Alfred Stein, Gao Wenxiu , Salma Anwar

1. Quantifying the changes in land use in developing countries using remote sensing: challenges and solutions Alfred Stein, GaoWenxiu, Salma Anwar Alfred Stein, a.stein@utwente.nl Salma Anwar, anwar19631@itc.nl

2. This presentation Developing countries have specific problems Data availability can be poor, the areas are big but sometimes inaccessible There is much to be gained from earth observation satellites Problems can be specific Solutions can be drawn from spatial statistics

3. Le menu du jour Use of modern techniques leads to novel ways of mapping Differences with existing methods can be big Automatic procedures may lead to odd situations that have to be resolved Spatial statistics may lead to tools and methods that can be of use to improve automation There is the common story:

4. The common story Problem Mathematics Statistics Data Solution Reporting

5. Premier plat Landuse change in china

6. Land use change in China In China there are different classification systems for land use There is land owned by many owners A main concern is the updating of existing maps Classifications may have changed: object oriented classification in stead of pixel based classification Increasingly satellite images are used for the purpose

7. Land use in China

8. Two Landuse Maps A traditional land-use map An image-derived land-use map

9. Improving Representation of Land-use Maps Derived from Object-oriented Image Classification • Intention: derive the vector landuse map from image with OO image • Problems: • For individual polygons: small, congested and twisted polygons exist with step-like boundaries. • For a group of polygons: geometric conflicts between polygons (e.g. unreadable small areas and narrow corridors) • Unclassified polygons • Methodology: • Map generalization combining with a polygon similarity model and spectral information from images.

10. Problems in an OO image-derived Landuse Map (1) • Individual polygons: • Congested polygons • Twisted polygons • Narrow corridors • Step-like boundaries.

11. Problems in OO image-derived Landuse Map (2) • A group of polygons: • Geometric conflicts • Unreadable small areas • Narrow corridors

12. Methodology A framework for improving representation of OO image-derived land-use maps. Polygon similarity model Outward-inward-buffering Elimination of small polygons

13. Original image-derived land-use map Detect problematic polygons Resolve problematic polygons Manipulate unclassified polygons Resolve geometric conflicts - Eliminate small polygons - Resolve narrow-corridor conflicts - Smoothen boundaries of polygons Preliminary optimized output Evaluate optimized output Final land-use map The Framework

14. Spectral similarity Spectral similarity (SP) quantifies the degree of resemblance in spectral characteristics of Pi and Pk and is calculated as the difference between their spectral values. The spectral values are described as the standard deviation of DN values the pixels covered by a polygon (brightness). Brightness contains the spectral characteristics of different layers of the image. A lower SP value corresponds with more similar spectral characteristics of two polygons.

15. Semantic similarity • Semantic similarity (SE)measures the equivalence in land-use of Pi and Pk • It is determined by the relationship between land-use classes of Pi and Pk based on a hierarchical land-use classification system: • n: nr of class levels in the land-use classification system. • Vl= 1 if Pi and Pk belong to the same land-use class at thelthlevel, and 0 otherwise. • If Vl = 1 and l > 1, then V1 =…= Vl-1 = Vl=1 and Vl+1 = …=Vn=0. • A larger SE value corresponds with a closer semantic relationship.

16. Semantic similarity: some cases The land-use classes of Pi and Pk are identical at l = 3, e.g. the both polygons belong to Class I. Then V1 = V2 = V3 = 1, and thus SE = 2. The land-use classes of Pi and Pkare different at l = 3, e.g. Pibelongs to Class 1 and Pk belongs to Class 2, but they belong to the same class A at Level 2. Then V1 = V2 = 1, V3 = 0, and thus SE = 1. The land-use classes of Pi and Pkdiffer at Levels 2 and 3, e.g. Pibelongs to Class A and Pk belongs to Class B, but they belong to the same class (e.g. Class II) at Level 1. Then V1 = 1 and V2 =V3 = 0, and thus SE = 1/3. The land-use classes of Pi and Pkare different at all levels, e.g. Pibelongs to Class I and Pk one belongs to Class X. Then SE = 0.

17. Geometric similarity Geometric similarity (GE) measures the resemblance in shape (size, perimeter) characteristics SIi of Pi and SIkof Pk. For eliminating a small polygon Pi, GEequals the ratio of the length of the sharing boundaries Piwith its neighbor polygon Pkto its perimeter. This shape index quantifies the difference in shape between a polygon and the circle with the same area. The small polygon is merged with its neighbor with the largest GE value. Thus the possibility is eliminated of introducing new narrow-corridor conflicts when eliminating the small polygon. For unclassified polygons, GE adopts the difference in the shape index of two polygons as

18. Polygon similarity model • Polygon similarity (S) is defined as the degree of similarity of two polygons depending on their contextual characteristics. • Spectral characteristics (SP) • Semantic characteristics (SE) • geometric characteristics (GE)

19. Eliminate small polygons Basic solution: merged with the neighbor with the highest polygon similarity

20. Outward-inward-buffering To resolve narrow-corridor conflicts existing in polygons. Basic rationale: an outward-buffering process (dilation process) + an inward-buffering process (erosion process)

21. Improved Map image-derived land-use map at 1:10000 image-derived land-use map at 1:50000

22. We notice… Well developed spatial statistical techniques are able to resolve emerging problems in new classification procedures Further optimization is to be done Automating updating steps is receiving a new flavor There is room for a further (probabilistic) approach

23. Seconde plat Deforestation in the Amazonian

25. Selective Logging In the Brazilian Amazonia, selective logging is a major source of forest degradation Detection and analysis of selective logging is an important challenge to forest researchers Log-landing sites serve as proxy for selective logging activities Spatial point pattern statistics may serve as an important tool for analyzing patterns of log-landing sites

26. Selective Logging Detection

27. Study area

28. Map of log-landings (2000) 650 locations

29. Point pattern statistics First order characteristics where dx is a small region located at x of the log-landing pattern X, |dx| being its area and N(dx) is the number of log-landings in dx Second order characteristics

30. Distance summary functions Nearest neighbor distance distribution function Empty space distance distribution function The J-function

31. Stationarityvd. Non-stationarity • Stationarity: all properties of a pattern remain invariant under translation (constant density) • Non-stationarity: configuration of the pattern depends on the locations (variable density) • variability due to environmental heterogeneity • interactions between the points • In case of non-stationarity: Markov Chain Monte Carlo methods (MCMC) become computationally extensive

32. Estimation of the intensity function and choice of the kernel bandwidth Intensity function is generally unknown and estimated non-parametrically using kernel smoothing Suitable choice of kernel bandwidth is the main challenge in estimation of the intensity function

33. Kernel density estimate with kernel size=10 km kernel size=50 kernel size=30 kernel size=40 kernel size=10

34. Kernel density estimate with kernel size=20 km

35. Kernel density estimate with kernel size=30 km

36. Kernel density estimate with kernel size=40 km

37. Kernel density estimate with kernel size=50 km

38. Observations A larger value of kernel bandwidth r reduces the interaction distance between the log-landing sites, thus reducing the effective range of interaction distance r over which the J-function is calculated. As the value of r increases beyond its effective range, the simulated envelopes span over wider range and relative noise in the simulated envelopes also increases. Relative noise in the calculated J-function also increases beyond the effective range of r

39. Map of loglandings (2001) 917 locations

40. kernel size=10 kernel size=20 kernel size=30 kernel size=40 Kernel density estimate with kernel size=20 km

41. Kernel density estimate with kernel size=30 km

42. Kernel density estimate with kernel size=40 km

43. To summarize The presented visual and graphical methods provide a useful tool to get an insight into the spatial characteristics of log-landings distribution. Spatial statistics was useful for analysis and interpretation of the pattern of log-landing sites. The inhomogeneous J-functions helps to infer the type and ranges of interaction using non-parametric form of intensity. The selective logging operations are strongly aggregated with in the study area The appropriates bandwidth increased from 20 to 30 km within a single year, indicating an increase in the extent of the clustering of log-landing sites.

44. Further work Fitting a spatial point pattern model to explain the clustered pattern of log-landing sites in terms of related environmental and geographic factors

45. Le desert A new scientific journal

46. A new journal ees.elsevier.com\spasta

47. The history • First ideas date back from 2007 • Aims and scope were defined • A key word analysis was done • 2007 – 2010: discussing it @ Elsevier • Reluctance because of the economic crisis • Reluctance because of increasing e-journals and internet • There was a recent journal in a related area: Spatial and Spatio-Temporal Epidemiology, Andrew Lawson editor in chief • No new journals

48. Then, in 2010… We had the idea for a conference to check the support Elsevier organized the meeting Conference took place in Enschede, in 2011 It was a great success (> 300 participants) This convinced Elsevier that it was a good idea to continue I was formally invited to become the ed-in-chief The first issue appeared in 2012, containing a wide range of publications The second issue is in press

49. Aims and scope (1) • The aim of the journal is to be the leading journal in the field of spatial statistics. • It publishes articles at the highest scientific level concerning important and timely developments in the theory and applications of spatial and spatio-temporal statistics. • It favors manuscripts that present theory generated by new applications, or where new theory is applied to an important spatial problem. • A purely theoretical study will only rarely be acceptable without a proper application, whereas a single case study is not acceptable for publication.

50. Aims and scope (2) • Spatial statistics concerns the quantitative analysis of spatial data, including their dependencies and uncertainties. • The extension to spatio-temporal statistics includes the time dimension as well. • The three major groups of data are covered: • lattice data that are collected on a predefined lattice • geostatisticaldata that represent continuous spatial variation • spatial point data that are observed at random locations. • These types of data have their logical extension into the space-time domain, where the relations remain similar, but estimation may be different.