Canopy Structural Properties Research Articles

Forest canopies as nature‐based solutions to mitigate global change effects on people and nature

Abstract Via sheltering, decoupling and buffering mechanisms, tree canopies have the capacity to mitigate impacts of multiple global‐change drivers on below‐canopy processes and organisms in forests. As a result, canopies have an important potential as nature‐based solution. The optimal combinations of forest canopy structural attributes to jointly mitigate the impacts of multiple global‐change drivers on below‐canopy organisms and processes have received little attention to date. To help solving this research gap, here we review how forest canopies modulate the effects of four important global‐change drivers—climate warming, drought, air pollution and biological invasions—on below‐canopy conditions. Particular attention is paid to mitigating canopy attributes that can be influenced by forest management, including canopy cover, tree species composition and vertical and horizontal structure. Synthesis. We show that the potential of forest canopies to mitigate global‐change effects is highly context‐dependent and that optimal canopy‐based solutions strongly depend on the environmental context and the targeted subcanopy organisms. Hence, holistic approaches, which maximize synergies and minimize trade‐offs, are needed to optimize the solution potential of forest canopies.

Surface Energy Dynamics and Canopy Structural Properties in Intact and Disturbed Forests in the Southern Amazon

AbstractThe Brazilian Amazon has been a focus of land development with large swaths of forests converted to agriculture. Forest degradation by selective logging and fires has accompanied the agricultural frontier and has resulted in significant impacts on Amazonian ecosystems. Changes in forest structure resulting from forest disturbances have large impacts on the surface energy balance, including on land surface temperature (LST) and evapotranspiration (ET). This study's objective is to assess the effects of forest disturbances on water fluxes and forest structure in a transitional forest site in the Southern Amazon. We used ET and LST products from MODIS and Landsat 8 and GEDI‐derived forest structure data to address our research questions. We found that disturbances induced seasonal water stress, more pronounced in croplands/pastures than in forests (differences up to 20% in the dry season), and more pronounced in second‐growth and recently burned areas than in logged and intact forests (differences up to 12% in the dry season). Moreover, ET and LST were negatively related, with more consistent relationships across disturbance classes in the dry season (R2: 0.41–0.87) than in the wet season (R2: 0.18–0.49). Forest and cropland or pasture classes showed contrasting relationships in the dry season. Finally, we found that forest structure exhibited stronger relationships with ET and LST in the most disturbed forests (R2: 0.01–0.43) than in the least disturbed forests (R2 < 0.05). Our findings help to elucidate degraded forests functioning under a changing climate and to improve estimates of water and energy fluxes in Amazonian degraded forests.

Estimation of Potato Chlorophyll Content from UAV Multispectral Images with Stacking Ensemble Algorithm

Rapid and accurate crop chlorophyll content estimation is crucial for guiding field management and improving crop yields. This study explored the potential for potato chlorophyll content estimation based on unmanned aerial vehicle (UAV) multispectral imagery. To search the optimal estimation method, three parts of research were conducted as following. First, a combination of support vector machines (SVM) and a gaussian mixture model (GMM) thresholding method was proposed to estimate fractional vegetation cover (FVC) during the potato growing period, and the proposed method produced efficient estimates of FVC; among all the selected vegetation indices (VIs), the soil adjusted vegetation index (SAVI) had the highest accuracy. Second, the recursive feature elimination (RFE) algorithm was utilized to screen the VIs and texture features derived from multispectral images: three Vis, including modified simple ratio (MSR), ratio vegetation index (RVI) and normalized difference vegetation index (NDVI); three texture features, including correlation in the NIR band (corr-NIR), correlation in the red-edge band (corr-Red-edge) and homogeneity in the NIR band (hom-NIR), showed higher contribution to chlorophyll content estimation. Finally, a stacking model was constructed with K-Nearest Neighbor (KNN), a light gradient boosting machine (light-GBM), SVM algorithm as the base model and linear fitting as the metamodel, and four machine learning algorithms (SVM, KNN, light-GBM and stacking) were used to build the chlorophyll content estimation model suitable for different growing seasons. The results were: (1) The performance of the estimation model could be improved based on both VIs and texture features over using single-type features, and the stacking algorithm yielded the highest estimation accuracy with an R2 value of 0.694 and an RMSE value of 0.553; (2) When FVC was added, the estimation model accuracy was further improved, and the stacking algorithm also produced the highest estimation accuracy with R2 value of 0.739, RMSE value of 0.511 (3) When comparing modeling algorithms, stacking algorithms had greater advantages in the estimation chlorophyll content with potato plants than using single machine learning algorithms. This study indicates that taking into account the combination of VIs reflecting spectral characteristics, texture features reflecting spatial information and the FVC reflecting canopy structure properties can accomplish higher chlorophyll content estimation accuracy, and the stacking algorithm can integrate the advantages of a single machine learning model, with great potential for estimation of potato chlorophyll content.

Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data

The Tree-based Pipeline Optimization Tool (TPOT) is a state-of-the-art automated machine learning (AutoML) approach that automatically generates and optimizes tree-based pipelines using a genetic algorithm. Although it has been proven to outperform commonly used machine techniques, its capability to handle high-dimensional datasets has not been investigated. In vegetation mapping and analysis, multi-date images are generally high-dimensional datasets that contain embedded information, such as phenological and canopy structural properties, known to enhance mapping accuracy. However, without the implementation of a robust classification algorithm or a feature selection tool, the large sets and the presence of redundant variables in multi-date images can impede accurate and efficient landscape classification. Hence, this study sought to test the efficacy of the TPOT on a multi-date Sentinel-2 image to optimize the classification accuracies of a landscape infested by a noxious invasive plant species, the parthenium weed (Parthenium hysterophorus). Specifically, the models created from the multi-date image, using the TPOT and an algorithm system that combines feature selection and the TPOT, dubbed “ReliefF-Svmb-EXT-TPOT”, were compared. The results showed that the TPOT could perform well on data with large feature sets, but at a computational cost. The overall accuracies were 91.9% and 92.6% using the TPOT and ReliefF-Svmb-EXT-TPOT models, respectively. The study findings are crucial for automated and accurate mapping of parthenium weed using high-dimensional geospatial datasets with limited human intervention.

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Detecting successional changes in tropical forest structure using GatorEye drone‐borne lidar

AbstractDrone‐based remote sensing is a promising new technology that combines the benefits of ground‐based and satellite‐derived forest monitoring by collecting fine‐scale data over relatively large areas in a cost‐effective manner. Here, we explore the potential of the GatorEye drone‐lidar system to monitor tropical forest succession by canopy structural attributes including canopy height, spatial heterogeneity, gap fraction, leaf area density (LAD) vertical distribution, canopy Shannon index (an index of LAD), leaf area index (LAI), and understory LAI. We focus on these variables’ relationship to aboveground biomass (AGB) stocks and species diversity. In the Caribbean lowlands of northeastern Costa Rica, we analyze nine tropical forests stands (seven second‐growth and two old‐growth). Stands were relatively homogenous in terms of canopy height and spatial heterogeneity, but not in their gap fraction. Neither species density nor tree community Shannon diversity index was significantly correlated with the canopy Shannon index. Canopy height, LAI, and AGB did not show a clear pattern as a function of forest age. However, gap fraction and spatial heterogeneity increased with forest age, whereas understory LAI decreased with forest age. Canopy height was strongly correlated with AGB. The heterogeneous mosaic created by successional forest patches across human‐managed tropical landscapes can now be better characterized. Drone‐lidar systems offer the opportunity to improve assessment of forest recovery and develop general mechanistic carbon sequestration models that can be rapidly deployed to specific sites, an essential step for monitoring progress within the UN Decade on Ecosystem Restoration.

A study on the drivers of canopy reflectance variability in a boreal forest

ABSTRACTThe degree of which the observable canopy bidirectional reflectance factors (BRF) express plant trait variation at leaf and canopy scales is the fundamental physical basis underlying the use of optical remote sensing data for discriminating tree species and estimating forest biophysical variables. In this study, we quantified the relative contribution of variations in leaf optical properties (LOP), canopy structural properties, and understory reflectance, to canopy BRF variability in a boreal forest, at the spatial and spectral resolutions of Sentinel-2 (S2) Multi-Spectral Instrument. Our approach was based on physically-based forest reflectance model and global sensitivity analysis (SA) parameterized entirely with field measurements. Results showed LOP had dominant contribution to canopy BRF in shortwave infrared (SWIR) in multispecies forest areas, while canopy gap fraction in sensor’s view direction (i.e. nadir) was consistently found as the main driver of canopy BRF in red. This implies the satellite-measured BRF in red is the most robust predictor of effective canopy cover (ECC), while BRF in SWIR are optimal for tree species classification based on interspecific differences in mean leaf traits.

Forest Canopy Structural Complexity and Light Absorption Relationships at the Subcontinental Scale

AbstractUnderstanding how the physical structure of forest canopies influences light absorption is a long‐standing area of inquiry fundamental to the physical sciences, including the modeling and interpretation of biogeochemical cycles. Conventional measures of forest canopy structure used to infer canopy light absorption are often limited to leaf or vegetation area indexes. However, LiDAR‐derived measures of canopy structural complexity (CSC) that describe the arrangement of vegetation may improve prediction of canopy light absorption by providing novel information on canopy‐light interactions not regulated by leaf area alone. We measured multiple indexes of CSC, vegetation area index (VAI), and the fraction of photosynthetically active radiation absorbed (fPAR) across the eastern United States using portable canopy LiDAR to evaluate how different canopy structural attributes relate to fPAR. Our survey included sites from the National Ecological Observation Network and university field stations. Measures of CSC were more strongly coupled with fPAR under high light (>1,000 μmol m−2 s−1PAR). Under low light conditions, when diffuse light predominates, light scattering weakens the dependency of fPAR on CSC. A multivariate model including CSC parameters and VAI explains ~89% of the intersite variance in fPAR, an improvement of over a VAI only linear model (r2 = 0.73). The inclusion of CSC metrics in canopy light absorption models could increase confidence in predictions of biogeochemical cycles and energy balance.

Vegetation Indices for Mapping Canopy Foliar Nitrogen in a Mixed Temperate Forest

Hyperspectral remote sensing serves as an effective tool for estimating foliar nitrogen using a variety of techniques. Vegetation indices (VIs) are a simple means of retrieving foliar nitrogen. Despite their popularity, few studies have been conducted to examine the utility of VIs for mapping canopy foliar nitrogen in a mixed forest context. In this study, we assessed the performance of 32 vegetation indices derived from HySpex airborne hyperspectral images for estimating canopy mass-based foliar nitrogen concentration (%N) in the Bavarian Forest National Park. The partial least squares regression (PLSR) was performed for comparison. These vegetation indices were classified into three categories that are mostly correlated to nitrogen, chlorophyll, and structural properties such as leaf area index (LAI). %N was destructively measured in 26 broadleaf, needle leaf, and mixed stand plots to represent the different species and canopy structure. The canopy foliar %N is defined as the plot-level mean foliar %N of all species weighted by species canopy foliar mass fraction. Our results showed that the variance of canopy foliar %N is mainly explained by functional type and species composition. The normalized difference nitrogen index (NDNI) produced the most accurate estimation of %N (R2CV = 0.79, RMSECV = 0.26). A comparable estimation of %N was obtained by the chlorophyll index Boochs2 (R2CV = 0.76, RMSECV = 0.27). In addition, the mean NIR reflectance (800–850 nm), representing canopy structural properties, also achieved a good accuracy in %N estimation (R2CV = 0.73, RMSECV = 0.30). The PLSR model provided a less accurate estimation of %N (R2CV = 0.69, RMSECV = 0.32). We argue that the good performance of all three categories of vegetation indices in %N estimation can be attributed to the synergy among plant traits (i.e., canopy structure, leaf chemical and optical properties) while these traits may converge across plant species for evolutionary reasons. Our findings demonstrated the feasibility of using hyperspectral vegetation indices to estimate %N in a mixed temperate forest which may relate to the effect of the physical basis of nitrogen absorption features on canopy reflectance, or the biological links between nitrogen, chlorophyll, and canopy structure.

Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different “canopy structural types” by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment.

The effect of clumping on canopy scattering and its directional properties: a model simulation using spectral invariants

The spectral invariants theory predicts that the bidirectional reflectance factor (BRF) of a vegetation canopy can be expressed in terms of the canopy interceptance (i0), the recollision probability (p), and the directional escape probability (ρ). These spectral invariant parameters together form a novel canopy structural parameter – the directional area scattering factor (DASF). The DASF can be retrieved from remotely sensed hyperspectral imagery and has been found to be useful, e.g. for the separation of tree species. The spectral invariants theory, however, does not provide an interpretation of which specific canopy structural properties are captured by the DASF. In this study, we examined the possible link between the DASF and the canopy clumping index (β). A simple model was designed to simulate the effect of β on canopy first order scattering, which was assumed to govern the directional behaviour of the DASF. The model is based on a modified spectral invariants approach, where the assumption of constant p is relaxed so that the first order recollision probability (p1) and single scattering are calculated separately, and canopy BRF is expressed as the sum of the first and multiple order components. Simulations were performed on model canopies, where radiation penetration is described using a traditional statistical approach but allowing non-random foliage distributions caused by clumping. The results indicated a strong dependency between the modelled DASF and the canopy clumping index.

Polar grid fraction as an estimator of montane tropical forest canopy structure using airborne lidar

The structure of a forest canopy is the key determinant of light transmission, use and understory availability. Airborne light detection and ranging (LiDAR) has been used successfully to measure multiple canopy structural properties, thereby greatly reducing the fieldwork required to map spatial variation in structure. However, lidar metrics to date do not reflect the full extent of the three-dimensional information available from the data. To this end, we developed a new metric, the polar grid fraction (GRID), based on gridding lidar returns in polar coordinates, in order to more closely match measurements provided by field instruments on leaf area index (LAI), gap fraction (GF) and percentage photosynthetically active radiation transmittance (tPAR). The metric summarizes the arrangement of lidar point returns for a single ground location rather than to an area surrounding the location.Compared with more traditional proportion-based and height percentile-based estimators, the GRID estimator increased validation R2 by 14.5% for GF and 6.0% for tPAR over the next best estimator. LAI was still best estimated with the more traditional statistic based on the proportion of ground returns in 14 m × 14 m moving kernels. By applying the models to a 2 × 2 m grid across the lidar coverage area, extreme values occurred in the estimations of all three response variables when using proportion-based and height percentile-based estimators. However, no extreme values were estimated by models using the GRID estimator, indicating that models based on GRID may be less influenced by spurious data. These results suggest that the GRID estimator is a strong candidate for any project requiring estimates of canopy metrics for large areas.

Modeling acoustic diversity using soundscape recordings and LIDAR-derived metrics of vertical forest structure in a neotropical rainforest

We determined the relationship between acoustic diversity and metrics of vertical forest structure derived from light detection and ranging (LIDAR) data in a neotropical rainforest in Costa Rica. We then used the LIDAR-derived metrics to predict acoustic diversity across the forest landscape. Sound recordings were obtained from 14 sites for six consecutive days during dusk chorus (6 pm). Acoustic diversity was calculated for each day as the total intensity across acoustic frequency bands using the Shannon index and then averaged over the 6 days at each site. A 10 m radius around each site was used to obtain several LIDAR- derived metrics describing the vertical structural attri- butes of the forest canopy. Multiple linear regression (MLR) with Akaike information criterion was used to determine a top-ranked model with acoustic diversity as the dependent variable and the LIDAR metrics as independent variables. Acoustic diversity was modeled for forested areas (where canopy height was (20 m) at 20 m resolution using coefficients obtained from the MLR, and a hotspot analysis was conducted on the resulting layer. Acoustic diversity was strongly corre- lated (R 2 = 0.75) with the LIDAR metrics suggesting that LIDAR-derived metrics can be used to determine canopy structural attributes important to vocal fauna species. The hotspot analysis revealed that the spatial distribution of these canopy structural attributes across the La Selva forest is not random. Our approach can be used to identify forest patches of potentially high acoustic diversity for conservation or management purposes.

IDENTIFYING SPECTRA IMPORTANT FOR PREDICTION OF SENESCENT GRASSLAND CANOPY STRUCTURE

Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of vegetation properties to ensure conservation of grassland structure for wildlife and forage for livestock. Spectral imaging data may be useful in assessment of large (>100,000 ha) landscapes, as in the Grand River National Grassland (GRNG), South Dakota. Here, we examined the predictive potential for the Advanced High Resolution Spectrometer (AVIRIS) to estimate mixed-grass prairie canopy structural attributes (photosynthetically active vegetation (kg PV ha-1), non-photosynthetically active vegetation (kg NPV ha-1), total standing crop (kg PV+NPV ha-1), nitrogen content (kg N ha-1), and visual estimates of bare ground (%) in October 2010. We conducted the study on a 36,000-ha herbaceous area using 24 randomly selected plots divided into summit, midslope and toeslope positions. Field data were collected during the AVIRIS flyover, and three approaches for building a prediction model of canopy attributes based on spectra were evaluated based on R2 values. These approaches included Partial Least Squares Regression (PLS), a variable selection method with predictor variables based on functions of the AVIRIS spectra, and a variable selection method using individual bands or combinations of individual bands of spectra as predictors. All variable selection methods involved randomly partitioning the data into training and validations sets and choosing a final prediction model based on model selection frequency. PLS regression out-performed regression models (based on the variable selection methods) with R2 values of 0.73, 0.56, 0.62, 0.67, and 0.58, for PV, NPV, total standing crop, nitrogen content, and bare ground, respectively.

Satellite lidar vs. small footprint airborne lidar: Comparing the accuracy of aboveground biomass estimates and forest structure metrics at footprint level

The use of lidar remote sensing for mapping the spatial distribution of canopy characteristics has the potential to allow an accurate and efficient estimation of tree dimensions and canopy structural properties from local to regional and continental scales. The overall goal of this paper was to compare biomass estimates and height metrics obtained by processing GLAS waveform data and spatially coincident discrete-return airborne lidar data over forest conditions in east Texas. Since biomass estimates are derived from waveform height metrics, we also compared ground elevation measurements and canopy parameters. More specific objectives were to compare the following parameters derived from GLAS and airborne lidar: (1) ground elevations; (2) maximum canopy height; (3) average canopy height; (4) percentiles of canopy height; and (5) above ground biomass. We used the elliptical shape of GLAS footprints to extract canopy height metrics and biomass estimates derived from airborne lidar. Results indicated a very strong correlation for terrain elevations between GLAS and airborne lidar, with an r value of 0.98 and a root mean square error of 0.78 m. GLAS height variables were able to explain 80% of the variance associated with the reference biomass derived from airborne lidar, with an RMSE of 37.7 Mg/ha. Most of the models comparing GLAS and airborne lidar height metrics had R-square values above 0.9.

Predicting the dry deposition of aerosol‐sized particles using layer‐resolved canopy and pipe flow analogy models: Role of turbophoresis

A number of synthesis activities, mathematical modeling, and experiments on dry deposition of aerosol‐sized particles over forested surfaces point to three disjointed findings: (1) deposition velocities measured over tall forests do not support a clearly defined minimum for particle sizes in the range of 0.1–2 μm; (2) when measurements of the normalized deposition velocity (Vd+) are presented as a function of the normalized particle timescale (τp+), where the normalizing variables are the friction velocity and air viscosity, a power law scaling in the form of Vd+ ∼ (τp+)2 emerges in the so‐called inertial‐impaction regime for many laboratory and crop experiments, but none of the forest measurements fall on this apparent scaling law; and (3) two recent models with entirely different assumptions about the representation of the particle deposition process reproduce common data sets for forests. We show that turbophoresis, when accounted for at the leaf scale in vertically resolved or multilayer models (MLMs), provides a coherent explanation for the first two findings and sheds light on the third. The MLM resolves the canopy vertical structure and its effects on both the flow statistics and the leaf particle collection mechanisms. The proposed MLM predictions agree with a recent two‐level particle‐resolving data set collected over 1 year duration for a Scots pine stand in Hyytiälä (southern Finland). Such an approach can readily proportion the particle deposition onto foliage and forest floor and can take advantage of recent advances in measurements of canopy structural properties derived from remote sensing platforms.

Predicting leaf area index in wheat using angular vegetation indices derived from in situ canopy measurements

Canopy structure significantly affects ecosystem function by influencing light attenuation, and thus there is a great need to characterize changes in canopy structural attributes such as leaf area index (LAI). This study presents an evaluation of a set of anisotropic vegetation indices (VIs) used in the estimation of LAI in the growth cycle of wheat. An analytical two-layer canopy reflectance model (ACRM) was used to simulate a range of bidirectional reflectance with different LAI variations and view zenith angles (VZAs). A number of indices, including the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the hot spot - dark spot index (HDS), the hot spot – dark spot NDVI (NDVIHD), and a new proposed hot spot – dark spot difference index (HDDI), were selected and compared for linearity with increasing LAI in the sensitivity study. Simulation results indicated that EVI was a better candidate than NDVI for LAI estimation in linearity. HDS560 and HDS670 showed nonmonotonical patterns with increasing LAI, and better resistance to saturation limits was observed for both HDS750 and HDS870. Indices of NDVIHD (NDVIHD870 and NDVIHD750) were found to have low sensitivity at high LAI values, with a clear saturation for LAI values above 3. For the two new angular indices, HDDI705 was found to have a relatively linear relationship in an LAI range of 1–5. HDDI750 showed the best linear relationship with LAI, and no saturation was observed for LAI values in the range 1–8. A validation study was conducted for the growth cycle of two types of wheat. NDVI and EVI showed reasonable potential in the estimation of LAI, with determination coefficients R2 of 0.68 and 0.78, respectively, and corresponding root mean square errors (RMSE in LAI units) of 0.79 and 0.66. Band selection was demonstrated to have significant effects on HDS indices. HDS560 showed a very low correlation with LAI (R2 = 0.40, RMSE = 0.85), and no correlation was found between HDS670 and LAI. However, both HDS750 and HDS870 can provide moderate estimates of LAI, with R2 values of 0.76 and 0.73, respectively, and corresponding RMSE values of 0.61 and 0.65. For the NDVIHD indices, low determination coefficients R2 of 0.51 and 0.47 were obtained (RMSE = 0.81 and 0.88). For the two new angular indices, significant correlations (R2 = 0.84 and 0.85, respectively, and RMSE = 0.48 and 0.45) were observed for HDDI705 and HDDI750. These preliminary results provide certain insights for the development of future multi-angle remote sensing models for LAI estimation with satellite observations in other ecosystems.

Energy balance and canopy conductance of a tropical semi‐deciduous forest of the southern Amazon Basin

Deforestation and climate change have the capacity to alter rainfall regimes, water availability, and surface‐atmosphere flux of water and energy of tropical forests, especially in ecotonal, semi‐deciduous tropical forests of the southern Amazon Basin, which have experienced rapid regional warming and deforestation over the last three decades. To reduce uncertainty regarding current and future energy and water flux, micrometeorological measurements of latent (Qe) and sensible heat flux (Qh) and canopy conductance (Gc) were combined with measurements of sap flux density (Fd) and maximum leaf conductance (gsmax) to characterize the seasonal controls on mass (H2O) and energy exchange of an ecotonal, semi‐deciduous forest in northern Mato Grosso, Brazil over the 2005‐2006 annual cycle. Average diel patterns and daily rates of energy flux and conductance declined during the dry season; however, the decline in Fd and Qe was smaller and/or more gradual than Gc and gsmax. Weekly averages of transpiration calculated from sap flow measurements during the dry‐wet season transition period were positively correlated (r2 = 0.47; p < 0.05; n = 11) with estimates of leaf area index (LAI) derived from the Modis‐Aqua satellite platform while estimates of evapotranspiration ET derived from eddy covariance were not, presumably because these estimates also include an evaporation component. Overall, our results suggest that access to deep water reserves can support high rates of Fd and Qe during the dry season, but because of high evaporative demand, declines in plant water potential lead to a corresponding decline in Gc. Furthermore, seasonal variations in LAI, that are likely to be controlled in part by plant water status and phenology, constrain tree and stand transpiration. Thus the consistency of Qe over the annual cycle appears to be the result of trade‐offs between water availability (rainfall, soil moisture, water potential), canopy structural properties (LAI), and meteorological conditions including vapor pressure deficit and net radiation.

EFFECTS OF METEOROLOGICAL VARIATIONS ON THE CO2 EXCHANGE OF A BRAZILIAN TRANSITIONAL TROPICAL FOREST

The net ecosystem CO2 exchange (NEE) of a 28–30 m tall transitional (ecotonal) tropical forest of the Brazilian Amazon was quantified using tower‐based eddy covariance. Measurements were made between August 1999 and July 2001 and were used to develop nonlinear statistical models to assess daily variations in ecophysiological parameters and provide annual estimates of NEE, gross ecosystem CO2 exchange (GEE), and respiration (Re). Diurnal trends in NEE were correlated with variations in photosynthetic photon flux density (Q), vapor pressure deficit (V), and temperature. Seasonal trends in the CO2‐flux components estimated from nonlinear regression (Amax and R0) were highly correlated with soil water availability and canopy structural properties (LAI and litter production). These results suggest that variations in soil water content can affect rates of canopy photosynthesis and whole forest respiration by altering both physiological processes and canopy structural properties. Estimates of the annual NEE suggest that the forest was in balance with respect to CO2 during the study period, which, in terms of rainfall, was a relatively typical period compared to the 30‐yr average rainfall regime. Our results also suggest that the warmer and dryer microclimate and decline in LAI that accompany land‐cover change will cause transitional forests to be sources of CO2 to the atmosphere.

Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification

The aim of this work was to investigate different approaches for the estimation of canopy structure properties from multiangular measurements at the field scale. Hyperspectral multiangular data were acquired on potato canopies using a spectroradiometer (GER-1500) and corresponding multiangular images using the VIFIS (Variable Interference Filter Imaging Spectrometer). The data obtained using the spectroradiometer were employed in the inversion of the PROSAIL model. The images obtained from the VIFIS were classified into the component image fractions: shaded and sunlit leaves and soil. These classification results were then used directly in the inversion of a simple ray-tracing canopy model. The inversion technique was based on a look-up table approach using a simple ray-tracing model of a plant canopy. Field sampling was carried out for the direct measurement of leaf area index (LAI) and other canopy properties. The experimental error in the data of both sensors was large since the canopy appeared non-homogeneous at the measurement height used, mainly because of the crop row structure. However LAI values retrieved from both approaches were realistic and allowed the discrimination of potato canopies that had received different nitrogen fertilization treatments. The relative merits and practicalities of the two approaches (multiangular hyperspectral reflectance versus image classification) are discussed.