A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions

Kyle Hartfield, Jeffrey K. Gillan, Cynthia L. Norton, Charles Conley and Willem J. D. van Leeuwen
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Kyle Hartfield: University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA
Jeffrey K. Gillan: University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA
Cynthia L. Norton: University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA
Charles Conley: University of Arizona School of Natural Resources and the Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA
Willem J. D. van Leeuwen: University of Arizona School of Natural Resources and the Environment and School of Geography, Development & Environment, Arizona Remote Sensing Center, The University of Arizona, 1064 E. Lowell Street, Tucson, AZ 85721, USA

Land, 2022, vol. 11, issue 6, 1-11

Abstract: Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences.

Keywords: hyperspectral; cacti; drone (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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