太原市黑臭水体遥感识别与地面验证

城市黑臭水体整治是城市水环境治理的一项重要工作。本文通过构建黑臭水体遥感识别模型，解决城市黑臭水体整治过程中黑臭水体的识别问题，实现城市黑臭水体空间分布快速定位与排查。通过分析黑臭水体形成机理和试验数据，从光谱特征上构建反映水体清洁程度的光谱指数（WCI），从图像特征上构建水体颜色、次生环境、河道淤塞、岸边带垃圾堆放等解译标志，综合光谱指数和解译标志共同进行黑臭水体遥感识别。利用该方法对太原市建成区内黑臭水体空间分布进行排查识别，共得到疑似黑臭河段14条，长度为52.530 km。经验证，除去断流影响外，遥感识别黑臭水体点位精度为92.86%，遥感识别黑臭河段长度精度为78.19%。在精度验证基础之上，进一步分析了黑臭水体遥感识别光谱指数和各项解译标志在黑臭识别中所占的权重，其中光谱指数和水体颜色二者权重最大，分别占29.60%和27.10%，是构成遥感识别黑臭水体的主要特征标志。通过两时相影像识别结果比对表明，WCI指数能够明显反应黑臭水体变化特征。因此，利用本文方法进行城市黑臭水体遥感识别具有很高的精度，在城市水环境整治过程中具有重要的应用价值。     

基于MODIS的鄱阳湖区水体水灾遥感影像图制作

 提出了利用空间分辨率为250 m的MODIS影像第1、2波段数据制作水体遥感影像图、水体变化遥感影像图和水灾遥感影像图的方法。对鄱阳湖区的遥感影像图制作实例表明，该方法不仅具有快速、简便和易于操作等优点，还能有效保留MODIS的空间分辨率，较清晰地识别水体和洪水，可用于水灾的遥感动态监测与制图。     

高分二号的沈阳市黑臭水体遥感识别

中国城市黑臭水体情况严重,基于遥感监测黑臭水体刚刚起步,很多问题待解决。以沈阳市城市建成区内主要河流为研究区,于2015年—2016年开展地面调查,获取了浑河和蒲河46个一般水体的样点,和辉山明渠、满堂河、细河以及微山湖路附近、丁香湖北部50个黑臭水体的样点数据,包括水面光谱和主要水质参数。分析了黑臭水体与一般水体的光谱特征,发现城市黑臭水体反射率光谱在绿光—红光波段变化比一般水体平缓,基于这一特点提出了一种基于反射率光谱指数BOI(Black and Odorous water Index)的黑臭水体识别模型,并将其与红绿波段比值指数进行对比,具有更好的识别精度。结果表明:(1)基于遥感反射率(Rrs)计算的BOI小于0.065时,可判为黑臭水体。(2)由于GF2水体图像精确大气校正存在困难,可以利用瑞利散射校正反射率(Rrc)替代R_(rs),BOI小于阈值0.05时,可判别为黑臭水体;同时模拟证明,当气溶胶光学厚度逐渐增大时,黑臭水体与一般水体的光谱差异将逐渐减小,因此这种方法主要适用于比较清晰的图像、气溶胶光学厚度比较小(如AOT(550)≤0.5时)。(3)基于R_(rc)的BOI模型可以较好的应用于GF2图像上,具有较好的识别精度。对2015年—2016年3景GF-2影像提取的结果显示,满堂河和新开河黑臭现象得到逐步改善,辉山明渠黑臭现象依然严峻。本文发展的黑臭水体遥感识别算法主要是基于沈阳黑臭水体的光谱特征,仅在沈阳市进行了验证,将来还需在其他城市进一步验证,并且需要更多地考虑多种因素对水体反射率的影响。     

基于MODIS遥感数据的水体提取模型及算法改进

针对MODIS遥感数据采用多波段谱间关系模型提取水体容易与阴影混淆,产生提取不精确的问题,作者改进了模型中的水体提取算法,然后对鄱阳湖的MODIS遥感数据进行水体提取实验。最终,实验结果表明,改进后的水体提取算法能够有效地提升水体提取精度。该方法现已运用到一些采用MODIS数据进行水体监测与提取的实践作业中。     

城区重度污染水体遥感识别研究

城市污染水体整治对城市良好水环境具有重大意义。本文基于污染水体形成机理,构建污染水体遥感识别模型,实现了城市污染水体快速、高效、大范围的提取。本文以银川市为研究区,开展银川市污染水体遥感识别,并进行实地验证。验证结果表明污染水体遥感识别精度可达62.96%,共确定银川市污染水体12条,为银川市污染水体整治工作提供参考。此外,本文还分析了影响识别精度的原因,以提高后续污染水体遥感识别精度。由于遥感识别污染水体尚属首次,因此识别精度一般。在今后研究工作中,将会加入其他指标继续研究,以提高精度。     

应用气象卫星识别薄云覆盖下的水体

本文根据地物光谱特性和卫星的信号接收原理，提出了一种利用气象卫星识别水体的简单而有效的方法，使得薄云覆盖下的水体和云影中的水体得到较好的识别效果。在洪水监测中使用该方法，可以充分利用气象卫星数据获取丰富的洪水动态信息．并以１９９１年江淮洪涝灾害为背景，对试验结果进行了分析．     

ASTER数据的煤矿区水体信息提取

为实现多光谱数据对煤矿区水体信息提取，以萍乡市芦新岭及周边煤矿为试验区，在国内外学者对水体指数法提取水体信息和应用的启发下，对试验区不同地物的波谱特征进行分析。本文首先利用数学统计方法建立一种ASTER数据综合归一化差异水体指数模型SWI ASTER_xy，然后利用这一模型对试验区进行水体信息提取。试验表明，该模型能够有效地提取矿区各类水体，还可以运用在ASTER数据（或其他多光谱数据）对不同区域不同地物的提取，在遥感地物定量提取上具有很好的效果和潜力，具有一定的推广价值。     

基于高分一号遥感影像的农村黑臭水体识别方法研究

农村黑臭水体治理是改善农村人居环境的一项非常重要的内容,黑臭水体识别是黑臭水体治理的前提和基础。本文在反射率光谱指数BOI(Black and Odorous water Index)的黑臭水体识别模型基础上,结合省基础测绘数据、高分辨率数字正射影像等专题资料,提出了基于高分一号(GF-1)遥感影像的农村黑臭水体识别模型,能够初步判别农村黑臭水体分布情况,为快速获取农村黑臭水体空间位置提供了解决方案,在农村黑臭水体治理中具有一定的应用价值。     

遥感影像水体追踪识别提取算法及应用

针对大尺度区域所有规模以上水体一次性智能化水体提取的问题,提出了水体追踪识别提取算法。这种方法可以追踪图像中所有的连通区域,并对区域进行识别与提取。以花马湖区域的水体为例,使用水体追踪识别算法提取湖泊,能够准确地追踪各种形状的湖泊,与两边扫描法、区域增长法相比,扫描重复率低、精度高。     

利用景观格局指数的空间分辨率对水域面积提取影响的分析

对8个不同地区对应的同一时间的ETM+数据和MODIS数据,利用谱间关系法得到30 m和250 m分辨率具有不同景观格局分布的水体专题图,研究分辨率对不同景观格局分布的水体提取的影响。通过比较发现,区域内水体边缘密度很小时,ETM+和MODIS提取结果的误差很小;当区域内水体边缘密度很大时,ETM+和MODIS提取结果的误差相应就变大。通过引入景观格局指数与两种分辨率的提取结果进行回归分析发现,对于不破碎区域的水体,MODIS和ETM+可以得到相近的精度;而对于中度破碎的水体,引入景观格局指数信息能显著地提高中度破碎水体的精度;但对于高度破碎的水体,通过引入景观格局指数信息的多元回归几乎不能提高精度。     

Validation of MODIS integrated water vapor product against reference GPS data at the Iberian Peninsula

In this work, the water vapor product from MODIS (MODerate-resolution Imaging Spectroradiometer) instrument, on-board Aqua and Terra satellites, is compared against GPS water vapor data from 21 stations in the Iberian Peninsula as reference. GPS water vapor data is obtained from ground-based receiver stations which measure the delay caused by water vapor in the GPS microwave signals. The study period extends from 2007 until 2012. Regression analysis in every GPS station show that MODIS overestimates low integrated water vapor (IWV) data and tends to underestimate high IWV data. R² shows a fair agreement, between 0.38 and 0.71. Inter-quartile range (IQR) in every station is around 30–45%. The dependence on several parameters was also analyzed. IWV dependence showed that low IWV are highly overestimated by MODIS, with high IQR (low precision), sharply decreasing as IWV increases. Regarding dependence on solar zenith angle (SZA), performance of MODIS IWV data decreases between 50° and 90°, while night-time MODIS data (infrared) are quite stable. The seasonal cycles of IWV and SZA cause a seasonal dependence on MODIS performance. In summer and winter, MODIS IWV tends to overestimate the reference IWV value, while in spring and autumn the tendency is to underestimate. Low IWV from coastal stations is highly overestimated (∼60%) and quite imprecise (IQR around 60%). On the contrary, high IWV data show very little dependence along seasons. Cloud-fraction (CF) dependence was also studied, showing that clouds display a negligible impact on IWV over/underestimation. However, IQR increases with CF, except in night-time satellite values, which are quite stable.     

Evaluation of seasonal water body extents in Central Asia over the past 27 years derived from medium-resolution remote sensing data

In this study medium resolution remote sensing data of the AVHRR and MODIS sensors were used for derivation of inland water bodies extents over a period from 1986 till 2012 for the region of Central Asia. Daily near-infrared (NIR) spectra from the AVHRR sensor with 1.1 km spatial resolution and 8-day NIR composites from the MODIS sensor with 250 m spatial resolution for the months April, July and September were used as input data. The methodological approach uses temporal dynamic thresholds for individual data sets, which allows detection of water pixel independent from differing conditions or sensor differences. The individual results are summed up and combined to monthly composites of areal extent of water bodies. The presented water masks for the months April, July, and September were chosen to detect seasonal patterns as well as inter-annual dynamics and show diverse behaviour of static, decreasing, or dynamic water bodies in the study region. The size of the Southern Aral Sea, as the most popular example for an ecologic catastrophe, is decreasing significantly throughout all seasons (R² 0.96 for April; 0.97 for July; 0.96 for September). Same is true for shallow natural lakes in the northern Kazakhstan, exemplary the Tengiz-Korgalzhyn lake system, which have been shrinking in the last two decades due to drier conditions (R² 0.91 for July; 0.90 for September). On the contrary, water reservoirs show high seasonality and are very dynamic within one year in their areal extent with maximum before growing season and minimum after growing season. Furthermore, there are water bodies such as Alakol-Sasykol lake system and natural mountainous lakes which have been stable in their areal extent throughout the entire time period. Validation was performed based on several Landsat images with 30 m resolution and reveals an overall accuracy of 83% for AVHRR and 91% for MODIS monthly water masks. The results should assist for climatological and ecological studies, land and water management, and as input data for different modelling applications.     

MODIS遥感数据辅助的鄱阳湖水体范围变化数值模拟

针对水体范围高动态变化的鄱阳湖,充分发挥数值模拟和遥感各自的优势,使用从MODIS影像上提取的水体范围初始化鄱阳湖水体范围数值模拟边界和率定模型参数,并与模拟的水体范围结果进行交叉验证分析。对鄱阳湖2001-07-08～11-30水体范围动态变化数值模拟的结果显示,水位模拟平均绝对误差小于15cm,数值模拟获取的水体范围与MODIS无云影像提取的湖泊水体范围具有较高的一致性,水体面积平均相对误差为5.7%。     

A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level

Optical Earth Observation data with moderate spatial resolutions, typically MODIS (Moderate Resolution Imaging Spectroradiometer), are of particular value to environmental applications due to their high temporal and spectral resolutions. Time-series of MODIS data capture dynamic phenomena of vegetation and its environment, and are considered as one of the most effective data sources for land cover mapping at a regional and national level. However, the time-series, multiple bands and their derivations such as NDVI constitute a large volume of data that poses a significant challenge for automated mapping of land cover while optimally utilizing the information it contains. In this study, time-series of 10-day cloud-free MODIS composites and its derivatives – NDVI and vegetation phenology information, are fully assessed to determine the optimal data sets for deriving land cover. Three groups of variable combinations of MODIS spectral information and its derived metrics are thoroughly explored to identify the optimal combinations for land cover identification using a data mining tool.The results, based on the assessment using time-series of MODIS data, show that in general using a longer time period of the time-series data and more spectral bands could lead to more accurate land cover identification than that of a shorter period of the time-series and fewer bands. However, we reveal that, with some optimal variable combinations of few bands and a shorter period of time-series data, the highest possible accuracy of land cover classification can be achieved.     

Testing estimation of water surface in Italian rice district from MODIS satellite data

Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R² = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies.     

多时相MODIS影像水田信息提取研究

水稻种植及其分布信息是土地覆被变化、作物估产、甲烷排放、粮食安全和水资源管理分析的重要数据源。基于遥感的水田利用监测中,通常采用时序NDVI植被指数法和影像分类法分别进行AVHRR和TM影像的水田信息获取。针对8天合成MODIS陆地表面反射比数据的特点和水稻生长特征,选取水稻种植前的休耕期、秧苗移植期、秧苗生长期和成熟期等多时相MODIS地表反射率影像数据,通过归一化植被指数、增强植被指数及利用对土壤湿度和植被水分含量较敏感的短波红外波段计算得到的陆表水指数进行水田信息获取。将提取结果与基于ETM＋影像的国土资源调查水田数据,通过网格化计算处理并进行对比分析,结果表明,利用MODIS影像的8天合成地表反射率数据,进行区域甚至全国的水田利用监测是可行的。     

基于谱间关系的MODIS遥感影像水体提取研究

水是地球上万物的命脉所在,水体提取对于水资源调查、洪水灾害预测评估和环境监测等有着重要作用。而可见光和近红外波段水体与植被、城市和土壤光谱反射率的差异是利用遥感手段提取水体的基本原理。本文利用谱间关系法进行MOD IS图像的水体提取。结果表明,用于提取水体的谱间关系法比归一化植被指数(NDVI)法快速,但谱间关系法有把云错误地当作水体提取的缺点。     

Assessment of the abnormal growth of floating macrophytes in Winam Gulf (Kenya) by using MODIS imagery time series

The objective of this research study is to assess the capability of time-series of MODIS imagery to provide information suitable for enhancing the understanding of the temporal cycles shown by the abnormal growth of the floating macrophytes in order to support monitoring and management action of Lake Victoria water resources.The proliferation of invasive plants and aquatic weeds is of growing concern. Starting from 1989, Lake Victoria has been interested by the high infestation of water hyacinth with significant socio-economic impact on riparian populations.In this paper, we describe an approach based on the time-series of MODIS to derive the temporal behaviour, the abundance and distribution of the floating macrophytes in the Winam Gulf (Kenyan portion of the Lake Victoria) and its possible links to the concentrations of the main water constituencies.To this end, we consider the NDVI values computed from the MODIS imagery time-series from 2000 to 2009 to identify the floating macrophytes cover and an appropriate bio-optical model to retrieve, by means of an inverse procedure, the concentrations of chlorophyll a, coloured dissolved organic matter and total suspended solid.The maps of the floating vegetation based on the NDVI values allow us to assess the spatial and temporal dynamics of the weeds with high time resolution.A floating vegetation index (FVI) has been introduced for describing the weeds pollution level.The results of the analysis show a consistent temporal relation between the water constituent concentrations within the Winam Gulf and the FVI, especially in the proximity of the greatest proliferation of floating vegetation in the last 10 years that occurred between the second half of 2006 and the first half of 2007.The adopted approach will be useful to implement an automatic system for monitoring and predicting the floating macrophytes proliferation in Lake Victoria.     

GNSS/MODIS信号紧耦合水汽层析算法

GNSS水汽层析技术凭借高精度、高时空分辨率及全天候监测等优点,已成为探测大气水汽最具潜力的技术之一。目前,融合多源大气遥感数据逐步成为弥补传统层析模型GNSS信号几何缺陷的研究热点。本文利用Terra卫星上的中分辨率成像光谱仪(moderate resolution imaging spectroradiometer,MODIS)提供的观测数据,首先分析了传统体素模型融合MODIS信号的不足;然后提出了基于体素节点模型的GNSS/MODIS信号紧耦合水汽层析算法,该算法将高分辨率MODIS PWV以三维信号的形式引入层析模型中;最后利用2016年7月徐州地区的15幅MODIS影像及同步GNSS数据对3种模型的层析结果质量进行了评估。试验结果表明:利用本文所提出的紧耦合算法,层析模型的平均有效观测信号数量提高了34.15%,层析结果平均RMSE(root mean square error)值降低了25.10%。此外,以邻近时刻探空站数据作为参考值,发现0~2 km的近地层,紧耦合算法的层析结果明显优于传统算法,这表明融合MODIS观测信号可改善近地层三维水汽场的重构质量。