Sistema de Monitoreo de la Cobertura del Suelo de América del Norte

1. Sistema de Monitoreo de la Cobertura del Suelo de América del Norte

2. What is NALCMS ? • North American Land Change Monitoring System • Developing land cover change monitoring capacity for North America • A tri-national initiative united by CEC • Canada: CCRS • USA: USGS • Mexico: INEGI, CONAFOR, CONABIO • Founded in 2006 • Goal: Develop an operational system for monitoring land cover change of the continent with satellite data • Resolution: 10 – 250m

3. NALCMS products • Medium spatial resolution • Continental satellite data • Annual continental land cover classification • Land change products • Fractional products • High spatial resolution • Hot-spot change analysis • Border analysis • Training / validation data

4. Coarse resolution data Land cover is a continuous variable Small patchlandscape Transitionzone • Estimate fractions of each class for every pixel • Discrete classification has to be accompanied by a pixel-level confidence

5. NALCMS Legend Italic: Class does not exist in Mexico

6. NALCMS Processing

7. Input data Radiance March Radiance October • Satellite data: Monthly composites of MODIS radiance data + NDVI • Ancillary data: DEM, Temperature, Precipitation • Regionalization: CEC Ecosystems L1 • Reference set: INEGI Serie-III Vegetation map • Samples • Masks for post-processing Elevation Precipitation Reference, Ecosystems Samples

8. Samples High number of samples necessary buffering improves clasification

9. C5: classification tree Preicts categorial variables (like land cover) Non-parametric Processes continuous and discrete variables Generates interpretabe rules Fast and high accuracy NDVI < 0.5 :...Red < 0.2 : :...SWIR < 0.3: B (20,70,40) : : SWIR >= 0.3 : : :...NDVI < 0.2 B: (10,90,30) : : NDVI >= 0.2 B: (30,60,10) : Red >= 0.2: C (60,40,70) NDVI >= 0.5 :...NIR < 0.5 :...NDVI < 0.8: C (30,60,80) : NDVI >= 0.8: B (40, 60, 30) :NIR >= 0.5: A (90,20,10) Samples like proportion per class

10. Data classification Process Application of single trees Fusion of single classifications by boosting rules (Quinlan, 1993) Fusion of boosted classifications Knowledge-based correction • Result • Individual classification • Boosted classification • Mean of boosted clasifications • Corrected classification Wetland Urban Urban Buffer 2km Wetland Buffer 2km Before After

11. Class memberships Temperate herbaceous Tropical shrubland Class membership [%] 100 0

12. Discrete map México 0 100 Confidence

13. Central Mexico

14. Accuracy Overall normalized accuracy: 82 % • Classeswithhigherrors: • Mixedforest • Temperateshrubland • Temperategrassland

15. Map agreement 2005-2006

16. Continental product

17. Conclusions • Mexico has complex land cover composition • Continuous land surface requires appropriate classification strategies for medium to coarse spatial resolution mapping • High map consistency for repeated classifications • Classification of several years • Change detection with appropriate methods • Surface fractional cover products

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