Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. If one is to extract pixel information as input to a crop model it is critical that the pixel be correctly identified with its crop type. In this study a Landsat TM classification was aggregated to the 250-meter pixel size of MODIS. Only pixels that re at least 80% of a particular crop re used as ground truth for the MODIS classification. The MODIS classification was performed using both standard techniques and decision tree techniques. The MODIS classifications re compared at the county, agricultural statistics district and state levels to the 80% mask and to the original TM land-use classification. When compared with the 80% mask, 60-80% accuracies re obtainable at the state level. Hover, the MODIS classification was only approximately 40% accurate when compared with the Landsat TM classification. It does appear that decision tree methodology resulted in better accuracies in most cases.
['Alan J. Stern', 'Paul C. Doraiswamy', 'Bakhyt Akhmedov']
I am writing a research paper on a idea Creating a Land-use Classification for Iowa using MODIS 250-meter Imagery
[{"from": "human", "value": "I am writing a research paper on a idea Creating a Land-use Classification for Iowa using MODIS 250-meter Imagery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. If one is to extract pixel information as input to a crop model it is critical that the pixel be correctly identified with its crop type. In this study a Landsat TM classification was aggregated to the 250-meter pixel size of MODIS. Only pixels that re at least 80% of a particular crop re used as ground truth for the MODIS classification. The MODIS classification was performed using both standard techniques and decision tree techniques. The MODIS classifications re compared at the county, agricultural statistics district and state levels to the 80% mask and to the original TM land-use classification. When compared with the 80% mask, 60-80% accuracies re obtainable at the state level. Hover, the MODIS classification was only approximately 40% accurate when compared with the Landsat TM classification. It does appear that decision tree methodology resulted in better accuracies in most cases."}]