Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling

Fawcett, Dominic; Verhoef, W.; Schläpfer, Daniel; Schneider, Fabian; Schaepman, Michael E.; Damm, Alexander

doi:10.1016/j.rse.2017.09.040

Cited by 21 publications

(21 citation statements)

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“…A typical hyperspectral flight has duration of 4-5 h and is influenced by varying sky conditions (i.e., the ratio of direct and diffuse radiation). However, because of the challenge to accurately describe the contribution of direct and diffuse irradiance to total irradiance in the actual operating process [32,33], in the RT-BRDF model we simplified this contribution by neglecting the influence of diffuse and adjacent surrounding radiance in the correction process. Another critical aspect of our study is that the CAF-LiCHy system was simultaneously acquired for both the airborne hyperspectral imagery and the LiDAR data, enabling the creation of an accurate high resolution LiDAR-derived DEM to describe the canopy and the underlying topography.…”

Section: Future Developmentsmentioning

confidence: 99%

“…Quantitative research on the correction of remotely sensed reflectance over rugged terrain has recently gained increasing attention, with the creation of new models such as the Kernel-Driven reflectance model for Sloping Terrain (KDST; [47]) and the Geometric-Optical model for Sloping Terrains (GOST; [111][112][113]). In addition, recent studies have worked towards a more accurate description of direct and diffuse irradiance components at the pixel level by considering a physically based approach with the combination of fine resolution DEM/DSM and the Discrete Anisotropic Radiative Transfer (DART) model [33,114].…”

Section: Future Developmentsmentioning

confidence: 99%

“…Physical-based BRDF corrections usually require more detailed surface characteristics like the canopy structure and the ratio of direct and diffuse radiation for each pixel in order to simulate the radiative transfer mechanisms. This can be a time consuming procedure, which clearly limits its large scale use [30][31][32][33], while it remains still a challenging approach for even VHR airborne hyperspectral imagery. In this paper, the BRDF-RT model presented is based on a semi-empirical kernel-driven BRDF approach (e.g., [34]) utilizing the Ross-Li-Reciprocal model employed for Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo product suite (e.g., [35][36][37][38]) to normalize the off-nadir viewing directional reflectance to nadir BRDF adjusted reflectance.…”

Section: Introductionmentioning

confidence: 99%

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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: Future Developmentsmentioning

confidence: 99%

Section: Future Developmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…individual-based forest model [106,108], and • forest growth model (eco-physiological model, [109]) • combining imaging spectroscopy, digital object models, and 3D canopy modelling [110] 3.…”

Section: Requirements For Merging Different Fh Modeling Approachesmentioning

confidence: 99%

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

et al. 2018

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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.forest ecosystem change through effective global coordination [3] good observations, indicators and scenarios of biodiversity and ecosystem services change [4].There is still a large discrepancy between the information required by forest managers and scientists and the information that is available for understanding and assessing the complexity and multidimensionality of forest health drivers, stressors, disturbances, and effects. Decision makers require information on forest health in high spatial and temporal accuracy, from the local to the global, for short and long-term periods that can be recorded ...

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“…Different approaches have been proposed for assessing and monitoring the spatial and temporal patterns of species diversity and distribution (Jetz et al, 2019;Mateo et al, 2017;Pereira et al, 2013), including biodiversity models-i.e., models that aim to predict species diversity and composition (Ferrier and Guisan, 2006;Guisan and Rahbek, 2011) and remote sensing technologies and products (Caughlin et al, 2016;Fawcett et al, 2018;Féret and Asner, 2014;Randin et al, 2020;Turner, 2014). Among biodiversity models, the approach of Stacked Species Distribution Models (S-SDMs) has been successfully implemented for predicting species diversity and composition (D'Amen et al, 2015;Pottier et al, 2013;Zurell et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Predicting species distributions and community composition using remote sensing products

Pinto‐Ledezma

Cavender‐Bares

2020

Preprint

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AbstractAccurate predictions of species composition and diversity are critical to the development of conservation actions and management strategies. In this paper using oak assemblages distributed across the conterminous United States as study model, we assessed the performance of stacked species distribution models (S-SDMs) and remote sensing products in building the next-generation of biodiversity models. This study represents the first attempt to evaluate the integrated predictions of biodiversity models—including assemblage diversity and composition—obtained by stacking next-generation SDMs. We found three main results. First, environmental predictors derived entirely from remote sensing products represent adequate covariates for biodiversity modeling. Second, applying constraints to assemblage predictions, such as imposing the probability ranking rule, results in more accurate species diversity predictions. Third, independent of the stacking procedure (bS-SDM versus cS-SDM), biodiversity models do not recover the observed species composition with high spatial resolution, i.e., correct species identities at the scale of individual plots. However, they do return reasonable predictions at macroecological scales (1 km). Our results provide insights for the prediction of assemblage diversity and composition at different spatial scales. An important task for future studies is to evaluate the reliability of combining S-SDMs with direct detection of species using image spectroscopy to build a new generation of biodiversity models to accurately predict and monitor ecological assemblages through time and space.

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Advancing retrievals of surface reflectance and vegetation indices over forest ecosystems by combining imaging spectroscopy, digital object models, and 3D canopy modelling

Cited by 21 publications

References 54 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

Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches

Predicting species distributions and community composition using remote sensing products

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