Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the st...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	International journal of remote sensing Jg. 34; H. 5; S. 1724 - 1743
Hauptverfasser:	Axelsson, Christoffer, Skidmore, Andrew K, Schlerf, Martin, Fauzi, Anas, Verhoef, Wouter
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Abingdon Taylor & Francis 01.01.2013
Schlagworte:	absorption features Animal, plant and microbial ecology Applied geophysics band-depth analysis Biological and medical sciences calcium canopy canopy nitrogen continuum removal deciduous forests Earth sciences Earth, ocean, space ecosystems Exact sciences and technology Fundamental and applied biological sciences. Psychology General aspects. Techniques hyperspectral imagery Indonesia infrared reflectance spectroscopy Internal geophysics landscapes leaf-area index least squares magnesium nitrogen nitrogen concentration nitrogen content pasture quality phosphorus potassium reflectance remote sensing remote-sensing data sodium Teledetection and vegetation maps wavelengths Far East Asia Indonesia models calcium potassium Biochemical property nitrogen magnesium accuracy vegetation concentration Plant leaf cartography Support vector machine Hyperspectral imaging sensor PLS regression sodium Mangrove biochemistry Hyperspectral characteristic deltas performances Canopy(vegetation) phosphorus
ISSN:	1366-5901, 0143-1161, 1366-5901
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to (1) assess the accuracy of foliar chemistry retrieval, (2) compare the performance of models based on support vector regression (SVR), i.e. ϵ-SVR, ν-SVR, and least squares SVR (LS-SVR), to models based on partial least squares regression (PLSR), and (3) investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level (R² = 0.67, root mean square error (RMSE) = 0.17, normalized RMSE (nRMSE) = 15%), whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.
Bibliographie:	http://dx.doi.org/10.1080/01431161.2012.725958 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1366-5901 0143-1161 1366-5901
DOI:	10.1080/01431161.2012.725958