Mapping canopy chlorophyll content in a temperate forest using airborne hyperspectral data

摘要: Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances stressors. Accurate timely estimation of canopy chlorophyll content (CCC) essential for effective ecosystem monitoring to allow successful management interventions occur. Hyperspectral remote sensing offers possibility accurately estimate map content. In past, research has predominantly focused on use hyperspectral data retrieval crops grassland ecosystems. Therefore, in this study, a temperate mixed forest, Bavarian Forest National Park Germany, was chosen study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) random forest (RF)) their accuracy predict CCC using airborne data. The imagery acquired AisaFenix sensor (623 bands; 3.5 nm spectral resolution visible near-infrared (VNIR) region, 12 shortwave infrared (SWIR) region; 3 m spatial resolution) 6 July 2017. situ leaf area index measurements were sampled from upper coniferous, mixed, deciduous stands August yielded highest accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 0.66). It further indicated specific regions within (390–400 470–540 nm), red edge (680–780 (1050–1100 nm) (2000–2270 that important retrieval. results showed can be mapped relatively high image spectroscopy.