1. U.S. Department of the Interior U.S. Geological Survey Assessment of Conifer Health in Grand County, Colorado using Remotely Sensed Imagery Chris Cole Parallel Incorporated U.S. Geological Survey Rocky Mountain Geographic Science Center

2. Purposes of study  Evaluate the feasibility of and develop methodology for the use of medium resolution remotely sensed imagery for conifer health assessment  Evaluate the potential to apply study results and methodologies to provide a strategic assessment of coniferous forest health statewide

3. Study Area Grand County, Colorado  A pilot study area for a host of USGS Fire Science Activities  Boasts a diverse range of land-cover, land ownership  Contains a wide range of coniferous forest health conditions

4. Grand County, Colorado

5. Methodology  Summer/Fall 2008 Landsat TM and ASTER imagery were collected spanning Grand County  Persistent cloud cover complicated analysis and classification efforts  Imagery were radiometrically normalized via reflectance transformation (rescaled), linear regression  Mosaicked to form a single, cloud minimized three band multispectral dataset (green, red, NIR)

6. Methodology  Data derivatives from multispectral image  Band Ratios  Normalized Difference Vegetation Index (NDVI) – sensitive to vegetation health  Samples selected – healthy and non-healthy conifers  Were collected from 30-m multispectral data, based upon image interpretation and spectral reflectance characteristics  High-resolution multispectral imagery also employed  Multi-year Aerial Surveys  Samples include range of conifer species type and health

7. Methodology  Sample signatures used to perform a supervised classification (maximum likelihood algorithm)  Produced an updated USGS NLCD for Grand County based upon 2008 remotely sensed data  Focused upon characterization of changes in conifer and mixed vegetation cover  Thinning/clearcutting  Emergent conifer regrowth  This dataset was used to exclude non-coniferous vegetation from final classification

8. Methodology – Spectral Plot Unhealthy Conifer Healthy Conifer

9. Results  Accuracy assessment confirms this approach produced a consistent conifer health classification at 30-m resolution within Grand County  Overall Classification accuracy 95.71%  Producer’s accuracy 91.43%  Kappa.9143  Methodologies are sound, flexible, and could be adapted and expanded to assess statewide coniferous forest health

10. 1995 Conifer Health Conditions Green = Likely Healthy Conifers

11. 2006 Conifer Health Assessment Green = Likely Healthy Conifers Orange = Likely Unhealthy Conifers

12. 2008 Conifer Health Assessment Green = Likely Healthy Conifers Orange = Likely Unhealthy Conifers

13. Next Steps  Field verification of Grand County classification results  Exploitation and classification of high resolution remotely sensed imagery for finer scale conifer health assessment  QuickBird (2.4-meter)  CAP ARCHER (1-meter)

14. Example – preliminary fine scale forest health analysis CAP ARCHER Hyperspectral image, 1-meter spatial resolution Preliminary generalized forest health classification using CAP ARCHER

15. Conclusions  Medium resolution remotely sensed imagery can be employed to assess coniferous forest health conditions in Grand County, Colorado  Results from these efforts, and methodologies can be applied to provide strategic assessment of forest health conditions statewide