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1.
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   

2.
Within the last few decades mangrove forests worldwide have been experiencing high annual rates of loss and many of those that remain have undergone considerable degradation. To understand the condition of these forests, various optical remote sensing platforms have been used to map and monitor these wetlands, including the use of these data for biophysical parameter mapping. For many mangrove forests a reliable source of optical imagery is not possible given their location in quasi-permanent cloud cover or smoke covered regions. In such cases it is recommended that Synthetic Aperture Radar (SAR) be considered. The purpose of this investigation was to examine the relationships between various ALOS-PALSAR modes, acquired from eight images, and mangrove biophysical parameter data collected from a black mangrove (Avicennia germinans) dominated forest that has experienced considerable degradation. In total, structural data were collected from 61 plots representing the four common stand types found in this degraded forest of the Mexican Pacific: tall healthy mangrove (n = 17), dwarf healthy mangrove (n = 15), poor condition mangrove (n = 13), and predominantly dead mangrove (n = 16).Based on backscatter coefficients, significant negative correlation coefficients were observed between filtered single polarization ALOS PALSAR (6.25 m) HH backscatter and Leaf Area Index (LAI). When the dead stands were excluded (n = 45) the strength of these relationships increased. Moreover, significant negative correlation coefficients were observed with stand height, Basal Area (BA) and to a lesser degree with stem density and mean DBH. With the coarser spatial resolution dual-polarization and quad polarization data (12.5 m) only a few, and weaker, correlation coefficients were calculated between the mangrove parameters and the filtered HH backscatter. However, significant negative values were once again calculated for the HH when the 16 dead mangrove stands were removed from the sample. Conversely, strong positive significant correlation coefficients were calculated between the cross-polarization HV backscatter and LAI when the dead mangrove stands were considered. Although fewer in comparison to the HH correlations, a number of VV backscatter based relationships with mangrove parameters were observed from the quad polarization mode and, to a lesser extent, with the one single VV polarization data.In addition to backscatter coefficients, stepwise multiple regression models of the mangrove biophysical parameter data were developed based on texture parameters derived from the grey level co-occurrence matrix (GLCM) of the ALOS data. A similar pattern to the backscatter relationships was observed for models based on the single polarization unfiltered data, with fairly strong coefficients of determination calculated for LAI and stem height when the dead stands were excluded. In contrast, similar coefficients of determination with biophysical parameters were observed for the dual and quad polarization multiple regression models when the dead stands were both included and excluded from the analyses. An estimated mangrove LAI map of the study area, derived from a multiple regression model of the quad polarization texture parameters, showed comparable spatial patterns of degradation to a map derived from higher spatial resolution optical satellite data.  相似文献   

3.
The geopotential coefficients of a number of recent and past models have been examined with the aim of calibrating their formal (or published) errors, principally by direct comparison with the same coefficients of a precise and wholly independent satellite-only reference field determined (in 2010) predominantly from over 7 years of μ/s low–low GRACE inter-satellite range rate observations (ITG-GRACE2010S). In all of these comparisons the reference field used, over specified spectral ranges, has much smaller reported errors than the ones to be calibrated. In particular we find that a recently published field (in 2010) using gravity gradient and position data from ESA’s GOCE satellite, GO_CONS_GCF_2_TIM_R1, has formal errors which are significantly optimistic for the lowest degrees (n) but with increasing realism where the gradiometer gains influence over the position information (15 < n < 120). Other GOCE models do not afford an unbiased error calibration. Validating error calibration with the independent reference model, of two past comprehensive fields (containing both satellite and surface data), confirms that one of them (JGM3, published in 1994, complete to n = 70) reported generally realistic formal errors while another (EGM96, published in 1998, complete to n = 360, tested only for n < 150) had significantly optimistic ones for most n < 100 but with better realism when affected only by the surface information.  相似文献   

4.
In an attempt to model regular variations of the ionosphere, the least-squares harmonic estimation is applied to the time series of the total electron contents (TEC) provided by the JPL analysis center. Multivariate and modulated harmonic estimation spectra are introduced and estimated for the series to detect the regular and modulated dominant frequencies of the periodic patterns. Two significant periodic patterns are the diurnal and annual signals with periods of 24/n hours and 365.25/n days (n = 1, 2, …), which are the Fourier series decomposition of the regular daily and yearly periodic variations of the ionosphere. The spectrum shows a cluster of periods near 27 days, thereby indicating irregularities at this solar cycle period. A series of peaks, with periods close to the diurnal signal and its harmonics, are evident in the spectrum. In fact, the daily signal harmonics of ω i  = 2πi are modulated with the annual signal harmonics of ω j  = 2πj/365.25 as ω ijM  = 2πi(1 ± j/365.25i). Among them, at low and midlatitudes, the largest variations belong to the diurnal signal modulated to the semiannual signal. Some preliminary results on the modulated part are presented. The maximum ranges of the modulated daily signal are ±15 TECU and ±6 TECU at high and low solar periods, respectively. A model consisting of purely harmonic functions plus modulated ones is capable of studying known regular anomalies of the ionosphere, which is currently in progress.  相似文献   

5.
Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.  相似文献   

6.
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7.
This paper highlights the spatial and temporal variability of atmospheric columnar methane (CH4) concentration over India and its correlation with the terrestrial vegetation dynamics. SCanning IMaging Absorption spectrometer for Atmospheric CHartographY (SCIAMACHY) on board ENVIronmental SATellite (ENVISAT) data product (0.5° × 0.5°) was used to analyze the atmospheric CH4 concentration. Satellite Pour l'Observation de la Terre (SPOT)-VEGETATION sensor’s Normalized Difference Vegetation Index (NDVI) product, aggregated at 0.5° × 0.5° grid level for the same period (2004 and 2005), was used to correlate the with CH4 concentration. Analysis showed mean monthly CH4 concentration during the Kharif season varied from 1,704 parts per billion volume (ppbv) to 1,780 ppbv with the lowest value in May and the highest value in September. Correspondingly, mean NDVI varied from 0.28 (May) to 0.53 (September). Analysis of correlation between CH4 concentration and NDVI values over India showed positive correlation (r = 0.76; n = 6) in Kharif season. Further analysis using land cover information showed characteristic low correlation in natural vegetation region and high correlation in agricultural area. Grids, particularly falling in the Indo-Gangetic Plains showed positive correlation. This could be attributed to the rice crop which is grown as a predominant crop during this period. The CH4 concentration pattern matched well with growth pattern of rice with the highest concentration coinciding with the peak growth period of crop in the September. Characteristically low correlation was observed (r = 0.1; n = 6) in deserts of Rajasthan and forested Himalayan ecosystem. Thus, the paper emphasizes the synergistic use of different satellite based data in understanding the variability of atmospheric CH4 concentration in relation to vegetation.  相似文献   

8.
Abstract

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean. In this study, we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China. A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses. Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater. Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness [20th band (R 2=0.92938, n=49, p<0.01), 26th band (R 2=0.93806, n=49, p<0.01), 29th band (R 2=0.93288, n=49, p<0.01)]. By using these statistical models, we successfully determined the normalized oil slick thickness with the Hyperion image. Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water. The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water. The high, stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.  相似文献   

9.
Citizen science projects encourage the general public to participate in scientific research. Participants can contribute large volumes of data over broad spatial and temporal frames; however, the challenge is to generate data of sufficient quality to be useable in scientific research. Most observations made by citizen‐scientists can be independently verified by “experts”. However, verification is more problematic when the phenomena being recorded are short‐lived. This article uses a GIS methodology to verify the quality of contrail observations made by the general public as part of the OPAL Climate Survey. We verify observations using datasets derived from a variety of different sources (experts, models and amateur enthusiasts) with different spatial and temporal properties which reflect the complex 3D nature of the atmosphere. Our results suggest that ～70% of citizen observations are plausible, based on favorable atmospheric conditions and the presence or absence of aircraft; a finding which is in keeping with other, more conventional citizen science projects. However, questions remain as to why the quality of the citizen‐based observations was so high. Given the lack of supporting data on observers, it is impossible to determine whether the dataset was generated by the activities of many participants or a small but dedicated number of individual observers.  相似文献   

10.
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   

11.
This study demonstrates the potentials of IRS P6 LISS-IV high-resolution multispectral sensor (IGFOV  6 m)-based estimation of biomass in the deciduous forests in the Western Ghats of Karnataka, India. Regression equations describing the relationship between IRS P6 LISS-IV data-based vegetation index (NDVI) and field measured leaf area index (ELAI) and estimated above-ground biomass (EAGB) were derived. Remote sensing (RS) data-based leaf area index (PLAI) image is generated using regression equation based on NDVI and ELAI (r2 = 0.68, p ≤ 0.05). RS-based above-ground biomass (PAGB) image was generated based on regression equation developed between PLAI and EAGB (r2 = 0.63, p ≤ 0.05). The mean value of estimated above-ground biomass and RS-based above-ground biomass in the study area are 280(±72.5) and 297.6(±55.2) Mg ha−1, respectively. The regression models generated in the study between NDVI and LAI; LAI and biomass can also help in generating spatial biomass map using RS data alone. LISS-IV-based estimation of biophysical parameters can also be used for the validation of various coarse resolution satellite products derived from the ground-based measurements alone.  相似文献   

12.
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   

13.
The validation study of leaf area index (LAI) products over rugged surfaces not only gives additional insights into data quality of LAI products, but deepens understanding of uncertainties regarding land surface process models depended on LAI data over complex terrain. This study evaluated the performance of MODIS and GLASS LAI products using the intercomparison and direct validation methods over southwestern China. The spatio-temporal consistencies, such as the spatial distributions of LAI products and their statistical relationship as a function of topographic indices, time, and vegetation types, respectively, were investigated through intercomparison between MODIS and GLASS products during the period 2011–2013. The accuracies and change ranges of these two products were evaluated against available LAI reference maps over 10 sampling regions which standed for typical vegetation types and topographic gradients in southwestern China.The results show that GLASS LAI exhibits higher percentage of good quality data (i.e. successful retrievals) and smoother temporal profiles than MODIS LAI. The percentage of successful retrievals for MODIS and GLASS is vulnerable to topographic indices, especially to relief amplitude. Besides, the two products do not capture seasonal dynamics of crop, especially in spring over heterogeneously hilly regions. The yearly mean LAI differences between MODIS and GLASS are within ±0.5 for 64.70% of the total retrieval pixels over southwestern China. The spatial distribution of mean differences and temporal profiles of these two products are inclined to be dominated by vegetation types other than topographic indices. The spatial and temporal consistency of these two products is good over most area of grasses/cereal crops; however, it is poor for evergreen broadleaf forest. MODIS presents more reliable change range of LAI than GLASS through comparison with fine resolution reference maps over most of sampling regions. The accuracies of direct validation are obtained for GLASS LAI (r = 0.35, RMSE = 1.72, mean bias = −0.71) and MODIS LAI (r = 0.49, RMSE = 1.75, mean bias = −0.67). GLASS performs similarly to MODIS, but may be marginally inferior to MODIS based on our direct validation results. The validation experience demonstrates the necessity and importance of topographic consideration for LAI estimation over mountain areas. Considerable attention will be paid to the improvements of surface reflectance, retrieval algorithm and land cover types so as to enhance the quality of LAI products in topographically complex terrain.  相似文献   

14.
Past laboratory and field studies have quantified phenolic substances in vegetative matter from reflectance measurements for understanding plant response to herbivores and insect predation. Past remote sensing studies on phenolics have evaluated crop quality and vegetation patterns caused by bedrock geology and associated variations in soil geochemistry. We examined spectra of pure phenolic compounds, common plant biochemical constituents, dry leaves, fresh leaves, and plant canopies for direct evidence of absorption features attributable to plant phenolics. Using spectral feature analysis with continuum removal, we observed that a narrow feature at 1.66 μm is persistent in spectra of manzanita, sumac, red maple, sugar maple, tea, and other species. This feature was consistent with absorption caused by aromatic CH bonds in the chemical structure of phenolic compounds and non-hydroxylated aromatics. Because of overlapping absorption by water, the feature was weaker in fresh leaf and canopy spectra compared to dry leaf measurements. Simple linear regressions of feature depth and feature area with polyphenol concentration in tea resulted in high correlations and low errors (% phenol by dry weight) at the dry leaf (r2 = 0.95, RMSE = 1.0%, n = 56), fresh leaf (r2 = 0.79, RMSE = 2.1%, n = 56), and canopy (r2 = 0.78, RMSE = 1.0%, n = 13) levels of measurement. Spectra of leaves, needles, and canopies of big sagebrush and evergreens exhibited a weak absorption feature centered near 1.63 μm, short ward of the phenolic compounds, possibly consistent with terpenes. This study demonstrates that subtle variation in vegetation spectra in the shortwave infrared can directly indicate biochemical constituents and be used to quantify them. Phenolics are of lesser abundance compared to the major plant constituents but, nonetheless, have important plant functions and ecological significance. Additional research is needed to advance our understanding of the spectral influences of plant phenolics and terpenes relative to dominant leaf biochemistry (water, chlorophyll, protein/nitrogen, cellulose, and lignin).  相似文献   

15.
In the present study, we aimed to map canopy heights in the Brazilian Amazon mainly on the basis of spaceborne LiDAR and cloud-free MODIS imagery with a new method (the Self-Organizing Relationships method) for spatial modeling of the LiDAR footprint. To evaluate the general versatility, we compared the created canopy height map with two different canopy height estimates on the basis of our original field study plots (799 plots located in eight study sites) and a previously developed canopy height map. The compared canopy height estimates were obtained by: (1) a stem diameter at breast height (D) – tree height (H) relationship specific to each site on the basis of our original field study, (2) a previously developed D–H model involving environmental and structural factors as explanatory variables (Feldpausch et al., 2011), and (3) a previously developed canopy height map derived from the spaceborne LiDAR data with different spatial modeling method and explanatory variables (Simard et al., 2011). As a result, our canopy height map successfully detected a spatial distribution pattern in canopy height estimates based on our original field study data (r = 0.845, p = 8.31 × 10−3) though our canopy height map showed a poor correlation (r = 0.563, p = 0.146) with the canopy height estimate based on a previously developed model by Feldpausch et al. (2011). We also confirmed that the created canopy height map showed a similar pattern with the previously developed canopy height map by Simard et al. (2011). It was concluded that the use of the spaceborne LiDAR data provides a sufficient accuracy in estimating the canopy height at regional scale.  相似文献   

16.
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   

17.
In this paper, we evaluate the extent to which the resampled field spectra compare with the actual image spectra of the new generation multispectral WorldView-2 (WV-2) satellite. This was achieved by developing models from resampled field spectra data and testing them on an actual WV-2 image of the study area. We evaluated the performance of reflectance ratios (RI), normalized difference indices (NDI) and random forest (RF) regression model in predicting foliar nitrogen concentration in a grassland environment. The field measured spectra were used to calibrate the RF model using a randomly selected training (n = 70%) nitrogen data set. The model developed from the field spectra resampled to WV-2 wavebands was validated on an independent field spectral test dataset as well as on the actual WV-2 image of the same area (n = 30%, bootstrapped a 100 times). The results show that the model developed using RI could predict nitrogen with a mean R2 of 0.74 and 0.65 on an independent field spectral test data set and on the actual WV-2 image, respectively. The root mean square error of prediction (RMSE %) was 0.17 and 0.22 for the field test data set and the WV-2 image, respectively. Results provide an insight on the magnitude of errors that are expected when up-scaling field spectral models to airborne or satellite image data. The prediction also indicates the unceasing relevance of field spectroscopy studies to better understand the spectral models critical for vegetation quality assessment.  相似文献   

18.
Discriminating commercial tree species using hyperspectral remote sensing techniques is critical in monitoring the spatial distributions and compositions of commercial forests. However, issues related to data dimensionality and multicollinearity limit the successful application of the technology. The aim of this study was to examine the utility of the partial least squares discriminant analysis (PLS-DA) technique in accurately classifying six exotic commercial forest species (Eucalyptus grandis, Eucalyptus nitens, Eucalyptus smithii, Pinus patula, Pinus elliotii and Acacia mearnsii) using airborne AISA Eagle hyperspectral imagery (393–900 nm). Additionally, the variable importance in the projection (VIP) method was used to identify subsets of bands that could successfully discriminate the forest species. Results indicated that the PLS-DA model that used all the AISA Eagle bands (n = 230) produced an overall accuracy of 80.61% and a kappa value of 0.77, with user’s and producer’s accuracies ranging from 50% to 100%. In comparison, incorporating the optimal subset of VIP selected wavebands (n = 78) in the PLS-DA model resulted in an improved overall accuracy of 88.78% and a kappa value of 0.87, with user’s and producer’s accuracies ranging from 70% to 100%. Bands located predominantly within the visible region of the electromagnetic spectrum (393–723 nm) showed the most capability in terms of discriminating between the six commercial forest species. Overall, the research has demonstrated the potential of using PLS-DA for reducing the dimensionality of hyperspectral datasets as well as determining the optimal subset of bands to produce the highest classification accuracies.  相似文献   

19.
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20.
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   

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