Classifying California plant species temporally using airborne hyperspectral imagery

Meerdink, Susan; Roberts, Dar A.; Roth, Keely L.; King, Jennifer Y.; Gader, Paul D.; Koltunov, Alexander

doi:10.1016/j.rse.2019.111308

Cited by 39 publications

(22 citation statements)

References 69 publications

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“…The past two decades have seen significant advances in the use of airborne remote sensing data in the field of Earth system science and for ecosystem process modeling, from the canopy/stand level to more recently larger regions, increasing the demand for hyperspectral and LiDAR measurements. However, only few studies explored the applicability of BRDF correction methods to airborne hyperspectral imagery [6,7,[92][93][94], especially for the forested areas with rugged topography. A better understanding of the BRDF effects on airborne hyperspectral imagery will provide clearer insights into their influence on remote sensing-based quantitative inversion products (e.g., LAI, PRI, canopy biochemistry parameters).…”

Section: Discussionmentioning

confidence: 99%

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

et al. 2020

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Airborne hyper-spectral imaging has been proven to be an efficient means to provide new insights for the retrieval of biophysical variables. However, quantitative estimates of unbiased information derived from airborne hyperspectral measurements primarily require a correction of the anisotropic scattering properties of the land surface depicted by the bidirectional reflectance distribution function (BRDF). Hitherto, angular BRDF correction methods rarely combined viewing-illumination geometry and topographic information to achieve a comprehensive understanding and quantification of the BRDF effects. This is in particular the case for forested areas, frequently underlaid by rugged topography. This paper describes a method to correct the BRDF effects of airborne hyperspectral imagery over forested areas overlying rugged topography, referred in the reminder of the paper as rugged topography-BRDF (RT-BRDF) correction. The local viewing and illumination geometry are calculated for each pixel based on the characteristics of the airborne scanner and the local topography, and these two variables are used to adapt the Ross-Thick-Maignan and Li-Transit-Reciprocal kernels in the case of rugged topography. The new BRDF model is fitted to the anisotropy of multi-line airborne hyperspectral data. The number of pixels is set at 35,000 in this study, based on a stratified random sampling method to ensure a comprehensive coverage of the viewing and illumination angles and to minimize the fitting error of the BRDF model for all bands. Based on multi-line airborne hyperspectral data acquired with the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) in the Pu’er region (China), the results applying the RT-BRDF correction are compared with results from current empirical (C, and sun-canopy-sensor (SCS) adds C (SCS+C)) and semi-physical (SCS) topographic correction methods. Both quantitative assessment and visual inspection indicate that RT-BRDF, C, and SCS+C correction methods all reduce the topographic effects. However, the RT-BRDF method appears more efficient in reducing the variability in reflectance of overlapping areas in multiple flight-lines, with the advantage of reducing the BRDF effects caused by the combination of wide field of view (FOV) airborne scanner, rugged topography, and varying solar illumination angle over long flight time. Specifically, the average decrease in coefficient of variation (CV) is 3% and 3.5% for coniferous forest and broadleaved forest, respectively. This improvement is particularly marked in the near infrared (NIR) region (i.e., >750 nm). This finding opens new possible applications of airborne hyperspectral surveys over large areas.

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Section: Discussionmentioning

confidence: 99%

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

et al. 2020

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“…HISUI is expected to enable identification of land cover classifications at the levels of vegetation species and plant communities (Meerdink et al, 2019 ). Thus, it is expected to contribute to improved understanding of biodiversity patterns in the mid to low latitudes, and to better quantify high spatial resolution changes in land cover.…”

Section: Europe’s Earth Observation Programmentioning

confidence: 99%

Current and near-term advances in Earth observation for ecological applications

2021

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There is an unprecedented array of new satellite technologies with capabilities for advancing our understanding of ecological processes and the changing composition of the Earth’s biosphere at scales from local plots to the whole planet. We identified 48 instruments and 13 platforms with multiple instruments that are of broad interest to the environmental sciences that either collected data in the 2000s, were recently launched, or are planned for launch in this decade. We have restricted our review to instruments that primarily observe terrestrial landscapes or coastal margins and are available under free and open data policies. We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum. The suite of instruments we describe measure land surface characteristics, including land cover, but provide a more detailed monitoring of ecosystems, plant communities, and even some species then possible from historic sensors. The newer instruments have potential to greatly improve our understanding of ecosystem functional relationships among plant traits like leaf mass area (LMA), total nitrogen content, and leaf area index (LAI). They provide new information on physiological processes related to photosynthesis, transpiration and respiration, and stress detection, including capabilities to measure key plant and soil biophysical properties. These include canopy and soil temperature and emissivity, chlorophyll fluorescence, and biogeochemical contents like photosynthetic pigments (e.g., chlorophylls, carotenoids, and phycobiliproteins from cyanobacteria), water, cellulose, lignin, and nitrogen in foliar proteins. These data will enable us to quantify and characterize various soil properties such as iron content, several types of soil clays, organic matter, and other components. Most of these satellites are in low Earth orbit (LEO), but we include a few in geostationary orbit (GEO) because of their potential to measure plant physiological traits over diurnal periods, improving estimates of water and carbon budgets. We also include a few spaceborne active LiDAR and radar imagers designed for quantifying surface topography, changes in surface structure, and 3-dimensional canopy properties such as height, area, vertical profiles, and gap structure. We provide a description of each instrument and tables to summarize their characteristics. Lastly, we suggest instrument synergies that are likely to yield improved results when data are combined.

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“…This can influence conclusions drawn from analyses of such non-randomly sampled collections records (Syfert et al, 2013). Temporal data is increasingly used in a wide range of applications in ecology and evolutionary studies including tracking changes in phenology -the timing of seasonal events such as flowering, leafing, and fruitingand monitoring the spread of invasive species (Iler et al, 2013;Veeneklaas et al, 2013;Daru et al, 2019;Meerdink et al, 2019). Yet, while there is general agreement that climate change can influence phenological patterns by disrupting the timing of life cycle events and consequently drive changes in fitness and population demography (Ovaskainen et al, 2013;CaraDonna et al, 2014;Thackeray et al, 2016;Kharouba and Wolkovich, 2020), most have been observed in terrestrial species and to a lesser extent in marine flowering plants.…”

Section: Biases In Temporal Samplingmentioning

confidence: 99%

Impediments to Understanding Seagrasses’ Response to Global Change

Rock

Daru

2021

Front. Mar. Sci.

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Uncertainties from sampling biases present challenges to ecologists and evolutionary biologists in understanding species sensitivity to anthropogenic climate change. Here, we synthesize possible impediments that can constrain research to assess present and future seagrass response from climate change. First, our knowledge of seagrass occurrence information is prevalent with biases, gaps and uncertainties that can influence inferences on species response to global change. Second, research on seagrass diversity has been focused on species-level metrics that can be measured with data from the present – but rarely accounting for the shared phylogenetic relationships and evolutionary distinctiveness of species despite species evolved and diversified from shared ancestors. Third, compared to the mass production of species occurrence records, computational tools that can analyze these datasets in a reasonable amount of time are almost non-existent or do not scale well in terms of computer time and memory. These impediments mean that scientists must work with incomplete information and often unrepresentative data to predict how seagrass diversity might change in the future. We discuss these shortfalls and provide a framework for overcoming the impediments and diminishing the knowledge gaps they generate.

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Classifying California plant species temporally using airborne hyperspectral imagery

Cited by 39 publications

References 69 publications

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

A Kernel-Driven BRDF Approach to Correct Airborne Hyperspectral Imagery over Forested Areas with Rugged Topography

Current and near-term advances in Earth observation for ecological applications

Impediments to Understanding Seagrasses’ Response to Global Change

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