Changes In Canopy Reflectance Research Articles

Spectral monitoring of wheat leaf nitrogen content based on canopy structure information compensation

Nitrogen is an important nutrient element for crop growth. A timely understanding of plant nitrogen information helps to adopt appropriate agricultural production management to maintain high yield and quality in wheat production. Crop spectral monitoring technology can obtain the leaf nitrogen content (LNC) information of wheat quickly and nondestructively. However, optical radiation interacts with the atmosphere, the canopy, and the soil before being captured by the sensor, and the capability to intercept, reflect, and transmit the radiation is different for different canopy structures. In wheat LNC monitoring, differences in target canopy structures will lead to changes in canopy reflectance, which can affect the monitoring accuracy. In this study, RGB and depth images were used to obtain wheat canopy structure indices. By analyzing the correlations between wheat LNC, canopy structure indices, and spectral reflectance, the indices that had large influence on spectral reflectance were screened; the change dynamics of these canopy structure indices under different internal and external factors were compared, and factors with greater impact were selected as the basis for grouping. On this basis, this study used the spectral indices RVI (660, 815) and RVI (730, 815) as fixed-effect variables and wheat population grouping variables as random-effect variables to construct linear mixed models of wheat LNC. In addition, the spectral indices and canopy structure indices were used as input parameters and wheat population grouping variables as output parameters to construct a random forest classifier for wheat canopy types. When monitoring the target population, we first predicted the classification of the unknown canopy by the classifier. The classification results and spectral indices were then input into the linear mixed models to realize the prediction of wheat LNC. The R2 of the prediction for wheat LNC with RVI (660, 815) and RVI (730, 815) increased from 0.57 and 0.71 to 0.76 and 0.80, respectively, and the RRMSE decreased from 20.86% and 17.33% to 15.58% and 14.20%, respectively. This study used digital image information to compensate for spectral information, broadened the amount of information used for the remote sensing monitoring of farmland, and effectively improved the accuracy and universality of wheat LNC monitoring.

Phenology of leaf optical properties and their relationship to mesophyll development in cool-temperate deciduous broad-leaf trees.

Leaf optical properties (reflectance and transmittance spectra of a single leaf) are the key to interpreting remote sensing data regarding the structure and functions of vegetation canopies. These properties are determined by leaf biochemical and anatomical traits, which change seasonally in deciduous species. To reveal the species-specific seasonal patterns in leaf optical properties and their relationship to chlorophyll content and/or mesophyll structure in deciduous trees, we examined these mechanistic relationships in a pioneer species, Betula ermanii, and in sunlit and shade leaves of a late-successional species, Quercus crispula, during their seasonal development. Leaf optical properties were measured from leaf emergence to leaf fall in four years. After leaf unfolding, reflectance in the photosynthetically active radiation (PAR) region decreased gradually, while transmittance dropped rapidly. Both reflectance and transmittance increased before leaf fall. In the near-infrared (NIR) region, reflectance increased and transmittance decreased during the development period. Values of parameter N (the number of compact layers within a leaf in PROSPECT-5 model) in young leaves was very low (1.1 for B. ermanii and 1.0 for sunlit and shade leaves of Q. crispula) but increased rapidly by 30% for B. ermanii and sunlit leaves of Q. crispula and by 20% for shade leaves. N was higher in the order of B. ermanii > Q. crispula sun leaves > shade leaves. The PROSPECT-5 simulation with our measured data showed that the development of mesophyll tissue increases reflectance and decreases transmittance in both NIR and PAR regions, and an increase in chlorophyll decreases both reflectance and transmittance in PAR region. Our results indicate that structural and biochemical development of leaf mesophyll and its interspecific variations are essential biological characteristics for understanding seasonal changes in canopy reflectance. The results of this study will help us to better analyze remotely sensed data.

In-Field Detection and Quantification of Septoria Tritici Blotch in Diverse Wheat Germplasm Using Spectral-Temporal Features.

Hyperspectral remote sensing holds the potential to detect and quantify crop diseases in a rapid and non-invasive manner. Such tools could greatly benefit resistance breeding, but their adoption is hampered by i) a lack of specificity to disease-related effects and ii) insufficient robustness to variation in reflectance caused by genotypic diversity and varying environmental conditions, which are fundamental elements of resistance breeding. We hypothesized that relying exclusively on temporal changes in canopy reflectance during pathogenesis may allow to specifically detect and quantify crop diseases while minimizing the confounding effects of genotype and environment. To test this hypothesis, we collected time-resolved canopy hyperspectral reflectance data for 18 diverse genotypes on infected and disease-free plots and engineered spectral–temporal features representing this hypothesis. Our results confirm the lack of specificity and robustness of disease assessments based on reflectance spectra at individual time points. We show that changes in spectral reflectance over time are indicative of the presence and severity of Septoria tritici blotch (STB) infections. Furthermore, the proposed time-integrated approach facilitated the delineation of disease from physiological senescence, which is pivotal for efficient selection of STB-resistant material under field conditions. A validation of models based on spectral–temporal features on a diverse panel of 330 wheat genotypes offered evidence for the robustness of the proposed method. This study demonstrates the potential of time-resolved canopy reflectance measurements for robust assessments of foliar diseases in the context of resistance breeding.

Estimating Leaf Area Index with a New Vegetation Index Considering the Influence of Rice Panicles

The emergence of rice panicle substantially changes the spectral reflectance of rice canopy and, as a result, decreases the accuracy of leaf area index (LAI) that was derived from vegetation indices (VIs). From a four-year field experiment with using rice varieties, nitrogen (N) rates, and planting densities, the spectral reflectance characteristics of panicles and the changes in canopy reflectance after panicle removal were investigated. A rice “panicle line”—graphical relationship between red-edge and near-infrared bands was constructed by using the near-infrared and red-edge spectral reflectance of rice panicles. Subsequently, a panicle-adjusted renormalized difference vegetation index (PRDVI) that was based on the “panicle line” and the renormalized difference vegetation index (RDVI) was developed to reduce the effects of rice panicles and background. The results showed that the effects of rice panicles on canopy reflectance were concentrated in the visible region and the near-infrared region. The red band (670 nm) was the most affected by panicles, while the red-edge bands (720–740 nm) were less affected. In addition, a combination of near-infrared and red-edge bands was for the one that best predicted LAI, and the difference vegetation index (DI) (976, 733) performed the best, although it had relatively low estimation accuracy (R2 = 0.60, RMSE = 1.41 m2/m2). From these findings, correcting the near-infrared band in the RDVI by the panicle adjustment factor (θ) developed the PRDVI, which was obtained while using the “panicle line”, and the less-affected red-edge band replaced the red band. Verification data from an unmanned aerial vehicle (UAV) showed that the PRDVI could minimize the panicle and background influence and was more sensitive to LAI (R2 = 0.77; RMSE = 1.01 m2/m2) than other VIs during the post-heading stage. Moreover, of all the assessed VIs, the PRDVI yielded the highest R2 (0.71) over the entire growth period, with an RMSE of 1.31 (m2/m2). These results suggest that the PRDVI is an efficient and suitable LAI estimation index.

Reliability and limitations of a novel terrestrial laser scanner for daily monitoring of forest canopy dynamics

Leaf area index (LAI) or plant area index (PAI) are commonly used to represent canopy structure and dynamics, but daily estimation of these variables using traditional ground-based methods is impractical and prone to multiple errors during data acquisition and processing. Existing terrestrial laser scanners can provide accurate representation of forest canopy structure, but the sensors are expensive, data processing is complex, and measurements are typically confined to a single event, which severely limits their utility in the interpretation of canopy trends indicated by remotely sensed data. We tested a novel, low-cost terrestrial laser scanner for its capacity to provide reliable and successive assessments of canopy PAI in an evergreen eucalypt forest. Daily scans comprised of 920 range measurements were made by three scanners at one forest site over a two-year period, providing mostly consecutive estimates of PAI, and of vertical structure profiles (as Plant Area Volume Density, PAVD). Data filtering, involving objective statistical methods to identify outliers, indicated that scan quality was adversely affected by moist weather and moderate wind speeds (>4ms−1), suggesting limited utility in some forest environments. Data cleaning (associated with sensor malfunctions) plus filtering removed 32 to 49% of scans, leaving on average 57% of data over the two-year period. Nonetheless, we found strong agreement between lidar-derived PAI estimates, and those from monthly hemispherical images (±0.1 PAI); with both methods indicating mostly stable PAI over multiple seasons. The PAVD profiles from the laser scanner indicated that leaf flush in the upper canopy concomitantly balanced leaf loss from the middle canopy in summer, which was consistent with measured summer peaks in litter fall. This clearly illustrated the advantages of three-dimensional lidar data over traditional two-dimensional PAI estimates in monitoring tree phenology, and in interpreting changes in canopy reflectance as detected by air- and space-borne remotely sensed data.

Modelling the spectral response of the desert tree Prosopis tamarugo to water stress

In this paper, we carried out a laboratory experiment to study changes in canopy reflectance of Tamarugo plants under controlled water stress. Tamarugo (Prosopis tamarugo Phil.) is an endemic and endangered tree species adapted to the hyper-arid conditions of the Atacama Desert, Northern Chile. Observed variation in reflectance during the day (due to leaf movements) as well as changes over the experimental period (due to water stress) were successfully modelled by using the Soil-Leaf-Canopy (SLC) radiative transfer model. Empirical canopy reflectance changes were mostly explained by the parameters leaf area index (LAI), leaf inclination distribution function (LIDF) and equivalent water thickness (EWT) as shown by the SLC simulations. Diurnal leaf movements observed in Tamarugo plants (as adaptation to decrease direct solar irradiation at the hottest time of the day) had an important effect on canopy reflectance and were explained by the LIDF parameter. The results suggest that remote sensing based assessment of this desert tree should consider LAI and canopy water content (CWC) as water stress indicators. Consequently, we tested fifteen different vegetation indices and spectral absorption features proposed in literature for detecting changes of LAI and CWC, considering the effect of LIDF variations. A sensitivity analysis was carried out using SLC simulations with a broad range of LAI, LIDF and EWT values. The Water Index was the most sensitive remote sensing feature for estimating CWC for values less than 0.036g/cm2, while the area under the curve for the spectral range 910–1070nm was most sensitive for values higher than 0.036g/cm2. The red-edge chlorophyll index (CIred-edge) performed the best for estimating LAI. Diurnal leaf movements had an effect on all remote sensing features tested, particularly on those for detecting changes in CWC.

Early Detection of Soybean Plant Injury from Glyphosate by Measuring Chlorophyll Reflectance and Fluorescence

Early detection of crop injury from off-target drift of herbicide is critical in crop production. Subtle changes in canopy reflectance could present useful information to detect the onset of crop stress. This study was conducted in a greenhouse to evaluate a portable spectroradiometer and a portable chlorophyll fluorometer for the detection of crop injury caused by glyphosate spray. Non-glyphosate resistant soybean (Glycine max L. Merr.) plants were sprayed with glyphosate using a pneumatic track sprayer in a spray chamber. Four plants received a rate of 0.86 kg ae/ha glyphosate and four plants received 0.086 kg ae/ha. Additional four non-sprayed plants were used as controls. After the glyphosate spray, the chlorophyll reflectance of the plants was measured with the spectroradiometer at 4, 24, 48, and 72 hours to determine the plant response to herbicide. Simultaneously, fluorescence induction kinetics of the crop under stress was measured with the portable chlorophyll fluorometer. Results of the statistical mean separation indicated that the plant chlorophyll reflectance measurement could be used to differentiate crop stress from glyphosate at 24 hours after spray among treatments and to identify the effect of herbicide at 24 hours after spray in each treatment. Moreover, linear discriminant analysis with the reflectance data showed that the crop stress of the soybean plants from glyphosate could be identified at 24 hours or more post application. Results of the statistical mean separation also indicated that use of plant chlorophyll fluorescence measurement could not differentiate crop stress until 48 hours after spray among treatments while it could identify the effect of herbicide 24 hours after spray in each treatment. These findings demonstrate that chlorophyll reflectance and fluorescence measurements both could be used for early detection of crop stress.

Seasonal variation in canopy reflectance and its application to determine the water status and water use by citrus trees in the Western Cape, South Africa

This study describes the diurnal and seasonal dynamics of the canopy reflectance, water use and water status of Midknight Valencia citrus trees under semi-arid conditions. Hyperspectral canopy reflectance data was collected on 30 trees at monthly intervals over a period of 16 months in a commercial orchard in South Africa. The mean canopy reflectance in the wavelength range 350–2500 nm followed a clear seasonal trend influenced by environmental conditions and tree phenology. Mean monthly reflectance peaked in summer (∼22%) while the lowest value (∼15%) was reached in winter with the seasonal changes in the sun's position accounting for a significant proportion of the variations. A sensitivity analysis of a Penman–Monteith transpiration model showed that water use by individual trees changed by up to 13% when the canopy reflectance was varied over the seasonal range of measured values. This suggested that the seasonal changes in tree water use influenced the seasonal trend of the canopy reflectance. Thus monitoring the canopy reflectance of citrus trees could offer information on the tree water status. To test this, sap flow data of water uptake and loss by the trees were compared with the canopy spectra. Sap flow data showed a heavy reliance by the citrus trees on the internally stored water with up to 25% of the daily total transpiration withdrawn from the trees’ internal water storage pools when soil water was limited. This depletion of internally stored water, and hence the change in tree water status, was detected using spectral indices based on the first order derivatives of the canopy reflectance centered at two and, at most, four spectral bands. We conclude that even if citrus trees are evergreen, their canopy reflectance changes significantly throughout the year with a considerable impact on tree energy balance and water use. In addition, the contribution of the internally stored water to daily transpiration is a possible indicator of drought stress for citrus trees detectable from changes in canopy reflectance and it has potential applications in irrigation scheduling using canopy level spectral information.

Improving predictions of forest growth using the 3-PGS model with observations made by remote sensing

Measurements made by remote sensing can characterize the leaf area density and nitrogen/chlorophyll content of forest canopies, as well as maximum photosynthetic capacity and above-ground structure and biomass. Combining these with climate data estimated from relationships based on temperature measurements and using an appropriate process-based model, it is possible to calculate, with useful accuracy, carbon sequestration and wood production by different forest types covering large land areas. To broaden its application and reduce the need for detailed information on stand characteristics, a satellite-driven version of the model 3-PG, was developed. The 3-PGS model incorporates the major first-order physiological processes that determine forest growth, and the biophysical factors that affect and govern those processes. It incorporates remotely sensed estimates of seasonal variation in canopy light interception ( fPAR) and includes physiological variables (stomatal conductance and canopy quantum efficiency) that can be estimated by remote-sensing measurements of factors that influence those variables. 3-PGS therefore provides a useful framework within which to evaluate how data from the array of airborne and satellite-borne sensors now available might be used to initialize, drive, and test process-based growth models across regions with diverse soils and climates. We address the question: to what extent might additional remote-sensing techniques improve 3-PGS predictions? Sensitivity analyses indicate that model accuracy would be most improved through better estimates of seasonal changes in canopy photosynthetic capacity ( α) and canopy conductance ( G c ). Canopy photosynthetic capacity depends on the amount of light absorbed by the canopy, estimated as a fraction of photosynthetically active radiation ( fPAR), and on foliage nitrogen or chlorophyll content, which can be estimated using multi-spectral imagery. G c depends on canopy leaf area index ( L) and stomatal conductance of the foliage ( g s ), which is affected by the vapor pressure deficit of the air and soil water content. The onset and effects of drought can be determined from changes in canopy reflectance and fPAR identified from sequential measurements; the same measurements, coupled with calculations of evapotranspiration using climatic data and standard formulae, provide estimates of total available water in forest root zones. Periodic surveys with Light Detection and Ranging (LiDAR) and interferometric RADAR may serve to validate model predictions of above-ground growth ( NPP A ), while progressive reduction in light-use efficiency ( NPP A / APAR) may identify forests with declining vigor that are likely to succumb to attack from insects and pathogens.

Detection of foliage conditions and disturbance from multi-angular high spectral resolution remote sensing

Disturbance of forest ecosystems, an important component of the terrestrial carbon cycle, has become a focus of research over recent years, as global warming is about to increase the frequency and severity of natural disturbance events. Remote sensing offers unique opportunities for detection of forest disturbance at multiple scales; however, spatially and temporally continuous mapping of non-stand replacing disturbance remains challenging. First, most high spatial resolution satellite sensors have relatively broad spectral ranges with bandwidths unsuitable for detection of subtle, stress induced, features in canopy reflectance. Second, directional and background reflectance effects, induced by the interactions between the sun-sensor geometry and the observed canopy surface, make up-scaling of empirically derived relationships between changes in spectral reflectance and vegetation conditions difficult. Using an automated tower based spectroradiometer, we analyse the interactions between canopy level reflectance and different stages of disturbance occurring in a mountain pine beetle infested lodgepole pine stand in northern interior British Columbia, Canada, during the 2007 growing season. Directional reflectance effects were modelled using a bidirectional reflectance distribution function (BRDF) acquired from high frequency multi-angular spectral observations. Key wavebands for observing changes in directionally corrected canopy spectra were identified using discriminant analysis and highly significant correlations between canopy reflectance and field measured disturbance levels were found for several broad and narrow waveband vegetation indices (for instance, r 2 NDVI = 0.90; r 2 CHL3 = 0.85; p < 0.05). Results indicate that multi-angular observations are useful for extraction of disturbance related changes in canopy reflectance, in particular the temporally and spectrally dense data detected changes in chlorophyll content well. This study will help guide and inform future efforts to map forest health conditions at landscape and over increasingly coarse scales.

Hyperspectral indices for detecting changes in canopy reflectance as a result of underground natural gas leakage

Natural gas leakage from underground pipelines is known to affect vegetation adversely, probably by displacement of the soil oxygen needed for respiration. This causes changes in plant and canopy reflectance, which may serve as indicators of gas leakage. In this study, a covariance analysis was performed between reflectance indices of maize (Zea mays) and wheat (Triticum aestivum) canopies and oxygen concentrations in a simulated natural gas leak. Twenty‐nine days after oxygen shortage occurred, the reflectance indices had the highest correlation with oxygen concentrations in the soil, for both species. The effect was consistent within species but the absolute values varied between the species. Normalization by adding a constant value to the control index of one species resulted in significant linear regression models for several indices. The indices with the highest regression coefficients were used to predict the oxygen concentration in the soil. This showed that the gas leakage caused reflectance changes up to 0.5 m from the source. As it could not be proven that oxygen shortage was the cause of the reflectance changes, further work is needed to study the side‐effects of gas leakage, such as bacterial oxygen depletion, on plant growth and reflectance.

Novel Analysis of Hyperspectral Reflectance Data for Detecting Onset of Pollen Shed in Maize

Knowledge of pollen shed dynamics in and around seed production fields is critical for ensuring a high yield of genetically pure corn seed. Recently, changes in canopy reflectance using hyperspectral reflectance have been associated with tassel emergence, which is known to precede pollen shed in a predictable manner. Practical application of this remote sensing technology, however, requires a simple and reliable method to evaluate changes in spectral images associated with the onset of tassel emergence and pollen shed. In this study, several numerical methods were investigated for estimating percentage of plants with visible tassels (VT) and percentage of plants that initiated pollen shed (IPS) from remotely sensed hyperspectral reflectance data (397 to 902 nm). Correlation analysis identified regions of the spectra that were associated with tassel emergence and anthesis (i.e., 50% of plants shedding pollen). No single band, however, generated correlations greater than 0.40 for either VT or IPS. Classification using an artificial neural network (ANN) was predictive, correctly classifying 83.5% and 88.3% of the VT and IPS data, respectively. The extensive preprocessing necessary and the black box nature of ANNs, however, rendered analysis of spectral regions difficult using this method. Partial least squares (PLS) analysis yielded models with high predictive capability (R2 of 0.80 for VT and 0.79 for IPS). The PLS coefficients, however, did not exhibit a spectrally consistent pattern. A novel range operator-enabled genetic algorithm (ROE-GA), designed to consider the shape of the spectra, had similar predictive capabilities to the ANN and PLS, but provided the added advantage of allowing information transfer for increased domain knowledge. The ROE-GA analysis is the preferred method to evaluate hyperspectral reflectance data and associate spectral changes to tassel emergence and the onset of pollen shed in corn on a field scale.

Response of an Indeterminate Soybean {Glycine Max (L.) Merr} to Chronic Water Deficit During Reproductive Development Under Greenhouse Conditions

Although crops are commonly exposed to chronic moderate water deficit stress, most research has been focused on catastrophic and extreme moisture deficit stress. A greenhouse experiment was conducted to: (i) assess the impact of chronic water stress, during reproductive development, on soybean growth and productivity; (ii) determine the effects of water stress on soybean canopy architecture; and (iii) evaluate changes in canopy reflectance due to water deficit. Soybean plants were grown under three moisture regimes: daily watering at 100% (control), 50% (medium stress W2) and 25% (severe stress W1) of evapotranspiration. Water stress during reproductive development decreased plant physiological activity, vegetative growth, and productivity and had a visible impact on plant canopy architecture at both stress levels. Water deficit reduced leaf chlorophyll content at the severe stress level. At the physiological level, the major effect of chronic water stress was a decrease in stomatal conductance. Water stress decreased the rate of plant vegetative growth for all variables measured, suggesting that water deficit during reproductive growth might have induced an early switch of plant development from vegetative to reproductive. Water deficit, at W1 and W2, did not affect the pod dry weight to total canopy dry weight ratio. This suggested that the proportional allocation of dry matter to reproductive structures was maintained and, although the stress was imposed during reproductive development, vegetative and reproductive structures were equally affected. This may be a function of the indeterminate nature of the soybean cultivar. Paraheliotropism behaviour was observable at W2, but not at W1. Thus, there might be a threshold stress level beyond which leaf movement cannot be activated, preventing control of transpiration through canopy movement. Water stress affected the spectral reflectance signature of soybean leaves, increasing leaf reflectance in the visible and decreasing it in the infrared spectral ranges at both stress levels.

Early Detection of Douglas-Fir Beetle Infestation with Subcanopy Resolution Hyperspectral Imagery

Abstract Early detection of insect or pathogen infestations in forests would be useful to forest managers who want to make decisions that minimize timber losses. Typical methods of forest reconnaissance to detect infestations have included analysis of multispectral imagery. Multispectral imagery, however, often lacks the sensitivity to detect subtle changes in tree canopy reflectance because of physiologic stress from insects or pathogens. Most hyperspectral imaging has the sensitivity to detect subtle changes in canopy reflectance but lacks high spatial resolution to identify affected trees. Our study examined the use of subcanopy spatial resolution hyperspectral imagery for differentiating Douglas-fir trees attacked by the Douglas-fir beetle. Comparison of the accuracies of step-wise discriminant analysis and classification and regression tree analysis (CART) revealed that CART provided the best separability among tree health classes (93% overall) because of CART's ability to use different band combinations for each class. Predictive accuracy of the CART method was estimated through cross-validation of the dataset using a jackknife resampling technique. Overall classification accuracy was promising (69%), as was classification among healthy and attacked, but still living, trees (50–70%). The results of our study provide support that hyperspectral imagery might be used for detecting and mapping tree stress in Douglas-fir stands. Although the rapid progress of beetle infestation somewhat limited the ability to differentiate among tree stress classes, which might limit the utility of this approach for fast moving infestations, the results were well beyond what might be expected from alternative detection methods. Slower moving infestations would benefit from the use of hyperspectral imagery because a lower percentage of infested trees would be asymtomatic. West. J. Appl. For. 18(3):202–206.

Canopy Reflectance as a Measure of Disease in Tall Fescue

Measurement of changes in canopy reflectance of tall fescue (Festuca arundinacea Schreb.), which result from deterioration of tissues caused by disease organisms, is a potential unbiased method for quantifying disease. Canopy reflectance in eight spectral bands between 430 and 840 nm and 18 vegetation indices derived from the spectral bands were regressed against visual severity estimates of Rhizoctonia blight, caused by Rhizoctonia solani Kühn, and gray leaf spot, caused by Pyricularia grisea (Cooke) Sacc. Reflectance within the 810‐nm band exhibited the strongest relationship (19% ≤ r2 ≤ 63%) with visual severity estimates of Rhizoctonia blight and gray leaf spot. Reflectance in the visible or near infra‐red wavelengths was similar to tall fescue blighted by either fungal pathogen. A significant (P ≤ 0.05) negative linear relationship was observed between canopy reflectance in the 810‐nm band and the severity of either Rhizoctonia blight or gray leaf spot. As Rhizoctonia blight decreased with increases in the application rate of the fungicide flutolanil (N‐[3‐(1‐methylethoxy)‐phenyl]‐2‐(trifluoromethyl)benzamide) and the proportion of a resistant cultivar in tall fescue blends, canopy reflectance in the 810‐nm band increased. However, models based on canopy reflectance had twice as much unexplained variability than models based on visual severity estimates of Rhizoctonia blight. Factors other than disease influenced variability in reflectance of light from tall fescue canopies.

Shinnery oak bidirectional reflectance properties and canopy model inversion

The authors present field measurements and the results of a three-dimensional canopy model inversion for sand shinnery oak. Spectral bidirectional radiance measurements in three spectral channels, 0.65-0.67 mu m, 0.81-0.84 mu m, and 1.62-1.69 mu m, encompassing both the complete land surface and sky hemispheres, were acquired for a sand shinnery oak plant community in west Texas. The changes in canopy reflectance that occur with variations in solar zenith angle and view direction and for two seasons of the year were evaluated. A three-dimensional radiation interaction model (TRIM) was then inverted to estimate oak leaf area index (LAI) and canopy density, expressed as percentage of cover, from the bidirectional reflectance data. >

A geobotanical investigation based on linear discriminant and profile analyses of airborne thematic mapper simulator data

This paper discusses the application of linear discriminant and profile analyses to detailed investigation of an airborne Thematic Mapper Simulator (TMS) image collected over a geobotanical test site. The test site was located on the Keweenaw Peninsula of Michigan's Upper Peninsula, and remote sensing data collection coincided with the onset of leaf senescence in the regional deciduous flora, Linear discriminant analysis revealed that sites overlying soil geochemical anomalies were distinguishable from background sites by the reflectance and thermal emittance of the tree canopy imaged in the airborne TMS data. The correlation of individual bands with the linear discriminant function suggested that the TMS thermal Channel 7 (10.32–12.33 μm) contributed most, while TMS Bands 2 (0.53–0.60 μm), 3 (0.63–0.69 μm), and 5 (1.53–1.73 μm) contributed somewhat more modestly to the separation of anomalous and background sites imaged by the TMS. The observed changes in canopy reflectance and thermal emittance of the deciduous flora overlying geochemically anomalous areas are consistent with the biophysical changes which are known or presumed to occur as a result of injury induced in metal-stressed vegetation.

Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1

AbstractSome plant growth models require estimates of leaf area and absorbed radiation for simulating evapotranspiration and photosynthesis. Previous studies indicated that spectral reflectance, absorption of photosynthetically active radiation (PAR), and leaf area index (LAI) are interrelated. The objective of this study was to establish a procedure by which spectral reflectance can be used to simultaneously estimate PAR absorption and LAI. A method is presented for estimating the quantity of absorbed PAR by wheat (Triticum aestivumL.) plants and their LAI based on the normalized difference (ND), transformation of the near infrared (ρn= 800 to 1100 nm) and red (ρr= 600 to 700 nm) canopy reflectances. The results, from a theoretical analysis and field measurements, indicated that ND correlates with the fraction of PAR absorbed by wheat canopies. Bare soil reflectance and scattering of near infrared radiation by foliage elements were the major factors that influenced the relation between ND and PAR absorption. The estimated PAR absorption values, based on the ND, and four classes of assumed leaf angles (45°, 60°, 75°, and spherical), were used to indirectly evaluate LAI of wheat for three different geographical locations. The standard deviation on mean predicted to measured LAI's for the three locations varied from 0.5 to 0.9 for a range of 0 to 6 LAI. The method is considerably less sensitive in predicting LAI above 6.0 since the sensitivity of ND to changes in LAI becomes small (&lt;0.01), due to small changes in canopy reflectance.