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

RMA Pasture, Range, and Forage--Vegetation Index

Jesslyn Brown

jfbrown@usgs.gov

Phone: 605-594-6003

Application of EROS NDVI to PRF Program

Source: EROS AVHRR NDVI data AVHRR (and future) timeline

EROS processing flow

Post-processing by GMS Temperature Constrained NDVI Index

8 X 8 km grids (i.e., spatial averaging)

Intervals (i.e., 3-month averaging)

Determining “normal” (i.e. long-term maximum/minimum)

Issues and Recommendations

Future Plans

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

2006 Satellite Vegetation Phenology for the Conterminous U.S.

April 2, 2006 April 30, 2006 May 28, 2006

June 25, 2006 July 23, 2006 August 20, 2006

September 17, 2006 October 15, 2006 October 29, 2006

March 2007

NDVI Normalized Difference Vegetation Index

NDVI changes in response to multiple terrestrial phenomena

Drought

Phenological cycles of emergence, maturity, scenesence

Flood

Pests

Hail

Wildfire

Land cover conversion

Advantages of NDVI

The NDVI is successful as a vegetation measure—it is sufficiently stable to permit meaningful comparisons of seasonal and inter-annual changes in vegetation growth and activity.

The strength of the NDVI is in its ratioing concept, which reduces (not removes) many forms of multiplicative noise present in different magnitudes in the red and NIR bands: Illumination differences

Cloud and relief shadows

Atmospheric contamination

Certain topographic illumination variations

NDVI Limitations

The main limitation of the NDVI is the inherent non-linearity of ratio-based indices

Additive noise effects, such as atmospheric path radiance, are not removed by ratioing

The NDVI also exhibits scaling problems, asymptotic (saturated) signals over high biomass conditions

The NDVI is very sensitive to canopy background variations, with NDVI degradation particularly strong with higher canopy background brightness

NDVI of the same cover is different when derived by different sensors –due to spectral band pass differences (band width and spectral response) between sensors.

EROS AVHRR NDVI Data: Platform/Sensor Sequence

89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13*

NOAA-11 AVHRR/2 pmNOAA-14 AVHRR/2 pmNOAA-16 AVHRR/3 pmNOAA-17 AVHRR/3 amNOAA-18 AVHRR/3 pmNOAA-19 AVHRR/3 pm

* Missing data at end of year due to satellite drift

Calibration of the 1 km AVHRR time series

Satellite Start Date End Date Source

 NOAA 11 09/26/1988 03/26/1989 prelaunch

NOAA 11 03/27/1989 01/01/2020 Teillet and Holben (1994)

NOAA 14 12/30/1994 06/30/1995 prelaunch

NOAA-14 06/31/1995 01/01/2020 Vermote and Kaufman (1995)

NOAA 16 09/01/2000 06/24/2003 prelaunch

NOAA-16 06/25/2003 01/01/2020 NOAA

NOAA-17 06/24/2002 12/31/2002 prelaunch

NOAA-17 01/01/2003 01/01/2020 NOAA

NOAA-18 05/20/2005 09/12/2005 prelaunch

NOAA-18 09/13/2005 01/01/2020 NOAA

 

EROS eMODIS NDVI Data: Platform/Sensor Sequence

00 01 02 03 04 05 06 07 08 09 10 11 12 13

NASA TERRA MODIS amNASA AQUA MODIS pm

PRF-VI post-processing: Temperature Constrained NDVI Index

1. Process 1-km gridded NDVI to 8 x 8 km grid cells

2. Define Major Land Resource Area (MLRA) and elevation classes for the GRP NDVI grids so that temperature constraint variables could be assigned to the appropriate geographic areas and indexing interval

3. Calculate the daily temperature constrained NDVI values for each 8 x 8 km grid cells

4. Calculate the daily max/min index value for each 8 x 8 km grid cell and average these over the indexing interval

5. Calculate the final temperature constrained index value for each 8 x 8 km grid cell and interval

Provided by J. Angerer, GMS in 2007Provided by J. Angerer, GMS in 2007

Intervals—3 month

Interval I: Apr 1 – Jun 30

Interval II: Jul 1 – Sep 30

Interval III: Oct 1 – Dec 31

Interval IV: Jan 1 – Mar 31

Issues

Multiple cover types within 8 x 8 km grid cells Forest

Irrigated agriculture

Intervals

Lack of transparency of methods

NM example: Irrigated and Non-irrigated Agriculture

NM example: Irrigated and Non-irrigated Agriculture

Irrigated Agriculture: NE New Mexico

II IIII IIIIIIIVIV

II IIII IIIIIIIVIVII IIII

Recommendations

Eliminate option to purchase coverage outside of the growing season (i.e., in intervals where the NDVI will not be related to vegetative growth)

Focus on forage (i.e., screen out the cover types that aren’t covered by this insurance). Land cover (USGS--NLCD), Crop maps (USDA-NASS), and Irrigated agriculture (USGS) data are all available.

Please expand the description of the methodology on the RMA website. Documentation still points directly to EROS NDVI and this is misleading.

Future of AVHRR, eMODIS, and VIIRS

Summary

Accurate and frequent communication on multiple topics (sensors, NDVI time series, etc.)

Remove confusion from irrigated agriculture and other land cover types within 8 x 8 km grid cells

Insurance intervals need to make sense for the geographic region, consider removing intervals outside the growing season

Collaboration amongst government agencies will be critical to transition applications to VIIRS

Extra slides

eMODIS Expedited

Terra MODIS

LANCE

T+10hrs

EDOS

MODIS L0 Data

T+3hrs

T+6hrs

MODISL2, L1B Data

MODAPS

LAADS

eMODIS Historical

Input Data Target: Monday 10:30 a.m.

USGS Drought Monitoring

NDMC Vegetation Drought Response Index

NIDIS Drought Portal

U.S. Drought Monitor

VegDRI

eMODIS Production Flow User decision support systems

Start of SeasonEnd of SeasonLength of SeasonGrowing season productionGreenness “to-date”

Processing remote sensing data to create information

Normalized Difference Vegetation Index (NDVI)

PASG(10)= 64.7%

Percent of Average Seasonal Greenness (PASG)

Seasonal GreennessSeasonal Greenness

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

24 22

16

X SG(89-09)= 21

6/28/08 6/28/09 6/28/10

What is VegDRI?

VegDRI is a new ‘hybrid’ drought index that integrates:

satellite-based observations of vegetation conditions

climate-based drought index data

biophysical characteristics of the environment

to produce maps of drought-related vegetation stress that have high spatial resolution (1-km) and are regularly updated (1-week intervals) throughout the growing season.

National Irrigated Lands

• National Irrigation Mapping

• CONUS maps of irrigation status for 2002 and 2007

• Journal publication on evaluation and validation http://dx.doi.org/10.3390/rs2102388

• Analysis of irrigation change in progress.

[CLICK TO CLOSE][CLICK TO OPEN]

Validation of MIrAD-US

Year Region Category Producer’s Accuracy

Omission error

Users’ Accuracy

Commission error

Overall accuracy

Kappa

2002

California Irrigated 0.75 0.25 0.85 0.15

0.92 0.75Non-irrigated

0.97 0.03 0.94 0.06

Great Plains Irrigated 0.76 0.24 0.92 0.08

0.80 0.58Non-Irrigated

0.80 0.20 0.65 0.35

2007

California Irrigated 0.71 0.29 0.88 0.12

0.92 0.74Non-Irrigated

0.97 0.03 0.93 0.07

Great Plains Irrigated 0.90 0.10 0.94 0.06

0.88 0.68Non-Irrigated

0.80 0.20 0.71 0.29

Idaho-ESPA Irrigated 0.75 0.25 0.87 0.13

0.94 0.77Non-Irrigated

0.98 0.02 0.95 0.05

Error matrix