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1.
The research evaluated the information content of spectral reflectance (laboratory and airborne data) for the estimation of needle chlorophyll (CAB) and nitrogen (CN) concentration in Norway spruce (Picea abies L. Karst.) needles. To identify reliable predictive models different types of spectral transformations were systematically compared regarding the accuracy of prediction. The results of the cross-validated analysis showed that CAB can be well estimated from laboratory and canopy reflectance data. The best predictive model to estimate CAB was achieved from laboratory spectra using continuum-removal transformed data (R2cv = 0.83 and a relative RMSEcv of 8.1%, n = 78) and from hyperspectral HyMap data using band-depth normalised spectra (R2cv = 0.90, relative RMSEcv = 2.8%, n = 13). Concerning the nitrogen concentration, we observed somewhat weaker relations, with however still acceptable accuracies (at canopy level: R2cv = 0.57, relative RMSEcv = 4.6%). The wavebands selected in the regression models to estimate CAB were typically located in the red edge region and near the green reflectance peak. For CN, additional wavebands related to a known protein absorption feature at 2350 nm were selected. The portion of selected wavebands attributable to known absorption features strongly depends on the type of spectral transformation applied. A method called “water removal” (WR) produced for canopy spectra the largest percentage of wavebands directly or indirectly related to known absorption features. The derived chlorophyll and nitrogen maps may support the detection and the monitoring of environmental stressors and are also important inputs to many bio-geochemical process models.  相似文献   

2.
The Normalized Area Over reflectance Curve (NAOC) is proposed as a new index for remote sensing estimation of the leaf chlorophyll content of heterogeneous areas with different crops, different canopies and different types of bare soil. This index is based on the calculation of the area over the reflectance curve obtained by high spectral resolution reflectance measurements, determined, from the integral of the red–near-infrared interval, divided by the maximum reflectance in that spectral region. For this, use has been made of the experimental data of the SPARC campaigns, where in situ measurements were made of leaf chlorophyll content, LAI and fCOVER of 9 different crops – thus, yielding 300 different values with broad variability of these biophysical parameters. In addition, Proba/CHRIS hyperspectral images were obtained simultaneously to the ground measurements. By comparing the spectra of each pixel with its experimental leaf chlorophyll value, the NAOC was proven to exhibit a linear correlation to chlorophyll content. Calculating the correlation between these variables in the 600–800 nm interval, the best correlation was obtained by computing the integral of the spectral reflectance curve between 643 and 795 nm, which practically covers the spectral range of maximum chlorophyll absorption (at around 670 nm) and maximum leaf reflectance in the infrared (750–800 nm). Based on a Proba/CHRIS image, a chlorophyll map was generated using NAOC and compared with the land-use (crops classification) map. The method yielded a leaf chlorophyll content map of the study area, comprising a large heterogeneous zone. An analysis was made to determine whether the method also serves to estimate the total chlorophyll content of a canopy, multiplying the leaf chlorophyll content by the LAI. To validate the method, use was made of the data from another campaign ((SEN2FLEX), in which measurements were made of different biophysical parameters of 7 crops, and hyperspectral images were obtained with the CASI imaging radiometer from an aircraft. Applying the method to a CASI image, a map of leaf chlorophyll content was obtained, which on, establishing comparisons with the experimental data allowed us to estimate chlorophyll with a root mean square error of 4.2 μg/cm2, similar or smaller than other methods but with the improvement of applicability to a large set of different crop types.  相似文献   

3.
Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970 nm and 1200 nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT + SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970 nm and 1200 nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970 nm can best be used for estimating CWC.  相似文献   

4.
This study describes the retrieval of state variables (LAI, canopy chlorophyll, water and dry matter contents) for summer barley from airborne HyMap data by means of a canopy reflectance model (PROSPECT + SAIL). Three different inversion techniques were applied to explore the impact of the employed method on estimation accuracies: numerical optimization (downhill simplex method), a look-up table (LUT) and an artificial neural network (ANN) approach. By numerical optimization (Num Opt), reliable estimates were obtained for LAI and canopy chlorophyll contents (LAI × Cab) with r2 of 0.85 and 0.94 and RDP values of 1.81 and 2.65, respectively. Accuracies dropped for canopy water (LAI × Cw) and dry matter contents (LAI × Cm). Nevertheless, the range of leaf water contents (Cw) was very narrow in the studied plant material. Prediction accuracies generally decreased in the order Num Opt > LUT > ANN. This decrease in accuracy mainly resulted from an increase in offset in the obtained values, as the retrievals from the different approaches were highly correlated. The same decreasing order in accuracy was found for the difference between the measured spectra and those reconstructed from the retrieved variable values. The parallel application of the different inversion techniques to one collective data set was helpful to identify modelling uncertainties, as shortcomings of the retrieval algorithms themselves could be separated from uncertainties in model structure and parameterisation schemes.  相似文献   

5.
The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m−2) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m−2), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m−2). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m−2) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m−2, 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.  相似文献   

6.
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7.
Determining the foliar N:P ratio provides a tool for understanding nutrient limitation on plant production and consequently for the feeding patterns of herbivores. In order to understand the nutrient limitation at landscape scale, remote sensing techniques offer that opportunity. The objective of this study is to investigate the utility of field spectroscopy and a potential of hyperspectral mapper (HyMap) spectra to estimate foliar N:P ratio. Field spectral measurements were undertaken, and grass samples were collected for foliar N and P extraction. The foliar N:P ratio prediction models were developed using partial least square regression (PLSR) with original spectra and transformed spectra for field and the resampled field spectra to HyMap. Spectral transformations included the continuum removal (CR), water removal (WR), first difference derivative (FD) and log transformation (Log(1/R)). The results showed that CR and WR spectra in combination with PLSR predicted foliar N:P ratio with higher accuracy as compared to FD and R, using field spectra. For HyMap spectral analysis, addition to CR and WR, FD achieved higher estimation accuracy. The performance of FD, CR and WR spectra were attributed to their ability to minimize sensor and water effects on the fresh leaf spectra, respectively. The study demonstrated a potential to predict foliar N:P ratio using field and HyMap simulated spectra and shortwave infrared (SWIR) found to be highly sensitive to foliar N:P ratio. The study recommends the prediction of foliar N:P ratio at landscape level using airborne hyperspectral data and could be used by the resource managers, park managers, farmers and ecologists to understand the feeding patterns, resource selection and distribution of herbivores (i.e. wild and livestock).  相似文献   

8.
The pH is one of the major chemical parameters affecting the results of remediation programs carried out at abandoned mines and dumps and one of the major parameters controlling heavy metal mobilization and speciation. This study is concerned with testing the feasibility of estimating surface pH on the basis of airborne hyperspectral (HS) data (HyMap). The work was carried on the Sokolov lignite mine, as it represents a site with extreme material heterogeneity and high pH gradients. First, a geochemical conceptual model of the site was defined. Pyrite, jarosite or lignite were the diagnostic minerals of very low pH (<3.0), jarosite in association with goethite indicated increased pH (3.0–6.5) and goethite alone characterized nearly neutral or higher pH (>6.5). It was found that these minerals have absorption feature parameters which are common for both forms, individual minerals as well as parts of the mixtures, while the shift to longer wavelengths of the absorption maximum centered between 0.90 and 1.00 μm is the main parameter that allows differentiation among the ferric minerals. The multi range spectral feature fitting (MRSFF) technique was employed to map the defined end-members indicating certain pH ranges in the HS image datasets. This technique was found to be sensitive enough to assess differences in the desired spectral parameters (e.g., absorption shape, depth and indirectly maximum absorption wavelength position). Furthermore, the regression model using the fit images, the results of MRSFF, as inputs was constructed (R2 = 0.61, Rv2 = 0.76) to estimate the surface pH. This study represents one of the few approaches employing image spectroscopy for quantitative pH modeling in a mining environment and the achieved results demonstrate the potential application of hyperspectral remote sensing as an efficient method for environmental monitoring.  相似文献   

9.
The possibility of quantifying iron content in the topsoil of the slopes of the El Hacho Mountain complex in Southern Spain using imaging spectroscopy is investigated. Laboratory, field and airborne spectrometer (ROSIS) data are acquired, in combination with soil samples, which are analysed for dithionite extractable iron (Fed) content. Analysis of the properties of two iron related absorption features present in laboratory spectra demonstrates good relations, especially between the standard deviation (S.D.) of the values in an absorption feature and the Fed content (R2 = 0.67) as well as the ratio based Redness Index (R2 = 0.51). Such derived relations are less strong for the ROSIS data (R2 for S.D. = 0.26 and R2 for Redness Index = 0.22). The spatial distribution of iron in vegetated areas shows a strong sensitivity of these relations with the presence of vegetation. A combination of both methods shows that the overestimation of the Fed content with the one method is (partly) compensated by the underestimation with the other method.  相似文献   

10.
In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.  相似文献   

11.
Hyperspectral sensing can provide an effective means for fast and non-destructive estimation of leaf nitrogen (N) status in crop plants. The objectives of this study were to design a new method to extract hyperspectral spectrum information, to explore sensitive spectral bands, suitable bandwidth and best vegetation indices based on precise analysis of ground-based hyperspectral information, and to develop regression models for estimating leaf N accumulation per unit soil area (LNA, g N m−2) in winter wheat (Triticum aestivum L.). Three field experiments were conducted with different N rates and cultivar types in three consecutive growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and LNA under the various treatments. Then, normalized difference spectral indices (NDSI) and ratio spectral indices (RSI) based on the original spectrum and the first derivative spectrum were constructed within the range of 350–2500 nm, and their relationships with LNA were quantified. The results showed that both LNA and canopy hyperspectral reflectance in wheat changed with varied N rates, with consistent patterns across different cultivars and seasons. The sensitive spectral bands for LNA existed mainly within visible and near infrared regions. The best spectral indices for estimating LNA in wheat were found to be NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516), and the regression models based on the above four spectral indices were formulated as Y = 26.34x1.887, Y = 5.095x − 6.040, Y = 0.609 e3.008x and Y = 0.388x1.260, respectively, with R2 greater than 0.81. Furthermore, expanding the bandwidth of NDSI (R860, R720) and RSI (R990, R720) from 1 nm to 100 nm at 1 nm interval produced the LNA monitoring models with similar performance within about 33 nm and 23 nm bandwidth, respectively, over which the statistical parameters of the models became less stable. From testing of the derived equations, the model for LNA estimation on NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) gave R2 over 0.79 with more satisfactory performance than previously reported models and physical models in wheat. It can be concluded that the present hyperspectral parameters of NDSI (R860, R720), RSI (R990, R720), NDSI (FD736, FD526) and RSI (FD725, FD516) can be reliably used for estimating LNA in winter wheat.  相似文献   

12.
Spectral properties of volcanic materials in the optical region (350–2500 nm) of the electromagnetic spectrum are analyzed. The goal is to characterize air-fall deposits, recent lava flows, and old lava flows based on their spectral reflectance properties and on the textural characteristics (grain size) of pyroclastic deposits at an active basaltic volcano. Data were acquired during a spectroradiometric field survey at Mt. Etna (Italy) in summer 2003 and combined with hyperspectral satellite (Hyperion) and airborne LiDAR (Light Detection and Ranging) data. In addition, air-fall deposits produced by the highly explosive 2002–2003 eruption have been sampled and spectrally characterized at different distances from the new vents. The spectral analysis shows that air-fall deposits are characterized by low reflectance values besides variations in grain size. This distinguishes them from other surface materials. Old lava flows show highest reflectance values due to weathering and vegetation cover. The spectral data set derived from the field survey has been compared to corrected satellite hyperspectral data in order to investigate the Hyperion capabilities to differentiate the surface cover using the reflectance properties. This has allowed us to identify the 2002–2003 air-fall deposits in a thematic image just few months after their emplacement. Moreover, the observed differences in the field spectra of volcanic surfaces have been compared with differences in the signal intensity detected by airborne LiDAR survey showing the possibility to include information on the texture of volcanic surfaces at Mt. Etna. The approach presented here may be particularly useful for remote and inaccessible volcanic areas and also represents a potentially powerful tool for the exploration of extraterrestrial volcanic surfaces.  相似文献   

13.
Estimating forest structural attributes using multispectral remote sensing is challenging because of the saturation of multispectral indices at high canopy cover. The objective of this study was to assess the utility of hyperspectral data in estimating and mapping forest structural parameters including mean diameter-at-breast height (DBH), mean tree height and tree density of a closed canopy beech forest (Fagus sylvatica L.). Airborne HyMap images and data on forest structural attributes were collected from the Majella National Park, Italy in July 2004. The predictive performances of vegetation indices (VI) derived from all possible two-band combinations (VI(i,j) = (Ri − Rj)/(Ri + Rj), where Ri and Rj = reflectance in any two bands) were evaluated using calibration (n = 33) and test (n = 20) data sets. The potential of partial least squares (PLS) regression, a multivariate technique involving several bands was also assessed. New VIs based on the contrast between reflectance in the red-edge shoulder (756–820 nm) and the water absorption feature centred at 1200 nm (1172–1320 nm) were found to show higher correlations with the forest structural parameters than standard VIs derived from NIR and visible reflectance (i.e. the normalised difference vegetation index, NDVI). PLS regression showed a slight improvement in estimating the beech forest structural attributes (prediction errors of 27.6%, 32.6% and 46.4% for mean DBH, height and tree density, respectively) compared to VIs using linear regression models (prediction errors of 27.8%, 35.8% and 48.3% for mean DBH, height and tree density, respectively). Mean DBH was the best predicted variable among the stand parameters (calibration R2 = 0.62 for an exponential model fit and standard error of prediction = 5.12 cm, i.e. 25% of the mean). The predicted map of mean DBH revealed high heterogeneity in the beech forest structure in the study area. The spatial variability of mean DBH occurs at less than 450 m. The DBH map could be useful to forest management in many ways, e.g. thinning of coppice to promote diameter growth, to assess the effects of management on forest structure or to detect changes in the forest structure caused by anthropogenic and natural factors.  相似文献   

14.
The latent heat of evapotranspiration (ET) plays an important role in the assessment of drought severity as one sensitive indicator of land drought status. A simple and accurate method of estimating global ET for the monitoring of global land surface droughts from remote sensing data is essential. The objective of this research is to develop a hybrid ET model by introducing empirical coefficients based on a simple linear two-source land ET model, and to then use this model to calculate the Evaporative Drought Index (EDI) based on the actual estimated ET and the potential ET in order to characterize global surface drought conditions. This is done using the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) products, AVHRR-NDVI products from the Global Inventory Modeling and Mapping Studies (GIMMS) group, and National Centers for Environmental Prediction Reanalysis-2 (NCEP-2) datasets. We randomly divided 22 flux towers into two groups and performed a series of cross-validations using ground measurements collected from the corresponding flux towers. The validation results from the second group of flux towers using the data from the first group for calibration show that the daily bias varies from −6.72 W/m2 to 12.95 W/m2 and the average monthly bias is −1.73 W/m2. Similarly, the validation results of the first group of flux towers using data from second group for calibration show that the daily bias varies from −12.91 W/m2 to 10.26 W/m2 and the average monthly bias is −3.59 W/m2. To evaluate the reliability of the hybrid ET model on a global scale, we compared the estimated ET from the GEWEX, AVHRR-GIMMS-NDVI, and NECP-2 datasets with the latent heat flux from the Global Soil Wetness Project-2 (GSWP-2) datasets. We found both of them to be in good agreement, which further supports the validity of our model's global ET estimation. Significantly, the patterns of monthly EDI anomalies have a good spatial and temporal correlation with the Palmer Drought Severity Index (PDSI) anomalies from January 1984 to December 2002, which indicates that the method can be used to accurately monitor long-term global land surface drought.  相似文献   

15.
Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been used for the blending of Landsat and MODIS data. Specifically, the 30 m Landsat-7 ETM+ (Enhanced Thematic Mapper plus) surface reflectance was predicted for a period of 10 years (2000–2009) as the product of observed ETM+ and MODIS surface reflectance (MOD09A1) on the predicted and observed ETM+ dates. A pixel based analysis for six observed ETM+ dates covering winter and summer crops showed that the prediction method was more accurate for NIR band (mean r2 = 0.71, p ≤ 0.01) compared to green band (mean r2 = 0.53; p ≤ 0.01). A recently proposed chlorophyll index (CI), which involves NIR and green spectral bands, was used to retrieve gross primary productivity (GPP) as the product of CI and photosynthetic active radiation (PAR). The regression analysis of GPP derived from closet observed and synthetic ETM+ showed a good agreement (r2 = 0.85, p ≤ 0.01 and r2 = 0.86, p ≤ 0.01) for wheat and sugarcane crops, respectively. The difference between the GPP derived from synthetic and observed ETM+ (prediction residual) was compared with the difference in GPP values from observed ETM+ on the two dates (temporal residual). The prediction residuals (mean value of 1.97 g C/m2 in 8 days) was found to be significantly lower than the temporal residuals (mean value of 4.46 g C/m2 in 8 days) that correspondence to 12% and 27%, respectively, of GPP values (mean value of 16.53 g C/m2 in 8 days) from observed ETM+ data, implying that the prediction method was better than temporal pixel substitution. Investigating the trend in synthetic ETM+ GPP values over a growing season revealed that phenological patterns were well captured for wheat and sugarcane crops. A direct comparison between the GPP values derived from MODIS and synthetic ETM+ data showed a good consistency of the temporal dynamics but a systematic error that can be read as bias (MODIS GPP over estimation). Further, the regression analysis between observed evapotranspiration and synthetic ETM+ GPP showed good agreement (r2 = 0.66, p ≤ 0.01).  相似文献   

16.
光谱重建与光谱真实性检验中地物光谱的作用   总被引：2，自引：0，他引：2  
 将地物光谱与高光谱重建、影像光谱真实性检验相结合,重点阐述地物光谱在高光谱数据预处理中的主要作用。以新疆东天 山航空HyMap数据为例,在光谱重建过程中导入数据区特征地物（白云母和高岭石）光谱,以对利用大气模型校正的视反射率影像光 谱进行平滑、去“伪”或增强处理。进一步利用2种矿物的特征光谱评价重建光谱的真实性,从数据源头确保高光谱遥感数据光谱特 性的准确与可靠。  相似文献   

17.
利用多时相的高光谱航空图像监测冬小麦条锈病   总被引：31，自引：1，他引：31  
冬小麦发生锈病 ,叶绿素被大量破坏 ,水分蒸滕量大大增加 ,叶片细胞大小、形态、叶片结构发生了改变 ,从而改变了叶片和冠层的光学特性 ,使得遥感探测与评价成为可能。利用多时相的高光谱航空飞行图像数据 ,了解、分析和发现条锈病病害对作物光谱的影响及其光谱特征 ;设计了病害光谱指数 ,成功地监测了冬小麦条锈病病害程度与范围。对比 3个生育期的条锈病与正常生长冬小麦的PHI图像光谱及光谱特征 ,发现 :5 6 0— 6 70nm黄边、红谷波段 ,条锈病病害冬小麦的冠层反射率高于正常生长的冬小麦光谱反射率 ;近红外波段 ,条锈病病害的冠层反射率低于正常生长的冬小麦光谱反射率 ;条锈病冬小麦冠层光谱红谷吸收深度和绿峰的反射峰高度都会减小  相似文献   

18.
Quantification of chlorophyll content provides useful insight into the physiological performance of plants. Several leaf chlorophyll estimation techniques, using hyperspectral instruments, are available. However, to our knowledge, a non-destructive bark chlorophyll estimation technique is not available. We set out to assess Boswellia papyrifera tree bark chlorophyll content and to provide an appropriate bark chlorophyll estimation technique using hyperspectral remote sensing techniques. In contrast to the leaves, the bark of B. papyrifera has several outer layers masking the inner photosynthetic bark layer. Thus, our interest includes understanding how much light energy is transmitted to the photosynthetic inner bark and to what extent the inner photosynthetic bark chlorophyll activity could be remotely sensed during both the wet and the dry season. In this study, chlorophyll estimation using the chlorophyll absorption continuum index (CACI) yielded a higher R2 (0.87) than others indices and methods, such as the use of single band, simple ratios, normalized differences, and conventional red edge position (REP) based estimation techniques. The chlorophyll absorption continuum index approach considers the increase or widening in area of the chlorophyll absorption region, attributed to high concentrations of chlorophyll causing spectral shifts in both the yellow and the red edge. During the wet season B. papyrifera trees contain more bark layers than during the dry season. Having less bark layers during the dry season (leaf off condition) is an advantage for the plants as then their inner photosynthetic bark is more exposed to light, enabling them to trap light energy. It is concluded that B. papyrifera bark chlorophyll content can be reliably estimated using the chlorophyll absorption continuum index analysis. Further research on the use of bark signatures is recommended, in order to discriminate the deciduous B. papyrifera from other species during the dry season.  相似文献   

19.
Fine scale maps of vegetation biophysical variables are useful status indicators for monitoring and managing national parks and endangered habitats. Here, we assess in a comparative way four different retrieval methods for estimating leaf area index (LAI) in grassland: two radiative transfer model (RTM) inversion methods (one based on look-up-tables (LUT) and one based on predictive equations) and two statistical modelling methods (one partly, the other entirely based on in situ data). For prediction, spectral data were used that had been acquired over Majella National Park in Italy by the airborne hyperspectral HyMap instrument. To assess the performance of the four investigated models, the normalized root mean squared error (nRMSE) and coefficient of determination (R2) between estimates and in situ LAI measurements are reported (n = 41). Using a jackknife approach, we also quantified the accuracy and robustness of empirical models as a function of the size of the available calibration data set. The results of the study demonstrate that the LUT-based RTM inversion yields higher accuracies for LAI estimation (R2 = 0.91, nRMSE = 0.18) as compared to RTM inversions based on predictive equations (R2 = 0.79, nRMSE = 0.38). The two statistical methods yield accuracies similar to the LUT method. However, as expected, the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples. The results of this study are of interest for the remote sensing community developing improved inversion schemes for spaceborne hyperspectral sensors applicable to different vegetation types. The examples provided in this paper may also serve as illustrations for the drawbacks and advantages of physical and empirical models.  相似文献   

20.
Locally computed statistics of image texture and a case-based reasoning (CBR) system were evaluated for mapping of forest attributes. Cluster analysis was preferred to regression models, as a pre-selection method of features. The best stand-based accuracy using satellite sensor images was 74.64 m−3 ha−1 (36%) RMSE for stand volume, 1.98 m−3 ha−1 a−1 (49%) for annual increase in stand volume, where κ = 0.23 for stand growth classes and κ = 0.41 for dominant tree species in stands. The top pixel-based accuracy using orthophotos was 76.54 m−3 ha−1 (41%) RMSE for stand volume, 1.87 m−3 ha−1 a−1 (44%) for annual increase in stand volume, where κ = 0.24 for stand growth classes and κ = 0.38 for dominant tree species in stands. Mean saturation in 30 m radius was the most useful feature when orthophotos were used, and standard deviation of Landsat ETM 6.2 values in 80 m radius was the best when satellite sensor images were used. The most valuable feature components (radii, channels and local statistics) for orthophotos were: 30 m kernel radius, lightness and the mean of pixel values; for satellite sensor images: 80 m kernel radius, near-infrared channel (ETM 4) and the mean of pixel values. Locally computed statistics.  相似文献   

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