Multilayer Canopy Research Articles

Wildfire effects on mercury fate in soils of North-Western Siberia

Arctic soils store 49 Gg mercury (Hg) – an extremely toxic heavy metal, whereas soil Hg can be released to the atmosphere by wildfires. For the first time we investigated the effects of wildfires on the fate of soil Hg in North-Western (NW) Siberia based on GIS maps of areas burned during the last 38 years and a field paired comparison of unburned and burned areas in tundra (mosses, lichens, some grasses, and shrubs) and forest-tundra (multi-layered canopy of larch trees, shrubs, mosses, and lichens). These field surveys were deepened by soil controlled burning to assess the Hg losses from organic horizon and mineral soil. The soil Hg stocks in the organic horizon and in the top 10 cm of the mineral soil were 3.3 ± 0.6 and 16 ± 3 mg Hg m−2 for unburned tundra and forest-tundra, respectively. After the burning by wildfires, the soil Hg stocks decreased to 2.4 ± 0.1 and 6.6 ± 0.2 mg Hg m−2 for tundra and forest-tundra, respectively. By the averages annual burned areas in NW Siberia 527 km2, wildfires in tundra and forest-tundra released 0.19 and 2.9 Mg soil Hg per year, respectively, corresponding to 28 % and 59 % of the initial soil Hg stocks. These direct effects of wildfires on Hg volatilization are raised by indirect post-pyrogenic consequences on Hg fate triggered by the vegetation succession and adsorption of atmospheric Hg on the surface of charred biomass. Charred lichens and trees accumulated 4–16 times more Hg compared to the living biomass. Blackened burned vegetation and soil reduced surface albedo and slowly increased soil temperatures in Arctic after wildfires. This created favorable conditions for seeding grasses and shrubs after wildfire and transformed burned high-latitude ecosystems into greener areas, increasing their capacity to trap atmospheric Hg by vegetation, which partly compensate the burning losses of soil Hg.

Effects of diffuse light on the gross ecosystem primary productivity of a winter wheat (Triticum aestivum L.) cropland in northern China

Diffuse light is produced by clouds and aerosols in the atmosphere. Exploring the effects of diffuse light on ecosystem productivity is important for understanding the terrestrial carbon (CO2) cycle. Here, 2 years of gross ecosystem primary productivity (GEP) from a (winter) wheat cropland in China was assessed using eddy covariance technology to explore the effects of diffuse photosynthetic active radiance (PAR) on wheat GEP. Wheat GEP increased significantly and positively along with diffuse PAR. In addition, wheat GEP was significantly affected by total PAR, air temperature, and vapor press deficit in different diffuse PAR fraction (fDIF) change stages. Because significant autocorrelations existed among the controlling factors, a path analysis was used to quantify the effects of diffuse light on GEP. Diffuse PAR was the primary and secondary importance factors affecting GEP with direct path coefficients of 0.54 and 0.48, respectively, in different fDIF change stages. A multilayer canopy model revealed that the middle and lower canopy levels intercepted more light when diffuse PAR increased. This resulted in the photosynthetic enhancement of middle and lower canopy levels, which contributed approximately 65% and 35%, respectively, to the increase in photosynthesis for the entire canopy (~ 30.5%). Overall, our study provided new evidence regarding the importance of diffuse light for CO2 uptake in agroecosystems, which is important for predicting the responses of ecosystem CO2 budgets to future climate-related light changes.

Development of a multi-layer canopy model for E3SM Land Model with support for heterogeneous computing

The vertical structure of vegetation canopies creates micro-climates. However, the land components of most Earth System Models, including the Energy Exascale Earth System Model (E3SM), typically neglect vertical canopy structure by using a single layer big-leaf representation to simulate water, CO2, and energy exchanges between the land and the atmosphere. In this study, we developed a Multi-Layer Canopy Model for the E3SM Land Model to resolve the micro-climate created by vegetation canopies. The model developed in this study re-implements the CLM-ml_v1 to support heterogeneous computing architectures consisting of CPUs and GPUs and includes three additional optimization-based stomatal conductance models. The use of Portable, Extensible Toolkit for Scientific Computation provides a speedup of 25–50 times on a GPU relative to a CPU. The numerical implementation of the model was verified against CLM-ml_v1 for a month-long simulation using data from the Ameriflux US-University of Michigan Biological Station site. Model structural uncertainty was explored by performing control simulations for five stomatal conductance models that exclude and include the control of plant hydrodynamics (PHD) on photosynthesis. The bias in simulated sensible and latent heat fluxes was lower when PHD was accounted for in the model. Additionally, six idealized simulations were performed to study the impact of three environmental variables (i.e. air temperature, atmospheric CO2, and soil moisture) on canopy processes (i.e. net CO2 assimilation, leaf temperature, and leaf water potential). Increasing air temperature reduced net CO2 assimilation and increased air temperature. Net CO2 assimilation increased at higher atmospheric CO2, while decreasing soil moisture resulted in lower leaf water potential.

Maximizing the Radiation Use Efficiency by Matching the Leaf Area and Leaf Nitrogen Vertical Distributions in a Maize Canopy: A Simulation Study.

The radiation use efficiency (RUE) is one of the most important functional traits determining crop productivity. The coordination of the vertical distribution of light and leaf nitrogen has been proven to be effective in boosting the RUE from both experimental and computational evidence. However, previous simulation studies have primarily assumed that the leaf area is uniformly distributed along the canopy depth, rarely considering the optimization of the leaf area distribution, especially for C4 crops. The present study hypothesizes that the RUE may be maximized by matching the leaf area and leaf nitrogen vertical distributions in the canopy. To test this hypothesis, various virtual maize canopies were generated by combining the leaf inclination angle, vertical leaf area distribution, and vertical leaf nitrogen distribution and were further evaluated by an improved multilayer canopy photosynthesis model. We found that a greater fraction of leaf nitrogen is preferentially allocated to canopy layers with greater leaf areas to maximize the RUE. The coordination of light and nitrogen emerged as a property from the simulations to maximize the RUE in most scenarios, particularly in dense canopies. This study not only facilitates explicit and precise profiling of ideotypes for maximizing the RUE but also represents a primary step toward high-throughput phenotyping and screening of the RUE for massive numbers of inbred lines and cultivars.

Evaluating Forest Site Quality Using the Biomass Potential Productivity Approach

Biomass productivity is of great significance for the evaluation of forest quality, which is important for the improvement of forest management. We propose the computational methods of biomass potential productivity (BPP) and biomass realistic productivity (BRP), both of which provide reliable practical guides for predicting forest growth under multi-aged, multi-species, and multi-layered canopy conditions. We used 2222 national forest inventory plots that were measured in four consecutive periods in the Jilin Province for this purpose. We analyzed and verified the computational methods of BPP based on the BRP and evaluated its practical significance. The results showed that growth models of the stand height, stand basal area, and stand biomass of four forest types (pure larch forest, larch broadleaf mixed forest, Mongolian oak pure forest, and Mongolian oak broadleaf mixed forest) fit adequately, BPP was greater than BRP, and this difference decreased with an increasing stand age, suggesting that the potential productivity of the middle-aged and young forest was higher than that of the mature forest, although the difference is minimal. In addition, the realistic productivity of stands with better site quality was close to the potential productivity, which is consistent with the biological significance of the potential productivity of the biomass. The degree of difference between the potential productivity of the biomass and the realistic productivity of biomass also decreases with the decline in site quality, and it can be termed as the potentially improved stand biomass. The BPP model was able to perform well in both the pure and mixed forests. The BRP not only verifies the rationality of the BPP but can be also used to quantify the forest site quality, which is helpful for evaluating forest growth and informed decision making in forestry.

Community-based plant diversity monitoring of a dense-canopy and species-rich tropical forest using airborne LiDAR data

Tropical forests are widely regarded as the Earth’s most important ecosystems, yet they are severely threatened by anthropogenic disturbances. Rapid and extensive monitoring of forest structure and biodiversity is crucial for developing ecologically sound conservation and restoration strategies. Airborne light detection and ranging (LiDAR) can effectively monitor three-dimensional forest canopy structures. Traditionally, LiDAR-based plant diversity estimation has relied on individual tree-based and area-based approaches. However, these approaches face significant challenges in tropical forests due to their complex canopy structures and diverse plant compositions. Therefore, by proposing a novel community-based approach, this study aims to examine the relationship between field-derived biodiversity indices and LiDAR-derived canopy structural metrics of plant communities in a species-rich ForestGEO forest dynamic plot in tropical Hong Kong. Our goal is to determine whether canopy structural metrics extracted from airborne LiDAR data can serve as a robust and efficient alternative for expediting plant diversity monitoring in highly dense and diverse tropical forests. Our results indicate that an integration watershed segmentation technique (for automatic patch-scale plant community delineation), LiDAR-derived canopy structural metrics, and machine learning-based random forest regression analysis can provide accurate predictions of community-based species diversity indices. Among various diversity indices, species richness and the Shannon-Wiener index are most accurately estimated using LiDAR-derived metrics. This study reveals that species richness is predominantly influenced by the existence of multi-layered canopy structures, whereas the Shannon-Wiener index is associated with both multi-layered structures and canopy morphologies. Overall, our findings showcase the immense potential of airborne LiDAR data in advancing the monitoring of structure and biodiversity in dense-canopy and species-rich tropical forests in a spatially explicit manner.

Stem shape and structural complexity change in beech forests along a management gradient

For about half a century, attempts have been made to manage forests as close to nature as possible. This management targeted the creation of structures that resemble those found in primary forests. Laser scanning provides the opportunity to quantify such structural “naturalness” and allows to evaluate which management practices come closest to forest structures found in primary forests. In this paper, European beech (Fagus sylvatica L.) forests with different management intensity were compared to primary forests in terms of their structural complexity and the shape of tree stems. For this purpose, data from mobile and terrestrial laser scans (MLS and TLS) of managed forests, of forests whose management has been abandoned, and of primary forests was used. We found that management intensity influenced the distribution of plant material in the forest stand and thus the structural complexity on stand level. Also, management affected the shape of the stems. Here, it is important to consider the management history of the forest stands and the forest development phase in which management was abandoned. The stem shapes of trees in primary forests were significantly different (larger stem diameters and longer branch free boles) from those of the other investigated forests. Nevertheless, our results showed that it is possible to achieve old-growth-like structures such as high standing volumes, a multi layered canopy, high number of large trees and high variation in tree size and age through targeted silvicultural treatments in order to accelerate the close-to-nature development of forests. The present study illustrates the possibility of using mobile and terrestrial laser scanning to objectively compare and evaluate the structural effects of different silvicultural concepts.

Automated characterization of forest canopy vertical layering for predicting forest inventory attributes by layer using airborne LiDAR data

Abstract Forest canopy vertical layering influences stand development and yield and is critical information for forest management planning and wood supply analysis. It is also relevant for other applications including habitat modelling, forest fuels management and assessing forest resilience. Forest inventories that use coincident airborne Light Detection and Ranging (LiDAR) data and field plots (i.e. area-based approach) to predict forest attributes generally do not consider the multi-layer canopy structure that may be found in many natural and managed forest stands. With airborne LiDAR, it is possible to separate single-layer and multi-layer stands. This information can be used to allocate predictions of forest attributes such as timber volume (m3 ha−1), by canopy layer. In this study, we used single-photon LiDAR data to automate the mapping of vertical stand layering in a temperate mixedwood forest with a variety of forest types and vertical complexities. We first predicted whether each 25 × 25 m grid cell had one or two canopy layers, and then partitioned inventory attributes (e.g. basal area (BA), gross total stem volume (GTV)) by canopy layer. We compared two methods for estimating attributes by layer at the stand level using nine independent validation stands. Overall agreement between the reference and predicted structure for the calibration plots was 74% (n = 266). At the grid-cell level, attributes were generally underestimated for the upper layer and overestimated for the lower layer. For the validation stands, the relative height of the lower layer was under-predicted compared to the reference data (46–52% versus 57%), while the proportion of BA and GTV in the lower layer were very similar to the reference values (17–19% versus 18% for BA and 12–15% versus 12% for GTV). Overall, the approach showed promise in distinguishing single- and two-layered stand conditions and partitioning estimates of inventory attributes such as BA and GTV by layer—both for grid cells and at the stand level. The inclusion of forest information by canopy layer enhances the utility of LiDAR-derived forest inventories for forest management in forest areas with complex, multi-layer stand conditions.

Influence of Organized Turbulence on OH Reactivity at a Deciduous Forest

AbstractOxidation of reactive carbon fuels climate‐ and pollution‐relevant chemistry. Deciduous forests are important sources of reactive carbon (particularly isoprene). Organization in turbulence can physically separate (“segregate”) oxidants from reactive carbon, causing oxidation to increase or decrease relative to the (ubiquitous) assumption of well‐mixed conditions. We use large eddy simulation coupled to a multilayer canopy model and simplified chemistry to quantify the impact of segregation on near‐canopy hydroxyl radical (OH) reactivity. Simulations mimic summer clear‐sky midday and morning conditions at a homogeneous deciduous forest. OH‐isoprene segregation alters OH reactivity inside the canopy by up to 9%, but the impact strongly depends on height, soil NO emissions, and sunlight. Uniquely, we identify the drivers of changes by isolating the roles of isoprene and OH. Our findings also suggest that segregation may create discrepancies between direct measurements and bottom‐up estimates of OH reactivity, separate from the issue of mischaracterized or unknown OH sinks.

Species-Specific Contribution to Atmospheric Carbon and Pollutant Removal: Case Studies in Two Italian Municipalities

In order to maximize ecosystem services (ES), a proper planning of urban green areas is needed. In this study, the urban greenery of two Italian cities (Milan and Bologna) exposed to high levels of atmospheric pollutants was examined. Vegetation maps were developed through a supervised classification algorithm, trained over remote sensing images, integrated by local trees inventory, and used as input for the AIRTREE multi-layer canopy model. In both cities, a large presence of deciduous broadleaves was found, which showed a higher capacity to sequestrate CO2 (3,953,280 g m2 y−1), O3 (5677.76 g m2 y−1), and NO2 (2358.30 g m2 y−1) when compared to evergreen needle leaves that, on the other hand, showed higher performances in particulate matter removal (14,711.29 g m2 y−1 and 1964.91 g m2 y−1 for PM10 and PM2,5, respectively). We identified tree species with the highest carbon uptake capacity with values up to 1025.47 g CO2 m2 y−1 for Celtis australis, Platanus x acerifolia, Ulmus pumila, and Quercus rubra. In light of forthcoming and unprecedent policy measures to plant millions of trees in the urban areas, our study highlights the importance of developing an integrated approach that combines modelling and satellite data to link air quality and the functionality of green plants as key elements in improving the delivery of ES in cities.

A Semiempirical Approach for Decomposition of Remotely Sensed Leaf Area Index Into Overstory and Understory Components Over Russian Forests

Forest is a multi-layered canopy, where overstory and understory implement different biogeochemical cycles, phenology and functional role. Remote sensing products typically estimate forest total Leaf Area Index (LAI), while few quantify its components. The theoretical understanding of foliage distribution between layers is still quite limited. In this study we’ve developed a semi-empirical model for decomposition of forest total LAI between layers. Decomposition was implemented over the full extent of Russian forests, exhibiting a wide dynamic range of the forest total LAI. This paper addresses both the theoretical and practical aspects of the problem. In terms of theory we formalized the principles of forest layers "biological/radiometric coupling" into a parametric model allowing to analyze the relationship between overstory/ understory LAI and overstory crown fraction. The model captures various features of layers foliage growth, including the "understory seasonal dip effect". In terms of practical aspects, we generated time series of the MODIS layered LAI product for 2001-2020 at the spatial resolution of 230-m over Russian forests. We calculated mean layered LAI of species and contrasted with typical values from the literature surveys. According to our estimates relative contribution of understory LAI increases from South to North- 28% of forests pixels have understory LAI which exceeds that of overstory, those pixels are located in the northern part of the eastern Siberia and occupied by larch forests. The layered LAI product was intercompared/validated with multiple ground and remote sensing data.

Effects of elevated temperature and abnormal precipitation on soil carbon and nitrogen dynamics in a Pinus densiflora forest

Forests have the largest terrestrial nutrient pools. The loss of soil carbon and nitrogen in forests under ongoing climate warming is subject to severe environmental degradation. To mitigate the negative effects of global warming on soil carbon and nitrogen in forest, it is important to obtain a better understanding of how elevated temperature and altered precipitation variability impact soil nutrient dynamics. To explore such interactions, we coupled an eco-hydrological model (Multi-Layer Canopy model, MLCan) with a biogeochemical model and applied the combined model to Pinus densiflora forest in Gwangneung Experimental Forest, South Korea, from 2004 to 2020. Our results showed that there was a time lag of 4 years to trigger soil organic carbon losses under the elevated temperature of +1.11°C during 2014–2020 compared to 2010–2013. A temperature rise over a prolonged period increased microbial biomass and activity, stimulating soil organic carbon decomposition. The combination of soil nitrate accumulation and exceptional but expected delay in heavy precipitation seasons of 2 months led to nitrate leaching four times higher than the average at 1 m depth in 2010. Reduced evapotranspiration and heavy precipitation during early fall caused intense subsurface water flux, resulting in a great increase in the risk of nitrate leaching. Our results highlight that the impacts of global warming on soil carbon decompositions has a time lag of 4 years and changes in precipitation characteristics will lead to excessive nitrate loss in P. densiflora forests under climate change.

Influence of canopy structure and light on the three-dimensional distribution of the iconic lichen Usnea longissima

Forest canopies modify microclimates and create habitats for nonvascular epiphytes, but we need to better understand the mechanisms regulating their vertical and horizontal distributions. Here we examine how canopy structure and light environment influence the 3D distribution of Usnea longissima, the world’s longest lichen, and associated with old-growth forests. We quantified forest structure, vertical profile of light (PPFD transmittance fraction), and horizontal as well as vertical distribution of the lichen in a 1 ha plot dominated by Picea abies. The forest had a multi-layered canopy with mortality driven by small-scale gap dynamics. The population size of the lichen had an approximate log-normal distribution with host trees showing strong clustering. The lichen extended up to mid-canopy and had a rather sharp upper limit. Population size increased with DBH and upper limit but did not correlate with basal area. The vertical profile of light was steeper in dead than in live trees, with the lichen occurring in a zone with low-intermediate light. The horizontal distribution was linked to the vertical distribution through short-distance asexual dispersal. The lichen’s 3D distribution was shaped by various interacting functional mechanisms. Its absence from the upper canopy was mainly explained by sensitivity to high light when desiccated and limited capacity for upward migration. The population dynamics was driven by source trees hosting large populations in mid-canopy. The lichen’s strong association with humid, old-growth forests is explained by narrow niche preferences and dispersal limitation, and not by slow growth. Protection of multi-layered forests with long continuity of tree cover is needed to secure substrates and suitable microclimates for development of viable lichen populations. Our study highlights that the 3D distribution of lichens in forest canopies is driven by forest dynamics, canopy structure, microclimate, and lichen functional traits.

Comparing Model Representations of Physiological Limits on Transpiration at a Semi‐Arid Ponderosa Pine Site

AbstractMechanistic representations of biogeochemical processes in ecosystem models are rapidly advancing, requiring advancements in model evaluation approaches. Here we quantify multiple aspects of model functional performance to evaluate improved process representations in ecosystem models. We compare semi‐empirical stomatal models with hydraulic constraints against more mechanistic representations of stomatal and hydraulic functioning at a semi‐arid pine site using a suite of metrics and analytical tools. We find that models generally perform similarly under unstressed conditions, but performance diverges under atmospheric and soil drought. The more empirical models better capture synergistic information flows between soil water potential and vapor pressure deficit to transpiration, while the more mechanistic models are overly deterministic. Although models can be parameterized to yield similar functional performance, alternate parameterizations could not overcome structural model constraints that underestimate the unique information contained in soil water potential about transpiration. Additionally, both multilayer canopy and big‐leaf models were unable to capture the magnitude of canopy temperature divergence from air temperature, and we demonstrate that errors in leaf temperature can propagate to considerable error in simulated transpiration. This study demonstrates the value of merging underutilized observational data streams with emerging analytical tools to characterize ecosystem function and discriminate among model process representations.

Large Eddy Simulation for Investigating Coupled Forest Canopy and Turbulence Influences on Atmospheric Chemistry

AbstractExchanges of reactive gases between forests and the atmosphere influence tropospheric chemistry, climate, and ecosystems. Chemistry inside canopies can alter these exchanges yet understanding of the chemistry and processes influencing the chemistry is incomplete. Previous work prioritizes complex chemical mechanisms over micrometeorology in the multilayer models of forests used to understand in‐canopy chemistry and its impacts. Instead, here we use a new simplified chemical mechanism and resolve turbulence in a multilayer canopy model. Specifically, we describe a new version of the National Center for Atmospheric Research large eddy simulation (LES) coupled to both a multilayer canopy model and a chemical mechanism. The mechanism has 41 reactions and 19 gases for ozone, NOx (=NO + NO2), HOx (=OH + HO2), and isoprene chemistry. We evaluate the mechanism against two more complex mechanisms using box modeling. We configure the LES for a temperate deciduous forest and summer midday weak‐wind buoyancy‐forced conditions. The multilayer canopy model necessitates estimates of reactant sources and sinks at each canopy level, and thus we describe multilayer parameterizations for dry deposition and biogenic emissions. For the first time with an LES coupled to both a multilayer canopy and chemistry, we demonstrate variability in chemical reactions due to turbulence‐induced segregation of reactants inside and just above the canopy. For the cases examined here, in‐canopy segregation alters reaction rates that only consider well‐mixed conditions by up to −48% to +23%. For many reactions, segregation is stronger when soil NO emissions reflect the upper bound of observed values for temperate forests.

Evaluating Ecohydrological Model Sensitivity to Input Variability with an Information-Theory-Based Approach.

Ecohydrological models vary in their sensitivity to forcing data and use available information to different extents. We focus on the impact of forcing precision on ecohydrological model behavior particularly by quantizing, or binning, time-series forcing variables. We use rate-distortion theory to quantize time-series forcing variables to different precisions. We evaluate the effect of different combinations of quantized shortwave radiation, air temperature, vapor pressure deficit, and wind speed on simulated heat and carbon fluxes for a multi-layer canopy model, which is forced and validated with eddy covariance flux tower observation data. We find that the model is more sensitive to radiation than meteorological forcing input, but model responses also vary with seasonal conditions and different combinations of quantized inputs. While any level of quantization impacts carbon flux similarly, specific levels of quantization influence heat fluxes to different degrees. This study introduces a method to optimally simplify forcing time series, often without significantly decreasing model performance, and could be applied within a sensitivity analysis framework to better understand how models use available information.

Throughfall drop sizes suggest canopy flowpaths vary by phenophase

Water flowpaths caused by incident rainfall onto forest canopy surfaces have a notable effect on the water budgets and chemistry of wooded ecosystems. This study revealed varying canopy flowpaths and residence times at the intra-event scale and across the phenological transition from leafed to leafless states for a set of three American beech (Fagus grandifolia Ehrh.) trees in a multilayered canopy. Simultaneous measurements of raindrops and throughfall drops by fifteen laser disdrometers over five rain events were analyzed during the transition from leafed to leafless phenophases. Throughfall was partitioned into free throughfall, splash throughfall, and canopy drip with four drop size classes. Throughfall drop size distributions and volume of each throughfall type varied at both intra-event and inter-event scales. Smaller canopy drips, <5.5 mm in diameter, were initiated earlier in rain events, whereas more rainfall accumulation was necessary to generate larger canopy drips, >5.5 mm in diameter. Smaller canopy drips were more dominant in the leafed phenophase when some structurally-mediated woody surface drip points were more muted. These results suggested throughfall from foliar surfaces generated smaller-sized canopy drip with shorter residence time, whereas throughfall from structurally-mediated woody surface drip points generated larger-sized canopy drip with longer residence time. There was also an increase in both free throughfall and splash droplets from leafed to leafless states, consistent with increased canopy gaps and direct interaction with woody surfaces in the leafless state. Based on the results, a conceptualization of the genesis and development of leaf and branch flowpaths in canopies is proposed.

Significant Loss of Ecosystem Services by Environmental Changes in the Mediterranean Coastal Area

Mediterranean coastal areas are among the most threated forest ecosystems in the northern hemisphere due to concurrent biotic and abiotic stresses. These may affect plants functionality and, consequently, their capacity to provide ecosystem services. In this study, we integrated ground-level and satellite-level measurements to estimate the capacity of a 46.3 km2 Estate to sequestrate air pollutants from the atmosphere, transported to the study site from the city of Rome. By means of a multi-layer canopy model, we also evaluated forest capacity to provide regulatory ecosystem services. Due to a significant loss in forest cover, estimated by satellite data as −6.8% between 2014 and 2020, we found that the carbon sink capacity decreased by 34% during the considered period. Furthermore, pollutant deposition on tree crowns has reduced by 39%, 46% and 35% for PM, NO2 and O3, respectively. Our results highlight the importance of developing an integrated approach combining ground measurements, modelling and satellite data to link air quality and plant functionality as key elements to improve the effectiveness of estimate of ecosystem services.

Habitat preferences, estimated abundance and behavior of tree hyrax (Dendrohyrax sp.) in fragmented montane forests of Taita Hills, Kenya

We studied a previously almost unknown nocturnal mammal, an apparently undescribed species of tree hyrax (Dendrohyrax sp.) in the moist montane forests of Taita Hills, Kenya. We used thermal imaging to locate tree hyraxes, observe their behavior, and to identify woody plants most frequently visited by the selective browsers. We also documented acoustic behavior in forest fragments of different sizes. Data on calling type and frequency were analyzed together with lidar data to estimate population densities and to identify forest stand characteristics associated with large populations. Viable populations were found only in the largest forest fragments (> 90 ha), where tree hyraxes preferred most pristine forest stands with high, multilayered canopies. The estimated population sizes in smaller forest fragments were very limited, and hyraxes were heard to call only during late night and early morning hours, presumably in order to avoid detection. While we frequently recorded tree hyrax songs in the largest forest fragments, we almost never heard songs in the small ones. All remaining subpopulations of the Taita tree hyrax are under threat of human disturbance and further habitat deterioration. Conservation efforts should include protection of all remaining habitat patches, but also reforestation of former habitat is urgently needed.

Evaluating Sentinel-2 red edge through hyperspectral profiles for monitoring LAI & chlorophyll content of Kinnow Mandarin orchards

Leaf area index (LAI) and chlorophyll content are efficient plant health and nutrition indicators. However, retrieving these traits is challenging for the multilayered canopy of evergreen fruit trees like Citrus. The spectral reflectance's Red Edge Position (REP) is a surrogate measure of LAI and chlorophyll content. Sentinel-2 (20 m) estimates REP at a much higher spatial resolution than before. However, retrieval of the traits in the dense canopy structure of orchards requires proper validation of the Sentinel-2 REP (S2REP) through the hyperspectral profiles of plants. The objective here is to evaluate the potential of S2REP to retrieve the LAI and chlorophyll of kinnow mandarin fruit orchards and validate the results with REP extracted from the hyperspectral profiles and ground measurements of these traits. For this, we first tested several algorithms to enumerate the REP range from hyperspectral data that corresponds well to the kinnow mandarin's LAI and Chl content. Next, we used the S2REP to estimate these traits at sampled locations. Hence, we calibrated the regression models of LAI and chlorophyll content with the Sentinel-2 REP as an explanatory variable and traits ground measurements as response variables. The prediction accuracy was tested through an independent validation data set. The results show that the kinnow mandarin trees demonstrated significant spectral response in the 695–725 nm S2REP range of the electromagnetic spectrum similar to the spectral profiles from ground-based hyperspectral data. With this S2REP range, LAI and chlorophyll content models outperformed to predict these traits for the orchards, with adjusted-R2 0.86 and 0.80 and RMSE 3.7 and 53.3%, respectively. We conclude that the S2REP can effectively evaluate kinnow mandarin's LAI and Chlorophyll over large areas. The plants' LAI and chlorophyll content are best represented by the REP derived from the hyperspectral profiles through Polynomial fitting and Linear Extrapolation. Such spectrally-validated Sentinel-2 REP for kinnow orchards can calibrate the LAI and chlorophyll models more accurately over large scales. Our methods can be extrapolated to other fruit traits to enable the real-time monitoring of orchards' health and nutritional status.