"USDA Forest Service Forest Inventory and Analysis (FIA)"
USDA Forest Service Forest Inventory and Analysis (FIA) MRLC Land Characterization Partners Meeting Nov. 7-8, 2000 OUTLINE • Federal mandates that FIA more effectively use remote sensing • FIA Business needs from satellite data • Classification detail • Classification accuracy • Geographic priorities • Information needed by FIA Management Team Federal mandates that FIA more effectively use remote sensing • 1998 Farm Bill • White House Office of Science and Technology, Committee on the Environment and Natural Resources • RAND Corporation review of forest monitoring conducted by federal agencies • FIA Staff Director Rich Guldin http://fia.fs.fed.us/library.htm - Papers Improve consistency of data and process using a top down approach • Consistent data is like a common language • Centralized data collection, documentation and dissemination • Decentralized analyses and decision making • Economies of scale FIA Business needs from satellite data • Stable, dependable and economical production of accurate and consistent forest cover and land use maps • Cover entire USA every 3 to 10 years • Adherence to Federal Geographic Data Committee (FGDC) standards FIA Business needs from satellite data • Automated image processing algorithms that require little human intervention – Product consistency and accuracy – Cost reduction – Timeliness – Diversity of geospatial products – Henry Ford analogy FIA Business needs from satellite data • Improve accuracy of FIA statistics – Improve statistical efficiency through stratification on forest v. nonforest cover – Improve statistical estimates for small geographic areas (e.g., counties) using remotely sensed ancillary data FIA Business needs from satellite data • Improve timeliness of statistics in annualized FIA – 10% - 15% of field plots re-measured each year – Remotely sensed data “refreshed” every 3 to 5 years – This is a goal, not an absolute design requirement – Could use change detection to update forest/nonforest in a 10-year MRLC product FIA Business needs from satellite data • Change detection – Keep forest/nonforest map current to maintain FIA statistical efficiency through stratification • 2005 update to 2000 landcover map – Better identify spatial patterns of change in broad landscapes FIA Business needs from satellite data • Change detection – Improve accuracy of FIA statistical estimates for • Timber removals • Reforestation • Afforestation FIA Business needs from satellite data • Help provide 30-m/1:24,000 products to FIA customers – User-friendly data base for GIS analyses – Attractive maps for distribution – Spatial analysis tool box (internal and external users) FIA Business needs from satellite data • Characterize context surrounding each FIA field plot that are not easily measured in field – Landscape fragmentation – Size and shape of forest stand – Distance to roads, surface waters, other land uses (important components of wildlife habitat) FIA Business needs from satellite data • Substitute satellite data for 1:40,000 NAPP – Reduce cost of FIA stratification with Phase 1 plots (1-km grid) – Continue to provide imagery for navigation by field crews – 15-m pan-sharpened Landsat 7 – 10-m pan-sharpened SPOT – Superimpose ancillary geospatial data (DLG, DEM, topos., etc.) – Downloadable to field crews (federal, state, contractors) FIA Business needs from satellite data • Implementation schedule – Prototype products available for 10% -20% of USA by September 2002 – Production system functional by September, 2003 FIA Business needs from satellite data • New remotely sensed products in the future – Net primary productivity or photosynthesis rates – Tree mortality – Indicators of drought, acidic deposition, or pest attack – Boundaries between different forest stands – Indicators of human infrastructure (e.g., individual buildings) FIA Business needs from satellite data • Developers’ tools to implement a variety of spatial models with centralized database – Linkages to other geospatial databases (e.g., Census Bureau) – Sharing geomatic models – Facilitate local improvements to national map products • Accuracy • Classification detail Minimum spatial resolution • 1-km pixel for global/national assessments • 250-m to 30-m pixel for regional assessments • FIA definition of forest requires 30-m scale • Special assessment needs require 30-m scale (e.g., riparian management zones) • Functionality request: – change spatial scale of data to balance assessment needs with technology Classification detail • Might need separate MRLC products for forest cover and timberland use • Forest v. nonforest (most valuable for statistical efficiency through stratification) Classification detail • FIA definition for forest uses – 10% stocking, which can be applied with field data but not directly with remotely sensed data – At least 1-acre and 120-foot wide – Includes non-stocked clearcuts and seedling/sapling stands – Accuracy of remotely sensed classifications need to be high, but not necessarily 100% Classification detail • FIA definition for nonforested land use includes – Urban and suburban areas with tree cover – tree stocking less than 10% • Pasture with tree cover • Rangeland Classification detail • Broad forest types (global/national assessments) – Softwoods – Bottomland hardwoods – Upland hardwoods – Mixed hardwoods and softwoods Classification detail More specific cover types • Softwood forest • Upland hardwood forest – White-red-jack pine – Oak-hickory – Spruce-fir – Maple-beech-birch – Longleaf-slash pine – Aspen-birch – Loblolly-shortleaf pine – Western hardwoods – Douglas-fir • Bottomland hardwoods – Hemlock-Sitka spruce – Oak-gum-cypress – Ponderosa pine – Elm-ash-cottonwood – Western white pine • Oak-pine – Lodgepole pine – Larch • Woodland – Chaparral – Fir-spruce – Pinyon-juniper – Redwood Classification detail • Open v. closed stands • Non-timber land use (e.g., urban with forest cover) • Special categories – Forested wetlands – Mesquite – Krummholtz Classification detail • National Forest System needs for Map Product 2 (Forest Planning) – Cover Type • 30-35 categories of forest • 6-10 categories of grass/forb/shrub types • 6 non-vegetated categories (rock, snow/ice, etc.) – Stand Size Class (5 categories) – Stand Crown Closure Class (4 categories) Classification detail • National Forest System needs for Map Product 2 (less detailed ) – Cover Type • 9 categories of forest • 4 categories of grass/forb/shrub types • 5 non-vegetated categories (rock, snow/ice, etc.) – Stand Size Class (2 categories) – Stand Crown Closure Class (3 categories) Classification detail • Need to agree on detailed description – Classification rules for each category – Devil is in the details Classification Accuracy • Forest v. nonforest 90% to 99% accuracy – Needed for stratification efficiency – Inaccuracies caused by FIA field-definition of forest included with usual classification error – No formal FIA accuracy standards for more detailed categorizations – Known accuracy relative to FIA field data Classification Accuracy • National Forest System (Montana, Idaho) Map Product 2 (most detailed) – 60-65% overall for cover types • at least 40% for any individual class – 40% overall for stand size class – 60%-70% for stand density classes Classification Accuracy • National Forest System (Montana, Idaho) Map Product 3 (less detailed) – 75% overall for cover types • at least 65% for any individual class – 75% overall for stand size class – 75% for stand density classes Timeliness • Less than 5% net change in forest cover since date of imagery – stratification efficiency • Less than 5 years old is desirable Registration Accuracy • Sufficient to link 1-acre FIA field plots to 30-m pixels Geographic priorities Forest/non-forest mask September 2002 Geographic priorities Forest/non-forest mask September 2002 • Maine • Alabama • Iowa • Virginia • Indiana • Georgia • Minnesota • Kentucky • Missouri • South Carolina • Wisconsin • Tennessee • Utah • Arizona • Colorado • Oregon Geographic priorities Forest/non-forest mask September 2003 • Arkansas • Pennsylvania • Louisiana • Michigan • Tennessee • Puerto Rico • Texas • Hawaii Information needed by FIA • Cost to FIA for Part II of MRLC Information needed by FIA • Timing of coverage – Will MRLC land characterizations always be 5 to 15 years out of date? – Can MRLC incorporate re-characterization or change detection in between 10-year MRLC cycle? Information needed by FIA • Classification detail – Potential role of FIA in determining detail of classification system – What decisions have already been made – What is on the table? – Need a thorough review of detailed classification descriptions and rules – Can MRLC produce map of forest cover optimized to FIA definitions of forest land use? – Consistency of MRLC and FGDC standards?