USDA Forest Service Remote Sensing Applications Center Forest Inventory and Analysis FIA, Remote Sensing and REDD Remote Sensing Applications and the Forest Inventory and Analysis Program Ken Brewer National Remote Sensing Research Program Leader Sean Healey IW-FIA Research Ecologist

Presentation Outline 1.USFS Forest Inventory and Analysis program use of remote sensing 2.Tree Canopy Cover information example 3.Monitoring Trends in Burn Severity example

Remote Sensing; General Principles 1.Improve efficiency 2.Increase precision of estimates 3.Provide new information

Forest Inventory and Analysis Program Conducted in three phases: Phase 1, FIA personnel stratify land areas to increase precision of the estimates. This phase has integrated remote sensing data for decades; aerial photos & satellite imagery. Phase 2, FIA field crews obtain observations and measurements of the traditional FIA suite of variables. Phase 3, FIA field crews obtain observations and measurements of additional variables related to the health of forest ecosystems.

FIA Strategic Plan: January 2007 Program Focus: Integrating new technology is critical to the efficient delivery of the FIA program. Emphasis Shifts: Increased use of remote sensing and spatial techniques. Improved land use/land cover change analysis. Five - Year Goals: Develop and document a suite of spatial tools and products.

2001 NLCD Tree Canopy Cover The USGS led the 2001 effort to map percent tree canopy cover for the United States at 30m resolution. The canopy cover layer is a popular product averaging over 400 downloads per month for the past several years. This dataset serves as one of the primary inputs for large interagency projects (e.g., Landfire fuel modeling). The US Forest Service examined these data for updating the 2000 assessment of urban tree cover as part of the Resource Planning Act Assessment.

Percent Tree Canopy Cover is important! (Example of the NLCD 2001 Percent Tree Canopy Layer) An integral part of both international and US forest land definitions Important both within forest land areas and in areas not traditionally considered forest. The percent tree canopy cover is an important dimension of fragmentation Knowing where trees are is an important first step in quantifying carbon and managing tree resources.

The Motivation for FIA Leadership If it’s related to trees, the Forest Service should be saying it FIA is a fundamental component of Forest Service research FIA is a data rich program Consistency between map based and plot based estimates

Pilot Phase – Study Design 4x Intensity Photo-based Sample Locations 105 photo points to estimate % tree canopy cover for model development

Pilot Phase – Key Research Outcomes Research on alternative pixel-level modeling techniques, alternative stratification/grouping strategies, using ordinal data for developing model, and model stability under different sampling intensification levels. (Moisen et al.,Tipton et al). Research on the impact of scale of observation on tree canopy cover estimates. Relationship among plot based, PI based, and modeled estimates (Toney et al. 2009) at multiple scales. (Toney et al., Frescino et al., Gatziolis et al.) Research on the impact of data normalization in the response variables. (Tipton et al.) Assessment and recommendations on photo interpretation repeatability (Jackson et al.) Research on modeling approaches for unique landscapes (Sen et al.) Synthesis of results (Coulston et al., Finco et al). 10 presentations, 3 journal papers, 8 proceedings papers

Prototype Phase – Study Design

Timelines Major Milestones 2010 Aug Prototype Kickoff Sept Oct Pilot Complete Nov Dec 2011 Jan SRS prototype data available Feb Mar Apr IW prototype data available May Jun Production Process Final Jul Aug Sept Production Begins Major Milestones Q 2Q 3QPilot Complete 4Q Q 2QProduction Process Defined 3QProduction Begins 4Q Q 2Q 3Q 4Q Q 2Q 3Q 4QCONUS Complete Q 2Q 3Q 4QCoastal Alaska Complete Q 2Q 3Q 4QHI, PR, VI Compelete

Implementation in Bhutan for Forest Monitoring Adapt USFS TCC approach for implementation in the Eastern Himalayan Region for REDD

Implementation in Bhutan for Forest Monitoring

High Resolution Satellite Imagery “Strip Samples” Landsat Imagery High Resolutio n Image Strip Tree Canopy Cover Sample Plot High Resolution Image Strip

Strategic Plan Direction – Emphasis Shifts Emphasis Shifts: 1.Increased use of remote sensing and spatial techniques 2.Improved land use/land cover change analysis  MTBS

Monitoring Trends in Burn Severity (MTBS) Project Overview Consistently map the location, extent and burn severity of large fires on all lands in the United States from 1984 and 2010Consistently map the location, extent and burn severity of large fires on all lands in the United States from 1984 and 2010 –> 400 hectares in the western United States –> 200 hectares in the eastern United States Project durationProject duration –1984 to 2010 data record to be completed between FY05 and FY11 –Annual maintenance/update planned for 2011 and beyond Jointly implemented and equally funded by USDA Forest Service and Department of InteriorJointly implemented and equally funded by USDA Forest Service and Department of Interior –USDA-FS RSAC –USGS-EROS

MTBS Methods – Burn Scar Delineation Goal is to utilize a consistent method and data to derive perimeters Perimeters digitized using dNBR and reflectance data Scale of delineation: 1:24,000 to 1:50,000 Incident perimeters do not directly affect delineation Perimeter confidence levels included as feature level metadata 2007 Chippy Creek Fire (western Montana) Create burn scar delineation

MTBS Methods - Burn Severity Mapping dNBR images are interpreted to derive 5 severity classes Analysts use knowledge of site ecology, and knowledge of fire behavior and effects in given ecological settings, as guidance for choosing severity thresholds Composite Burn Index (CBI) thresholds applied where available Analysts also have access to advice and feedback from regional experts 2007 Chippy Creek Fire (western Montana) Threshold dNBR images into burn severity classes

MTBS Geospatial Products Fire Level Datasets Available at at Pre/Post-fire Landsat imageryPre/Post-fire Landsat imagery Fire Occurrence DatabaseFire Occurrence Database Burn scar boundaryBurn scar boundary Burn severity indicesBurn severity indices Thematic burn severity dataThematic burn severity data Map, visualization and reporting productsMap, visualization and reporting products Prefire Image Postfire Image Burn Severity Indices Thematic Burn Severity Unburned to Low LowModerateHigh

Accelerated re-measurements of burned FIA plots were used for validation

Validation results were encouraging

An MTBS-like effort has been initiated covering the entire country of Bhutan Landsat path/row 138/41Landsat path/row 137/41

Satellite-based products developed by and with FIA can support monitoring, reporting, and verification needed for international agreements related to forest cover

Questions? USDA Forest Service Remote Sensing Applications Center Forest Inventory and Analysis