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

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

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

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

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

NALCMS Processing

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

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, Te. Grassland4,443 4,3973, Wetland1,096 1, Cropland74,558 72,23857,83930,560 16Barren land Urban and built-up5,937 5,9024, Total121, ,63394,96345,548 High number of samples necessary buffering improves clasification

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)

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

Class memberships Class membership [%] Tropical shrubland Temperate herbaceous

Discrete map México 0100 Confidence

Central Mexico

Accuracy IDClase Prod.Users 1Te. needleleaf evergreen Tr. broadleaf evergreen Tr. broadleaf deciduous Te. broadleaf deciduous Mixed forest Tr. Shrubland Te. Shrubland Tr. Grassland Te. Grassland Wetland Cropland Barren land Urban and built-up Overall normalized accuracy: 82 % Classes with high errors: Mixed forest Temperate shrubland Temperate grassland

Map agreement

Continental product

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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