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

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

Estimating biomass carbon stocks for a Mediterranean forest in central Spain using LiDAR height and intensity data

Biomass fractions (total aboveground, branches and foliage) were estimated from a small footprint discrete-return LiDAR system in an unmanaged Mediterranean forest in central Spain. Several biomass estimation models based on LiDAR height, intensity or height combined with intensity data were explored. Raw intensity data were normalized to a standard range in order to remove the range dependence of the intensity signal. In general terms, intensity-based models provided more accurate predictions of the biomass fractions. Height models selected were mainly based on a percentile of the height distribution. Intensity models selected included variables that consider the percentage of the intensity accumulated at different height percentiles, which implicitly take into account the height distribution. The general models derived considering all species together were based on height combined with intensity data. These models yielded R² values greater than 0.58 for the different biomass fractions considered and RMSE values of 28.89, 18.28 and 1.51 Mg ha⁻¹ for aboveground, branch and foliage biomass, respectively. Results greatly improved for species-specific models using the main species present in each plot, with R² values greater than 0.85, 0.70 and 0.90 for black pine, Spanish juniper and Holm oak, respectively, and with lower RMSE for the biomass fractions. Reductions in LiDAR point density had only a small effect on the results obtained, except for those models based on a variation of the Canopy Reflection Sum, which was weighted by the mean point density. Based on the species-specific equations derived, Holm oak dominated plots showed the highest average carbon contained by aboveground biomass and branch biomass 44.66 and 31.42 Mg ha^− 1 respectively, while for foliage biomass carbon, Spanish juniper showed the highest average value (3.04 Mg ha^− 1).     

Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model

Estimates of mean tree size and cover for each forest stand from an invertible forest canopy reflectance model are part of a new forest vegetation mapping system. Image segmentation defines stands which are sorted into general growth forms using per-pixel image classifications. Ecological models based on terrain relations predict species associations for the conifer, hardwood, and brush growth forms. The combination of the model-based estimates of tree size and cover with species associations yields general-purpose vegetation maps useful for a variety of land management needs. Results of timber inventories in the Tahoe and Stanislaus National Forests indicate the vegetation maps form a useful basis for stratification. Patterns in timber volumes for the strata reveal that the cover estimates are more reliable than the tree size estimates. A map accuracy assessment of the Stanislaus National Forest shows high overall map accuracy and also illustrates the problems in estimating tree size.     

Leaf area and clumping indices for a boreal mixed-wood forest: lidar,hyperspectral, and Landsat models

The leaf area index (LAI) and the clumping index (CI) provide valuable insight into the spatial patterns of forest canopies, the canopy light regime and forest productivity. This study examines the spatial patterns of LAI and CI in a boreal mixed-wood forest, using extensive field measurements and remote sensing analysis. The objectives of this study are to: (1) examine the utility of airborne lidar (light detection and ranging) and hyperspectral data to model LAI and clumping indices; (2) compare these results to those found from commonly used Landsat vegetation indices (i.e. the normalized difference vegetation index (NDVI) and the simple ratio (SR)); (3) determine whether the fusion of lidar data with Landsat and/or hyperspectral data will improve the ability to model clumping and LAI; and (4) assess the relationships between clumping, LAI and canopy biochemistry.

Regression models to predict CI were much stronger than those for LAI at the site. Lidar was the single best predictor of CI (r ² > 0.8). Landsat NDVI and SR also had a moderately strong predictive performance for CI (r ² > 0.68 with simple linear and non-linear regression forms), suggesting that canopy clumping can be predicted operationally from satellite platforms, at least in boreal mixed-wood environments. Foliar biochemistry, specifically canopy chlorophyll, carotenoids, magnesium, phosphorus and nitrogen, was strongly related to the clumping index. Combined, these results suggest that Landsat models of clumping could provide insight into the spatial distribution of foliar biochemistry, and thereby photosynthetic capacity, for boreal mixed-wood canopies. LAI models were weak (r ² < 0.4) unless separate models were used for deciduous and coniferous plots. Coniferous LAI was easier to model than deciduous LAI (r ² > 0.8 for several indices). Deciduous models of LAI were weaker for all remote sensing indices (r ² < 0.67). There was a strong, linear relationship between foliar biochemistry and LAI for the deciduous plots. Overall, our results suggest that broadband satellite indices have strong predictive performance for clumping, but that airborne hyperspectral or lidar data are required to develop strong models of LAI at this boreal mixed-wood site.     

Effects of chlorophyll fluorescence on the estimation of microphytobenthos biomass using spectral reflectance indices

The communities of benthic microalgae that form dense biofilms at the surface of aquatic sediments, or microphytobenthos, are important primary producers in estuarine intertidal flats and shallow coastal waters. The microalgal biomass present in the photic zone of the sediment is a key parameter for ecological and photophysiological studies on microphytobenthos, and has been routinely estimated using hyperspectral reflectance indices based on the chlorophyll (Chl) a red absorption peak at 675 nm, usually the Normalised Difference Vegetation Index (NDVI). This study reports that red region-based biomass indices measured on microphytobenthos biofilms can be significantly affected by the enrichment of reflected light with solar-induced Chl fluorescence emitted by the microalgae. Chl fluorescence emission peaks at 683 nm, counterbalancing the decrease in reflectance centered at 675 nm, thus causing the underestimation of NDVI. The interference of Chl fluorescence was found to be easily identified by a conspicuous double-peak feature in the 670-700 nm region of the second-derivative reflectance spectra. The fluorescence-induced NDVI underestimation was shown to be most pronounced for high surface biomass levels and low incident solar irradiance. Particular aspects of microphytobenthos biofilms, such as the increase in surface Chl fluorescence due the contribution of emission by subsurface layers, and vertical migratory responses by motile microalgae to changes in ambient light, further complicate the effects on biomass estimation using NDVI-like indices. By comparing NDVI with a fluorescence-independent biomass index for a wide range of natural light conditions, it was found that Chl fluorescence interference may cause the underestimation of microalgal biomass to reach over 25%, with errors above 10% being expected for more than half of the measuring occasions. These results indicate that the use of NDVI may compromise the correct assessment of important aspects of microphytobenthos ecology, such as the characterisation of migratory behaviour or the determination of biomass-specific productivity rates, and call for the use of alternative biomass indices, not based on the Chl a red absorption peak.     

Multi-angular reflectance properties of a hemiboreal forest: An analysis using CHRIS PROBA data

Forest types differ in their hyperspectral anisotropy patterns mainly due to species-specific geometrical structure, spatial arrangement of canopies and subsequent shadow patterns. This paper examines the multi-angular, hyperspectral reflectance properties of typical hemiboreal forests during summer time using three simultaneous CHRIS PROBA (mode 3) scenes and stand inventory data from the Järvselja Training and Experimental Forestry District in southeastern Estonia. We investigated the magnitude and reasons for the differences in the anisotropy patterns of deciduous and coniferous stands at three backward viewing angles. A forest reflectance model (FRT) was used as a tool to provide a theoretical basis to the discussion, and to estimate the directional contribution of scattering from crowns and ground to total stand reflectance for the two forest types. The FRT model simulated successfully the HDRF (hemispherical–directional reflectance factor) curves of the study stands to match those obtained from the CHRIS image, yet it produced a smaller and less wavelength-dependent angular reflectance effect than was observed in the satellite image. The main results of this study provide new information for separating the spectral contribution of the forest floor (or understory layer) from the tree canopy layer: (1) the red edge domain was identified to have the largest contribution from forest understory, and (2) the more oblique the viewing angle, the smaller the contribution from the understory. In addition, coniferous stands were observed to have a specific angular effect at the red and red edge domain, possibly as a result of the hierarchical structure and arrangement of coniferous canopies.     

Assessment of grape yield and composition using the reflectance based Water Index in Mediterranean rainfed vineyards

In rainfed vineyards water deficits play a major role in determining berry yield and composition. Therefore, reliable indicators of vine water status might be of great value for the optimization of grape yield and quality. In the present study the feasibility of using hyperspectral reflectance indices related to plant biophysical properties at predicting berry yield and quality attributes in rainfed vineyards is assessed. The study was conducted on Vitis vinifera cv. Chardonnay in commercial vineyards in the D.O. Penedès region (Catalonia, Spain) over two consecutive years (2007–2008). Field measurements of fractional intercepted Photosynthetic Active Radiation (fIPAR), canopy reflectance, predawn water potential (Ψ_p) and the canopy to air temperature difference at midday (ΔT_midday) were conducted at the stage of veraison. Yield, Total Soluble Solids (TSS), Titratable Acidity (TA) and the ratio TSS/TA (maturation index, IMAD) were determined at harvest. Contrasted water availability among vineyards prompted considerable variation in berry yield and quality attributes. Across years, higher yield was accompanied by higher TA (r = 0.59, p < 0.01) and lower IMAD (r = ? 0.63, p < 0.01) while no significant relationship was observed between yield and TSS. Yield was related to canopy vigor (fIPAR) in a variable extend: in 2007, yield was positively related to fIPAR (r = 0.71, p < 0.05) while yield was found to decrease along with increasing fIPAR in 2008 (r = ? 0.62, p < 0.05). Contrastingly, NDVI provided consistent estimates of yield across years (r = 0.57, p < 0.05). These results suggest that NDVI might be more appropriate to characterize the effects of varying water availability on yield than fIPAR. In addition, yield was found to be related to ΔT_midday (r = ? 0.63 and r = ? 0.66, in 2007 and 2008, respectively). Accordingly, the Water Index (WI), an indicator of vine water status, provided robust estimates of yield across years (r = 0.61, p < 0.01). The strength of the correlation between NDVI and WI vs. yield suggests that yield was influenced by changes in both leaf area (intercepted light) and photosynthesis (stomatal aperture) in a variable extent according to the timing and severity of water deficits in the years of study. Berry quality attributes did not show significant relationships against fIPAR but were related to ΔT_midday. Accordingly, NDVI did not show significant correlation with berry quality attributes, while WI was found to be consistently related to TA (r = 0.70, p < 0.01) and IMAD (r = ? 0.71, p < 0.01) across years. The results obtained suggest that the WI might provide reliable estimates of berry quality attributes in vineyards experiencing moderate to severe water deficits with potential application in precision viticulture activities such as selective harvesting according to grape quality attributes as well as for ripening assessment.     

The relationship between summer aggregation of fin whales and satellite-derived environmental conditions in the northwestern Mediterranean Sea

Few studies have tried to explain the summer distribution pattern of fin whales (Balaenoptera physalus) in the northwestern Mediterranean Sea, an area characterized with heterogeneous and transient hydrobiological features. Satellite imagery was used to gain knowledge on primary biomass over large time and space scales and to process environmental variables of significance to the problem of fin whale distribution.Fin whale distribution was obtained from survey data and expressed into sightings per unit of effort. Net primary production (g C/m²/day), NPP, can be estimated with a model by processing remote-sensed measurements of chlorophyll concentration, provided by SeaWIFS DAAC. NPP was integrated over different temporal scales, related to primary production cycles in the area. Additional variables were derived from sea surface temperature (AVHRR/NOAA sensors).Multiple cross-correlation coefficients were calculated between these environmental parameters and the fin whale summer distribution from 1998 to 2002. A predictive model, the potential grouping index, was developed from this statistical approach.This study improves our understanding of the variability of fin whale distribution in summer. While food availability at a particular time and place is a function of environmental conditions in the previous months, this study provides evidence that whales adapt their movements and group size directly to food availability rather than to instantaneous environmental conditions.     

Inverting a forest canopy reflectance model to retrieve the overstorey and understorey leaf area index for forest stands

In this article, the Kuusk–Nilson forest reflectance and transmittance (FRT) model was inverted to retrieve the overstorey and understorey leaf area index (OU-LAI) of forest stands in the Longmenhe forest nature reserve in China. Data from detailed sample sites were collected in 30 forest stands representing the typical vegetation community in the study area. An uncertainty and sensitivity matrix (USM) was used to analyse the sensitivity of the FRT model parameters based on these data. The results indicated that overstorey LAI strongly influenced stand reflectance, whereas understorey LAI had a much lower impact. To predict OU-LAI in forest stands, FRT model inversion is carried out by minimizing a merit function that provides a measure of the difference between the reflectance simulated by the FRT model and the reflectance originating from optimal band selection of Hyperion data. Various combinations of Hyperion bands were tested to evaluate the most effective wavelengths for the inversion of OU-LAI. The best estimates from 17 Hyperion bands (5 VIS, 8 NIR, 4 SWIR) by the FRT model inversion showed an R ²?=?0.41 and RMSE/mean?=?0.21 for overstorey LAI and R ²?=?0.49 and RMSE/mean?=?0.91 for understorey LAI. Advantages and disadvantages of FRT inversion for retrieval OU-LAI combined with Hyperion data are discussed.     

Integration of remote sensing and ecosystem modelling techniques to estimate forest net carbon uptake

Estimates of forest gross primary production (GPP) can be obtained using a parametric model (C‐Fix) that combines ground and remotely sensed data. A methodology is presented to convert these GPP estimates into values of net ecosystem exchange (NEE). The methodology is based on the use of a process model (BIOME‐BGC) that, after proper calibration, simulates all main functions of forest ecosystems at the climax condition. The estimated photosynthesis and respirations are transformed into net carbon fluxes of actual forests by using a simplified approach that relies on the difference between actual and potential stand biomass. The methodology was applied to eight forest sites in Italy where flux measurements were available and GPP estimates had been previously produced. The comparison of the obtained NEE estimates to the ground data indicates the potential of the approach and the prospects for future investigation.     

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

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

Towards a better assessment of the ecological status of coastal waters using satellite-derived chlorophyll-a concentrations

The application of the new Water Framework Directive (WFD) of the European Union will require a dense and frequent monitoring of chlorophyll-a near the coast. Not counting the transitional water bodies located in the vicinity of estuaries, not less than seventy four coastal water bodies have to be monitored along the coast of the French Atlantic continental shelf and the English Channel. All the available data have to be gathered to implement a comprehensive monitoring scheme. To this purpose, we evaluate the capacity of ocean colour imagery to complete the conventional in situ data set collected in coastal networks. Satellite-derived chlorophyll-a concentration is obtained by the application of a coastal Look-Up-Table to water-leaving radiance of the Sea-viewing Wide Field Instrument Sensor (SeaWiFS) for the 1998–2004 period. Seven years of satellite-derived and in situ chlorophyll-a concentrations are compared at seven representative stations of different water bodies. These comparisons show that the satellite products are reliable in most of the situations studied and throughout the seasons. Then the satellite imagery is used to classify the coastal waters following the eutrophication risk criterion of the WFD. This classification is made according to the percentile-90 of chlorophyll-a calculated during the productive season, from March to October. Despite a lack of sensor coverage over a small fraction of the near shore waters, this work shows that the satellite monitoring can considerably ease the application of the WFD.     

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

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

Mapping carbon and water vapor fluxes in a chaparral ecosystem using vegetation indices derived from AVIRIS

Using simple models derived from spectral reflectance, we mapped the patterns of ecosystem CO₂ and water fluxes in a semi-arid site in southern California during a period of extreme disturbance, marked by drought and fire. Employing a combination of low (∼ 2 km) and high (∼ 16 km) altitude images from the hyperspectral Airborne Visible Infrared Imaging Spectrometer (AVIRIS), acquired between April 2002 and September 2003, and ground data collected from an automated tram system, several vegetation indices were calculated for Sky Oaks field station, a FLUXNET and SpecNet site located in northern San Diego County (CA, USA). Based on the relationships observed between the fluxes measured by the eddy covariance tower and the vegetation indices, net CO₂ and water vapor flux maps were derived for the region around the flux tower. Despite differences in the scale of the images (from ∼ 2 m to 16 m pixel size) as well as marked differences in environmental conditions (drought in 2002, recovery in early 2003, and fire in mid 2003), net CO₂ and water flux modeled from AVIRIS-derived reflectance indices (NDVI, PRI and WBI) effectively tracked changes in tower fluxes across both drought and fire, and readily revealed spatial variation in fluxes within this landscape. After an initial period of net carbon uptake, drought and fire caused the ecosystem to lose carbon to the atmosphere during most of the study period. Our study shows the power of integrating optical and flux data in LUE models to better understand factors driving surface-atmosphere carbon and water vapor flux cycles, one of the main goals of SpecNet.     

Coupling forest canopy and understory reflectance in the Arctic latitudes of Finland

The Arctic region is predicted to experience considerable climatic and environmental changes as the global atmospheric CO₂ increases. Growing awareness of the role of tundra and taiga ecosystems and their transition zone in the climate change process has resulted in a recent increase in remote sensing studies focusing on the Arctic latitudes. Remote sensing of biophysical properties of the canopy layer in the forested part of the region is often, however, challenged by the dominating role of the understory in the spectral signal. In this paper, we examine the influence of understory vegetation on forest reflectance in the Arctic region of Finland during no-snow conditions. The study is based on SPOT HRVIR images, field goniospectrometry, 300 ground reference plots and a physically-based forest reflectance model (PARAS). The results indicate that lichen-dominated forest site types can be distinguished from sites dominated by dwarf shrubs. The paper also contains results from applying an analytical method for calculating photon recollision probability from canopy transmittance data for forest stands, and then using it to simulate the reflectance of the same stands.     

Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

The effects of soil moisture and leaf water content on canopy reflectance of MODIS shortwave infrared (SWIR) bands 5, 6, and 7 and water‐related indices are studied quantitatively using the coupled soil–leaf–canopy reflectance model. Canopy spectra simulations under various input conditions show that soil moisture has a large effect on each SWIR reflectance at low leaf area index (LAI) values, among which band 5 is the most sensitive to soil moisture variations, while band 7 responds strongest to dry soil conditions. Band 5 is also better suited to measure leaf water content change, since it obtains a higher variation when leaf water content changes from dry to wet. In general, each SWIR band responds to soil moisture and leaf water content differently. By using the normalized calculation between the water absorption‐sensitive band and insensitive band, the Normalized Difference Water Index shows the most capability to remove the soil background effect and enhance the sensitivity to leaf water content. These two moisture variables may be separated by combining multiple rather than one SWIR band with a near‐infrared band considering that each SWIR band has a different response to soil moisture and leaf water content.     

Discrimination of vegetation strata in a multi-layered Mediterranean forest ecosystem using height and intensity information derived from airborne laser scanning

Height and intensity information derived from Airborne Laser Scanning (ALS) was used to obtain a quantitative vertical stratification of vegetation in a multi-layered Mediterranean ecosystem. A new methodology for the separation of different vegetation strata was implemented using supervised classification of a two-dimensional feature space spanned by ALS return height (terrain corrected) and intensity. The classification was carried out using Gaussian mixture models tuned on a control plot. The approach was validated using extensive field measurements from treated plots, ranging from single vegetation strata to a more complex multi-layered ecosystem. Plot-level canopy profiles derived from ALS and from a geometric reconstruction based on field measurements were in very good agreement, with correlation coefficients ranging from 0.73 (for complex, 3-layered) to 0.96 (simple, single-layered). In addition, it was possible to derive plot-level information on layer height, vertical extent and coverage with absolute accuracies of some decimetres (simple plots) to a meter (complex plots) for both height and vertical extent and about 10 to 15% for layer coverage. The approach was then used to derive maps of the layer height, vertical extent and percentage of ground cover for a larger area, and classification accuracy was evaluated on a per-pixel basis. The method performed best for single-layered plots or dominant layers on multi-layered plots, obtaining an overall accuracy of 80 to 90%. For subdominant layers in the more complex plots, accuracies obtained were as low as 48%.Our results demonstrate the possibility of deriving qualitative (presence and absence of specific vegetation layers) and quantitative, physical data (height, vertical extent and ground cover) describing the vertical structure of complex multi-layered forest ecosystems using ALS-based height and intensity information.     

An analysis of GOES and NOAA derived land surface temperatures estimated over a boreal forest

Precise estimates of land surface temperature (LST) are important for land monitoring investigations. This study compares LST values calculated using different satellite platforms (Geostationary Operational Environmental Satellite-Imager and National Oceanographic and Atmospheric Administration-Advanced Very High Resolution Radiometer) and five different split window algorithms. The analysis includes (1) a fitting test with the reference dataset, (2) a comparison of differences between algorithms, and (3) an inter-sensor comparison. Considering the hypothesis of the Temperature/Vegetation Index (TVX) technique, the reference dataset was made with air temperature measured over dense canopy having maximum Normalized Difference Vegetation Index (NDVI). The first and second analyses show that algorithms used by Becker and Li and Ulivieri et al. have smaller estimation errors (less than 2.3 K) than the other algorithms, for example, best-fit linear regression. Although these algorithms show a good agreement in the paired algorithms analysis, the final analysis presents a considerable difference in the root mean square error between Imager and AVHRR (1.7 K for the Ulivieri et al. algorithm and 5.3 K for the Becker and Li algorithm). Finally we considered that the Ulivieri et al. method is more stable for both satellites.