Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Terrestrial net primary production (NPP) is sensitive to a number of controls, including aspects of climate, topography, soils, plant and microbial characteristics, disturbance, and anthropogenic impacts. Yet, at least at the global scale, models based on very different types and numbers of parameters yield similar results. Part of the reason for this is that the major NPP controls influence each other, resulting, under current conditions, in broad correlations among controls. NPP models that include richer suites of controlling parameters should be more sensitive to conditions that disrupt the broad correlations, but the current paucity of global data limits the power of complex models. Improved data sets will facilitate applications of complex models, but many of the critical data are very difficult to produce, especially for applications dealing with the past or future. It may be possible to overcome some of the challenges of data availability through increased understanding and modeling of ecological processes that adjust plant physiology and architecture in relation to resources. The CASA (Carnegie, Stanford, Ames Approach) model introduced by Potter et al. (1993) and expanded here uses a combination of ecological principles, satellite data, and surface data to predict terrestrial NPP on a monthly time step. CASA calculates NPP as a product of absorbed photosynthetically active radiation, APAR, and an efficiency of radiation use, ϵ. The underlying postulate is that some of the limitations on NPP appear in each. CASA estimates annual terrestrial NPP to be 48 Pg and the maximum efficiency of PAR utilization (ϵ∗) to be 0.39 g C MJ−1 PAR. Spatial and temporal variation in APAR is more than fivefold greater than variation in ϵ. <s> BIB001 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> An algorithm based on the physics of radiative transfer in vegetation canopies for the retrieval of vegetation green leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FPAR) from surface reflectances was developed and implemented for operational processing prior to the launch of the moderate resolution imaging spectroradiometer (MODIS) aboard the TERRA platform in December of 1999. The performance of the algorithm has been extensively tested in prototyping activities prior to operational production. Considerable attention was paid to characterizing the quality of the product and this information is available to the users as quality assessment (QA) accompanying the product. The MODIS LAI/FPAR product has been operationally produced from day one of science data processing from MODIS and is available free of charge to the users from the Earth Resources Observation System (EROS) Data Center Distributed Active Archive Center. Current and planned validation activities are aimed at evaluating the product at several field sites representative of the six structural biomes. Example results illustrating the physics and performance of the algorithm are presented together with initial QA and validation results. Potential users of the product are advised of the provisional nature of the product in view of changes to calibration, geolocation, cloud screening, atmospheric correction and ongoing validation activities. D 2002 Published by Elsevier Science Inc. <s> BIB002 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Until recently, continuous monitoring of global vegetation productivity has not been possible because of technological limitations. This article introduces a new satellite-driven monitor of the global biosphere that regularly computes daily gross primary production (GPP) and annual net primary production (NPP) at 1-kilometer (km) resolution over 109,782,756 km 2 of vegetated land surface. We summarize the history of global NPP science, as well as the derivation of this calculation, and current data production activity. The first data on NPP from the EOS (Earth Observing System) MODIS (Moderate Resolution Imaging Spectroradiometer) sensor are presented with different types of validation. We offer examples of how this new type of data set can serve ecological science, land management, and environmental policy. To enhance the use of these data by nonspecialists, we are now producing monthly anomaly maps for GPP and annual NPP that compare the current value with an 18-year average value for each pixel, clearly identifying regions where vegetation growth is higher or lower than normal. <s> BIB003 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> [1] This paper compares by land cover type seasonal and spatial variations of MODIS leaf area index (LAI) and fraction of photosynthetically active radiation (0.4-0.7 μm) absorbed by vegetation (FPAR) from 2.5 years with those from the Common Land Model (CLM) and investigates possible reasons for notable differences. The FPAR value is mainly determined by LAI in MODIS and both LAI and stem area index (SAI) in CLM. On average, the model underestimates FPAR in the Southern Hemisphere and overestimates FPAR over most areas in the Northern Hemisphere compared to MODIS observations during all seasons except northern middle latitude summer. Such overestimation is most significant in winter over northern high latitudes. The MODIS LAI is generally consistent with the model during the snow-free periods but may be underestimated in the presence of snow, especially for evergreen trees. The positive FPAR bias is mainly attributed to CLM SAI of deciduous canopy and higher LAI than MODIS for evergreen canopy as well. The negative FPAR bias results from several factors, including differences in LAI and soil albedo between CLM and MODIS or limitations of the geometric optics scheme used in the model. Therefore the MODIS algorithm needs to better represent the winter LAI retrievals, while the model needs to better quantify LAI and SAI. Since stems will not have the same single-scattering albedo as green leaves, it may be inappropriate for the model to treat LAI and SAI the same in the FPAR and albedo parameterizations. If so, the role of SAI in these parameterizations needs reformulation. <s> BIB004 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> [1] The fraction of absorbed photosynthetically active radiation (FAPAR) is a vegetation biophysical variable that characterizes energy, mass, and momentum exchanges and is used extensively in models that represent the transfer of energy, carbon, water, and the biogeochemistry of terrestrial ecosystems. This paper compares three estimates of FAPAR by an Amazonian tropical rain forest. In the Tapajos National Forest, near Santarem, state of Para, Brazil, FAPAR is estimated based on field measurements, modeling (using IBIS which simulates the fluxes in the soil-vegetation-atmosphere system considering two vegetation layers) and remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) monthly FAPAR product, which has not been validated for a tropical rain forest yet. FAPAR based on field observations is calculated from incoming and reflected PAR measurements taken above the canopy, and downward PAR at a 15 m height, corrected to be representative of the entire canopy, obtaining an annual mean value of 0.91. FAPAR simulated by the Integrated Biosphere Simulator (IBIS) is 0.76, while the annual average FAPAR estimated by MODIS is 0.85. If we consider that MODIS estimates include only PAR absorbed by leaves, the remote sensing estimates are very close to field measurements corrected to include only the absorption by leaves (0.87); hence we conclude that the MODIS FAPAR product for the tropical rain forest is reliable to be used in future studies. However, model estimates of FAPAR for the tropical forest are low, and adjustments on the algorithm used to calculate the absorbed radiation by the canopy are necessary. <s> BIB005 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Exchange of carbon between forests and the atmosphere is a vital component of the global carbon cycle. Satellite laser altimetry has a unique capability for estimating forest canopy height, which has a direct and increasingly well understood relationship to aboveground carbon storage. While the Geoscience Laser Altimeter System (GLAS) onboard the Ice, Cloud and land Elevation Satellite (ICESat) has collected an unparalleled dataset of lidar waveforms over terrestrial targets, processing of ICESat data to estimate forest height is complicated by the pulse broadening associated with large-footprint, waveform-sampling lidar. We combined ICESat waveforms and ancillary topography from the Shuttle Radar Topography Mission to estimate maximum forest height in three ecosystems; tropical broadleaf forests in Brazil, temperate broadleaf forests in Tennessee, and temperate needleleaf forests in Oregon. Final models for each site explained between 59% and 68% of variance in field-measured forest canopy height (RMSE between 4.85 and 12.66 m). In addition, ICESat-derived heights for the Brazilian plots were correlated with field-estimates of aboveground biomass (r(2) = 73%, RMSE = 58.3 Mgha(-1)). <s> BIB006 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Estimates of daily gross primary production (GPP) and annual net primary production (NPP) at the 1 km spatial resolution are now produced operationally for the global terrestrial surface using imagery from the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor. Ecosystem-level measurements of GPP at eddy covariance flux towers and plot-level measurements of NPP over the surrounding landscape offer opportunities for validating the MODIS NPP and GPP products, but these flux measurements must be scaled over areas on the order of 25 km 2 to make effective comparisons to the MODIS products. Here, we report results for such comparisons at 9 sites varying widely in biome type and land use. The sites included arctic tundra, boreal forest, temperate hardwood forest, temperate conifer forest, tropical rain forest, tallgrass prairie, desert grassland, and cropland. The ground-based NPP and GPP surfaces were generated by application of the Biome-BGC carbon cycle process model in a spatially-distributed mode. Model inputs of land cover and leaf area index were derived from Landsat data. The MODIS NPP and GPP products showed no overall bias. They tended to be overestimates at low productivity sites — often because of artificially high values of MODIS FPAR (fraction of photosynthetically active radiation absorbed by the canopy), a critical input to the MODIS GPP algorithm. In contrast, the MODIS products tended to be underestimates in high productivity sites — often a function of relatively low values for vegetation light use efficiency in the MODIS GPP algorithm. A global network of sites where both NPP and GPP are measured and scaled over the local landscape is needed to more comprehensively validate the MODIS NPP and GPP products and to potentially calibrate the MODIS NPP/GPP algorithm parameters. <s> BIB007 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> [1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies. <s> BIB008 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Remotely sensed data have become the primary source for biomass estimation. A summary of previous research on remote sensing‐based biomass estimation approaches and a discussion of existing issues influencing biomass estimation are valuable for further improving biomass estimation performance. The literature review has demonstrated that biomass estimation remains a challenging task, especially in those study areas with complex forest stand structures and environmental conditions. Either optical sensor data or radar data are more suitable for forest sites with relatively simple forest stand structure than the sites with complex biophysical environments. A combination of spectral responses and image textures improves biomass estimation performance. More research is needed to focus on the integration of optical and radar data, the use of multi‐source data, and the selection of suitable variables and algorithms for biomass estimation at different scales. Understanding and identifying major uncertainties cause... <s> BIB009 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Plants are finely tuned to the seasonality of their environment, and shifts in the timing of plant activity (i.e. phenology) provide some of the most compelling evidence that species and ecosystems are being influenced by global environmental change. Researchers across disciplines have observed shifting phenology at multiple scales, including earlier spring flowering in individual plants and an earlier spring green-up' of the land surface revealed in satellite images. Experimental and modeling approaches have sought to identify the mechanisms causing these shifts, as well as to make predictions regarding the consequences. Here, we discuss recent advances in several fields that have enabled scaling between species responses to recent climatic changes and shifts in ecosystem productivity, with implications for global carbon cycling. <s> BIB010 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> • Canopy chemistry and spectroscopy offer insight into community assembly and ecosystem processes in high-diversity tropical forests, but phylogenetic and environmental factors controlling chemical traits underpinning spectral signatures remain poorly understood. • We measured 21 leaf chemical traits and spectroscopic signatures of 594 canopy individuals on high-fertility Inceptisols and low-fertility Ultisols in a lowland Amazonian forest. The spectranomics approach, which explicitly connects phylogenetic, chemical and spectral patterns in tropical canopies, provided the basis for analysis. • Intracrown and intraspecific variation in chemical traits varied from 1.4 to 36.7% (median 9.3%), depending upon the chemical constituent. Principal components analysis showed that 14 orthogonal combinations were required to explain 95% of the variation among 21 traits, indicating the high dimensionality of canopy chemical signatures among taxa. Inceptisols and lianas were associated with high leaf nutrient concentrations and low concentrations of defense compounds. Independent of soils or plant habit, an average 70% (maximum 89%) of chemical trait variation was explained by taxonomy. At least 10 traits were quantitatively linked to remotely sensed signatures, which provided highly accurate species classification. • The results suggest that taxa found on fertile soils carry chemical portfolios with a deep evolutionary history, whereas taxa found on low-fertility soils have undergone trait evolution at the species level. Spectranomics provides a new connection between remote sensing and community assembly theory in high-diversity tropical canopies. <s> BIB011 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Changes in vegetative growing seasons are dominant indicators of the dynamic response of ecosystems to climate change. Therefore, knowledge of growing seasons over the past decades is essential to predict ecosystem changes. In this study, the long-term changes in the growing seasons of temperate vegetation over the Northern Hemisphere were examined by analyzing satellite-measured normalized difference vegetation index and reanalysis temperature during 1982-2008. Results showed that the length of the growing season (LOS) increased over the analysis period; however, the role of changes at the start of the growing season (SOS) and at the end of the growing season (EOS) differed depending on the time period. On a hemispheric scale, SOS advanced by 5.2 days in the early period (1982-1999) but advanced by only 0.2 days in the later period (2000-2008). EOS was delayed by 4.3 days in the early period, and it was further delayed by another 2.3 days in the later period. The difference between SOS and EOS in the later period was due to less warming during the preseason (January-April) before SOS compared with the magnitude of warming in the preseason (June-September) before EOS. At a regional scale, delayed EOS in later periods was shown. In North America, EOS was delayed by 8.1 days in the early period and delayed by another 1.3 days in the later period. In Europe, the delayed EOS by 8.2 days was more significant than the advanced SOS by 3.2 days in the later period. However, in East Asia, the overall increase in LOS during the early period was weakened in the later period. Admitting regional heterogeneity, changes in hemispheric features suggest that the longer-lasting vegetation growth in recent decades can be attributed to extended leaf senescence in autumn rather than earlier spring leaf-out. <s> BIB012 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> One of the major uncertainties in predicting climate change comes from a full accounting of carbon-cycle feedbacks, which roughly double physical feedbacks (1, 2). Most of this uncertainty is a result of the many pathways and time scales at which ecosystems interact with the climate system and how these will respond to change (3). The relationship between leaf nitrogen and the carbon cycle is key to many ecosystem processes because photosynthesis provides the energy and carbon-cycle molecules for growth and reproduction (4⇓⇓–7) and decomposition for nutrient cycling (7, 8). Ecologists have long recognized that nitrogen was the most limited nutrient for plant growth (9, 10). Quantifying changes in canopy nitrogen content provides direct information about ecosystem functioning and a method to detect and monitor changes in response to climate forcing (9, 10); thus, it has been a long-term objective for airborne and spaceborne imaging spectroscopy (11⇓–13). Several papers have reported direct detection of canopy nitrogen from airborne imaging spectrometers (14⇓⇓–17). Ollinger (18) argues that selective pressure on plant competition for light, water, and nutrients should result in suites of biochemical and structural traits that integrate their functional strategies. Thus, structural traits affecting light scattering “over scales ranging from cells to canopies” (18) will be convergent with their biochemical traits. Knyazikhin et al. … <s> BIB013 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> As the world enters the Anthropocene – a new geologic period, defined by humanity's massive impact on the planet – the Earth's rapidly changing environment is putting critical ecosystem services at risk. To understand and forecast how ecosystems will change over the coming decades, scientists will require an understanding of the sensitivity of species to environmental change. The current distribution of species and functional groups provides valuable information about the performance of various species in different environments. However, when the rate of environmental change is high, information inherent in the ranges of many species will disappear, since that information exists only under more or less steady-state conditions. The amount of information about species' relationships to climate declines as their distributions move farther from steady state. New remote-sensing technologies can map the chemical and structural traits of plant canopies and will allow for the inference of traits and, in many cases, species' ranges. Current satellite remote-sensing data can only produce relatively simple classifications, but new techniques will produce data with dramatically higher biological information content. <s> BIB014 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> • Context ::: In the framework of climate change, decreased tree growth and enhanced mortality induced by hot and dry conditions are increasing in many forests around the world, and particularly in Mediterranean forests. <s> BIB015 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> The goal of this study is to identify the current state of in situ observations and remote sensing data and methods used to assess biomass and net primary production (NPP) in West Siberian natural ecosystems, and consider perspectives for future developments. The natural ecosystems of the boreal region mainly consist of two classes: wetlands and forests, where one is very different from the other, requiring different methods for biomass assessment. Basically, two methods are available to estimate NPP and biomass in regional terrestrial ecosystems: (1) extrapolating the local field measurements up to a larger region, using the vegetation or land cover maps and (2) modeling productivity and plant biomass at regional and grid point scales, with or without the use of remote sensing data and techniques. The first method was predominantly used to estimate wetland biomass, having an extensive dataset of direct in situ measurements in both the above- and below-ground fractions of biomass. So far, no direct methods based on remote sensing data have been elaborated for biomass estimations in wetland ecosystems and soil carbon inventories. In forest ecosystems, the biomass can be estimated by processing satellite data from high-resolution radiometers (AVHRRs). The radar or LIDAR remote sensing approaches hold great promise for direct observations of the three-dimensional structure (3D) of the above-ground vegetation that can be used for relatively straight-forward calculations of carbon storage, but the method works only in low to medium biomass ecosystems. The SAR-based biomass retrievals were found to be fairly uncertain in mature forests with high biomass values, as the Synthetic Aperture Radar (SAR) signal often saturates at ~70 tonnes/ha. The estimation errors in terms of RMSE are typically found at 25–30 % of the mean biomass. The methods should be further refined to reduce uncertainties and to make them operational over the vast region of Siberia. <s> BIB016 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Our research group has recently proposed a strategy to simulate net forest carbon fluxes based on the coupling of a NDVI-driven parametric model, Modified C-Fix, and of a biogeochemical model, BIOME-BGC. The outputs of the two models are combined through the use of a proxy of ecosystem distance from equilibrium condition which accounts for the occurred disturbances. This modeling strategy is currently applied to all Italian forest areas using an available set of NDVI images and ancillary data descriptive of an 8-year period (1999–2006). The obtained estimates of forest net primary production (NPP) are first analyzed in order to assess the importance of the main model drivers on relevant spatial variability. This analysis indicates that growing stock is the most influential model driver, followed by forest type and meteorological variables. In particular, the positive influence of growing stock on NPP can be constrained by thermal and water limitations, which are most evident in the upper mountain and most southern zones, respectively. Next, the NPP estimates, aggregated over seven main forest types and twenty administrative regions in Italy, are converted into current annual increment of standing volume (CAI) by specific coefficients. The accuracy of these CAI estimates is finally assessed by comparison with the ground data collected during a recent national forest inventory. The results obtained indicate that the modeling approach tends to overestimate the ground CAI for most forest types. In particular, the overestimation is notable for forest types which are mostly managed as coppice, while it is negligible for high forests. The possible origins of these phenomena are investigated by examining the main model drivers together with the results of previous studies and of older forest inventories. The implications of using different NPP estimation methods are finally discussed in view of assessing the forest carbon budget on a national basis. <s> BIB017 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract The Suomi NPP VIIRS Vegetation Index (VI) Environment Data Record (EDR) includes the Top Of the Atmosphere Normalized Difference Vegetation Index (TOA NDVI) and the Top Of the Canopy Enhanced Vegetation Index (TOC EVI). TOC NDVI will be included in the VI EDR suite for the upcoming JPSS-1 and JPSS-2 missions. Currently, the VIIRS VI suite is undergoing extensive Calibration and Validation (Cal/Val) activities to quantify product performance and to provide guidance for algorithm improvement. In this study we utilized AErosol RObotic NETwork (AERONET) based Surface Reflectance (SR) match-up data sets. Match-ups are pairs of VIIRS SR and SR derived from atmospheric correction of VIIRS TOA reflectances using AERONET ground measurements (aerosol and water vapor parameters) and ancillary data. Atmospheric correction is performed with the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer code, the same code used in the operational VIIRS atmospheric correction. Daily time series January 1, 2013–March 31, 2014 of 101 × 101 pixel window match-up subsets at 375 m VIIRS Imagery resolution, centered at the selected AERONET site locations were used. The overall objective of the study was to characterize the performance of the VIIRS TOC VIs (TOC EVI and TOC NDVI) over AERONET sites as a function of accuracy of inputs of atmospheric correction algorithm (aerosol, water vapor and others) and quality control screening (cloud and snow masks). We performed three types of analyses: (1) analysis of overall performance of VI product over all sites, (2) analysis of time series over select sites representative of vegetation types, and (3) sensitivity analysis of VI to cloud, aerosols and snow contamination. Over the period of time series average Accuracy, Precision and Uncertainty statistics were 0.009, 0.035, and 0.038, respectively, for TOC NDVI and − 0.004, 0.015, and 0.016, respectively, for TOC EVI. Those statistics were derived based on screening to retain only confidently clear, snow free, non-urban pixels. The reason for the substantial difference in the performance of VIs is a different sensitivity of VIs to residual atmospheric contamination. According to our study VIIRS cloud mask performed reasonably. However, Aerosol Optical Thickness (AOT) was substantially overestimated in the vicinity of clouds, which resulted in overcorrection of VIIRS visible channels and ultimately overestimation of TOC NDVI. TOC EVI, on the other hand, remained largely unaffected, as an atmospherically resistant index. TOC EVI was also found to be less sensitive to residual snow contamination. Results of this study indicate the need to develop VI-specific quality control, which effectively screens data based on index sensitivity to atmospheric contamination. Monitoring VIIRS VIs over AERONET sites has been automated with a web-based STAR JPSS VI Monitor. <s> BIB018 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Vegetation productivity metrics such as gross primary production (GPP) at the canopy scale are greatly affected by the efficiency of using absorbed radiation for photosynthesis, or light use efficiency (LUE). Thus, close investigation of the relationships between canopy GPP and photosynthetically active radiation absorbed by vegetation is the basis for quantification of LUE. We used multiyear observations over irrigated and rainfed contrasting C3 (soybean) and C4 (maize) crops having different physiology, leaf structure, and canopy architecture to establish the relationships between canopy GPP and radiation absorbed by vegetation and quantify LUE. Although multiple LUE definitions are reported in the literature, we used a definition of efficiency of light use by photosynthetically active "green" vegetation (LUE(green)) based on radiation absorbed by "green" photosynthetically active vegetation on a daily basis. We quantified, irreversible slowly changing seasonal (constitutive) and rapidly day-to-day changing (facultative) LUE(green), as well as sensitivity of LUE(green) to the magnitude of incident radiation and drought events. Large (2-3-fold) variation of daily LUE(green) over the course of a growing season that is governed by crop physiological and phenological status was observed. The day-to-day variations of LUE(green) oscillated with magnitude 10-15% around the seasonal LUE(green) trend and appeared to be closely related to day-to-day variations of magnitude and composition of incident radiation. Our results show the high variability of LUE(green) between C3 and C4 crop species (1.43 g C/MJ vs. 2.24 g C/MJ, respectively), as well as within single crop species (i.e., maize or soybean). This implies that assuming LUE(green) as a constant value in GPP models is not warranted for the crops studied, and brings unpredictable uncertainties of remote GPP estimation, which should be accounted for in LUE models. The uncertainty of GPP estimation due to facultative and constitutive changes in LUE(green) can be considered as a critical component of the total error budget in the context of remotely sensed based estimations of GPP. The quantitative framework of LUE(green) estimation presented here offers a way of characterizing LUE(green) in plants that can be used to assess their phenological and physiological status and vulnerability to drought under current and future climatic conditions and is essential for calibration and validation of globally applied LUE algorithms. <s> BIB019 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input parameter in many climate and ecological models. The accuracy of satellite FAPAR products directly influences estimates of ecosystem productivity and carbon stocks. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. It is important to evaluate satellite FAPAR products and understand differences between the products to effectively use them in carbon cycling models. In this study, five global FAPAR products, namely MODIS, MISR, MERIS, SeaWiFS, and GEOV1 are intercompared over different land covers and directly validated with ground-based measurements at VAlidation of Land European Remote sensing Instruments (VALERI) and AmeriFlux sites. Intercomparison results show that MODIS, MISR, and GEOV1 agree well with each other and so do MERIS and SeaWiFS, but the difference between these two groups can be as large as 0.1. The temporal trends of these products agree better with each other in the Northern Hemisphere than in the Southern Hemisphere. The trends in the Northern Hemisphere are similar to those globally. However, the conclusions from the northern hemispheric scale could not be extended to the global scale for land covers such as savannahs and broadleaf evergreen forests. The differences between the products are consistent throughout the year over most of the land cover types, except over the forests, because of the different assumptions in the retrieval algorithms and the differences between green and total FAPAR products over forests. Direct validation results show that MERIS, MODIS, MISR, and GEOV1 FAPAR products have an uncertainty of 0.14 when validating with total FAPAR measurements, and 0.09 when validating with green FAPAR measurements. Overall, current FAPAR products are close to, but have not fulfilled, the accuracy requirement, and further improvements are still needed. <s> BIB020 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Terrestrial ecosystem and carbon cycle feedbacks will significantly impact future climate, but their responses are highly uncertain. Models and tipping point analyses suggest the tropics and arctic/boreal zone carbon-climate feedbacks could be disproportionately large. In situ observations in those regions are sparse, resulting in high uncertainties in carbon fluxes and fluxes. Key parameters controlling ecosystem carbon responses, such as plant traits, are also sparsely observed in the tropics, with the most diverse biome on the planet treated as a single type in models. We analyzed the spatial distribution of in situ data for carbon fluxes, stocks and plant traits globally and also evaluated the potential of remote sensing to observe these quantities. New satellite data products go beyond indices of greenness and can address spatial sampling gaps for specific ecosystem properties and parameters. Because environmental conditions and access limit in situ observations in tropical and arctic/boreal environments, use of space-based techniques can reduce sampling bias and uncertainty about tipping point feedbacks to climate. To reliably detect change and develop the understanding of ecosystems needed for prediction, significantly, more data are required in critical regions. This need can best be met with a strategic combination of remote and in situ data, with satellite observations providing the dense sampling in space and time required to characterize the heterogeneity of ecosystem structure and function. <s> BIB021 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Biomass and net primary productivity (NPP) are important factors for studying terrestrial carbon storage and the carbon cycle. Using data from existing literature, this study synthesized and analyzed a comprehensive database of direct field observations of forest biomass and NPP for Southwestern China. The biomass of mature natural forests and mature planted forests range from 81.2 Mg·ha−1 to 692.6 Mg·ha−1 (mean = 288.1 Mg·ha−1) and from 76.8 Mg·ha−1 to 670.1 Mg·ha−1 (mean = 181.5 Mg·ha−1), respectively. Mature natural forests have higher biomass than mature planted ones. The NPP values of natural and planted forests range from 1.4 Mg·ha−1·year−1 to 29.6 Mg·ha−1·year−1 (mean = 13.6 Mg·ha−1·year−1) and from 0.6 Mg·ha−1·year−1 to 26.5 Mg·ha−1·year−1 (mean = 9.9 Mg·ha−1·year−1), respectively. Correlations among biomass, NPP, and environmental factors show that NPP significantly decreases with latitude and increases with mean annual temperature, mean annual precipitation, growing degree-days on a 0 °C base, and mean annual drought index, whereas biomass positively correlates with stand age and leaf area index strongly. Karst forests exhibit almost the same NPP as non-karst forests, but the former have significantly lower biomass compared to the latter. Comprehensive regional data synthesis and analysis based on direct field observations of forest biomass and NPP are important for benchmarking global and regional vegetation and carbon models, estimating regional carbon content, restoring vegetation, and mitigating climate change. <s> BIB022 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract It is of great significance to analyze the long time-series spatiotemporal dynamics of water use efficiency (WUE) to formulating appropriate management measures in response to the growing water scarcity in arid and semi-arid regions. This study analyzed the long time-series variations of WUE in the Lower Heihe River Basin, a typical arid and semi-arid region in China. The net primary productivity (NPP) was first estimated with the C-fix model, then WUE during 2001–2010 was calculated with the NPP and evapotranspiration (ET) data, and the accumulative WUE was further calculated. The results showed that the annual NPP and WUE in the study area ranged from zero to 448.70 gC/(m 2 a) and from zero to 2.20 gC kg −1 H 2 O, respectively, both of which showed an overall increasing trend during 2001–2010. Besides, the spatial pattern of WUE kept overall unchanged during 2001–2010, but with remarkable change in some part of the study area. In addition, the accumulative WUE of the whole study area showed a first sharply decreasing and then gradually increasing trend, but there was still some scope to improve the WUE, and it is necessary to carry out some more specific policies to further improve the water allocation and WUE within the Lower Heihe River Basin. Although with some uncertainties, these results still can provide valuable reference information for improving the water resource management and ecological conservation to guarantee provision of essential ecosystem services in arid and semi-arid regions. <s> BIB023 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO. <s> BIB024 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> We present the first comparison between new fAPAR and LAI products derived from the GlobAlbedo dataset and the widely-used MODIS fAPAR and LAI products. The GlobAlbedo-derived products are produced using a 1D two-stream radiative transfer (RT) scheme designed explicitly for global parameter retrieval from albedo, with consistency between RT model assumptions and observations, as well as with typical large-scale land surface model RT schemes. The approach does not require biome-specific structural assumptions (e.g., cover, clumping, understory), unlike more detailed 3D RT model approaches. GlobAlbedo-derived values of fAPAR and LAI are compared with MODIS values over 2002–2011 at multiple flux tower sites within selected biomes, over 1200 × 1200 km regions and globally. GlobAlbedo-derived fAPAR and LAI values are temporally more stable than the MODIS values due to the smoothness of the underlying albedo, derived via optimal estimation (assimilation) using an a priori estimate of albedo derived from an albedo “climatology” (composited multi-year albedo observations). Parameters agree closely in timing but with GlobAlbedo values consistently lower than MODIS, particularly for LAI. Larger differences occur in winter (when values are lower) and in the Southern hemisphere. Globally, we find that: GlobAlbedo-derived fAPAR is ~0.9–1.01 × MODIS fAPAR with an intercept of ~0.03; GlobAlbedo-derived LAI is ~0.6 × MODIS LAI with an intercept of ~0.2. Differences arise due to the RT model assumptions underlying the products, meaning care is required in interpreting either set of values, particularly when comparing to fine-scale ground-based estimates. We present global transformations between GlobAlbedo-derived and MODIS products. <s> BIB025 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysical variable for vegetation productivity estimation, vegetation growth condition monitoring, and climate change analysis. The object of this study is to conduct a comprehensive sensitivity analysis on the contributing factors of FPAR variation, in order to reveal the impacts of the interference factors on FPAR estimation using remote sensing and to improve the accuracy of the model. The analysis was conducted based on a coupled leaf-canopy–atmosphere radiative transfer model and a global sensitivity analysis algorithm. Due to the impact of diffuse FPAR on the variation of FPAR, we also analyzed the determinant factors of diffuse FPAR. The results showed that leaf area index (LAI) and the average leaf inclination angle (ALA) are two important canopy structural parameters determining FPAR variability, especially before canopy closure. FPAR was found to be affected differently by these two variables with canopy development. Canopy background reflectance has a great impact on FPAR estimation at low LAI values (LAI <s> BIB026 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract The fraction of absorbed photosynthetically active radiation (FAPAR) is a critical input in numerous climatological and ecological models. The targeted accuracy of FAPAR products is 10%, or 0.05, for many applications. However, most of the FAPAR products in current usage have not yet fulfilled the accuracy requirement, thus requiring further improvements. In this study, a new FAPAR estimation model is developed on the basis of the radiative transfer (RT) for a horizontally homogeneous continuous canopy. The spatially explicit parameterization of leaf-scattering and soil background reflectance is derived from a 13-year Moderate Resolution Imaging Spectroradiometer (MODIS) albedo database. The new algorithm requires the input of leaf area index (LAI), which is estimated by a hybrid geometric optical-RT model suitable for both continuous and discrete vegetation canopies in this study. The model calculated radiative surface fluxes, i.e., canopy reflectance, absorption, and transmittance, are compared with the reference data from Radiation transfer Model Intercomparison (RAMI) exercise. The evaluation results show that the model estimated FAPAR has an uncertainty of 0.08 over homogeneous and heterogeneous canopies. The FAPAR estimates from the new model are intercompared with reference satellite FAPAR products and validated with ground-based measurements at the Validation of Land European Remote Sensing Instruments (VALERI) AmeriFlux experimental sites. The validation results show that the FAPAR estimates from the new model are comparable to or slightly better in performance than the MODIS and the Multi-angle Imaging SpectroRadiometer (MISR) FAPAR products when using corresponding satellite LAI product values as the input. The FAPAR estimates are further improved when using the new LAI estimates from the hybrid model as the input. The new model adequately identifies the growing seasons and produces smooth time series curves of estimated FAPAR during a specific duration. The uncertainty is reduced to 0.1 when validating with total FAPAR measurements, and 0.08 when validating with green FAPAR measurements. The improvements are apparent in grasslands and forests with an uncertainty reduction of 0.06. The regional-scale application of the presented model generates consistent FAPAR maps at spatial resolutions of 30 m, 500 m, and 1 km from the Landsat, MODIS, and MISR data, respectively. <s> BIB027 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract Field surveys are often a primary source of data for aboveground biomass (AGB) and forest area estimates — two fundamental parameters in forest resource assessments and for measurement, reporting, and verification (MRV) under the United Nations Collaborative Program on Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD +). However, plot-based estimates of such parameters are often not sufficiently precise for their intended purposes, and especially so in developing and tropical countries in which implementation of extensive sample surveys can be cost-prohibitive or infeasible due to inaccessibility. Remotely sensed data can improve the precision of estimates and thereby reduce the need for field samples. To guide investment decision in MRV systems, comparative analyses of the contribution of different types of remotely sensed data to improve precision of estimates are required. The aim of the current study was to quantify the contribution of data from (1) airborne laser scanning (ALS), (2) interferometric synthetic aperture radar (InSAR) derived from TanDEM-X, (3) RapidEye optical imagery, and global forest map products derived from (4) Landsat and (5) ALOS PALSAR L-band radar imagery to improve precision of AGB and forest area estimates beyond the precision that could be obtained by a pure field-based survey in miombo woodlands of Tanzania. Miombo woodlands is one among the most wide-spread vegetation types in eastern, central, and southern Africa, occupying about 9% of the entire African land area. A 365.6 km 2 region in Liwale district in Tanzania served as area of interest for this study. Eighty-eight ground plots distributed on 11 clusters of eight plots each according to a probability-based single-stage cluster sampling design served as field data for regression model calibration used for mapping and estimation of AGB and forest area. Model-assisted estimators were used in the estimation. The relative efficiency (RE) of the ALS-assisted estimates of mean AGB per hectare (variance of the field-based estimate relative to the variance of the ALS-assisted estimate) was 3.6. Relative efficiency translates directly to the factor by which the sample size used for the ALS-assisted estimate would have to be multiplied to arrive at the same precision for a pure field-based estimate. RE values for InSAR and RapidEye were 2.8 and 3.3, while the global Landsat and PALSAR map products contributed only marginally to improve precision (RE = 1.3–1.4). For forest area estimation, ALS-assisted estimates showed an RE of 3.7–4.6, while InSAR, RapidEye, and global Landsat and PALSAR maps resulted in RE values of 1.0–1.3, 2.0–2.1, 1.4–1.8, and 1.7, respectively. <s> BIB028 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> This study presents a comparison and integration of three methods commonly used to estimate the amount of forest ecosystem carbon (C) available for storage. In particular, we examine the representation of living above- and below-ground biomass change (net accumulation) using plot-level biometry and repeat airborne laser scanning (ALS) of three dimensional forest plot structure. These are compared with cumulative net CO2 fluxes (net ecosystem production, NEP) from eddy covariance (EC) over a six-year period within a jack pine chronosequence of four stands (~ 94, 30, 14 and 3 years since establishment from 2005) located in central Saskatchewan, Canada. Combining the results of the two methods yield valuable observations on the partitioning of C within ecosystems. Subtracting total living biomass C accumulation from NEP results in a residual that represents change in soil and litter C storage. When plotted against time for the stands investigated, the curve produced is analogous to the soil C dynamics described in Covington (1981). Here, ALS biomass accumulation exceeds EC-based NEP measured in young stands, with the residual declining with age as stands regenerate and litter decomposition stabilizes. During the 50–70 year age-period, NEP and live biomass accumulation come into balance, with the soil and litter pools of stands 70–100 years post-disturbance becoming a net store of C. Biomass accumulation was greater in 2008–2011 compared to 2005–2008, with the smallest increase in the 94-year-old “old jack pine” stand and greatest in the 14-year-old “harvested jack pine 1994” stand, with values of 1.4 (± 3.2) tC ha− 1 and 12.0 (± 1.6) tC ha− 1, respectively. The efficiency with which CO2 was stored in accumulated biomass was lowest in the youngest and oldest stands, but peaked during rapid regeneration following harvest (14-year-old stand). The analysis highlights that the primary source of uncertainty in the data integration workflow is in the calculation of biomass expansion factors, and this aspect of the workflow needs to be implemented with caution to avoid large error propagations. We suggest that the adoption of integrated ALS, in situ and atmospheric flux monitoring frameworks is needed to improve spatio-temporal partitioning of C balance components at sub-decadal scale within rapidly changing forest ecosystems and for use in national carbon accounting programs. <s> BIB029 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Unmanned aerial vehicles (UAVs) can provide new ways to measure forests and supplement expensive or labor-intensive inventory methods. Forest carbon, a key uncertainty in the global carbon cycle and also important for carbon conservation programs, is typically monitored using manned aircraft or extensive forest plot networks to estimate aboveground carbon density (ACD). Manned aircraft are only cost-effective when applied to large areas (>100,000 ha), while plot networks are most effective for total C stock estimation across large areas, not for quantifying spatially-explicit variation. We sought to develop an effective method for frequent and accurate ACD estimation at intermediate scales (100–100,000 ha) that would be sensitive to small-scale disturbance. Using small UAVs, we collected imagery of 516 ha of lowland forest in the Peruvian Amazon. We then used a structure-from-motion (SFM) approach to create a 3D model of forest canopy. Comparing SFM- and airborne Light Detection and Ranging (LiDAR)-derived estimates of canopy height and ACD, we found that SFM estimates of top-of-canopy height (TCH) and ACD were highly correlated with previous LiDAR estimates (r = 0.86–0.93 and r = 0.73–0.94 for TCH and ACD, respectively, at 0.1–4 ha grain sizes), with r = 0.92 for ACD determination at the 1 ha scale, despite SFM and LiDAR measurements being separated by two years in a dynamic forest. SFM and LiDAR estimates of mean TCH and mean ACD were highly similar, differing by only 0.4% and 0.04%, respectively, within mature forest. The technique allows inexpensive, near-real-time monitoring of ACD for ecological studies, payment for ecosystem services (PES) ventures, such as reducing emissions from deforestation and forest degradation (REDD+), forestry enterprises, and governance. <s> BIB030 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R 2 = 0.98). At the plot-level, lidar-derived height and intensity metrics provided the most robust estimate for aboveground biomass, producing dominant species-based aboveground models with errors ranging from 4 to 14 Mg ha −1 across all woodland and forest species. Land-sat-8 imagery produced dominant species-based aboveground biomass models with errors ranging from 10 to 28 Mg ha −1 . Thus, airborne lidar allowed for estimates for fine-scale aboveground biomass mapping with low uncertainty, while Landsat-8 seems best suited for broader spatial scale products such as a national biomass essential climate variable (ECV) based on land cover types for the United States. <s> BIB031 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization. <s> BIB032 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Abstract Estimating the change in the magnitude and distribution of forest biomass is important for monitoring carbon dynamics and understanding the implications on the terrestrial carbon cycle. In this study, we assessed the capacity of multi-temporal airborne Light Detection and Ranging (LiDAR) data to estimate forest biomass dynamics at a mixed subtropical forest of southeast China, and analyzed the magnitude and spatial patterns of the above ground biomass (AGB) change over a 6-year period. To do so, we evaluated different approaches to estimate change in AGB, specifically direct (i.e., predicting biomass change using the differences in LiDAR metrics) and indirect (i.e., modeling biomass for each point in time and predicting the change as their differences) approaches. Once models were developed, the change in AGB was mapped across the study site and examined in relation to forest type and age. Our results demonstrated that the direct approach (CV- R 2 = 0.63, CV-rRMSE = 25.64%) produced more accurate estimates in change of AGB than the indirect approach (CV- R 2 = 0.59, CV-rRMSE = 28.35%). Canopy height metrics of delta mean height (∆ h mean ) and a number of upper percentile heights (i.e., ∆ h 75 and ∆ h 95 ) , calculated from the LiDAR first returns, were found to be most sensitive to changes in biomass, whereas metrics based on maximum canopy height or canopy density had less predictive power. Furthermore, differences in point cloud densities did not significantly impact the estimations of AGB and change of AGB at the plot-scale (30 × 30 m). Spatial extrapolation of the AGB change indicated that, in general, most of the forest area had an overall gain in biomass. The middle-aged stands (typically dominated by Quercus acutissima between 41 and 60 years old) show marked growth of biomass (median ∆ AGB = 2.29 Mg ha − 1 year − 1 ), followed by mature (typically dominated by Pinus massoniana between 31 and 60 years old) (median ∆ AGB = 1.72 Mg ha − 1 year − 1 ) and young stands (typically consisting of regenerated broadleaved trees younger than 40 years old) (median ∆ AGB = 1.48 Mg ha − 1 year − 1 ), whereas over-mature stands (typically dominated by Cunninghamia lanceolata older than 35 years) show the lowest growth (median ∆ AGB = 0.76 Mg ha − 1 year − 1 ). This study demonstrates benefits of using multi-temporal LiDAR data to help identify specific areas for management interventions, to enhance forest productivity, maintain biomass stocks and optimize carbon sequestration. <s> BIB033 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Ecosystem service-based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot-level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community-weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management. <s> BIB034 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> The Spectranomics approach to tropical forest remote sensing has established a link between foliar reflectance spectra and the phylogenetic composition of tropical canopy tree communities vis-a-vis the taxonomic organization of biochemical trait variation. However, a direct relationship between phylogenetic affiliation and foliar reflectance spectra of species has not been established. We sought to develop this relationship by quantifying the extent to which underlying patterns of phylogenetic structure drive interspecific variation among foliar reflectance spectra within three Neotropical canopy tree communities with varying levels of soil fertility. We interpreted the resulting spectral patterns of phylogenetic signal in the context of foliar biochemical traits that may contribute to the spectral-phylogenetic link. We utilized a multi-model ensemble to elucidate trait-spectral relationships, and quantified phylogenetic signal for spectral wavelengths and traits using Pagel’s lambda statistic. Foliar reflectance spectra showed evidence of phylogenetic influence primarily within the visible and shortwave infrared spectral regions. These regions were also selected by the multi-model ensemble as those most important to the quantitative prediction of several foliar biochemical traits. Patterns of phylogenetic organization of spectra and traits varied across sites and with soil fertility, indicative of the complex interactions between the environmental and phylogenetic controls underlying patterns of biodiversity. <s> BIB035 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Monitoring Land Surface Phenology (LSP) is important for understanding both the responses and feedbacks of ecosystems to the climate system, and for representing these accurately in terrestrial biosphere models. Moreover, by shedding light on phenological trends at a variety of scales, LSP has the potential to fill the gap between traditional phenological (field) observations and the large-scale view of global models. ::: In this study, we review and evaluate the variability and evolution of satellite-derived Growing Season Length (GSL) globally and over the past three decades. We used the longest continuous record of Normalized Difference Vegetation Index (NDVI) data available to date at global scale to derive LSP metrics consistently over all vegetated land areas and for the period 1982-2012. We tested GSL, Start- and End-Of Season metrics (SOS and EOS, respectively) for linear trends as well as for significant trend shifts over the study period. We evaluated trends using global environmental stratification information in place of commonly used land cover maps to avoid circular findings. ::: Our results confirmed an average lengthening of the growing season globally during 1982-2012 – averaging 0.22-0.34 days/year, but with spatially heterogeneous trends. 13-19% of global land areas displayed significant GSL change, and over 30% of trends occurred in the boreal/alpine biome of the Northern Hemisphere, which showed diverging GSL evolution over the past 3 decades. Within this biome, the “Cold and Mesic” environmental zone appeared as an LSP change hotspot. We also examined the relative contribution of SOS and EOS to the overall changes, finding that EOS trends were generally stronger and more prevalent than SOS trends. These findings constitute a step towards the identification of large-scale phenological drivers of vegetated land surfaces, necessary for improving phenological representation in terrestrial biosphere models. <s> BIB036 </s> Understanding Forest Health with Remote Sensing -Part I—A Review of Spectral Traits, Processes and Remote-Sensing Characteristics <s> Aggregated ST <s> Summary ::: Average responses of forest foliar traits to elevation are well understood, but far less is known about trait distributional responses to elevation at multiple ecological scales. This limits our understanding of the ecological scales at which trait variation occurs in response to environmental drivers and change. ::: We analyzed and compared multiple canopy foliar trait distributions using field sampling and airborne imaging spectroscopy along an Andes-to-Amazon elevation gradient. Field-estimated traits were generated from three community-weighting methods, and remotely sensed estimates of traits were made at three scales defined by sampling grain size and ecological extent. ::: Field and remote sensing approaches revealed increases in average leaf mass per unit area (LMA), water, nonstructural carbohydrates (NSCs) and polyphenols with increasing elevation. Foliar nutrients and photosynthetic pigments displayed little to no elevation trend. Sample weighting approaches had little impact on field-estimated trait responses to elevation. Plot representativeness of trait distributions at landscape scales decreased with increasing elevation. Remote sensing indicated elevation-dependent increases in trait variance and distributional skew. ::: Multiscale invariance of LMA, leaf water and NSC mark these traits as candidates for tracking forest responses to changing climate. Trait-based ecological studies can be greatly enhanced with multiscale studies made possible by imaging spectroscopy. <s> BIB037
Net Primary Production (NPP) FD   BIB003 BIB001 BIB007 BIB016 BIB022 BIB023 BIB017 BIB018 BIB024 Fraction of Photosynthetically Active Radiation (fPAR) FD BIB004 BIB025 BIB019 BIB020 BIB005 BIB008 BIB002 BIB026 BIB027 Biomass ST, FD BIB028 BIB029 BIB030 BIB022 BIB006  BIB015 BIB031 BIB032 BIB009 BIB033 Scaling traits TD, ST, FD BIB037 BIB034 Phylogenetic information of traits TD, ST, FD BIB035 BIB011 ST, FD BIB013   BIB021 BIB010 BIB036  BIB014 BIB012 