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 patch landscapeTransition zone Estimate fractions of each class for every pixel Discrete classification has to be accompanied by a pixel-level confidence

5. NALCMS Legend Level 1Level II 1. Needle leaved forest 1. Temperate or sub-polar needleleaf evergreen forest 2. Sub-polar taiga needleleaf forest 2. Broadleaved forest 3. Tropical or sub-tropical broadleaf evergreen forest 4. Tropical or sub-tropical broadleaf deciduous forest 5. Temperate or sub-polar broadleaf deciduous forest 3. Mixed forest6. Mixed forest 4. Shrubland 7. Tropical or sub-tropical shrubland 8. Temperate or sub-polar shrubland 5. Herbaceous 9. Tropical or sub-tropical grassland 10. Temperate or sub-polar grassland 6. Lichens/moss 11. Sub-polar or polar shrubland-lichen-moss 12. Sub-polar or polar grassland-lichen-moss 13. Sub-polar or polar barren-lichen-moss 7. Wetland14. Wetland 8. Cropland15. Cropland 9. Barren lands16. Barren land 10. Urban and built-up17. Urban and built-up 11. Water18. Water 12. Snow and ice 19. Snow and ice Italic: Class does not exist in Mexico

6. NALCMS Processing

7. Input data 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 Radiance MarchRadiance October ElevationPrecipitation SamplesReference, Ecosystems

8. Samples IDClasePuntosPíxelesTrainingBuffer 1Te. needleleaf evergreen3,184 3,1812,5461,536 3Tr. broadleaf evergreen3,832 3,8223,0581,833 4Tr. broadleaf deciduous3,844 3,8423,0741,651 5Te. broadleaf deciduous4,124 4,1193,2961,387 6Mixed forest4,695 4,6933,7551,312 7Tr. Shrubland6,599 6,5935,2753,066 8Te. Shrubland4,091 4,0853,2691,537 9Tr. Grassland4,035 3,9393,152588 10Te. Grassland4,443 4,3973,518730 14Wetland1,096 1,094876619 15Cropland74,558 72,23857,83930,560 16Barren land731 728583469 17Urban and built-up5,937 5,9024,722260 Total121,169 118,63394,96345,548 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 Samples like proportion per class 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)

10. Data classification Process 1.Application of single trees 2.Fusion of single classifications by boosting rules (Quinlan, 1993) 3.Fusion of boosted classifications 4.Knowledge-based correction Result Individual classification Boosted classification Mean of boosted clasifications Corrected classification Wetland Buffer 2km Urban Buffer 2km BeforeAfter Urban Wetland

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

12. Discrete map México 0100 Confidence

13. Central Mexico

14. Accuracy IDClase Prod.Users 1Te. needleleaf evergreen60.473.9 3Tr. broadleaf evergreen88.974.6 4Tr. broadleaf deciduous84.367.1 5Te. broadleaf deciduous66.774.6 6Mixed forest80.162.9 7Tr. Shrubland94.979.7 8Te. Shrubland34.749.4 9Tr. Grassland63.181.9 10Te. Grassland59.936.1 14Wetland62.296.4 15Cropland73.597.2 16Barren land50.094.6 17Urban and built-up58.289.1 Overall normalized accuracy: 82 % Classes with high errors: Mixed forest Temperate shrubland Temperate grassland

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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