u.s. department of the interior u.s. geological survey usgs/eros data center global land cover...

U.S. Department of the InteriorU.S. Geological Survey

USGS/EROS Data Center Global Land Cover Project – Experiences and Research Interests

GLC2000-JRCMarch 2001

U.S. Department of the InteriorU.S. Geological Survey

The USGS/IGBP Global Land Cover Database

U.S. Department of the InteriorU.S. Geological Survey

USGS/IGBP Global Land Cover Database Strategy

U.S. Department of the InteriorU.S. Geological Survey

Classification Methods

Flexible land cover database Unsupervised multi-temporal classification of

1992-1993 AVHRR NDVI data Classification implemented on a continent by

continent basis

Team interpretation to encourage consistency

External peer review of draft results

Validated IGBP land cover layer

U.S. Department of the InteriorU.S. Geological Survey

Continents to World – Combine Maps

Set rules for top and middle level classification systems

Describe land cover, vegetation seasonality, structure, and leave longevity consistently

Hold frequent project meetings to review consistency

Accuracy measured separately for each mapping area

U.S. Department of the InteriorU.S. Geological Survey

Quality of Reference Data is an Important Factor

U.S. Department of the InteriorU.S. Geological Survey

Global Forest Cover Mapping

EROS Data Center FRA2000

50% FOREST

100% AG

DARK

MODEL

BRIGHT

MODEL

Ch

ann

el 2

(N

IR)

100% FOREST

AVHRR Channel 1 (Visible)

Canopy Density Model

U.S. Department of the InteriorU.S. Geological Survey

EDC FRA2000 Project:— Estimating density of forest canopy cover …

62 78 82 68 5846 72 78 80 6454 54 78 78 8248 28 52 84 8226 60 58 60 70

Estimated percent forest

U.S. Department of the InteriorU.S. Geological Survey

A New Global Forest Cover MapImproved USGS global land cover database

U.S. Department of the InteriorU.S. Geological Survey

Current and Future R&D Interests

Continue global land cover database research using new coarse/moderate resolution sensors

Test new techniques/algorithms Integrate satellite imagery with sampling-based

field data

Sampling-based field data

Focus on attributes, themes that are useful for both science and land management

U.S. Department of the InteriorU.S. Geological Survey

Land Cover Techniques at EDC

Unsupervised classification

Decision-tree models

Spectral mixture analysis

Experimental: Co-kriging, KNN, NN

Continued emphasis on database strategy and its

improvement

Stratification before and after clustering

U.S. Department of the InteriorU.S. Geological Survey

Example of Tree Canopy Density

0 20 40 60 80 100

actual (%)

02

04

06

08

01

00

pre

dic

ted

(%

)

A comparison of predicted forest% to actual values

R2 = 0.7374

0 100%

U.S. Department of the InteriorU.S. Geological Survey

Spatial Modeling Techniques for Satellite Imagery-Field Data Integration

= pz pi

k

ipi zw ,

1,

• Spatial models such as KNN, Co-kriging are nonparametric spatial statistics

• Potential tool for extending field measurements to image data/maps

• Mapping vegetation structure measured on permanent plots

Sample1: U1, V1 at (x1, y1)

Sample2: U2, V2 at (x2, y2)

Sample3: V3at (x3, y3)

Calculate U0at (x0, y0)

U.S. Department of the InteriorU.S. Geological Survey

Key Experimental Vegetation Type and Structure Variables

Biomass Net primary productivity Canopy density Canopy height Age Size class DBH Vegetation species, types, associations

U.S. Department of the InteriorU.S. Geological Survey

Summary

EDC is committed to continuing its global land cover R&D

Working with partners is important for USGS