TY - GEN A1 - Limberger, Oliver A1 - Homeier, Jürgen A1 - Farwig, Nina A1 - Pucha-Cofrep, Franz A1 - Fries, Andreas A1 - Leuschner, Christoph A1 - Trachte, Katja A1 - Bendix, Jörg T1 - Classification of Tree Functional Types in a Megadiverse Tropical Mountain Forest from Leaf Optical Metrics and Functional Traits for Two Related Ecosystem Functions T2 - Forests N2 - Few plant functional types (PFTs) with fixed average traits are used in land surface models (LSMs) to consider feedback between vegetation and the changing atmosphere. It is uncertain if highly diverse vegetation requires more local PFTs. Here, we analyzed how 52 tree species of a megadiverse mountain rain forest separate into local tree functional types (TFTs) for two functions: biomass production and solar radiation partitioning. We derived optical trait indicators (OTIs) by relating leaf optical metrics and functional traits through factor analysis. We distinguished four OTIs explaining 38%, 21%, 15%, and 12% of the variance, of which two were considered important for biomass production and four for solar radiation partitioning. The clustering of species-specific OTI values resulted in seven and eight TFTs for the two functions, respectively. The first TFT ensemble (P-TFTs) represented a transition from low to high productive types. The P-TFT were separated with a fair average silhouette width of 0.41 and differed markedly in their main trait related to productivity, Specific Leaf Area (SLA), in a range between 43.6 to 128.2 (cm2/g). The second delineates low and high reflective types (E-TFTs), were subdivided by different levels of visible (VIS) and near-infrared (NIR) albedo. The E-TFTs were separated with an average silhouette width of 0.28 and primarily defined by their VIS/NIR albedo. The eight TFT revealed an especially pronounced range in NIR reflectance of 5.9% (VIS 2.8%), which is important for ecosystem radiation partitioning. Both TFT sets were grouped along elevation, modified by local edaphic gradients and species-specific traits. The VIS and NIR albedo were related to altitude and structural leaf traits (SLA), with NIR albedo showing more complex associations with biochemical traits and leaf water. The TFTs will support LSM simulations used to analyze the functioning of mountain rainforests under climate change. KW - ecosystem productivity KW - energy fluxes KW - leaf hyperspectra KW - functional traits KW - tree functional types KW - tropical forest Y1 - 2021 U6 - https://doi.org/10.3390/f12050649 SN - 1999-4907 VL - 12 IS - 5 ER - TY - GEN A1 - Murkute, Charuta A1 - Sayeed, Mostafa A1 - Pucha-Cofrep, Franz A1 - Carrillo-Rojas, Galo A1 - Homeier, Jürgen A1 - Limberger, Oliver A1 - Fries, Andreas A1 - Bendix, Jörg A1 - Trachte, Katja T1 - Turbulent energy and carbon fluxes in an Andean Montane Forest—energy balance and heat storage T2 - Forests N2 - High mountain rainforests are vital in the global energy and carbon cycle. Understanding the exchange of energy and carbon plays an important role in reflecting responses to climate change. In this study, an eddy covariance (EC) measurement system installed in the high Andean Mountains of southern Ecuador was used. As EC measurements are affected by heterogeneous topography and the vegetation height, the main objective was to estimate the effect of the sloped terrain and the forest on the turbulent energy and carbon fluxes considering the energy balance closure (EBC) and the heat storage. The results showed that the performance of the EBC was generally good and estimated it to be 79.5%. This could be improved when the heat storage effect was considered. Based on the variability of the residuals in the diel, modifications in the imbalances were highlighted. Particularly, during daytime, the residuals were largest (56.9 W/m 2 on average), with a clear overestimation. At nighttime, mean imbalances were rather weak (6.5 W/m 2 ) and mostly positive while strongest underestimations developed in the transition period to morning hours (down to −100 W/m 2 ). With respect to the Monin–Obukhov stability parameter ((z − d)/L) and the friction velocity (u*), it was revealed that the largest overestimations evolved in weak unstable and very stable conditions associated with large u* values. In contrast, underestimation was related to very unstable conditions. The estimated carbon fluxes were independently modelled with a non-linear regression using a light-response relationship and reached a good performance value (R 2 = 0.51). All fluxes were additionally examined in the annual course to estimate whether both the energy and carbon fluxes resembled the microclimatological conditions of the study site. This unique study demonstrated that EC measurements provide valuable insights into land-surface–atmosphere interactions and contribute to our understanding of energy and carbon exchanges. Moreover, the flux data provide an important basis to validate coupled atmosphere ecosystem models. KW - energy balance closure KW - heat storage KW - carbon fluxes KW - eddy covariance KW - Andes Mountains Y1 - 2024 U6 - https://doi.org/10.3390/f15101828 SN - 1999-4907 VL - 15 IS - 10 PB - MDPI ER - TY - GEN A1 - Limberger, Oliver A1 - Homeier, Jürgen A1 - Gonzalez-Jaramillo, Victor A1 - Fries, Andreas A1 - Murkute, Charuta A1 - Trachte, Katja A1 - Bendix, Jörg T1 - Foliar trait retrieval models based on hyperspectral satellite imagery perform well in a biodiversity hotspot of the SE Ecuadorian Andes T2 - International journal of remote sensing N2 - The determination of the spatial distribution of canopy traits is crucial for understanding the spatio-temporal dynamics of ecosystem functions, such as carbon sequestration, water and energy fluxes in tropical montane forests. Especially in remote areas such as the Andes of south-east Ecuador, remote sensing using satellites has been proven to provide valuable information on canopy traits. However, the performance of the multispectral models to date is limited. Here, we analyse the potential of DESIS (DLR Earth Sensing Imaging Spectrometer) hyperspectral surface reflectance data for the prediction of specific leaf area (SLA), foliar toughness, nitrogen (N) and phosphorus (P) content by calibrating a PLSR model for each foliar trait. Model validation showed a high explanation of variance (R2 : 0.78–0.92) and a low model error (mean-normalized RMSE: 8–13%) for all traits, showing an improved performance compared to models based on multispectral imagery and ancillary data within the same forest type. Hyperspectral models predicting foliar traits from individual tree crowns showed a lower performance for most traits, probably related to the difference in spatial resolution compared to this study. Given the high species richness of the Andean biodiversity hotspot and the complex topography, the high quality of the DESIS models is remarkable, considering that the multispectral models from the region require topographic predictors in addition to reflectances to achieve a good performance. The spatial trait maps generated by our models based on the reflectances only show clear relationships with terrain elevation. Thus, the hyperspectral trait maps from satellite orbit are capable of supporting large-scale ecological analyses between features, diversity and ecosystem functions. Y1 - 2025 U6 - https://doi.org/10.1080/01431161.2025.2511211 SN - 1366-5901 SP - 1 EP - 19 PB - Taylor & Francis CY - London ER -