Ph.D. University of California-Riverside, Center for Conservation Biology 1)Time: Tuesday, August 25, 2015, 15:30- 16:30 (1)Location: Amirkabir University of Technology, Department of Civil and Environmental Engineering (2)Time: Wednesday, August 26, 2015, 14:00- 16:00 (2)Location: Department of Surveying Engineering, University of Tehran, N. Kargar St.

1. 1

2.  University of Tehran, Iran
 Survey and Geomatic Engineer (GIS, Remote Sensing,
Photogrammetry and Geodesy)
 University of Tehran, Iran
 GIS
 Purdue University, USA
 Department of Forestry and Natural Resource
 University of Wisconsin-Madison, USA
 Wisconsin Energy Institute
 Landscape Ecology
 University of California-Riverside, USA
 Center for Conservation Biology
 Department of Botany and Plant Sciences 2

3.  Introduction
 Land cover and land use
 History of land change science
 Sustainability
 Big data and land use change science
 Software development -> LTM-HPC
 Summary of other projects
3

4.  Introduction
 Land cover and land use
 History of land change science
 Sustainability
 Big data and land use change science
 Software development -> LTM-HPC
 Summary of other projects
4

5.  Land cover and land use
 One third to one-half (Turner, 1995)
 Land use cover change (Foley et al., 2005)
 Land change science
5

6.  Moving to urban areas
 In 1900 (<10%)
 In 2050 (>50%)
 Occurred on <3%
 Doubles every 30 years
 Approach 10% by 2070
 78% of carbon emissions
 60% of water use
 …
6

7.  Conversion of forest to agriculture in the Amazon
 Local temperature
 Carbon dioxide
7

8. 8

9.  Fragmentation of natural habitats
 Richness and abundance
9

10.  Earth as a system
 Sustainability
 Current and future needs
 Land change science (Turner, 2007)
 Future and historical land use map
10

11.  Introduction
 Land cover and land use
 History of land change science
 Sustainability
 Big data and land use change science
 Software development -> LTM-HPC
 Summary of other projects
11

12. Using Big Data to Simulate Land Use Change at a
National Scale: An Application of Land
Transformation Model-High Performance
Computing (LTM-HPC)
12

13.  Managing the nation’s fish habitat at multiple spatial
and temporal scales in a rapidly changing climate
 Land use
 Climate change
 Fish habitat
 Research team
 Scientists from the USGS, University of Missouri,
Michigan State University, and Purdue University
 Series of meeting (3 years)
13

14.  Limitations
 Discrete time periods
 Particular regions
 Coarse spatial resolution
 Multiple land uses
 Understand global process
 Forecasting annual multiple land use changes at
continental scale -> 2000 to 2100
14

15.  Modeling land use change
 Big data (GIS and remote sensing)
 Data mining (Artificial neural network)
 Calibration
 Validation
 Forecasting
 Products and applications
 Software
 Programming (Python, C#, C++ and batch)
 Communication (XML)
 Parallel processing (High performance
computing)

16. 16

17.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Land use change quantity (C# executable)
 Suitability map (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 17

18. Drivers in time 1 Land use change
18

19.  Workstation
 Quantity of files
 Size of files
 Server
 Parallel processing
 …
19

20. 20
Drivers in 1992 Land use map Results
Distance to road NLCD 1992 Pattern file
Distance to urban NLCD 2001 Suitability map
Distance to stream Change map Simulated map
Distance to city center ---- Error map
Distance to highway ---- ----
Slope ---- ----
Gross domestic product ---- ----
Exclusionary zone (existing urban,
water, state parks and others)
---- ----

21. 20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
21

22. Slope_16_003.asc
22

23. 23

24. 20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
24

25. 25

26. 26

27.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Land use change quantity (C# executable)
 Suitability map (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 27

28. 1
2
3
4
5
6
7
8
9
10 11
12
13
14
15
16
18
17
28

29. 29

30. 30

31.  10000.net file
31

32.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Suitability map (C# executable)
 Land use change quantity (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 32

33.  Quantity of change between times 1 and 2
 Simulated map in time 2
 Sort suitability values
Reference map (time 2)
Status 1 (Non-Urban) Status 2 (Urban)
Reference map (time 1) Status 1 (Non-Urban) A B
Status 2 (Urban) C D
Drivers in time 1 Suitability Map
33

34. B
34

35. 20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
35

36. 36

37. 37

38.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Land use change quantity (C# executable)
 Suitability map (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 38

39. Reference map (time 2)
Status 1 (Non-Urban) Status (Urban)
Simulated map (time 2) Status 1 (Non-Urban) True Negative (TN) False Negative (FN)
Status 2 (Urban) False Positive (FP) True Positive (TP)
Future Scenario
39

40. 20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
40

41. 41

42. 42

43. 43

44. 44

45.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Land use change quantity (C# executable)
 Suitability map (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 45

46. 46

47. 20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
47

48. 48

49. 49

50. 50

51. 51

52. 52

53.  Big data (Python)
 Create pattern file (C# executable)
 Data mining
 Artificial neural network (C++ executable)
 Calibration
 Land use change quantity (C# executable)
 Suitability map (C# executable)
 Simulated map (C# executable)
 Validation (C# executable)
 Forecasting (C# executable)
 Products and applications 53

54.  In Great Lakes area, LaBeau et al., (2014), used the
future land use maps (between 2010-2050)
 Land use (agriculture and urban) and phosphorus
delivery
 Increase P loadings by 3.5–9.5%
54

55. 55

56. 56

57.  Developing a model to simulate land use change at
continental scale
 LTM-HPC
 Sustainability
 Climate, water quality and biodiversity
 Big data and land change science
 Land use legacy
57

58.  Tayyebi, A., Pekin, B. K., Pijanowski, B. C., Plourde, J. D., Doucette, J. S.,
and D. Braun. (2013). Hierarchical modeling of urban growth across the
conterminous USA: Developing meso-scale quantity drivers for the Land
Transformation Model. Journal of Land Use Science, 8(4), 422-442.
 Pijanowski, B. C., Tayyebi, A., Doucette, J., Pekin, B. K., Braun, D., and J.
Plourde. (2014). A big data urban growth simulation at a national scale:
Configuring the GIS and neural network based Land Transformation Model
to run in a High Performance Computing environment. Environmental
Modelling & Software, 51, 250-268.
 Tayyebi, A., Pekin, B. K., and B. C. Pijanowski. (In review). Urbanization
trends across the conterminous of USA from 1900 to 2100: Lessons learned
from studies in 11 mega-regions. Regional Environmental Change.
58

59. Advisor -> Bryan C Pijanowski
Post Doc -> Burak K Pekin
GIS Specialist -> Jarrod Doucette
GIS Specialist -> James Plourde
IT Specialist -> David Braun
59

60.  Introduction
 Land cover and land use
 History of land change science
 Sustainability
 Big data and land use change science
 Software development -> LTM-HPC
 Summary of other projects
60

61. SmartScape™: A web-based decision support
system for strategic agricultural land use
policy development
61

62. 62

63. 63

64. Urban Heat Island Variation across a Dramatic
Coastal to Desert Climate Gradient: An
Application to Los Angeles, CA Metropolitan Area
64

65. 65

66.  Video time
66

67. 67

68. 68

69. 69

70.  Mike Batty
70