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FundingThis work was funded by the EU LIFE Healthy Forest project (LIFE14 ENV/ES/000179) and the German Scholars Organization/Carl Zeiss Foundation.

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Iturritxa, EugeniaAuthor
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Monitoring Forest Health Using Hyperspectral Imagery: Does Feature Selection Improve the Performance of Machine-Learning Techniques?

Publicated to:Remote Sensing. 13 (23): 4832- - 2021-12-01 13(23), DOI: 10.3390/rs13234832

Authors: Schratz, Patrick; Muenchow, Jannes; Iturritxa, Eugenia; Cortes, Jose; Bischl, Bernd; Brenning, Alexander

Affiliations

Friedrich Schiller Univ Jena, Dept Geog, GISci Grp, Loebdergraben 32, D-07743 Jena, Germany - Author
Ludwig Maximilians Univ Munchen, Dept Stat, Akademiestr 1-1, D-80799 Munich, Germany - Author
NEIKER Tecnalia, Tecnalia 48160, Spain - Author

Abstract

This study analyzed highly correlated, feature-rich datasets from hyperspectral remote sensing data using multiple statistical and machine-learning methods. The effect of filter-based feature selection methods on predictive performance was compared. In addition, the effect of multiple expert-based and data-driven feature sets, derived from the reflectance data, was investigated. Defoliation of trees (%), derived from in situ measurements from fall 2016, was modeled as a function of reflectance. Variable importance was assessed using permutation-based feature importance. Overall, the support vector machine (SVM) outperformed other algorithms, such as random forest (RF), extreme gradient boosting (XGBoost), and lasso (L1) and ridge (L2) regressions by at least three percentage points. The combination of certain feature sets showed small increases in predictive performance, while no substantial differences between individual feature sets were observed. For some combinations of learners and feature sets, filter methods achieved better predictive performances than using no feature selection. Ensemble filters did not have a substantial impact on performance. The most important features were located around the red edge. Additional features in the near-infrared region (800-1000 nm) were also essential to achieve the overall best performances. Filter methods have the potential to be helpful in high-dimensional situations and are able to improve the interpretation of feature effects in fitted models, which is an essential constraint in environmental modeling studies. Nevertheless, more training data and replication in similar benchmarking studies are needed to be able to generalize the results.

Keywords
Canopy defoliationCarotenoid contentFeature selectionForest health monitoringHyperspectral imageryImaging spectroscopyLeaf chlorophyll contentMachine learningModel comparisonPinus-sylvestrisRed edge positionRemote estimationSpectral reflectanceVegetation indexesWater-content

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2021, it was in position 30/202, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 3.9, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Apr 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-04-30, the following number of citations:

  • WoS: 10
  • Scopus: 17
  • OpenCitations: 7
Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-04-30:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 50.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 49 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 1.75.
  • The number of mentions on the social network X (formerly Twitter): 4 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Germany.