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1.
森林生态系统在调节生态气候与碳循环方面发挥着重要作用,森林高度是衡量森林生态系统功能的重要参数。利用单一遥感数据获取森林冠层高度会受到多种制约。因此,本文使用星载激光雷达ICESat-2提供的高质量离散森林冠层高度点,结合Sentinel-1、Landsat 8及地形数据,采用随机森林方法建立不同影像特征组合森林冠层高度的回归模型,并分析各特征对森林高度反演的影响,最后将模型应用于广西森林冠层高度制图。试验结果表明,多源遥感数据可有效提高森林冠层高度反演精度,在所利用遥感数据中,特征重要性从大到小依次为光学特征、地形特征、SAR特征,“L8+SRTM+Sentinel-1+邻域均值”特征组合的反演精度最高,加入邻域均值特征进行森林冠层高度反演效果最佳,随机森林模型能精确绘制森林冠层高度。 相似文献
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参数不确定性是SAR反演土壤水分的重要不确定性来源,为控制土壤水分反演精度,提出一种基于参数不确定性的有效控制土壤水分反演精度的方法,使用该方法可以控制参数的误差范围。首先使用全局敏感性分析方法,确定后向影响散射系数输出的主要参数;在不同量级高斯噪声随机扰动下,将大量各参数采值输入AIEM模型中,得到带噪声的后向散射系数集合;再使用LUT法反演土壤水分,计算反演结果满足误差量级控制范围。以此为基础,利用ENVISAT ASAR双极化数据(VV、VH)和实测土壤水分数据进行验证,利用LUT法反演得到带噪声的土壤水分,计算ASAR影像中采样点土壤水分反演值RMSE0.04cm3/cm3。结果表明各影响参数误差量级控制范围可有效控制土壤水分反演精度,在较大的入射角范围内都适用。 相似文献
3.
以北京市为研究区域,联合使用光学遥感数据和雷达数据,对植被覆盖区地表土壤水分进行反演研究。在利用同期光学数据提取出归一化水分指数(normalized differential water index,NDWI)之后,利用water-cloud模型去除植被层在土壤水分后向散射中的贡献,然后考虑到地表粗糙度,在构建后向散射数据库的基础上分别利用HH和HV极化方式的后向散射系数构建土壤水分反演模型,并对反演结果进行对比研究。结果表明,采用HH极化方式反演土壤水分的均方根误差为0.044,相对误差为15.5%;采用HV极化方式反演土壤水分的均方根误差为0.057,相对误差为20.3%;相比而言,HH极化的反演效果更好。 相似文献
4.
作为中国发射的风云四号系列首颗卫星,FY-4A搭载了先进的静止轨道辐射成像仪AGRI。本文利用AGRI传感器的中红外到热红外波段观测数据、ERA5水汽再分析产品以及全球无线电探空数据集IGRA等数据进行晴空大气可降水量反演研究。在海洋表面,分别利用回归分析法与随机森林算法反演大气可降水量,反演结果与ERA5水汽产品相比,均方根误差为0.493 cm和0.247 cm。在陆地表面,利用随机森林模型反演大气可降水量,反演结果与ERA5水汽产品相比,均方根误差为0.155 cm;与IGRA水汽产品相比,均方根误差为0.215 cm。结果表明本文使用的随机森林算法可以有效地提升热红外遥感数据反演大气可降水量的精度。 相似文献
5.
针对以光谱特征差异为依据,提取森林湿地信息精度低的问题,该文采用兼容多源数据的分类回归树(CART)提取方法,并以大沾河国家森林湿地进行实证研究。基于Landsat8遥感数据、Radarsat-2极化雷达数据和地形辅助数据,采用SPM软件分别构建3种特征变量组合的CART决策树模型,并获取分类规则,最后根据规则对研究区的森林湿地信息进行提取。结果表明:3种特征变量组合中,兼容光谱、纹理、雷达与地形辅助数据的CART决策树的森林湿地信息提取精度最高,用户精度和制图精度分别达到了88.46%和82.14%。研究结果体现了雷达数据与地形辅助数据有助于提取森林湿地信息。 相似文献
6.
随机森林回归模型用于土壤重金属含量多光谱遥感反演 总被引：1，自引：0，他引：1
本文以陕西省柞水县大西沟矿区为研究区域,通过实地采集土壤样本,结合在Landsat 8多光谱遥感影像上提取的辐射亮度值和光谱衍生指数,以及从ASTER GDEM提取的3种地形因素,通过相关性分析确定了建模因子,并以K折交叉验证法建立了砷、铜、铅3种重金属元素的随机森林回归模型。试验结果表明,所建立模型的预测精度优于多元线性回归模型和CART模型,可见随机森林回归模型适用于在小样本情况下的矿区重金属含量反演。经现场调查,空间反演结果与实际情况较符合,证明了基于多光谱遥感的随机森林回归模型在矿区土壤重金属反演中的准确性。 相似文献
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时序双极化SAR开采沉陷区土壤水分估计 总被引：1，自引：0，他引：1
开采沉陷地质灾害诱发矿区生态环境恶化的关键因子是土壤水分变化。研究提出了一种利用Sentinel-1A双极化SAR和OLI地表反射率数据联合反演土壤含水量的方法,即基于归一化水体指数(NDWI)反演植被含水量;采用Water-Cloud Model(WCM)模型消除植被对Sentinel-1A后向散射系数产生的影响,将其转化为裸土区的后向散射系数;利用基于AIEM模型和Oh模型建立的经验模型反演研究区地表参数,并用OLI光学反演结果进行验证;最后比较了开采沉陷区内外土壤水分含量。研究表明:(1)与基于OLI的土壤水分监测指数(SMMI)的土壤水分含量反演结果相比,两种极化方式中VH极化反演的水分结果具有更好的一致性,且两种极化方式反演结果也表明荒漠化草原区比黄土丘陵沟壑区反演效果更好,说明地形对后向散射的影响不可忽略。(2)在2016年内72期数据中,VH极化反演结果对比区土壤水分含量大于沉陷区的有41期,所占比例为57%;VV极化反演结果对比区土壤水分含量大于沉陷区的有36期,所占比例为50%,且不同矿区内的沉陷区受到的影响不同。说明开采沉陷造成的地表粗糙度的增加会对地表土壤水分产生负面影响,但不同矿区之间又有差异。 相似文献
9.
基于PLSR的陕北土壤盐分高光谱反演 总被引：3，自引：0，他引：3
选取陕北盐渍土为研究对象,通过采集高光谱数据及土壤样品测定,研究土壤盐分含量与反射率之间相关性,遴选盐分特征波段,利用常规回归分析及偏最小二乘回归分析建立土壤盐分的定量反演模型,并利用检验样点进行对比分析和精度检验。研究结果表明,482 nm,1 365 nm,1 384 nm,2 202 nm及2 353 nm为土壤盐分含量的特征波段,利用高光谱数据进行盐分定量反演具有良好的精度;精度检验结果表明,通过Matlab进行偏最小二乘回归计算的反演模型,实测值与预测值相关性更好,精度较高。 相似文献
10.
为了更好地进行土壤水分反演,发展了一种基于ALOS/PALSAR数据、利用自适应神经模糊推理系统(adaptive neuro fuzzy inference system,ANFIS)反演土壤水分的方法.首先,根据研究区实际情况,利用AIEM和Oh模型模拟了试验区裸土区的后向散射特性,建立了后向散射系数与地表粗糙度之间的关系;然后,考虑到研究区地表粗糙度几乎没有变化这一情况,设定了地表粗糙度对后向散射系数的影响为常量;在此基础上,分别利用ANFIS,BP神经网络、多元线性回归和多元非线性回归方法构建了裸土区土壤水分的反演模型,并利用野外实测数据对模型进行了验证.研究结果表明,采用ANFIS方法构建的模型反演精度最高,其均方根误差为0.030,相对误差为14.5％.因此,可以利用ANFIS方法反演裸土区的土壤水分含量,其反演结果具有较高的精度. 相似文献
11.
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
12.
Ankita Jain 《国际地球制图》2017,32(9):1017-1033
Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved. 相似文献
13.
K. S. Rao Y. S. Rao H. K. Al Jassar 《Journal of the Indian Society of Remote Sensing》2008,36(2):109-122
Spaceborne Imaging Radar (SIR-C) polarimetric data acquired over Gujarat test site, India, during April and October 1994 were processed to retrieve soil moisture and surface roughness using multi-polarization techniques. Synchronous field data were collected and compared with the results obtained using SIR-C data. Indian Remote Sensing Satellite (IRS) images in visible region were used for locating groundtruth points. Multi-polarization inversion techniques are found to be sensitive to retrieval of soil moisture and surface roughness parameters. However, the accuracy is not adequate. There is a need to improve the existing inversion models to suit to the Indian agricultural fields. 相似文献
14.
Single, interferometric dual, and quad-polarization mode data were evaluated for the characterization and classification of seven land use classes in an area with shifting cultivation practices located in the Eastern Amazon (Brazil). The Advanced Land-Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data were acquired during a six month interval. A clear-sky Landsat-5/TM image acquired at the same period was used as additional ground reference and as ancillary input data in the classification scheme. We evaluated backscattering intensity, polarimetric features, interferometric coherence and texture parameters for classification purposes using support vector machines (SVM) and feature selection. Results showed that the forest classes were characterized by low temporal backscattering intensity variability, low coherence and high entropy. Quad polarization mode performed better than dual and single polarizations but overall accuracies remain low and were affected by precipitation events on the date and prior SAR date acquisition. Misclassifications were reduced by integrating Landsat data and an overall accuracy of 85% was attained. The integration of Landsat to both quad and dual polarization modes showed similarity at the 5% significance level. SVM was not affected by SAR dimensionality and feature selection technique reveals that co-polarized channels as well as SAR derived parameters such as Alpha-Entropy decomposition were important ranked features after Landsat’ near-infrared and green bands. We show that in absence of Landsat data, polarimetric features extracted from quad-polarization L-band increase classification accuracies when compared to single and dual polarization alone. We argue that the joint analysis of SAR and their derived parameters with optical data performs even better and thus encourage the further development of joint techniques under the Reducing Emissions from Deforestation and Degradation (REDD) mechanism. 相似文献
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全极化SAR获取的信息量远多于传统SAR,但信息量的增加并不能确保分类精度的提高,如何有效进行特征选择至关重要。针对自适应特征选择问题,提出一种顾及分类器参数的特征选择和分类方法。该方法以支持向量数为评估依据,结合遗传算法进行特征选择,并同时对分类器参数进行寻优;最后利用优选的特征集和模型参数进行分类。为验证算法的有效性,利用两组全极化数据进行了监督分类实验。实验结果表明,提出方法降低了SVM分类器对自身参数的敏感性,而且能在较少特征个数下具备良好的泛化性能,分类精度优于未经过特征选择和参数优化的方法。 相似文献
16.
单变量特征选择的苏北地区主要农作物遥感识别 总被引：2，自引：0，他引：2
遥感识别多源特征综合和特征优选是提高遥感影像分类精度的关键技术。农作物遥感识别中,识别特征的相对单一和数量过多均会导致作物识别精度不理想。随机森林(random forests)采用分类与回归树(CART)算法来生成分类树,结合了bagging和随机选择特征变量的优点,是一种有效的分类方法。单变量特征选择(univariate feature selection)能够对每一个待分类的特征进行测试,衡量该特征和响应变量之间的关系,根据得分舍弃不好的特征,优选得到的特征用于分类。本文基于随机森林和单变量特征选择,利用多时相光谱信息、植被指数信息、纹理信息及波段差值信息,设计多组分类实验方案,对江苏省泗洪县的高分一号(GF-1)和环境一号(HJ-1A)影像进行分类研究,旨在选择最佳的分类方案对实验区主要农作物进行识别和提取。实验结果表明:(1)多源信息综合的农作物分类精度明显高于单一的原始光谱特征分类,说明不同类型特征的引入能改善分类效果;(2)基于单变量特征选择算法的优选特征分类效果最佳,总体精度97.07%,Kappa系数0.96,表明了特征优选在降低维度的同时,也保证了较高的分类精度。随机森林和单变量特征选择结合的方法可以提高遥感影像的分类精度,为农作物的识别和提取研究提供了有效的方法。 相似文献
17.
Liangliang Tao Jing Li Xi Chen Qingkong Cai Yunfei Zhang 《Journal of the Indian Society of Remote Sensing》2017,45(4):621-629
This study investigated an appropriate method for soil moisture retrieval from radar images and coincident ground measurements acquired over bare soil and sparsely vegetated regions. The adopted approach based on a single scattering integral equation method (IEM) was developed to establish the relationship between backscatter coefficient and surface soil parameters including volumetric soil moisture content and surface roughness. The performance of IEM in 0–7.6 cm is better than that in 0–20 cm. Moreover, IEM can simulate correctly the backscatter coefficients only for the root mean square (RMS) height s < 1.5 cm at C-band and s < 2.5 cm at L-band by using an exponential correlation function and for s > 1.5 cm at C-band and s > 2.5 cm at L-band by using Gaussian function. However, due to the difficulties involved in the parameterization of soil surface roughness, the estimated accuracy is not satisfactory for the inversion of IEM. This paper used a combined roughness parameter and Fresnel reflection coefficient to develop an empirical model. Simulations were performed to support experimental results and to highlight soil moisture content and surface roughness effects in different polarizations. Results showed that a good agreement was found between the IEM simulations and the SAR measurements over a wide range of soil moisture and surface roughness characteristics. The model had a significant operational advantage in soil moisture retrieval. The correlation coefficients were 77.03 % at L-band and 81.45 % at C-band with the RMSEs of 0.515 and 0.4996 dB, respectively. Additionally, this work offered insight into the required application accuracy of soil moisture retrieval at a large area of arid regions. 相似文献
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Mehdi Hosseini Mohammad Reza Saradjian 《Journal of the Indian Society of Remote Sensing》2014,42(4):719-726
High difference between dielectric constant of water (dielectric constant about 80) and dielectric constant of dried soil (dielectric constant about 2–3) makes Synthetic Aperture Radar (SAR) highly capable in soil moisture estimation. However, there are other factors which affect on radar backscattering coefficient. The most important parameters are vegetation cover, surface roughness and sensor parameters (frequency, polarization and incidence angle). In this paper, the importance of considering the effects of these parameters on SAR backscatter coefficients is shown by comparing different soil moisture estimation models. Moreover, an experimental soil moisture estimation model is developed. It is shown that this model can be used to estimate soil moisture under a variety of vegetation cover densities. The new developed model is based on combination of different indices derived from Landsat5-Thematic Mapper and AIRSAR images. The AIRSAR image is used for extraction of backscattering coefficient and incidence angle while TM image is used for calculation of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI) and Brightness Temperature. Then a soil moisture estimation model which is named as Hybrid model is developed based on integration of all of these parameters. The accuracies of this model are assessed in the NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7 by using SAR data in C band and L band frequencies and also in different polarizations of HH, HV, VV and TP. The results show that for instance in L band with HV polarization, R-square values of 0.728, 0.628 and 0.527 are obtained between ground measured soil moisture and estimated soil moisture values using the Hybrid model for NDVI ranges of 0–0.2, 0.2–0.4 and 0.4–0.7, respectively. 相似文献
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Kai Liu Dong Zhao Jun-yong Fang Xia Zhang Qing-yun Zhang Xue-ke Li 《Journal of the Indian Society of Remote Sensing》2017,45(5):805-813
Heavy-metal-contaminated soil is a critical environmental issue in suburban regions. This paper focuses on utilizing field spectroscopy to predict the heavy metal contents in soil for two suburban areas in the Jiangning District (JN) and the Baguazhou District (BGZ) in China. The relationship between the surface soil heavy metal contents and spectral features was investigated through statistical modeling. Spectral features of several spectral techniques, including reflectance spectra (RF), the logarithm of reciprocal spectra (LG) and continuum-removal spectra (CR), were employed to establish and calibrate models regarding to Cd, Hg and Pb contents. The optimal bands for each spectral feature were first selected based on the spectra of soil samples with artificially added heavy metals using stepwise multiple linear regressions. With the chosen bands, the average predictive accuracies of the cross-validation, using the coefficient of determination R2, for estimating the heavy metal contents in the two field regions were 0.816, 0.796 and 0.652 for Cd; 0.787, 0.888 and 0.832 for Pb; and 0.906 and 0.867 for Hg based on partial least squares regression. Results show that better prediction accuracies were obtained for Cd and Hg, while the poorest prediction was obtained for Pb. Moreover, the performances of the LG and CR models were better than that of the RF model for Pb and Hg, indicating that LG and CR can provide alternative features in determining heavy metal contents. Overall, it’s concluded that Cd, Hg and Pb contents can be assessed using remote-sensing spectroscopy with reasonable accuracy, especially when combined with library and field-collected spectra. 相似文献