Submitted to: Geocarto International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 4, 2006
Publication Date: September 15, 2007
Citation: Yang, C., Everitt, J.H., Fletcher, R.S., Murden, D. 2007. Using high resolution QuickBird imagery for crop identification and area estimation. Geocarto International. 22(3):219-233. Interpretive Summary: Timely and accurate information on crop types and areas obtained during the growing season is of vital importance for regional crop management. High-resolution imagery acquired by a fairly new satellite named QuickBird was evaluated for crop identification and area estimation within an intensively cropped area in south Texas in 2003. Image analysis and ground verification indicate that the imagery can be used to successfully identify and map the crops (grain sorghum, cotton, melons, sugarcane, and citrus) growing in the imaging area. This type of imagery provides a promising tool for farmers and agricultural scientists to obtain more accurate information concerning the crops grown over a large area.
Technical Abstract: High spatial resolution imagery from recently launched satellite sensors offers new opportunities for accurate crop identification and area estimation. A QuickBird image covering an intensively cropped area of 64 square kilometers in south Texas was acquired in the 2003 growing season. The imagery was rectified with a set of ground control points to improve positional accuracy. Field boundaries were digitized from the imagery to determine field areas and mask unnecessary areas during image classification. The crops grown in the imaging area were grain sorghum, cotton, melons, sugarcane, and citrus. Other cover types included mixed grass, mixed brush, and water bodies. Supervised classification techniques were used to classify the image first into eight classes (five crops and three non-crop classes), then into the five crop classes by masking non-crop areas, and finally into three major crop classes by further masking citrus and sugarcane. To correct the problem that multiple classes coexisted within the same field on the pixel-based classification maps, each field was assigned to a single dominant class. Overall accuracy for the corrected eight-, five-, and three-category classification maps was 87, 94, and 97% respectively. Percentage area estimates based on the corrected five-category classification (53.4, 29.9, 2.5, 3.7, and 10.5% for grain sorghum, cotton, citrus, sugarcane and melons, respectively) agreed well with estimates from the polygon map (53.6, 27.4, 3.7, 3.2, and 12.2% for the respective crops). These results indicate that QuickBird imagery can be a useful data source for identifying crop types and estimating crop areas at a regional level.