Uttam Kumar and Ramachandra T.V. Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore

Transcription

1 Remote Sensing and GIS for Monitoring Urban Dynamics Uttam Kumar and Ramachandra T.V. Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore

2 Remote Sensing is a widely used technology for providing surface information in the form of image data. These images of the earth s surface are acquired by space borne sensors in different spatial, spectral, temporal and radiometric resolutions. Introduction

3 What is Remote Sensing?

4 Remote Sensing Processes Atmosphere Sensor System Onboard calibration And Preprocessing Data to earth station Preprocessing -Radiometric -Geometric Information Extraction Distribution & use of information Incorporation of ancillary data

5

6 Spectra of Surface Materials Bare soil Reflectance Water Vegetation Wavelength

7 Reflectance from various materials

8 Cartosat-1 Chandigarh Manasarovar Lake IRS P6 Kuwait LISS-IV IRS-P6 South India and Sri Lanka AWIFS Source: NRSA, Hyderabad

9 GIS Remote sensing data when integrated with Geographic Information and other attribute data (such as data obtained from ground, other measurements and observations) can yield better results in terms of interpretability and easier understanding.

10 GIS A geographic information System (GIS) is a system for capturing, storing, analyzing and managing data and associated attributes which are spatially referenced to the earth. It is a computer system capable of integrating, storing, editing, analyzing, sharing, and displaying geographically- referenced information.

11 1. Cartographic Irrigation Land evaluation Crop Analysis Air Quality Traffic patterns Planning and facilities management 2. Digital Terrain Modeling Earth science resources Civil Engineering & Military Evaluation Soil Surveys Pollution Studies Flood Control 3. Geographic objects Car navigation systems Utility distribution and consumption Consumer product and services

12 Digital Numbers Raster Data

13 Vector data Types: Basic Shapes, Multi- Shapes, Derived Shapes, Alternate Shapes, Any possible Shape, User- Defined Shapes Basic Shapes Alternate Shapes Multi-Shapes Any Possible Shape N 0, N * Derived Shapes User Defined Shape!

14 Lines Polygons

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16 Land cover and Land use Land cover refers to what is actually present on the ground. It provides the ground cover information for baseline thematic maps. Land use refers to the various applications and the context of its use. Land use itself is the human employment of a land-cover type. Thematic spatial outputs aid planners and policy makers to take holistic decisions.

17 Study Area Kolar District, Karnataka state, India Area sq. km. Location to E to N

18 Data Used 1. LISS-3 MSS (23.5 m) 2. MODIS data a) MODIS Bands 1 to 7 - MOD 09 Surface Reflectance 8-day L3 global (Spatial resolution - Band 1 and m, Band 3 to m) b) MODIS Bands 1 to 36 - MOD 02 Level-1B Calibrated Geolocation Data Set (Spatial resolution - 1 km) 3. The main sources of primary data were from field (using GPS), the Survey of India (SOI) topographic maps of 1:50,000, 1:250,000 scale. 4. The secondary data were collected from the Government agencies (Directorate of census, Agriculture department and Forest department) etc.

19 Data Used Satellite/ Sensor Resolution Swath IRS 1C LISS 3 IRS 1D LISS m (visible and near IR region) 70.5 m (shortwave IR region) 141 km (visible and near IR region) 148 km (shortwave IR region) 23.5 m Repetitivity 24 days 25 days Spectral Bands Primary Application microns (B2) microns (B3) microns (B4) microns (B5) 127 km (bands 2, 3, 4) 134 km (band 5 MIR) microns (B2) microns (B3) microns (B4) microns (B5) These data have a diverse range of applications such as land use and land cover mapping, urban planning, biodiversity characterisation, forest survey, wetland mapping, environmental impact, crop acreage and production estimation of major crops, drought monitoring and assessment based on vegetation condition, snowmelt run-off estimation, mineral prospecting, coastal studies and so on. Band Number Spectral Range Bands 1 19 nm Bands µm Spatial Resolution Primary Application 250 m Land / Cloud / Aerosols Boundaries 500 m Land / Cloud / Aerosols Properties 1000 m Ocean Colour / Phytoplankton Biochemistry 1000 m Atmospheric Water Vapour 1000 m Surface / Cloud Temperature 1000 m Atmospheric Temperature 1000 m Cirrus Clouds Water Vapour m Cloud Properties m Ozone m Surface / Cloud Temperature 1000 m Cloud Top Altitude

20 LISS-3 3 classification IRS 1C/1D LISS-3 MSS (23.5 m) Georeferencing / Geo-correction FCC Generation Classification Ground Truth Unsupervised Classification Supervised Classification High resolution Classified Map

21 Land Cover Analysis using NDVI Vegetation Non-vegetation % % (LISS-3)

22 Supervised Classification

23 Unsupervised Classification (Clustering) Types of Clustering Algorithms K- Means Density Based (DBSCAN) Balance Iterative reducing and Clustering using Hierarchies (BIRCH) K- Mediod Grid Based Model based

24 Comparison of classes and clusters Class 1 Cluster 1 Cluster 2 A A A A A A A A A A A A A A A A B B A x 2 One possible cluster assignment B B B B B B B B BBB B A A B B A A B B B Another possible cluster assignment A A B B Class 2 Cluster 4 cluster 3 x 1

25 Evaluation of Accuracy of Classifiers

26 FCC and Training data

27 Overall Accuracy of LISS-3 MSS Supervised Classified map %

28 Landuse classification of Kolar district Year 1998 Year 2002 N N Built up Built up

29 Landuse details of Kolar district Categories Area (in ha) Area (%) Area (in ha) Area (%) Agriculture Built up Forest Plantation Waste land Categories Non vegetation Vegetation

30 Classified MSS and PAN fused image 1998 Chikballapur taluk 2002 N

31 Types of Classification Algorithms Maximum likelihood classification Neural network Genetic algorithms Decision tree K-nearest neighbor Bayesian classification Support vector machines

32 MODIS preprocessing MODIS Data B1, B2 250m B3 -B7 500m B1-B36 1Km Reprojection of all MODIS Bands Georeferencing of all MODIS Bands Georeferenced MODIS Band 1and 2 Georeferenced MODIS Bands B3 to B7 (500m) B1 to B36 (1 Km) Resampling to 250m INPUT B1, B2, B3, B4, B5, B6 and B7 (all 250m) B1 to B36 (all 250m) Hard Classification Soft Classification

33 Classification of MODIS data INPUT I MODIS Bands 1 to 7 (MOD 09 Surface Reflectance Product) MODIS Bands 1 to 36 (MOD 02 Level-1B Product) Principal Component Analysis (PCA) Minimum Noise Fraction (MNF) INPUT II PC of MODIS INPUT - III MNFComponents of MODIS C L A S S I F I C A T I O N A L G O R I T H M S

34 PCA and MNF Output PC Eigenvalue PCA MNF Components Eigenvalue MNF

35 Classification of MODIS data Hard Classification B1,B2,B3,B4,B5,B6,B7 PCA (B 1 to Band 36) Selection of appropriate Bands MNF (B1 to B36) Selection of appropriate Bands NDVI (B1, B2) K-Means Clustering Maximum Likelihood Classification (M LC) Spectral Angle Mapper (SAM) Neural Network (NN) Decision Tree Approach (DTA)

36 Land Cover Analysis using NDVI Vegetation Non-vegetation % % (MODIS)

37 Maximum Likelihood Neural Network K-Means Clustering Decision Tree

38 MODIS classified maps

39 Accuracy Assessment Using Error matrix Algorithms Overall Accuracy NN (MNF) MLC (B1 to B7) DTA (MNF) DTA (B1 to B7) NN (PCA) DTA (PCA) SAM (B1 to B7) NN (B1 to B7) K-Means (B1 to B7) SAM (MNF) MLC (MNF) SAM (PCA) MLC (PCA) 30.44

40 Discussions 1. Hard classification technique performs well with high spatial resolution (LISS-3) with the use of training sites and ancillary data having minimum difference between the date of acquisition and date of ground truth. 2. MODIS classified image with coarse spatial resolution had many misclassified pixels. 3. Further, within the land cover parameter, errors were generated when the classification algorithm selected the wrong class. The errors (errors of omission & errors of commission) occured when the signal of a pixel is ambiguous, as a result of spectral mixing, or when the signal is produced by a cover type is not accounted for in the training process. 4. As the pixel size increases, the chances of high accuracies in hard classifications being product of random assignment of values declined.

41 Linear Unmixing Natural surfaces are rarely composed of a single uniform category. For example, each pixel produced by MODIS instrument covers approximately 250 m to 1 Km square area on the ground, which is larger than the surface expression of interesting features.

42 Thus the problem of mixed pixel classification is a major issue in remote sensing. B C

43 Spectral unmixing contd Hence, it is possible to model each pixel spectrum of this image as a linear combination of a finite set of components: r 1 = a 11 * x 1 + a 12 * x a 1n * x n + e 1 r 2 = a 21 * x 1 + a 22 * x a 2n * x n + e 2 : : r m = a m1 * x 1 + a m2 * x a mn * x n + e m where, r i = Spectral response of the pixel in ith spectral band. a ij = Spectral response of the jth component in the pixel for ith spectral band. x j = Proportion value of the jth component in the pixel; e i = Error term for the ith spectral band.

44 Spectral unmixing contd This error is due to the assumption made that the response of each pixel in any spectral wavelength is a linear combination of the proportional responses of each component. j = 1, 2, 3... n (Number of components assumed for the problem) i = 1, 2, 3... m (Number of Spectral bands used in problem) A linear constraint is added, since the sum of the proportions for any pixel must be one. Also, the proportion values must be non-negative.

45 Linear Unmixing Soft Classification MODIS Bands (B1, B2, B3, B4, B5, B6, B7) MNF Transformation Endmemeber Selection Estimation of Proportion of each Endmember Abundance Maps 5 6

46 LMM: Endmember Collection Pixel Purity Index (PPI) - This is generated by randomly generating lines in the N-dimensional space comprising the scatter plot. All of the points in the space are then projected on to the line. The pixels falling at the extremes of the lines are counted above a certain threshold are declared pure. Scatter Plot N-Dimensional Visualization Training sites obtained from Ground Truths

47 LMM: Endmember selection Class spectral characteristics of the Endmembers 3-Dimensional visualisation of the Endmembers showing their separability

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49 Accuracy Assessment Using Error matrix - Ground truths collected from the study area with the GPS. - Computation of User s, Producer s and Overall accuracy.

50 Comparison on the basis of on land cover class % area with LISS-3 MSS % of class A = A % of class B = B % of class C = C % of class D = D % of class E = E Administrative boundary % of class F = F Algorithms Agriculture Built up Forest Plantation Waste land Water bodies MLC (LISS3) K-Means (B1 to B7) MLC (B1 to B7) SAM (B1 to B7) NN (B1 to B7) DTA (B1 to B7) MLC (PCA) SAM (PCA) NN (PCA) DTA (PCA) MLC (MNF) SAM (MNF) NN (MNF) DTA (MNF)

51 Pixel to pixel analysis with LISS-3 MSS classified image 11 x 11 1 x 1 LISS3 (23.5 m) F A P B W 1 PIXEL OF MODIS (250 m) A W W AGRICULTURE BUILTUP FOREST PLANTATION WASTELAND WATER MIXED

52 Accuracy Assessment Comparison on the basis of percentage area with LISS-3 MSS Class LMM LISS-3 Agriculture (%) Built up (%) Forest (%) Plantation (%) Waste land (%) Water bodies (%) Land Cover details of fraction images for Chikballapur taluk

53 Accuracy Assessment based on Pixel Analysis Class Total no. of pixels identified Pure pixels Mixed pixels Wrongly classified Agriculture Built up (Urban / Rural) Evergreen/Semi- Evergreen Forest Plantation/Orchards Waste land / Barren Rock / Stony waste Water bodies Total Validation of land cover classes in Chikballapur

54 Discussions 1. For LMM, the selection of the endmembers had an high adjacency effect existing between contrasting features (for example, forest and plantation). 2. This could be a limitation to the use of MODIS data in areas of high contrasting nature, as is the case of water bodies, and raises concerns about its application especially on heavily fragmented or small isolated areas. 3. When unmixing an image to generate the abundance maps, from the presence of endmembers which do not mix in a linear fashion raises concern about the abundance maps. 4. These errors took place when the selection of endmembers was wrong, for example when these did not corresponded to pure pixels. 5. The determination of endmember fractions for MODIS exhibited good behaviors in places where the endmembers are present, generally showing the capacity of estimating values within 20 % to 25 % of the actual values except for the water bodies endmembers.

55 Seasonal Variations in Landuse (Urban) reflectance using MODIS data

56 Seasonal Variations in Urban reflectance using MODIS data Hyderabad Bangalore Chennai

57 Dec 2006 Nov 2005 Nov 2004 Nov 2003 June2003 July 2002 Oct 2002 Sep 2002 Aug 2002 Weekly data for 1 year

58 False Colour Composite of MODIS data, B1-R, B2-NIR, B4-G

59 Hyderabad Bangalore Chennai

60 FCC of Bangalore FCC of Hyderabad FCC of Chennai

61 NDVI of Bangalore Aug, 2002 and July, 2003 FCC of Bangalore

62 NDVI of Hyderabad Aug, 2002 and July, 2003 FCC of Hyderabad

63 NDVI of Chennai Aug, 2002 and July, 2003 FCC of Chennai

64 Unsupervised classification of MODIS data Bangalore Hyderabad FCC of Bangalore FCC of Hyderabad

65 FCC of Chennai Unsupervised classification of MODIS data for Chennai

66 Conclusion Remote Sensing GIS Classification Supervised and Unsupervised Endmember Selection and Linear Unmixing Temporal and Seasonal changes in reflectance of landuse classes using MODIS data

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