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Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare?

School of Biological Sciences, University of Adelaide, Adelaide, SA 5005, Australia
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Remote Sens. 2019, 11(21), 2589; https://doi.org/10.3390/rs11212589
Received: 1 October 2019 / Revised: 30 October 2019 / Accepted: 31 October 2019 / Published: 4 November 2019
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
Remotely sensed ground cover maps are routinely validated using field data collected by observers who classify ground cover into defined categories such as photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), and rock. There is an element of subjectivity to the classification of PV and NPV, and classifications may differ between observers. An alternative is to estimate ground cover based on in situ hyperspectral reflectance measurements (HRM). This study examines observer consistency when classifying vegetation samples of wheat (Triticum aestivum var. Gladius) covering the full range of photosynthetic activity, from completely senesced (0% PV) to completely green (100% PV), as photosynthetic or non-photosynthetic. We also examine how the classification of spectra of the same vegetation samples compares to the observer results. We collected HRM and photographs, over two months, to capture the transition of wheat leaves from 100% PV to 100% NPV. To simulate typical field methodology, observers viewed the photographs and classified each leaf as either PV or NPV, while spectral unmixing was used to decompose the HRM of the leaves into proportions of PV and NPV. The results showed that when a leaf was ≤25% or ≥75% PV observers tended to agree, and assign the leaf to the expected category. However, as leaves transitioned from PV to NPV (i.e., PV ≥ 25% but ≤ 75%) observers’ decisions differed more widely and their classifications showed little agreement with the spectral proportions of PV and NPV. This has significant implications for the reliability of data collected using binary methods in areas containing a significant proportion of vegetation in this intermediate range such as the over/underestimation of PV and NPV vegetation and how reliably this data can then be used to validate remotely sensed products. View Full-Text
Keywords: wheat; photosynthetic; non-photosynthetic; vegetation; spectral unmixing; validation fractional cover wheat; photosynthetic; non-photosynthetic; vegetation; spectral unmixing; validation fractional cover
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MDPI and ACS Style

Fisk, C.; Clarke, K.D.; Delean, S.; Lewis, M.M. Distinguishing Photosynthetic and Non-Photosynthetic Vegetation: How Do Traditional Observations and Spectral Classification Compare? Remote Sens. 2019, 11, 2589.

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