2018
DOI: 10.3390/rs10081263
|View full text |Cite
|
Sign up to set email alerts
|

Machine Learning Using Hyperspectral Data Inaccurately Predicts Plant Traits Under Spatial Dependency

Abstract: Spectral, temporal and spatial dimensions are difficult to model together when predicting in situ plant traits from remote sensing data. Therefore, machine learning algorithms solely based on spectral dimensions are often used as predictors, even when there is a strong effect of spatial or temporal autocorrelation in the data. A significant reduction in prediction accuracy is expected when algorithms are trained using a sequence in space or time that is unlikely to be observed again. The ensuing inability to g… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

1
30
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 28 publications
(31 citation statements)
references
References 56 publications
1
30
0
Order By: Relevance
“…As anticipated, predictive models based on airborne hyperspectral measurements are less accurate, because tree-level spectral signatures combine light reflected by overlapping and adjacent crowns, in addition to canopy structural properties, that further modulate the signal (Asner et al, 2011(Asner et al, , 2017Doughty et al, 2017). Assessments of predictive performance are likely to be inflated when field samples are collected in a clustered manner, which is not accounted for during analysis (Rocha, Groen, Skidmore, Darvishzadeh, & Willemen, 2018), particularly if both trait and spectral variation is small within clusters relative to between clusters (e.g. Chadwick & Asner, 2016).…”
Section: Pixel-level Accuracy Versus Statistical Powermentioning
confidence: 99%
“…As anticipated, predictive models based on airborne hyperspectral measurements are less accurate, because tree-level spectral signatures combine light reflected by overlapping and adjacent crowns, in addition to canopy structural properties, that further modulate the signal (Asner et al, 2011(Asner et al, , 2017Doughty et al, 2017). Assessments of predictive performance are likely to be inflated when field samples are collected in a clustered manner, which is not accounted for during analysis (Rocha, Groen, Skidmore, Darvishzadeh, & Willemen, 2018), particularly if both trait and spectral variation is small within clusters relative to between clusters (e.g. Chadwick & Asner, 2016).…”
Section: Pixel-level Accuracy Versus Statistical Powermentioning
confidence: 99%
“…The other layers of plant traits were utilised only to simulate the hyperspectral data (see Rocha et al 2018 for full details on the simulation). The simulated spectra were used as explanatory variables to fit spatial and non-spatial regression models to predict LAI.…”
Section: Methodsmentioning
confidence: 99%
“…Machine learning regression can deal with the serially correlated predictors derived from the spectral domain if properly tuned (Dormann et al, 2007). However, these algorithms may pose challenges when dealing with serially correlated observations from spatially dependent plant traits and remote sensing data (Rocha et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
See 2 more Smart Citations