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Folders: ROOT > ScienceBase Catalog > John Wesley Powell Center for Analysis and Synthesis > Global Croplands and Their Water Use for Food Security in the Twenty-first Century > Algorithms > Global and regional cropland extent, area, and characteristics (e.g., crop types, cropping intensities, irrigated versus rainfed) classification algorithms ( Show all descendants )

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_ScienceBase Catalog
__John Wesley Powell Center for Analysis and Synthesis
___Global Croplands and Their Water Use for Food Security in the Twenty-first Century
____Algorithms
_____Global and regional cropland extent, area, and characteristics (e.g., crop types, cropping intensities, irrigated versus rainfed) classification algorithms
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The tools described here were developed for use within the ERDAS Imagine 8.7 software environment, and are for use with the Rulequest Research Cubist and See5/C5.0 software packages above version 1.12 of cubist, and above version 1.18 of see5. The software executables were compiled using the Imagine Toolkit 8.7 and Microsoft Visual C++ 6.0.
An automated cropland classification algorithm (ACCA) that is rule-based is illustrated here for the state of California, USA. The goal of the ACCA is to automatically compute cropland characteristics such as: (a) cropland extent\area; (b) crop type, (c) cropping intensity, and (d) irrigated versus rainfed. However, ACCA here is focused on automatically determining cropland extent using multi-sensor remote sensing and secondary data for the state of California. First, a Mega-file data cube (MFDC) (see section 2.0) was created using Moderate Resolution Imaging Spectroradiometer (MODIS) for year 2010 monthly maximum value composite (MVC) normalized difference vegetation index (NDVI) time-series and Landsat TM5 July...