Quantifying spatial and temporal instability of land change.

Transcription

1 Quantifying spatial and temporal instability of land change. Dan Runfola Robert Gilmore Pontius Jr. 1

2 What is temporal instability? When change in the past has not been consistent. 2

3 What is temporal instability? 10% Business As Usual % Annual Change 0%

4 What is temporal instability? 10% Business Is Complicated % Annual Change 0%

5 Why do we care about instabiilty? If Business is Complicated, assumptions of land change models which calibrate based on past events are incorrect. 5

6 Why do we care about instabiilty? If Business is Complicated, assumptions of land change models which calibrate based on past events are incorrect. Phenomenon occur at different temporal scales, which measures of temporal instability can help identify. 6

7 Hasn t this been done before? Yes! (Bell and Hinojosa 1977, Petit et al. 2001, Pueyo and Beguería 2007, Pelorosso et al. 2011) 7

8 Hasn t this been done before? Yes! (Bell and Hinojosa 1977, Petit et al. 2001, Pueyo and Beguería 2007, Pelorosso et al. 2011) So why bother doing this? Past approaches relied on the Markov matrix. 8

9 The Markov Matrix Widely used in Land Change Modeling Assumes proportional change across time, and an eventual approach to equilibrium. 9

10 Why not just use Markov? It s quite difficult to compute annualized matrices. 10

11 Why not just use Markov? It s quite difficult to compute annualized matrices. In fact, so difficult that positive definite solutions do not exist for the annualization of every matrix! Takada et al

12 Why not just use Markov? It s quite difficult to compute annualized matrices. In fact, so difficult that positive definite solutions do not exist for the annualization of every matrix! Takada et al The assumption of equilibrium implicit in Markovian matrices is an assumption we have no evidence for (at least in our data). 12

13 Something Different (The Flow Matrix) Assumes linearity in transitions over time (no equilibrium is reached). A solution always exists for annualization. 13

14 Exploring Temporal Stability with the Flow Matrix 14

15 Exploring Temporal Stability with the Flow Matrix 15

16 Case Study : Georgia Coastal Ecosystems 16

17 17

18 Study Site Selection 18

19

20

21

22

23

24

25 20% of Change would have to be reallocated for change to be perfectly consistent. 25

26 Change is stable at coarse temporal resolutions. 26

27 GCE is relatively stable compared to some LTER sites. 27

28 Instable Change (Percent of Total Change) LUQ CWT PIE FCE GCE HBR CAP KNZ JRN AND Site GCE is relatively stable compared to some LTER sites. 28

29 Web: Instable Change (Percent of Total Change) LUQ CWT PIE FCE GCE HBR CAP KNZ JRN AND Site This material is based upon work supported by the National Science Foundation under Grant Nos. BCS and BCS BCS , and DEB Any opinions, findings, & conclusions or recommendations expressed in this material are those of the author(s) & do not necessarily reflect the views of the National Science Foundation. Thanks to Safaa Aldwaik for the development of many programs that support this research. Thanks to the many students at Clark University who participated in this research. Special thanks to Gil Pontius and Colin Polsky for their ongoing support of this work. 29

30

31 2001 2x Persistence in Water due to different extent 31

32 2001 ½ Persistence in Water due to Mask 32

33 1991 Category Stocks 33

34 2001 Category Stocks 34

35 2005 Category Stocks 35

36 Net Change Analysis: How did Urban Grow from ? s 36

37 Intensity Analysis: How did Urban Grow from ? 37

38 Moving Forward: Cross-site Synthesis Issues Sites have variable class definitions Sites have different map resolutions Sites have different time periods Quality of data variable across sites Map Error missing in some cases 38

39 Moving Forward: Cross-site Synthesis Solutions Index of Temporal Stationarity: Is the rate of change stationary across intervals? Two Research Questions: What is the suitability of data for modeling? What does the data tell us about the landscape s temporal stationarity? Extensive, Iterative Quality Assurance Communication with participants 39

40 Harvard Forest Poster 40