遥感影像空间分辨率变化对湖泊水体提取精度的影响

湖泊面积是表征湖泊水情变化的重要指示因子,如何从不同空间分辨率遥感数据中获取客观准确的水面信息,是当前遥感应用研究中的难点问题.本文以鄱阳湖为例,通过选用丰水期和枯水期代表性Landsat ETM+遥感影像,采用最邻近法(NN)和像元聚合法(PA)两种重采样方法,分别获取分辨率逐渐降低的不同分辨率的影像数据,结合归一化差异水指数法研究水域面积随遥感影像分辨率降低的变化趋势及其误差变化特征,同时深入分析不同影响因素对水体提取精度的差异.研究结果表明:(1)空间分辨率是影响鄱阳湖水体提取精度的重要因素之一,随着遥感影像空间分辨率的降低,提取水域面积的精度相对30 m分辨率时呈逐渐降低的趋势,但整体精度较高,最低精度在67.64%以上;(2)NN重采样方法对遥感影像波段亮度值的均值影响不大,但PA重采样后影像的均值和标准差随分辨率逐渐降低且变化更有规律;(3)水体阈值在PA重采样后变化较大,NN重采样后变化较小,因而采用30 m分辨率时获取的阈值提取PA重采样后鄱阳湖水体误差较大,提取NN重采样后的湖泊水体误差较小.本研究结果对于全球变化影响下湖泊水体信息遥感精确提取具有重要的参考价值.     

近30 a云贵高原湖泊表面水体面积变化遥感监测与时空分析

以云贵高原湖泊近30 a来的TM、ETM~+和OLI遥感影像为数据源,采用归一化水体指数(NDWI)、改进归一化水体指数(MNDWI)、新型水体指数(NWI)、增强型水体指数(EWI)和自动水体提取指数5种水体指数提取了1985—2015年云贵高原10个湖泊表面水体面积,并对各种算法进行精度对比分析.针对湖泊各自特点采用不同的水体指数提取其表面水体面积,并进行水体面积变化时空分析.结果表明:云贵高原湖泊表面水体面积总体呈现先增加后缩减趋势,1985—1995年湖泊表面水体面积增加了30.86 km~2,1995—2015年湖泊水体表面面积减少了48.12 km~2,其中,面积变化最大的是杞麓湖与异龙湖.对云贵高原湖泊表面水体面积变化与该区域的年降水量、蒸发量、平均气温、流域植被覆盖面积和人类活动时空进行相关分析,结果表明:1)高原湖泊对区域气候变化的响应具有明显的空间差异性,云贵高原湖泊的表面水体面积与气候相关性较显著,气温升高引起蒸发加速,降水量下降,湖面不断缩小,与逐年上升的气温呈负相关,与逐年波动上升的蒸发量呈负相关,与逐年减少的降水量呈正相关;2)云贵高原湖泊各流域的植被覆盖面积与湖泊面积变化相关性较弱;3)人类活动是影响湖泊面积变化的重要因素,大肆围湖造田、围湖养殖以及旅游开发等人类活动直接导致云贵高原湖泊面积的锐减,并对湖泊生态环境产生重要影响.     

一种新的水体指数及其稳定性研究

针对地表水提取时易受山体阴影、建筑物阴影和雪等因素影响精度问题,本文提出一种新的研究思路:将地物分为水和非水两种类型,统计各地物的反射率,研究阴影之类的低反射率地物与雪之类的高反射率地物与水体的关系.在水体和其他非水体像元最大反射率基础上,构建新的水体指数模型Water Index(WI2019).用Landsat 8 Surface Reflectance Tier 1影像的实验结果表明,与已有的7种水体指数相比,WI2019平均总体精度、Kappa系数、错分与漏分误差分别是97.98%、0.9569、3.88%和1.22%,总体上与其他水体指数比较具有一定的优势.实验发现,在有雪环境下,缨帽变换湿度指数、改进的归一化水指数、自动水体提取指数、水体指数2015和多波段水体指数均不能有效的进行水体提取.本文的创新在于利用Landsat 8的三个波段,构建了一个可以抑制阴影和雪的新的水体指数,补充了其他已有的水体指数的不足.本文方法能够稳定地提取地表水,且能够稳定地去除山体阴影、建筑物阴影和雪等的影响.     

离散型湖泊水体提取方法精度对比分析

基于卫星遥感的陆地水体提取方法多种多样,并且应用广泛.对于水体分布支离破碎的枯水期湖泊,准确的水体提取方法尚不明晰,直接影响湖泊水域面积的提取精度.以鄱阳湖湖区为研究对象,利用ALOS遥感影像,以2.5 m高分辨率全色波段融合影像非监督分类(ISODATA)得到的水体面积为参考值,分别使用归一化水体指数(NDWI)法、NDWIISODATA法和基于近红外(NIR)的ISODATA法提取了10 m分辨率的水体分布,分析了不同方法提取结果之间的差异性及产生原因.结果表明:3种方法均可以较好地提取出水体,但利用ISODATA法提取的水体细部信息更为明显,面积值较NDWI法更大;相对于近红外单波段而言,基于NDWI图像的ISODATA法提取水体的精度更高.纵观3种方法,基于NDWI图像的ISODATA法提取的水体精度最高,基于近红外波段的ISODATA法提取结果次之,NDWI阈值法的提取效果最差.研究结果对于离散型湖泊水体提取方法及数据源的选择等具有重要的借鉴和参考意义.     

基于Landsat TM/ETM+数据提取巢湖悬浮泥沙相对浓度的信息与空间分布变化

悬浮泥沙是重要的水质参数之一.应用遥感技术监测悬浮泥沙,学者们提出了众多的悬浮泥沙遥感的经验模型和推导模型.但在缺乏大气参数或没有足够实测数据的情况下,这些模式的精度和准确性得不到保证.针对这种情况,以巢湖为实验区,对三景的巢湖卫星遥感数据进行了如下的数据处理:(1)利用内部平均相对反射率法进行图像的大气校正,得到的相对反射率与真实反射率具有相似的波谱特征;(2)对图像进行了水体提取、二值化、掩膜处理,并通过湖泊泥沙指数SI=(TM2 TM3)/(TM2/TM3)提取了TM数据下的泥沙信息,得到水体含沙量图;(3)按照本文提出的基于遥感图像的不同浓度等级泥沙的划分依据,在泥沙指数图上进行密度分割处理,得到了巢湖泥沙相对浓度分布图.在上述的处理基础上,利用谱间关系法对巢湖水体进行准确提取;结果表明,与实测资料对比,巢湖泥沙相对浓度分布与验证数据一致,实测数据和SI值相关系数为0.89(置信度水平在0.001),表明泥沙指数方法可以直观和定量地反映悬浮泥沙相对浓度的分布与变化;研究结果显示,1987-2000年间,巢湖高浓度悬浮泥沙范围增大了约1.5倍.通过影像差值图清楚地识别出变化区域,主要位于西湖的中心、河口入湖区和东湖的南岸,这种变化的最主要原因是由于各入湖河流携带的大量悬浮泥沙进入水体,其次是岸坡崩塌物形成的.     

基于叶绿素荧光峰特征的浑浊水体悬浮物浓度遥感反演

内陆水体光学特性复杂,其水质参数遥感反演是当前环境遥感研究的热点与难点.2004年10月在太湖实测了67个站点的遥感反射率与相应站点水质参数浓度,通过对水体反射率光谱的分析发现,秋季太湖悬浮物主导了水体光学特性,叶绿素荧光峰的特征主要体现为悬浮物浓度的变化.据此建立了基于水面实测岛光谱遥感反射率数据的叶绿素荧光峰特征与悬浮物浓度之间的拟合关系,发现二者具有很好的响应关系.具体分析了叶绿素荧光峰绝对高度、基线高度、归一化高度(分别归一化到560nm附近最大反射率波段与近红外810nm附近最大反射率波段)及荧光峰积分面积(包括积分总面积、基线以下面积与基线以上面积)等儿种光谱特征与悬浮物浓度之间的关系,其相关系数(R2)分别为0.8822、0.7483、0.8901、0.8547、0.8927、0.8877、0.8632,平均相对误差分别为27.25%、41.03%、27.11%、25.75%、24.91%、25.47%、27.54%.总体反演精度较高,其中总积分面积法效果最好,基线高度法效果最差,而叶绿索荧光峰波段的位移与悬浮物浓度之间不存在明显的相关性.研究结果表明叶绿素荧光峰特征在浑浊内陆水体悬浮物浓度信息提取中具有很好的应用前景,该方法可为浑浊的二类水体悬浮物遥感反演提供了一个新思路.     

海洋水体指数模型及其稳定性研究

基于遥感光学影像数据,针对海洋水体的特性,本文提出一种指数模型:在海洋水体平均反射率基础上,构建新的水体指数模型Ocean Water Index(OWI).主要以华南沿海地区、英国泽西岛为实验区,以Landsat 8 Surface Reflectance Tier 1影像、高分四号影像作为主要的数据源,与常用的8种水体指数相比,OWI的错漏率较低.实验发现,本文的亮点在于:(1)仅使用三个可见光波段、一个近红外波段,充分地利用海洋水体的光谱信息,构建了一个新的、可以抑制非水体噪声的水体指数模型;(2)因为新的水体指数模型选择的波段与国产光学卫星的波段吻合,并且使用了一景高分四号数据进行验证新指数的稳定性.实验结果证明了本文方法能够稳定地提取海洋水体,可应用于沿海地区的海岸线变化研究、沿海城市的围填海监测.     

巢湖水体漫衰减系数空间差异及其遥感反演

根据2009年6月巢湖32个样点的实测数据,分析巢湖水体漫衰减系数光谱特征、主导因子、空间分布规律以及400-700nm各波段Kd与Kd(490)之间的关系,并在此基础上,建立了Kd的遥感反演模型.结果表明:巢湖水体Kd具有一致的光谱特征,400-600nm之间Kd随波长的增加呈指数衰减趋势,在600-700nm之间的...     

基于环境一号卫星高光谱遥感数据的巢湖水体叶绿素a浓度反演

三波段模型是基于生物光学模型构建的叶绿素a浓度反演半分析模型,是目前反演内陆富营养化浑浊水体叶绿素a浓度效果较好的方法.本文通过星地同步实验,分析巢湖水体各组分光谱特征,分别基于地面实测数据与环境一号卫星高光谱遥感数据建立三波段模型反演巢湖水体叶绿素a浓度.结果表明,由于特征波段在不同数据源的位置不同,导致了两个模型波段选择及反演精度的差异.因此,只有在充分考虑遥感数据的光谱特征的条件下,分析遥感信息理论和实际图幅影像有效结合在一起的地物信息,才能进一步优化三波段模型的波段选择,实现遥感数据定量反演水体叶绿素a浓度的目标.     

基于地面实测光谱的太湖水体富营养化水平估算

富营养化指数是评价水体污染情况的一个重要的综合性指标．通过对高光谱遥感数据和水体富营养化指数的分析,确立了反演水体富营养化水平的高光谱敏感波段,进而用选择的敏感波段和波段组合来建立模型．通过对几个模型的比较,选出了较为理想的估算模型．最后对模型进行精度分析,认为该模型具有一定的可靠性和实用性．从而确定了直接由高光谱遥感数据监测水体富营养化水平的可能性,为实现由高光谱遥感数据开展大范围的水质调查奠定了一定的理论基础．     

三峡工程调节作用对洞庭湖水面面积(2000-2010年)的影响

以洞庭湖为研究对象,以11年(2000-2010年)Terra/MODIS 16 d最大值合成的植被指数数据产品集MOD13Q1和同期城陵矶水文监测站的水位数据为主要数据源,通过对NDVI和NIR分别设定阈值的方法,实现了洞庭湖水面面积的综合提取,分析了三峡工程建设背景下,洞庭湖水面面积的年际变化特征和年内变化规律,再结合城陵矶水位数据,对水位与水面面积之间的定量关系进行了深入分析.研究结果表明:三峡工程的运行,很大程度上控制着洞庭湖的入湖水量,对洞庭湖防汛工作有利;在气候变化、三峡水库的共同影响下,洞庭湖区水面面积整体上呈减少趋势;水面面积与水位的拟合结果显示两者具有良好的相关性,其中2000-2003年两者的确定性系数达到0.975.     

Delineation of riparian vegetation from Landsat multi‐temporal imagery using PCA

A deficiency in crucial digital data, such as vegetation cover, in remote regions is a challenging issue for water management and planning, especially for areas undergoing rapid development, such as mining in the Pilbara, Western Australia. This is particularly relevant to riparian vegetation, which provides important ecological services and, as such, requires regional protection. The objective of this research was to develop an approach to riparian vegetation mapping at a regional scale using remotely sensed data. The proposed method was based on principal component analysis applied to multi‐temporal Normalized Difference Vegetation Index datasets derived from Landsat TM 5 imagery. To delimit the spatial extent of riparian vegetation, a thresholding method was required and various thresholding algorithms were tested. The accuracy of results was estimated for various Normalized Difference Vegetation Index multi‐temporal datasets using available ground‐truth data. The combination of a 14‐dry‐date dataset and Kittler's thresholding method provided the most accurate delineation of riparian vegetation.     

Large‐scale regional delineation of riparian vegetation in the arid and semi‐arid Pilbara region,WA

Multiscene Landsat 5 TM imagery, Principal Component Analysis, and the Normalized Difference Vegetation Index were used to produce the first region‐scale map of riparian vegetation for the Pilbara (230,000 km²), Western Australia. Riparian vegetation is an environmentally important habitat in the arid and desert climate of the Pilbara. These habitats are supported by infrequent flow events and in some locations by groundwater discharge. Our analysis suggests that riparian vegetation covers less than 4% of the Pilbara region, whereas almost 10.5% of this area is composed of groundwater dependent vegetation (GDV). GDV is often associated with open water (river pools), providing refugia for a variety of species. GDV has an extremely high ecological value and are often important Indigenous sites. This paper demonstrates how Landsat data calibrated to Top of Atmosphere reflectance can be used to delineate riparian vegetation across 16 Landsat scenes and two Universal Transverse Mercator spatial zones. The proposed method is able to delineate riparian vegetation and GDV, without the need for Bidirectional Reflectance Distribution Function correction. Results were validated using ground truth data from local and regional scale vegetation surveys.     

Cotton canopy reflectance under variable solar zenith angles: Implications of use in evapotranspiration models

Evapotranspiration (ET) is an important parameter in hydrologic processes and modelling. In agricultural watersheds with competing uses of fresh water including irrigated agriculture, estimating crop evapotranspiration (ET_c) accurately is critical for improving irrigation system and basin water management. The use of remote sensing-based basal crop coefficients is becoming a common method for estimating crop evapotranspiration for multiple crops over large areas. The Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI), based on reflectance in the red and near-infrared bands, are commonly used for this purpose. In this paper, we examine the effects of row crop orientation and soil background darkening due to shading and soil surface wetness on these two vegetation indices through modelling, coupled with a field experiment where canopy reflectance of a cotton crop at different solar zenith angles, was measured with a portable radiometer. The results show that the NDVI is significantly more affected than the SAVI by background shading and soil surface wetness, especially in north–south oriented rows at higher latitudes and could lead to a potential overestimation of crop evapotranspiration and irrigation water demand if used for basal crop coefficient estimation. Relationships between the analysed vegetation indices and canopy biophysical parameters such as crop height, fraction of cover and leaf area index also were developed for both indices.     

Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment

Estimation of runoff is a prerequisite for many applications involving conservation and management of water resources. This study is undertaken in the Upper Damodar Valley Catchment (UDVC) having a drainage area of 17513.08 km² for prediction of monthly runoff. Thirty one microwatersheds and 15 sub-watersheds were selected from a total of 716 microwatersheds in the catchment area for this study. The feasibility of using different soil attributes (particle size distribution, organic matter content and apparent density), topographic attributes (primary, secondary and compound), geomorphologic attributes (basin, relief and network indices) and vegetation attribute as Normalized Difference Vegetation Index (NDVI), on prediction of monthly runoff were explored in this study. Principal Component Analysis (PCA) was applied to minimize the data redundancy of the input variables. Ten significant input variables namely; watershed length (km), elongation ratio, bifurcation ratio, area ratio, coarse sand (%), fine sand (%), elevation (m), slope (°), profile curvature (rad/m) and NDVI were selected. The selected input variables were added in hierarchy with monthly rainfall (mm) as inputs for prediction of monthly runoff (mm) using Bootstrap based artificial neural networks (BANN). The performance of the models was tested using Spearman’s correlation coefficient (r), coefficient of efficiency (COE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Best performance was observed for model with monthly rainfall, slope, coarse sand, bifurcation ratio and Normalized Difference Vegetation Index (NDVI) as inputs (r = 0.925 and COE = 0.839). Increase in number of input variables did not necessarily yield better performances of the BANN models. Selection of relevant inputs and their combinations were found to be key elements in determining the performance of BANN models. Annual runoff map was generated for all the microwatersheds utilizing the weights of the best performing BANN model. This study reveals that the specific combinations of soil, topography, geomorphology and vegetation inputs can be utilized for better prediction of monthly runoff.     

Research on the evolution law of permafrost under the influence of urbanization based on remote sensing technology

The permafrost of Mohe County and its suburbs in the Daxing′an Mountains has been influenced by the urbanization. Remote sensing, GIS technology and numerical simulation was used to study the temperature variations of permafrost with the changes in surface vegetation that cover Mohe County and suburban areas, and the law of permafrost degradation on the study area was analyzed. The research results show that the urban area of the study area increased 114.42%from 2000 to 2016, and the urbanization process is continuing to accelerate. The Normalized Difference Vegetation Index map of 2017 in Mohe County and its suburbs was studied and the maximum proportion of vegetation coverage was different in the four seasons. The numerical calculation model results show that the permafrost temperature change in the study area cyclically fluctuates in a cosine form. The annual variation curve of permafrost temperature gradually decreased and its accompanying phase lag increased with depth. The annual temperature change value with the different depths of the town was greater than the natural ground. The maximum permafrost thawing depths of the town and natural ground were 4.2 m and 2.82 m in 50 a, and the degradation rates of the two permafrost are, respectively, 0.88 cm/a and 0.46 cm/a. These results show that urbanization has accelerated the degradation of permafrost.     

1975-2007年中亚干旱区内陆湖泊面积变化遥感分析

中亚干旱区内陆湖泊的湖面变化反映了气候波动和人类活动对流域水文过程的影响.本文以中亚干旱区平原区尾闾湖泊、吞吐湖泊和高山湖泊三类典型内陆湖泊为研究对象,利用1975-2007年Landsat遥感影像,基于归一化水体指数提取湖泊水域边界信息,分析近30年来内陆湖泊湖面变化特征.结果表明,近30年来,研究区内有超过一半的内...     

Response of remotely sensed Normalized Difference Water Deviation Index to the 2006 Drought of eastern Sichuan Basin

One of the most serious droughts in last century occurred in eastern Sichuan Basin in the summer of 2006 (hereinafter called the Drought). The response of Moderate Resolution Imaging Spectroradiometer (MODIS, boarding on NASA satellites of Terra and Aqua) to the Drought was analyzed in order to reach one practicable monitoring solution for regional soil moisture. Temporal process and spatial extension of the Drought were firstly estimated with ground meteorological and hydrological observations. Then, for the whole region of Sichuan and Chongqing, the remotely sensed Normalized Difference Water In- dex (NDWI) for the summers of 2001―2006 were calculated based on 8-day composite MODIS products, which were further used to construct a new water index (Normalized Difference Water Deviation Index, NDWDI) to examine the sensitivity of remote sensing in the Drought. The study showed that the NDWDI is more sensitive to regional drought than other absolute-soil-moisture-based indices. With the new index, the study extracted the spatial-temporal characteristics of the 2006 Drought, and explored its developing and withdrawing processes, which agreed with related statistics. Compared with ground method of drought observation, the NDWDI-based remote sensing solution of this paper is more pref- erable and practicable in that the local soil properties of water consumption and supply are implicitly taken into account, and the spatial representativity limit of ground observation is circumvented to a degree as satellite remotely senses the earth surface in a way of two-dimensional pixel matrix. So, the NDWDI-based method can be used to monitor regional soil water stress situation more practically and efficiently.