{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T14:53:35Z","timestamp":1768402415309,"version":"3.49.0"},"reference-count":118,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T00:00:00Z","timestamp":1604102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010661","name":"Horizon 2020","doi-asserted-by":"publisher","award":["834709"],"award-info":[{"award-number":["834709"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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\u2013400 nm and 470\u2013540 nm), red edge (680\u2013780 nm), near-infrared (1050\u20131100 nm) and shortwave infrared regions (2000\u20132270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.<\/jats:p>","DOI":"10.3390\/rs12213573","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T21:39:56Z","timestamp":1604180396000},"page":"3573","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data"],"prefix":"10.3390","volume":"12","author":[{"given":"J. Malin","family":"Hoeppner","sequence":"first","affiliation":[{"name":"Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW 2109, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7446-8429","authenticated-orcid":false,"given":"Andrew K.","family":"Skidmore","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW 2109, Australia"},{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-0574","authenticated-orcid":false,"given":"Roshanak","family":"Darvishzadeh","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands"}]},{"given":"Marco","family":"Heurich","sequence":"additional","affiliation":[{"name":"Department of Visitor Management and National Park Monitoring, Bavarian Forest National Park, Freyunger Str. 2, 94481 Grafenau, Germany"},{"name":"Chair of Wildlife Ecology and Management, University of Freiburg, Tennenbacher Stra\u00dfe 4, 79106 Freiburg, Germany"}]},{"given":"Hsing-Chung","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Earth and Environmental Sciences, Macquarie University, Sydney, NSW 2109, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-4849","authenticated-orcid":false,"given":"Tawanda W.","family":"Gara","sequence":"additional","affiliation":[{"name":"Department of Geography and Environmental Science, University of Zimbabwe, P.O. 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