Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

We utilized an ecosystem process model (SIPNET, simplified photosynthesis and evapotranspiration model) to estimate carbon fluxes of gross primary productivity and total ecosystem respiration of a high-elevation coniferous forest. The data assimilation routine incorporated aggregated twice-daily measurements of the net ecosystem exchange of CO2 (NEE) and satellite-based reflectance measurements of the fraction of absorbed photosynthetically active radiation (fAPAR) on an eight-day timescale. From these data we conducted a data assimilation experiment with fifteen different combinations of available data using twice-daily NEE, aggregated annual NEE, eight-day f AP AR, and average annual fAPAR. Model parameters were conditioned on three years of NEE and fAPAR data and results were evaluated to determine the information content from the different combinations of data streams. Across the data assimilation experiments conducted, model selection metrics such as the Bayesian Information Criterion and Deviance Information Criterion obtained minimum values when assimilating average annual fAPAR and twice-daily NEE data. Application of wavelet coherence analyses showed higher correlations between measured and modeled fAPAR on longer timescales ranging from 9 to 12 months. There were strong correlations between measured and modeled NEE (R2, coefficient of determination, 0.86), but correlations between measured and modeled eight-day fAPAR were quite poor (R2 = −0.94). We conclude that this inability to determine fAPAR on eight-day timescale would improve with the considerations of the radiative transfer through the plant canopy. Modeled fluxes when assimilating average annual fAPAR and annual NEE were comparable to corresponding results when assimilating twice-daily NEE, albeit at a greater uncertainty. Our results support the conclusion that for this coniferous forest twice-daily NEE data are a critical measurement stream for the data assimilation. The results from this modeling exercise indicate that for this coniferous forest, average annuals for satellite-based fAPAR measurements paired with annual NEE estimates may provide spatial detail to components of ecosystem carbon fluxes in proximity of eddy covariance towers. Inclusion of other independent data streams in the assimilation will also reduce uncertainty on modeled values.

Modelling spectral reflectance of open cork oak woodland: a simulation analysis of the effects of vegetation structure and background

This study analyses the influence of vegetation structure (i.e. leaf area index and canopy cover) and seasonal background changes on moderate-resolution imaging spectrometer (MODIS)-simulated reflectance data in open woodland. Approximately monthly spectral reflectance and transmittance field measurements (May 2011 to October 2013) of cork oak tree leaves (Quercus suber) and of the herbaceous understorey were recorded in the region of Ribatejo, Portugal. The geometric-optical and radiative transfer (GORT) model was used to simulate MODIS response (red, near-infrared) and to calculate vegetation indices, investigating their response to changes in the structure of the overstorey vegetation and to seasonal changes in the understorey using scenarios corresponding to contrasting phenological status (dry season vs. wet season). The performance of normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and enhanced vegetation index (EVI) is discussed. Results showed that SAVI and EVI were very sensitive to the emergence of background vegetation in the wet season compared to NDVI and that shading effects lead to an opposing trend in the vegetation indices. The information provided by this research can be useful to improve our understanding of the temporal dynamic of vegetation, monitored by vegetation indices.

Signal classification of submerged aquatic vegetation based on the hemispherical-conical reflectance factor spectrum shape in the yellow and red regions

The water column overlying the submerged aquatic vegetation (SAV) canopy presents difficulties when using remote sensing images for mapping such vegetation. Inherent and apparent water optical properties and its optically active components, which are commonly present in natural waters, in addition to the water column height over the canopy, and plant characteristics are some of the factors that affect the signal from SAV mainly due to its strong energy absorption in the near-infrared. By considering these interferences, a hypothesis was developed that the vegetation signal is better conserved and less absorbed by the water column in certain intervals of the visible region of the spectrum; as a consequence, it is possible to distinguish the SAV signal. To distinguish the signal from SAV, two types of classification approaches were selected. Both of these methods consider the hemispherical-conical reflectance factor (HCRF) spectrum shape, although one type was supervised and the other one was not. The first method adopts cluster analysis and uses the parameters of the band (absorption, asymmetry, height and width) obtained by continuum removal as the input of the classification. The spectral angle mapper (SAM) was adopted as the supervised classification approach. Both approaches tested different wavelength intervals in the visible and near-infrared spectra. It was demonstrated that the 585 to 685-nm interval, corresponding to the green, yellow and red wavelength bands, offered the best results in both classification approaches. However, SAM classification showed better results relative to cluster analysis and correctly separated all spectral curves with or without SAV. Based on this research, it can be concluded that it is possible to discriminate areas with and without SAV using remote sensing. © 2013 by the authors; licensee MDPI, Basel, Switzerland.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest

Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change

3D modelling of mango fruit skin blush in the tree canopy

The fruit of certain mango cultivars (e.g., 'Honey Gold') can develop blush on their skin. Skin blush due to red pigmentation is from the accumulation of anthocyanins. Anthocyanin biosynthesis is related to environmental determinants, including light received by the fruit. It has been observed that mango skin blush varies with position in the tree canopy. However, little investigation into this spatial relationship has been conducted. The objective of this preliminary study was to describe a 'Honey Gold' mango tree by capturing its three-dimensional (3D) architecture. A light path tracing model QuasiMC was then used to predict light received by fruit. The use of this 3D model was to better understand the relationship between mango fruit skin blush and fruit position in the canopy. The digitised mango tree mimicked the real tree at a high level of detail. Observations on mango skin blush distribution supported the proposition that sunlight exposure is an absolute requirement for anthocyanin development. No blush development occurred on shaded skin. It was affirmed that 3D mapping could allow for virtual experiments. For example, for virtual canopy thinning (e.g., 'window pruning') to admit more sunlight with a view to improve fruit blush. Improvements to 3D modelling of mango skin blush could focus on increasing accuracy, e.g., measurement of leaf light reflectance and transmission and the inclusion of the effect shading by branches.

Visual Navigation In The Neotropical Ant Odontomachus Hastatus (formicidae, Ponerinae), A Predominantly Nocturnal, Canopy-dwelling Predator Of The Atlantic Rainforest.

The arboreal ant Odontomachus hastatus nests among roots of epiphytic bromeliads in the sandy forest at Cardoso Island (Brazil). Crepuscular and nocturnal foragers travel up to 8m to search for arthropod prey in the canopy, where silhouettes of leaves and branches potentially provide directional information. We investigated the relevance of visual cues (canopy, horizon patterns) during navigation in O. hastatus. Laboratory experiments using a captive ant colony and a round foraging arena revealed that an artificial canopy pattern above the ants and horizon visual marks are effective orientation cues for homing O. hastatus. On the other hand, foragers that were only given a tridimensional landmark (cylinder) or chemical marks were unable to home correctly. Navigation by visual cues in O. hastatus is in accordance with other diurnal arboreal ants. Nocturnal luminosity (moon, stars) is apparently sufficient to produce contrasting silhouettes from the canopy and surrounding vegetation, thus providing orientation cues. Contrary to the plain floor of the round arena, chemical cues may be important for marking bifurcated arboreal routes. This experimental demonstration of the use of visual cues by a predominantly nocturnal arboreal ant provides important information for comparative studies on the evolution of spatial orientation behavior in ants. This article is part of a Special Issue entitled: Neotropical Behaviour.

Comparing Near-infrared Conventional Diffuse Reflectance Spectroscopy And Hyperspectral Imaging For Determination Of The Bulk Properties Of Solid Samples By Multivariate Regression: Determination Of Mooney Viscosity And Plasticity Indices Of Natural Rubber.

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Estimativa de parâmetros biofísicos de plantios de café a partir de imagens orbitais de alta resolução espacial

Remote sensing data are each time more available and can be used to monitor the vegetal development of main agricultural crops, such as the Arabic coffee in Brazil, since that the relationship between spectral and agronomical data be well known. Therefore, this work had the main objective to assess the use of Quickbird satellite images to estimate biophysical parameters of coffee crop. Test area was composed by 25 coffee fields located between the cities of Ribeirão Corrente, Franca and Cristais Paulista (SP), Brazil, and the biophysical parameters used were row and between plants spacing, plant height, LAI, canopy diameter, percentage of vegetation cover, roughness and biomass. Spectral data were the reflectance of four bands of QUICKBIRD and values of four vegetations indexes (NDVI, GVI, SAVI and RVI) based on the same satellite. All these data were analyzed using linear and nonlinear regression methods to generate estimation models of biophysical parameters. The use of regression models based on nonlinear equations was more appropriate to estimate parameters such as the LAI and the percentage of biomass, important to indicate the productivity of coffee crop.

Fluorescence and reflectance spectroscopy for identification of atherosclerosis in human carotid arteries using principal components analysis

Objectives: The aim of this work was to verify the differentiation between normal and pathological human carotid artery tissues by using fluorescence and reflectance spectroscopy in the 400- to 700-nm range and the spectral characterization by means of principal components analysis. Background Data: Atherosclerosis is the most common and serious pathology of the cardiovascular system. Principal components represent the main spectral characteristics that occur within the spectral data and could be used for tissue classification. Materials and Methods: Sixty postmortem carotid artery fragments (26 non-atherosclerotic and 34 atherosclerotic with non-calcified plaques) were studied. The excitation radiation consisted of a 488-nm argon laser. Two 600-mu m core optical fibers were used, one for excitation and one to collect the fluorescence radiation from the samples. The reflectance system was composed of a halogen lamp coupled to an excitation fiber positioned in one of the ports of an integrating sphere that delivered 5 mW to the sample. The photo-reflectance signal was coupled to a 1/4-m spectrograph via an optical fiber. Euclidean distance was then used to classify each principal component score into one of two classes, normal and atherosclerotic tissue, for both fluorescence and reflectance. Results: The principal components analysis allowed classification of the samples with 81% sensitivity and 88% specificity for fluorescence, and 81% sensitivity and 91% specificity for reflectance. Conclusions: Our results showed that principal components analysis could be applied to differentiate between normal and atherosclerotic tissue with high sensitivity and specificity.

Rare canopy species in communities within the Atlantic Coastal Forest in Rio de Janeiro State, Brazil

Rare species are one of the principal components of the species richness and diversity encountered in Dense Ombrophilous Tropical Forests. This study sought to analyze the rare canopy species within the Atlantic Coastal Forest in Rio de Janeiro State, Brazil. Six different communities were examined: Dense Ombrophilous alluvial Forest; Dense sub-montane Ombrophilous Forest; Dense Montane Ombrophilous in Serra do Mar and Serra da Mantiqueira. In each area the vegetation was sampled within forty 10 x 25 m plots alternately distributed along a linear transect. All trees with DBH (1.3 m above ground level) a parts per thousand yen5 cm were sampled. The canopy was characterized using the allometric relationship between diameter and height, and included all trees with BDH a parts per thousand yen10 cm and height a parts per thousand yen10 m. A total of 64 families, 206 genera, and 542 species were sampled, of which 297 (54.8%) represented rare species (less than one individual per hectare). The percentage of rare species varied from 34 to 50% in each of the different communities sampled. A majority of these rare trees belonged to the Rosidae, and a smaller proportion to the Dilleniidae. It was concluded that there was no apparent pattern to rarity among families, that rarity was probably derived from a number of processes (such as gap formation), and that a great majority of the rare species sampled were consistently rare. This indicates that the restricted geographic distribution and high degree of endemism of many arboreal taxa justifies the conservation of even small fragments of Atlantic Forest.

Repeated disturbances and canopy disturbance regime in a tropical semi-deciduous forest

The canopy disturbance regime and the influence of gap methods on the interpretation of forest structure and dynamics were evaluated in a tropical semi-deciduous forest in south-eastern Brazil. We encountered a gap density of 11.2 gaps ha(-1) and an average size which varied from 121 to 333 m(2) depending on the gap delimitation method considered (minimum gap size was 10 m(2)). Although average size was slightly higher, the median value obtained (78 m(2)) was comparable to other tropical forest sites and the gap size-class distribution found supported the pattern described for such forest sites. Among 297 gap makers, snapping and uprooting were the most common modes of disturbance. The number and basal area of gap makers were good predictors of gap size. Almost 25% of all gaps suffered from repeated disturbance events that brought about larger gap sizes. Such processes, along with delimitation methods, strongly influenced the estimation of turnover rate and therefore the interpretation of forest dynamics. These results demonstrated the importance of further studies on repeated disturbances, which is often neglected in forest studies.

Soil density evaluated by spectral reflectance as an evidence of compaction effects

Soil compaction, reflected by high bulk density, is an environmental degradation process and new technologies are being developed for its detection. Despite the proven efficiency of remote sensing, it has not been widely used for soil density. Our objective was to evaluate the density of two soils: a Typic Quartzpisament (TQ) and a Rhodic Paleudalf (RP), using spectral reflectance obtained by a laboratory spectroradiometer between 450 and 2500 nm. Undisturbed samples were taken at two depths (0-20 and 60-80 cm), and were artificially compacted. Spectral data, obtained before and after compaction, were compared for both wet and dried compacted samples. Results demonstrated that soil density was greater in RP than in TQ at both depths due to its clayey texture. Spectral data detected high density (compacted) from low density (non-compacted) clayey soils under both wet and dry conditions. The detection of density in sandy soils by spectral reflectance was not possible. The intensity of spectral reflectance of high soil bulk density (compacted) samples was higher than for low density (non-compacted) soils due to changes in soil structure and porosity. Dry samples with high bulk density showed differences in the spectral intensity, but not in the absorption features. Wet samples in equal condition had statistically higher reflectance intensity than that of the low soil bulk density (non-compacted), and absorption differences at 1920 nm, which was due to the altered position of the water molecules. Soil line and spectral reflectance used together could detect soil bulk density variations for the clay soil. This technique could assist in the detection of high soil density in the laboratory by providing new soil information.

MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass

The use of remote sensing is necessary for monitoring forest carbon stocks at large scales. Optical remote sensing, although not the most suitable technique for the direct estimation of stand biomass, offers the advantage of providing large temporal and spatial datasets. In particular, information on canopy structure is encompassed in stand reflectance time series. This study focused on the example of Eucalyptus forest plantations, which have recently attracted much attention as a result of their high expansion rate in many tropical countries. Stand scale time-series of Normalized Difference Vegetation Index (NDVI) were obtained from MODIS satellite data after a procedure involving un-mixing and interpolation, on about 15,000 ha of plantations in southern Brazil. The comparison of the planting date of the current rotation (and therefore the age of the stands) estimated from these time series with real values provided by the company showed that the root mean square error was 35.5 days. Age alone explained more than 82% of stand wood volume variability and 87% of stand dominant height variability. Age variables were combined with other variables derived from the NDVI time series and simple bioclimatic data by means of linear (Stepwise) or nonlinear (Random Forest) regressions. The nonlinear regressions gave r-square values of 0.90 for volume and 0.92 for dominant height, and an accuracy of about 25 m(3)/ha for volume (15% of the volume average value) and about 1.6 m for dominant height (8% of the height average value). The improvement including NDVI and bioclimatic data comes from the fact that the cumulative NDVI since planting date integrates the interannual variability of leaf area index (LAI), light interception by the foliage and growth due for example to variations of seasonal water stress. The accuracy of biomass and height predictions was strongly improved by using the NDVI integrated over the two first years after planting, which are critical for stand establishment. These results open perspectives for cost-effective monitoring of biomass at large scales in intensively-managed plantation forests. (C) 2011 Elsevier Inc. All rights reserved.