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 dataLand cover is a continuous variable

Small patch landscape Transition zone

• Estimate fractions of each class for every pixel

• Discrete classification has to be accompanied by a pixel-level confidence

NALCMS LegendLevel 1 Level II

1. Needle leaved forest1. Temperate or sub-polar needleleaf evergreen forest2. Sub-polar taiga needleleaf forest

2. Broadleaved forest3. Tropical or sub-tropical broadleaf evergreen forest4. Tropical or sub-tropical broadleaf deciduous forest5. Temperate or sub-polar broadleaf deciduous forest

3. Mixed forest 6. Mixed forest

4. Shrubland7. Tropical or sub-tropical shrubland8. Temperate or sub-polar shrubland

5. Herbaceous9. Tropical or sub-tropical grassland10. Temperate or sub-polar grassland

6. Lichens/moss11. Sub-polar or polar shrubland-lichen-moss12. Sub-polar or polar grassland-lichen-moss13. Sub-polar or polar barren-lichen-moss

7. Wetland 14. Wetland8. Cropland 15. Cropland9. Barren lands 16. Barren land10. Urban and built-up 17. Urban and built-up11. Water 18. Water12. 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 March Radiance October

Elevation Precipitation

SamplesReference, Ecosystems

Samples

ID Clase Puntos Píxeles Training Buffer

1 Te. needleleaf evergreen 3,184 3,181 2,546 1,536

3 Tr. broadleaf evergreen 3,832 3,822 3,058 1,833

4 Tr. broadleaf deciduous 3,844 3,842 3,074 1,651

5 Te. broadleaf deciduous 4,124 4,119 3,296 1,387

6 Mixed forest 4,695 4,693 3,755 1,312

7 Tr. Shrubland 6,599 6,593 5,275 3,066

8 Te. Shrubland 4,091 4,085 3,269 1,537

9 Tr. Grassland 4,035 3,939 3,152 588

10 Te. Grassland 4,443 4,397 3,518 730

14 Wetland 1,096 1,094 876 619

15 Cropland 74,558 72,238 57,839 30,560

16 Barren land 731 728 583 469

17 Urban and built-up 5,937 5,902 4,722 260

Total 121,169 118,633 94,963 45,548

High number of samples necessarybuffering improves clasification

C5: classification treePreicts categorial variables (like land cover)Non-parametricProcesses continuous and discrete variablesGenerates interpretabe rulesFast 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 classificationProcess

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

WetlandBuffer 2km

UrbanBuffer 2km

Before After

Urban

Wetland

Class memberships

0 100

Class membership [%]

Tropical shrubland

Temperate herbaceous

Discrete map México

0 100Confidence

Central Mexico

Accuracy

ID Clase Prod. Users1 Te. needleleaf evergreen 60.4 73.9

3 Tr. broadleaf evergreen 88.9 74.6

4 Tr. broadleaf deciduous 84.3 67.1

5 Te. broadleaf deciduous 66.7 74.6

6 Mixed forest 80.1 62.9

7 Tr. Shrubland 94.9 79.7

8 Te. Shrubland 34.7 49.4

9 Tr. Grassland 63.1 81.9

10 Te. Grassland 59.9 36.1

14 Wetland 62.2 96.4

15 Cropland 73.5 97.2

16 Barren land 50.0 94.6

17 Urban and built-up 58.2 89.1

Overall normalized accuracy: 82 %

Classes with high errors:•Mixed forest•Temperate shrubland•Temperate grassland

Map agreement 2005-2006

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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http://www.cec.org

http://www.cec.org/naatlas/nalcms.cfm