共查询到17条相似文献，搜索用时 671 毫秒
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针对广西茅尾海入海河口池塘养殖污染问题,本文利用无人机多光谱遥感影像和实测水质数据,建立了反映水体营养状态的叶绿素a (Chl-a)、化学需氧量(COD)、悬浮物(SS)、总氮(TN)、总磷(TP)5种水质参数,反演光谱特征及遥感反演模型,并利用湖泊综合营养指数法对水体富营养化状态进行评价。研究结果表明:①Chl-a与蓝、近红外波段相关性显著,COD与红、红边波段相关性显著,SS与红边波段相关性显著,TN与近红外波段相关性显著,TP与蓝、绿波段相关性显著;②在建立的几种水质参数反演模型中,二次多项式函数反演模型综合效果最佳;③池塘养殖区水体富营养指数多集中在60～80,属于中度和重度富营养化程度,且近岸水体富营养化程度大多低于远岸。 相似文献
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基于GF-1号卫星WFV数据的太湖水质遥感监测 总被引：9，自引：0，他引：9
为研究高分一号(GF-1)卫星数据监测太湖水质的可行性,基于新发射的GF-1卫星16 m分辨率的多光谱宽覆盖(wide field of view,WFV)相机和HJ-1A CCD数据,对太湖的叶绿素a、悬浮物、透明度和富营养化状况进行遥感监测,以评价GF-1 WFV相机的应用潜力。研究结果表明:GF-1 WFV与HJ-1A CCD数据对水质参数的反演结果具有一致性,可有效反映叶绿素a浓度、悬浮物浓度、透明度和富营养化指数的空间变化规律。其中,太湖西北部分布有少量水华蓝藻,在大面积蓝藻爆发区域附近,叶绿素a浓度明显高于其他区域的水体,平均浓度为62.46 mg·m-3;悬浮物浓度以竺山湾及西部沿岸湖区较大,沿西北向东南方向递减,平均浓度为26.07 mg·L-1;透明度整体从西北向东南递增,与悬浮物浓度的分布趋势相反,平均值为22.1 cm;富营养化指数整体从西北向东南递减,与叶绿素浓度的分布趋势相同,平均值为69.62。遥感监测指标的结果均符合常规监测规律。 相似文献
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基于高光谱遥感反射比的太湖水体叶绿素a含量估算模型 总被引：19，自引：1，他引：19
旨在寻找叶绿素a的高光谱遥感敏感波段并建立其定量估算模型。通过对太湖水体的连续监测，获得了从2004年6月到8月3个月的太湖水体高光谱数据和水质化学分析数据。利用实测的高光谱数据分析计算太湖水体的离水辐亮度和遥感反射比；然后，通过相关分析寻找反演叶绿素a浓度的高光谱敏感波段，进而建立反演太湖水体叶绿素a浓度的高光谱遥感定量估算模型，并用相关数据对模型进行精度分析。研究发现，水体的遥感反射比光谱在719nm和725nm存在两个峰，其中719nm处的峰更明显且稳定。通过模型的对比分析，发现用这两个峰值处的遥感反射比参与建模可以提高叶绿素a的估算精度；并且认为由反射比比值变量R719／R670所建立的线性模型对叶绿素a浓度的估算精度最理想。 相似文献
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基于叶片光谱的森林叶绿素浓度反演研究 总被引：2，自引：0，他引：2
准确估测森林叶片叶绿素浓度有助于进一步理解和模拟森林生态系统。基于江西省千烟州试验区主要树种光谱数据和相应叶绿素浓度等化学参量数据,使用高光谱指数和偏最小二乘(PLS)回归方法进行森林叶绿素浓度的反演,对具代表性的几种叶绿素相关的光谱指数进行反演能力评价,构造出表征叶绿素吸收特征的叶绿素吸收面积指数(CAAI),发现TCAR I(改进型的叶绿素吸收比率指数)和CAAI能较好地估测试验区森林主要树种的叶绿素浓度。针对叶片生化参量之间强相关现象,首先使用了叶片生化参量吸收特征分析方法,选定特定波段。然后对叶绿素浓度进行PLS回归估测,并从PLS获得估测模型的结果来评价和解释几种高光谱指数的叶绿素反演能力。 相似文献
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目前, 针对太湖水体的叶绿素波段敏感性的分析, 大多集中在实测的高光谱反射率数据或者图像提取反射率与叶绿素浓度的统计分析结果上, 缺乏基于水体光学特性的研究, 并且两者之间的一致性也一直缺少论证。研究中采用2004 年4 月和2007 年8 月的两期数据, 首先从水质参数的生物光学特性入手, 基于生物光学模型, 利用叶绿素和其他水质参数的吸收和后向散射系数, 模拟计算其他水质参数不变, 叶绿素浓度处于不同水平时的水面反射率, 分析实测反射率对叶绿素浓度变化的响应; 利用MODIS 的波段响应函数把实测光谱模拟成宽波段的MODIS 反射率, 以此作为桥梁进而对实测的高光谱反射率和MODIS 图像提取反射率与叶绿素浓度的相关程度的一致性进行分析, 为利用MODIS 图像进行水质参数反演时的反演因子选择提供了依据。 相似文献
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昆承湖水质状况遥感监测与空间特征分析 总被引：2，自引：0，他引：2
针对镇域水资源常规的监测方法不能满足对水质适时、大尺度的监测评价要求等问题,该文以江苏常熟辛庄镇昆承湖为例,分析叶绿素a(Chl-a)和悬浮物(SS)浓度值与水体归一化反射光谱、一阶微分反射光谱特征的关系,运用PEARSON法分析两者之间的相关性,确定Chl-a和SS的敏感波段,利用Chl-a和SS敏感波段归一化光谱反射值和实测浓度数据建立了水质参数反演模型。采用同时相ETM遥感数据对水质参数(叶绿素a和悬浮物)浓度进行遥感定量反演,并根据反演结果分析镇域水体污染空间分布特征。结果表明,对水质参数几个最大正(负)相关的光谱值进行波段组合处理可以提高反演精度,并且模型反演值和实测浓度值之间误差较低。通过对叶绿素、悬浮物等水质参数反演实现了对昆承湖水体污染状况的快速、准确和动态的信息获取和评价,有效地实时监测镇域水体在空间和时间上的变化状况和特性。 相似文献
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应用实测光谱估测太湖梅梁湾附近水体叶绿素浓度 总被引：40，自引：1，他引：40
遥感方法测定水体中叶绿素含量的核心问题是建立遥感数据和叶绿素含量的定量关系;利用太湖梅梁湾附近水体的实测光谱和水质采样实验室分析,从数量上揭示了位于682nm附近和706nm附近对叶绿素含量估测最重要的两个光谱特征,分别通过比值(R706/R682、R706/R572)、微分、面积、峰高、峰谷距离以及反射峰位置等建立与叶绿素浓度的线性或非线性相关回归模型,通过R2、平均误差以及RMS误差等的分析对比,认为比值和反射峰位置对叶绿素浓度有很好的指示作用,是估测太湖梅梁湾附近水体叶绿素浓度的最好方法,其中与反射峰位置的指数关系R2达0.9199,与R706/R682的直接线性关系R2达0.9038。相对于706nm附近的峰谷距离、峰高以及反射峰面积而言,反射峰的位置是指示叶绿素浓度最敏感的变量;572nm附近、682nm附近以及706nm附近等三个波段对探测叶绿素a具有重要作用。 相似文献
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鄱阳湖叶绿素a浓度遥感定量模型研究 总被引：1，自引：0，他引：1
叶绿素a浓度是反映湖泊水体营养状况的重要指标,本研究通过分析水体叶绿素a浓度与高光谱反射特征的相互关系,采用一阶微分值和峰值比值法分别建立了叶绿素a的高光谱定量反演模型,在此基础上与同步MODIS数据敏感波段建立卫星定量反演模型。结果表明:叶绿素a荧光峰出现在波段690nm-700nm,波段696nm一阶微分值相关系数最大;波段700nm与波段680nm的比值与其对数相关性较好,MODIS数据波段2和波段1比值的指数模型为最佳的回归模型。 相似文献
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《International Journal of Digital Earth》2013,6(5):364-382
To understand the mechanism of wetland cover change with both moderate spatial resolution and high temporal frequency, this research evaluates the applicability of a spatiotemporal reflectance blending model in the Poyang Lake area, China, using 9 time-series Landsat-5 Thematic Mapper images and 18 time-series Terra Moderate Resolution Imaging Spectroradiometer images acquired between July 2004 and November 2005. The customized blending model was developed based on the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). Reflectance of the moderate-resolution image pixels on the target dates can be predicted more accurately by the proposed customized model than the original ESTARFM. Water level on the input image acquisition dates strongly affected the accuracy of the blended reflectance. It was found that either of the image sets used as prior or posterior inputs are required when the difference of water level between the prior or posterior date and target date at Poyang Hydrological Station is <2.68 m to achieve blending accuracy with a mean average absolute difference of 4% between the observed and blended reflectance in all spectral bands. 相似文献
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Youichi Oyama Bunkei Matsushita Takehiko Fukushima Kazuo Matsushige Akio Imai 《ISPRS Journal of Photogrammetry and Remote Sensing》2009,64(1):73-85
The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member. 相似文献
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付海军 《测绘与空间地理信息》2017,(6):109-112
为分析高分一号WFV传感器16 m遥感影像在水质反演方面的能力,本文选取南四湖为研究区,以高分一号卫星影像与Landsat-8卫星OLI影像为数据源,结合地面同步实测水体浊度数据,建立反演水体浊度的原始光谱反射率模型、归一化反射率模型和波段比值模型,并对各模型进行精度评价,分别比较两个传感器在浊度反演能力方面的差异。结果表明:利用高分一号WFV 16m遥感影像进行水质反演具有较高的精度,且具备更高的空间分辨率和更短的重访周期,可以替代Landsat-8多光谱数据。 相似文献
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悬浮泥沙是湖泊水体的重要水质要素之一,是湖泊水生态系统重要影响因子。利用遥感提取的鄱阳湖底质类型数据,对各类型底质区域上泥沙模型参数进行敏感性分析,基于2011年实测的不同底质区上泥沙浓度数据率定得到空间上不一致的泥沙模型参数,从而建立鄱阳湖丰水期二维悬浮泥沙输运模型。利用该模型模拟预测2013年丰水期悬浮泥沙输移过程,实测泥沙数据验证表明,模拟悬浮泥沙均方根误差为19.8 mg/L,相关系数R2为0.78,该模型能够较好地进行悬浮泥沙模拟预测。对比模型模拟和遥感反演的悬浮泥沙空间分布,两者具有较好的空间一致性,进一步表明该模型能够可靠地模拟预测鄱阳湖悬浮泥沙的时空动态变化。本研究充分利用遥感数据辅助建立鄱阳湖泥沙输移模型,为湖泊水环境动态变化监测提供有力的技术方法支撑,为进一步进行鄱阳湖水环境动态变化的数值模拟预测研究提供基础。 相似文献
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Applying remote sensing techniques to develop the retrieval models and further to obtain the spatiotemporal information of water quality parameters is necessary for understanding, managing, and protecting lake ecosystems. This study aimed to calibrate and validate the retrieval models for estimating the concentrations of chlorophyll a (CCHL), suspended particulate matter (CSPM), and dissolved organic carbon (CDOC) with the in situ hyperspectral measurements in Poyang Lake, China in 2010 and 2011. The model calibration and validation results indicated that: (1) for CCHL retrieval, significantly strong and moderate correlations existed between the measured and estimated values (with the correlation coefficient r = 0.92 and r = 0.76) using the exponential model and the three-band model, respectively, with biased estimation observed for the exponential model; (2) for retrieving CSPM, there was a strong correlation between the measured and estimated values (r = 0.95) using the exponential model; and (3) no significant correlation between measured and estimated CDOC values was found with our developed models. More work is needed to allow the water quality of Poyang Lake to be accurately and steadily estimated, especially for CCHL and CDOC. 相似文献
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Abstract Upper Lake is the lifeline of Bhopal City, India for drinking and other water needs. In recent years, environmentalists have expressed their serious concern on deteriorating water quality of this lake. Conventional field sampling methods for monitoring lake water quality lack spatial information about the pollution in the lake. It is desirable to have spatial information about the lake for better management and control. In the present paper the remote sensing data from IRS-1C LISS III have been integrated into a GIS environment to analyse and create a pollution zone map of the Upper Lake. Spectral reflectance analysis was carried out to find the suitability of wavelengths for determining chlorophyll‐a concentration (chl‐a), suspended solid concentration (SSC) and secchi depth (SD). Empirical models relating spectral reflectance and chl‐a, SSC and SD were developed using least square regression analysis. These models were found valid on unused samples. Chl‐a, SSC and SD distribution maps were generated using proposed models and were incorporated as datalayers in the GIS for further analysis of pollution zones. The spatial information of pollution offered by the pollution zone map could delineate regions of lake having high pollution load. The methodology employed in this work can be used for regular monitoring of the pollution in surface water bodies and serve the data needs for better management of the water quality. 相似文献