Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/1/2012
Publication Date: 5/23/2013
Citation: Long, D.S., Mccallum, J.D. 2013. Mapping straw yield using on-combine light detection and ranging (LiDAR). International Journal of Remote Sensing. 34: 6127-6134.
Interpretive Summary: Straw from production of wheat is available for conversion to energy, but not all of this straw is available for conversion because a certain amount must be returned to the soil for conservation. County and state-wide inventories do not account for variation within farm fields. In this study, a technique is described that applies information from an off-the-shelf, LiDAR sensor into estimation of straw yield across fields. Straw yield could be accurately predicted using LiDAR-based measurements of crop height during actual harvest. Further testing and development is needed to identify erroneous measurements resulting from flying chaff in front of the sensor.
Technical Abstract: Wheat (Triticum aestivum L.) straw is not only important for long-term soil productivity, but also as a raw material for biofuel, livestock feed, building, packing, and bedding. Inventory figures in the United States for potential straw availability are largely based on whole states and counties. Site-specific information is needed to determine where sufficient straw is available for removal within farm fields. The objective of this study was to assess the accuracy and feasibility of light detection and ranging (LiDAR) measurements of crop height for predicting the straw yield of wheat at site-specific field locations and apply this information into determining where excess straw is available beyond soil conservation needs. An inexpensive LiDAR sensor was mounted to the top of a Case International Harvester 1470 combine and aimed to point forward at a 45° angle over the combine’s reel. LiDAR measured crop height was correlated with manually measured crop height in small plots (r = 0.91) and was a good predictor of straw productivity across three farm fields (r2 = 0.85). Crop height was better correlated with straw yield than grain yield or grain protein concentration. Crop height predicted by LiDAR could then be used to estimate straw yield from a predetermined linear relationship between crop height and straw yield. A straw yield map was generated during harvest by programming the LiDAR sensor to compute the average of 91 readings in each scan. A map of harvestable straw was computed by subtracting the amount of straw required to maintain soil organic carbon from a map of total straw yield. LiDAR is potentially useful for measuring crop biomass on a combine harvester and determining in farm fields where excess crop residue could be removed for commercial purposes. Further work is needed to resolve the issue of flying chaff with LiDAR.