曹妃甸近海表层悬浮泥沙遥感定量监测研究

在曹妃甸近海水体实测光谱数据分析的基础上,与同步采集的水体悬浮泥沙浓度进行相关分析,选用合适的TM数据的遥感变量参数,根据2008年实测的四个剖面29个站点的海洋水色数据建立了水体悬浮泥沙浓度与TM数据对应的遥感定量反演模型,并用18个独立样本点进行了精度评价.研究结果表明:曹妃甸近海水体光谱曲线基本呈现单峰现象,敏感...     

近海岸悬浮泥沙遥感反演模型的分析探讨

简要介绍了国内外近海岸悬浮泥沙定量化遥感反演的研究进展及悬浮泥沙遥感反演模型的建立方法,通过对国内外近年研究的部分悬浮泥沙遥感反演模型进行简单分类、对比分析得出其优缺点,并针对部分悬浮泥沙遥感反演模型的不足和目前遥感技术在悬浮泥沙定量反演中的应用提出了一些肤浅见解。     

近岸水体表层悬浮泥沙平均粒径遥感反演

悬浮泥沙的粒径分布特征不仅体现了悬浮颗粒态物质的存在状态,而且可以指示水动力及再悬浮作用的过程和强度,因此研究悬浮泥沙粒径分布特征具有重要意义。利用Mie理论建立悬浮泥沙平均粒径反演模型,结果表明,悬浮泥沙后向散射系数与其平均粒径的三次方线性关系明显,4个波段(412nm、443nm、555nm、667nm)拟合方程决定系数均在0.93以上,拟合误差最小值为16.6322%(412nm),最大值为20.3143%(667nm)。利用QAA算法从MODIS影像上反演研究区域悬浮泥沙后向散射系数,并结合反演的悬浮泥沙浓度推算研究区域表层悬浮泥沙的平均粒径。对比发现,近岸高悬浮泥沙区域的反演结果与实测数据吻合较好,相关研究可以为深入开展陆海相互作用、海洋生态系统演变和海洋参与全球碳循环等研究提供重要数据支持。     

基于MODIS 数据的悬浮泥沙定量遥感方法

以黄海及东海海域为对象,研究用MOD IS数据提取我国海域悬浮泥沙时空分布的定量遥感方法,建立了基于MOD IS数据的悬浮泥沙定量遥感实用模式。研究表明,用250 m和1 000 m分辨率的MOD IS数据进行悬浮泥沙浓度的定量遥感,可以达到实际应用的精度要求。这说明,MOD IS数据是研究近岸水体中悬浮物输运变化规律的一种经济实用数据源。     

河口表层悬浮泥沙气象卫星遥感定量模式研究

根据多时相NOAA AVHRR卫星遥感数据和准同步实测表层含沙量资料，分别利用经暗像元大气校正的遥感图像灰度值、R1－R2相关关系曲线的斜率，以及泥沙指数等参数，建立泥沙遥感定量模式（灰度法、斜率法和泥沙指数法），并对3种模式的效果和适用性进行了比较。灰度法由于只用了单个波段的光谱信息，模式的相关性较差；斜率法通过计算R1－R2关系曲线的斜率，达到大气校正的目的。但由于在泥沙浓度交界面存在斜率值跃变现象，会影响最终效果；泥沙指数法综合运用了Ch1,Ch2波段的光谱信息，模式的相关性较好，可获得层次丰富泥沙图像。     

福建省海岸带泥沙分布的气象卫星遥感监测

根据水体对太阳辐射光谱的反射率变化特性,利用NOAA极轨气象卫星遥感资料,动态监测福建省海岸带悬浮泥沙的分布,并从十几幅质量较好的不同时相的泥沙分布图中得出初步的监测结果。     

三峡坝区水体悬浮泥沙含量遥感估算模型研究

通过对水槽中不同含沙量水体所进行的光谱测试,获得了不同含沙量水体的光谱曲线,得到了不同含沙量水体与光谱反射率之间的相关估算模型.     

悬浮泥沙遥感定量的统一模式及其在珠江口中的应用

本文推导出来悬浮泥沙遥感定量的统一式,其形式包含了Gordon关系式和负指数关系式,其它各悬浮泥沙的关系式可看作是该统一式的近似。由多个例子的计算表明统一式的相关性和精度要高于目前国内外使用的悬浮泥沙定量的遥感模式。该统一式被成功地应用于珠江口伶仃洋的遥感定量分析中。     

机器学习模型的珠江口悬浮泥沙遥感反演与规律分析

本文以珠江口悬浮泥沙水质采样数据及高光谱数据为基础,构建了人工神经网络(ANN)、支持向量回归机(SVR)、随机森林(RF)悬浮泥沙反演模型,结果发现模型预测精度从高到低依次为：RF、ANN、SVR。采用随机森林模型对珠江口悬浮泥沙反演,结果得到珠江口岸悬浮泥沙浓度呈现西高东低,从近岸到离岸逐渐递减趋势。主要是由于珠江口地形为喇叭形,在季风和潮汐共同作用下,珠江口顶部区域受潮汐和风向混合作用强烈。在远离珠江口顶部区域地形较为开阔,各支流径流的悬浮泥沙汇聚堆积在珠江口西南区域,导致珠江口西南区域悬浮泥沙浓度高。     

基于遥感反演结果的悬浮泥沙时空动态规律研究--以珠江河口及邻近海域为例

选择了珠江口及其邻近海域1995年~2000年152个时相的NOAA/AVHRR影像反演的悬浮泥沙浓度结果,通过对悬浮泥沙反演结果影像的叠加,求算叠加像素点像素平均值,计算了研究海域在1995~2000年间月、季及丰枯水期几个时间尺度上的悬浮泥沙平均浓度及其平均变化。结果大致表明了陆地径流对海域的影响范围和悬浮泥沙在海域的运动方向。     

杭州湾滨海湿地时空格局变化遥感调查研究

采用TM、SPOT-5卫星遥感数据,通过建立杭州湾滨海湿地分类体系和解译标志并进行人机交互解译,完成了杭州湾1987、1995、2003和2009年滨海湿地提取和分类。研究了4期杭州湾滨海湿地的利用状况、面积,以及时空格局变化情况,研究显示:1987~2009年期间,杭州湾滨海湿地主要表现为滩涂湿地的逐年减少和库塘湿地的逐年增加;1995年之前湿地变化以自然驱动力为主,之后人类活动影响明显,对近海域、滩涂不断开发,尤以南岸为主;以杭州湾湿地公园为典型的沼泽草甸湿地得到了较好的保护。     

A three-band semi-analytical model for deriving total suspended sediment concentration from HJ-1A/CCD data in turbid coastal waters

The accurate assessment of total suspended sediment (TSM) concentration in coastal waters by means of remote sensing is quite challenging, due to the optical complexity and significant variability of these waters. In this study, three-band semi-analytical TSM retrieval (TSTM) model with HJ-1A/CCD spectral bands was developed for the retrieval of TSM concentration from turbid coastal waters. This model was calibrated and validated by means of one calibration dataset and three independent validation datasets obtained from three different turbid waters. It was found that the TSTM model may be used to retrieve accurate TSM concentration data from highly turbid waters without the spectral slope of the model requiring further optimization. Finally, the TSM concentration data were quantified from the HJ-1A/CCD images after atmospheric correction using the dark-object subtraction technique. Upon comparing the model-derived and field-measured TSM concentration data, it was observed that the TSTM model produced <29% uncertainty in deriving TSM concentration from the HJ-1A/CCD data. These findings imply that the TSTM model may be used for the quantitative monitoring of TSM concentration in coastal waters, provided that the atmospheric correction scheme for the HJ-1A/CCD imagery is available.     

遥感卫星CCD相机数据的系统几何校正

本文叙述ＣＣＤ相机遥感数据的几何校正方法，特别是在预处理系统中实现校正的算法。     

随机森林回归模型的悬浮泥沙浓度遥感估算

三峡工程蓄水以来,清水下泄,坝下游河段发生了长时间、长距离的沿程冲刷,河流悬浮泥沙浓度发生改变,给沿岸生态系统带来了不利影响。随机森林算法灵活、稳健,已被广泛应用于各类生态环境变量的回归预测分析,但其在水体悬浮泥沙浓度估算方面的能力尚未得到充分认识。基于泥沙站点监测数据和MODIS卫星遥感反射率数据,通过构建随机森林非参数回归预测模型,对三峡工程坝下游宜昌至城陵矶河段在建坝前后14年间(2002年—2015年)各月的悬浮泥沙浓度进行遥感估算。研究表明:(1)基于随机森林的悬浮泥沙浓度估算模型表现较好,模型预测值与实测值间相关性好、预测精度高,优于其他模型(线性回归、支持向量机、人工神经网络模型)。(2)在参与模型构建的MODIS波段变量中,红波段被认为是最重要的预测变量,但不能单独使用它进行预测,悬浮泥沙遥感预测需要多变量共同参与。(3)将悬浮泥沙数据按季节分类所构建的随机森林模型,其平均误差为0.46 mg/L,平均相对均方根误差为12.33%,估算效果最优,能够满足较高精度下悬浮泥沙浓度估算的需求。综上,可以考虑以季节为划分依据,用随机森林回归模型估算悬浮泥沙浓度,并用于后期坝下游河道悬浮泥沙浓度时空反演。     

Estimation of forest aboveground biomass from HJ1B imagery using a canopy reflectance model and a forest growth model

Accurately estimating the spatial distribution of forest aboveground biomass (AGB) is important because of its carbon budget forms part of the global carbon cycle. This paper presented three methods for obtaining forest AGB based on a forest growth model, a Multiple-Forward-Mode (MFM) method and a stochastic gradient boosting (SGB) model. A Li-Strahler geometric-optical canopy reflectance model (GOMS) with the ZELIG forest growth model was run using HJ1B imagery to derive forest AGB. GOMS-ZELIG simulated data were used to train the SGB model and AGB estimation. The GOMS-ZELIG AGB estimation was evaluated for 24 field-measured data and compared against the GOMS-SGB model and GOMS-MFM biomass predictions from multispectral HJ1B data. The results show that the estimation accuracy of the GOMS-MFM model is slightly higher than that of the GOMS-SGB model. The GOMS-ZELIG and GOMS-MFM models are considerably more accurate at estimating forest AGB in arid and semiarid regions.     

Assessment of Landsat atmospheric correction methods for water color applications using global AERONET-OC data

With the longest archive of satellite remote sensing images, the Landsat series of satellites have demonstrated their great potential in aquatic environmental studies. However, although various atmospheric correction (AC) methods have been developed for Landsat observations in water color applications, a comprehensive assessment of their accuracies across different AC methods and instruments has yet to be performed. Using in situ spectral data collected by Aerosol Robotic Network-Ocean Color (AERONET-OC) sites, the performances of five types of AC methods over three different Landsat missions (i.e., Landsat 5/7/8) were evaluated. The Landsat 8 Operational Land Imager (OLI) showed more accurate AC retrievals than the other two instruments, and the results for its green and red bands appeared more reliable than those for the other wavelengths (uncertainty levels of ∼30 %). The iterative NIR algorithm with 2-bands (NIR-SWIR2) model selection embedded in SeaDAS showed the best performances for OLI in two blue bands. Moreover, larger residual errors were found for most Landsat 5/7 bands regardless of the AC methods and spectral bands employed with an uncertainty of >50 %. Interestingly, a simple aerosol subtraction method over the Rayleigh-corrected reflectance (Rrc) outperformed the exponential extrapolation (EXP) algorithms, especially for Landsat 5/7. Neither the image-based AC algorithm nor the surface reflectance (SR) products provided by the United States Geological Survey (USGS) showed acceptable performances over coastal environments. The uncertainties in the various Landsat reflectance products over water surfaces could be associated with a relatively poor signal-to-noise ratio (SNR) in addition to radiometric calibration uncertainties, imperfect aerosol removal methods. Future research is required to collect in situ data across a wider range of water optical properties (particularly more turbid inland waters) to examine the corresponding applicability of Landsat-series observations.     

Spectral material mapping using hyperspectral imagery: a review of spectral matching and library search methods

Imaging spectroscopy is an emerging and versatile technique that finds applications in diverse fields concerned with remote identification, discrimination and mapping of materials. The large amount of spectral data produced by hyperspectral imaging necessitates the development of automated techniques that convert imagery directly into thematic maps. Spectral library search method, a method of choice for organic compound identification by the mass spectroscopy, has caught the attention of researchers as one of the appropriate methods for an efficient exploitation of high quality spectral data available from the hyperspectral imaging systems. Given the apparent increase in the number of papers appearing on the subject as well as the variety of methods proposed, it is reasonable to say that the field of automated interpretation of reflectance spectral data has passed its infancy now gaining important space in the scientific community. We present an overall view of the literature relevant to the development of library search method, the various search algorithms and systems available in the purview for developing an automated hyperspectral data analysis system for material identification.     

基于Sentinel-2卫星数据的内陆湖泊水体大气校正

利用Acolite、FLAASH和C2RCC 3种大气校正算法,参考内陆湖泊历史实测遥感反射率数据,开展针对Sentinel-2卫星数据的内陆湖泊水体大气校正研究.试验结果表明:在数值方面,C2RCC校正结果和实测结果位于相同数量级,Acolite和FLAASH校正结果高估;在光谱曲线变化趋势方面,C2RCC校正结果与...     

Empirical models for estimating the suspended sediment concentration in Amazonian white water rivers using Landsat 5/TM

Suspended sediment yield is a very important environmental indicator within Amazonian fluvial systems, especially for rivers dominated by inorganic particles, referred to as white water rivers. For vast portions of Amazonian rivers, suspended sediment concentration (SSC) is measured infrequently or not at all. However, remote sensing techniques have been used to estimate water quality parameters worldwide, from which data for suspended matter is the most successfully retrieved. This paper presents empirical models for SSC retrieval in Amazonian white water rivers using reflectance data derived from Landsat 5/TM. The models use multiple regression for both the entire dataset (global model, N = 504) and for five segmented datasets (regional models) defined by general geological features of drainage basins. The models use VNIR bands, band ratios, and the SWIR band 5 as input. For the global model, the adjusted R² is 0.76, while the adjusted R² values for regional models vary from 0.77 to 0.89, all significant (p-value < 0.0001). The regional models are subject to the leave-one-out cross validation technique, which presents robust results. The findings show that both the average error of estimation and the standard deviation increase as the SSC range increases. Regional models were more accurate when compared with the global model, suggesting changes in optical proprieties of water sampled at different sampling stations. Results confirm the potential for the estimation of SSC from Landsat/TM historical series data for the 1980s and 1990s, for which the in situ database is scarce. Such estimates supplement the SSC temporal series, providing a more comprehensive SSC temporal series which may show environmental dynamics yet unknown.