U.S. Department of the Interior U.S. Geological Survey RMA Pasture, Range, and Forage-- Vegetation...
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Transcript of U.S. Department of the Interior U.S. Geological Survey RMA Pasture, Range, and Forage-- Vegetation...
U.S. Department of the InteriorU.S. Geological Survey
RMA Pasture, Range, and Forage--Vegetation Index
Jesslyn Brown
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