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1.
Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. 相似文献
2.
精确估算森林生物量对全球碳平衡以及气候变化的研究有重要意义。以亚热带天然次生林为研究对象,借助地面实测样地数据,通过对机载LiCHy(LiDAR,CCD and Hyperspectral)传感器同时获取的高光谱和高空间分辨率数据进行信息提取和数据融合,建模反演森林生物量。首先通过面向对象分割方法进行单木冠幅提取,然后融合从高光谱数据提取的光谱特征变量和从高空间分辨率数据提取的单木冠幅统计变量,构建多元回归模型估算地上、地下生物量,最后利用地面实测生物量经交叉验证评价模型精度。结果表明,综合模型的精度(R~2为0.54—0.62)高于高光谱模型(R~2为0.48—0.57);在高光谱模型中地上生物量模型精度(R~2为0.57)高于地下生物量模型(R~2为0.48);在综合模型中地上生物量模型精度(R~2为0.62)同样高于地下生物量模型(R~2为0.54)。交叉验证结果表明,与仅使用高光谱数据(单一数据源)相比,通过集成高光谱和高空间分辨率数据的生物量反演效果有所提升,可以更加有效地估算亚热带森林生物量。 相似文献
3.
The Hyperspectral Imager for the Coastal Ocean (HICO) was used to derive chlorophyll-a (chl-a) based on the normalized difference chlorophyll index (NDCI) in two Gulf of Mexico coastal estuaries. Chl-a data were acquired from discrete in situ water sample analysis and above-water hyperspectral surface acquisition system (HyperSAS) remote sensing reflectance in Pensacola Bay (PB) and Choctawhatchee Bay (CB). NDCI algorithm calibrations and validations were completed on HICO data. Linear and best-fit (polynomial) calibrations performed strongly with R2 of 0.90 and 0.96, respectively. The best validation of NDCI resulted with an R2 of 0.74 and root-mean-square error (RMSE) of 1.64 µg/L. A strong spatial correspondence was observed between NDCI and chl-a, with higher NDCI associated with higher chl-a and these areas were primarily located in the northern PB and eastern CB at the river mouths. NDCI could be effectively used as a qualitative chl-a monitoring tool with a reduced need for site-specific calibration. 相似文献
4.
Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction of R2 0.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite. 相似文献
5.
In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. 相似文献
6.
《地理信息系统科学与遥感》2013,50(4):492-509
A relationship between the likelihood of wildfires and various drought metrics (soil moisture-based fire potential indices) were examined over the southern part of Mississippi. The following three indices were tested and used to simulate spatial and temporal wildfire probability changes: (1) the accumulated difference between daily precipitation and potential evapotranspiration (P - E); (2) simulated moisture content of the top 10 cm of soil; and (3) the Keetch-Byram Drought Index (KBDI). These indices were estimated from gridded meterological data and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). The relationships between normalized fire potential index deviations and the probability of at least one fire occurring during the following five consecutive days were evaluated using a 23-year (1986-2008) forest fire record for an evenly spaced grid (0.25° x 0.25°) across the state of Mississippi's coastal plain. Two periods were selected and examined (January-mid June and mid September-December). There was good agreement between the observed and logistic model-fitted fire probabilities over the study area during both seasons. The fire potential indices based on the top 10 cm soil moisture and KBDI had the largest impact on wildfire odds, increasing it by almost 2 times in response to each unit change of the corresponding fire potential index during January-mid-June period and by nearly 1.5 times during mid-September-December. These results suggest that soil moisture-based fire potential indices are good indicators of fire occurrence probability across this region. 相似文献
7.
Surface soil water content plays an important role in driving the exchange of latent and sensible heat between the atmosphere and land surface through transpiration and evaporation processes, regulating key physiological processes affecting plants growth. Given the high impact of water scarcity on yields, and of irrigated agriculture on the overall withdrawal rate of freshwater, it is important to define models that help to improve water resources management for agricultural purposes, and to optimize rainfed crop yield. Recent advances in satellite-based remote sensing have led to valuable solutions to estimate soil water content based on microwave or optical/thermal-infrared data. This study aims at improving soil water content estimation at high spatial and temporal resolution, by means of the Optical Trapezoid Model (OPTRAM) driven by Copernicus Sentinel-2 data. Two different model variations were considered, based on linear and nonlinear parameters constraints, and validated against in situ soil water content measurements made with time domain reflectometry (TDR) on irrigated maize in central Italy and on rainfed maize and pasture in northern Italy. For the first site the non-linear model shows a better correlation between measured and estimated soil water content values (r = 0.80) compared to the linear model (r = 0.73). In both cases the modeled soil moisture tends to overestimate the measured values at medium to high water content level, while both models underestimate soil moisture at low water content level. Estimated versus measured normalized surface soil water for rainfed pasture plots from nonlinear OPTRAM parametrized based on irrigated maize parameterization (SIM1), and site-specific parametrization for rainfed pasture (SIM2), indicate that both models (SIM1 and SIM2) are comparable for rotational grazing pasture (RMSEsim1 = 0.0581 vs. RMSEsim2 = 0.0485 cm3 cm-3) and the continuous grazing pasture (RMSEsim1 = 0.0485 vs. RMSEsim2 = 0.0602 cm3 cm-3), while for the rainfed maize plots SIM1 shows lower RMSE (average for all plots RMSE = 0.0542 cm3 cm-3) compared to the site-specific calibration model (SIM2 – average for all plots RMSE = 0.0645 cm3 cm-3). Finally, OPTRAM estimations are close to in situ measurement values while Surface Soil Moisture at 1 km (SSM1 km) tends to underestimate the measurements during maize crop growing season. Soil moisture retrieval from high-resolution Sentinel-2 optical images allows water stress conditions to be effectively mapped, supporting decision making in irrigation scheduling and other crop management. 相似文献
8.
The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding. 相似文献
9.
Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors
This paper compared two soil moisture downscaling methods using three scaling factors. Level 3 soil moisture product of advanced microwave scanning radiometer for EOS (AMSR-E) is downscaled from 25 to 1?km. The downscaled results are compared with the soil moisture observations from polarimetric scanning radiometer (PSR) microwave radiometer and field sampling. The results show that (1) the scaling factor of normalized soil thermal inertia (NSTIs) and vegetation temperature condition index (VTCI) are better than soil evaporative efficiency in reflecting soil moisture; (2) for method 1, NSTIS is the best in the downscaling of soil moisture. For method 2, VTCI is the best; (3) no significant differences of the correlation coefficients (R2) and the biases were found between the two methods for the same scaling factors. However, method 2 shows a better potential than method 1 in the time-series applications of the downscaling of soil moisture; (4) compared with the relationship between the area-averaged soil moisture of AMSR-E and that of PSR, R2 of the 6 sets of the downscaled soil moisture almost do not decrease, which suggests the validity of the downscaling of soil moisture with the two downscaling methods using the three scaling factors. 相似文献
10.
Long-term soil moisture dynamics derived from GNSS interferometric reflectometry: a case study for Sutherland,South Africa 总被引：3，自引：0，他引：3
Sibylle Vey Andreas Güntner Jens Wickert Theresa Blume Markus Ramatschi 《GPS Solutions》2016,20(4):641-654
Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m2. In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r 2 = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m3/m3. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m3/m3. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m3/m3). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide. 相似文献
11.
光学与微波数据协同反演农田区土壤水分 总被引：1，自引：0，他引：1
光学和微波协同遥感反演对于提高农田土壤水分遥感反演精度十分重要。本文采用SMEX02数据集,研究了L波段土壤发射率与地表土壤水分之间的关系,分析了地面植被覆盖对L波段土壤发射率与地表水分之关系的影响规律,推导了以L波段土壤发射率和归一化植被指数NDVI为自变量的土壤水分反演模型。研究表明:L波段土壤发射率与地表土壤水分之间的相关性随NDVI的增加而下降。验证结果表明,本文算法相对常规经验算法,土壤水分反演精度明显提高,H极化条件下,土壤水分的反演精度RMSE由0.0553提高到0.0407,相关系数R2由0.70提高到0.81;V极化条件下,反演精度RMSE由0.0452提高到0.0348,相关系数R2由0.79提高到0.86。 相似文献
12.
Roshanak Darvishzadeh Clement Atzberger Andrew Skidmore Martin Schlerf 《ISPRS Journal of Photogrammetry and Remote Sensing》2011,66(6):894-906
Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R2 between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R2 = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R2 = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements. 相似文献
13.
机载激光雷达及高光谱的森林乔木物种多样性遥感监测 总被引：1，自引：0，他引：1
利用机载LiDAR和高光谱数据并结合37个地面调查样本数据,基于结构差异与光谱变异理论,通过相关分析法分别筛选了3个最优林冠结构参数和6个最优光谱指数,在单木尺度上利用自适应C均值模糊聚类算法,在神农架国家自然保护区开展森林乔木物种多样性监测,实现了森林乔木物种多样性的区域成图。研究结果表明,(1)基于结合形态学冠层控制的分水岭算法可以获得较高精度的单木分割结果(R~2=0.88,RMSE=13.17,P0.001);(2)基于LiDAR数据提取的9个结构参数中,95%百分位高度、冠层盖度和植被穿透率为最优结构参数,与Shannon-Wiener指数的相关性达到R~2=0.39—0.42(P0.01);(3)基于机载高光谱数据筛选的16个常用的植被指数中,CRI、OSAVI、Narrow band NDVI、SR、Vogelmann index1、PRI与Shannon-Wiener指数的相关性最高(R~2=0.37—0.45,P0.01);(4)在研究区,利用以30 m×30 m为窗口的自适应模糊C均值聚类算法可预测的最大森林乔木物种数为20,物种丰富度的预测精度为R~2=0.69,RMSE=3.11,Shannon-Wiener指数的预测精度为R~2=0.70,RMSE=0.32。该研究在亚热带森林开展乔木物种多样性监测,是在区域尺度上进行物种多样性成图的重要实践,可有效补充森林生物多样性本底数据的调查手段,有助于实现生物多样性的长期动态监测及科学分析森林物种多样性的现状和变化趋势。 相似文献
14.
利用AMSR2和MODIS数据的土壤冻融相变水量降尺度方法 总被引：1，自引：0，他引：1
本文基于站点实测土壤温度和土壤湿度数据分析,发现温度指数TI(Temperature Index)和土壤冻融相变水量呈现幂函数关系,温度指数能够反映相变水量的变化。使用MODIS地表温度产品计算温度指数,在AMSR2卫星观测尺度上与相变水量建立了关系,从而对土壤冻融相变水量进行了降尺度研究。采用CTP-SMTMN数据采集仪观测网络上的站点观测到土壤水分对土壤冻融相变水量降尺度结果进行了验证。结果表明,土壤冻融相变水量降尺度结果与实测值较为接近,在土壤相变水量大于0.01(m3/m3)时,RMSE为0.0085(m3/m3),MAE为0.0059(m3/m3)。这种通过温度指数对土壤相变水量进行降尺度的方法具有简便,可行,可靠的优势,适合在冻融交替期计算较湿润土壤在冻融过程中产生的相变水量。同时,这种降尺度方法能够生成小尺度上的相变水量产品,实现了热红外遥感和被动微波遥感的优势整合,对研究地气水热平衡,气候变化,土壤冻结强度以及冻融侵蚀强度等具有重要意义。 相似文献
15.
Shaohua Zhao Yonghui Yang Guoyu Qiu Qiming Qin Yunjun Yao Yujiu Xiong Chunqiang Li 《International Journal of Applied Earth Observation and Geoinformation》2010
An approach for estimating soil moisture is presented and tested by using surface-temperature-based soil evaporation transfer coefficient (ha), a coefficient recently proposed through the equation ha = (Ts − Ta)/(Tsd − Ta), where Ts, Tsd, and Ta are land surface temperature (LST), reference soil (dry soil without evaporation) surface temperature, and air temperature respectively. Our analysis and controllable experiment indicated that ha closely related to soil moisture, and therefore, a relationship between field soil moisture and ha could be developed for soil moisture estimation. Field experiments were carried out to test the relationship between ha and soil moisture. Time series Aqua-MODIS images were acquired between 11 Sep. 2006 and 1 Nov. 2007. Then, MODIS derived ha and simultaneous measured soil moisture for different soil depths were used to establish the relations between the two variables. Results showed that there was a logarithmic relationship between soil moisture and ha (P < 0.01). These logarithmic models were further validated by introducing another ground-truth data gathered from 46 meteorological stations in Hebei Province. Good agreement was observed between the measured and estimated soil moisture with RMSE of 0.0374 cm3/cm3 and 0.0503 cm3/cm3 for surface energy balance method at two soil depths (10 cm and 20 cm), with RMSE of 0.0467 cm3/cm3 and 0.0581 cm3/cm3 for maximum temperature method at two soil depths. For vegetated surfaces, the ratio of ha and NDVI suggested to be considered. The proposed approach has a great potential for soil moisture and drought evaluation by remote sensing. 相似文献
16.
《地理信息系统科学与遥感》2013,50(2):225-244
Geospatial tools and techniques are playing important roles in determining the location and spatial extents of invasive species infestations and in evaluating the performances of various management activities aimed at controlling their spread. In this study, hyperspectral image processing techniques were used to map purple loosestrife and to assess the effectiveness of biological control agents in controlling its infestations along the Niobrara River in Nebraska. Validation based on field survey showed an overall map accuracy of 82.1% and comparison with in situ data on biocontrol release indicated that biocontrol agents were effective in the areas where they were released. 相似文献
17.
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
18.
A. Brosinsky A. Lausch D. Doktor C. Salbach I. Merbach S. Gwillym-Margianto M. Pause 《Journal of the Indian Society of Remote Sensing》2014,42(2):311-324
The assessment and quantification of spatio-temporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of the plants (R 2?=?0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments. 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.
Wetland biomass is essential for monitoring the stability and productivity of wetland ecosystems. Conventional field methods to measure or estimate wetland biomass are accurate and reliable, but expensive, time consuming and labor intensive. This research explored the potential for estimating wetland reed biomass using a combination of airborne discrete-return Light Detection and Ranging (LiDAR) and hyperspectral data. To derive the optimal predictor variables of reed biomass, a range of LiDAR and hyperspectral metrics at different spatial scales were regressed against the field-observed biomasses. The results showed that the LiDAR-derived H_p99 (99th percentile of the LiDAR height) and hyperspectral-calculated modified soil-adjusted vegetation index (MSAVI) were the best metrics for estimating reed biomass using the single regression model. Although the LiDAR data yielded a higher estimation accuracy compared to the hyperspectral data, the combination of LiDAR and hyperspectral data produced a more accurate prediction model for reed biomass (R2 = 0.648, RMSE = 167.546 g/m2, RMSEr = 20.71%) than LiDAR data alone. Thus, combining LiDAR data with hyperspectral data has a great potential for improving the accuracy of aboveground biomass estimation. 相似文献