应用MODIS监测太湖水体叶绿素a浓度的研究

以太湖作为实验区,将MODIS影像不同空间分辨率的波段反射率与叶绿素a浓度实测值进行相关分析,在此基础上通过回归拟合建立遥感监测模型,并应用模型计算出太湖水体叶绿素a浓度的分布情况,对太湖水质进行了评价。研究结果表明,MODIS影像在太湖的水质监测中是可用的,其中250m分辨率波段1、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.903),适于用来反演叶绿素a浓度。     

基于TM遥感图像的近海岸带水深反演研究

以江苏南通小洋口港外辐射沙脊群海域为研究区,在TM数据地表反射率反演的基础上,通过实测水深数据与TM4波段反射率、TM4/TM2波段反射率比值因子的相关研究,分别建立了线性、对数、指数和幂指数的水深反演模型,研究不同模型对该研究区0~5m、5~10m、10~15m不同水深的适用情况以及水深反演精度分析。研究结果表明:针对本研究区,以TM4/TM2波段反射率比值建立的线性水深反演模型的精度最好,并依据该模型对研究区进行遥感水深制图。     

巢湖水体悬浮物的遥感分析

利用Landsat TM/ETM 卫星遥感影像为数据源,根据TM/ETM 的3、4波段反射率与悬浮物浓度存在着线性相关的性质,利用每个时相的3、4波段进行加法运算后的影像,对巢湖悬浮物浓度分布及其变化趋势进行了分析。研究结果表明,巢湖的悬浮物浓度较高,所占的面积在增大,悬浮物的污染还是很严重;但是,通过对1987年和2003年巢湖水体悬浮物悬浓度等级变化转移矩阵分析发现,悬浮物浓度等级降低的区域比悬浮物浓度等级升高的区域高出了17.64个百分点。这说明巢湖悬浮物的污染在减轻,国家对巢湖的治理已见成效。     

基于PSO-RBF神经网络模型反演闽江下游水体悬浮物浓度

总悬浮物浓度是水环境重要参数之一,二类水体光谱特征复杂,光谱特征与悬浮物浓度之间关系不能用简单的线性模型来表示。利用2017年7月12日～13日2d时间对闽江40点位进行水质采样和光谱测量,结合光谱响应函数模拟GF-1 WFV1各波段遥感反射率,分析遥感因子与总悬浮物浓度相关性。利用相关系数较高的波段及组合b3、b3/b2和b3/b1,构建PSO-RBF和传统RBF神经网络总悬浮物浓度反演模型,同时建立以b3/b2为自变量的经验比值模型。结果表明:与传统RBF神经网络和经验模型相比,PSO-RBF神经网络模型效果更佳,R2=0.890,RMSE=3.01mg·L-1。基于训练好的PSO-RBF模型,应用GF-1 WFV1遥感影像对闽江下游水体总悬浮物浓度进行反演,影像反演的总悬浮物浓度RMSE=3.65mg·L-1,MRE=14.11%,遥感影像反演结果精度明显高于克里金空间插值结果。分析其空间分布特征,从上游方向往下游方向呈现增加趋势,马尾至闽江入海口河段总悬浮物浓度增加明显。     

基于Sentinel-3A的北部湾海域叶绿素a浓度遥感反演研究

以北部湾为研究对象,基于Sentinel-3A卫星搭载的OCLI水色传感器,探索了叶绿素浓度的遥感反演方法。通过利用实测光谱数据对北部湾海域进行了分区,结合实测的叶绿素a浓度和Sentinel-3A遥感数据尝试不同的反演因子,包括波段比值、波段差值和波段差比,构建了叶绿素a浓度的遥感反演模型。研究结果表明：(1)北部湾海域的遥感反射率曲线呈现明显的分区的特征,结合光谱特征将北部湾海域分为近岸水体、过渡水体和离岸水体;(2)不同水体类型适用不同的反演因子构建模型,其中R_rs(764.375)/R_rs(681.25)用于近岸水体,[1/R_rs(620)-1/R_rs(708.75)]/R_rs(753.75)用于过渡水体,R_rs(708.75)-R_rs(764.375)用于离岸水体,均取得了较好的拟合效果,相应的R2值分别为0.67、0.80和0.8;(3)分区的方法有效的提高了遥感反演北部湾叶绿素浓度模型的适用性和精度。研究基于Sentin...     

应用Landsat TM影像估算渤海叶绿素a和总悬浮物浓度

利用23个实测样点的渤海叶绿素a和总悬浮物浓度数据及同步Landsat TM影像数据,分别分析了Landsat TM离水辐射亮度对渤海叶绿素a和总悬浮物浓度的敏感性,选择合适的波段,通过回归分析构建了基于Landsat TM离水辐射亮度的渤海叶绿素a和总悬浮物浓度反演模型。结果表明,TM1、TM2和TM3波段对叶绿素a的敏感性较高,以TM4/TM1和TM3/TM2的对数为自变量,以叶绿素a浓度的对数为因变量的线性估算模型可以有效反演渤海叶绿素a浓度,决定系数R2达到0.97;TM3波段对悬浮物的敏感性最高,以TM2、TM3和TM3/TM2为自变量,以总悬浮物浓度的以10为底的对数为因变量的多元线性模型获得的结果最佳,决定系数R2达到0.91。     

基于ERDAS IMAGING的TM影像中较小水体识别方法

针对利用Landsat TM遥感影像中水体信息识别的常用方法对细小水体识别的局限性作了深入研究,提出了新的基于ERDAS IMAGING的较小水体识别方法.将TM影像中某3个波段组成的假彩色合成图转换到IHS彩色空间,构建模型进行细小水体的识别.该方法有效地提高了细小水体识别的精度.     

基于TM影像的平原湖泊水体信息提取的研究

以洪泽湖Landsat TM影像为例,分析了利用单波段阈值法和多波段增强图阈值法进行水体信息提取的差异,从而确定出不同时期不同用途所采用的最佳水体综合提取方法,即综合利用多波段谱间关系(TM2+TM3TM4+TM5)和单波段TM5建立起适合于平原湖泊水域的水体提取方法。     

应用MODIS数据监测巢湖蓝藻水华的研究

以巢湖为研究区域,以MODIS 卫星影像为数据源,结合准同步的地面水质监测数据,将MODIS 250 m分辨率的波段反射率与叶绿素a浓度实测值进行相关分析。在此基础上通过回归拟合,构建基于中分辨率成像光谱仪(MODIS) 的叶绿素遥感提取模型。应用模型成功提取出蓝藻爆发水域chl-a的分布。从MODIS遥感图像上可以清晰地反映出巢湖这次蓝藻爆发的强度、地点和分布范围 。研究结果表明:用MODIS影像监测巢湖蓝藻水华是可行的,其中250m分辨率波段1 、2的比值组合r2/r1与叶绿素a浓度实测值高度相关(R=0.909 3),适于反演叶绿素a浓度。     

CBERS与TM在水体污染遥感监测中的比较研究

以南京市溧水县的主要水体为研究对象,利用CBERS数据和TM数据分别监测水污染状况形成污染等级图,通过分析两者的差异,研究CBERS数据和TM数据在水体污染遥感监测方面的异同情况.结果表明,分析结果与溧水县的水体污染现状一致,且利用CBERS数据和TM数据分析分别得出的水体污染分布具有较强的一致性;免费的CBERS数据在一定程度上可以替代TM数据应用于水体污染遥感监测方面.     

基于高光谱数据和MODIS影像的鄱阳湖悬浮泥沙浓度估算

本文旨在寻找悬浮泥沙浓度的MODIS遥感影像估算模型,并利用实测的高光谱数据对其敏感波段和反演模型进行测试和验证。以鄱阳湖为研究区域,利用光谱数据进行分析,为利用遥感影像建模提供依据。进一步利用同步进行的鄱阳湖水质采样分析与MODIS影像中等分辨率各个波段反射率及其组合进行相关分析,寻找反演悬浮泥沙浓度的敏感波段。实验表明,MODIS的第一波段反射率对于悬浮泥沙浓度有很好的匹配(R² = 0.91; n = 25),进而建立了鄱阳湖地区的悬浮泥沙浓度遥感定量估算模型。利用估算模型和鄱阳湖地区历史MODIS影像,得到了鄱阳湖悬浮泥沙浓度分布图。基于对汛期鄱阳湖悬浮泥沙浓度的连续监测,可对长江倒灌入鄱阳湖现象的形态进行观测。     

运用SPOT数据进行河流水体悬浮固体浓度的研究——以台湾淡水河为例

根据含沙水体的光谱特征,通过对比分析认为ＳＰＯＴ影像是河流水质遥感的理想数据源,其中１ 波段和２ 波段对反映水体悬浮固体比较敏感。根据遥感影像灰度值与水中悬浮固体含量之间的相关关系,运用ＳＰＯＴ影像的１、２ 波段和实测数据将淡水河悬浮固体含量分为４ 级,并对结果进行了评价。通过对悬浮固体污染等级图的分析,得出淡水河１０％ 以下的水体悬浮固体含量较高。悬浮固体含量从上游向下游递增,流经城市的河段悬浮物含量高,说明水体悬浮物含量受植被覆盖和人为作用影响。     

基于GF-2影像的沈阳市黑臭水体遥感分级识别

城市黑臭水体遥感分级识别对于黑臭水体的监管及治理具有重要作用。针对目前黑臭水体遥感识别算法无法对河流黑臭程度分级这一问题,在沈阳市建成区内开展野外实验,对比分析一般水体、轻度黑臭水体和重度黑臭水体的反射率光谱差异,利用绿波段反射率的基线差值与红波段反射率之比,提出了一种城市黑臭水体遥感分级指数BOCI(Black and Odorous water Classification Index)模型。首先采用实测光谱数据对BOCI模型检验,并将其与改进后归一化比值模型进行对比,结果表明,BOCI模型具有更高的黑臭水体识别精度,且可以将重度黑臭水体与轻度黑臭水体区分开,解决了现有模型无法对黑臭水体污染程度分级的问题;然后将BOCI模型应用于沈阳市同步GF-2影像进一步检验,同样取得了较高的识别精度;最后将该模型应用于2015~2018年4景GF-2影像,对研究区内黑臭水体进行动态监测,结果显示,新开河、南运河和满堂河黑臭现象逐步得到改善,辉山明渠黑臭现象依然很严峻。     

Remote Sensing Classification and Recognition of Black and Odorous Water in Shenyang based on GF-2 Image

The classification and recognition of urban black-odor water by remote sensing plays an important role in the supervision and treatment of the black-odor water. Aiming at the problem that the current remote sensing recognition algorithm of black-odor water cannot classify the pollution degree of black-odor water, we conducted field experiments in Shenyang built-up area. The reflectance spectra and water quality parameters of general water, mild and heavy black-odor water were measured. According to the spectral characteristics of different water, based on the ratio of baseline difference of green band reflectance to red band reflectance, a remote sensing classification index BOCI (Black and Odorous water Classification Index) model is proposed. Firstly, BOCI is checked by the measured data on the ground, compared with improved normalized ratio model. The results show that BOCI has higher recognition accuracy. Moreover, BOCI can distinguish between mild and heavy black-odor water, which solves the problem that the existing model cannot classify the pollution degree of the black-odor water. Then, BOCI is applied to the synchronous GF-2 image of Shenyang for further tested, and the recognition accuracy is also high. Finally, BOCI is applied to the four GF-2 images of Shenyang from 2015 to 2018 to monitor the dynamic changes of black-odor water. The results show that the black-odor phenomena of Xinkai River, Nanyun River and Mantang River are gradually improved, but the black-odor phenomena of Huishan Canal are still very serious.     

Applications of Landsat‐5 TM imagery in assessing and mapping water quality in Reelfoot Lake,Tennessee

Water quality in Reelfoot Lake, Tennessee, was investigated in the field over 15 years ago. However, the spatial variations of water quality were not studied. The remote sensing technique has been proved a powerful tool in mapping spatial distributions of some water quality parameters such as chlorophyll‐a concentration. Additionally, different regression methods and various independent variables have been used to establish relationships between water quality parameters and spectral reflectance. The results from this study indicate that Landsat TM2 and TM3, as a set of independent variables in multivariate regression analysis, are good predictors of water quality in Reelfoot Lake. TM2 is positively correlated to water quality, and TM3 is negatively correlated to water quality. Poor water quality, or a high algae load, results in a high reflectance measured by TM2 and a low reflectance measured by TM3. Maps of spatial distribution of Secchi disk depth, turbidity, chlorophyll‐a, and total suspended solids present apparent spatial variations of water quality in the lake.     

An optimal model for estimating suspended sediment concentration from Landsat TM images in the Caofeidian coastal waters

Suspended sediment concentration (SSC) is one of the most critical parameters in water quality and environmental evaluations. Remote sensing has the potential for monitoring the dynamics and spatial distribution of SSC efficiently. The primary objective of this study is to develop retrieval models that are reliable and sensitive to SSC levels in the Caofeidian area, a new seaport in northeast China, based on Landsat-5 Thematic Mapper (TM) images and a set of in situ data sets, including spectral reflectance data and water quality data. The study finds that the band reflectance ratio and binary combination factor (i.e. the ratio of the reflectance to the particle size) are more effective than single band reflectance, and a non-linear model is more potent than a linear model for predicting SSC in the Caofeidian waters. A quadratic polynomial regression model of the R_TM3/R_TM2 ratio is proposed as the optimal retrieval model after evaluating various models with respect to different sensitive factors. The accuracy of the model is acceptable with a relative error and a root mean square error of 25.35% and 7.22 mg l^–1, respectively; the correlation coefficient between the observed and estimated SSCs is 0.986. This study also indicates that the band reflectance ratio and binary combination factor are effective in weakening and even partially eliminating the effects of the changes in the sediment type (i.e. particle size and refractive index). And the band reflectance ratio is more efficient. Using the proposed model and TM data, SSC levels for the entire region were estimated. Such results can serve as a baseline for future environmental monitoring efforts.     

Automatic detection of coastal plumes using Landsat TM/ETM+ images

Riverine fresh water outflows create coastal plumes that are distinguished from surrounding sea water by their specific spectral signature. Coastal waters are unique ecosystems, and they are very important in terms of living resources and oceanographic processes. River plumes and coastal turbid waters have important effects on coastal marine ecosystems, and they also influence marine life cycles, sediment distribution, and pollution. Remote sensing and digital image-processing techniques provide an effective tool to detect and monitor these plume zones over large areas. The primary goal of this study was automatic detection and monitoring of coastal plume zones using multispectral Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) imagery. For that purpose, the proposed algorithm exploits spectral features of the multispectral images by using feature extraction and decision-making steps. The procedure has two main stages: (1) some pre-processing operations were applied to the images in order to extract the plume core reflectance values with maximum turbidity and offshore water mass reflectance values; (2) a k-means algorithm was applied with initial seed values of reflectance computed from the pre-processing stage to classify coastal plume zones. Spatial pattern and variability of optical characteristics of coastal plume zones were then defined following the results of the classification process. The algorithm was automatically applied in three different regions with three multispectral Landsat images acquired on different dates, and yielded a very high classification accuracy in detecting coastal plume zones.     

Developing a two-step retrieval method for estimating total suspended solid concentration in Chinese turbid inland lakes using Geostationary Ocean Colour Imager (GOCI) imagery

Total suspended solid (TSS) concentration is an important water quality parameter. Mapping its varying distribution using satellite images with high temporal resolution is valuable for studying suspended sediment transportation and diffusion patterns in inland lakes. A total of 255 sites were used to make remote-sensing reflectance measurements and surface water sampling at four Chinese inland lakes, i.e. Taihu Lake, Chaohu Lake, Dianchi Lake, and the Three Gorges Reservoir, at different seasons. A two-step retrieval method was then developed to estimate TSS concentration for contrasting Chinese inland lakes, which is described in this article. In the first step, a cluster method was applied for water classification using eight Geostationary Ocean Colour Imager (GOCI) channel reflectance spectra simulated by spectral reflectance measured by an Analytical Spectral Devices (ASD) Inc. spectrometer. This led to the classification of the water into three classes (1, 2, and 3), each with distinct optical characteristics. Based on the water quality, spectral absorption, and reflectance, the optical features in Class 1 were dominated by TSS, while Class 3 was dominated by chl-a and the optical characteristics of Class 2 were dominated jointly by TSS and chl-a. In the second step, class-specific TSS concentration retrieval algorithms were built. We found that the band ratio Band 8/Band 4 was suitable for Class 1, while the band ratio of Band 7/Band 4 was suitable for both Class 2 and Class 3. A comprehensive determination value, combining the spectral angle mapper and Euclidean distance, was adopted to identify the classes of image pixels when the method was applied to a GOCI image. Then, based on the pixel’s class, the class-specific retrieval algorithm was selected for each pixel. The accuracy analysis showed that the performance of this two-step method was improved significantly compared to the unclassed method: the mean absolute percentage error decreased from 38.9% to 24.3% and the root mean square error decreased from 22.1 to 16.5 mg l^–1. Finally, the GOCI image acquired on 13 May 2013 was used as a demonstration to map the TSS concentration in Taihu Lake with a reasonably good accuracy and highly resolved spatial structure pattern.