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1.
Sagebrush (Artemisia tridentata), a dominant shrub species in the sagebrush-steppe ecosystem of the western US, is declining from its historical distribution due to feedbacks between climate and land use change, fire, and invasive species. Quantifying aboveground biomass of sagebrush is important for assessing carbon storage and monitoring the presence and distribution of this rapidly changing dryland ecosystem. Models of shrub canopy volume, derived from terrestrial laser scanning (TLS) point clouds, were used to accurately estimate aboveground sagebrush biomass. Ninety-one sagebrush plants were scanned and sampled across three study sites in the Great Basin, USA. Half of the plants were scanned and destructively sampled in the spring (n = 46), while the other half were scanned again in the fall before destructive sampling (n = 45). The latter set of sagebrush plants was scanned during both spring and fall to further test the ability of the TLS to quantify seasonal changes in green biomass. Sagebrush biomass was estimated using both a voxel and a 3-D convex hull approach applied to TLS point cloud data. The 3-D convex hull model estimated total and green biomass more accurately (R2 = 0.92 and R2 = 0.83, respectively) than the voxel-based method (R2 = 0.86 and R2 = 0.73, respectively). Seasonal differences in TLS-predicted green biomass were detected at two of the sites (p < 0.001 and p = 0.029), elucidating the amount of ephemeral leaf loss in the face of summer drought. The methods presented herein are directly transferable to other dryland shrubs, and implementation of the convex hull model with similar sagebrush species is straightforward. 相似文献
2.
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
3.
Leaf mass per area (LMA), the ratio of leaf dry mass to leaf area, is a trait of central importance to the understanding of plant light capture and carbon gain. It can be estimated from leaf reflectance spectroscopy in the infrared region, by making use of information about the absorption features of dry matter. This study reports on the application of continuous wavelet analysis (CWA) to the estimation of LMA across a wide range of plant species. We compiled a large database of leaf reflectance spectra acquired within the framework of three independent measurement campaigns (ANGERS, LOPEX and PANAMA) and generated a simulated database using the PROSPECT leaf optical properties model. CWA was applied to the measured and simulated databases to extract wavelet features that correlate with LMA. These features were assessed in terms of predictive capability and robustness while transferring predictive models from the simulated database to the measured database. The assessment was also conducted with two existing spectral indices, namely the Normalized Dry Matter Index (NDMI) and the Normalized Difference index for LMA (NDLMA).Five common wavelet features were determined from the two databases, which showed significant correlations with LMA (R2: 0.51–0.82, p < 0.0001). The best robustness (R2 = 0.74, RMSE = 18.97 g/m2 and Bias = 0.12 g/m2) was obtained using a combination of two low-scale features (1639 nm, scale 4) and (2133 nm, scale 5), the first being predominantly important. The transferability of the wavelet-based predictive model to the whole measured database was either better than or comparable to those based on spectral indices. Additionally, only the wavelet-based model showed consistent predictive capabilities among the three measured data sets. In comparison, the models based on spectral indices were sensitive to site-specific data sets. Integrating the NDLMA spectral index and the two robust wavelet features improved the LMA prediction. One of the bands used by this spectral index, 1368 nm, was located in a strong atmospheric water absorption region and replacing it with the next available band (1340 nm) led to lower predictive accuracies. However, the two wavelet features were not affected by data quality in the atmospheric absorption regions and therefore showed potential for canopy-level investigations. The wavelet approach provides a different perspective into spectral responses to LMA variation than the traditional spectral indices and holds greater promise for implementation with airborne or spaceborne imaging spectroscopy data for mapping canopy foliar dry biomass. 相似文献
4.
Developing spectral models of soil properties is an important frontier in remote sensing and soil science. Several studies have focused on modeling soil properties such as total pools of soil organic matter and carbon in bare soils. We extended this effort to model soil parameters in areas densely covered with coastal vegetation. Moreover, we investigated soil properties indicative of soil functions such as nutrient and organic matter turnover and storage. These properties include the partitioning of mineral and organic soil between particulate (>53 μm) and fine size classes, and the partitioning of soil carbon and nitrogen pools between stable and labile fractions. Soil samples were obtained from Avicennia germinans mangrove forest and Juncus roemerianus salt marsh plots on the west coast of central Florida. Spectra corresponding to field plot locations from Hyperion hyperspectral image were extracted and analyzed. The spectral information was regressed against the soil variables to determine the best single bands and optimal band combinations for the simple ratio (SR) and normalized difference index (NDI) indices. The regression analysis yielded levels of correlation for soil variables with R2 values ranging from 0.21 to 0.47 for best individual bands, 0.28 to 0.81 for two-band indices, and 0.53 to 0.96 for partial least-squares (PLS) regressions for the Hyperion image data. Spectral models using Hyperion data adequately (RPD > 1.4) predicted particulate organic matter (POM), silt + clay, labile carbon (C), and labile nitrogen (N) (where RPD = ratio of standard deviation to root mean square error of cross-validation [RMSECV]). The SR (0.53 μm, 2.11 μm) model of labile N with R2 = 0.81, RMSECV= 0.28, and RPD = 1.94 produced the best results in this study. Our results provide optimism that remote-sensing spectral models can successfully predict soil properties indicative of ecosystem nutrient and organic matter turnover and storage, and do so in areas with dense canopy cover. 相似文献
5.
As a preparatory study for future hyperspectral missions that can measure canopy chemistry, we introduce a novel approach to investigate whether multi-angle Moderate Resolution Imaging Spectroradiometer (MODIS) data can be used to generate a preliminary database with long-term estimates of chlorophyll. MODIS monthly chlorophyll estimates between 2000 and 2015, derived from a fully coupled canopy reflectance model (ProSAIL), were inspected for consistency with eddy covariance fluxes, tower-based hyperspectral images and chlorophyll measurements. MODIS chlorophyll estimates from the inverse model showed strong seasonal variations across two flux-tower sites in central and eastern Amazon. Marked increases in chlorophyll concentrations were observed during the early dry season. Remotely sensed chlorophyll concentrations were correlated to field measurements (r2 = 0.73 and r2 = 0.98) but the data deviated from the 1:1 line with root mean square errors (RMSE) ranging from 0.355 μg cm−2 (Tapajós tower) to 0.470 μg cm−2 (Manaus tower). The chlorophyll estimates were consistent with flux tower measurements of photosynthetically active radiation (PAR) and net ecosystem productivity (NEP). We also applied ProSAIL to mono-angle hyperspectral observations from a camera installed on a tower to scale modeled chlorophyll pigments to MODIS observations (r2 = 0.73). Chlorophyll pigment concentrations (ChlA+B) were correlated to changes in the amount of young and mature leaf area per month (0.59 ≤ r2 ≤ 0.64). Increases in MODIS observed ChlA+B were preceded by increased PAR during the dry season (0.61 ≤ r2 ≤ 0.62) and followed by changes in net carbon uptake. We conclude that, at these two sites, changes in LAI, coupled with changes in leaf chlorophyll, are comparable with seasonality of plant productivity. Our results allowed the preliminary development of a 15-year time series of chlorophyll estimates over the Amazon to support canopy chemistry studies using future hyperspectral sensors. 相似文献
6.
This paper assesses the capability of hyperspectral remote sensing to detect hydrocarbon leakages in pipelines using vegetation status as an indicator of contamination. A field experiment in real scale and in tropical weather was conducted in which Brachiaria brizantha H.S. pasture plants were grown over soils contaminated with small volumes of liquid hydrocarbons (HCs). The contaminations involved volumes of hydrocarbons that ranged between 2 L and 12.7 L of gasoline and diesel per m3 of soil, which were applied to the crop parcels over the course of 30 days. The leaf and canopy reflectance spectra of contaminated and control plants were acquired within 350–2500 nm wavelengths. The leaf and canopy reflectance spectra were mathematically transformed by means of first derivative (FD) and continuum removal (CR) techniques. Using principal component analysis (PCA), the spectral measurements could be grouped into either two or three contamination groups. Wavelengths in the red edge were found to contain the largest spectral differences between plants at distinct, evolving contamination stages. Wavelengths centred on water absorption bands were also important to differentiating contaminated from healthy plants. The red edge position of contaminated plants, calculated on the basis of FD spectra, shifted substantially to shorter wavelengths with increasing contamination, whereas non-contaminated plants displayed a red shift (in leaf spectra) or small blue shift (in canopy spectra). At leaf scale, contaminated plants were differentiated from healthy plants between 550–750 nm, 1380–1550 nm, 1850–2000 nm and 2006–2196 nm. At canopy scale, differences were substantial between 470–518 nm, 550–750 nm, 910–1081 nm, 1116–1284 nm, 1736–1786 nm, 2006–2196 nm and 2222–2378 nm. The results of this study suggests that remote sensing of B. brizantha H.S. at both leaf and canopy scales can be used as an indicator of gasoline and diesel contaminations for the detection of small leakages in pipelines. 相似文献
7.
The objective of this study was to investigate the entire spectra (from visible to the thermal infrared; 0.390–14.0 μm) to retrieve leaf water content in a consistent manner. Narrow-band spectral indices (calculated from all possible two band combinations) and a partial least square regression (PLSR) were used to assess the strength of each spectral region. The coefficient of determination (R2) and root mean square error (RMSE) were used to report the prediction accuracy of spectral indices and PLSR models. In the visible-near infrared and shortwave infrared (VNIR–SWIR), the most accurate spectral index yielded R2 of 0.89 and RMSE of 7.60%, whereas in the mid infrared (MIR) the highest R2 was 0.93 and RMSE of 5.97%. Leaf water content was poorly predicted using two-band indices developed from the thermal infrared (R2 = 0.33). The most accurate PLSR model resulted from MIR reflectance spectra (R2 = 0.96, RMSE = 4.74% and RMSE cross validation RMSECV = 6.17%) followed by VNIR–SWIR reflectance spectra (R2 = 0.91, RMSE = 6.90% and RMSECV = 7.32%). Using thermal infrared (TIR) spectra, the PLSR model yielded a moderate retrieval accuracy (R2 = 0.67, RMSE = 13.27% and RMSECV = 16.39%). This study demonstrated that the mid infrared (MIR) and shortwave infrared (SWIR) domains were the most sensitive spectral region for the retrieval of leaf water content. 相似文献
8.
Soil respiration (Rs) data from 45 plots were used to estimate the spatial patterns of Rs during the peak growing seasons of winter wheat and summer maize in Julu County, North China, by combining satellite remote sensing data, field-measured data, and a support vector regression (SVR) model. The observed Rs values were well reproduced by the model at the plot scale, with a root-mean-square error (RMSE) of 0.31 μmol CO2 m−2 s−1 and a coefficient of determination (R2) of 0.73. No significant difference was detected between the prediction accuracy of the SVR model for winter wheat and summer maize. With forcing from satellite remote sensing data and gridded soil property data, we used the SVR model to predict the spatial distributions of Rs during the peak growing seasons of winter wheat and summer maize rotation croplands in Julu County. The SVR model captured the spatial variations of Rs at the county scale. The satellite-derived enhanced vegetation index was found to be the most important input used to predict Rs. Removal of this variable caused an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.42 μmol CO2 m−2 s−1. Soil properties such as soil organic carbon (SOC) content and soil bulk density (SBD) were the second most important factors. Their removal led to an RMSE increase from 0.31 μmol CO2 m−2 s−1 to 0.37 μmol CO2 m−2 s−1. The SVR model performed better than multiple regression in predicting spatial variations of Rs in winter wheat and summer maize rotation croplands, as shown by the comparison of the R2 and RMSE values of the two algorithms. The spatial patterns of Rs are better captured using the SVR model than performing multiple regression, particularly for the relatively high and relatively low Rs values at the center and northeast study areas. Therefore, SVR shows promise for predicting spatial variations of Rs values on the basis of remotely sensed data and gridded soil property data at the county scale. 相似文献
9.
This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2 = 0.64 and RMSE = 42.67 gm−2) than the exponential regression (R2 = 0.48 and RMSE = 41.65 gm−2) and the ANN (R2 = 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2 = 0.55) but higher RMSE (RMSE = 37.79 gm−2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology. 相似文献
10.
11.
Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350–2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg−1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg−1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg−1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and 2290 nm, leaf spectral bands near 700, 890 and 900 nm in PLSR models were important wavelengths for soil arsenic prediction. Moreover, soil arsenic showed significantly positive correlations with soil organic matter (r = 0.62, p < 0.01) and leaf arsenic (r = 0.77, p < 0.01), and a significantly negative correlation with leaf chlorophyll (r = −0.67, p < 0.01). The results showed that the prediction of arsenic contents using soil and leaf spectra may be based on their relationships with soil organic matter and leaf chlorophyll contents, respectively. Although RPD of 1.47 was below the recommended RPD of >2 for soil analysis, arsenic prediction in agricultural soils can be improved by combining the leaf and soil spectra. 相似文献
12.
In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρnir–LAI and ρred–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R2) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R2 reached 0.82 and RMSD was 0.25 for early July 2011. 相似文献
13.
The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters.Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R2 = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R2 = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m. 相似文献
14.
Detailed knowledge of vegetation structure is required for accurate modelling of terrestrial ecosystems, but direct measurements of the three dimensional distribution of canopy elements, for instance from LiDAR, are not widely available. We investigate the potential for modelling vegetation roughness, a key parameter for climatological models, from directional scattering of visible and near-infrared (NIR) reflectance acquired from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). We compare our estimates across different tropical forest types to independent measures obtained from: (1) airborne laser scanning (ALS), (2) spaceborne Geoscience Laser Altimeter System (GLAS)/ICESat, and (3) the spaceborne SeaWinds/QSCAT. Our results showed linear correlation between MODIS-derived anisotropy to ALS-derived entropy (r2 = 0.54, RMSE = 0.11), even in high biomass regions. Significant relationships were also obtained between MODIS-derived anisotropy and GLAS-derived entropy (0.52 ≤ r2 ≤ 0.61; p < 0.05), with similar slopes and offsets found throughout the season, and RMSE between 0.26 and 0.30 (units of entropy). The relationships between the MODIS-derived anisotropy and backscattering measurements (σ0) from SeaWinds/QuikSCAT presented an r2 of 0.59 and a RMSE of 0.11. We conclude that multi-angular MODIS observations are suitable to extrapolate measures of canopy entropy across different forest types, providing additional estimates of vegetation structure in the Amazon. 相似文献
15.
Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R2 = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R2 = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha−1 to 11.73 ± 1.84 Mg ha−1, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level. 相似文献
16.
This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 − 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = −1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08–0.12 and 0.1–0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = −0.019 m3 m−3, ubRMSE = 0.062 m3 m−3 for the IGBP-based calibration; against R = 0.54, bias = −0.034 m3 m−3 and ubRMSE = 0.070 m3 m−3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product. 相似文献
17.
Leaf carotenoids content (LCar) is an important indicator of plant physiological status. Accurate estimation of LCar provides valuable insight into early detection of stress in vegetation. With spectroscopy techniques, a semi-empirical approach based on spectral indices was extensively used for carotenoids content estimation. However, established spectral indices for carotenoids that generally rely on limited measured data, might lack predictive accuracy for carotenoids estimation in various species and at different growth stages. In this study, we propose a new carotenoid index (CARI) for LCar assessment based on a large synthetic dataset simulated from the leaf radiative transfer model PROSPECT-5, and evaluate its capability with both simulated data from PROSPECT-5 and 4SAIL and extensive experimental datasets: the ANGERS dataset and experimental data acquired in field experiments in China in 2004. Results show that CARI was the index most linearly correlated with carotenoids content at the leaf level using a synthetic dataset (R2 = 0.943, RMSE = 1.196 μg/cm2), compared with published spectral indices. Cross-validation results with CARI using ANGERS data achieved quite an accurate estimation (R2 = 0.545, RMSE = 3.413 μg/cm2), though the RBRI performed as the best index (R2 = 0.727, RMSE = 2.640 μg/cm2). CARI also showed good accuracy (R2 = 0.639, RMSE = 1.520 μg/cm2) for LCar assessment with leaf level field survey data, though PRI performed better (R2 = 0.710, RMSE = 1.369 μg/cm2). Whereas RBRI, PRI and other assessed spectral indices showed a good performance for a given dataset, overall their estimation accuracy was not consistent across all datasets used in this study. Conversely CARI was more robust showing good results in all datasets. Further assessment of LCar with simulated and measured canopy reflectance data indicated that CARI might not be very sensitive to LCar changes at low leaf area index (LAI) value, and in these conditions soil moisture influenced the LCar retrieval accuracy. 相似文献
18.
We developed a method to produce a 3-D voxel-based solid model of a tree based on portable scanning lidar data for accurate estimation of the volume of the woody material. First, we obtained lidar measurements with a high laser pulse density from several measurement positions around the target, a Japanese zelkova tree. Next, we converted lidar-derived point-cloud data for the target into voxels. The voxel size was 0.5 cm × 0.5 cm × 0.5 cm. Then, we used differences in the spatial distribution of voxels to separate the stem and large branches (diameter > 1 cm) from small branches (diameter ⩽ 1 cm). We classified the voxels into sets corresponding to the stem and to each large branch and then interpolated voxels to fill out their surfaces and their interiors. We then merged the stem and large branches with the small branches. The resultant solid model of the entire tree was composed of consecutive voxels that filled the outer surface and the interior of the stem and large branches, and a cloud of voxels equivalent to small branches that were discretely scattered in mainly the upper part of the target. Using this model, we estimated the woody material volume by counting the number of voxels in each part and multiplying the number of voxels by the unit voxel volume (0.13 cm3). The percentage error of the volume of the stem and part of a large branch was 0.5%. The estimation error of a certain part of the small branches was 34.0%. 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.
Soil erodibility, which is difficult to estimate and upscaling, was determined in this study using multiple spectral models of soil properties (soil organic matter (SOM), water-stable aggregates (WSA) > 0.25 mm, the geometric mean radius (Dg)). Herein, the soil erodibility indicators were calculated, and soil properties were quantitatively analyzed based on laboratory simulation experiments involving two selected contrasting soils. In addition, continuous wavelet transformation was applied to the reflectance spectra (350–2500 nm) of 65 soil samples from the study area. To build the relationship, the soil properties that control erodibility were identified prior to the spectral analysis. In this study, the SOM, Dg and WSA >0.25 mm were selected to represent the most significant soil properties controlling erodibility and describe the erodibility indicator based on a logarithmic regression model as a function of SOM or WSA > 0.25 mm. Five, six and three wavelet features were observed to calibrate the estimated soil properties model, and the best performance was obtained with a combination feature regression model for SOM (R2 = 0.86, p < 0.01), Dg (R2 = 0.79, p < 0.01) and WSA >0.25 mm (R2 = 0.61, p < 0.01), respectively. One part of the wavelet features captured amplitude variations in the broad shape of the reflectance spectra, and another part captured variations in the shape and depth of the soil dry substances. The wavelet features for the validated dataset used to predict the SOM, WSA >0.25 mm and Dg were not significantly different compared with the calibrated dataset. The synthesized spectral models of soil properties, and the formation of a new equation for soil erodibility transformed from the spectral models of soil properties are presented in this study. These results show that a spectral analytical approach can be applied to complex datasets and provide new insights into emerging dynamic variation with erodibility estimation. 相似文献