Spectral indices in N-Space

Linear combinations of $\text{n}$ spectral bands form physically significant indices in $\text{n-}\text{dimensional}$ space. The 2-dimensional (2-D) perpendicular vegetation index (PVI) of Richardson and Wiegand and the 4-D tasseled cap of Kauth and Thomas are special cases of $\text{n-}\text{space}$ indices. A procedure for calculating the coefficients of $\text{n-}\text{space}$ indices is described. Spectra from 12 wheat and two bare soil (wet and dry) plots were multiplied point by point (at 1-nm intervals) by response functions representing five satellite sensors. Reflectance values were obtained for each band for each sensor (atmospheric effects and sensor characteristics such as noise, resolution, and calibration, were not considered). $\text{N-}\text{Space}$ indices were calculated for various band combination for the several sensors and their dynamic range for a 0–100% change in vegetation was compared. A 6-D vegetation index (greenness) calculated using six of the thematic mapper bands had the greatest dynamic range, followed closely by two 5-D and one 4-D greenness from the same sensor. The 2-D greenness using bands 4 (near-IR) and 7 (mid-IR) of the thematic mapper had a greater dynamic range than any band combination of the other four satellite sensors. The 4-D greenness of the Landsat-4 MSS and the 3-D index of the SPOT HRV were similar. The 2-D indices from the AVHRR sensors on NOAA-6 and NOAA-7 changed less with vegetation changes than did the other three.     

Influence of topography and sensor view angles on NIR/red ratio and greenness vegetation indices of wheat

Abstract

Reflectance factors of winter wheat were measured with aground-based radiometer to determine the effect of topography and sensor view angle on the diurnal behaviour of two spectral vegetation indices. Data are presented for fields with 10^° slopes in a topographically complex area of central Italy. The ratio of reflectances in near-infrared (NIR) (0.78 to 0.89 μm) to red (0.63 to 0.69 μm) was less sensitive to field aspect than greenness. However, when nadir and off-nadir view angles were compared for the same aspect, greenness displayed less variability. Field aspect and view angle had less effect on both indices when solar zenith angles were small.     

New spectral vegetation indices based on the near-infrared shoulder wavelengths for remote detection of grassland phytomass

This article examines the possibility of exploiting ground reflectance in the near-infrared (NIR) for monitoring grassland phytomass on a temporal basis. Three new spectral vegetation indices (infrared slope index, ISI; normalized infrared difference index, NIDI; and normalized difference structural index, NDSI), which are based on the reflectance values in the H25 (863–881 nm) and the H18 (745–751 nm) Chris Proba (mode 5) bands, are proposed. Ground measurements of hyperspectral reflectance and phytomass were made at six grassland sites in the Italian and Austrian mountains using a hand-held spectroradiometer. At full canopy cover, strong saturation was observed for many traditional vegetation indices (normalized difference vegetation index (NDVI), modified simple ratio (MSR), enhanced vegetation index (EVI), enhanced vegetation index 2 (EVI 2), renormalized difference vegetation index (RDVI), wide dynamic range vegetation index (WDRVI)). Conversely, ISI and NDSI were linearly related to grassland phytomass with negligible inter-annual variability. The relationships between both ISI and NDSI and phytomass were however site specific. The WinSail model indicated that this was mostly due to grassland species composition and background reflectance. Further studies are needed to confirm the usefulness of these indices (e.g. using multispectral specific sensors) for monitoring vegetation structural biophysical variables in other ecosystem types and to test these relationships with aircraft and satellite sensors data. For grassland ecosystems, we conclude that ISI and NDSI hold great promise for non-destructively monitoring the temporal variability of grassland phytomass.     

Spectral estimation of absorbed photosynthetically active radiation in corn canopies

Most models of crop growth and yield require an estimate of canopy leaf area index or absorption of radiation; however, direct measurement of LAI or light absorption can be tedious and time-consuming. The object of this study was to develop relationships between photosynthetically active radiation (PAR) absorbed by corn (Zea mays L.) canopies and the spectral reflectance of the canopies. Absorption of PAR was measured near solar noon in corn canopies planted in north-south rows at densities of 50,000 and 100,000 plants ha.^?1 Reflectance factor data were acquired with a radiometer with spectral bands similar to the Landsat MSS. Three spectral vegetation indices (ratio of near infrared to red reflectance, normalized difference, and greenness) were associated with more than 95% of the variability in absorbed PAR from planting to silking. The relationships developed between absorbed PAR and the three indices were tested with reflectance factor data acquired from corn canopies planted in 1979–1982 that excluded those canopies from which the equations were developed. Treatments included in these data were two hybrids, four planting densities (25, 50, 75, and 100 thousand plantsha^?1), three soil types (Typic Argiaquoll, Udollic Ochraqualf, and Aeric Ochraqualf), and several planting dates. Seasonal cumulations of measured LAI and each of the three indices were associated with greater than 50% of the variation in final grain yields from the test years. Seasonal cumulations of daily absorbed PAR, estimated indirectly from the multispectral reflectance of the canopies, were associated with up to 73% of the variation in final grain yields. Absorbed PAR, cumulated through the growing season, is a better indicator of yield than cumulated leaf area index.     

Estimating forage quantity and quality under different stress and senescent biomass conditions via spectral reflectance

Assessing forage quantity and quality through remote sensing can facilitate grassland and pasture management. However, the high spatial and temporal variability of canopy conditions may limit the predictive accuracy of models based on reflectance measurements. The objective of this work was to develop this type of models, and to challenge their capacity to predict plant properties under a wide range of environmental conditions. We manipulated Paspalum dilatatum canopies through different stress treatments (flooding, drought, nutrient availability, and control) and by artificially varying the amount of senescent biomass. We measured canopy reflectance and constructed simple models, based on either normalized vegetation indices or a few selected wavebands, to estimate biomass and two variables related to forage quality: proportion of photosynthetic vegetation and biomass C:N ratio. General models satisfactorily predicted plant properties for the whole set of environmental conditions, but failed under specific conditions such as drought (for estimates of plant biomass), fertilization (for estimates of C:N ratio), and different levels of senescent tillers (for estimates of the proportion of photosynthetic vegetation). Where general models failed, specific models, based on different bands, achieved satisfactory accuracy. The general models performed better when based on a few selected bands than when based on two-band vegetation indices, having better accuracy (higher R²) and parsimony (lower BIC). However specific models performed similarly for both approaches (similar R² and BIC). These results indicate that these plant properties can be predicted from reflectance information under a broad range of conditions, but not for some particular conditions, where ancillary data or more complex models are probably needed to increase predictive accuracy.     

Nadir and off‐nadir hyperspectral field data: strengths and limitations in estimating grassland biophysical characteristics

In recent years, hyperspectral and multi‐angular approaches for quantifying biophysical characteristics of vegetation have become more widely used. In fact, as both hyperspectral and multi‐angle reflectance decrease the level of noise on retrieved geophysical parameter values, they increase their reliability by also reducing the saturation problem of the relationships between vegetation indices and biophysical characteristics. To test which is the best methodology in estimating some important biophysical grassland parameters (biomass, total and percent biomass nitrogen content, phytomass and its total and percent nitrogen content), nadir and off‐nadir measurements were carried out, three times during the vegetative period of 2004, in a permanent flat meadow located in the experimental farm of the University of Padua, Italy. The two approaches and the broad band vegetation indices calculated using Landsat bands were compared considering both the best determination coefficients of five vegetation indices, calculated with the two analysis, and through a partial least squares regression using different spectral regions measured at different angles as predictive variables. Using nadir data the red edge region was the most useful for the prediction of biophysical variables, especially phytomass, but also nitrogen content. The off‐nadir data did not provide any significance differences in results to that of data obtained in nadir view but both methods seem to be better adapted to describe biophysical parameters of vegetation than the use of broad band vegetation indices.     

Spectral indices for remote sensing of phytomass,deciduous shrubs,and productivity in Alaskan Arctic tundra

The relationships among in situ spectral indices, phytomass, plant functional types, and productivity were determined through field observations of moist acidic tundra (MAT), moist non-acidic tundra (MNT), heath tundra (HT), and sedge-shrub tundra (SST) in the Arctic coastal tundra, Alaska, USA. The two-band enhanced vegetation index (EVI2) was found more useful for estimating vascular plant green phytomass, leaf carbon and nitrogen, leaf carbon and nitrogen turnover, and vascular plant net primary productivity (NPP) without root production than the normalized difference vegetation index (NDVI). Deciduous shrub green phytomass was strongly correlated with deciduous shrub index (DSI), defined as EVI2 × (R_blue + R_green – R_red)/(R_blue + R_green + R_red) (with a coefficient of determination (R²) of 0.63). R_blue, R_green, and R_red denote the blue, green, and red bands, respectively. This is because R_blue and R_green values were higher than the R_red values for green leaves, deciduous shrub stems, lichens, and rocks compared with other ecosystem components, and EVI2 values of lichens and rocks were very low. The vascular plant NPP without root production was estimated with an R² of 0.67 using DSI and EVI2. Our results offer empirical evidence that a new spectral index predicts the distribution of deciduous shrub and plant production, which influences the interactions between tundra ecosystems and the atmosphere.     

Spectral vegetation indices for estimating shrub cover,green phytomass and leaf turnover in a sedge‐shrub tundra

Using field observations, we determined the relationships between spectral indices and the shrub ratio, green phytomass and leaf turnover of a sedge-shrub tundra community in the Arctic National Wildlife Refuge, Alaska, USA. We established a 50‐m × 50‐m plot (69.73°N 143.62°W) located on a floodplain of the refuge. The willow shrub (Salix lanata) and sedge (Carex bigelowii) dominated the plot vegetation. In July to August 2007, we established ten 0.5‐m × 0.5‐m quadrats on both shrub‐covered ground (shrub quadrats) and on ground with no shrubs (sedge quadrats). The shrub ratio was more strongly correlated with the normalized difference vegetation index (NDVI, R2 of 0.57) than the normalized difference infrared index (NDII), the soil-adjusted vegetation index (SAVI) or the enhanced vegetation index (EVI). On the other hand, for both green phytomass and leaf turnover, the strongest correlation was with NDII (R ² of 0.63 and 0.79, respectively).     

Improved forest biomass estimates using ALOS AVNIR-2 texture indices

Optical remote sensing is still one of the most attractive choices for obtaining biomass information, as new sensors are available with fine spatial and spectral resolutions. Better biomass estimates may be possible if suitable processing techniques for these sensors can be demonstrated. This research investigates the potential of high resolution optical data from the ALOS AVNIR-2 sensor for biomass estimation in a mountainous, subtropical forested region using four different types of image processing techniques including i) spectral reflectance and simple spectral band ratio, ii) commonly used vegetation indices, iii) texture parameters and iv) ratio of texture parameters. Simple linear and stepwise multiple regression models were developed between biomass data from 50 field plots, and image parameters derived from these techniques.Results indicate that spectral reflectance, the simple band ratio, and commonly used vegetation indices have relatively low potential for biomass estimation, as only about 58% of the variability in the field data was explained by the model (adjusted r² = 0.58 and RMSE = 64 t/ha). However, the texture parameters of spectral bands were found to be effective for biomass estimation with an explained variability of ca. 76% (adjusted r² = 0.76 and RMSE = 46 t/ha). The result was further improved to adjusted r² = 0.88 (RMSE = 32 t/ha) using the simple ratio of texture parameters. The results suggest that the performance of biomass estimation can be improved significantly using the texture parameters of high resolution optical data, and further improvement can be obtained using the ratio of texture parameters, as this combines the advantages of both texture and ratio.     

Modelling forest stand volume and tree density using Landsat ETM+ data

Estimation of stand volume and tree density in a large area using remotely sensed data has considerable significance for sustainable management of natural resources. In this paper, we explore likely relationships between forest stand characteristics and Landsat Enhanced Thematic Mapper Plus (ETM+) reflectance values. We used multivariate regression technique to predict stand volume and tree density. The result showed that a linear combination of greenness and difference vegetation index (DVI) were better predictors of stand volume (adjusted R² = 43%; root mean square error (RMSE) = 97.4 m³ ha^?1) than other ETM+ bands and vegetation indices. In addition, the regression model with ETM4 (near infrared band) and ETM5 (first shortwave band) as independent variables was a better predictor of tree density (adjusted R² = 73.4%; RMSE = 170.13 ha^?1) than other combinations of ETM⁺ bands and vegetation indices. Results obtained from this study demonstrate the significant relationship between forest stand characteristics and ETM+ reflectance values and the utility of transformed bands in modelling stand volume and tree density. Based on the results of this study, we conclude that ETM+ data are useful to estimate forest volume and density and to gain insights into its structural characteristics in our study area. Forest managers could use ETM+ data for gaining insights into stand characteristics and generating maps required for developing forest management plans and identifying locations within stands that require treatments and other interventions.     

Using in‐situ measurements to evaluate the new RapidEye™ satellite series for prediction of wheat nitrogen status

This study assessed whether vegetation indices derived from broadband RapidEye^? data containing the red edge region (690–730 nm) equal those computed from narrow band data in predicting nitrogen (N) status of spring wheat (Triticum aestivum L.). Various single and combined indices were computed from in‐situ spectroradiometer data and simulated RapidEye^? data. A new, combined index derived from the Modified Chlorophyll Absorption Ratio Index (MCARI) and the second Modified Triangular Vegetation Index (MTVI2) in ratio obtained the best regression relationships with chlorophyll meter values (Minolta Soil Plant Analysis Development (SPAD) 502 chlorophyll meter) and flag leaf N. For SPAD, r ² values ranged from 0.45 to 0.69 (p<0.01) for narrow bands and from 0.35 and 0.77 (p<0.01) for broad bands. For leaf N, r ² values ranged from 0.41 to 0.68 (p<0.01) for narrow bands and 0.37 to 0.56 (p<0.01) for broad bands. These results are sufficiently promising to suggest that MCARI/MTVI2 employing broadband RapidEye^? data is useful for predicting wheat N status.     

Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CI_green; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CI_green had a large linear correlation with chlorophyll content ranging from 100–1000 mg m^?2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m^?2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m^?2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r ² ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CI_green. EVI had a greater precision (r ²=0.81) than MASVI (r ²=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.     

Narrow band vegetation indices overcome the saturation problem in biomass estimation

Remotely sensed vegetation indices such as NDVI, computed using the red and near infrared bands have been used to estimate pasture biomass. These indices are of limited value since they saturate in dense vegetation. In this study, we evaluated the potential of narrow band vegetation indices for characterizing the biomass of Cenchrus ciliaris grass measured at high canopy density. Three indices were tested: Modified Normalized Difference Vegetation Index (MNDVI), Simple Ratio (SR) and Transformed Vegetation Index (TVI) involving all possible two band combinations between 350?nm and 2500?nm. In addition, we evaluated the potential of the red edge position in estimating biomass at full canopy cover. Results indicated that the standard NDVI involving a strong chlorophyll absorption band in the red region and a near infrared band performed poorly in estimating biomass (R ²=0.26). The MNDVIs involving a combination of narrow bands in the shorter wavelengths of the red edge (700–750?nm) and longer wavelengths of the red edge (750–780?nm), yielded higher correlations with biomass (mean R ²=0.77 for the highest 20 narrow band NDVIs). When the three vegetation indices were compared, SR yielded the highest correlation coefficients with biomass as compared to narrow band NDVI and TVI (average R ²=0.80, 0.77 and 0.77 for the first 20 ranked SR, NDVI and TVI respectively). The red edge position yielded comparable results to the narrow band vegetation indices involving the red edge bands. These results indicate that at high canopy density, pasture biomass may be more accurately estimated by vegetation indices based on wavelengths located in the red edge than the standard NDVI.     

Biotic controls over spectral reflectance of arctic tundra vegetation

In this study, seasonal field measurements of the normalized difference vegetation index (NDVI), using a field spectroradiometer, and leaf area index (LAI), using a LI‐COR LAI‐2000 Plant Canopy Analyzer, were compared with above‐ground phytomass data to investigate relationships between vegetation properties and spectral indices for four distinct tundra vegetation types at Ivotuk, Alaska (68.49°?N, 155.74°?W). NDVI, LAI and above‐ground phytomass data were collected biweekly from four 100?m×100?m grids, each representative of a different vegetation type, during the 1999 growing season. Shrub phytomass, especially the live foliar deciduous shrub phytomass, was the major factor controlling NDVI across all vegetation types. LAI showed the strongest relationship with the overstorey component (total above‐ground excluding moss and lichen) of phytomass and also showed a significant relationship with NDVI. The results from this study illustrated that time of the growing season in which sampling is conducted, non‐linearity of relationships, and plant composition are important factors to consider when using relationships between NDVI, LAI and phytomass to parameterize or validate ecological models. The relationships established in this study also suggest that NDVI is useful for estimating levels of total live above‐ground phytomass and LAI in tundra vegetation.     

Evaluation of hyperspectral indices for retrieval of canopy equivalent water thickness and gravimetric water content

There are two main parameters describing the amount of water in vegetation: the gravimetric water content (GWC) and the equivalent water thickness (EWT). In this study, we investigated the applicability of hyperspectral water-sensitive indices from canopy spectra for estimating canopy EWT (CEWT) and GWC. First, the spectral reflectance’s response to different levels of canopy water content was analysed and a noticeable increase in the slope of the near-infrared (NIR) shoulder of the canopy spectrum was observed. Next, the correlation between the CEWT and various hyperspectral water-sensitive indices was investigated. It was found that all of the indices could retrieve the CEWT of winter wheat well, with the coefficients of determination (R²) all being higher than 0.80. Finally, the retrieval performance of these indices for canopy GWC was evaluated and no significant correlation was observed between canopy GWC and the water-sensitive indices except for the spectral ratio index in the NIR shoulder region (NSRI). These results showed that the traditional water-sensitive vegetation indices are more sensitive to CEWT than to GWC, especially when the LAI is not highly correlated with the GWC, and that the NSRI is a potential vegetation index for use in the retrieval of GWC.     

Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest

Structural and functional analyses of ecosystems benefit when high accuracy vegetation coverages can be derived over large areas. In this study, we utilize IKONOS, Landsat 7 ETM+, and airborne scanning light detection and ranging (lidar) to quantify coniferous forest and understory grass coverages in a ponderosa pine (Pinus ponderosa) dominated ecosystem in the Black Hills of South Dakota. Linear spectral mixture analyses of IKONOS and ETM+ data were used to isolate spectral endmembers (bare soil, understory grass, and tree/shade) and calculate their subpixel fractional coverages. We then compared these endmember cover estimates to similar cover estimates derived from lidar data and field measures. The IKONOS-derived tree/shade fraction was significantly correlated with the field-measured canopy effective leaf area index (LAI_e) (r²=0.55, p<0.001) and with the lidar-derived estimate of tree occurrence (r²=0.79, p<0.001). The enhanced vegetation index (EVI) calculated from IKONOS imagery showed a negative correlation with the field measured tree canopy effective LAI and lidar tree cover response (r²=0.30, r=−0.55 and r²=0.41, r=−0.64, respectively; p<0.001) and further analyses indicate a strong linear relationship between EVI and the IKONOS-derived grass fraction (r²=0.99, p<0.001). We also found that using EVI resulted in better agreement with the subpixel vegetation fractions in this ecosystem than using normalized difference of vegetation index (NDVI). Coarsening the IKONOS data to 30 m resolution imagery revealed a stronger relationship with lidar tree measures (r²=0.77, p<0.001) than at 4 m resolution (r²=0.58, p<0.001). Unmixed tree/shade fractions derived from 30 m resolution ETM+ imagery also showed a significant correlation with the lidar data (r²=0.66, p<0.001). These results demonstrate the power of using high resolution lidar data to validate spectral unmixing results of satellite imagery, and indicate that IKONOS data and Landsat 7 ETM+ data both can serve to make the important distinction between tree/shade coverage and exposed understory grass coverage during peak summertime greenness in a ponderosa pine forest ecosystem.     

Comparison of ERS-2 SAR and Landsat TM imagery for monitoring agricultural crop and soil conditions

Studies over the past 25 years have shown that measurements of surface reflectance and temperature (termed optical remote sensing) are useful for monitoring crop and soil conditions. Far less attention has been given to the use of radar imagery, even though synthetic aperture radar (SAR) systems have the advantages of cloud penetration, all-weather coverage, high spatial resolution, day/night acquisitions, and signal independence of the solar illumination angle. In this study, we obtained coincident optical and SAR images of an agricultural area to investigate the use of SAR imagery for farm management. The optical and SAR data were normalized to indices ranging from 0 to 1 based on the meteorological conditions and sun/sensor geometry for each date to allow temporal analysis. Using optical images to interpret the response of SAR backscatter (σ^o) to soil and plant conditions, we found that SAR σ^o was sensitive to variations in field tillage, surface soil moisture, vegetation density, and plant litter. In an investigation of the relation between SAR σ^o and soil surface roughness, the optical data were used for two purposes: (1) to filter the SAR images to eliminate fields with substantial vegetation cover and/or high surface soil moisture conditions, and (2) to evaluate the results of the investigation. For dry, bare soil fields, there was a significant correlation (r²=.67) between normalized SAR σ^o and near-infrared (NIR) reflectance, due to the sensitivity of both measurements to surface roughness. Recognizing the limitations of optical remote sensing data due to cloud interference and atmospheric attenuation, the findings of this study encourage further studies of SAR imagery for crop and soil assessment.     

Spectral response of a plant canopy with different soil backgrounds

The spectral behavior of a cotton canopy with four soil types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra.     

Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands: A case study

Quantification of biophysical parameters is needed by terrestrial process modeling and other applications. A study testing the role of multispectral data for monitoring biophysical parameters was conducted over a network of grassland field sites in the Great Plains of North America. Grassland biophysical parameters [leaf area index (LAI), fraction of absorbed photosynthetically active radiation (fPAR), and biomass] and their relationships with ground radiometer normalized difference vegetation index (NDVI) were established in this study (r²=.66–.85) from data collected across the central and northern Great Plains in 1995. These spectral/biophysical relationships were compared to 1996 field data from the Tallgrass Prairie Preserve in northeastern Oklahoma and showed no consistent biases, with most regression estimates falling within the respective 95% confidence intervals. Biophysical parameters were estimated for 21 “ground pixels” (grids) at the Tallgrass Prairie Preserve in 1996, representing three grazing/burning treatments. Each grid was 30×30 m in size and was systematically sampled with ground radiometer readings. The radiometric measurements were then converted to biophysical parameters and spatially interpolated using geostatistical kriging. Grid-based biophysical parameters were monitored through the growing season and regressed against Landsat Thematic Mapper (TM) NDVI (r²=.92–.94). These regression equations were used to estimate biophysical parameters for grassland TM pixels over the Tallgrass Prairie Preserve in 1996. This method maintained consistent regression development and prediction scales and attempted to minimize scaling problems associated with mixed land cover pixels. A method for scaling Landsat biophysical parameters to coarser resolution satellite data sets (1 km²) was also investigated.     

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.