oct 16, 2012 slide 1 change detection: an inter-disciplinary investigation across climate sc.,...

Oct 16, 2012Slide 1

Change Detection: An Inter-disciplinary Investigation Across

Climate Sc., Computer Sc./Eng., Statistics, & Remote sensing

On site review of NSF Expedtions in Computing: Understanding Climate Change: A Data Driven Approach.

Minneapolis, MN, Oct. 16, 2012Sponsor: NSF CISE?/EIA?

Peter K. Snyder

Abdollah Homaifar

Snigdhansu Chatterjee Joseph F. Knight

Shashi Shekhar

Stefan Liess

Students:Zhe Jiang

Keith Harding

Mohammad Gorji Sefidmazgi

Ansu’s student

Lian Rampi

Xun Zhou

Oct 16, 2012Slide 2

Change Detection Questions in Climate Sc.

• Sahel: Characterize spatial Extent of the Sahel over time• How does one define Savanna using remotely sensed data ?• Identify appropriate variable to detect Sahel (and droughts) from among precipitation,

soil moisture, vegetation, water supplies, etc.• How does one efficiently find Sahel-footprint given Savanna definition• How will statistical distribution of top k-percentile change?

• Regimes:• How does one efficiently detect interesting interval in a time series?• How does one detect persistent regime-intervals in time series collection?

Oct 16, 2012Slide 3

Contributions to Computer Sc./Eng. & Statistics

• Statistics:• Optimally detect change in multiple climate characteristics, their

statistics, and relationship among these characteristics and variables,• Quantify the uncertainty and confidence in change detection, with

incomplete, and spatio-temporally dependent

• Computer Sc./Eng.• Efficiently discover Interesting sub-path from ST datasets: A Sub-path

Enumeration and Pruning (SEP) approach• Spatial Decision Tree Learning algorithm (global spatial autocorrelation)• Finding common intervals of change among time series (need name of

the approach/algorithm from Abbie’s group)

Oct 16, 2012Slide 4

Computer Sc. Problem : Interesting Sub-path Query (ISQ)• Input

• An interest measure and thresholds.• A path and its attribute

• Output• All dominant interesting sub-path

• Constraints• Correctness & completeness• Automation & scalability

Change : 7 -6 1 -1 5 5 4 -3 5 5 -11

Unit interval : 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12

Average change (slope) ≥ 3.5

Slope = 7 Slope = 3.5

[1,2], [5,11]

Oct 16, 2012Slide 5

Computational Structure & A Naive Algorithm• Naive approach :

• Phase 1: Collect qualifying sub-paths • For each possible sub-paths, evaluate interest measure

• Phase 2: Identify dominant sub-paths by comparing pairs of qualifying sub-paths.

• Will Dynamic Programming reduce computational cost?

End

loca

tion

1

2

3

4

5

6

7

8

9

10

11

12

1 2 3 4 5 6 7 8 9 10 11 12Start location

Dominated by

Examined interval Skipped interval

Dominant Interesting sub-path

Invalid intervalDominated interesting sub-path

O(n4) in worst case

Oct 16, 2012Slide 6

Why is ISQ Problem Hard?• Concept Definitions

• Sahel Footprint: Rectangle or irregular polygon• Interest Measure: Characterize Sahel signature in remotely sensed data

• Large Data Volume and Computations• Trillion computations per time step for GIMMS/MODIS (resolution 0.07 degree)• Thousand time steps per variable

• Non-monotonic Interest Measure • Example: Average Slope (AS)• AS (interval) does not bound AS (sub-interval)

• Dynamic programming principle violated• Lack of (optimal) sub-structure

Oct 16, 2012Slide 7

Computer Sc. Contributions for ISQ Problem

• Formalize Interesting (change) sub-path Query problem

• Characterized computational structure

• A novel algorithm: Sub-path Enumeration and Pruning (SEP)

• Evaluation • Cost model • Computational experiments • Case study with Eco-climate data

Oct 16, 2012Slide 8

Related Work, Its Limitations, Novelty of Our Approach

Interesting sub-region query

Change-points sub-pathse.g., SEP (Our Work)e.g., CUSUM[3]

CUSUM score: S0 = 0, Sn+1 = max(0, Sn + xn - Ɵn)Here Ɵ is chosen to be the mean of the dataChange below mean above mean

sub-regions(Future Work)

[1,2], [5,11][6]

Oct 16, 2012Slide 9

The SEP approach

• Insight 1 : Interest measure is a algebraic function

• Insight 2: Dominance imposes a partial order among sub-paths

• Insight 3: The partial order is a grid-based DAG • Better way to traverse the G-DAG ? • BFS? DFS (preorder)? DFS (postorder)?

Grid-based Directed Acyclic Graph (G-DAG)

1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 10-11 11-12

SUM

7

1

2

1

6

11

15

12

17

22

12

1-3

1-4

1-5

1-6

1-7

1-8

1-9

1-10

1-11 5-11

1-12

1-2

1

2

3

4

5

6

7

8

9

10

11

12

End

lo

cati

on

1 2 3 4 5 6 7 8 9 10 11 12Start location

1-3

1-4

1-5

1-6

1-7

1-8

1-9

1-10

1-11

1-12

1-2

5-11

Traversal Direction

End

loca

tion

1

2

3

4

5

6

7

8

9

10

11

12

1 2 3 4 5 6 7 8 9 10 11 12Start location

1-21-2

5-11

Cnt

1

2

3

4

5

6

7

8

9

10

11

5-11

1-2

1-12

• AVG = SUM/COUNT. - Build lookup table for SUM and COUNT - pre-compute for O(n), access for O(1)• Row-wise : scan each row, stop when pattern found

• Top-down : Smart BFS over G-DAG - A node has 2 parents: a pruned node may reappear! - No phase 2 needed – more space for recording

Oct 16, 2012Slide 10

Backup slides start here

A Comparison of Techniques for Traversing G-DAG

DFS (wo/ pruning)

BFS (wo/ pruning)

BFS (w/ leaf scan and pruning)

SEP Pruning border approach

A: Redundant leaf visits

Yes Yes None None

B: Unnecessary dominated non-leaf visits

Yes Yes None None

C: Memory needs to avoid B

O(n) O(n) O(n2) O(1)



Generalizable contribution to computer science

• New graph traversal order (for G-DAG)• Can benefit many other problems for scaling up to larger datasets

• Space (e.g., spatial field data)• Time (e.g., time series)• Space-time (e.g., Lagrangian path?)• Trajectories• Hui’s paper (see if apply)

• Space-filling curves are designed for traversing planar space not graph• Hillbert

Hillbert curve (source: wikipedia)


• Theoretical Evaluation: • SEP is Correct and Complete• Correct: All the reported sub-paths are qualifying dominant sub-paths • Complete: All the dominant interesting sub-paths are reported

• Experimental Evaluation• SEP is orders of magnitude faster than competition• SEP top-down is faster for longer patterns• SEP row-wise is faster for shorter patterns

Case 1: short patterns (PLR = 0.1) Case 2: long patterns PLR = 1

Theoretical and Experimental Evaluations of SEP

* Synthetic dataset: length 10k-50k, unit difference follow Gaussian distribution. Code in Matlab.** Pattern Length Ratio is the length of longest interesting sub-path by the length of the entire path, between 0 and 1.

Case 3: Row-wise vs. Top-down


Case Study (1)

• Data: Vegetation Data (in NDVI) by GIMMS [4], Africa, 1981 August. Resolution: 8km. Smoothed within 1x1 degree.

• Path: along each longitude (south north)

• Interest measure: (Slope) Sameness degree , ∆ : unit slope

• Thresholds: α= 20% percentile, SD ≥0.5

AVG{∆}AVG≥α{∆}


Case Study (2)

• The Sahara desert is growing towards south

• What is the spatial pattern of the Sahel over time• Time: August, 1982-1985, 1990, 2000


(Path to) Contribution to Climate Science

• Current• Identify the spatial extent of the Sahel and its change over time.• Characterize existing land cover/use applicable to climate studies (e.g. savanna)

• Near Future: Understand Sahel Drought Occurrences• Attribution: Human Influence Vs. natural processes • Changes in intensity, location, frequency• Tele-connections • Predict future changes using projected climate information (CMIP5)• How is regional climate changing (e.g., moisture content, evapo-transpiration, boundary

layer energetics)? • Characterizing changes in the general circulation and its affect on extreme events -

detecting changes in Rossby wave amplitude and wave number

• Long Term

• Improve vegetation representation in climate simulations


Future research directions in Computer Sc. & Statistics

• Computer science directions• Exploring two dimensional change patterns

• Two dimensional transitional zone (e.g., rectangle)• Arbitrary change direction

• Exploring three dimensional change pattern• Space-time change zone

• Reduce memory needs of the SEP algorithm• Spatial Decision Tree Learning algorithm + local autocorrelation (from zhe)

• Statistics Future Directions• Needs input from Ansu


List of Publications and References

Contributors’ Publications:[1] Xun Zhou, Shashi Shekhar, Pradeep Mohan, Stefan Liess, Peter K. Snyder:

Discovering interesting sub-paths in spatiotemporal datasets: a summary of results. GIS 2011: 44-53

[2] Need publications from Ansu, Abby and Joe’s group

References:[3] E. Page. Continuous inspection schemes. Biometrika, 41(1/2):100--115,

1954. [4] Tucker, C. J., J. E. Pinzon, M. E. Brown. Global inventory modeling and

mapping studies. Global Land Cover Facility, University of Maryland, College Park, Maryland, 1981--2006.

[5]. Needs references from Ansu, Abby, and Joe’s group


Backup Slides Start here


Traversal order on the G-DAG (Top-down/smart BFS)


5-11

1-2

1-12


Traversal order on the G-DAG (Pruning bordar/smart DFS)


5-11

1-2

1-12



General contribution to computer science1. General contribution to computer science

1. New graph traversal order2. Can benefit many other problems for scaling up to larger datsets

1. Space2. Time3. Space-time4. Trajectories5. Hui’s paper (see if apply)

3. Space-filling curves for space not for graph space 1. + pictures of Hillbert


What is a drought

• A period of unusually persistent dry weather that persists long enough to cause serious problems such as crop damage and/or water supply shortages

• Four different ways to define drought• Meteorological-a measure of departure of precipitation from normal.

Due to climatic differences, what might be considered a drought in one location of the country may not be a drought in another location.

• Agricultural-refers to a situation where the amount of moisture in the soil no longer meets the needs of a particular crop.

• Hydrological-occurs when surface and subsurface water supplies are below normal.

• Socioeconomic-refers to the situation that occurs when physical water shortages begin to affect people.

sources: NOAA http://www.wrh.noaa.gov/fgz/science/drought.php?wfo=fgz


Desertification (1)

• Sahel is transition zone between the desert and Savannas.• Arabic word Sahel means shore (coastline of Sahara desert)• Sahel droughts have occur numerous time over centuries including 2012, 2010,

1984-85 (Ethiopia), 1968-73,1940s, 1910s, 1898, etc.• Possible correlates include AMO, global warming/dimming, Solar(89-120 years)

Wolf-Gleissberg cycles, overgrazing/deforrestation, land management practices, ...• UN Convention to Combat Desertification shows a map of areas of high risk for

dessertification. This map looks very similar to the map produced in our case study with vegetation data http://en.wikipedia.org/wiki/Desertification

• Deserification is the the process of fertile land transforming into desert typically as a result of deforestation, drought or improper/inappropriate agriculture Regards,

• A billion people are under threat from further desertification Sahara is currently expanding southward 48 km/year.

• desertification creates increasingly larger empty spaces over a large strip of land, a phenomenon known as "tiger fur pattern".

• Pictorial details of Sahel dessertification are at http://oceanworld.tamu.edu/resources/environment-book/desertificationinsahel.html


Desertification (2)

• Current decade (2010-2020) is UN decade Decade for Deserts and the Fight against Desertification.

• Last week, Colorado State U hosted a UN meeting on desertification. See http://www.today.colostate.edu/story.aspx?id=4888 It suggests that desertification is a key issue for US (West, Mid-west).

• A recent paper lists six research priorities including Increase understanding of the nature, extent and severity of desertification, drought and dryland degradation, and develop more effective ways to measure and monitor it. See page 8 , 12-13, 25-26 (Dust Bowl), 27-28 (Sahel) of Desertification, Drought, Poverty and Agriculture: Research Lessons and Opportunities, Mark Winslow et al, 2004. http://www.iwmi.cgiar.org/Assessment/files/Synthesis/Land%20Degradation/DDPAARLO_text.pdf

• Another report on desertification from 2009-2010 is atIDEntifying and Analysing New Issues in Desertification: Research Trends and Research NeedShttp://www.uni-marburg.de/fb02/ike/forschung/projekte/finalreport.pdf

oct 16, 2012 slide 1 change detection: an inter-disciplinary investigation across climate sc.,...

Documents