Forest and land cover mapping applications

1. Forest and land cover mapping applications Sept. 18, 2013

2. But first, vegetation indices… A vegetation index is an algebraic combination of two or more wavebands of information is sensitive to the presence or condition of vegetation. • Vegetation index values are correlated with… • green biomass • leaf area • leaf moisture content • various related biophysical parameters • (net primary production, CO2 flux…)

3. near infrared - red NDVI = near infrared + red Normalized difference vegetation index (NDVI): • One of the first indices published • One of the simplest (easy to interpret, easy to calculate) • Virtually all earth resource observation sensors record in the visible red and near-infrared wavelengths needed for NDVI

4. near infrared - red NDVI = Landsat TM Band 3 Landsat TM Band 4 near infrared + red

5. near infrared - red NDVI = Landsat TM Band 3 Landsat TM Band 4 near infrared + red Chlorophyll absorbs red and leaf structure scatters (reflects) near infrared NDVI contrasts near infrared reflectance with red reflectance

6. near infrared - red NDVI = Landsat TM Band 3 Landsat TM Band 4 near infrared + red

7. near infrared - red NDVI = near infrared + red Partial canopy removal Intact forest canopy Partial canopy removal, exposure of soils, woody debris NDVI decreases

8. 1991 NDMI 1993 NDMI 1995 NDMI

9. SWIR: Short-wave infrared • reflected infrared, generally synonymous with • mid-infrared, but some include near infrared as part • of the SWIR wavelengths • LWIR: Long-wave infrared • emitted infrared, synonymous with thermal infrared

10. OK, now forest cover and vegetation mapping

11. Sample TM signatures from lab 2 – supervised classification of a piece of South-central Maine Average radiance (average pixel value) Image layers (TM bands 1-5 and 7)

12. Vegetation and Land Cover of Maine, 1993 (MeGAP)

13. Location and Dates of Maine TM Scenes

14. Schriever and Congalton 1995: Exploiting natural phenological variation to improve forest vegetation discrimination • Results of forest type mapping using TM imagery acquired in… • May bud break • September hardwood leaf-on • October leaf senescence Best results from October and May imagery – spectral signatures of different forest types are most dissimilar during first growth and during during autumnal senescence differences in foliar biomass, pigment content, and moisture content affect spectral response patterns

15. Creating a statewide map- Maine GAP (1993) Modify Class Assignments to remove scene seams • Scene Classifications: • Unsupervised • Supervised • Cluster-busted • Strata • Agricultural Areas • Urban/Residential • Blueberry Fields Digital NWI data Scene Mosaic

16. Testing Map Accuracy (using interpreted videography polygons) Accuracy Assessment of Map Superclasses 88.1% Statewide Accuracy

17. Categorical Land Cover 2001 National Land Cover Database

18. 2001 National Land Cover Database • Supervised classification using… • multiple dates of TM imagery (and derived data layers) • digital elevation (and derived data layers) • and a variety of ancillary data • (population density, city lights • imagery, roads data, wetlands • from air photo interpretation)

19. National Land Cover Data (NLCD) 1992 & 2001(Homer et al. 2006) • Homer et al. does not recommend comparing NLCD 92 with NLCD 2001 because of different methods and datasets used to create them! • This creates a dilemma for users interested in tracking forest and land cover changes. As of 2009; however, they have developed a “bridge” product re-coded (crosswalked) to make the two classifications more compatible for change comparison. • NLCD 2001 was outdated (was not available until 2006) • Maine contracted Sanborn Inc. to develop a 2004 statewide Land cover/use map (MeLCD) at 5 m resolution through cooperation with NLCD 2001 program

20. Sader and Legaard 2008 • We can’t use the Maine Land Cover Data (MeLCD) 2004 or NLCD 2001 maps for spatially-explicit forest inventory, wildlife habitat modeling, & biodiversity analysis because neither map includes updated forest harvest and regeneration stand boundaries • We needed to develop our own updated MeGAP map using time-series Landsat to support forest research and applications.

21. Why Integrate Change Detection into Forest and Land Cover Map Updates? • Statewide land cover/use mapping is expensive thus precluding frequent updating (every 2-3 years) due to Federal and State budget constraints. • Landsat is accurate in detecting forest disturbance and regrowth (to approximately 1-2 acres). • Simple time-series change detection methods applied every 2-3 years will capture even light disturbance and maintain the harvest history legacy and age classes in newer maps • Landsat change detection, integrated with older forest type maps, is an appropriate, cost-effective and powerful tool for forest cover monitoring at the state or regional level

22. Landsat path 12, row 28 Thematic Mapper: 2004 2001 2000 1999 1997 1995 1993 1991 1988 1 - 3 year intervals Multispectral Scanner: 1985 1982 1978 1975 1973 2 - 4 year intervals

23. Thematic Mapper bandwidths: ~60% basal area removal Intact forest canopy Normalized difference moisture index: • correlated with leaf area, canopy cover, • green biomass, canopy moisture content • sensitive to stand structure, canopy disturbance

24. Red = 1991 Green = 1993 NDMI color composite Blue = 1995

25. MeGAP Update RS/GIS Methods

26. Comparisons of MeLCD and MeGAP Update Maps (2004)

27. MeGAP Update Map Accuracy

28. MeLCD (Sanborn) Map Accuracy

32. Sader and Legaard 2008 • Older forest area and forest patch size & connectivity are over-represented, as regeneration stands are under-represented in MeLCD 2004 and NLCD 2001 • No forest cover information in the recent harvest areas • The MeLCD 5m (compared to 30m for MeGAP) may be of little practical benefit where distinct landscape patterns are not resolved (due to methodology) in the final product. • The MeLCD 5m statewide vector data locks up most computers when trying to analyze statewide or large regional datasets.