Presentation is loading. Please wait.

Presentation is loading. Please wait.

MODIS-based Cropland Classification in North America Teki Sankey and Richard Massey Northern Arizona University.

Published byRose Copeland Modified over 9 years ago

Similar presentations

Presentation on theme: "MODIS-based Cropland Classification in North America Teki Sankey and Richard Massey Northern Arizona University."— Presentation transcript:

1 MODIS-based Cropland Classification in North America Teki Sankey and Richard Massey Northern Arizona University

2 Outline Datasets chosen for processing: 2000 and 2007 Preprocessing Classification Spatial and temporal extrapolation Crop type labeling

3 Preprocessing workflow MODIS data Re-projection Mosaicking Layer stacking NDVIBand 1 (RED)Band 2 (NIR) Min-value 16-day Composite Max-value 16-day Composite Cloud Filtering/smoothing 47 tiles, 8-day composites (Feb 2000 to Feb 2001) (Feb 2007 to Feb 2008)

4 Composites Cloud-covered pixels make up for most of the noise in the data stack 16-day minimum value composites for band 1 and band 2 as cloud reflectance is higher 16-day maximum value composite for NDVI as cloud NDVI is lower NDVI maximum value composite is more useful in classification

5 Cloud filtering NDVI Days NDVI Days Return Value Difference Direction Return Value Difference Direction NDVI Days NDVI Days Thresholds:- Return Value < 0.20 Difference > 0.15

6 Cloud filtering NDVI Days NDVI Days Difference NDVI Days NDVI Days Thresholds:- Difference > 0.15

7 Cluster computing workflow The NAU computing cluster has 32 cores each with 500 nodes, shared memory of 1.5 TB per node ENVI services engine and ENVI version 5.1 Master C Program Batch file for execution IDL code for each process Batch file for IDL process Node Allocation IDL parallel process

8 NDVI stack - 2000

9 Year 2007 No existing crop type map for 2000 2000 classification needs labeling 2007 = 2000 in region-wise annual precipitation NASS CDL available for year 2007 Assumption: Similar spectral signatures between the two years

10 Region-wise annual precipitation statistics US (2000-2013, National Climatic Data Center)

11 NASS CDL availability for conterminous US

12 Spatial and Temporal Extrapolation USA  North America 2007  2000

13 Spatial extrapolation: GCE v1.0 Most accurate irrigated class = AOI-1 (4/4 maps) ( 63,102,129 acres) Further split: Agro-Ecological Zones

14 Agro-Ecological Zones based on length of growing period (GAEZ-FAO)

15 GCE v1.0 AOI-1 and Agro-Ecological zones

16 MODIS-based US Irrigation map, 2001 (Ozdogan and Gutman, 2008)

17 GCE v1.0 AOI-1, Agro-Ecological Zones, and Irrigated map

18 Irrigated map 2001 (US) GCE v1.0 Class1GCE v1.0 Class3 AEZ 1AEZ 2 AEZ 3 AEZ 14 AEZ 1AEZ 2AEZ 3 AEZ 14 ISODATA Classification Overlay …… Irrigated map 2001 (US) GCE v1.0 AOI-1GCE v1.0 AOI-3 AEZ 1AEZ 2 AEZ 3 AEZ 14 AEZ 1AEZ 2AEZ 3 AEZ 14 Class 1Class 2 Class 25 Class 1Class 2 Class 25 Group 1 Group 2 Group 3 Group 4Group 5 Group 6 Group 100 ISODATA Classification Class Grouping Overlay ……….. …… …… Spatial extrapolation: Spatial subsets

19 Spatial extrapolation

20 Spatial extrapolation: Labels Spectral correlation matrix Classes are grouped together (R 2 > 0.98) 123456789101112131415 10.9660.9950.9790.910.9870.9690.9440.9890.8880.9720.9250.9620.8860.6580.547 20.390.9070.920.990.8840.8770.9570.8250.8880.8780.8720.7960.5720.447 30.8630.9960.978 0.9630.9740.8970.9830.9090.970.8690.6750.556 40.9610.90.9310.8980.9710.8130.9450.8480.9230.7910.5550.426 50.9840.9440.9370.9810.9110.9370.960.9350.9080.6930.589 60.9730.9640.9590.7920.9870.8890.9740.850.6790.548 70.9640.9260.9670.9450.9830.960.9830.8340.771 80.9380.6740.8630.9740.8810.9590.8340.628 90.9250.9460.970.9670.9760.7690.696 100.8780.9710.8970.9520.9240.863 110.9510.9530.9630.6770.619 120.7130.8120.9990.963 130.9870.7420.725 140.9340.994 150.826

21 Temporal extrapolation: 2007  2000 2000 AOI + Irrigated map + Agro Eco zone2007 NASS CDL 2007 AOI + Agro Eco zone Labeling of classes using NASS CDL 2007

22 Master-file Primary layers – Cropland extent – Crop type – Crop intensity – Irrigated/Rainfed Secondary layers – Temperature – Precipitation – Elevation AttributeNameValue Cropland ExtentNon-Cropland0 Cropland1 Irrigated/RainfedRainfed0 Irrigated1 Crop TypeNon-Cropland0 Wheat1 Rice2 Corn3 Barley4 Soybean5 Pulses6 Potatoes7 Cotton8 Others9 IntensityNo crop0 Single crop1 Double crop2 Double+ crop3 TemperatureCelsius-value- PrecipitationCentimeters-value- Elevationmeters-value- NASS CDL 2007 MIrAD US 2007 NCEP NARR 2007 SRTM DEM

23 Spectral database Isodata classification for each AOI Class comparison with master-file Group classes based on attributes Group member classes lie in ± 0.1 buffer of the group mean spectra for more than 80% of bands Spectral database for each attribute combination Corn: Irrigated, Single crop Wheat: Irrigated, Single crop Soybean: Irrigated, Single crop

24 Spectral database AttributeNameValue Cropland ExtentNon-Cropland0 Cropland1 Irrigated/RainfedIrrigated0 Rainfed1 Crop TypeNon-Cropland0 Wheat1 Rice2 Corn3 Barley4 Soybean5 Pulses6 Potatoes7 Cotton8 Others9 IntensitySingle crop0 Double crop1 Triple crop2 Triple+ crop3 TemperatureCelsius-value- PrecipitationCentimeters-value- Elevationmeters-value- Class IDBand 1Band 2………Band N Class 10.510.59………0.60 Class 20.500.58………0.66...………0.59...………0.46...………0.62...………0.61 Class M0.490.57………0.63 Cropland attributes Grouped classes for one set of attributes

25 Extrapolation rules: Correlation Spectral match between classes in 2007 and 2000 ISOdata classification 2007 result ISOdata classification 2000 result 123456789101112131415 10.9660.9950.9790.910.9870.9690.9440.9890.8880.9720.9250.9620.8860.6580.547 20.390.9070.920.990.8840.8770.9570.8250.8880.8780.8720.7960.5720.447 30.8630.9960.978 0.9630.9740.8970.9830.9090.970.8690.6750.556 40.9610.90.9310.8980.9710.8130.9450.8480.9230.7910.5550.426 50.9840.9440.9370.9810.9110.9370.960.9350.9080.6930.589 60.9730.9640.9590.7920.9870.8890.9740.850.6790.548 70.9640.9260.9670.9450.9830.960.9830.8340.771 80.9380.6740.8630.9740.8810.9590.8340.628 90.9250.9460.970.9670.9760.7690.696 100.8780.9710.8970.9520.9240.863 110.9510.9530.9630.6770.619 120.7130.8120.9990.963 130.9870.7420.725 140.9340.994 150.826

26 Extrapolation rules: Buffer If the input spectra lies within ± 0.1 buffer of the database spectra for more than 80% of bands it is assigned the same label If secondary parameters indicate Drought or Abundance, buffer is adjusted accordingly Overall validation threshold: 90% Buffer

27 Extrapolation Generation of NDVI stack for non-US region Spatial extrapolation of labels to non-US region using updated spectral database Input spectra is assigned the same label if lies within ± 0.1 buffer of the database spectra for more than 80% of bands it Verification of extent using GCE v1.0 and secondary parameters

28 Class Labels Irrigated map 2000 (US) GCE v1.0 Class1GCE v1.0 Class3 AEZ 1AEZ 2 AEZ 3 AEZ 14 AEZ 1AEZ 2AEZ 3 AEZ 14 Class 1Class 2 Class 25 Class 1Class 2 Class 25 ISODATA Classification Class Grouping and Labeling: 2007 Overlay Label 1 Label 2 Label 3 Label 4Label 5 Label 6 Label 100 Temporal Extrapolation: 2007  2000 ……….. …… …… Cropland map 2000 (US) Cropland map 2007 Cropland map 2000 (North America) Spatial Extrapolation: US  non-US

29 Thank you!

30 Class labeling 2007 classes are labeled by geolocating at least 10 random points within the 2007 CDL class Classes between 2000 and 2007 are matched together via correlation (R 2 >0.98) Labeled as crop type ‘A’

31 Spatial and temporal extrapolation US classesNA_class1NA_class2NA_class3NA_class4NA_class5NA_class6NA_class7NA_class8NA_class9NA_class10NA_class11NA_class12NA_class13NA_class14NA_class15NA_class16NA_class17NA_class18 10.9660.9950.9790.9810.9870.9690.9440.9890.8880.9720.9250.9620.8860.6580.5470.8810.7610.809 20.990.9970.920.9930.8840.8770.9570.8250.8880.8780.8720.7960.5720.4470.7980.6760.738 30.9630.9960.978 0.9630.9740.8970.9830.9090.970.8690.6750.5560.8550.7350.77 40.9610.990.9310.8980.9710.8130.9450.8480.9230.7910.5550.4260.7880.6420.705 50.9840.9440.9370.9810.9110.9370.960.9350.9080.6930.5890.9060.8090.853 60.9730.9640.9590.8920.9870.8890.9740.850.6790.5480.8340.7110.741 70.9640.9260.9670.9450.9830.960.9830.8340.7710.9660.9180.915 80.9380.9740.8630.9740.8810.9590.8340.6280.4960.6920.739 90.9250.9460.970.9670.9760.7690.6960.9790.9060.932 100.8780.9710.8970.9520.9240.8630.9230.9210.874 110.9510.9530.9630.6770.6190.9710.8770.934 120.7130.8120.9990.9630.7690.8470.728 130.9870.7420.7250.9960.960.992 140.9340.9940.6840.8160.674 150.8260.9760.9940.981 Spectral correlation matrix

32 Validation Preprocessing of MODIS data for validation year (2009) Generation of NDVI stack Generation of validation-file using spectral database Validation-file has same structure as master-file of normal year (2008) Comparison of NASS CDL for 2009 with the validation file

Download ppt "MODIS-based Cropland Classification in North America Teki Sankey and Richard Massey Northern Arizona University."

Similar presentations

Applications of one-class classification

Applications of one-class classification
INSEA biophysical modelling: data pre-processing Workshop at JRC in Ispra, Italy 11 th – 12 th April, 2005 By Juraj Balkovič & Rastislav Skalský SSCRI.

INSEA biophysical modelling: data pre-processing Workshop at JRC in Ispra, Italy 11 th – 12 th April, 2005 By Juraj Balkovič & Rastislav Skalský SSCRI.
A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy.

A Land Cover Map of Eurasia’s Boreal Ecosystems S. BARTALEV, A. S. BELWARD Institute for Environment and Sustainability, EC Joint Research Centre, Italy.
GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) LAND COVER MAP OF.

GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) GLC 2000 ‘Final Results’ Workshop (JRC-Ispra, 24 ~ 26 March, 2003) LAND COVER MAP OF.
The University of New Mexico Continual Compositing of NDVI MODIS Products for Theresa R. Watson, Richard P. Watson, Louis A. Scuderi, Enrique Montaño,

The University of New Mexico Continual Compositing of NDVI MODIS Products for Theresa R. Watson, Richard P. Watson, Louis A. Scuderi, Enrique Montaño,
Incorporating regional knowledge into global data sets: Some ideas for rice in Asia Andy Nelson Thursday11 th Sep, 2014.

Incorporating regional knowledge into global data sets: Some ideas for rice in Asia Andy Nelson Thursday11 th Sep, 2014.
Geoprocessing with GDAL and Numpy in Python Delong Zhao 11-03-2011.

Geoprocessing with GDAL and Numpy in Python Delong Zhao
Change Detection. Digital Change Detection Biophysical materials and human-made features are dynamic, changing rapidly. It is believed that land-use/land-cover.

Change Detection. Digital Change Detection Biophysical materials and human-made features are dynamic, changing rapidly. It is believed that land-use/land-cover.
Global Particulate Matter (PM 10 and PM 2.5 ) Emissions from Agricultural Tilling and Harvesting 1 Northeast Institute of Geography and Agroecology, Chinese.

Global Particulate Matter (PM 10 and PM 2.5 ) Emissions from Agricultural Tilling and Harvesting 1 Northeast Institute of Geography and Agroecology, Chinese.
North American Croplands Richard Massey & Dr. Teki Sankey.

North American Croplands Richard Massey & Dr. Teki Sankey.
ASTER image – one of the fastest changing places in the U.S. Where??

ASTER image – one of the fastest changing places in the U.S. Where??
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.

Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Crop Yield Modeling through Spatial Simulation Model.

Crop Yield Modeling through Spatial Simulation Model.
CS 128/ES 228 - Lecture 5a1 Working with Rasters.

CS 128/ES Lecture 5a1 Working with Rasters.
CS 128/ES 228 - Lecture 5a1 Raster Formats (II). CS 128/ES 228 - Lecture 5a2 Spatial modeling in raster format  Basic entity is the cell  Region represented.

CS 128/ES Lecture 5a1 Raster Formats (II). CS 128/ES Lecture 5a2 Spatial modeling in raster format  Basic entity is the cell  Region represented.
April 28-29, 2015 at Hotel Serena, Islamabad

April 28-29, 2015 at Hotel Serena, Islamabad
U.S. Geological Survey U.S. Department of Interior GFSAD30m Global Cropland Extent Products of Nominal 250 m (GCE V2.0) Updates Pardhasaradhi.

U.S. Geological Survey U.S. Department of Interior GFSAD30m Global Cropland Extent Products of Nominal 250 m (GCE V2.0) Updates Pardhasaradhi.
U.S. Geological Survey U.S. Department of Interior GFSAD30m Global Cropland Extent Products of Nominal 250 m (GCE V2.0) Updates Pardhasaradhi.

U.S. Geological Survey U.S. Department of Interior GFSAD30m Global Cropland Extent Products of Nominal 250 m (GCE V2.0) Updates Pardhasaradhi.
Ten State Mid-Atlantic Cropland Data Layer Project Rick Mueller Program Manager USDA/National Agricultural Statistics Service Remote Sensing Across the.

Ten State Mid-Atlantic Cropland Data Layer Project Rick Mueller Program Manager USDA/National Agricultural Statistics Service Remote Sensing Across the.
Mapping of mountain pine beetle red-attack forest damage: discrepancies by data sources at the forest stand scale Huapeng Chen and Adrian Walton.

Mapping of mountain pine beetle red-attack forest damage: discrepancies by data sources at the forest stand scale Huapeng Chen and Adrian Walton.

Similar presentations

Ads by Google