1. FIA AND REMOTE SENSING FOR LAND USE CHANGE John Coulston, Greg Reams 2015 FIA National User’s Group Meeting, San Antonio, TX April 1-2

2. Rationale Farm Bill provision to ‘understand and report on changes in land cover and use’ NASF recommendation to ‘Use of remote imagery to track harvest intensity, land-use change, and land cover change’

3. FIA Corporate Cover Products 2011 National Land Cover Database Percent Tree Canopy Cover (30m) complete and available at mrlc.gov Canopy cover models based on % tree canopy cover photo interpretation of FIA plot locations on ‘all’ lands 2016 % Tree Canopy Cover Product and 2011-2016 change product. Research underway for delivery circa 2018. FIA partners (Remote Sensing Application Center, VA Tech, USGS) Funded by FS R&D, ST&PF, NFS

4. Land Use vs. Land Cover Land use is a function of the social and economic purposes for which land is managed. Land cover is a function of the biological and human-made cover classifications observed on the land. Land cover does not always equal land use. This confounds the change issue particularly for forests

5. Forest Use and Forest Cover: definitions and drivers of change FIA Example forest land use definition: Land spanning more than 0.5 ha with trees higher than 5 m and a canopy cover of more than 10%, or trees able to reach these thresholds in situ. It does not include land that is predominately under agricultural or urban land use. NLCD Example forest land cover definition: Areas dominated by trees generally greater than 5 meters tall, and greater than 20 percent of total vegetation cover Drivers of change Conversion to developed Natural conversion from agriculture to forest Tree planting Harvesting Severe fire and insect outbreaks Some thinning operations Forest Use Forest Cover Forest Use & Cover

6. Quiz: Forest or not?

7. Remote Sensing Land Use Change: Aren’t a lot of people doing this? Many organizations ‘say’ they do land use change but… Efforts are often a mixture of land use and land cover. Some efforts are intentionally misleading (e.g. gross forest cover loss) With the exception of FIA and NRI all other broad scale efforts treat forest as a cover (rather than a use) However, NRI land use change data do not cover all lands and have not been available at either the State or County-level since the 1997 survey.

8. Needs We need to expand our thinking beyond just how much forest land we have. We need to understand the flows of land uses into and out of forest as well as flows among other uses. We need to understand the ‘ecosystem service’ impacts of these land use flows. We need to understand the management impacts of the changing land base

9. For example: Greenhouse Gas Inventories Background: Accounting framework Tracks carbon emission and reductions by sector, source, and activity Purpose Policy makers use inventories to establish a baseline for tracking emission trends, develop mitigation strategies and policies, assess progress. Land use, land use change, and forestry (LULUCF) is a sector that covers emissions and removals of greenhouse gases resulting from direct human-induced land use, land-use change and forestry activities. Unambiguously separating land use change effects from forest management effects is key to informing both forestry and land use policies.

10. A Southern Example

11. Coterminous United States Over the past 10 years we seen a reduced amount of land transitioning into forest land use. This substantially impacts land use carbon transfers into forests. We need work with our partners at EPA and CEQ to ensure a complete accounting of land uses and associated carbon stocks. Harvest Reduction Reduced Afforestation

12. Remote Sensing for land use change Previous (circa 2011) initiatives of the FIA program called for a classification of each FIA plot (forest and non-forest). The classification includes a determination of Land use Land cover Percent tree canopy cover. These classifications are performed using either field observation or photo interpretation of high resolution imagery based on the rotating panel design and schedule.

13. But Wait --- FIA plots are only in forest? A common misconception FIA is a longitudinal study over space and time across all land uses and covers. Plot locations are permanent.

14. Remote Sensing for land use change Current efforts implement the land use, land cover, and agent of change information using photo interpretation of National Agricultural Imagery Program (NAIP) imagery (1m). The land use, land cover, and change classification would be done for all FIA plots (forest and non-forest) on the NAIP schedule (~ every 3 years). Image Change Estimation (ICE)

15. Why Use Photo Interpretation The classification of use requires an interpretation of intent. This is best done on the ground (but most expensive too) High resolution imagery can provide an alternative but QA on photo work will be important Manual interpretation of high resolution imagery is a common way to develop response variable for remote sensing products. Because we have tied this PI work to the original sample data arising from this work can be used for ‘estimation’ or for subsequent geospatial product development.

16. Image Change Pilot Efforts

17. Land Use Classification Land Use Classification System General Classification Intermediate Classification Detailed Classification 100Natural/Semi -Natural (MMU) 110Forest (MMU) 111Natural Forest (MMU) 112Artificial Forest (MMU) 120Other Natural/Se mi-Natural (MMU) 121 Wetland/Riparian (MMU) 122Nonforest-Chaparral (MMU) 200Water (MMU) 210Noncensu s Water (MMU) 211Noncensus Stream/River (MMU) 212Noncensus Lake (MMU) 213Noncensus Canal (MMU) 214Noncensus Reservoir (MMU) 215Noncensus Coastal Water (MMU) 220Census Water (MMU) 221Census Stream/River (MMU) 222Census Lake (MMU) 223Census Canal (MMU) 224Census Reservoir (MMU) 225Census Coastal Water (MMU) 300Agricultur al (some MMU) 310Farmlan d (MMU) 311Cropland (MMU) 312Pasture (MMU) 313Idle Farmland (MMU) 320Agricultur al Trees (some MMU) 321Orchards, Groves, Nurseries (MMU) 322Christmas Tree Plantation (MMU) 323Windbreak/Shelterbel t 330Other Agricultur al (some MMU) 331Maintained Wildlife Opening (MMU) 332Vineyard (MMU) 333Confined Feeding Operations (MMU) 400Developed410Cultural411Residential 412Commercial and Services 413Industrial 414Mixed Urban/Built-up land 420Right-of- Way 421Road 422Railway 423Utility/Communication Lines and Areas 424Maintained Canal 425Airport Facility 430Recreati on 431Park 432Ski Area 433Golf Course 434Athletic Fields and Tracks 435Camp Ground 440Strip Mines, Quarries, and Gravel Pits 500Rangeland (MMU) 900Other910Nonvegetated (MMU) 920Beach (MMU) 999Uninterpretable Agent of Change Classification System 0No Change 11Development 21 Harvest (Forested: >10% canopy cover visible on T2) 22 Harvest (Forested: <10% canopy cover visible on T2) 31Natural Regeneration of Vegetation 32Artificial Regeneration of Vegetation 33Removal or Loss of Vegetation 34Stress or Mortality - Insect/Disease/Drought 35Channing 41Fire 42Erosion 43Landslide 44Avalanche 45Weather Event 46Animal Damage 91Crop Rotation 92Leaf on vs. Leaf off imagery 93Seasonal Snow Cover 94Seasonal High Water

18. Photo Interpretation Time 1Time 2 Land use and land cover are classified at time 1 and time 2 (5dots) If change observed land use, land cover, and change agent classified (45 dots)

19. Georgia Example Agent of Change (% of total)

20. Getting it done Funding vehicle – Remote Sensing Application Center (RSAC) is a key Forest Service Partner In the South the States are key partners Texas pilot being done by Texas A&M Forest Service in coordination with FIA and RSAC. Other regions may choose to do work ‘in house’ or contract with RSAC.

21. Still work to be done Initiate field protocols to ensure adequate QA information available for photo work. Some estimation work – there is a need to incorporate both the field observed information with the photo information in the estimation phase Funding – securing of additional funds to move effort to production Additional Research Using Image Change data as bases for land use projections (RPA) Working with EPA to have a more consistent view of land use and land use change for carbon reporting Modeling of land use dynamics impact on timber availability and harvest probability

22. Questions?