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SPDSS_Task89-1_ImageryMethodsOrdering_20030708
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SPDSS_Task89-1_ImageryMethodsOrdering_20030708
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Last modified
4/17/2013 9:52:12 AM
Creation date
6/5/2008 9:09:29 AM
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Decision Support Systems
Title
SPDSS Task 89.1 - Finalize Methods and Order Imagery
Description
The purpose of this memo is to review relevant literature, including reports of researchers and previous State contractors engaged in mapping land use and crop types for water resources applications, and consequently describe the recommended technical approach for mapping current land use.
Decision Support - Doc Type
Task Memorandum
Date
7/8/2003
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 -TASK 89.1 <br />5.5 Crop classification <br />The crop types to be classified will be determined according to the following criteria: (1) crops ad acreage <br />reported by the Colorado Agricultural Statistics, (2) spectral characteristics of the crops obtained from <br />expert knowledge and research literature, and (3) input from Consumptive Use contractor on categories of <br />crops with similar water requirements. Based on this information, we anticipate a final classification of <br />approximately 9 to 10 crops or crop groups. <br />The crop classification algorithm will be hierarchical, combining multi-temporal NDVI or Tasseled Cap <br />techniques, multi-spectral information from the individual Landsat TM images, GIS data (i.e., slope, <br />proximity to stream), as well as other ancillary data in a knowledge-based classification algorithm. In <br />addition, inputs of water commissioners, water users, ditch companies, and field observation will be used <br />to refine the classification map. <br />5.6 Accuracy assessment <br />Using fifty percent of the "ground truth" or reference data, a quantitative evaluation of the classification <br />will be performed, consisting of an analysis of the overall accuracy, the producer's accuracy, the user's <br />accuracy, and the individual and overall Kappa coefficients (Jensen 1996, Congalton and Green 1999, <br />Lillesand and Kiefer 2000). In delineating polygons for extraction of reference data, a buffer distance will <br />be excluded as recommended in a recent South Platte River basin study (Cipra, 2003). <br />For details on the standard remote sensing accuracy assessment procedures to be applied in this project, <br />please refer to Jensen 1996, Congalton and Green 1999, and Lillesand and Kiefer 2000. <br />6. Comments/Concerns <br />This memorandum describes the most relevant existing studies and the recommended approach for the <br />classification of irrigated crops. However, there are some concerns that will need to be addressed as we <br />develop and apply the definitive classification procedure. The following issues are particularly relevant: <br />• Acquisition of reliable reference data for the entire project area is tenuous. We have identified the <br />FSA as the primary source for reference data. However, FSA has yet to give full access to data <br />from all the county offices in the SPDSS study area. <br />The spectral complexity of the some of the cover types to be classified will generate classification <br />confusion problems. For example, recently cut alfalfa hay fields can appear spectrally similar to <br />fallow, non-irrigated lands. Another example is the spectral similarity of some riparian vegetation <br />to certain irrigated agriculture cover types. There are different approaches to reduce the impact of <br />spectral similarities on the classification and these will be applied on a specific case basis, <br />including, for example use of a ancillary GIS data, masking operations, texture/spatial pattern <br />layers, etc. However, errors will not be completely eliminated. <br />• Although most of the imagery that we expect to use in this study is nearly cloud-free, there are <br />some areas of scattered cloud and areas of excessive haze. One of the advantages of the multi- <br />temporal approach that we will use is the possibility to replace areas obscured by clouds on a <br />particular date with data from another date. Nevertheless, there may be extreme cases where <br />cloud-covered areas may compromise the classification method. <br />• Although the root mean square (RMS) errors obtained in the co-registration were minimal (i.e., <br />below 0.5 pixels or 15 meters), due to the 30-meter spatial resolution of Landsat imagery there <br />will be mixed pixels (pixels that contain information of multiple cover types) along the edges or <br />boundaries of fields. This can create problems when developing training statistics for the <br />classification algorithms and is particularly problematic in the case of long and narrow <br />Page 11 of 13 ~,~tversFde TecAnafagy, lnc. <br />4'J:.fr: !?es'ourccs Frrr~mec.•r.?g ~~nd L'nr+suilrnq <br />
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