire is to avoid it.
• GIS has proved its potential in forest fire management. There are different
applications of GIS in forest fire management out of which the most
important ones are hazard map production, forest fire simulation, and
resource management.
• Simulation by itself has a main role in the management of forest fire. GIS
uses various information layers such as Digital Elevation Model (DEM)
and index of flammability along with different models for the purpose of
forest fire management.
38. CASE STUDY 1
• Topic: GIS-Based Forest Fire Risk Assessment and
Mapping
• Authors: Chengcheng Gai, Wenguo Weng, Hongyong Yuan
39. Introduction
• Forest fire is a usual disaster in real life, causing huge live, property and ecology
losses.
• A risk assessment model to identify, classify and map forest fire risk areas is
presented in this paper. This model considers three parts, i.e. hazards identification,
vulnerability analysis, and emergency response capacity analysis.
• The first part concentrates on several influence factors in forest fires, including the
land use, topography and meteorology where the forest situate.
• The second part is made up of population density and value of forest resources.
• The forest fire response capacity including forest fire-brigade, watch-tower and
helicopter water source is the third part
40. METHODS
• The proposed method in this article is scenario-based and
involves natural and human factors in each step.
• It implements and expands the analytic-deliberative process.
43. There is a positive correlation
between the speed of fire spread
and the temperature, wind force,
and a negative correlation with
relative humidity
44. As for vulnerability factors population
density and value of forest resources
are considered. Human activity is one
of the main causes of forest fires.
When the population density is small,
forest fires usually is caused by
natural forces, such as lightning,
spontaneous combustion, etc. When
the population density is large, human
activity becomes dominated. There are
different interpretations of the value of
forest resources. Broadly speaking,
forest price is the monetary value of
the forest performance, which
includes the stumpage value of
forests, and animals, plants,
microorganisms, ecological benefits
of forests. In a narrow sense, it is the
monetary value of the stumpage price
For a certain region, the forest fire risk is
calculated with the data acquired and the
weights for each factors based on the local
condition. Equation (1) is used in GIS to
determine the fire hazard model:
R=∑WiXi (1)
(1) Where R is the numerical index of
forest fire risk value, Xi is the value of
factors, and Wi is the weight of factors,
which is determined by Grey Relativity
Analysis described below.
45. Weights of Factors
• Grey Relativity Analysis is used to assess the degree of the influence of each factor.
• Grey relation analysis is an effective technique that can be used to solve the
uncertainty problems under the discrete data and information incompleteness.
• xi(j) are the values of the factors in the forest risk index. i=1…m represents the
geographic regions, and j=1…n is the number of factors, i.e. land use, elevation,
slope, aspect, temperature, relative humidity, wind force, population density, value
of forest resources, forest fire-brigade, watchtower and helicopter water source.
Using the grey relativity analysis procedures, the weights of various factors can be
got, shown in Table Ⅳ.
46.
47. Results
Hazard Identification: Forest fire risk
factors involve land use, elevation, slope,
aspect, temperature, relative humidity and
wind force that are performed using
ArcGIS software.
The hazard identification map is the
combination of seven forest fire risk
factors through map algebra algorithm, in
order to reflect the risk of forest fires in
the region.
Figure 3: Red area indicated the highest
level of risk, blue area shows the lowest
level of risk. It is shown that, the north area
has greater risk of forest fires, mainly due to
wide distribution of vegetation and strong
wind.
48. Fig. 4 is combined of the population density
and value of forest resources. It is shown that,
the north area has greater vulnerability,
mainly due to the higher value of forest
resources.
Vulnerability Analysis Map
49. Emergency response map
Fig.5 is combined of distribution
of forest fire-brigade, watch-tower
and helicopter water point. It is
shown that, the central area has
poor emergency response, mainly
due to the distribution of watch-
tower.
50. Final map of forest fire risk zone map
Fig.6 is combined of hazard map,
vulnerability map and emergency
response map, in order to reflect the
region’s integrated risk. It is clear
from the figure that, the northern
part of the region has a higher risk of
forest fires, mainly due to the hazard
identification in that area.
51. CASE STUDY 2
• Topic: Depicting changes in land surface cover at AlHassa
oasis of Saudi Arabia using remote sensing and GIS
techniques
• Authors: Abdulrahman Mohamed Almadini, Abdalhaleem
Abdalla Hassaballa
52. Introduction
• Since the economic history of the Al-Hassa oasis is tightly associated with
agricultural practices where the oasis (in its old geometry) produces a considerable
share of dates in the Kingdom of Saudi Arabia (KSA)
• This study aimed to depict the spatial variations in the oasis’s green cover using two
scenarios corresponding to urban sprawl over the past 32 years.
• Scenario (i) included the old oasis beside the surrounding cities, irrigation
discharge lakes, and the newly embedded agricultural areas over the southern part
of the oasis (i.e., the new oasis). In this scenario, the quantitative share of the new
agricultural areas that extended out of the old oasis, where the new extended
agricultural areas were aimed at compensating the degraded agricultural land inside
the old oasis, was studied.
53. • Scenario (ii) was applied over the old oasis only in order to examine the actual
change in vegetation cover (i.e., degradation) within this oasis, with respect to the
other classes of surface cover throughout the estimated period (i.e., the last 30
years)
55. Data collection and processing
• Four cloud-free satellite images from the Landsat series were acquired for the
assessment period (1985 to 2017), with a spatial resolution of 30 m and calibrated
using the data-specific utilities of ENVI (Ver. 5.3) software, where the image’s
digital number was transformed into spectral radiance (Lλ). Subsequently,
reflectance images were generated from the radiance pixels.
• Atmospheric correction tools such as dark object removal, haze removal, and cloud
masking were used to correct the sensor radiance for atmospheric effects using Fast
Line-of-sight Atmosphere Analysis of Spectral Hypercubes (FLAASH)
56. • Image enhancement and linear histogram stretching were also performed.
• Exo-atmospheric reflectance was applied using published post-launch gain value in
ENVI, which is a value that is multiplied by the pixel value to scale it into
physically meaningful units of radiance
• The Lλ was calculated using the calibration coefficients from the metadata of the
acquired image. e. Hence, reflectance value of images were determined from the
obtained radiance values.
57. Image classification
• The acquired images were processed using supervised
classification and five basic class types in scenario
• (i) were determined; namely: vegetation cover, urban area,
bare lands, sand dunes, and water bodies.
• Water bodies as a class was not included in scenario (ii) as no
water body was located within the borders of this scenario.
58. Accuracy assessment
• A confusion matrix is typically used as a numerical technique for
portraying the accuracy of the classified image.
• It is set in a tabular form that illustrates correspondence between
the result of the classification process and a reference image.
• In order to generate the confusion matrix, ground truth data, such
as field observations documented with a GPS, map information,
or a digitized image, are needed.
59. Change detection
• In the post-classification process, image differencing
technique was applied for each of the two images. This
technique uses change detection statistics to provide a
detailed tabulation of changes between the two classified
images
61. Results
Figs 3 and 4 show the resultant
classification maps of the study area
for the years of (a) 1985, (b) 1999, (c)
2013, and (d) 2017obtained from
scenarios (i) and (ii), respectively. A
clear spatial variability in vegetation
cover class was observed in scenario
(i) due to the compensation plans
62.
63. Confusion matrix
• The overall accuracies of surface cover were found to be 97.6%,
100%, 97.8%, and 98.7% for the urban area, vegetation cover, bare
soil, and sand dune classes, respectively.
• This indicates a high similarity between the classifiers and the
predictors, especially for the year 1999.
• During the process of error evaluation, > 94% for both producer
and user accuracies were achieved with a kappa coefficient of more
than 0.96 for all classified images.
64.
65. Classification statistics
• The summary statistics of the acquired areas of each surface cover class (ha) under
scenarios (i) and (ii) throughout the analyzed periods (i.e., 1985, 1999, 2013, and
2017) is presented in Tables 3 and 4, respectively.
• The range value (ha) for each class was also produced as the difference between
the early state (1985) and the later state (2017), in order to reveal the final state for
each class. Therefore, the resulting ranges showed that the urban area class
produced the highest change in surface cover (347.29%).
• Scenario (i) shows that the sand dunes class was the biggest and the most dominant
among the others (Table 3). scenario (i) shows that the area of bare land class was
the second highest, followed by the vegetation class (Table 3).
66. • The urban area that was estimated at 4,597.02 ha in 1985 reached 20,562.21 ha by
2017 due to urban sprawl. The urban area expanded nearly 16,000 ha over the other
classes by the end of the analyzed period
• The class of water bodies, represented by agricultural drainage water evaporation
lakes, occupied only a small portion of the surface cover of the new oasis (Table 3)
• . Finally, it is worth to mention from scenario (i) of the new oasis that the areas of
the sand dunes and bare lands classes were the most dominant in terms of area,
which reflects the area geographical identity, where the area of these two classes
represented together (i.e., 87%) of the total area (Table 3)
• Though scenario (ii) was applied in order to examine the actual change in
vegetation cover within the old oasis (only) with respect to the other classes of
surface cover, that both sand dunes and bare soil classes masked most of the old
oasis surface cover (73.81%) (Table 4).
67. • The area of vegetation cover in scenario (ii) was third largest in the category among
other classes (Table 4).
• The urban class showed an increasing trend throughout the study period (1985 to
2017) in both the scenarios. A major part of this sprawl has occurred in the new
oasis, as was verified from Figs 2 and 3. This increase reflects the continuous
increase in population and their endeavor to settle within the green spots, alongside
some other social and economic considerations
70. CASE STUDY 3
• Topic: Climate change vulnerability in a tropical region based on
environmental and socio-economic factors
• Authors: Sarun Savith, Andrea Ghermandi, Sheela A.M, Vineetha
.P
71. Introduction
• Sheela et.al.(2018) assessed the local dimensions of vulnerability in the tropical
state of Kerala, India, using a purposely developed vulnerability index, which
accounts for both environmental and socio-economic factors.
• The large extents of coastal wetlands and lagoons and high concentration of
mangrove forests make the state environmentally vulnerable.
• Low human development index, large population of socially deprived groups,
which are dependent on the primary sector, and high population density make
the state vulnerable from a socioeconomic point of view.
• Present study investigates climate change vulnerability at the district level in
the State of Kerala relying on a purposely developed composite vulnerability
index that encompasses both socioeconomic and environmental factors
73. Methodology
• The first methodological step deals with the identification of nine key
environmental and socio-economic variables covering important aspects related to
climate change vulnerability in the context of Kerala
• Lagoons, dense forests, Shola forests, coastal wetlands,and sand beaches are
important vulnerable systems and susceptible to the effect of cumulative stressors.
The relative extent of these ecosystems was measured in each district.
• The socioeconomic variables human development index, population dependent on
primary sector (agricultural and fisheries sectors), socially deprived classes, and
population density have also been included.
74. • Weights were assigned to each indicator of every district based on vulnerability
importance to the particular phenomenon as per various reports, namely, Census Report
2011, State Environment Report, State Wetlands Atlas, Human Development Report
2005, and State Economic Review.
• The composite climate change vulnerability index was also tested by applying different
normalization methods and using different weighting factors for the selected indicators
• In the environmental vulnerability index, the ranking was given to each variable as 2, 4,
6, and 8 based on degree of vulnerability(rank value 2 indicates that the district is least
vulnerable, 4-medium, 6-high and rank value 8-very vulnerable to climate change)
• The individual shares of population density, population depending on primary sectors
and socially deprived classes, have been calculated as percentages.
75. • Weighing for the socio-economic variables relied on expert judgment, where 4-
Variable most affected by climate change (population density), 3-Population
dependent to the primary sector, 2-Socially deprived section, and 1 to the human
development index.
• Socio-economic and environmental vulnerability indexes were developed by
cumulating the corresponding values of each of the variables.
• Accordingly, separate district-wide maps of socio-economic and environmental
vulnerability have been developed.
• A composite climate change vulnerability index has finally been developed using
simple additive weighting from environmental and socio-economic vulnerability
indexes
76. • Based on composite vulnerability index,14 districts are clustered into four classes
which are characterized by very high, high, medium and low vulnerability.
• In order to confirm the above results, Cluster analysis was conducted using SPSS
Chicago 16.0 software. The classification of districts have been done using Cluster
analysis.
• The Euclidian distance method and Ward’s method were used for the analysis
89. Conclusion
• Among the 14 districts that make up the state, the coastal district of Alappuzha is
found to be the most highly vulnerable because of the high population density with
very exposed coastal plain physiographic regions like wetlands, lagoons, and sandy
beaches which are exposed to the anticipated climate change risk.
• Backwater banks and filtration ponds/paddy fields are other sections of the coastal
zone which are highly susceptible to sea level rise (SAPCC), which are
predominant geographical peculiarities in the district.
• The hilly districts of Idukki and Wayanad and Palakkad have similar environmental
and social characteristics such as high dependence of the primary sector, deprived
classes, the low performance of human development index, and high concentration
of forest density and Shola forest.
90. • There is increase in the temperature across the highland region and change in the
distribution of rainfall in the Palaghat district.
• A composite vulnerability index based on environmental and socio-economic
factors revealed that a higher percentage of the population relying on agricultural
related activities and social deprivation groups and comparatively low performance
in the human development index, existence of coastal wetlands, lagoons, mangrove
forest, and beaches make the region highly vulnerable. Climate change
vulnerability risk is highest in the coastal areas.
91. General Conclusion
• With the rising pressure on the use of natural resources due to the
increasing human population, geo-informatics can be used to
manage these precious limited resources in an efficient and effective
manner. Geospatial information is quite useful in the identification
& analysis of factors that effect the utilization of these resources.
• Hence with a detailed understanding of these factors, sound
decision could be reached in order to ensure the sustainable use of
natural resources to meet the needs of the current as well as future
generations.
