Authors: J. A. Gamon · B. Somers · Z. Malenovský · E. M. Middleton · U. Rascher · M. E. Schaepman
Abstract: Healthy vegetation function supports diverse biological communities and ecosystem processes, and provides crops, forest products, forage, and countless other benefts. Vegetation function can be assessed by examining dynamic processes and by evaluating plant traits, which themselves are dynamic. Using both trait-based and process-based approaches, spectroscopy can assess vegetation function at multiple scales using a variety of sensors and platforms ranging from proximal to airborne and satellite measurements. Since spectroscopic data are defned by the instruments and platforms available, along with their corresponding spatial, temporal and spectral scales, and since these scales may not always match those of the function of interest, consideration of scale is a necessary focus. For a full understanding of vegetation processes, combined (multi-scale) sampling methods using empirical and theoretical approaches are required, along with improved informatics.
Introduction: Vegetation plays a crucial role in many Earth system processes, including carbon and water cycling, biogeochemistry, and climate regulation. Additionally, plants provide humans with a wide range of important goods and services, ranging from forest products and fodder to food production. Our society and economy depend upon the healthy function of plants as well as the resilience of ecosystems of which they are a part. Remote sensing is playing an increasingly important role in monitoring, understanding, and maintaining these critical functions.
From a remote sensing perspective, vegetation function can have several meanings. One defnition refers to the detection or quantifcation of plant traits, structural, physiological, biochemical, or phenological attributes that represent plant functional adaptations or reveal underlying plant ecophysiological processes (Kattge et al. 2011). A complementary defnition of vegetation function addresses the dynamics of essential physiological, or ecological processes themselves. This defnition emphasizes variation in key processes over time and space and implies that traits themselves can be dynamic.
In this review, we consider both trait- and process-based approaches used to assess plant function, and explore the history, promise and challenge of integrating these approaches in the context of optical remote sensing. The primary focus is on optical measurements, including spectral refectance and fuorescence sampled with spectrometers, realizing that other types of remote sensing are also useful. Since our ability to detect plant function is scale-dependent, we start with a brief discussion of scale. Next, we review plant functional traits detectable with remote sensing, followed by a consideration of proximal and airborne remote sensing. We then discuss how global satellite remote sensing has been applied to vegetation assessment along with a presentation of new directions in satellite applications. We conclude with current challenges and opportunities, and provide suggestions and directions that are likely to further advance the study of vegetation function.
The PSR+ Spectroradiometer was one of the instruments used during this study:
• Fast, full-spectrum UV-VIS-NIR measurements (350 – 2,500 nm)
• High Resolution Field Portable Spectroradiometer with 512 element Si array and two 256 element extended InGaAs arrays
• Various optics ranging from 1° to 25° for reflectance, radiance and irradiance measurements
• The PSR+ spectroradiometer is powered by batteries and connected to a rugged tablet which provides GPS, photo tagging, and voice notes
• Our self-developed software allows in-field mineral identification and classification
• Usage of handheld contact probe allows field measurements on outcrops or mapping open pits even at cloudy conditions
• Best signal-to-noise ratio for improved reflectance values by using full range tungsten lamps
• Detailed analysis of field samples in order to build Arctic spectral libraries from natural covers
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