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North American Croplands Teki Sankey and Richard Massey Update: 8/21/2014.

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Presentation on theme: "North American Croplands Teki Sankey and Richard Massey Update: 8/21/2014."— Presentation transcript:

1 North American Croplands Teki Sankey and Richard Massey Update: 8/21/2014

2 United States Cropland Distribution: 2008 (source: USDA CDL) Landcover typePixels (56 meter)% of total croplands % Sum Winter Wheat5902131913.5 Spring Wheat260753546.0 Durum Wheat44713851.0 Corn 137534136 31.5 Sweet Corn4841630.1 Pop or Orn Corn1355180.0 Rice44481421.0 Barley54204881.2 Soybeans 112384082 25.7 Lentils3817710.1 Cotton149038153.4 Potatoes14529500.3 Sweet Potatoes461820.083.9 Alfalfa212433574.9 Dbl Crop WinWht/Soybeans 100089902.3 Sorghum92745812.1 Sunflower32462090.793.9

3 NDVI spectral signatures for 2007 Pure pixel spectra by 5x5 buffer window Pixel size = 56m, resampling to 250m will not mix boundaries after buffer Random points inside boundaries, max 10,000 per crop per Agricultural Ecological zone Extraction of NDVI spectra for each of the point (spectral database) Correlation of CDL NDVI spectra with obtained class spectra from Isodata classification of each Ag-eco zone Labelling of classes

4 5x5 buffer window (pixel size 56m)

5 FAO Ag Eco zones

6 Random Sample points per crop type per Ag-Eco zone for 2007 CropAEZ3AEZ4AEZ5AEZ6AEZ7AEZ8AEZ9AEZ10AEZ11AEZ12Total Winter Wheat10000 39121000084 -73996 Spring Wheat10000 8 - -70008 Durum Wheat3610057443100003166 - - - -18581 Corn296910000 - -72969 Sweet Corn383683258 - -11362 - -1373 Pop Corn - - - -3 - - - - -3 Rice -110418610000 4878 - - -25169 Barley701885666127937633832278 -2 -36750 Soybean -102610000 -71026 Lentils123281595250258 - - - - -4495 Cotton 10210833311565 -11725 Potato591499221288249279106546813822 -19345 Sweet Potato - - -13 - - - - - - Alfalfa10000 929575652917161316 -61406 WinWht/Soybean -214491831000068514899 - - - -33077 Sorghum -142113165865267419265619211407 Sunflower74276100006730181622918645759 -25722 Total46339679528605089379719646937134994342032580013

7 NDVI spectra for Ag-Eco zone 6

8 AOI-3 Ag-Eco zone 6 WWSWDWCORIBASBLTCTPTSPAAWWSBSGSF CLASS1 0.5976890.9538680.9764760.9543010.87410.9062960.9208980.9258790.7316860.9750850.565210.9417190.9460060.7199770.863624 CLASS2 0.5300380.9509610.9775670.9683620.9206150.8965930.9313380.915390.7847190.9877250.6545770.9000690.9552280.6614880.859231 CLASS3 0.6753250.9960190.982580.8958210.8463260.9840440.81910.9778730.6220160.9293420.5409370.8739290.852180.6161260.714179 CLASS4 0.9585210.7754510.7293610.5527280.4293110.855020.4514160.8468150.120210.5612320.0215930.7445990.457120.6482920.375306 CLASS5 0.6634050.9635280.9454790.9025150.8089620.9189420.8602970.9102460.6447180.9396470.4600480.9481260.8963810.7126920.807479 CLASS6 0.5812780.9097090.9037970.9085860.8032190.8417070.8932370.842390.697730.9357530.4561160.9608970.9228570.7409510.874697 CLASS7 0.5567550.9413350.9479630.95080.8828560.8808250.9230720.8856240.7610150.9707140.5731390.9351560.9473980.7043590.874156 CLASS8 0.3830380.8373460.9018080.9755240.9142830.7513380.986080.7893210.8777530.9770670.6814790.8836360.9969420.7210930.951787 CLASS9 0.3450620.7865290.8810210.9836410.934380.7169040.9974850.7816420.9076090.9461980.7143230.8527170.9785440.7308670.959311 CLASS10 0.4275620.8685070.9436450.9957230.9670840.8224980.9739720.8737160.8684840.9701460.7494910.8425540.9617030.6812790.890983 CLASS11 0.480660.8998130.9520840.9954190.950390.8521090.9726730.8898080.8443820.9763760.6843730.8929610.9662670.7120060.904927 CLASS12 0.4793790.8525130.9048270.9699820.8804530.7904480.9714180.8365370.8153710.9459080.5652540.9439140.9634810.777370.953225 CLASS13 0.5790760.9159070.9206980.9366210.8484250.854870.9191870.859420.7423560.9493620.5055250.9612620.9379370.7568520.894685 CLASS14 0.6186080.8510240.8720240.8903630.7588220.8046320.8835720.8357970.666070.876970.364960.9899880.8849970.8178980.898553 CLASS15 0.30052-0.18208-0.04659-0.07987-0.06039-0.0614-0.08965-0.00096-0.07701-0.17625-0.08107-0.04947-0.155620.335445-0.05312

9 Further analysis 1.Spectral correlation similarity using R 2 values (> 0.95) 2.Euclidian distance similarity using a buffer for CDL NDVI and class spectra 3.Class grouping and re-running the R 2 comparison 4.Testing for Significant differences in R 2 5.Grouping small crop cover types in adjacent Ag-Eco zones

10 Thank you

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