Assessing Vegetation Function with Imaging Spectroscopy

Gamon, John A.; Somers, Ben; Malenovský, Zbyněk; Middleton, Elizabeth M.; Rascher, Uwe; Schaepman, Michael E.

doi:10.1007/s10712-019-09511-5

Cited by 119 publications

(92 citation statements)

References 149 publications

(174 reference statements)

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“…The differences among canopy layers are most relevant in proximal sensing [75], for example, in the estimation of photosynthetic capacity. Proximal sensing provides measurements at a scale that can be easily related to the leaf traits [76] and thus contribute background information for remote sensing applications, as highlighted in Serbin et al [77]. In the VNIR range, the highest spectral reflectance differences among the canopy layers occurred at 519 nm, 696 nm, and 725 nm.…”

Section: Discussionmentioning

confidence: 99%

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

et al. 2019

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The availability of light within the tree canopy affects various leaf traits and leaf reflectance. We determined the leaf reflectance variation from 400 nm to 2500 nm among three canopy layers and cardinal directions of three genetically identical cloned silver birches growing at the same common garden site. The variation in the canopy layer was evident in the principal component analysis (PCA), and the influential wavelengths responsible for variation were identified using the variable importance in projection (VIP) based on partial least squares discriminant analysis (PLS-DA). Leaf traits, such as chlorophyll, nitrogen, dry weight, and specific leaf area (SLA), also showed significant variation among the canopy layers. We found a shift in the red edge inflection point (REIP) for the canopy layers. The canopy layers contribute to the variability in the reflectance indices. We conclude that the largest variation was among the canopy layers, whereas the differences among individual trees to the leaf reflectance were relatively small. This implies that within-tree variation due to the canopy layer should be taken into account in the estimation of intraspecific variation in the canopy reflectance.

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Section: Discussionmentioning

confidence: 99%

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

et al. 2019

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“…Essentially, one can assess plant traits, vegetation structure and functioning, and phenology at an aggregated pixel level integrating signals from all individual plants and species present in the pixel [14]. Given that remote sensing measurements resolve these properties in space and time, they are key for investigating changes in plant diversity, ecosystem functioning, and services, globally and in near real time [15][16][17][18][19].…”

Section: What Do Satellites Measure?mentioning

confidence: 99%

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Migliavacca

Wirth

et al. 2020

Remote Sensing

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Plant functional diversity (FD) is an important component of biodiversity. Evidence shows that FD strongly determines ecosystem functioning and stability and also regulates various ecosystem services that underpin human well-being. Given the importance of FD, it is critical to monitor its variations in an explicit manner across space and time, a highly demanding task that cannot be resolved solely by field data. Today, high hopes are placed on satellite-based observations to complement field plot data. The promise is that multiscale monitoring of plant FD, ecosystem functioning, and their services is now possible at global scales in near real-time. However, non-trivial scale challenges remain to be overcome before plant ecology can capitalize on the latest advances in Earth Observation (EO). Here, we articulate the existing scale challenges in linking field and satellite data and further elaborated in detail how to address these challenges via the latest innovations in optical and radar sensor technologies and image analysis algorithms. Addressing these challenges not only requires novel remote sensing theories and algorithms but also urges more effective communication between remote sensing scientists and field ecologists to foster mutual understanding of the existing challenges. Only through a collaborative approach can we achieve the global plant functional diversity monitoring goal.

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“…In [11] the benefits of hyperspectral remote sensing data are summarised for agricultural application; retrieval methods and challenges for operational implementation are also discussed. A comprehensive and up-to-date summary to assess vegetation properties and function with remote sensing-based imaging spectroscopy are provided in [12].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle

et al. 2019

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In this paper we aim to show a proof-of-principle approach to detect and monitor weed management using glyphosate-based herbicides in agricultural practices. In a case study in Germany, we demonstrate the application of Sentinel-2 multispectral time-series data. Spectral broadband vegetation indices were analysed to observe vegetation traits and weed damage arising from herbicide-based management. The approach has been validated with stakeholder information about herbicide treatment using commercial products. As a result, broadband NDVI calculated from Sentinel-2 data showed explicit feedback after the glyphosate-based herbicide treatment. Vegetation damage could be detected after just two days following of glyphosate-based herbicide treatment. This trend was observed in three different application scenarios, i.e., during growing stage, before harvest and after harvest. The findings of the study demonstrate the feasibility of satellite based broadband NDVI data for the detection of glyphosate-based herbicide treatment and, e.g., the monitoring of latency to harvesting. The presented results can be used to implement monitoring concepts to provide the necessary transparency about weed treatment in agricultural practices and to support environmental monitoring.

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Assessing Vegetation Function with Imaging Spectroscopy

Cited by 119 publications

References 149 publications

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

Monitoring Plant Functional Diversity Using the Reflectance and Echo from Space

Monitoring Glyphosate-Based Herbicide Treatment Using Sentinel-2 Time Series—A Proof-of-Principle

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