Satellite remote sensing of wild rice

Abstract

Wild rice (Zizania aquatica) is a primary staple for Native Americans in the northern United States and there is a strong need to timely map and monitor its production on American Indian Reservations. This paper describes a methodology for the detection and classification of wild rice using satellite remote sensing. Landsat-7 data were used to map and estimate wild rice crop areas for the Leech Lake American Indian Reservation in Minnesota. A 14-band dataset was created using bands 1-5 and 7 from Landsat-7 images for July and October 1999. Two additional bands of image texture were also created from the 15-metre panchromatic channel of Landsat-7 for each time period and added to the dataset. Since wild rice grows in standing water, a data transform was developed to identify wet vegetation in the dataset. The data transform was created from the multi-temporal dataset and based on a minimum noise fraction transformation, image textural analysis, and hierarchical data masking. A lake and water mask was created and applied to the image to isolate wild rice beds from the wet vegetation transform. Training data for wild rice were collected using a GPS mounted to a laptop computer containing the image data. A spectral matched filtering algorithm was then applied to the image transform using ground collected data to classify wild rice within the data mask. Final classified data were verified using wild rice data collected in the field and from aerial photographs. We estimated nearly 2000 hectares of wild rice available for harvest for 1999 within the boundaries of the Leech Lake American Indian Reservation. This is over a 65% decrease in crop area of wild rice from 1994 (5750 hectares). We were able to verify the 1999 crop loss and subsequent insurance claim for the Leech Lake American Indian Reservation. Such mapping with Landsat-7 provides a more accurate wild rice database than can be routinely updated with repeat Landsat-7 coverage.