首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 568 毫秒
1.
Forests play a vital role in biological cycles and environmental regulation. To understand the key processes of forest canopies (e.g., photosynthesis, respiration and transpiration), reliable and accurate information on spatial variability of Leaf Area Index (LAI), and its seasonal dynamics is essential. In the present study, we assessed the performance of biophysical parameter (LAI) retrieval methods viz. Look-Up Table (LUT)-inversion, MLRA-GPR (Machine Learning Regression Algorithm- Gaussian Processes Regression) and empirical models, for estimating the LAI of tropical deciduous plantation using ARTMO (Automated Radiative Transfer Models Operator) tool and Sentinel-2 satellite images. The study was conducted in Central Tarai Forest Division, Haldwani, located in the Uttarakhand state, India. A total of 49 ESUs (Elementary Sampling Unit) of 30 m × 30 m size were established based on variability in composition and age of plantation stands. In-situ LAI was recorded using plant canopy imager during the leaf growing, peak and senescence seasons. The PROSAIL model was calibrated with site-specific biophysical and biochemical parameters before used to the predicted LAI. The plantation LAI was also predicted by an empirical approach using optimally chosen Sentinel-2 vegetation indices. In addition, Sentinel-2 and MODIS LAI products were evaluated with respect to LAI measurements. MLRA-GPR offered best results for predicting LAI of leaf growing (R2 = 0.9, RMSE = 0.14), peak (R2 = 0.87, RMSE = 0.21) and senescence (R2 = 0.86, RMSE = 0.31) seasons while LUT inverted model outperformed VI’s based parametric regression model. Vegetation indices (VIs) derived from 740 nm, 783 nm and 2190 nm band combinations of Sentinel-2 offered the best prediction of LAI.  相似文献   

2.
We propose 3D triangulations of airborne Laser Scanning (ALS) point clouds as a new approach to derive 3D canopy structures and to estimate forest canopy effective LAI (LAIe). Computational geometry and topological connectivity were employed to filter the triangulations to yield a quasi-optimal relationship with the field measured LAIe. The optimal filtering parameters were predicted based on ALS height metrics, emulating the production of maps of LAIe and canopy volume for large areas. The LAIe from triangulations was validated with field measured LAIe and compared with a reference LAIe calculated from ALS data using logarithmic model based on Beer’s law. Canopy transmittance was estimated using All Echo Cover Index (ACI), and the mean projection of unit foliage area (β) was obtained using no-intercept regression with field measured LAIe. We investigated the influence species and season on the triangulated LAIe and demonstrated the relationship between triangulated LAIe and canopy volume. Our data is from 115 forest plots located at the southern boreal forest area in Finland and for each plot three different ALS datasets were available to apply the triangulations. The triangulation approach was found applicable for both leaf-on and leaf-off datasets after initial calibration. Results showed the Root Mean Square Errors (RMSEs) between LAIe from triangulations and field measured values agreed the most using the highest pulse density data (RMSE = 0.63, the coefficient of determination (R2) = 0.53). Yet, the LAIe calculated using ACI-index agreed better with the field measured LAIe (RMSE = 0.53 and R2 = 0.70). The best models to predict the optimal alpha value contained the ACI-index, which indicates that within-crown transmittance is accounted by the triangulation approach. The cover indices may be recommended for retrieving LAIe only, but for applications which require more sophisticated information on canopy shape and volume, such as radiative transfer models, the triangulation approach may be preferred.  相似文献   

3.
冠层反射光谱对植被理化参数的全局敏感性分析   总被引：1，自引：0，他引：1  
植被理化参数与许多有关植物物质能量交换的生态过程密切相关,定量分析植被反射光谱对理化参数的敏感性是遥感反演理化参数含量的前提。本文采用EFAST(Extended Fourier Amplitude Sensitivity Test)全局敏感性分析方法,利用PROSAIL辐射传输模型分析了冠层疏密程度对叶片生化组分含量、冠层结构以及土壤背景等多种参数敏感性的影响,并对植被理化参数反演所需先验知识的精度问题进行了初步探讨。研究表明:(1)对于较为稠密的冠层,可见光波段的冠层反射率主要受叶绿素含量的影响,近红外和中红外波段的冠层反射率主要受干物质量和含水量的影响;(2)对于稀疏的冠层,LAI是影响400—2500 nm波段范围内冠层反射率的最重要参数,土壤湿度次之,叶片生化参数对冠层反射率的敏感性较低;(3)在已知稀疏冠层LAI的情况下进一步确定土壤的干湿状态,可显著提高冠层反射率对叶绿素含量的敏感度,有助于稀疏冠层叶绿素含量的反演。  相似文献   

4.
杜鹤娟  柳钦火  李静  杨乐 《遥感学报》2013,17(6):1587-1611
光学遥感是目前反演植被叶面积指数LAI（Leaf Area Index）的主要手段,但是当叶面积指数较大时存在光学遥感信息饱和、反演精度显著降低的问题。叶面积指数和平均叶倾角对光学、微波波段范围内反射和散射特性都有重要影响,主要表现在植被结构参数的变化可以引起冠层孔隙率和消光截面大小的改变。本文以典型农作物玉米为例,通过构建统一的PROSAIL和MIMICS模型输入参数,生成一套玉米全生长期光学二向反射率和全极化微波后向散射系数模拟库和冠层参数库。通过对模拟数据与LAI敏感性和相关性分析得出：（1）光学植被指数MNDVI(800 nm,2000 nm),在LAI为0—3时敏感,基于MNDVI与LAI的回归模型可以估算LAI变化 0.4的情况,RMSE是0.33,R2是0.958。（2）微波植被指数SARSRVI(1.4 GHz HH,9.6 GHz HV),在LAI为3—6时敏感,基于SARSRVI与LAI的回归模型可以估算LAI变化1的情况,RMSE为0.22,R2是0.9839。研究表明,采用分段敏感的植被指数,协同光学和微波遥感反演玉米全生长期叶面积指数是可行的。  相似文献   

5.
The current development of satellite technology particularly in the sensors like POLDER and MISR, has emphasized more on directional reflectance measurements (i.e. spectral reflectance of the target measured from different view zenith and azimuth angles) of the earth surface features mainly the vegetation for retrieval of biophysical parameters at regional scale using radiative transfer models. This approach being physical process based and uses directional reflectance measurement has been found to better and more reliable compared to the conventional statistical approach used till date and takes care of anisotropic nature (i.e. reflectance from the target is different if measured from different view angles) of the target. Keeping this in view a field experiment was conducted in mustard crop to evaluate the radiative transfer model for biophysical parameter retrieval through its inversion with the objectives set as (i) to relate canopy biophysical parameters and geometry to its bidirectional reflectance, (ii) to evaluate a canopy reflectance model to best represent the radiative transfer within the canopy for its inversion and (iii) to retrieve crop biophysical parameters through inversion of the model. Two varieties of the mustard crop (Brassica juncea L) were grown with two nitrogen treatments. The bidirectional reflectance data obtained at 5 nm interval for a range of 400–1100 nm were integrated to IRS LISS–II sensor’s four band values using Newton Cotes Integration technique. Biophysical parameters like leaf area index, leaf chlorophyll content, leaf length, plant height and average leaf inclination angle, biomass etc were estimated synchronizing with the bi-directional reflectance measurements. Radiative transfer model PROSAIL model was validated and its inversion was done to retrieve LAI and ALA. Look Up Table (LUT) of Bidirectional reflectance distribution function (BRDF) was prepared simulating through PROSAIL model varying only LAI (0.2 interval from 1.2 to 5.4 ) and ALA (5° interval from 40° to 55°) parameters and inversion was done using a merit function and numerical optimization technique given by Press et al. (1986). The derived LAI and ALA values from inversion were well matched with observed one with RMSE 0.521 and 5.57, respectively.  相似文献   

6.
The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R2 = 0.87, RMSE = 0.0022 g/cm2, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.  相似文献   

7.
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   

8.
Accurate representation of leaf area index (LAI) from high resolution satellite observations is obligatory for various modelling exercises and predicting the precise farm productivity. Present study compared the two retrieval approach based on canopy radiative transfer (CRT) method and empirical method using four vegetation indices (VI) (e.g. NDVI, NDWI, RVI and GNDVI) to estimate the wheat LAI. Reflectance observations available at very high (56 m) spatial resolution from Advanced Wide-Field Sensor (AWiFS) sensor onboard Indian Remote Sensing (IRS) P6, Resourcesat-1 satellite was used in this study. This study was performed over two different wheat growing regions, situated in different agro-climatic settings/environments: Trans-Gangetic Plain Region (TGPR) and Central Plateau and Hill Region (CPHR). Forward simulation of canopy reflectances in four AWiFS bands viz. green (0.52–0.59 μm), red (0.62–0.68 μm), NIR (0.77–0.86 μm) and SWIR (1.55–1.70 μm) were carried out to generate the look up table (LUT) using CRT model PROSAIL from all combinations of canopy intrinsic variables. An inversion technique based on minimization of cost function was used to retrieve LAI from LUT and observed AWiFS surface reflectances. Two consecutive wheat growing seasons (November 2005–March 2006 and November 2006–March 2007) datasets were used in this study. The empirical models were developed from first season data and second growing season data used for validation. Among all the models, LAI-NDVI empirical model showed the least RMSE (root mean square error) of 0.54 and 0.51 in both agro-climatic regions respectively. The comparison of PROSAIL retrieved LAI with in situ measurements of 2006–2007 over the two agro-climatic regions produced substantially less RMSE of 0.34 and 0.41 having more R2 of 0.91 and 0.95 for TGPR and CPHR respectively in comparison to empirical models. Moreover, CRT retrieved LAI had less value of errors in all the LAI classes contrary to empirical estimates. The PROSAIL based retrieval has potential for operational implementation to determine the regional crop LAI and can be extendible to other regions after rigorous validation exercise.  相似文献   

9.
Computer simulation models have seldom been applied for estimating the structural and biophysical variables of forest canopy. In this study, an approach for the estimation of leaf area index (LAI) using the information contained in hyperspectral, multi-angle images and the inversion of a computer simulation model are explored. For this purpose, L-systems combined with forest growth model ZELIG were applied to render 3-D forest architectural scenarios. The Radiosity-graphics combined model (RGM) was used to estimate forest LAI from the Compact High-Resolution Imaging Spectrometer/Project for On-Board Autonomy (CHRIS/PROBA) data. LAI inversion was performed using the look-up table (LUT) method. The estimated LAI was evaluated against in situ LAI measurement and compared against the LAI predictions from CHRIS data obtained using the Li-Strahler geometric-optical canopy reflectance model (GOMS). The results indicated that the method used in this study can be efficient strategy to estimate LAI by RGM model inversion.  相似文献   

10.
In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R2 ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.  相似文献   

11.
This study is aimed at demonstrating the feasibility of the large scale LAI inversion algorithms using red and near infrared reflectance obtained from high resolution satellite imagery. Radiances in digital counts were obtained in 10 m resolution acquired on cloud free day of August 23, 2007, by the SPOT 5 high resolution geometric (HRG) instrument on mostly temperate hardwood forest located in the Great Lakes – St. Lawrence forest in Southern Quebec. Normalized difference vegetation index (NDVI), scaled difference vegetation index (SDVI) and modified soil-adjusted vegetation index (MSAVI) were applied to calculate gap fractions. LAI was inverted from the gap fraction using the common Beer–Lambert's law of light extinction under forest canopy. The robustness of the algorithm was evaluated using the ground-based LAI measurements and by applying the methods for the independently simulated reflectance data using PROSPECT + SAIL coupled radiative transfer models. Furthermore, the high resolution LAI was compared with MODIS LAI product. The effects of atmospheric corrections and scales were investigated for all of the LAI retrieval methods. NDVI was found to be not suitable index for large scale LAI inversion due to the sensitivity to scale and atmospheric effects. SDVI was virtually scale and atmospheric correction invariant. MSAVI was also scale invariant. Considering all sensitivity analysis, MSAVI performed best followed by SDVI for robust LAI inversion from high resolution imagery.  相似文献   

12.
A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view-illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra-annual and inter-annual variability of structural properties of the canopy on the light extinction coefficient were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+ LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.  相似文献   

13.
Forest canopy height is an important indicator of forest carbon storage, productivity, and biodiversity. The present study showed the first attempt to develop a machine-learning workflow to map the spatial pattern of the forest canopy height in a mountainous region in the northeast China by coupling the recently available canopy height (Hcanopy) footprint product from ICESat-2 with the Sentinel-1 and Sentinel-2 satellite data. The ICESat-2 Hcanopy was initially validated by the high-resolution canopy height from airborne LiDAR data at different spatial scales. Performance comparisons were conducted between two machine-learning models – deep learning (DL) model and random forest (RF) model, and between the Sentinel and Landsat-8 satellites. Results showed that the ICESat-2 Hcanopy showed the highest correlation with the airborne LiDAR canopy height at a spatial scale of 250 m with a Pearson’s correlation coefficient (R) of 0.82 and a mean bias of -1.46 m, providing important evidence on the reliability of the ICESat-2 vegetation height product from the case in China’s forest. Both DL and RF models obtained satisfactory accuracy on the upscaling of ICESat-2 Hcanopy assisted by Sentinel satellite co-variables with an R-value between the observed and predicted Hcanopy equalling 0.78 and 0.68, respectively. Compared to Sentinel satellites, Landsat-8 showed relatively weaker performance in Hcanopy prediction, suggesting that the addition of the backscattering coefficients from Sentinel-1 and the red-edge related variables from Sentinel-2 could positively contribute to the prediction of forest canopy height. To our knowledge, few studies have demonstrated large-scale vegetation height mapping in a resolution ≤ 250 m based on the newly available satellites (ICESat-2, Sentinel-1 and Sentinel-2) and DL regression model, particularly in the forest areas in China. Thus, the present work provided a timely and important supplementary to the applications of these new earth observation tools.  相似文献   

14.
This paper analyzes the backscatter of the microwave signal in a boreal forest environment based on a Ku -band airborne Frequency-Modulated Continuous Waveform (FMCW) profiling radar—Tomoradar. We selected a half-managed boreal forest in the southern part of Finland for a field test. By decomposing the waveform collected by the Tomoradar, the vertical canopy structure was achieved. Based on the amplitude of the waveform, the Backscattered Energy Ratio of Canopy-to-Total (BERCT) was calculated. Meanwhile, the canopy fraction was derived from the corresponding point cloud recorded by a Velodyne VLP-16 LiDAR mounted on the same platform. Lidar-derived canopy fraction was obtained by counting the number of the first/ the strongest returns versus the total amount of returns. Qualitative and quantitative analysis of radar-derived BERCT on lidar-derived canopy fraction and canopy height are investigated. A fitted model is derived to describe the Ku-band microwave backscatter in the boreal forest to numerically analyze the proportion contributed by four factors: lidar-derived canopy fraction, radar-derived canopy height, the radar-derived distance between trees and radar sensor and other factors, from co-polarization Tomoradar measurements. The Root Mean Squared Error (RMSE) of the proposed model was 0.0958, and the coefficient of determination R2 was 0.912. The fitted model reveals that the correlation coefficient between radar-derived BERCT and lidar-derived canopy fraction is 0.84, which illustrates that lidar surface reflection explains the majority of the profiling /waveform radar response. Thus, vertical canopy structure derived from lidar can be used for the benefit of radar analysis.  相似文献   

15.
Three-dimensional (3-D) Monte Carlo-based radiative transfer (MCRT) models are usually used for benchmarking in intercomparisons of the canopy radiative transfer (RT) simulations. However, the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests, due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales. Fortunately, some of important tree structure parameters such as canopy height and tree density distribution have been available globally. This enables to run the intermediate complexities of the 3-D MCRT models. We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density. It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms. The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA, respectively. Results demonstrated that the simulations of bidirectional reflectance factor (BRF) based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error (RMSE) and relative RMSE (rRMSE) ranging from 0.002 to 0.006 and from 0.7% to 19.8%, respectively. Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%, respectively. Although the results from the current study are limited in two boreal forest stands, our approach has the potential to generate stand structures for different forest biomes.  相似文献   

16.
The retrieval of canopy biophysical variables is known to be affected by confounding factors such as plant type and background reflectance. The effects of soil type and plant architecture on the retrieval of vegetation leaf area index (LAI) from hyperspectral data were assessed in this study. In situ measurements of LAI were related to reflectances in the red and near-infrared and also to five widely used spectral vegetation indices (VIs). The study confirmed that the spectral contrast between leaves and soil background determines the strength of the LAI–reflectance relationship. It was shown that within a given vegetation species, the optimum spectral regions for LAI estimation were similar across the investigated VIs, indicating that the various VIs are basically summarizing the same spectral information for a given vegetation species. Cross-validated results revealed that, narrow-band PVI was less influenced by soil background effects (0.15 ≤ RMSEcv ≤ 0.56). The results suggest that, when using remote sensing VIs for LAI estimation, not only is the choice of VI of importance but also prior knowledge of plant architecture and soil background. Hence, some kind of landscape stratification is required before using hyperspectral imagery for large-scale mapping of vegetation biophysical variables.  相似文献   

17.
Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility.  相似文献   

18.
For the soil moisture retrieval from passive microwave sensors, such as ESA’s Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active and Passive (SMAP) mission, a good knowledge about the vegetation characteristics is indispensable. Vegetation cover is a principal factor in the attenuation, scattering and absorption of the microwave emissions from the soil; and has a direct impact on the brightness temperature by way of its canopy emissions. Here, brightness temperatures were measured at three altitudes across the TERENO (Terrestrial Environmental Observatories) Rur catchment site in Germany to achieve a range of spatial resolutions using the airborne Polarimetric L-band Multibeam Radiometer 2 (PLMR2). The L-band Microwave Emission of the Biosphere (L-MEB) model which simulates microwave emissions from the soil–vegetation layer at L-band was used to retrieve surface soil moisture for all resolutions. A Monte Carlo approach was developed to simultaneously estimate soil moisture and the vegetation parameter b’ describing the relationship between the optical thickness τ and the Leaf Area Index (LAI). LAI was retrieved from multispectral RapidEye imagery and the plant specific vegetation parameter b′ was estimated from the lowest flight altitude data for crop, grass, coniferous forest, and deciduous forest. Mean values of b’ were found to be 0.18, 0.07, 0.26 and 0.23, respectively. By assigning the estimated b′ to higher flight altitude data sets, a high accuracy soil moisture retrieval was achieved with a Root Mean Square Difference (RMSD) of 0.035 m3 m−3 when compared to ground-based measurements.  相似文献   

19.
There is growing evidence that imaging spectroscopy could improve the accuracy of satellite-based retrievals of vegetation attributes, such as leaf area index (LAI) and biomass. In this study, we evaluated narrowband vegetation indices (VIs) for estimating overstory effective LAI (LAIeff) in a southern boreal forest area for the period between the end of snowmelt and maximum LAI using three Hyperion images and concurrent field measurements. We compared the performance of narrowband VIs with two SPOT HRVIR images, which closely corresponded to the imaging dates of the Hyperion data, and with synthetic broadband VIs computed from Hyperion images. According to the results, narrowband VIs based on near infrared (NIR) bands, and NIR and shortwave infrared (SWIR) bands showed the strongest linear relationships with LAIeff over its typical range of variation and for the studied period of the snow-free season. The relationships were not dependent on dominant tree species (coniferous vs. broadleaved), which is an advantage in heterogeneous boreal forest landscapes. The best VIs, particularly those based on NIR spectral bands close to the 1200 nm liquid water absorption feature, provided a clear improvement over the best broadband VIs.  相似文献   

20.
This paper evaluates the potential of a terrestrial laser scanner (TLS) to characterize forest canopy fuel characteristics at plot level. Several canopy properties, namely canopy height, canopy cover, canopy base height and fuel strata gap were estimated. Different approaches were tested to avoid the effect of canopy shadowing on canopy height estimation caused by deployment of the TLS below the canopy. Estimation of canopy height using a grid approach provided a coefficient of determination of R2 = 0.81 and an RMSE of 2.47 m. A similar RMSE was obtained using the 99th percentile of the height distribution of the highest points, representing the 1% of the data, although the coefficient of determination was lower (R2 = 0.70). Canopy cover (CC) was estimated as a function of the occupied cells of a grid superimposed upon the TLS point clouds. It was found that CC estimates were dependent on the cell size selected, with 3 cm being the optimum resolution for this study. The effect of the zenith view angle on CC estimates was also analyzed. A simple method was developed to estimate canopy base height from the vegetation vertical profiles derived from an occupied/non-occupied voxels approach. Canopy base height was estimated with an RMSE of 3.09 m and an R2 = 0.86. Terrestrial laser scanning also provides a unique opportunity to estimate the fuel strata gap (FSG), which has not been previously derived from remotely sensed data. The FSG was also derived from the vegetation vertical profile with an RMSE of 1.53 m and an R2 = 0.87.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号