PHD third Semester Presentation-Jan2021-V.Shiva Chandra.pptx

presentation

1. Improving Runoff Predictions in Urbanized Catchments with Pixel based Estimates of Impervious Surface Cover Dr.T. Reshma Vaddiraju Shiva Chandra Research Supervisor Research Scholar Dept. Of Civil Engineering Dept. Of Civil Engineering NIT Andhra Pradesh NIT Andhra Pradesh

2. INTRODUCTION Urbanization Urbanization is the increase in the proportion of people living in towns and cities. Urbanization occurs because people move from rural areas to urban areas This usually occurs when a country is still developing. An area is considered to urbanize when over 50 per cent of its population live in the urban areas Urban population is expected to increase to 68% from todays 55% by 2050 90% of this migration is expected to be in countries of Asia and Africa. The process of urbanization is ongoing ,it is dynamic and multi-faceted. Urban areas have the potential that generate ecological and environmental impacts at multiple scales. Urbanization is not only causing land use change but excessive use of energy and resources have overburdened the ecosystem .

3. TREND OF URBANIZATION Source:Equity Master Survey Fig 1: Trend of Urbanization

4. EFFECTS OF URBANIZATION Positive Migration of rural people to urban areas Employment opportunities in urban centers Transport and communication facilities Educational facilities Increase in the standard of living Negative Stress on natural resources Poverty Urban floods Over crowding Increased crime Fig 2: Flooding in Urban Areas

5. Fig 3: Present Day photos of the study area Fig 4: Photos showing complete concrete cover leaving no scope for infiltration

6. Fig 5: Photos showing the real world scenario in Hyderabad during rainy season

7. Author Observations Alessandra et al: (2020) In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multi-sensor analysis. A new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index was developed. The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation) Iswari Nur Hidayati et al:(2019) In this study, author demonstrated that BCI (Biophysical Composition Index) has the highest value when compared to NDVI, NDBI, EBBI, SAVI and NDISI. BCI has the most significant impervious surface index, as evidenced by the most significant correlation coefficient. The study concludes that BCI is the most useful index for categorizing impervious surfaces into bare land, residential areas or vegetation . LITERATURE REVEIW

8. Author Observations Suman Sinha (2018) Author used various spectral indices using spectral information from remote sensing sensor. Several indices like, soil-adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI); water index, modified normalized difference water index (MNDWI); and urban indices, normalized difference built-up index (NDBI), built-up index (BUI) and index-based built-up index (IBI), and a combination of various indices were implemented in the study. The proposed methodology has the potential to identify and extract urban impervious features automatically. Fenghua Huang et al: (2018) Proposed a method for automatic extraction of impervious surfaces from High Resolution Remote Sensing Images based on deep learning techniques. (AEISHIDL). Convolutional Neural Network (CNN) is used to calculate CNN feature averages of all pixel neighbourhoods in each object. Finally, fuzzy c-means clustering (FCM) algorithm is employed to extract the impervious features. The experimental results showed that AEISHIDL has higher accuracy compared to other techniques. LITERATURE REVEIW

9. Author Observations Rudong et al: (2018) This study explores the performance of Sentinel-2A Multispectral Instrument (MSI) imagery for extracting urban impervious surface using a modified linear spectral mixture analysis (MLSMA) method. In MLSMA, a built-up image was first extracted from the normalized difference built-up index (NDBI) using the Otsu’s method; the high-albedo, low-albedo, vegetation, and soil fractions were then estimated using conventional linear spectral mixture analysis (LSMA). The 10 m impervious surface map of Sentinel-2A is capable of recovering more detail than the 30 m map of Landsat 8. In the Sentinel-2A impervious surface map, continuous roads and the boundaries of buildings in urban environments were clearly identified. Chen et al:(2018) Proposed an index for Impervious Surface extraction [i.e., enhanced normalized difference impervious surfaces index (ENDISI)] by integrating the spectrum character of Landsat operational land imager (OLI) images, and an automatic threshold selection method using the generalized Gaussian model. The results show that: (a) the ENDISI can reduce the impacts of arid land, bare rock, and bare soil on IS extraction effectively; The overall accuracy and kappa coefficient values of IS extraction via automatic threshold selection exceed 93.9% and 82.4%, respectively. LITERATURE REVEIW

10. Author Observations Bhatti et al:(2014) Proposed built-up area extraction method (BAEM). Instead of using individual bands, BAEM employed principal component analysis (PCA) images of the highly correlated bands pertinent to NDBI computation. Through integration of temperature data, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) BAEM was able to improve the overall accuracy of built-up area extraction by 11.84 % compared to the modified NDBI approach. Rather than employing the binary NDBI, NDVI and MNDWI images, continuous images of these indices were used and the final output was recoded by determining the threshold value through a double window flexible pace search (DFPS) method. Results indicate that BAEM was more accurate at mapping urban built-up areas when applied to OLI imagery as compared to the modified NDBI approach; omission and commission errors were reduced by 75.96% and 33.36%, respectively. Chithra S.V et al: (2013) Estimated the Total Impervious Area (TIA) of the city of Kochi using high resolution satellite image (LISS IV, 5m. resolution). Additionally, the study maps the Effective Impervious Area (EIA) by coupling the capabilities of GIS and Remote Sensing. Land use/Land cover map of the study area was prepared from the LISS IV image acquired for the year 2012. The classes were merged to prepare a map showing pervious and impervious area. Supervised Maximum Likelihood Classification (Supervised MLC),which is a simple but accurate method for image classification, is used in calculating TIA and an overall classification accuracy of 86.33% was obtained. LITERATURE REVEIW

11. INDEX EQUATION Normalized Difference Vegetation Index NDVI = (NIR-R) / (NIR+R) Soil-Adjusted Vegetation Index SAVI = (1+L)(NIR-RED) / (NIR+RED+L) Normalized Difference Built up Index NDBI = (SWIR-NIR) / (SWIR+NIR) Modified Normalized Difference Water Index MNDWI = (G-SWIR) / (G+SWIR) Built up Index BUI = NDBI – NDVI Index-Based Built-up Index IBI = [NDBI-(SAVI+MNDWI)/2] / [NDBI+(SAVI+MNDWI/2)] Enhanced Built up and Bareness Index ) Modified Built-up Index MBUI = BUI-MNDWI SwirTirRed Index STRed Index = (SWIR1+R-TIR1) / (SWIR1 + R + TIR1) SwiRed Index SwiRed = (SWIR1 –R) / (SWIR1+R) INDEX EQUATION Normalized Difference Vegetation Index NDVI = (NIR-R) / (NIR+R) Soil-Adjusted Vegetation Index SAVI = (1+L)(NIR-RED) / (NIR+RED+L) Normalized Difference Built up Index NDBI = (SWIR-NIR) / (SWIR+NIR) Modified Normalized Difference Water Index MNDWI = (G-SWIR) / (G+SWIR) Built up Index BUI = NDBI – NDVI Index-Based Built-up Index IBI = [NDBI-(SAVI+MNDWI)/2] / [NDBI+(SAVI+MNDWI/2)] Enhanced Built up and Bareness Index Modified Built-up Index MBUI = BUI-MNDWI SwirTirRed Index STRed Index = (SWIR1+R-TIR1) / (SWIR1 + R + TIR1) SwiRed Index SwiRed = (SWIR1 –R) / (SWIR1+R) Table 1: List of various Indices existing in Literature

12. Table2: Showing various bands existing in Landsat Series data

13. RESEARCH ENVISAGED Based on the literature survey, it is very clear that most of the studies are done only on determination of total impervious area from high resolution freely available satellite imagery using various conventional methods and Remote Sensing and GIS techniques Runoff estimation based on total impervious area is accounting to almost 30% excess runoff calculations which is very high in urbanized catchments. Determination of effective impervious area from total impervious areas were based on indirect methods using Empirical equations till now or using high resolution satellite data which is costly. Present research focusses on determining effective impervious area from low resolution remote sensing imagery and there by use it as an input in estimating the runoff in urbanized catchments.

14. To determine the impact of Urbanization on existing water resources in North and Eastern Regions of Hyderabad. To develop a Pixel-Based approach for determining effective impervious areas for urban hydrological modelling. To develop an urban hydrological model using SCS-CN method for estimation of surface runoff. Modifying the developed urban hydrological model with pixel-based approach for automatic determination of the effective impervious areas. OBJECTIVES

15. EXPECTED OUTPUT AND OUTCOME OF THE PROPOSAL The outcome of the project is finding out the impervious area in the urban catchment and subsequent estimation of runoff volume for various time durations. Precise estimation of runoff and ground water augmentation can be very much helpful to plan better water balance requirements. This outcome can help organizations such as Hyderabad Metropolitan Water Supply & Sewerage Board (HMWSSB) and Greater Hyderabad Municipal Corporation, Hyderabad (GHMC) for water management.

16. CASE STUDY

17. Saroor Nagar is selected as the study area for the present study. Study area is located in the Musi Sub-Basin (42 km 2 ), which is part of the larger Krishna Basin (265,000 km 2 ). Most Developed area in the recent past. Fig 6:Google Earth image and watershed boundary of Study Area STUDY AREA Fig 7:Mandal boundaries of Ranga Reddy District Fig 8:Figure Showing the distance between sources of Water supply to Hyderabad

18. METHODOLOGY Fig 9: Flow chart of the Methodology RADIOMETRIC CORRECTIONS NDVI NDBI MNDWI EBBI SWIRED BUI MBUI LANDSAT 8 COMPARISION AND ACCURACY ASSESSMENT SPECTRAL INDICES

19. The following indices were calculated in the current study Index Equation Normalized Difference Vegetation Index (NDVI) Normalized Difference Built-up Index (NDBI) Built-up index Modified Normalized Difference Water Index Modified Built-up Index Enhanced Built-up and Bareness Index SwiRed index Index Equation Normalized Difference Vegetation Index (NDVI) Normalized Difference Built-up Index (NDBI) Built-up index Modified Normalized Difference Water Index Modified Built-up Index Enhanced Built-up and Bareness Index SwiRed index

20. RESULTS Indices   Min   Max   NDVI -0.162 0.651 NDBI -0.64 0.281 BUI -1.226 0.178 MNDWI -0.545 0.739 MBUI -1.345 0.57 EBBI -0.037 0.025 SWIRED -0.694 0.603 Indices Range of Impervious Area No of Pixels   MBUI 0.14-0.23 29103 EBBI 0.10-0.16 28341 SWIRED 0.19-0.23 26572   MBUI EBBI SWIRED Google Earth Impervious Area 26.19 25.5 22.41 20.26 Total Area 42.0 42.0 42.0 42.0 % of Impervious Area 62.3 60.7 57 48.2 Table 3:Min and Max Values of Various Indices Table 4:Index Values for impervious area Table 5: Comparison of Impervious area

21. MAPS OF SPECTRAL INDICES Fig 10:Maps showing NDVI, NDBI of study area

22. Fig 11:Maps showing BUI and MNDWI of study area

23. Fig 12:Maps showing MBUI,SWIRED,EBBI of study area

24. Fig 13:Maps showing Impervious area extracted using various indices

25. CONCLUSIONS Extraction of the impervious surface area is a challenging task especially when the environment is covered by heterogeneous land cover types. Applying urban indices is one of the practical utilities of remote sensing data for differentiating impervious areas from non- impervious areas. In this study, spectral indices such as NDBI, NDVI, BUI, MBUI , EBBI, SWIRED were extracted using Landsat 8 OLI-TIRS data and the results of these indices were evaluated for mapping the impervious surface area. SwiRed index presented better results than the other urban indices, and the impervious area extracted from SwiRed index is very close to the impervious area extracted from Google earth images. T he most appropriate index should be determined by comparing different indices as proposed in this case study, as the results of these indices can vary from site to site Determination of the impervious area can help researchers and policymakers to better understand the environmental response to urban development. The results can also be used for implementing new strategies in potential waterlogging areas due to high rainfall.

26. PUBLICATIONS DONE AFTER JOINING PHD V . Shiva Chandra, T. Reshma, Rajkumar “ Impact of Urbanization on Water Resource of Eastern Part of Hyderabad using Geomatics , in 8th International Conference on Emerging Technologies in Urban Water Management, organized by Asia Pacific Association of Hydrology and Water Resources, Japan(APHW) and IIT Roorkee during 21- 23 November 2019. V.Shiva Chandra , T.Reshma “ Urbanization Implications On Local Climate And Groundwater Levels Using Index Based Techniques”, Advanced Modelling and Innovations in Water Resources Engineering AMIWRE 2020, NIT Jamshedpur, Jharkhand, India( Paper Accepted for publications in Springer Conference Proceedings) V. Shiva Chandra , T.Reshma “ Determination of Impervious Area Using Remote Sensing Spectral Indices – A Case Study of Saroor Nagar Watershed ” ( Submitted to Geocarto International Journal, Taylor & Francis Publisher)

27. Replies to the Comments of Reviewer   Comment 1 This paper attempts to corelate urbanization with other parameters which is a novel attempt. Some more detail analysis is required and some changes in language is also necessary. Reply Authors are thankful to the reviewer for finding topic interesting and also in the revised manuscript analysis between Premonsoon ground water levels and Rainfall and the accuracy details of the work is also incorporated. The revised manuscript has been carefully studied to improve the grammar and readability.   Comment 2 Some of the references are not written properly. Reply References are re written as per the given template format. On Thu, Nov 12, 2020 at 12:40 AM AMIWRE-2020 < [email protected] > wrote: Dear Authors Please find the reviewer's comment on your revised paper. Minor revision is required.  Please submit the final version of the paper, the paper with changes highlighted, and answer to reviewer comments. Regards. Dr. Sangeeta Kumari. Reviewer's Comments The  paper is acceptable for including in the conference proceedings with the following suggestion. The location of Temperature stations, rain gauge stations and groundwater observation wells to be marked in the contour maps.

28. Activity Months   1-3 4-6 7-9 10-12 13-15 16-18 19-21 22-24 25-27 28-30 31-33 34-36 Literature Review                         Collection of data: Satellite Imagery from NRSC/USGS Soil Map from SLUSI Population Data from census Ground Water Level data from SGWB/CGWB Rainfall and Runoff data from IMD/Collectorate                         Development of pixel-based approach for determining the effective impervious area                         Development of urban hydrological modelling using Curve Number method                           Modifying the developed Urban hydrological model with pixel-based approach for automatic determination of effective impervious area                         Simulation and Validation of the developed model                         Applicability of the developed model on various urban regions                         Report preparation                         Table 3: Time Schedule Of Activities Giving Milestones Through BAR Diagram Partially Completed In progress Not Started

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32. Thank You For Your Attention!