Remote sensing of biophysical variables in boreal forest stands of Picea mariana

Using two hybrid radiative transfer models to represent conifer canopies and stands, algorithms to infer several important structural parameters of stands of black spruce (Picea mariana), the most common boreal forest dominant, were developed and evaluated. Spectral mixture analysis and multi-spectral reflectance data for 31 black spruce stands of varying density and structure were used to infer the values for the areal proportions of sunlit canopy, sunlit background and shadow fraction, which we call radiometric elements, and the areal proportions of these radiometric elements were strongly related to leaf area index, biomass density, and annual above ground net primary productivity. The best overall correspondence between the radiometric elements and biophysical variables was found from the shadow fraction obtained with the cone-based canopy reflectance model corrected for variations in solar zenith angle.     

Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imagery

Hyperspectral remote sensing has great potential for accurate retrieval of forest biochemical parameters. In this paper, a hyperspectral remote sensing algorithm is developed to retrieve total leaf chlorophyll content for both open spruce and closed forests, and tested for open forest canopies. Ten black spruce (Picea mariana (Mill.)) stands near Sudbury, Ontario, Canada, were selected as study sites, where extensive field and laboratory measurements were carried out to collect forest structural parameters, needle and forest background optical properties, and needle biophysical parameters and biochemical contents chlorophyll a and b. Airborne hyperspectral remote sensing imagery was acquired, within one week of ground measurements, by the Compact Airborne Spectrographic Imager (CASI) in a hyperspectral mode, with 72 bands and half bandwidth 4.25-4.36 nm in the visible and near-infrared region and a 2 m spatial resolution. The geometrical-optical model 4-Scale and the modified leaf optical model PROSPECT were combined to estimate leaf chlorophyll content from the CASI imagery. Forest canopy reflectance was first estimated with the measured leaf reflectance and transmittance spectra, forest background reflectance, CASI acquisition parameters, and a set of stand parameters as inputs to 4-Scale. The estimated canopy reflectance agrees well with the CASI measured reflectance in the chlorophyll absorption sensitive regions, with discrepancies of 0.06%-1.07% and 0.36%-1.63%, respectively, in the average reflectances of the red and red-edge region. A look-up-table approach was developed to provide the probabilities of viewing the sunlit foliage and background, and to determine a spectral multiple scattering factor as functions of leaf area index, view zenith angle, and solar zenith angle. With the look-up tables, the 4-Scale model was inverted to estimate leaf reflectance spectra from hyperspectral remote sensing imagery. Good agreements were obtained between the inverted and measured leaf reflectance spectra across the visible and near-infrared region, with R² = 0.89 to R² = 0.97 and discrepancies of 0.02%-3.63% and 0.24%-7.88% in the average red and red-edge reflectances, respectively. Leaf chlorophyll content was estimated from the retrieved leaf reflectance spectra using the modified PROSPECT inversion model, with R² = 0.47, RMSE = 4.34 μg/cm², and jackknifed RMSE of 5.69 μg/cm² for needle chlorophyll content ranging from 24.9 μg/cm² to 37.6 μg/cm². The estimates were also assessed at leaf and canopy scales using chlorophyll spectral indices TCARI/OSAVI and MTCI. An empirical relationship of simple ratio derived from the CASI imagery to the ground-measured leaf area index was developed (R² = 0.88) to map leaf area index. Canopy chlorophyll content per unit ground surface area was then estimated, based on the spatial distributions of leaf chlorophyll content per unit leaf area and the leaf area index.     

Retrieval of the canopy leaf area index in the BOREAS flux tower sites using linear spectral mixture analysis

This paper reports on the use of linear spectral mixture analysis for the retrieval of canopy leaf area index (LAI) in three flux tower sites in the Boreal Ecosystem-Atmosphere Study (BOREAS) southern study area: Old Black Spruce, Old Jack Pine, and Young Jack Pine (SOBS, SOJP, and SYJP). The data used were obtained by the Compact Airborne Spectrographic Imager (CASI) with a spatial resolution of 2 m in the winter of 1994. The convex geometry method was used to select the endmembers: sunlit crown, sunlit snow, and shadow. Along transects for these flux tower sites, the fraction of sunlit snow was found to have a higher correlation with the field-measured canopy LAI than the fraction of sunlit crown or the fraction of shadow. An empirical equation was obtained to describe the relation between canopy LAI and the fraction of sunlit snow. There is a strong correlation between the estimated LAI and the field-measured LAI along transects (with R² values of 0.54, 0.71, and 0.60 obtained for the SOBS, SYJP, and SOJP sites, respectively). The estimated LAI for the whole tower site is consistent with that obtained by the inversion of a canopy model in our previous study where values of 0.94, 0.92, and 0.63 were obtained for R² for the SOBS, SYJP and SOJP sites, respectively.The CASI 2-m summer data over the SOBS site was also employed to investigate the possibility of deriving canopy LAI from the summer data using linear mixture analysis. At a spatial resolution of 10 m, the correlation between the field-measured LAI and the estimated LAI along transects is small at R² less than 0.3, while R² increases to 0.6 at a spatial resolution of 30 m. The difficulty in canopy LAI retrieval from the summer data at a spatial resolution of 10 m is likely due to the variation of the understory reflectance across the scene, although spatial misregistration of the CASI data used may also be a possible contributing factor.     

Evaluating image-based estimates of leaf area index in boreal conifer stands over a range of scales using high-resolution CASI imagery

Leaf area index (LAI) is an important surface biophysical parameter as a measure of vegetation cover, vegetation productivity, and as an input to ecosystem process models. Recently, a number of coarse-scale (1-km) LAI maps have been generated over large regions including the Canadian boreal forest. This study focuses on the production of fine-scale (≤30-m) LAI maps using the forest light interaction model-clustering (FLIM-CLUS) algorithm over selected boreal conifer stands and the subsequent comparison of the fine-scale maps to coarse-scale LAI maps synthesized from Landsat TM imagery. The fine-scale estimates are validated using surface LAI measurements to give relative root mean square errors of under 7% for jack pine sites and under 14% for black spruce sites. In contrast, finer scale site mean LAI ranges between 49% and 86% of the mean of surface estimates covering only part of the sites and 54% to 110% of coarse-scale site mean LAI. Correlations between fine-scale and coarse-scale estimates range from near 0.5 for 30-m coarse-scale images to under 0.3 to 1-km coarse-scale images but increase to near 0.90 after imposing fine-scale zero LAI areas in coarse-scale estimates. The increase suggests that coarse-scale image-based LAI estimates require consideration of sub-pixel open areas. Both FLIM-CLUS and coarse-scale site mean LAI are substantially lower than surface estimates over northern sites. The assumption of spatially random residuals in regression-based estimates of LAI may not be valid and may therefore add to local bias errors in estimating LAI remotely. Differences between fine-scale airborne LAI maps and 30-m-scale Landsat TM LAI maps suggests that, for sparse boreal conifer stands, LAI maps produced from Landsat TM alone may not always be sufficient for validation of coarser scale LAI maps. In addition, previous studies may have used biased LAI estimates over the study site. Fine-scale spatial LAI maps offer one means of assessing and correcting for effects of sub-pixel open area patches and for characterising the spatial pattern of residuals in coarse-scale LAI estimates in comparison to the true distribution of LAI on the surface.     

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

Leaf chlorophyll content in coniferous forest canopies, a measure of stand condition, is the target of studies and models linking leaf reflectance and transmittance and canopy hyperspectral reflectance imagery. The viability of estimation of needle chlorophyll content from airborne hyperspectral optical data through inversion of linked leaf level and canopy level radiative transfer models is discussed in this paper. This study is focused on five sites of Jack Pine (Pinus banksiana Lamb.) in the Algoma Region (Canada), where field, laboratory and airborne data were collected in 1998 and 1999 campaigns. Airborne hyperspectral CASI data of 72 bands in the visible and near-infrared region and 2 m spatial resolution were collected from 20×20 m study sites of Jack Pine in 2 consecutive years. It was found that needle chlorophyll content could be estimated at the leaf level (r²=0.4) by inversion of the PROSPECT leaf model from needle reflectance and transmittance spectra collected with a special needle carrier apparatus coupled to the Li-Cor 1800 integrating sphere. The Jack Pine forest stands used for this study with LAI>2, and the high spatial resolution hyperspectral reflectance collected, allowed the use of the SPRINT canopy reflectance model coupled to PROSPECT for needle chlorophyll content estimation by model inversion. The optical index R750/R710 was used as the merit function in the numerical inversion to minimize the effect of shadows and LAI variation in the mean canopy reflectance from the 20×20 m plots. Estimates of needle pigment content from airborne hyperspectral reflectance using this linked leaf-canopy model inversion methodology showed an r²=0.4 and RMSE=8.1 μg/cm² when targeting sunlit crown pixels in Jack Pine sites with pigment content ranging between 26.8 and 56.8 μg/cm² (1570-3320 μg/g).     

MODIS spectral signals at a flux tower site: Relationships with high-resolution data, and CO2 flux and light use efficiency measurements

In this study, spectral indices were calculated from single date HyMap (3 m; 126 bands), Hyperion (30 m; 242 bands), ASTER (15/30 m; 9 bands), and a time series of MODIS nadir BRDF-adjusted reflectance (NBAR; 1 km, 7 bands) for a study area surrounding the Tumbarumba flux tower site in eastern Australia. The study involved: a) the calculation of a range of physiologically-based vegetation indices from ASTER, HyMap, Hyperion and MOD43B NBAR imagery over the flux tower site; b) comparison across scales between HyMap, Hyperion and MODIS for the normalized difference water index (NDWI) and the Red-Green ratio; c) analysis of relationships between tower-based flux and light use efficiency (LUE) measurements and seasonal and climatic constraints on growth; and d) examination of relationships between fluxes, LUE and time series of NDVI, NDWI and Red-Green ratio. Strong seasonal patterns of variation were observed in NDWI and Red/Green ratio from MODIS NBAR. Correlations between fine (3 and 30 m) and coarse (1 km) scale indices for a small region around the flux tower site were moderately good for Red/Green ratio, but poor for NDWI. Hymap NDWI values for the understorey canopy were much lower than values for the tree canopy. MODIS NDWI was negatively correlated with CO₂ fluxes during warm and cool seasons. The correlation indicated that surface reflectance, affected by a spectrally bright grassland understorey canopy, was decoupled from growth of trees with access to deep soil moisture. The application of physiologically-based indices at earth observation scale requires careful attention to applicability of band configurations, contribution of vegetation components to reflectance signals, mechanistic relationships between biochemical processes and spectral indexes, and incorporation of ancillary information into any analysis.     

Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data

The potential of canopy reflectance modelling to retrieve simultaneously several structural variables in managed Norway spruce stands was investigated using the “Invertible Forest Reflectance Model”, INFORM. INFORM is an innovative extension of the FLIM model, with crown transparency, infinite crown reflectance and understory reflectance simulated using physically based sub-models (SAILH, LIBERTY and PROSPECT). The INFORM model was inverted with hyperspectral airborne HyMap data using a neural network approach. INFORM based estimates of forest structural variables were produced using site-specific ranges of stand structural variables. A relatively simple three layer feed-forward backpropagation neural network with two input neurons, one neuron in the hidden layer and three output neurons was employed to map leaf area index (LAI), crown coverage and stem density.To identify the optimum 2-band spectral subset to be used in the inversion process, all 2-band combinations of the HyMap dataset were systematically evaluated for model inversion. Field measurements of structural variables from 39 forest stands were used to validate the maps produced from HyMap imagery. Using two HyMap wavebands at 837 nm and 1148 nm the obtained accuracy of the LAI map amounts to an rmse of 0.58 (relative rmse = 18% of mean, R² = 0.73). With HyMap data resampled to Landsat TM spectral bands and using two “optimum” bands at 840 nm and 1650 nm, rmse was 0.66 and relative rmse 21%. In contrast to approaches based on empirical relations between spectral vegetation indices and structural variables, the main advantage of the inversion approach is that it does not require previous calibration.     

Characterization of the Reflectance Anisotropy of Three Boreal Forest Canopies in Spring–Summer

As a part of the Boreal Ecosystem-Atmosphere Study (BOREAS), measurements of the spectral reflectance anisotropy of three boreal forest canopies were studied for cloudless sky conditions at the phenological growth stages which were at or near maximum leaf area index at each site. The three sites were relatively homogeneous mature stands of black spruce, jack pine, and aspen located in the southern boreal zone of central Saskatchewan. Measurements of the spectal bidirectional reflectance factors with a 15° instrument field of view in three spectral bands centered at 662 nm, 826 nm, and 1658 nm were made with the PARABOLA instrument over a range of solar zenith angles typically varying from 35° (near solar noon) to 70°. The measured reflectance factors showed large anisotropy at all three sites and for all three wavelengths, with prominant backscatter peak reflectances, and strong retro solar view angle (hot spot) maximum reflectances in the visible (662 nm) and shortwave infrared (1658 nm) for the jack pine and black spruce sites, with a less pronounced hot spot at the aspen site. Pronounced effects of canopy and understory shadowing in the visible, as a function of solar zenith angle (SZA), were observed for the black spruce and jack pine sites, with resultant large linear increases in computed normalized difference and simple ratio vegetation indices as SZA increased for near-nadir view angles. Hemispheric spectral reflectances or spectral albedos were computed from angular integration of PARABOLA measured bidirectional reflectances. Visible (662 nm) hemispheric reflectances for the jack pine and black spruce canopies showed very little variation with solar zenith angle, while near-infrared hemispheric reflectances increased strongly with increasing SZA. Estimates were made of the total shortwave albedo for the aspen and jack pine sites from irradiance and reflectance weighting of the spectral hemispheric reflectances in the three measured wavelengths. Comparison of estimated to pyranometer measured total albedo showed all estimates to be biased high, but only by about 0.007–0.018, depending on which of two sets of pyranometer measured albedos were utilized for the comparison. The measured bidirectional reflectance factor (BRF) data sets reported in this study coupled with ancillary data of biophysical parameters collected at the same sites by BOREAS researchers provide a unique data set for the development and characterization of canopy bidirectional reflectance modeling and for the interpretation of remotely sensed data for boreal forest canopies.     

Mapping and monitoring conifer mortality using remote sensing in the Lake Tahoe Basin

A prolonged drought in the western United States has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15 % of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11 % for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.     

Satellite estimation of tropical secondary forest above-ground biomass: Data from Brazil and Bolivia

This paper reports on a test of the ability to estimate above-ground biomass of tropical secondary forest from canopy spectral reflectance using satellite optical data. Landsat Thematic Mapper data were acquired concurrent with field surveys conducted in secondary forest fallows near Manaus, Brazil and Santa Cruz de la Sierra, Bolivia. Measurements of age and above-ground live biomass were made in 34 regrowth stands. Satellite data were converted to surface reflectances and compared with regrowth stand age, biomass and structural variables. Among the Brazilian stands, significant relationships were observed between middle-infrared reflectance and stand age, height, volume and biomass. The canopy reflectance-biomass relationship saturated at around 15.0 kg m^-2, or over 15 years of age (r > 0.80, p < 0.01). In the Bolivian study area, no significant relationship between canopy spectral reflectance and biomass was observed. These contrasting results are probably caused by a low Sun angle during the satellite measurements from Bolivia. However, regrowth structural and general compositional differences between the two study areas could explain the lack of a significant relationship in Bolivia. The results demonstrate a current potential for biomass estimation of secondary forests with satellite optical data in some, but not all, tropical regions. A discussion of the potential for regional extrapolation of spectral relationships and future satellite imagery is included.     

A simple single layer model to estimate transpiration from vegetation using multi-spectral and meteorological data

A methodology is developed here to model evapotranspiration (λE_c ) from the canopy layer over large areas by combining satellite and ground measurements of biophysical and meteorological variables. The model developed here follows the energy balance approach, where λE_c is estimated as a residual when the net radiation (Rn), sensible heat flux (H) and ground flux (G) are known. Multi-spectral measurements from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used along with routine meteorological measurements made on the ground to estimate components of the energy balance. The upwelling long wave radiation, and H from the canopy layer were modelled using the canopy temperature, obtained from a linear relation between the Normalized Difference Vegetation Index (NDVI) and surface temperature. This method separates flux measurements from the canopy and bare soil without the need for a complex two layer model. From theoretical analysis of canopy reflectance, leaf area, and canopy resistance, a model is developed to scale the transpiration estimates from the full canopy to give an area averaged estimate from the mean NDVI of the study area. The model was tested using data collected from the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), and the results show that the modelled values of total surface evapotranspiration from the soil and canopy layers vary from the ground measurements by less than 9 per cent.     

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.     

The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data

In mixed-species forests of complex structure, the delineation of tree crowns is problematic because of their varying dimensions and reflectance characteristics, the existence of several layers of canopy (including understorey), and shadowing within and between crowns. To overcome this problem, an algorithm for delineating tree crowns has been developed using eCognition Expert and hyperspectral Compact Airborne Spectrographic Imager (CASI-2) data acquired over a forested landscape near Injune, central east Queensland, Australia. The algorithm has six components: 1) the differentiation of forest, non-forest and understorey; 2) initial segmentation of the forest area and allocation of segments (objects) to larger objects associated with forest spectral types (FSTs); 3) initial identification of object maxima as seeds within these larger objects and their expansion to the edges of crowns or clusters of crowns; 4) subsequent classification-based separation of the resulting objects into crown or cluster classes; 5) further iterative splitting of the cluster classes to delineate more crowns; and 6) identification and subsequent merging of oversplit objects into crowns or clusters. In forests with a high density of individuals (e.g., regrowth), objects associated with tree clusters rather than crowns are delineated and local maxima counted to approximate density. With reference to field data, the delineation process provided accuracies > ∼70% (range 48-88%) for individuals or clusters of trees of the same species with diameter at breast height (DBH) exceeding 10 cm (senescent and dead trees excluded), with lower accuracies associated with dense stands containing several canopy layers, as many trees were obscured from the view of the CASI sensor. Although developed using 1-m spatial resolution CASI data acquired over Australian forests, the algorithm has application elsewhere and is currently being considered for integration into the Definiens product portfolio for use by the wider community.     

Seasonal reflectance trends of hemiboreal birch forests

MODIS, AVHRR and SPOT VEGETATION satellite images have recently been used to track coarse scale seasonal vegetation dynamics of boreal and temperate forests. However, the understanding of driving factors of reflectance seasonality at forest stand level is still in its infancy, and has only preliminarily been linked to, for example, forest structure or site fertility. We present results from a study on the seasonal reflectance trends of 145 hemiboreal birch stands in Estonia from budburst to initial senescence. A time series comprising 32 high resolution Landsat ETM+, TM and SPOT HRVIR, HRV images from April to September was assembled for analyzing empirical reflectance courses of birch stands. The most noteworthy seasonal reflectance dynamics were observed in the red and NIR channels, changes in the green and SWIR spectral channels were relatively small. The most stable period in stand reflectance in all the spectral channels occurred in midsummer i.e. when stand leaf area index (LAI) reached its highest level and changes in solar angle were the smallest. A twenty-day difference was observed between the reflectance development of birch stands growing on infertile and fertile sites. Next, to provide an explanation for the observed reflectance changes, we simulated the mean seasonal reflectance trajectories of the study stands at 10 day intervals for the same period using a radiative transfer model (FRT). Simulated seasonal reflectance courses for the different site fertility classes followed the general pattern of the measured courses. Simulation results indicated that the main driving factors for reflectance seasonality for all the site fertility classes in the red and green bands were stand LAI and leaf chlorophyll content, in the NIR band stand LAI, and in the SWIR band LAI and general water content. Finally, we discuss current limitations related to applying forest radiative transfer models in investigating the driving factors of seasonal reflectance changes in the boreal zone.     

Application of photon recollision probability in coniferous canopy reflectance simulations

A new semi-physical forest reflectance model, PARAS, is presented in the paper. PARAS is a simple parameterization model for taking into account the effect of within-shoot scattering on coniferous canopy reflectance. Multiple scattering at the small scale represented by a shoot is a conifer-specific characteristic which causes the spectral signature of coniferous forests to differ from that of broadleaved forests. This has for long led to problems in remote sensing of canopy structural variables in coniferous dominated regions. The PARAS model uses a relationship between photon recollision probability and leaf area index (LAI) for simulating forest reflectance. The recollision probability is a measurable, wavelength independent variable which is defined as the probability with which a photon scattered in the canopy interacts with a phytoelement again. In this study, we present application results using PARAS in simulating reflectance of coniferous forests for approximately 800 Scots pine and Norway spruce dominated stands. The results of this study clearly indicate that a major improvement in simulating canopy reflectance in near-infrared (NIR) is achieved by simply accounting for the within-shoot scattering. In other words, the low NIR reflectance observed in coniferous areas is mainly due to within-shoot scattering. In the red wavelength the effect of within-shoot scattering was not pronounced due to the high level of needle absorption in the red range. To conclude the paper, further application possibilities of the presented parameterization model are discussed.     

Validation of the MODIS reflectance product under UK conditions

Surface reflectance obtained from remote-sensing data is the main input to almost all remote-sensing applications. The availability and special characteristics of Moderate Resolution Imaging Spectroradiometer (MODIS) products have led to their use worldwide. Validation of the MODIS reflectance product is then crucial to provid information on uncertainty in the reflectance data, and in other MODIS products and in the applied surface–atmosphere models. Compact Airborne Spectrographic Imager (CASI) and Système Pour l'Observation de la Terre (SPOT) data, collected during the Network for Calibration and Validation in Earth Observation (NCAVEO) 2006 Field Campaign, were applied to validate daily MODIS reflectance data over a site in the southern UK. The difference in the view geometry of at-nadir CASI and SPOT data and off-nadir MODIS data was dealt with using a semi-empirical bidirectional reflectance distribution function (BRDF) model. The validation results showed that for our particular study site, the absolute errors in the MODIS reflectance product were too large to allow the albedo data to be used directly in climate models. The errors were mainly related to the uncertainties in the MODIS atmospheric variables, the BRDF model, and undetected clouds and cloud shadows. More generally, the study highlights the extreme difficulty of achieving pixel-level validation of coarse spatial resolution satellite sensor data in an environment in which the atmosphere is constantly changing, and in which the landscape is characterized by high space–time heterogeneity.     

Detection of initial damage in Norway spruce canopies using hyperspectral airborne data

Current broadband sensors are not capable of separating the initial stages of forest damage. The current investigation evaluates the potential of hyperspectral data for detecting the initial stages of forest damage at the canopy level in the Norway spruce (Picea abies (L.) Karst) forests of Czech Republic. Hyperspectral canopy reflectance imagery and foliar samples were acquired contemporaneously for 23 study sites in August 1998. The sites were selected along an air pollution gradient to represent the full range of damage conditions in even-aged spruce forests. The changes in canopy and foliar reflectance, chemistry and pigments associated with forest damage were established. The potential of a large number of spectral indices to identify initial forest damage was determined. Canopy hyperspectral data were able to separate healthy from initially damaged canopies, and therefore provided an improved capability for assessment of forest physiology as compared to broadband systems. The 673-724 nm region exhibited maximum sensitivity to initial damage. The nine spectral indices having the highest potential as indicators of the initial damage included: three simple band ratios, two derivative indices, two modelled red-edge parameters and two normalized bands. The sensitivity of these indices to damage was explained primarily by their relationship to foliar structural chemical compounds, which differed significantly by damage class.     

Quantifying empirical relations between planted species mixtures and canopy reflectance with PROTEST

Mapping plant species composition of mixed vegetation stands with remote sensing is a complicated task. Uncertainties may arise from similar spectral signatures of different plant species as well as from variable influences of prevailing plant states (e.g., growth stages, vigor, or stress levels). Despite these uncertainties, empirical approaches may often be able to take up the challenge. However, their performance is likely to be affected by the temporal variability of empirical relations between reflectance and plant species composition. To assess some aspects of this temporal variability, we performed a greenhouse study. Three mixed stands of grassland species were planted with defined spatial variation in species proportions. The canopy reflectance of these mixed stands was measured with a field spectrometer over a period of three months. Confounding external influences on plant states apart from maturation were minimized.The suitability of canopy reflectance and derivative reflectance to draw conclusions on differences in qualitative species mixtures between the stands was tested with a classification approach (Spectral Angle Mapper, SAM). Procrustean randomization test (PROTEST), which is to our knowledge new to the field of remote sensing, was applied in combination with Isometric Feature Mapping to quantify the spectral variation caused by within-stand spatial variation in species proportions. Model fits in both analyses increased with progressing plant development; further, utilization of derivative reflectance improved the model fits. Regardless of the within-stand variation, SAM enabled a successful discrimination of the three stands with an average overall accuracy of 85% (reflectance) and 92% (derivative reflectance). In PROTEST analysis, spatial variation in reflectance was successfully related to within-stand variation in species proportions. However, observed influences of variable growth stages and health states on these relations were considerable. The temporal variation of these relations (r = 0.27-0.73 for reflectance and 0.48-0.73 for derivative reflectance) was quantified for the first time under controlled conditions.     

Using ASTER satellite data to calculate riparian evapotranspiration in the Middle Rio Grande,New Mexico

Riparian evapotranspiration (ET) in the Rio Grande Basin in New Mexico, USA is a major component of the hydrological system. Over a period of several years, ET has been measured in selected locations of dense saltcedar and cottonwood vegetation. Riparian vegetation varies in density, species and soil moisture availability, and to obtain accurate measurements, multiple sampling points are needed, making the process costly and impractical. An alternative solution involves using remotely sensed data to estimate ET over large areas. In this study, daily ET values were measured using eddy covariance flux towers installed in areas of saltcedar and cottonwood vegetation. At these sites, remotely sensed satellite data from the National Aeronautics and Space Administration (NASA) Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to calculate the albedo, normalized difference vegetation index (NDVI) and surface temperature. A surface energy balance model was used to calculate ET values from the ASTER data, which were available for 7 days in the year. Comparison between the daily ET values of saltcedar and cottonwood measured from the flux towers and calculated from remote sensing resulted in a mean square error (MSE) of 0.16 and 0.37 mm day^?1, respectively. The regional map of ET generated from the remote sensing data demonstrated considerable variation in ET, ranging from 0 to 9.8 mm day^?1, with a mean of 5.5 mm day^?1 and standard deviation of 1.85 mm day^?1 (n = 427481 pixels) excluding open water. This was due to variations in plant variety and density, soil type and moisture availability, and the depth to water table.