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。     

针对资源三号卫星影像水体提取的谱间关系法

针对现阶段水体提取的数据源多是国外测绘卫星数据且需要多波段、丰富光谱信息的问题,通过研究资源三号卫星影像中水体的光谱特征以及阴影的光谱特征,提出一种在资源三号卫星影像中水体提取的方法。运用单波段阈值法、归一化差分水体指数法、支持向量机法、基于阈值的谱间关系法4种方法对提取水体的效果进行比较分析。实验结果表明,本文提出的基于阈值的谱间关系法提取水体的效果比另外3种方法有明显提升,能有效剔除阴影的影响,并且能较好地提取影像中的细小水体。     

基于TM影像的几种常用水体提取方法的比较和分析

随着遥感技术的飞速发展,利用遥感数据来进行水资源的监测、调查和分析已成为一种必然的趋势。从遥感影像中快速、准确地提取水体信息,是进行水资源调查和监测的一种重要的方法和手段。目前进行水体提取的方法有很多,本文选取了常用的3种水体提取方法,即单波段阈值法、基于阈值的多波段谱间关系法、基于阈值的水体指数法,然后分别选取典型的武汉平原地区和宜昌山地地区为研究区,以Landsat5TM影像为数据源,通过实验来比较和分析这3种水体提取方法分别在平原地区和山地地区的优势和不足。     

基于分布式计算的遥感图像水体识别研究

为了提高遥感数据的处理速度,解决遥感信息提取中的数据密集与计算密集问题,将并行计算的思想引入到遥感图像的处理与信息提取中,构建基于Landsat ETM+影像的分布式遥感图像水体提取模型。以渭干河流域为研究区,利用单波段阈值法、多波段谱间关系法、水体指数法等方法进行水体信息自动提取的实验。实验结果表明,该模型具有较高的识别精度,能够快速识别水体,并具有稳定的可扩展性和伸缩性。     

基于TM影像的典型内陆淡水湿地水体提取研究

水是维系湿地生态系统稳定和健康的决定性因子,利用卫星遥感影像快速、准确地提取湿地水体信息已经成为湿地调查、研究与保护的重要手段。鉴于TM遥感影像具有较高的空间分辨率、波谱分辨率、极为丰富的信息量、较高的定位精度和相对较低的价格,其必然成为近一段时期湿地调查、研究与保护的重要数据源之一。研究基于TM遥感影像,运用多种方法针对典型内陆淡水湿地的水体信息进行了提取实验,通过对实验结果的分析得出：在面积的准确性、提取的准确度以及视觉效果3种指标下,光谱分类法较其它方法效果要好,其次为单波段阈值分析法与植被指数法,较差的是多波段谱间关系法与水体指数法;影响提取效果的主要原因是湿地水体提取不够完全,这是由影像的分辨率及湿地特殊的水文条件所造成的,采用像元分解及多源遥感数据融合技术将成为提高水体提取精度的重要手段。     

一种利用多时相GF-4影像的快速水体提取方法

鉴于GF-4在洪涝灾害领域的重要应用,为了更好地消除山体阴影和城区建筑对水体提取的影响,以GF-4的PMS传感器影像为数据源,针对其影像的高时相与水体在近红外波段高吸收的特点,提出了一种基于多时相影像在近红外波段变化方差的快速水体提取方法。以长江中下游地区的2个典型区域为研究对象,对比分析了该方法与NDWI阈值法的水体信息提取结果,并进行了精度评价。实验结果表明,该方法不仅能快速准确地提取水体信息,而且能很好地去除山体阴影和城区建筑的影响,对细小水体也有很好的提取能力,在2个区域的水体提取精度分别达到99.04%与99.37%,高于NDWI阈值法。该方法可以有效地提取水体信息,为GF-4水体提取提供技术支持。     

马尾藻遥感信息提取

以Landsat数据为基础,分析马尾藻的图像和波谱特征,对比分析单波段提取法、双波段比值法、双波段差值法和归一化植被指数法对马尾藻信息的提取结果,并利用IKONOS数据来验证4种方法的提取精度.结果表明:马尾藻在Landsat真彩色(TM3、TM2、TM1)和假彩色(TM4、TM3、TM1)合成图上均呈黄色,其生长边界在假彩色合成图上更为清晰.马尾藻水体与非藻类水体在TM4的差异最大,在TM3也存在细小差异,单波段提取法(TM4)、双波段比值法(TM4/TM3)、双波段差值法(TM4-TM3)和归一化植被指数法((TM4-TM3)/(TM4+TM3))都可以从自然水体中提取出马尾藻信息,与IKONOS的提取结果相比,归一化植被指数法的提取精度最高.     

Landsat数据水体自动提取研究进展

水体自动提取是当前遥感技术应用研究的热点之一。在简述水体遥感识别机理的基础上,回顾了国内外常用的Landsat数据水体自动提取方法,并将其划分为单波段阈值法、多波段谱间关系法、水体指数法和分类后提取法4类进行综合阐述,最后从辐射校正、混合像元和图像二值细化3方面对水体自动提取的未来研究方向做了展望,以期为水体自动提取及相关研究提供参考。     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光-近红外多光谱波段的基础上,新增加的8个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5%。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

WorldView卫星16波段影像光谱特征分类

传统的高分辨率遥感卫星光谱分辨率较低,WorldView卫星在8个可见光G近红外多光谱波段的基础上,新增加的８个短波红外(short wave infrared,SWIR)影像,有助于提高影像提取地物信息能力。分析了WorldView卫星的16波段影像上各种地物的光谱特征和分类性能,提出了新的植被指数、水体指数和建成区指数。实验表明,相比于8波段影像,使用16波段影像分类能够显著提高各类地物特别是裸地、建筑物和道路的分类精度,总体精度提高约5.5％。基于16波段设计的新地物特征指数能更好地避免干扰地物,通过简单阈值提取地物,取得较高的提取精度。     

Surface water classification from GF-4 images using a time series water index

Although satellite remote sensing imagery is suitable for mapping and monitoring the surface water, the application of surface water classification techniques in the literatures is still constrained by low accuracy in various situations. As the GF-4 satellite is one of the few moderate spatial resolution geostationary orbit remote sensing satellites, the purpose of this study is to introduce a method that consistently improves the accuracy of surface water classification by utilizing the temporal characteristics of the dense time series of GF-4 images. A new time series water index called the spectrum and solar altitude angle water index (SSWI) is proposed based on the characteristics of the variation in the top of atmosphere (TOA) radiance with the change in solar altitude angle in the near-infrared (NIR) band of GF-4 images. The thresholding technique is applied to the SSWI map to automatically extract surface water. The performance of the SSWI method for surface water classification is validated and compared with the results of two widely used water classification methods, the Normalized Difference Water Index (NDWI) and maximum likelihood (ML) classifier, at four representative sites in different parts of China, including Beijing, Hubei, Tibet and Guangdong. The results show that at all four test sites, the classification accuracy of SSWI is significantly higher than that of NDWI and ML. Averaged over the four test sites, the total error of the SSWI for water bodies is only approximately 36.9% of that of the NDWI method and 23.5% of that of the ML classifier. In particular, the SSWI method shows outstanding performance in terms of distinguishing surface water from backgrounds that are usually sources of surface water classification errors, e.g. mountain shadows and low-albedo built-up areas. Therefore, the SSWI method can be used to classify surface water from GF-4 images and holds the potential to be a useful surface water classification technology for water resource studies and applications.     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题，通过分析高分二号影像中典型地物的光谱特征，构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理，建立包含波段均值、标准差等特征的规则集，对影像的I和PC1分量进行多尺度分割 ，结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验，实验结果表明:与传统基于像素的阴影提取方法相比，该方法提取出的阴影斑块完整，且能有效地减弱水体和植被的影响。     

集成特征分量的高分二号影像阴影检测

针对高分辨率遥感影像中阴影检测精度易受水体、植被等因素干扰的问题,通过分析高分二号影像中典型地物的光谱特征,构建了一种集成特征分量与面向对象分类相结合的阴影检测方法。构建的特征分量包括：主成分第一分量PC1、亮度分量I、归一化差分植被指数NDVI及水体指数WI。将各特征分量进行归一化处理,建立包含波段均值、标准差等特征的规则集,对影像的I和PC1分量进行多尺度分割 ,结合面向对象的方法进行阴影检测。选取不同区域遥感影像进行实验,实验结果表明:与传统基于像素的阴影提取方法相比,该方法提取出的阴影斑块完整,且能有效地减弱水体和植被的影响。     

于桥水库蓝藻水华遥感长时序监测研究

针对水库富营养化给供水安全带来的严重威胁，利用2008~2017年Landsat时间序列卫星数据，基于归一化差值植被指数（NDVI）与实测水质参数的相关分析结果，运用阈值法动态提取了于桥水库的水华分布范围和程度。通过与自然和人为因子的协同分析，认为气温、降水和人类活动等共同驱动引发了水华爆发，其中人为干预的生态修复工程可抑制或减缓水华爆发，并有效改善水质状况。时间分辨率更高的气象因子数据和卫星遥感数据将更有助于对中小型饮用水水面蓝藻水华驱动力的分析，推动准实时遥感监测预警技术应用。     

Remote Sensing Long-term Monitoring of Cyanobacterial Blooms in Yuqiao Reservoir

Reservoir eutrophication leads serious threat to water supply safety. This paper apples Landsat time series satellite data from 2008 to 2017 to extract the distribution and degree of water bloom in Yuqiao Reservoir based on a threshold method to the correlation analysis results between Normalized Difference Vegetation Index (NDVI) and measured water quality parameters. Through the collaborative analysis of both natural and artificial factors, the water bloom was jointly drive by temperature, precipitation, and human activities. Among them, the ecological restoration project with human intervention could inhibit or slow down the blooms and effectively improve the water quality. Meteorological and spaceborne remote sensing data with higher temporal resolution will be more conducive the analyze the driver force of cyanobacteria blooms on small and medium-sized drinking water surfaces. Meanwhile, remote sensing data based monitoring and early warning technology could be promoted.     

Non-stationary and unequally spaced NDVI time series analyses by the LSWAVE software

ABSTRACT

Change detection within non-stationary and unequally spaced remote sensing time series has become a key methodology for a broad range of environmental applications. A new method of analysing vegetation variation over lands is proposed. Four regions in northern Tunisia with various characteristics are selected, and a non-stationary and unequally spaced Normalized Difference Vegetation Index (NDVI) time series is obtained for each region since 2000. The Landsat 7 remote sensing satellite imagery with insignificant cloud-shadow coverage is used to calculate the NDVI after atmospheric correction. The Least-Squares Wavelet (LSWAVE) software is implemented to rigorously analyse each NDVI time series and study the relationship between the vegetation of olive trees and temperature/precipitation in one of the regions. To investigate possible effects of temperature on the green cover caused by increasing water salinity, the coherency between the NDVI and sea surface temperature time series is also shown in the region of Lake Ichkeul in Tunisia.     

Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans

Information about vegetation water content (VWC) has widespread utility in agriculture, forestry, and hydrology. It is also useful in retrieving soil moisture from microwave remote sensing observations. Providing a VWC estimate allows us to control a degree of freedom in the soil moisture retrieval process. However, these must be available in a timely fashion in order to be of value to routine applications, especially soil moisture retrieval. As part of the Soil Moisture Experiments 2002 (SMEX02), the potential of using satellite spectral reflectance measurements to map and monitor VWC for corn and soybean canopies was evaluated. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data and ground-based VWC measurements were used to establish relationships based on remotely sensed indices. The two indices studied were the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The NDVI saturated during the study period while the NDWI continued to reflect changes in VWC. NDWI was found to be superior based upon a quantitative analysis of bias and standard error. The method developed was used to map daily VWC for the watershed over the 1-month experiment period. It was also extended to a larger regional domain. In order to develop more robust and operational methods, we need to look at how we can utilize the MODIS instruments on the Terra and Aqua platforms, which can provide daily temporal coverage.     

基于GF-1影像的耕地地块破碎区水稻遥感提取

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例,通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异,基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据,构建归一化差值植被指数（NDVI）差值法、归一化水体指数和比值植被指数（NDWI-RVI）差值法提取水稻分布,并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示：与非监督分类和监督分类方法相比,植被指数差值法水稻识别精度贡献率可提升30%以上,NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72;NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846,实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的,可实际服务于太湖地区农业生产及相关决策支持。     

基于GF-1影像的耕地地块破碎区水稻遥感提取

耕地地块破碎区水稻遥感提取是作物监测研究的热点问题之一。以苏州市高新区为例，通过挖掘关键物候期水稻与下垫面水体光谱特征组合差异，基于分蘖期与齐穗期两景16 m分辨率的GF-1 WFV数据，构建归一化差值植被指数（NDVI）差值法、归一化水体指数和比值植被指数（NDWI-RVI）差值法提取水稻分布，并深入探究了水稻面积提取精度及空间重合度影响因素。结果显示：与非监督分类和监督分类方法相比，植被指数差值法水稻识别精度贡献率可提升30%以上，NDVI差值法提取水稻种植面积的精度、空间重合度、制图总体精度和Kappa系数分别为86.2%、66.1%、92.2%和0.72；NDWI-RVI差值法上述指标分别高达95.5%、78.4%、93.5%和0.846，实现了利用少量中高分辨率遥感影像精确提取耕地地块破碎区水稻分布的目的，可实际服务于太湖地区农业生产及相关决策支持。