Abstract
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.
Highlights
Sensed fractional cover maps are critically important for understanding a variety of environmental issues such as the impacts of land use change, climate change variability, ecosystem function, and desertification [1,2]
Algorithms used to produce fractional cover maps decompose each pixel in an image into a measure of similarity to two or more spectrally distinct land cover types, typically including photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), shadow, and snow [3,4]
By having multiple observers assess the same samples, we have developed an insight into the consistency of observer classifications and have clarified the relationship between human and spectral assessments of PV and NPV
Summary
Sensed fractional cover maps are critically important for understanding a variety of environmental issues such as the impacts of land use change, climate change variability, ecosystem function, and desertification [1,2]. Algorithms used to produce fractional cover maps decompose each pixel in an image into a measure of similarity to two or more spectrally distinct land cover types, typically including photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil (BS), shadow, and snow [3,4]. Commonly-used field methods for estimating fractional ground cover require observers to walk across a study area and make point-based observations at defined intervals These methods use variants of point-based sampling techniques that were initially developed for vegetation ecology and rangeland assessment [5,6,7]. They can be used for more detailed surveys such as determining the presence or abundance of plant species across a survey area [8,9]
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