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Potential for boom-mounted remote sensing applications in seedling quality monitoringAuthor(s): Robert F. Keefe; Jan U. H. Eitel; Daniel S. Long; Anthony S. Davis; Paul Gessler; Alistair M. S. Smith
Source: In: Dumroese, R. K.; Riley, L. E., tech. coords. National Proceedings: Forest and Conservation Nursery Associations-2008. Proc. RMRS-P-58. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 48-51
Publication Series: Proceedings (P)
Station: Rocky Mountain Research Station
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DescriptionRemotely sensed aerial and satellite sensor imagery is widely used for classification of vegetation structure and health on industrial and public lands. More intensively than at any other time in the life of a planted tree, its health and status will be maintained and monitored while under culture in a bareroot or container nursery. As a case in point, inventories to track seedling root-collar diameter, height, bud development, and merchantability at the University of Idaho Center for Forest Nursery and Seedling Research greenhouses are conducted and discussed bi-weekly. Plant moisture and nutrient status, and the presence of pests and pathogens, are monitored continuously. Many nurseries are equipped with overhead irrigation boom systems designed to deliver fertigation uniformly. Because of their slow speed and complete coverage, these systems provide an opportunistic location on which to mount lightweight, portable sensors for remote seedling quality assessment. We conducted measurements with an ASD Field SpecProTM radiometer (Analytical Spectral Devices, Boulder, CO) in a laboratory setting to evaluate whether spectral indices used to predict biomass and nitrogen status from tractors in dryland wheat crops might also be capable of detecting differences in nitrogen effects on Scots pine (Pinus sylvestris). We regressed the Green Normalized Difference Vegetation Index (GNDVI) on seedling stem mass and the Canopy Chlorophyll Content Index (CCCI) on foliage chlorophyll content. GNDVI explained 77% of the variation in shoot biomass and had an average prediction error (RMSE) of 19% of the mean. CCCI predicted 61% of the variation in foliar chlorophyll content, with an average prediction error of 16%.
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CitationKeefe, Robert F.; Eitel, Jan U. H.; Long, Daniel S.; Davis, Anthony S.; Gessler, Paul; Smith, Alistair M. S. 2009. Potential for boom-mounted remote sensing applications in seedling quality monitoring. In: Dumroese, R. K.; Riley, L. E., tech. coords. National Proceedings: Forest and Conservation Nursery Associations-2008. Proc. RMRS-P-58. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 48-51
Keywordsseedling monitoring, container nursery
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