Next Article in Journal
Characterization of the Far Infrared Properties and Radiative Forcing of Antarctic Ice and Water Clouds Exploiting the Spectrometer-LiDAR Synergy
Previous Article in Journal
Weather Types Affect Rain Microstructure: Implications for Estimating Rain Rate
Previous Article in Special Issue
Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance
Article

Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

1
Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW 2109, Australia
2
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands
3
Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany
4
Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Straße 4, 79106 Freiburg, Germany
5
Department of Geography and Environmental Science, University of Zimbabwe, P.O. Box MP 167, Mt Pleasant, Harare, Zimbabwe
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3573; https://doi.org/10.3390/rs12213573
Received: 28 September 2020 / Revised: 23 October 2020 / Accepted: 27 October 2020 / Published: 31 October 2020
(This article belongs to the Special Issue Remote Sensing for Estimating Leaf Chlorophyll Content in Plants)
Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy. View Full-Text
Keywords: hyperspectral remote sensing; airborne; canopy chlorophyll content (CCC); vegetation indices; partial least squares regression; forest monitoring hyperspectral remote sensing; airborne; canopy chlorophyll content (CCC); vegetation indices; partial least squares regression; forest monitoring
Show Figures

Graphical abstract

MDPI and ACS Style

Hoeppner, J.M.; Skidmore, A.K.; Darvishzadeh, R.; Heurich, M.; Chang, H.-C.; Gara, T.W. Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data. Remote Sens. 2020, 12, 3573. https://doi.org/10.3390/rs12213573

AMA Style

Hoeppner JM, Skidmore AK, Darvishzadeh R, Heurich M, Chang H-C, Gara TW. Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data. Remote Sensing. 2020; 12(21):3573. https://doi.org/10.3390/rs12213573

Chicago/Turabian Style

Hoeppner, J. M., Andrew K. Skidmore, Roshanak Darvishzadeh, Marco Heurich, Hsing-Chung Chang, and Tawanda W. Gara. 2020. "Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data" Remote Sensing 12, no. 21: 3573. https://doi.org/10.3390/rs12213573

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop