APPLICATION OF RS AND GIS IN ENVIRONMENTAL ASSESSMENT M.B.RAJIV GANDHI(04B21) J.SAHAYA VETRI SELVAN(04B22) K.SHENBAGAVALLI(04B23) N.SIRAJUDEEN(04B24) B.SOWMIYA(04B25)
INTRODUCTION The environment, is a term that comprises all living and non-living things that occur naturally on Earth. Assessment is the process of documenting, usually in measurable terms
ENVIRONMENTAL ASSESSMENT Environmental assessment is a process to predict the environmental effects of proposed initiatives before they are carried out. An environmental assessment: identifies possible environmental effects proposes measures to mitigate adverse effects predicts whether there will be significant adverse environmental effects, even after the mitigation is implemented
NEED FOR ENVIRONMENTAL ASSESSMENT There are two main purposes of environmental assessment: minimize or avoid adverse environmental effects before they occur incorporate environmental decision making
WHEN IT IS DONE?? Should be conducted as early as possible in the planning and proposal stages of a project for the analysis to be valuable to decision makers and to incorporate the mitigative measures into the proposed plans.
BENEFITS increased protection of human health the sustainable use of natural resources reduced project costs and delays minimized risks of environmental disasters
APPLICATIONS Wild Land Analysis Emergency Services like Fire Prevention Hazard Mitigation and Future planning Air pollution & control Disaster Management Forest Fires Management
CONTD… Managing Natural Resources Waste Water Management Oil Spills and its remedial actions Sea Water - Fresh water interface Studies Coal Mine Fires
CASE STUDY: GIS model to assess Chennai city’s environmental performance, using green-cover as the parameter
Chennai city is the capital of Tamil Nadu, located in the Southeastern India. The average population growth of the city is 25% per decade that recurrently reduces the greencovered area. Exceptionally, during the post economic liberalization period, i.e. between the years 1997-2001, the city lost up to 99% of its green covered areas at some parts.
Subsequently, the Chennai city started to experience wide range of environmental issues, like urban heat island, pollution and ground water depletion, etc. Though other factors are also reason for that, the receding green-covers mainly lowers the urban system's self rejuvenation capacity. The diminishing green-covers simplified many aspects of the natural process, thus ultimately affected the Chennai city's environmental performance.
Through this model, the correlation between the Chennai city's green-cover change and its environmental performance change is appraised. To appraise this sensitive association between the green-cover and city's environmental performance, a GIS model has been developed by adapting the Chennai city as the case study area. The model is evolved, using the three sets of the green-cover services, namely the air quality amelioration, the hydrological process regulation and the micro-climatic amelioration.
The output confirms the positive relationship between per capita green cover modification and the Chennai city's environmental performance change. The result also shows that the Chennai city's environmental performance is reduced drastically across the city between the years 1997- 2001, at some parts to the degree of 38% is reduced.
CASE STUDY GIS and Natural Disaster Management
Disasters can be categorized in two forms, either natural or man-made. Natural disasters are natural processes or phenomena occurring in the biosphere that may constitute a damaging event. They can be classified as earthquakes, volcanic activity, mass movement (landslides, rock falls, avalanches), floods and mudflows, storms (hailstorms, blizzard, rain, wind, tropical cyclones, storm surges), drought, desertification, heat waves, sand or dust storms, fire.
Man-made disasters are man-induced phenomena that may constitute a damaging event. They can be classified terrorism, war, and engineering faults. The Economic and Human Cost of Disasters: In the past 20 years, natural disasters have killed over 3 million people, and inflicted injury, disease, homelessness and misery on over 1 billion people in addition to causing billions of dollars of material damage.
In 2004, it was estimated that the annual global economic costs related to disaster events average US$629 billion per year, five times that of 20 years ago. In 2003 there were about 700 natural disasters.
Below are a few statistics on the human and economic losses experienced from recent disaster events.
Kobe Japan 1995 earthquake
$126 billion to repair basic infrastructure
Izmit Turkey 1999 earthquake
$13 billion repair bill
Bam Iran 2003 earthquake
Ash Wednesday Fire, Victoria Australia Feb 1983
AUD $400 million property loss
Ash Wednesday Fire, Victoria Australia Feb 1983
AUD $400 million property loss
October Fire Siege 2004, California USA
cost of fire $12 billion
Hurricane Andrew, Florida USA, Aug 1992
Heat wave, France, Aug 2003
$26.5 billion in damages
Ice Storm, Canada, Jan 1998
Exxon Valdez oil spill 1989
World Trade Centre – building collapse on September 11, 2001
$2.5 billion cleanup costs alone
$5.4 billion in damages
Mitigation Once the types of hazards likely to impact a given area have been accessed and understood, the areas of most vulnerability can be easily identified from maps. Master plans can be formulated to address these areas of vulnerability.
For instance, in flood prone areas, a review of municipal zoning could be undertaken and recommendations made to restrict new buildings in the areas subject to most flood inundation, instead leaving these places as open public areas; river bank levees could be designed and constructed, etc.
Mitigation measures are usually left to the local, state or federal authorities. Whilst many authorities in developed nations are now being proactive in taking measures to reduce the impact of disasters, many developing nations are waiting until after an extreme event has occurred before addressing the issue.
In either case, GIS analysis is being used to assist authorities to identify areas where mitigation effort should be concentrated. The first example shown below depicts areas at risk to flood [red being high risk, yellow being medium risk and green being low risk].
whilst the second example shows areas at risk to landslide [areas depicted in orange to red being most at risk]. Using such maps generated by GIS, the authorities can then review policies such as building codes for these specific areas, rezoning effected areas, and/or develop a program of civil works to minimize potential risks.
CONCLUTION: Changing building codes so that building structures are raised above flood peaks, building water levees, building basement structures to withstand earthquakes, clearing zones around houses in fire risk areas, are just some of the ways that authorities and individuals are working towards minimizing the impact of extreme events when they occur.
GIS analysis can be used to identify the zone boundaries having mapped what was available. It can also be used to quickly identify where deficiencies exist. In addition to management zone maps, maps need to be communicated to the general public identifying the location of support facilities.The guidelines will identify who should receive warnings about potential hazards, its severity, when, how often, and by which means – newspapers, TV, radio, or emergency personnel door knocking.
GIS application for weather analysis and forecasting Various methods were developed and used by meteorologists for weather forecasting. The most important methods are the conventional Synoptic, and Numerical Weather Prediction (NWP) methods. Skill of these forecasts can be enhanced through use of GIS by relating different features of the atmosphere and their proper visualization.
Conventional synoptic method In this subjective method, conventional forecasting tools like, trend, persistence, Climatology, and analogue of weather systems, are popularly employed. These methods makes use of some basic assumptions for extrapolating the weather into the future. The forecaster blends these extrapolations with his own experience and the location specific weather quirks The inadequate human understanding of the various complex atmospheric processes leading to the weather development itself is one of the major problems associated with this method.
NWP method: The NWP method makes use of numerical solutions
(high speed super computers are generally required for this task) of complex system of mathematical prognostic equations/models representing both the physical and dynamical processes occurring in the atmosphere. The model integrations into the future automatically produce charts of important parameters such as surface pressure, wind circulations, etc. The forecaster interprets these charts for weather forecasting at the locations of his interest
Limitations of NWP weather forecasts : Inadequate representations of the initial conditions of the
atmosphere, Inadequate finite resolution of the model - presents difficulty in the representation of orography, giving rise to differences in station levels, Inadequate parameterizations of the boundary layer and other physical processes, and Incorrect representation of ground conditions
CONTD… Keeping the above limitations of NWP based medium range weather forecasts in view, this focuses on improving forecast skill by making use of new technological tools like Geographical Information System (GIS) software for plotting, analyses and visualization of observed meteorological parameters, superior to the conventional techniques otherwise followed for the purpose.
Materials and Methods for the Study The weather parameters viz. Wind Speed, Wind Direction, temperature and Geopotential height at vertical levels of the atmosphere at 850 hPa and 500 hPa at the T80 model grid points (approximately 150 km apart) over the globe. The 5-day weather forecasts for the same parameters based of the above initial conditions (analysis) were made use of as weather forecasts. Weather analysis is the process of drawing isobars, isohyets, isotachs, etc. and locating pressure systems, fronts, etc. on a base map of an area on which the weather observations from a wide area are plotted, following meteorological conventions.
The locations of the synoptic weather observation stations over the globe.
• Super imposition of the wind flow pattern and specific humidity (gm/kg) at 850 hPa level at 5.30 A.M. IST on 17th Dec. 2000, over the surface topography. The red arrows show the wind flow, and the green contour lines with blue colored numbers across show the specific humidity distribution. The multicolored background is the surface created out of the ten minute interval orography of the area.
• Super imposition of the wind flow pattern and specific humidity (gm/kg) at 850 hPa level at 5.30 A.M. IST on 17th Dec. 2000, over the surface topography. The red arrows show the wind flow, and the green contour lines with blue colored numbers across show the specific humidity distribution. The purple colored contours with black numbers across represent the ten minute interval orography of the area.
FOREST • CASE STUDY 1: Application of Satellite Based Remote Sensing for Monitoring and Mapping of India’s Forest and Tree Cover • CASE STUDY 2:Application of Remote Sensing and GIS for forest fire susceptibility mapping using likelihood ratio model
Application of Satellite Based Remote Sensing for Monitoring and Mapping of India’s Forest and Tree Cover The current National Forest Policy (1988) in India aims at maintaining a minimum of 33 percent of country’s geographical area under forest. FSI has been carrying out assessment of forest cover in the country using satellite based remote sensing data. FSI was using satellite data on 1:250,000 scale for assessment of forest cover. In its latest assessment, it used digital interpretation of satellite data on 1:50,000 scale.
STEPS INVOLVED STEP 1: Acquisition of satellite data The digital data of IRS-1C and 1D LISS III is acquired from NRSA India is covered in about 340 scenes, of IRS 1C and 1D. One scene covers an area of about 20000 km2, having an overlap of about 10% with adjoining scenes. While procuring the data, it should be cloud free (with not more than 10% cloud cover)
STEP 2: Geometric Rectification of raw data After downloading the data into computer, rectification is carried out in each image to provide Latitude and Longitude information into raw satellite scene using raster based geometric corrections. Rectification carried out in geographic projection is reprojected in shape of polygonal projection and the scene is geo-coded.
STEP 3: Mosaicing of rectified scenes Different scenes, which are already rectified, may have to be merged together to get one combined FCC (False Colour Composite). FCC of sheet is extracted from mosaiced scene in a chosen area of interest. Image is displayed in three bands 3, 2, 1. Masking of non-forest areas is done separately to extract forest areas
STEP 4: Classification of forest cover Interactive method of display is used for assigning threshold values for each class (open, dense and scrub) on the basis of the ground knowledge to highlight forest/vegetated areas. Density class of forest cover and colour is accordingly allocated.
• Raw images of IRS IC/D PAN and LISS III data for the period between Oct.-Dec. 2002 are acquired from National Remote Sensing Agency, Hyderabad. • Thereafter, the PAN image is geometrically rectified with the help of Survey of India toposheets on 1:50,000 Scale. • The LISS III image is then co registered with the rectified PAN images. PAN and LISS III images are fused using appropriate algorithm
RESULTS AND DISCUSSION For the first time FSI has interpreted the satellite data of the entire country digitally. Digital interpretation has the advantage of overcoming subjectivity prevalent in visual method. Due to absorption of digital image processing technique, it has been possible for FSI to interpret the data on 1:50,000 scale. This has resulted in providing more realistic information on forest cover as areas having forest cover down to 1 ha could be delineated while in earlier assessments, forest cover down to 25 ha could only be delineated.
CONTD.. For the first time an independent and systematic assessment of accuracy of satellite data interpretation was made. An error matrix was generated by comparing classified forest cover with the actual forest cover on the ground at 3,608 locations spread throughout the country. High resolution PAN data was used as proxy for ground verification. The overall accuracy of forest cover classification was found to be 95.9%.
CONTD… Though forest cover in areas as less as 1 ha in extent could be assessed using satellite data, significant tree cover exists in patches of less than 1 ha and in linear shapes along roads, canals, etc. and scattered trees that can not be assessed using remote sensing.
CASE STUD Y 2 Ap plica tion of Re mote Sensi ng an d GIS fo r for est f ire su scept ibili ty ma pping usin g like lihoo d rat io mo del
INTRODUCTION In the event of a prolonged spell without rain, and a lowering of the water table in the peat swamp forest, the organic layers becomes completely dry and is very prone to fire. Geospatial technology, including Remote Sensing and Geographic Information Systems (GIS), provides the information and the tools necessary to develop a forest fire susceptibility map in order to identify, classify and map fire hazard area. Forest Fire locations were identified in the study area from historical hotspots data from year 2000 to 2005 using AVHRR NOAA 12 and NOAA 16 satellite images.
STUDY AREA The study area is located approximately between Upper Left (3º 23’ 53.6”E and 101º 3’ 36.3”N) and Lower Right (3º 45’ 18.05”E and 101º 30’ 55.33”N). The area located within the Kuala Selangor District, northern part Selangor.
Sg Karang and Raja Muda Musa Forest Reserve, Selangor
Data using GIS and Remote Sensing
Data using Remote Sensing Recent advances in remote sensing, GIS and computer technologies provided an opportunity to assess and monitor the land cover changes. NOAA AVHRR satellite data with a spatial resolution of 1.1 km at nadir was found to be extremely useful for national-scale assessment and monitoring of major land cover types. Historical forest fire data were collected from satellite remote sensing NOAA AVHRR 12 and NOAA 14 sensors for last 5 years.
CONTD… • The imagery from Landsat-7 ETM of path 157 and row 058 acquired on 21 September 2001 was used in this study. • The spatial resolution for Landsat-7 ETM was 30 meter x 30 meter. • Fuel map were extracted from satellite imagery.
Data using GIS GIS data consists of biophysical and socio-economic variable is on 1: 25,000 scale. Hotspots prone areas, fire occurrence map, peat swamp map and soil maps have been acquired and digitized. Meteorological data such as temperature and relative humidity and Fire Danger Rating System (FDRS) map were obtained
CONTD… Image processing was carried out using ERDAS Imagine 8.7 and PCI Geomatica 9.0. There were six factors that were considered in calculating the probability, and the factors were extracted from the constructed spatial database. The factors were transformed into a vector-type spatial database using the GIS, and forest firerelated factors were extracted using the database.
CONTD… • A digital elevation model (DEM) was created first from the topographic database. • Contour and survey base points that had elevation values from the 1:25,000-scale topographic maps were extracted, and a DEM was constructed with a resolution of 20 m. • Using this DEM, the slope angle and slope aspect were calculated. • The soil map is obtained from a 1:100,000-scale soil map. Landsat-7 ETM, 30 meter x 30 meter resolution was used for extracting fuel map in the Sg Karang and Raja Muda Forest Reserve, Selangor.
RESULTS AND DISCUSSION The large amount of data can be processed in the GIS environment quickly and easily. Moreover, it is hard to process the large amount of data in the statistical package. Recently, forest fire susceptibility mapping has shown a great deal of importance for haze detection and fire prevention in forest area.
Applications of GIS and Remote Sensing in the Analysis of the Environment of Bay of Bengal
The Bay of Bengal is a northern extended arm of the Indian Ocean, situated in eastern part between equator and 220N latitude and 800E and 1000E longitude. The present study aims at understanding the seasonal variability of the environmental parameters of BoB. The present study focus on the application of Geographical Information System (GIS) and remote sensing for the analysis of environment parameters of Bay of Bengal.
The remote sensing data, used in the study, are (SST), (SSH) anomalies, Chlorophyll Concentration and Sea Surface Wind.
The monthly mean data on SST is from Modis Aqua, chlorophyll pigment concentrations is from SeaWiFS, and sea surface winds from Quikscat. Monthly mean sea surface height anomalies were obtained merged 7-day snap shot from Topex/Posidon and ERS-1/2 series of satellites.
GIS facilitates the modeling and analysis of data apart from generation of both 2-D and 3-D maps.Preliminary analysis showed that SST and chlorophyll pigment concentrations were correlated with cold SSTs located with high chlorophyll pigment concentrations. In the northern Bay the pigment concentration was always high and appears to be related to the influence of river discharge. High chlorophyll concentration was found near the coastal areas of Bay of Bengal, so these areas may become biologically highy productive.
Use of RS and GIS to estimate Air Quality Index over Peninsular Malaysia
•This is the report resulted in a study in order to compute API using satellite-based method •Five locations of air pollution station were selected where major pollutants have been measured conventionally. •Haze information was extracted from the satellite data
Relationship between the satellite recorded reflectance and the corresponding pollutant measurement was determined using regression analysis. The result proven that satellite-based method using space-borne remote sensing data was capable of computing API spatially and continuously
Haze (originating from open burning or forest fire) usually contains large amount of particulate matter (e.g., organic matter, graphitic carbon). The pollution rate can be measured from ground instruments such as air sampler, sun photometer and optical particle counter, however these instruments is impractical if measurement are to be made over relatively large areas or for continuous monitoring.
Study area • On 22nd September 1997 Malaysian government had declared that Kuching (capital of Sarawak) was in the state of emergency when the API exceeded 650 (hazardous level) • Their concentration and spatial distribution was quantified from NOAA-14 AVHRR satellite data
• Combination of band 1, 2 and 4 are used to visually differentiate between haze (orange), low clouds (yellow) and high clouds (white).
methods • Derivation of haze model, • Regression analysis • Accuracy Assessment.
Contd.. • This model can be described by: σ-R=L–V • where, σ : reflectance recorded by satellite sensor, R : reflectance from known object from earth • surface • L : skylight, and V : lost radiation caused by scattering and absorption
Regression analysis • Calibration pixels of NOAA-14 AVHRR data were sampled within a radius of 2.5 km from each of the air pollution stations. The relationship between AQI and satelliterecorded reflectance of band 1 AVHRR, were analysed using linear regression.
Accuracy Assessment • In order to verify the accuracy of the regression model, RMSE (Root-meansquared Error) was implemented to the AQI values obtained by the model.
Results and discussion • RS and GIS technology are useful in providing haze early warnings, so that necessary measures could be taken effectively by both government authorised party as well as public
ENVIRONMENTAL IMPACT ASSESSMENT FOR DAM CONSTRUCTION USING GIS/REMOTE SENSING
The Environmental Impact Assessment, for the dam construction on the Man river, Gujarat, India, was performed using GIS and Remote Sensing software-Arc/Info. The study attempts to assess the present problems of the area and to provide suggestive measures for the watershed improvement of the Man River after the construction of the dam. Various information layers were integrated through the GIS software Arc/Info using overlay methods. Suitable site selection for different types of treatments, using the criteria rules of Arc/Info has also been performed.
OBJECTIVE OF THE STUDY This study shall prepare a detailed environmental impact analysis of the proposed dam and environmental management plan in the catchment and the command areas of the Man River for clearance by the Ministry of Environment. The increased irrigation potential due to the proposed dam shall have a great impact on the socio-economic upliftment of the region. The construction of the dam is aimed at improving the land productivity in the command area. Proper utilization of the irrigation water from the dam is suggested to prevent increase in the land salinity in the command area. The study shall assess the status of erosion and land degradation in the catchment area to prevent siltation and to suggest the future plan of treatment.
STUDY AREA •
The Man River originates in the Nasik district of Maharashtra, India and flows through Valsad district of Gujarat, India and discharges directly into the Arabian Sea.
The area is located between 73o 15’ - 73o36’ E longitude and 20o 25’ 20o 40’ N latitude, and covers parts of the Survey of India topographic sheet numbers 73H/2,3,6,10. • The catchment area is demarcated into sub-watersheds and consists of 55 villages covering an area of 261.0 sq.km. The command area covers an area of 192.6 sq.km. consisting of 37 villages.
The input data from all of the above diverse sources are translated into the thematic maps by the methods of: interpretation Classification manipulation integration editing analysis • During the last few decades GIS software has gained importance for generating overlays and making site-specific decisions. Multi-spectral remotely sensed satellite data plays a vital role in the generation of the overlays. • The integration of the satellite imagery and GIS has eased the data integration and analysis of very large data sets.
Physiography and drainage • The catchment area of the proposed dam consists of flat topped, highly dissected plateaus and dyke ridges running in a west to east direction. • The command area of the proposed dam is surrounded by moderately dissected plateaus and piedmont slopes. • The largest portion of the command area is the alluvial plain, which has been formed by the river Man. The alluvial plain is studded with number of residual hills with degraded forests. • The river Man in the command area also flows in straight channels, which shows that the river is structurally controlled
Soil series • The area, being of basaltic formation, falls under the broad soil group of red loams and black clayey soils. The transmission of water through similar parent material seems to have influenced the development of different physiographic characteristics of the soils in the area.
Land use/ Land cover • Digital interpretation of IRS LISS-III FCC on 1:50,000scale for two season dates is done in ERDAS for identification of different land use land cover classes based on the image characteristics. • The multidate imagery are interpreted for the details of the crop land in the two harvest seasons known as the kharif and Rabi seasons. • Based on ground truth verification the boundaries are finalized which synchronizes well with the physiography, slope and soil of the area.
PREPARATION OF THE SECONDARY OVERLAYS • The secondary layers are derived from the above datasets in Arc/Info by the various overlay functions. Polygons below a threshold limit eliminated to generate the final layers based on which the decisions can be made.
Slope • The slope map is derived by using the GRID and TIN features of Arc/Info. The input data are the contours from the Survey of India topographic sheets. After converting from raster to vector layer it is processed for generating the secondary overlays. • In the catchment area, the plateau tops have slopes of 03% and the steep hillsides are above 8%. The piedmonts comprise of slopes mainly between 5 – 8%. In the river bed and the valley fills, the slope remains below 5%. • The command area, comprising mainly of the alluvial plain and the flood plain, has a slope ranging between 03%. In the surrounding dissected plateau and piedmont, the slope varies between 3 – 8%.
Hydrogeomorphology and groundwater • The hydrogeomorphological map is prepared by overlaying geomorphology, lithostratigraphy, structure and land use. • The hydrogeomorphologic conditions for each landform type are identified based on the above layers. Groundwater prospects are assigned to each unit. • A total of five classes of groundwater prospect areas have been identified in the catchment area. The groundwater status in most of the area varies between poor to moderate, especially in the dissected plateaus and dyke ridges.
Land-Irrigability • In the command area, based on the texture, structure, permeability, of the soil, soil-irrigability classes are assigned and each type of soil irrigability class is given a unique code. This soilirrigability layer is unionized with the slope layer to derive the land-irrigability classes. Based on the percent slope and soil irrigability classes, four land-irrigability classes have been identified .
Land Capability • Overlaying the slope, soil, land use and environmental factors of each CEIU/CLDU, land capability classes are generated. • Each land capability class is identified by a unique characteristic, having similar hazards of the soil to various factors, which causes soil damage, decreases soil fertility, and its potential for agriculture. • In the command area the land-capability has been assigned for the development of the area. In the catchment area land capability has been assigned for future treatment.
CONCLUSION • The entire study was carried out by using, Arc/info, ArcVIEW and ERDAS. The intricate overlays, which are manually impossible to generate, along with the detailed calculation have been successfully performed. • Due to the construction of the dam on the Man River, an area of 17.55 sq.km is going under submergence. This area consists of parts of five villages. • The affected inhabitants can be suitably rehabilitated in the non-submerged part of the villages.