Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Upcoming SlideShare
What to Upload to SlideShare
Next
Download to read offline and view in fullscreen.

Share

Land use cover pptx.

Download to read offline

mapping of land use land cover(LULC) using GIS and remote sensing

Land use cover pptx.

  1. 1. MAPPING LAND USE, LAND COVER CHANGE USING REMOTE SENSING AND GIS TECHNIQUE PRESENTED BY- PRATIK R RAMTEKE DEPT. OF SOIL SCIENCE,
  2. 2. CONTENT • Introduction • Objectives of study • Studies involved • Methodology used • Result and discussion
  3. 3. INTRODUCTION • Land use/cover are two separate terminologies which are often used interchangeably. • Land cover refers to the physical characteristics of earth’s surface (vegetation, water, soil and other physical features of the land, including those created by human activities e.g., settlements. • land-use refers to the way in which land has been used by humans.
  4. 4. • Land use/cover changes is a widespread and accelerating process, mainly driven by natural phenomena and anthropogenic activities, affecting natural ecosystem. • Accurate and up-to-date land cover change information is necessary to understanding and assessing the environmental consequences of such changes. • The basic premise in using remote sensing data for change detection is that Information is generally required about the ‘‘from-to’’ analysis.
  5. 5. OBJECTIVES OF STUDY • To utilize GIS and Remote Sensing applications to discern the extent of changes occurred over particular time period. • To identify and delineate different LULC categories and pattern of land use change • To examine the potential of integrating GIS with RS in studying the spatial distribution of different LULC changes • To determine the shift in LULC categories through spatial comparison of the LULC maps produced.
  6. 6. Why remote sensing • Application of remotely sensed data made possible to study the changes in land cover in less time, at low cost and with better accuracy in association with GIS that provides suitable platform for data analysis, update and retrieval. • The advent of high spatial resolution satellite imagery and more advanced image processing and GIS technologies, has resulted in updating land use/cover maps.
  7. 7. Success in methodology Regardless of the technique used, the success of change detection from imagery depend the 1. Nature of the change involved and 2. The success of the image preprocessing and classification procedures. Example - If the nature of change within a particular scene is either abrupt or at a scale appropriate to the imagery collected then change should be relatively easy to detect.
  8. 8. CLASSIFICATION TECHNIQUE • Unsupervised classification or clustering • Supervised classification • PCA • Hybrid classification • Fuzzy classification These are the most commonly applied techniques used in classification
  9. 9. CASE STUDY - 1 Study area The Hawalbagh block of District Almora of the Uttarakhand state. • Study analysed for a period of 20 years from 1990-2010 .
  10. 10. SOURCE OF DATA • Landsat-TM images represent valuable and continuous records of the earth’s surface during the last 3 decades. • The entire Landsat archive is now available free-of-charge to the scientific public, for identifying and monitoring changes in manmade and physical environments.
  11. 11. STEPS INVOLVED • Landsat Thematic Mapper at a resolution of 30 m of 1990 and 2010 were used for land use/cover classification. • The TM sensor primarily detect reflected radiation from the Earth’s surface in the visible and near-infrared (IR) wavelengths. • The TM sensor have seven spectral bands. • The wavelength range for the TM sensor is from the visible, through the mid-IR, into the thermal-IR portion of the EMS.
  12. 12. • The satellite data covering study area were obtained from global land cover facility (GLCF) and earth explorer site. • To work out the land use/cover classification, supervised classification method with maximum likelihood algorithm was applied in the ERDAS Imagine 9.3 Software. • Maximum likelihood algorithm (MLC) is one of the most popular supervised classification methods used with remote sensing image data • These data sets were imported in ERDAS Imagine version 9.3 satellite image processing software to create a false colour composite (FCC).
  13. 13. • The layer stack option in image interpreter tool box was used to generate FCCs for the study areas. • The sub-setting of satellite images were performed for extracting study area from both images by taking geo-referenced outline boundary of Hawalbagh block map as AOI (Area of Interest). • The spectral distance method is used for classifying those pixels that were unclassified. • Ground verification was done for doubtful areas. • Based on the ground truthing, the misclassified areas were corrected using recode option in ERDAS Imagine.
  14. 14. • For performing land use/cover change detection, a post- classification detection method was employed. • Post-classification comparison proved to be the most effective technique. • A pixel-based comparison was used to produce change information on pixel basis and thus, interpret the changes more efficiently taking the advantage of ‘‘-from, -to’’ information.
  15. 15. • Classified image pairs of two different decade data were compared using cross-tabulation in order to determine qualitative and quantitative aspects of the changes for the periods from 1990 to 2010. • A change matrix was produced with the help of ERDAS Imagine software. • Areal data of the overall land use/cover changes as well as gains and losses in each category between 1990 and 2010 were then compiled.
  16. 16. LAND USE COVER CHANGE DETECTED Five land use/cover types are identified in the study. 1. vegetation 2. agricultural land 3. barren land 4. built-up land 5. water body
  17. 17. RESULT AND DISCUSSION
  18. 18. Categories 1990 2010 Change 1990-2010 km2 % km2 % km2 % Vegetation 146.5 54.75 155.88 58.26 9.39 3.51 Agriculture 84.73 31.69 80.67 30.17 -4.06 -1.52 Barren 31.17 11.65 16.58 6.19 -14.59 -5.46 Built-up 2.72 1.01 12.2 4.56 9.48 3.55 Water body 2.42 0.9 2.2 0.82 -0.22 -0.08 total 267.53 100 267.53 100 0.00 0.00 Area and amount of change in different land use/cover
  19. 19. CASE STUDY - 2 Study area In the northwestern coast of Egypt For a period of 14 years from 1987-2001.
  20. 20. METHODOLOGY USED • Landsat TM images acquired in 1987-2001. • Ground information was collected for the purpose of supervised classification and classification accuracy assessment.
  21. 21. IMAGE PRE-PROCESSING Geometric correction • Change detection analysis is performed on a pixel-by-pixel basis. • Any mis-registration greater than one pixel will provide an anomalous result of that pixel. • To overcome this problem, the root mean-square error (RMSE) between any two dates should not exceed 0.5 pixel. • The RMSE between the two images was less that 0.4 pixel which is acceptable.
  22. 22. Image inhancement and visual interpretation • To improve the visual interpretability. • To optimize the complementary abilities of the human mind and the computer. • Contrast stretching was applied on the two images and two false color composites (FCC) were produced. • Some classes were spectrally confused and could not be separated well by supervised classification and hence visual interpretation was required to separate them.
  23. 23. Image classification • A supervised classification was carried for the two images individually with the aid of ground truth data • The overall objective of the image classification procedure is to automatically categorize all pixels in an image into land cover classes or themes. • Using ancillary data, visual interpretation and expert knowledge of the area through GIS further refined the classification results. • Post-classification change detection technique was used to produce change image through cross-tabulation
  24. 24. CROSSTAB Categories of one image are compared with those of a second image. The result of this operation is a table listing the tabulation totals as well as several measures of association between the images.
  25. 25. • A legend is automatically produced showing these combinations. • In order to increase the accuracy of land cover mapping of the two images, ancillary data and the result of visual interpretation was integrated with the classification results using GIS. • The module used is the overlay module in IDRISI Kilimanjaro software.
  26. 26. The area was classified into eight main classes: • seawater • salt marshes • Sabkha – (Arabic name for low lying , high water table area • cropland • grassland • bare land • urban and • quires - (Areas with active excavation and mining)
  27. 27. •Landsatimage 1987 •Landsatimage 2001 Result and discussion
  28. 28. year categories 1987 (Area in ha) Salt marsh Sabkh a Crop land Grass land Bare land Urban Quire s Total 2001 Salt marsh 1960 4116 386 145 50 0 0 6657 Sabkha 28 3890 289 2027 532 0 0 6766 Crop Land 14 5079 21,189 35,459 15,220 0 0 77,266 Grass Land 0 2715 185 60,951 12,857 0 98 78,806 Bare Land 0 924 8 60,737 131,911 0 340 193,92 0 urban 67 2262 202 3100 4563 1543 89 11,826 Quires 0 17 0 345 1391 0 242 1995 total 2069 19,00 3 22,259 163,06 4 166,524 1543 769 375,23 6 Cross tabulation of land cover classes between 1987-2001
  29. 29. CASE STUDY -03 • Study area Simly watershed, Islamabad, Pakistan. for the years 1992 and 2012
  30. 30. METHODOLOGY • satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 • Supervised classification • Data were pre-processed in ERDAS imagine 12 for geo- referencing, mosaicking and subsetting of the image on the basis of Area of Interest (AOI).
  31. 31. The watershed was classified into five major land cover/use classes viz- • Agriculture • Bare soil/rocks • Settlements • Vegetation and • Water Resultant land cover/land use and overlay maps generated in ArcGIS 10 software.
  32. 32. SATELLITE DATA SPECIFICATIONS Data Year of acquisition Band/colour Resolution (m) Source Landsat 5 TM 1992 Multi-spectral 250 USGS glovis SPOT imagery 2012 Multi-spectral 2.5 SUPARCO
  33. 33. The delineated classes were • Agriculture • Bare soil/rocks • Settlements • Vegetation and • water class
  34. 34. Result and discussion
  35. 35. LULC classes 1992 2012 Area (ha) % Area (ha) % Agriculture 1775 11 4681 29 Bare soil 1648 10 2691 16 Settlement 1038 6 1870 11 Vegetation 11,342 69 7008 43 Water 603 4 155 1 Land cover/land use classes and areas in hectares from year 1992-2012
  36. 36. CONCLUSION • The objectives of this study were to detect land cover types and land cover changes that have taken place. • Integration of visual interpretation with supervised classification using GIS and remote sensing was found to the best combination of detecting changes. • Integration leads to increase in the overall accuracy. • Especially the area having spatial distribution of different land cover changes. • Assessment of land degradation, and future planning.
  37. 37. Thank you
  • KanwarpreetSingh26

    Sep. 19, 2021
  • shefali8shekhawat95

    Aug. 22, 2021
  • sheilajayaraj

    Apr. 7, 2021
  • UmeshKalsariya4

    Oct. 26, 2020
  • penugondaskumar

    Sep. 11, 2020
  • VarunS68

    Dec. 17, 2019
  • anuragkumar519

    Dec. 5, 2019
  • JayJadhav8

    Jun. 24, 2019
  • HASRAT1996

    May. 22, 2019
  • abebawmengist

    Jan. 28, 2019
  • taryllgreen

    Dec. 11, 2018
  • MajeedulHassanchesti1

    Nov. 19, 2018
  • BharathiMandala

    Oct. 11, 2018

mapping of land use land cover(LULC) using GIS and remote sensing

Views

Total views

4,132

On Slideshare

0

From embeds

0

Number of embeds

7

Actions

Downloads

267

Shares

0

Comments

0

Likes

13

×