Application of Geo-informatics in Environmental Management
- 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.
- 8. GIS Integrated Environmental Management System
- 9. ESRI’s GIS Tools for Environmental Management
- 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.
- 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.
- 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.
- 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.
- 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.
- 37. CASE STUDIES
- 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.
- 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
- 60. Procedure of Change Detection
- 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
- 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.
- 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
- 69. Change detection
- 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
- 72. STUDY AREA
- 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
- 77. Results
- 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.
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