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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
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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
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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
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Land cover and land use
One third to one-half (Turner, 1995)
Land use cover change (Foley et al., 2005)
Land change science
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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
…
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Conversion of forest to agriculture in the Amazon
Local temperature
Carbon dioxide
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Fragmentation of natural habitats
Richness and abundance
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Earth as a system
Sustainability
Current and future needs
Land change science (Turner, 2007)
Future and historical land use map
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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
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Using Big Data to Simulate Land Use Change at a
National Scale: An Application of Land
Transformation Model-High Performance
Computing (LTM-HPC)
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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)
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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
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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)
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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
Drivers in time 1 Land use change
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Workstation
Quantity of files
Size of files
Server
Parallel processing
…
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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)
----
----
20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
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Slope_16_003.asc 22
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20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
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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
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10000.net file
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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
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
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B
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20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
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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
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
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20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
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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
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20722 × 11 = 227942 ~ 228K
20722 × 4 = 82888 ~ 83K
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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
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%
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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
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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.
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Advisor -> Bryan C Pijanowski
Post Doc -> Burak K Pekin
GIS Specialist -> Jarrod Doucette
GIS Specialist -> James Plourde
IT Specialist -> David Braun
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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
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SmartScape™: A web-based decision support
system for strategic agricultural land use
policy development
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Urban Heat Island Variation across a Dramatic
Coastal to Desert Climate Gradient: An
Application to Los Angeles, CA Metropolitan Area
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Video time
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Mike Batty
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