Mapping forest leaf dry matter content from hyperspectral data

Abebe Mohammed Ali, Andrew K. Skidmore, Roshanak Darvishzadeh, Iris van Duren, Stefanie Holzwarth, Joerg Mueller

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

Abstract

Leaf dry matter content (LDMC) is a central vegetation property that plays an important role in assessments of ecosystem functions. In this study, LDMC was estimated from hyperspectral airborne image by inversion of the INFORM radiative transfer model using Continuous Wavelet Analysis (CWA). Stand parameters were collected for 33 sample plots during a field campaign in July 2013 in the Bavarian Forest National Park, Germany. The INFORM model was used to simulate the canopy reflectance of the study area and was then inverted by applying CWA in the shortwave infrared region. The results were evaluated using R2 and RMSE of the estimated and measured LDMC. Our results revealed significant correlations of six wavelet features with LDMC. The wavelet feature at 1741 nm (scale 5) was the strongly correlated feature in the studied spectral region to LDMC variation. The combination of all the identified wavelet features for LDMC gave the most accurate prediction (R2= 0.59 and RMSE=4.39%).
Original languageEnglish
Title of host publicationIGTF 2016 conference proceedings
Place of PublicationMaryland, US
PublisherAmerican Society for Photogrammetry and Remote Sensing
Number of pages14
Publication statusPublished - 2016
Externally publishedYes
EventIGTF 2016 - ASPRS 2016 Annual Conference - Fort Worth, United States
Duration: 11 Apr 201615 Apr 2016

Conference

ConferenceIGTF 2016 - ASPRS 2016 Annual Conference
CountryUnited States
CityFort Worth
Period11/04/1615/04/16

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