nitiestodevelopa30-meterspatialdatabase

nitiestodevelopa30-meterspatialdatabase内容摘要：

1995 22 FIA joins MRLC 2020 Consortium • $45 per CD, including 3 dates of calibrated Landsat 7 data and Digital Elevation Model (DEM) used for terrain correction 1992 1997 1998 1999 2020 1995 23 National Land Cover Data (NLCD 2020) • FIA considers working with USGS EROS Data Center to replace NLCD 1992 forest cover map of USA using MRLC 2020 new Landsat 7 ETM+ data • Annualized FIA data more valuable as training data for classification of Landsat data 1992 1997 1998 1999 2020 1995 24 FIA partnership with USGS • FIA and USGS conduct pilot studies on highly automated digital classification of forest types with MRLC 2020 database and FIA plot data. 1992 1997 1998 1999 2020 2020 1995 25 MRLC Input Database •If bright 30 amp。 green 45 •amp。 wet 30 amp。 texture 25 •amp。 shape = 3 amp。 slope 5 •amp。 elevation 4500 •amp。 position 3 amp。 canopy10 •amp。 aspect = north amp。 soil = 2 •Then Deciduous Forest Brightness Greeness Wetness Texture Shape Slope Elevation Position Aspect Soils % Tree Canopy Training data FIA NRI IKONOS 3 seasons of state of the art Landsat 7 ETM+ data Tassel Cap Transformation to press data quantity Variability within a 5x5 moving window over 30m Landsat pixels Image segmentation (digital grouping of adjacent pixels into “polygons”) and FRAGSTATS indices of polygon shape assigned to each pixel . a square polygon is more likely a corn field than a forest stand 30m Digital Elevation Model for each pixel in the USA Derivatives of Digital Elevation Model Coarse resolution STATSGO soils data from NRCS. soil texture and soil depth Regression equation predicting crown cover from Landsat Land cover classification rulesets •CART model to predict land cover type from Landsat and geospatial data NLCD 2020 Land Cover •Apply CART model to wall MRLC Input Database NLCD 2020 Classification Process 26 FIA partnership with USGS • Pilot study using FIA plots from NE and SRS FIA Units –1100+ nonforest plots –535 forested plots for training digital classifier –134 forested plots plots for validation 1992 1997 1998 1999 2020 2020 1995 27 FIA partnership with USGS Classification detail Accuracy Forest v. nonforest 95% +2% 3 MRLC forest type groups 80% + 2% 6 FIA forest type groups 65% + 5% Preliminary accuracy results (Chesapeake Bay) 1992 1997 1998 1999 2020 2020 1995 28 FIA partnership with USGS • FIA decides to station an FIA scientist at USGS EROS Data Center in Sioux Falls SD to – Assure FIA has preeminent role in national mapping of forest cover – Improve coordination on NLCD。