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Lewis J. + , J. Li + , J. Rowland * , G. Tappan * , L. Tieszen *

Assessment of land cover performance using NDVI time series to identify degradation and change across local and regional scales. Lewis J. + , J. Li + , J. Rowland * , G. Tappan * , L. Tieszen * + Dept. of Geography, McGill University, Montreal H3A 2K6

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Lewis J. + , J. Li + , J. Rowland * , G. Tappan * , L. Tieszen *

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  1. Assessment of land cover performance using NDVI time series to identify degradation and change across local and regional scales • Lewis J.+, J. Li+, J. Rowland*, G. Tappan*, L. Tieszen* • + Dept. of Geography, McGill University, Montreal H3A 2K6 • Science & Applications Branch, EROS Data Center, Sioux Falls, SD • Work performed under U.S. Geological Survey contract 1434-CR-97-CN-40274

  2. Our basic question is --- How are my pixels performing?

  3. Patterns of Change • Seasonal Responses • Interannual Variability • Directional Vegetation Change • intrinsic vegetation response • land use & other human induced changes • global climate change

  4. Research Objectives • Develop tools for assessing land performance in Senegal using coarse resolution NDVI • Investigate historical trends (degradation and improvement) from NDVI time series data • Test procedures for determining levels of human influence on land performance

  5. Outline • Senegal Rainfall • Rainfall/iNDVI – Correlation Analysis • Local Area Analysis - SEE • Multi-temporal PCA • Maximum NDVI • iNDVI • For the Sahel

  6. Mean satellite-derived rainfall estimate for 1995-1997 Mean interpolated rainfall for 1995-1997

  7. Mean season integrated NDVI for 1982-1997 Mean annual rainfall for 1982-1997

  8. . Annual rainfall for 1984 iNDVI for 1984 Annual rainfall for 1988 iNDVI for 1988

  9. Land Cover Map of Senegal

  10. Correlation coefficient between seasonal integrated NDVI and rainfall (1982-97) Correlation coefficient between season integrated NDVI and rainfall for the four land cover types

  11. 1982 iNDVI 1983 Rainfall(mm)

  12. 1988 iNDVI 1989 Rainfall(mm)

  13. Correlation Analysis between integrated NDVI and rainfall 88 88 87 87 86 86 85 85 84 84 83 83 82 82 pixel pixel NDVI Rainfall

  14. Slope of regression equation for iNDVI over time (1982-1997) . Classified map of slope coefficient for iNDVI (1982-1997)

  15. Site Description • 1.Dendoudi. This site reveals areas of degraded soil and vegetation around a Sahelian (semi-arid) borehole. Degradation has occurred since about 1950 because of livestock concentration. • 2.Patako. This is a large forest preserve that has been protected since about 1933. It is in relatively good condition, though slightly degraded from thinning of the forest by selective cutting since the early 1980s (Tappan et al., 2000). This site has relatively higher correlation coefficients for both time periods. Patako, surrounded by extensive agriculture, appears to be less affected by problems of high humidity and rainfall that complicate the NDVI signal for the woodland/forest region in southern Senegal. • 3.Touba. This is a rapidly growing city with a ring of very degraded, unproductive cropland around it. It lies within the generally degraded agricultural region (west central Senegal). Degradation has increased in the past 10 to 20 years..

  16. Integrated NDVI time series profiles

  17. Integrated NDVI time series profiles Dendoudi (mean=8.4) Independent site (mean=12.0) Touba (mean=12.3) Pakota (mean=24.9)

  18. Correlation coefficients of rainfall and iNDVI (1982-1993) Correlation coefficients of rainfall and iNDVI (1986-1997)

  19. Correlation coefficient for selected sites on the two correlation maps

  20. Z-score values and probabilities for three sites

  21. Analysis of outliers (SEE) A 'local' standard error of estimate (SEE) was defined by conducting a regression analysis on the pixels within a local window. Thus, the local SEE is a measure of the SEE for a moving NxN window. In an image, each pixel (except around the edge) can be considered as the center of a NxN window for which the SEE of the NxN values is computed. If the central pixel of each window is greater than +/-1 SEE, the pixel is regarded as a outlier for this window. Outliers greater than +1 SEE are positive; whereas, outliers less than -1 SEE are negative . Negative outliers specify those pixels that have low NDVI values and high precipitation for the local area.

  22. Linear regression analysis on NDVI and rainfall for local area NDVI Rainfall NDVI SEE -SEE • • • • • • • • Rainfall • • • • • •

  23. . Frequency maps of negative outliers (<-1 SEE) for rainfall and iNDVI in 6-year intervals

  24. . Frequency map of negative outliers (<-1 SEE) for rainfall and iNDVI from 1982-1997.

  25. 4, 5, and 6 year length for negative runs of integrated NDVI with color denoting starting year (4) (5) (6) 4, 5, and 6 year for positive runs with color denoting starting year

  26. Summary • For most of Senegal there exists a strong relationship between the amount and timing of rainfall and vegetation development. • If the climate effect is accounted for then an initial assumption can be made that a majority of the remaining variation should be related to human-induced effects on the land. • A simple regression approach and its correlation coefficients for integrated NDVI and rainfall appear to provide useful indications of land performance. • The simple procedure identified known areas where land degradation has been a problem.

  27. An Approach Using Principal Components Analysis (Multi-temporal PCA)

  28. Percent Variance Explained by Components

  29. Loadings : CMP 1 CMP 2 CMP 3 gmb1 0.973841 -0.10733 -0.0438 gmb2 0.962135 0.180684 -0.01825 gmb3 0.937641 0.288658 -0.05087 gmb4 0.974971 0.016813 -0.00267 gmb5 0.966035 -0.10675 -0.04305 gmb6 0.976179 -0.0744 -0.02459 gmb7 0.963939 -0.19732 0.014964 gmb8 0.973987 -0.07246 -0.01707 gmb9 0.973537 0.080992 0.003118 gmb10 0.973191 0.138903 -0.05487 gmb11 0.973313 0.081531 -0.07105 gmb12 0.977546 -0.04946 -0.06259 gmb13 0.966637 -0.05901 -0.11019 gmb14 0.96764 -0.13565 -0.02734 gmb15 0.950352 -0.09753 0.250297 gmb16 0.947186 0.122579 0.268269

  30. 0.2 0.3 0.15 0.2 0.1 0.1 0.05 0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 -0.05 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 -0.1 -0.1 -0.15 -0.2 -0.3 Time series of seasonal integrated NDVI (input) Principal components local variance analysis procedure Component 3 Component 2 spatial negative anomalies detect the positive anomalies no change pixel(s) (negative (NDVI is stable overlay undergoing relationship with over time) degradation rainfall ) degraded overlay areas Methodological procedures

  31. Percent variance of the first five components PCA loadings for first five components

  32. PCA Component 1 Map - iNDVI

  33. Rainfall for 1982-1997 and loadings for component 2

  34. Component 2 Map Threshold >155 - 60< Threshold< 60 (high negative relationship (no relationship with rainfall) with rainfall in forest area)

  35. PCA Loadings for component 3

  36. Local spatial NDVI anomaly detection A local variance method was designed to detect local anomalies of NDVI within Senegal. Local variance is measured as the mean value of the standard deviation of a moving NxN (3x3, 5x5, 7x7) window. In an image, each pixel (except around the edge) can be considered as the center of a NxN window. The standard deviation of the NxN values is computed. Generally, areas with anomalies in excess of one standard deviation have been found to be associated with significant environmental effects (Singh and Harrison, 1985). If central pixel's value is less than -2*STD+mean, the pixel will be set to 1 (a negative anomaly), if the pixel's value larger than 2*STD+mean, the pixel is 2 (a positive anomaly), or else 0 (normal). The advantage of using a moving window in place of regional sub-grouping or employing the entire country as a dataset is to focus exclusively on local spatial anomalies.

  37. Anomalies of local variance (threshold = 10 out of 16 years)

  38. Dendoudi Touba Independent site Resultant map of land performance assessment

  39. Integrated NDVI time series profiles

  40. Summary • the loadings of component 2 are related to inter-annual change of rainfall • loadings of component 3 seem to correspond to changes in iNDVI influenced by human factors especially after the early 1990s • a local variance method is used to extract the spatial local anomalies in iNDVI • combination of PCA & local variance provide an integrated picture of local and regional land cover

  41. Land Performance Analysis for the Sahel A start up attempt

  42. PCA for Sahel- Integrated NDVI Map of Component 1

  43. Map of Component 2

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