An Introduction to the Spatio-Temporal Analysis of Satellite Remote Sensing Data for Geostatisticians

Militino, Ana F.; Ugarte, M. D.; Pérez-Goya, Unai

doi:10.1007/978-3-319-78999-6_13

Cited by 9 publications

(12 citation statements)

References 53 publications

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“…Part of the unexplained variation in reflectance and plant trait values are inherent to the measuring system or related to patterns across space and time (Dormann et al, 2007;Legendre and Fortin, 1989). These domains are not independent, and they most probably interact considerably when continuous areas of heterogonous vegetation are imaged with sunlight as the primary source of illumination (Legendre et al, 2004;Militino et al, 2018;Roberts et al, 2017). Under these conditions, the only certainty when predicting ecological systems with remote sensing is the presence of uncertainty.…”

Section: Uncertainty and Stochasticitymentioning

confidence: 99%

“…To reduce uncertainties when modelling plant traits, essential variables that represent reality closely are needed. However, spatiotemporal fluctuation in environmental conditions imposes some degrees of unpredictability whether or not all essential variables are available for modelling (Militino et al, 2018). Ecological systems present inherent stochasticity or randomness, which make it somehow impossible to predict precisely their dynamics.…”

Section: Uncertainty and Stochasticitymentioning

confidence: 99%

“…Illumination, climatic and environmental variations introduce random and systematic noises into the spectral domain as mentioned before (Liang, 2005;Militino et al, 2018). Therefore, modelling with hyperspectral data presents a high risk to select spurious correlations rather than empirical relationships.…”

Section: Spectral Domainmentioning

confidence: 99%

“…The interaction between these two wavelengths may create a third spatial structure. Spatial structures derived from remote sensing procedures, for instance, soil background or cloud cover should be treated as an error and corrected before modelling (Militino et al, 2018). If remote sensing images are available before the sampling campaign, the spatial dependency of plant traits could be estimated and used for defining the optimal distance between samples as discussed in chapter five.…”

Section: Spatial Domainmentioning

confidence: 99%

“…Although the keyword spatiotemporal is quite recurrent in satellite imagery papers, the use of spatiotemporal (stochastic) models is rare in remote sensing applications (Militino et al, 2018). In most cases, spectra are modelled while the other domains are neglected, or individual pixels are analysed through a time series, or spatial pattern from images of different periods are compared (Nguyen and Lee, 2006).…”

Section: Temporal Domainmentioning

confidence: 99%

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Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Rocha¹

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Other components of variability related to the spatial alignment between spectra and ground references are errors in plot coordinates, upscale or downscale, distortions to departing from the nadir, among others (Manolakis et al., 2003). The discretisation of continuous domains such as spectra, space or time results in the loss of a certain amount of information (Bruce et al., 2002). The spatial resolution of a remote sensing data (pixel) or the sample The possible solutions for selecting covariates for modelling using hyperspectral data and avoid multicollinearity include: (1) extracting spectral indices that explain causally or empirically the relationship with the target plant trait based on a-priori knowledge; (2) searching a coefficient from a combination of two or more bands that is highest correlated with the plant trait (Darvishzadeh et al., 2008); (3) combining wavelengths to create latent Predictive models for plant traits are mostly selected by data rather than based on theory, and often elected among different regression techniques (James et al., 2013). If the model is assessed with the same data as was fitted, more complexity, directly means more accuracy, as the prediction error always reduces when the complexity increases (James et al., 2013). Consequently, it is improper to assess and report the accuracy of predictive models with the same data as used for selecting the final model. Predictive models require to split the data into training and testing (sub) sets to assess accuracy (Esbensen and Geladi, 2010). There are many alternatives, from splitting an independent This thesis is comprised of six chapters, of which four research chapters are submitted, and three are currently accepted as scientific articles to peerreviewed ISI journals. The general outline is indicated below. Chapter 1: the introductory chapter discusses the importance of plant traits and the role of remote sensing to monitoring and understanding the underlying process. The chapter is designed to highlight issues that need further improvement when modelling plant traits with hyperspectral data. Chapter 2: demonstrates that empirical models using hyperspectral data to predict traits are very likely to lead to significant overfitting, even when selected by commonly used robust cross-validation. A new method named Naïve Overfitting Index Selection (NOIS) was developed to quantify overfitting while selecting model complexity (tuning). The method was tested using five hyperspectral datasets and seven machine learning regression techniques. Chapter 3: shows that machine learning regressions using hyperspectral data are likely to lead to inaccurate predictions when significant autocorrelation is

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Section: Uncertainty and Stochasticitymentioning

confidence: 99%

Section: Uncertainty and Stochasticitymentioning

confidence: 99%

Section: Spectral Domainmentioning

confidence: 99%

Section: Spatial Domainmentioning

confidence: 99%

Section: Temporal Domainmentioning

confidence: 99%

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Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Rocha¹

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Quantifying bivalve phytoplankton depletion in a eutrophic system: an integrated approach

Taylor,

Jakobsen,

Lyngsgaard

et al. 2024

Limnology & Oceanography

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The removal of organic particulate matter, predominantly phytoplankton, in eutrophic coastal seas and estuaries is considered an ecosystem service performed by large bivalve assemblages. Mussel farming has been proposed as a measure to mitigate eutrophication, as filtration directly reduces the concentration of chlorophyll a (Chl a), a primary ecological indicator. Seston depletion is typically assessed by in situ investigation, which generally lacks spatiotemporal coverage of features relative to greater ecosystem dynamics. To assess the scale and structure of this service, the present study couples multiple measurement approaches, including moored stations, synoptic transect surveys, flow cytometry, a preliminary drone survey technique, and satellite remote sensing within and around a large mussel farm. Significant depletion patterns were observed with all methods, and mixing gradients could be detected hundreds of meters beyond the farm, with repeatable patterns but distinct findings between methods. The intensity of the depletion signal was correlated with mussel biomass loads, ambient conditions, and hydrodynamic regimes, ranging from 5% to 91% relative Chl a depletion and Secchi depth increases of up to 2 m. Changes in particle size distributions were impacted in all downstream areas, as well as phytoplankton diversity. Observed depletion gradients with satellite imagery were consistent with other measurements and can be used to complement in situ field measurements. The findings of this study, will inform carrying capacity assessments, farm configuration, and development of impact assessment programs on seston removal for bivalve aquaculture.

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On nonparametric ridge estimation for multivariate long-memory processes

Beran

Telkmann

2020

Lith Math J

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An Introduction to the Spatio-Temporal Analysis of Satellite Remote Sensing Data for Geostatisticians

Cited by 9 publications

References 53 publications

Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Quantifying bivalve phytoplankton depletion in a eutrophic system: an integrated approach

On nonparametric ridge estimation for multivariate long-memory processes

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