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SPDSS_Task89-2_CropLandUseClassificationProcedures_20060929
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Last modified
4/17/2013 9:54:12 AM
Creation date
6/5/2008 9:24:41 AM
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Decision Support Systems
Title
SPDSS Task 89.2 - Crop and Land Use Classification Procedures for Year 2001
Description
This memorandum describes the activities conducted under Task 89: ‘Mapping of Irrigated Land Use and Irrigated Parcel Boundaries’ for year 2001 and complements the SPDSS Memoranda for Task 89.1 and Task 90.2. This memorandum also provides details on the methods used to conduct a number of Task 89 activities, including Task 89.3: Determine Irrigated Vs. Non-irrigated Lands, Task 89.4: Identify Crop Types In Each Polygon, Task 89.5: Review, Revision and Final Classification, and Task 89.6: Conduct Accuracy Assessment, as well as the results obtained from these activities for year 2001.
Decision Support - Doc Type
Task Memorandum
Date
9/29/2006
DSS Category
GIS
DSS
South Platte
Basin
South Platte
Contract/PO #
C153960
Grant Type
Non-Reimbursable
Bill Number
SB01-157, HB02-1152, SB03-110, HB04-1221, SB05-084, HB06-1313, SB07-122
Prepared By
Riverside Technology inc.
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MEMO 89.2 <br />three times the amount of parcels added confirming the initial overestimation found in the accuracy <br />assessment. <br />Table 5. Analysis of Irrigated Parcels Based on Water User Information <br /> Number of <br />Parcels Number of Parcels <br />(Percenta e) Parcels Area <br />(Acres) Parcel Area <br />(Percenta e) <br />Final Irrigated Parcels 27,389 100.00% 1,017,130 100.00% <br />Added Irrigated Parcels <br />(Error of Omission) 1,369 5.00% 45,195 4.44% <br />Eliminated Irrigated <br />Parcels 1,685 6.15% 70,822 6.96% <br />(Error of Commission) <br />Total Parcel Changes <br />(Omission and 3,054 11.15% 116,017 11.41% <br />Commission) <br />2.7.2 Irrigated Crop Types Accuracy Assessment <br />As with the irrigated lands, the accuracy assessment of irrigated crop types used reference data collected <br />mostly from FSA (Section 2.5). After all reference data was acquired, fifty percent was selected for <br />testing in the accuracy assessment procedure using a randomization algorithm. These data were separated <br />and kept independent from the training data used in the classification. To ensure that a representative <br />sample was collected, reference data acquired from FSA was compared to 2001 Colorado Agricultural <br />Statistics (Table ~. From Table 6, with the exception of `Grass for Pasture and Hay' it is evident that all <br />major crop types in the reference sample have very similar proportions as compared to the Colorado <br />Agricultural Statistics. The reason for this is that the `Grass for Pasture and Hay' class was difficult to <br />find in FSA records because most FSA programs involve more valuable crops. On the other hand, as <br />mentioned in Section 2.4, RTi requested FSA personnel to perform database queries of crop types that <br />were difficult to find on a strictly random basis. Consequently, crop types, such as `Dry Beans' and <br />`Sugar Beets' had higher proportion in the reference data than in the Colorado Agricultural Statistics. <br />This was necessary to provide sufficient information on these crop types for the MLC classifier. <br />Table 6. Comparison Between the Proportion of Crop Types in the Colorado Agricultural Statistics 2001 and <br />the Test Sample Collected From FSA <br /> <br />Crop Type Proportion of Crops in Colorado <br />Agricultural Statistics for the SPDSS <br />Study Area Proportion of Crops in <br />Reference Test Sample <br />Collected from FSA <br />Small Grains 8.3% 10.5% <br />Dry Beans 3.6% 5.1% <br />Corn 43.8% 44.8% <br />Sugar Beets 3.3% 7.4% <br />Alfalfa 26.9% 28.5% <br />Grass for Pasture or <br />Ha <br />14.1% <br />3.7% <br />Total 100.0% 100.0% <br />Figure 17 is a diagram of the irrigated crops accuracy assessment procedure. Similar to the accuracy <br />assessment of irrigated lands, a quantitative evaluation of the classification was performed using the <br />reference data selected for testing. This evaluation consisted of an error matrix analysis and derived <br />accuracy assessment parameters, the overall accuracy, the producer's accuracy, the user's accuracy, and <br />the individual and overall Kappa coefficients (Jensen 1996, Congalton and Green 1999, Lillesand and <br />Page 25 of 45 ~R~versfde FecAnotogy, fnc. <br />4'JaYer Resources Errgi~ecr:np an~i CansuFlrnp <br />
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