Remote sensing of photosynthetic light-use efficiency across two forested biomes: Spatial scaling

Eddy covariance (EC) measurements have greatly advanced our knowledge of carbon exchange in terrestrial ecosystems. However, appropriate techniques are required to upscale these spatially discrete findings globally. Satellite remote sensing provides unique opportunities in this respect, but remote sensing of the photosynthetic light-use efficiency (ε), one of the key components of Gross Primary Production, is challenging. Some progress has been made in recent years using the photochemical reflectance index, a narrow waveband index centered at 531 and 570 nm. The high sensitivity of this index to various extraneous effects such as canopy structure, and the view observer geometry has so far prevented its use at landscape and global scales. One critical aspect of upscaling PRI is the development of generic algorithms to account for structural differences in vegetation. Building on previous work, this study compares the differences in the PRI: ? relationship between a coastal Douglas-fir forest located on Vancouver Island, British Columbia, and a mature Aspen stand located in central Saskatchewan, Canada. Using continuous, tower-based observations acquired from an automated multi-angular spectro-radiometer (AMSPEC II) installed at each site, we demonstrate that PRI can be used to measure ? throughout the vegetation season at the DF-49 stand (r² = 0.91, p < 0.00) as well as the deciduous site (r² = 0.88, p < 0.00). It is further shown that this PRI signal can be also observed from space at both sites using daily observations from the Moderate Resolution Imaging Spectro-radiometer (MODIS) and a multi-angular implementation of atmospheric correction (MAIAC) (r² = 0.54 DF-49; r² = 0.63 SOA; p < 0.00). By implementing a simple hillshade model derived from airborne light detection and ranging (LiDAR) to approximate canopy shadow fractions (α_s), it is further demonstrated that the differences observed in the relationship between PRI and ε at DF-49 and SOA can be attributed largely to differences in α_s. The findings of this study suggest that algorithms used to separate physiological from extraneous effects in PRI reflectance may be more broadly applicable and portable across these two climatically and structurally different biome types, when the differences in canopy structure are known.     

Modelling PRI for water stress detection using radiative transfer models

This paper presents a methodology for water stress detection in crop canopies using a radiative transfer modelling approach and the Photochemical Reflectance Index (PRI). Airborne imagery was acquired with a 6-band multispectral camera yielding 15 cm spatial resolution and 10 nm FWHM over 3 crops comprising two tree-structured orchards and a corn field. The methodology is based on the PRI as a water stress indicator, and a radiative transfer modelling approach to simulate PRI baselines for non-stress conditions as a function of leaf structure, chlorophyll concentration (Cab), and canopy leaf area index (LAI). The simulation work demonstrates that canopy PRI is affected by structural parameters such as LAI, Cab, leaf structure, background effects, viewing angle and sun position. The modelling work accounts for such leaf biochemical and canopy structural inputs to simulate the PRI-based water stress thresholds for non-stress conditions. Water stress levels are quantified by comparing the image-derived PRI and the simulated non-stress PRI (sPRI) obtained through radiative transfer. PRI simulation was conducted using the coupled PROSPECT-SAILH models for the corn field, and the PROSPECT leaf model coupled with FLIGHT 3D radiative transfer model for the olive and peach orchards. Results obtained confirm that PRI is a pre-visual indicator of water stress, yielding good relationships for the three crops studied with canopy temperature, an indicator of stomatal conductance (r² = 0.65 for olive, r² = 0.8 for peach, and r² = 0.72 for maize). PRI values of deficit irrigation treatments in olive and peach were consistently higher than the modelled PRI for the study sites, yielding relationships with water potential (r² = 0.84) that enabled the identification of stressed crowns accounting for within-field LAI and Cab variability. The methodology presented here for water stress detection is based on the visible part of the spectrum, and therefore it has important implications for remote sensing applications in agriculture. This method may be a better alternative to using the thermal region, which has limitations to acquire operationally high spatial resolution thermal imagery.     

Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery

A methodology for the assessment of fruit quality in crops subjected to different irrigation regimes is presented. High spatial resolution multispectral and thermal airborne imagery were used to monitor crown temperature and the Photochemical Reflectance Index (PRI) over three commercial orchards comprising peach, nectarine and orange fruit trees during 2008. Irrigation regimes included sustained and regulated deficit irrigation strategies, leading to high variability of fruit quality at harvest. Stem water potential was used to monitor individual tree water status on each study site. Leaf samples were collected for destructive sampling of xanthophyll pigments to assess the relationship between the xanthophyll epoxidation state (EPS) and PRI at leaf and airborne-canopy level. At harvest, fruit size, Total Soluble Solids (TSS) and Tritatable Acidity (TA) were measured to characterize fruit quality. A statistically significant relationship between EPS and PRI was found at the leaf (r² = 0.81) and canopy level (r² = 0.41). Airborne-derived crown PRI calculated from the imagery acquired during the fruit growth was related to the ratio of the total soluble solids normalized by the tritatable acidity (TSS/TA), an indicator of fruit quality measured on the same trees, yielding a coefficient of determination of r² = 0.50. The relationship between the integral of PRI time-series and TSS/TA yielded a coefficient of determination of r² = 0.72 (peach) and r² = 0.61 (nectarines). On the contrary, the relation between TSS/TA and the time-series of crown thermal imagery was very weak (r² = 0.21 and 0.25 respectively). These results suggest that a physiological remote sensing indicator related to photosynthesis, such as PRI, is more appropriate for fruit quality assessment than crown temperature, the established method of water stress detection, which is more related to crown transpiration. A radiative transfer modelling study was conducted to assess the potential validity of this methodology for fruit quality assessment when using medium spatial resolution imagery. The analysis shows important effects of soil and shadows on the PRI vs EPS relationship used for fruit quality assessment if non-pure crown reflectance was extracted from the imagery.     

Regional mapping of gross light-use efficiency using MODIS spectral indices

Direct estimation of photosynthetic light-use efficiency (LUE) from space would be of significant benefit to LUE-based models which use inputs from remote sensing to estimate terrestrial productivity. The Photochemical Reflectance Index (PRI) has shown promise in tracking LUE at the leaf- to small canopy levels, but its use at regional to global scales still remains a challenge. In this study, we used different formulations of PRI calculated from the MODIS ocean band centered at 531 nm and a set of alternative reference bands at 488, 551, and 678 nm to explore the relationship between PRI and LUE where LUE was measured at eight eddy covariance flux towers located in the boreal forest of Saskatchewan, Canada. The magnitude and variability of LUE was significantly lower at the times when useful MODIS ocean band images were available (i.e. around midday under clear-sky conditions) relative to the rest of the growing season. PRI₆₇₈ (reference band at 678 nm) showed the strongest relationship (r² = 0.70) with LUE_90a (i.e. 90-minute mean LUE calculated using Absorbed Photosynthetically Active Radiation, APAR), but only when all sites were combined. Overall, the relationships between the MODIS PRIs and LUE_90a were always stronger for observations closer to the backscatter direction and there were no significant differences in the strength of the correlations whether LUE was calculated based on incident PAR or on APAR. Predictions of ecosystem photosynthesis at the time of the MODIS overpasses were significantly improved by multiplying either PAR or APAR by MODIS PRI (r² improved from 0.09 to 0.44 and 0.54 depending on the PRI formulation).We used our PRI-LUE model to create a regional LUE_90a map for the three cover types covering 47,500 km² around the flux sites. The MODIS PRI-derived LUE_90a map appeared to capture more realistic spatial heterogeneity of LUE across the landscape compared to a daily LUE map derived using the look-up table in the MODIS GPP (MOD17) algorithm. While our LUE map is only a snapshot of minimum regional LUE_90a values, with appropriate gap-filling methods it could be used to improve regional-scale monitoring of GPP. Moreover, the strong relationship between midday and daily LUE on clear days (r² = 0.93) indicates that instantaneous MODIS observations of LUE_90a could be used to estimate daily LUE. Finally, pixel shadow fraction from the 5-Scale geometric-optical model was closely related to both MODIS PRI and tower-derived LUE suggesting that differences in stand leaf area and in diffuse illumination among flux sites play a role in the relationship we observed between LUE and MODIS PRI.     

Revised photochemical reflectance index (PRI) for predicting light use efficiency of wheat in a growth cycle: validation and comparison

The photochemical reflectance index (PRI) was developed to trace the changes in light use efficiency (LUE) as the two contributing reflectances at 531 nm and 570 nm are closely related to the xanthophyll pigment cycle. In this paper, two revised indices of PRI (PRI_R1 and PRI_R2) are derived for a better prediction of LUE during the growth cycle of wheat. The signal of chlorophyll content (reflectance at 550 nm) to PRI is incorporated so that the revised indices can be used to estimate LUE values at low chlorophyll concentration. A validation was conducted using ground data (reflectance and LUE data) during a growth cycle of wheat in 2007 (17 April, 28 April, 16, 29 May). The results demonstrate that PRI cannot be used as an index for LUE estimation during the growth cycle of wheat as the relationship between PRI and LUE significantly weakened (R² = 0.20) on 29 May when the leaves lost chlorophyll concentration in the senescent period. PRI_R1 and PRI_R2 are more robust than PRI for LUE estimationm, not only with a relatively stable precision (R² = 0.62, 0.76, 0.62, 0.57 for PRI_R1 and R² = 0.62, 0.76, 0.63, 0.59 for PRI_R2) but also with better linearity with LUE (standard error of regression equation between LUE and index is 0.00187, 0.00127, 0.00116, 0.00103 for PRI_R1 and 0.00186, 0.00117, 0.00114, 0.00102 for PRI_R2). The result of the comparison analysis indicates that the revised indices (PRI_R1 and PRI_R2) are more sensitive than PRI to low chlorophyll content and low leaf area index, which means they are more appropriate for LUE interpretation in these situations. Sensitivity of Sun-sensor geometry to all indices implies that all indices exhibit large variations with changes in solar zenith angle and view zenith angle. As solar zenith angle increases, all indices display different sensitivity patterns before and after hotspot positions. All indices vary greatly as the view zenith angle increases. An acceptable precision of all indices can be acquired within a departure of 10° from the nadir view.     

Separating physiologically and directionally induced changes in PRI using BRDF models

Monitoring of photosynthetic efficiency (ε) over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. While the past decade has seen progress in the remote estimation of ε at the leaf, canopy and stand level using the photochemical reflectance index PRI (based on the normalized difference of reflectance at 531 and 570 nm), little is known about the temporal and spatial requirements for up-scaling PRI to landscape and global levels using satellite observations. One potential way to investigate these requirements is using automated tower-based remote sensing platforms, which observe stand level reflectance with high spatial, temporal, and spectral resolution. Prediction of ε from PRI diurnally or over a full year requires observations of canopy reflectance over multiple view and sun-angles. As a result, these observations are subject to directional reflectance effects which can be interpreted in terms of the bidirectional reflectance distribution function (BRDF) using semi-empirical kernel driven models. These semi-empirical models use a combination of physically based BRDF shapes and empirical observations to standardize multi-angular observations to a common viewing and illumination geometry. Directional reflectance effects are thereby modeled as a linear superposition of mathematical kernels, representing the bi-direction variation in reflectance from isotropic, geometric, and volumetric scattering components of the vegetation canopy. However, because variations in plant physiological conditions can also introduce bidirectional reflectance variations, we introduce an approach to separate bidirectional effects arising purely from plant physiological status from other effects by stratifying PRI observations into categories based on environmental conditions for which the expected physiological variability is low. Within each of these PRI strata, the derived physically based BRDF shapes were used to standardize multi-angular PRI measurements to a common viewing and illumination geometry. The method significantly enhanced the relationship found between PRI and ε (from r² = 0.38 for the directionally uncorrected case to r² = 0.82 for the directionally corrected case) from data measured continuously over the course of 1 year over an evergreen conifer forest using an automated platform. Results show that isotropic PRI scattering is highly correlated to changes in ε, while geometric scattering can be related to canopy level shading. Instrumentation and approaches such as the one demonstrated in this study may be integrated into current efforts aiming at predicting ε at global scales using satellite observations.     

Disentangling the relationships between plant pigments and the photochemical reflectance index reveals a new approach for remote estimation of carotenoid content

The Photochemical Reflectance Index (PRI) is used as an indicator of leaf and plant canopy photosynthetic efficiency. However, the photosynthetic efficiency-PRI relationship has been shown to be inconsistent over time, likely due to changes in foliar pigment content.We measured reflectance spectra and biochemical properties from 24 leaves of two deciduous tree species and acquired pigment and reflectance data from the Leaf Optical Properties EXperiment database for an additional nine species. These data were used as inputs for the PROSPECT-5 leaf optical model. We found measurements of PRI to be significantly (p < 0.05) correlated with chlorophyll content, carotenoid content, and the carotenoid/chlorophyll ratio. However, only the PRI-carotenoid/chlorophyll ratio relationship was consistent across all analyses. Two predictive equations were derived from PROSPECT-5 simulations: a curvilinear PRI model (PRI(clm)) predicted the carotenoid/chlorophyll ratio (r² = 0.99), and a linear model using the chlorophyll index (CI(lm)) predicted chlorophyll content (r² = 0.98). Multiplying PRI(clm) with CI(lm) canceled the influence of chlorophyll content on PRI(clm) and thus allowed for prediction of carotenoid content from 11 deciduous tree species (r² = 0.83). Our results confirm that the PRI is significantly influenced by chlorophyll and carotenoid pools and demonstrate a new approach for non-destructive estimation of leaf carotenoid content using the PRI. Because variation in foliar physiological status is known to relate to leaf carotenoid content and the carotenoid/chlorophyll ratio, convolving the PRI with a chlorophyll index is likely to be useful for understanding the photosynthetic performance of deciduous vegetation across a wide range of temporal periods, ranging from daily to seasonal time scales.     

Spatio-temporal remote sensing of vertical and seasonal chlorophyll-a:b ratio in Tamarix ramosissima

Remotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R² > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRI_mod, R₅₁₀–R₅₇₀ nm) performed better than the original PRI (PRI, R₅₃₁–R₅₇₀ nm) with R² improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRI_mod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.     

Utility of spectral vegetation index for estimation of gross CO2 flux under varied sky conditions

To estimate the gross CO₂ flux (F_CO2) of deciduous coniferous forest from canopy spectral reflectance, we introduced spectral vegetation indices (VIs) into a light use efficiency (LUE) model of mature Japanese larch (Larix kaempferi) forest. We measured the eddy covariance CO₂ flux and spectral reflectance of larch canopy at half-hourly intervals during one growing season, and investigated the relationships between the parameters of the LUE model (FAPAR, ?) and 3 types of VIs (NDVI, PRI, EVI) in both clear sky and cloudy conditions.FAPAR (fraction of absorbed photosynthetically active radiation) had a positive linear relationship with both NDVI (normalized difference vegetation index) and EVI (enhanced vegetation index), and the sky condition had little effect on the relationships. The relative RMSE (root mean square error) of the APAR (absorbed photosynthetically active radiation) based on the incoming PAR and estimated FAPAR from a linear function of NDVI was less than 10.5%, irrespective of sky condition.Half-hourly values of ? (conversion efficiency of absorbed energy) showed both seasonal variation related to leaf phenology and short-term variation related to light intensity due to varied sun position and sky condition. Both EVI and PRI (photochemical reflectance index) were significantly correlated with ?. EVI showed a positive linear relationship with ? as a result of their similar seasonal variation. However, since EVI did not detect short-term variation of ?, their relationship differed among sky conditions. On the other hand, although PRI could trace the short-term variation of ? in green needles, the relationship became non-linear due to drastic reduction of PRI in the senescent needles.EVI/(PRI/PRI_min), a combined index based on a 6-day moving minimum value of PRI (PRI_min), showed a linear relationship with half-hourly values of ? throughout the seasons irrespective of sky condition. This index allow us to estimate ? in all sky conditions with a smaller error (rRMSE = 35.2%) than using EVI or PRI alone (38.7%-48.7%). Consequently, this combined index-derived ? and NDVI-based FAPAR gave a low estimation error of F_CO2 (rRMSE = 36.4%, RMSE = 8.3 μmol m^− 2 s^− 1). Although there are still various issues to resolve, including adaptive limit and combination of vegetation index type, we conclude that the combination of PRI and EVI increased the accuracy of estimation of CO₂ uptake in deciduous forest even though sky conditions varied.     

A LiDAR-derived canopy density model for tree stem and crown mapping in Australian forests

The retrieval of tree and forest structural attributes from Light Detection and Ranging (LiDAR) data has focused largely on utilising canopy height models, but these have proved only partially useful for mapping and attributing stems in complex, multi-layered forests. As a complementary approach, this paper presents a new index, termed the Height-Scaled Crown Openness Index (HSCOI), which provides a quantitative measure of the relative penetration of LiDAR pulses into the canopy. The HSCOI was developed from small footprint discrete return LiDAR data acquired over mixed species woodlands and open forests near Injune, Queensland, Australia, and allowed individual trees to be located (including those in the sub-canopy) and attributed with height using relationships (r² = 0.81, RMSE = 1.85 m, n = 115; 4 outliers removed) established with field data. A threshold contour of the HSCOI surface that encompassed ∼ 90% of LiDAR vegetation returns also facilitated mapping of forest areas, delineation of tree crowns and clusters, and estimation of canopy cover. At a stand level, tree density compared well with field measurements (r² = 0.82, RMSE = 133 stems ha^− 1, n = 30), with the most consistent results observed for stem densities ≤ 700 stems ha^− 1. By combining information extracted from both the HSCOI and the canopy height model, predominant stem height (r² = 0.91, RMSE = 0.77 m, n = 30), crown cover (r² = 0.78, RMSE = 9.25%, n = 30), and Foliage & Branch Projective Cover (FBPC; r² = 0.89, RMSE = 5.49%, n = 30) were estimated to levels sufficient for inventory of woodland and open forest structural types. When the approach was applied to forests in north east Victoria, stem density and crown cover were reliably estimated for forests with a structure similar to those observed in Queensland, but less so for forests of greater height and canopy closure.     

An assessment of photosynthetic light use efficiency from space: Modeling the atmospheric and directional impacts on PRI reflectance

Estimation of photosynthetic light use efficiency (ε) from satellite observations is an important component of climate change research. The photochemical reflectance index, a narrow waveband index based on the reflectance at 531 and 570 nm, allows sampling of the photosynthetic activity of leaves; upscaling of these measurements to landscape and global scales, however, remains challenging. Only a few studies have used spaceborne observations of PRI so far, and research has largely focused on the MODIS sensor. Its daily global coverage and the capacity to detect a narrow reflectance band at 531 nm make it the best available choice for sensing ε from space. Previous results however, have identified a number of key issues with MODIS-based observations of PRI. First, the differences between the footprint of eddy covariance (EC) measurements and the MODIS footprint, which is determined by the sensor's observation geometry make a direct comparison between both data sources challenging and second, the PRI reflectance bands are affected by atmospheric scattering effects confounding the existing physiological signal. In this study we introduce a new approach for upscaling EC based ε measurements to MODIS. First, EC-measured ε values were “translated” into a tower-level optical PRI signal using AMSPEC, an automated multi-angular, tower-based spectroradiometer instrument. AMSPEC enabled us to adjust tower-measured PRI values to the individual viewing geometry of each MODIS overpass. Second, MODIS data were atmospherically corrected using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which uses a time series approach and an image-based rather than pixel-based processing for simultaneous retrievals of atmospheric aerosol and surface bidirectional reflectance (BRDF). Using this approach, we found a strong relationship between tower-based and spaceborne reflectance measurements (r² = 0.74, p < 0.01) throughout the vegetation period of 2006. Swath (non-gridded) observations yielded stronger correlations than gridded data (r² = 0.58, p < 0.01) both of which included forward and backscatter observations. Spaceborne PRI values were strongly related to canopy shadow fractions and varied with different levels of ε. We conclude that MAIAC-corrected MODIS observations were able to track the site-level physiological changes from space throughout the observation period.     

Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north-south climatic gradient

The aim of this study was to evaluate the use of ground-based canopy reflectance measurements to detect changes in physiology and structure of vegetation in response to experimental warming and drought treatment at six European shrublands located along a North-South climatic gradient. We measured canopy reflectance, effective green leaf area index (green LAIe) and chlorophyll fluorescence of dominant species. The treatment effects on green LAIe varied among sites. We calculated three reflectance indices: photochemical reflectance index PRI [531 nm; 570 nm], normalized difference vegetation index NDVI₆₈₀ [780 nm; 680 nm] using red spectral region, and NDVI₅₇₀ [780 nm; 570 nm] using the same green spectral region as PRI. All three reflectance indices were significantly related to green LAIe and were able to detect changes in shrubland vegetation among treatments. In general warming treatment increased PRI and drought treatment reduced NDVI values. The significant treatment effect on photochemical efficiency of plants detected with PRI could not be detected by fluorescence measurements. However, we found canopy level measured PRI to be very sensitive to soil reflectance properties especially in vegetation areas with low green LAIe. As both soil reflectance and LAI varied between northern and southern sites it is problematic to draw universal conclusions of climate-derived changes in all vegetation types based merely on PRI measurements. We propose that canopy level PRI measurements can be more useful in areas of dense vegetation and dark soils.     

Testing LiDAR models of fractional cover across multiple forest ecozones

Four LiDAR-based models of canopy fractional cover (FC_LiDAR) have been tested against hemispherical photography fractional cover measurements (FC_HP) and compared across five ecozones, eight forest species and multiple LiDAR survey configurations. The four models compared are based on: i) a canopy-to-total first returns ratio (FC_LiDAR(FR)) method; ii) a canopy-to-total returns ratio (FC_LiDAR(RR)); iii) an intensity return ratio (FC_LiDAR(IR)); and iv) a Beer's Law modified (two-way transmission loss) intensity return ratio (FC_LiDAR(BL)). It is found that for the entire dataset, the FC_LiDAR(RR) model demonstrates the lowest overall predictive capability of overhead FC (annulus rings 1-4) (r² = 0.70), with a slight improvement for the FC_LiDAR(FR) model (r² = 0.74). The intensity-based FC_LiDAR(IR) model displays the best results (r² = 0.78). However, the FC_LiDAR(BL) model is considered generally more useful (r² = 0.75) because the associated line of best fit passes through the origin, has a slope near unity and produces a mean estimate of FC_HP within 5%. Therefore, FC_LiDAR(BL) requires the least calibration across a broad range of forest cover types. The FC_LiDAR(FR) and FC_LiDAR(RR) models, on the other hand, were found to be sensitive to variations in both canopy height and sensor pulse repetition frequency (or pulse power); i.e. changing the repetition frequency led to a systematic shift of up to 11% in the mean FC_LiDAR(RR) estimates while it had no effect on the intensity-based FC_LiDAR(IR) or FC_LiDAR(BL) models. While the intensity-based models were generally more robust, all four models displayed at least some sensitivity to variations in canopy structural class, suggesting that some calibration of FC_LiDAR might be necessary regardless of the model used. Short (< 2 m tall) or open canopy forest plots posed the greatest challenge to accurate FC estimation regardless of the model used.     

Linking physiological responses,chlorophyll fluorescence and hyperspectral imagery to detect salinity stress using the physiological reflectance index in the coastal shrub,Myrica cerifera

Measurements of physiology, chlorophyll fluorescence and hyperspectral reflectance were used to detect salinity stress in the evergreen coastal shrub, Myrica cerifera on Hog Island, Virginia. Two experimental sites were used in our study, the oceanside of a M. cerifera thicket, which is exposed to sea spray, and the protected, leeside of the thicket. Using the physiological reflectance index (PRI), we were able to detect stress at both the canopy and landscape level. Monthly variations in stomatal conductance, photosynthesis, and relative water content indicated a strong summer drought response that was not apparent in chlorophyll fluorescence or in the water band index (WBI) derived from canopy and airborne reflectance measurements. In contrast, there were significant differences in both physiological measurements and tissue chlorides between the two sites used in the study, indicating salinity stress. This was reflected in measurements of PRI. There was a positive relationship between PRI measured at the canopy-level and light-adapted fluorescence (ΔF/F′_m; r² = 0.69). PRI was significantly lower on the oceanside of the Myrica cerifera thicket. PRI was not significantly related to NDVI (r² = 0.01) at the canopy-level and only weakly related (r² = 0.04) at the landscape-level, suggesting that the indices are independent. The chlorophyll index (CI) did not show any significant changes between the two sites. Frequency histograms of pixels sampled from airborne hyperspectral imagery revealed that the distribution of PRI was shifted to the right on the backside of the thicket relative to the oceanside and there was a significant difference between sites. These results suggest that PRI may be used for early identification of salt-stress and to identify areas across the landscape where community structure may change due to sea-level rise.     

Evaluation of satellite based primary production modelling in the semi-arid Sahel

The MODIS (Moderate Resolution Imaging Spectroradiometer) primary productivity products are evaluated against observed Above-ground Net Primary Production (AGNPP) in the semi-arid Senegal 2001. MODIS net primary productivity (NPP) modelling is a light use efficiency (LUE) based approach incorporating constraints on vegetation productivity arising from simulated radiation, water demand and temperature data from NASA's Data Assimilation Office (DAO). Annually integrated MODIS PSN (MOD17A2 net photosynthesis, Collection 4) explains more of the observed biomass variation (r² = 0.77) than MODIS fAPAR (fraction Absorbed Photosynthetically Active Radiation, Collection 4) (r² = 0.72), indicating the effect of including the canopy stress scalar (ε_s) based on DAO data combined with modelled maintenance respiration costs (of leaf and fine roots). Annual MODIS NPP (MOD17A3, Collection 4 (C4) and Collection 4.5 (C4.5)) including growth respiration and live wood maintenance respiration costs and modified DAO input (C4.5) however increases the residual unexplained observed AGNPP variance (C4 NPP; r² = 0.49) (C4.5 NPP; r² = 0.37). The overall quality of the annual NPP MODIS C4 and C4.5 products are moderate for the semi-arid Senegal because of the annual respiration cost modelling and a change in C4.5 biome-specific parameters stored in a Biome Properties Look-Up Table (BPLUT) is the main contributor to the observed discrepancy between C4 and C4.5 NPP. The dynamic range of the values of all MOD17 products was too low when compared to observed AGNPP. An estimate of canopy water stress (SIWSI; Shortwave Infrared Water Stress Index) derived from MODIS channels 2 and 6 and photosynthetically active radiation (PAR) irradiance derived from geostationary METEOSAT data were tested for primary production modelling using a stepwise linear regression analysis. PAR irradiance was combined with MODIS fAPAR into APAR (Absorbed Photosynthetically Active Radiation) explaining 79% of the observed AGNPP variation. Introducing SIWSI significantly increased the explained variance of observed AGNPP (r² = 0.89). MODIS-derived percentage tree cover was tested as a predictor based on the hypothesis that tree cover provides information on differences in respiratory costs between trees and grasses thereby accounting for variations in the LUE conversion efficiency ε. No significant reduction in residual unexplained AGNPP variance was found. Earth observation based derivation of PAR and canopy water stress from SIWSI suggest potential improvements to primary production models in semi-arid biomes that can be implemented in general NPP modelling LUE methodology.     

Monitoring yield and fruit quality parameters in open-canopy tree crops under water stress. Implications for ASTER

Work on water stress detection at tree and orchard levels using a high-spatial airborne thermal sensor is presented, showing its connection with yield and some fruit quality indicators in olive and peach commercial orchards under different irrigation regimes. Two airborne campaigns were conducted with the Airborne Hyperspectral Scanner (AHS) over olive and peach orchards located in Córdoba, southern Spain. The AHS sensor was flown at three different times on 25 July 2004 and 16 July 2005, collecting 2 m spatial resolution imagery in 80 spectral bands in the 0.43-12.5 μm spectral range. Thermal bands were assessed for the retrieval of land surface temperature using the split-window algorithm and TES (Temperature-Emissivity-Separation) method, separating pure crowns from shadows and sunlit soil pixels using the reflectance bands. Stem water potential and stomatal conductance were measured on selected trees at the time of airborne flights over the orchards. Tree fruit yield and quality parameters such as oil, weight and water content (for the olive trees), and fruit volume and weight (for the peach trees) were obtained at harvest and through laboratory analysis. Relationships between airborne-estimated crown temperature minus air temperature and stem water potential yielded r² = 0.5 (12:30 GMT) at the olive tree level, and r² = 0.81 (9:00 GMT) at the treatment level in peach trees. These results demonstrate that water stress can be detected at the crown level even under the usual water management conditions of commercial orchards. Regressions of yield and fruit quality against remote sensing estimates of crown temperature as an indicator of water stress, yielded r² = 0.95 (olive fruit water content) and r² = 0.94 (peach fruit mean diameter). These results suggest that high-spatial remote sensing thermal imagery has potential as an indicator of some fruit quality parameters for crop field segmentation and irrigation management purposes. A simulation study using ASTER spectral bands and aggregated pixels for stress detection as a function of irrigation level was conducted in order to study the applicability of medium resolution thermal sensors for the global monitoring of open-canopy tree crops. The determination coefficients obtained between the ASTER-simulated canopy temperature minus air temperature and stem water potential yielded r² = 0.58 (12:30 GMT) for olive trees, suggesting the potential for extrapolating these methods to ASTER satellite for global monitoring of open tree canopies.     

A pseudo-waveform technique to assess forest structure using discrete lidar data

The use of airborne laser scanning systems (lidar) to describe forest structure has increased dramatically since height profiling experiments nearly 30 years ago. The analyses in most studies employ a suite of frequency-based metrics calculated from the lidar height data, which are systematically eliminated from a full model using stepwise multiple linear regression. The resulting models often include highly correlated predictors with little physical justification for model formulation. We propose a method to aggregate discrete lidar height and intensity measurements into larger footprints to create “pseudo-waves”. Specifically, the returns are first sorted into height bins, sliced into narrow discrete elements, and finally smoothed using a spline function. The resulting “pseudo-waves” have many of the same characteristics of traditional waveform lidar data. We compared our method to a traditional frequency-based method to estimate tree height, canopy structure, stem density, and stand biomass in coniferous and deciduous stands in northern Wisconsin (USA). We found that the pseudo-wave approach had strong correlations for nearly all tree measurements including height (cross validated adjusted R² (R²_cv) = 0.82, RMSE_cv = 2.09 m), mean stem diameter (R²_cv = 0.64, RMSE_cv = 6.15 cm), total aboveground biomass (R²_cv = 0.74, RMSE_cv = 74.03 kg ha^− 1), and canopy coverage (R²_cv = 0.79, RMSE_cv = 5%). Moreover, the type of wave (derived from height and intensity or from height alone) had little effect on model formulation and fit. When wave-based and frequency-based models were compared, fit and mean square error were comparable, leading us to conclude that the pseudo-wave approach is a viable alternative because it has 1) an increased breadth of available metrics; 2) the potential to establish new meaningful metrics that capture unique patterns within the waves; 3) the ability to explain metric selection based on the physical structure of forests; and 4) lower correlation among independent variables.     

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r²_NDVI = 0.90; r²_CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.     

Mapping tree and shrub leaf area indices in an ombrotrophic peatland through multiple endmember spectral unmixing

Leaf area index (LAI) is an important parameter used by most process-oriented ecosystem models. LAI of forest ecosystems has routinely been mapped using spectral vegetation indices (SVI) derived from remote sensing imagery. The application of SVI-based approaches to map LAI in peatlands presents a challenge, mainly due to peatlands characteristic multi-layer canopy comprising shrubs and open, discontinuous tree canopies underlain by a continuous ground cover of different moss species, which reduces the greenness contrast between the canopy and the background.Our goal is to develop a methodology to map tree and shrub LAI in peatlands and similar ecosystems based on multiple endmember spectral mixture analysis (MESMA). This new mapping method is validated using LAI field measurements from a precipitation-fed (ombrotrophic) peatland near Ottawa, Ontario, Canada. We demonstrate first that three commonly applied SVI are not suitable for tree and shrub LAI mapping in ombrotrophic peatlands. Secondly, we demonstrate for a three-endmember model the limitations of traditional linear spectral mixture analysis (SMA) due to the unique and widely varying spectral characteristics of Sphagnum mosses, which are significantly different from vascular plants. Next, by using a geometric-optical radiative transfer model, we determine the nature of the equation describing the empirical relationship between shadow fraction and tree LAI using nonlinear ordinary least square (OLS) regression. We then apply this equation to describe the empirical relationships between shadow and shrub fractions obtained from mixture decomposition with SMA and MESMA, respectively, and tree and shrub LAI, respectively. Less accurate fractions obtained from SMA result in weaker relationships between shadow fraction and tree LAI (R² = 0.61) and shrub fraction and shrub LAI (R² = 0.49) compared to the same relationships based on fractions obtained from MESMA with R² = 0.75 and R² = 0.68, respectively. Cross-validation of tree LAI (R² = 0.74; RMSE = 0.48) and shrub LAI (R² = 0.68; RMSE = 0.42) maps using fractions from MESMA shows the suitability of this approach for mapping tree and shrub LAI in ombrotrophic peatlands. The ability to account for a spectrally varying, unique Sphagnum moss ground cover during mixture decomposition and a two layer canopy is particularly important.