A second modified normalized difference water index(SMNDWI) in the case of extracting the shoreline

Due to the fast development of industrialization and urbanization, shorelineextraction is necessary for the sustainable development and environment protection inmany countries. This study focused on the accurate methods of extracting theinstantaneous waterline —shoreline obtained as the same instant as the satellite imageis acquired. Based on NDWI (Normalized Difference Water Index) and MNDWI(Modified Normalized Difference Water Index), the study changed the bandcombination and proposed a second modified normalized water index (SMNDWI) toextract the waterline. And, this new index is applied to three types of coast to evaluatethe performance of this method with traditional ones. Results show that SNDWI isbetter than NDWI and suitable for applying to the waterline extraction.

Drought trend analysis in a semi-arid area of Iraq based on Normalized Difference Vegetation Index, Normalized Difference Water Index and Standardized Precipitation Index

Drought was a severe recurring phenomenon in Iraq over the past two decades due to climate change despite the fact that Iraq has been one of the most water-rich countries in the Middle East in the past.The Iraqi Kurdistan Region(IKR)is located in the north of Iraq,which has also suffered from extreme drought.In this study,the drought severity status in Sulaimaniyah Province,one of four provinces of the IKR,was investigated for the years from 1998 to 2017.Thus,Landsat time series dataset,including 40 images,were downloaded and used in this study.The Normalized Difference Vegetation Index(NDVI)and the Normalized Difference Water Index(NDWI)were utilized as spectral-based drought indices and the Standardized Precipitation Index(SPI)was employed as a meteorological-based drought index,to assess the drought severity and analyse the changes of vegetative cover and water bodies.The study area experienced precipitation deficiency and severe drought in 1999,2000,2008,2009,and 2012.Study findings also revealed a drop in the vegetative cover by 33.3%in the year 2000.Furthermore,the most significant shrinkage in water bodies was observed in the Lake Darbandikhan(LDK),which lost 40.5%of its total surface area in 2009.The statistical analyses revealed that precipitation was significantly positively correlated with the SPI and the surface area of the LDK(correlation coefficients of 0.92 and 0.72,respectively).The relationship between SPI and NDVI-based vegetation cover was positive but not significant.Low precipitation did not always correspond to vegetative drought;the delay of the effect of precipitation on NDVI was one year.

Relationship between drought and soil erosion based on the normalized differential water index(NDWI)and revised universal soil loss equation(RUSLE)model

The Langat River Basin in Malaysia is vulnerable to soil erosion risks because of its exposure to intensive land use activities and its topography,which primarily consists of steep slopes and mountainous areas.Furthermore,climate change frequently exposes this basin to drought,which negatively affects soil and water conservation.However,recent studies have rarely shown how soil reacts to drought,such as soil erosion.Therefore,the purpose of this study is to evaluate the relationship between drought and soil erosion in the Langat River Basin.We analyzed drought indices using Landsat 8 satellite images in November 2021,and created the normalized differential water index(NDWI)via Landsat 8 data to produce a drought map.We used the revised universal soil loss equation(RUSLE)model to predict soil erosion.We verified an association between the NDWI and soil erosion data using a correlation analysis.The results revealed that the southern and northern regions of the study area experienced drought events.We predicted an average annual soil erosion of approximately 58.11 t/(hm^(2)·a).Analysis of the association between the NDWI and soil erosion revealed a strong positive correlation,with a Pearson correlation coefficient of 0.86.We assumed that the slope length and steepness factor was the primary contributor to soil erosion in the study area.As a result,these findings can help authorities plan effective measures to reduce the impacts of drought and soil erosion in the future.

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.

基于NDWI的海南岛西部沙漠化信息自动提取方法研究

分析海南岛西部沙漠化土地的光谱特征,主要表现为沙漠化土地整体反射光谱率偏高,在TM3和TM5波段表现尤其突出,反射光谱曲线的波峰形态也比其他背景地物更为明显。选择能较好反映沙漠化土地的植被信息(TM4)和土壤水分信息(TM5)的波段,经波段运算得出归一化水指数(NDWI),用以自动提取沙漠化土地信息。运用海南岛西部典型沙漠化区域对该方法进行了精度检验,结果表明,利用NDWI自动提取沙漠化土地信息是可行的,正确率达到89.71%。为快速自动提取沙漠化土地信息提供了一种新思路,以期能更快捷地为沙漠化治理决策提供准确的时空变化信息和科学依据。

基于NDVI-MNDWI特征空间的水体信息增强方法研究

以新疆吐鲁番为研究区域,以Landsat8/OLI影像为主要数据源,通过分析水体与地形阴影区以及建筑用地等典型地类在改进后归一化差异水体指数(MNDWI)与归一化植被指数(NDVI)所构成的二维特征空间中的表现特征,提出一种能够有效凸显水体影像特征的复合型水体指数(CNDWI),并据此进行水体信息的遥感自动提取实验.研究结果表明:CNDWI指数不仅明显增加研究区内的水体与山体阴影区之间的区分度,同时还保持了其他水体指数所具有的以极大值表达水体以及水体与建筑用地和植被等地物之间存在明显差异性的优点.

基于MNDWI的浅层地下水信息提取技术研究

为了实现宏观、快速、大范围圈定找水远景区,降低传统找水工作的风险性和盲目性。根据土壤湿度在一定程度上可反映出浅层地下水的富集程度这一特性,将修正归一化水体差异指数MNDWI作为提取浅层地下水的关键因子,利用ETM数据进行了定量反演,并通过最优密度分割的方法,提取那些中间色调作为找水信息,圈定了找水远景区。通过实地考察,勘察精度可达到85.76%,结果令人满意,说明该方法具有一定的可靠性和实用性。

基于NDWI和卷积神经网络的冬小麦产量估测方法

为进一步提高冬小麦单产估测的效率和准确性,利于宏观指导农业生产、制定冬小麦整个生长期的精准管理决策,针对目前已有的县域冬小麦单产估测方法存在时效性差、准确度低、成本高等问题,以中分辨率成像光谱仪(Moderate resolution imaging spectroradiometer,MODIS)为数据源,分别提取不同时段可见光与近红外波段信息,选择归一化差值植被指数(Normalized difference vegetation index,NDVI)、归一化差值水指数(Normalized difference water index,NDWI)、土壤调节植被指数(Soil adjusted vegetation index,SAVI)、调整土壤亮度植被指数(Optimal soil adjusted vegetation index,OSAVI)、绿色归一化植被指数(Green normalized difference vegetation index,GNDVI)、改进型土壤调节植被指数(Modified soil-adjusted vegetation index,MSAVI)以及绿红植被指数(Green red vegetation index,GRVI)7个遥感植被指数,以其直方图分布信息作为输入变量,应用卷积神经网络(Convolutional neural network,CNN)回归预测冬小麦产量,对比分析NDWI在冬小麦产量估测上的表现并探究其在霜冻害影响下的精度变化。研究表明,相对于植被指数NDVI、SAVI、OSAVI、GNDVI、MSAVI、GRVI,NDWI对冬小麦生育早期的产量预测表现出更好的预测效果,单产去趋势前后的NDWI对产量的预测精度均高于NDVI、SAVI等植被指数,决定系数最高可达到0.79,且在霜冻害影响下仍能保持较好的预测效果;NDWI在抽穗—灌浆阶段对冬小麦最终产量影响最大,4月23—30日时间段内NDWI对产量的决定系数可达到0.72;空间分布上,研究区域冬小麦具有东部单产最高、中部次之、西部单产最低的空间分布特征,西部和北部山区与东部黄淮海平原交界处误差较大。研究结果可为冬小麦生育早期产量预测提供科学依据。

一种NDWI自约束遥感影像水体信息的快速检测方法

针对传统遥感影像水体信息检测环节多、过程复杂的问题,本文提出一种基于归一化水体指数(NDWI)自约束的水体判别方法,该方法直接从影像中检测水体,能减少操作环节、提高检测速度,为水体应急监测提供快速响应的方法。通过对本方法与阈值法、分类法在不同数据中进行测试,结果显示:本方法对WorldView-2、Landsat8和Sentinel-2A影像水体检测的总体精度介于83.87%~91.53%,阈值法检测的总体精度介于85.07%~94.07%,支持向量机分类法检测的总体精度介于91.91%~97.70%;该方法对水体的检测精度不及阈值法和分类法高,但在具有较高检测精度的同时能明显简化操作,在水体监测应急响应中具有实际应用价值。

Effect of Water Level Fluctuations on Wintering Goose Abundance in Poyang Lake Wetlands of China

The Poyang Lake is a Ramsar site and is the important over-wintering site for migratory waterbirds along the East Asian-Australasian Fly way. Examining the effects of water level fluctuations on waterbird abundance and analyzing the influencing mechanism is critical to waterbird protection in the context of hydrological alteration. In this study, the effect of water level regime on wintering goose abundance was examined and the influencing mechanism was interpreted. Synchronous waterbirds survey data, hydro- logical data, Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index （MODIS-NDVI） data and habi- tat data derived from Landsat TNUETM data and HJ/CCD data were combined. The satellite-derived Green Wave Index （GWI） based on MODIS-NDVI dataset was applied to detect changes in goose food resources. It was found that habitat size and vegetation conditions are key factors determining goose abundance. Geese numbers were positively correlated with habitat area, while intermediate range of vegetation productivity might benefit the goose abundance. Water level affects goose abundance by changing available habitat areas and vegetation conditions. We suggested that matching hydrological regime and exposed meadows time to wintering geese dynamics was crucial in the Poyang Lake wetlands. Our study could provide sound scientific information for hydrological management in the context of waterbird conservation.

Wheat Yield Response to Water Deficit under Central Pivot Irrigation System Using Remote Sensing Techniques

Scarcity of rainfall and limited irrigation water resources is the main challenge for agricultural expanding policies and strategies. At the same time, there is a high concern to increase the area of wheat cultivation in order to meet the increasing local consumption. The big challenge is to incerese wheat production using same or less amount of irrigation water. In this trend, the study was carried out to analyze the sensitivity of wheat yield to water deficit using remotely sensed data in El-Salhia agricultural project which located in the eastern part of Nile delta. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were extracted from Landsat 7. Water Deficit Index (WDI) used both LST minus air temperature (Tair) and vegetation index to estimate the relative water status. Yield response factor (ky) was derived from relationship between relative yield decrease and relative evapotranspiration deficit. The relative Evapotranspiration deficit was replaced by WDI. Linear regression was found between predicted wheat yield and actual wheat yield with 0.2?6, 0.025, 0.252 and 0.76 as correlation coefficient on 30th of Dec. 2012, 15th of Jan. 2013, 16th of Feb. 2013 and 20th of Mar. 2013 respectively. The main objective of this study is using a combination between FAO 33 paper approach and remote sensing techniques to estimate wheat yield response to water.

Water bodies extraction from TM images

Aiming at the problems of high time-consuming, low accuracy and weak versatility of the existing methods of wa- ter extraction based on TM image, this paper combines principal component analysis （PCA） with the modified normalized difference water index （MNDWI） which was improved by XU Han-qiu to construct a false color composite image that could separate water from others easily. This method can realize the water extraction based on TM image by analyzing the spectral characteristics of water in this false color image and establishing a water extraction model. This paper also compares the effi- ciency of this method with MNDWI, （TM2 ＋ TM3） - （TM4 ＋ TM5） and new water index （NWI）, which were applied in the city and mountain of Taiyuan, respectively. The results show that the proposed method can extract water body from TM im- age more rapidly and efficiently and its accuracy is up to 94.03 %. In addition, this method does not require a manual selec- tion threshold, which meets the research reuuirement of high automaticm.

基于Sentinel-2的青铜峡灌区水稻和玉米种植分布早期识别

及时准确地掌握灌区内作物种植分布对于灌溉水资源高效配置、农田精准管理具有重要指导意义。以宁夏青铜峡灌区为研究对象,利用多时相Sentinel-2卫星数据,通过水稻和玉米早期特征分析,提取关键的“水淹”信号和“植被”信号,构建时序归一化差异水体指数(MNDWI)和归一化植被指数(NDVI)特征值数据集,并通过样本分析关键特征阈值,构建水稻和玉米早期种植分布决策树模型,提取2022年宁夏青铜峡灌区水稻和玉米种植的空间分布。结果表明(:1)玉米和水稻苗期的后半段5月15—31日,水淹信号和植被信号是区分二者关键时期。(2)基于早期作物物候特征的方法,在5月16—31日获取的水稻和玉米图像制图精度高于90%,用户精度超过91%,总体精度超过90%,Kappa系数高于0.88,明显高于同时期随机森林方法的分类精度。(3)本研究提出的方法在早期水稻和玉米种植分布提取方面具有较强的适用性,并且能够在时空尺度上以较少的实地样本进行延展,同时在时间上也更有优势。因此,该方法为青铜峡灌区水稻和玉米种植分布早期调查提供了重要的方法支撑。

Automatic Generation of Water Masks from RapidEye Images

Water is a very important natural resource and it supports all life forms on earth. It is used by humans in various ways including drinking, agriculture and for scientific research. The aim of this research was to develop a routine to automatically extract water masks from RapidEye images, which could be used for further investigation such as water quality monitoring and change detection. A Python-based algorithm was therefore developed for this particular purpose. The developed routine combines three spectral indices namely Simple Ratios (SRs), Normalized Green Index (NGI) and Normalized Difference Water Index (NDWI). The two SRs are calculated between the NIR and green band, and between the NIR and red band. The NGI is calculated by rationing the green band to the sum of all bands in each image. The NDWI is calculated by differencing the green to the NIR and dividing by the sum of the green and NIR bands. The routine generates five intermediate water masks, which are spatially intersected to create a single intermediate water mask. In order to remove very small waterbodies and any remaining gaps in the intermediate water mask, morphological opening and closing were performed to generate the final water mask. This proposed algorithm was used to extract water masks from some RapidEye images. It yielded an Overall Accuracy of 95% and a mean Kappa Statistic of 0.889 using the confusion matrix approach.

基于高分一号遥感影像的水体提取方法对比分析与改进

以高分一号遥感影像为数据源,以安徽省黄山市屯溪流域内的东方红水库为研究对象,采用单波段阈值法、两波段差值法、波段比值法、归一化差分水体指数(NDWI)法、归一化差分植被指数(NDVI)法共5种水陆像元度量方法,分别应用平均值法和最大类间方差迭代法两种阈值选取方法对东方红水库进行水体提取,探索最大类间方差迭代法在水体提取变量上的改进效果,在此基础上提出了一种改进的最大类间方差联合水体提取法,并对比了改进前后的水体提取效果。结果表明:改进后的水体提取方法可以很好地降低影像提取中产生的噪点,提高水体提取的精度,提取成果的平均相对误差为4.69%,决定系数为0.8579,相比于改进前平均相对误差降低了0.68%,决定系数提高了0.0539。

青藏高原多尔索洞错水深反演与水量变化估算

利用由实测水深空间插值获得的水下地形数据估算湖泊水量及其变化往往存在较大误差。以Landsat系列影像数据及多尔索洞错12 m以下实测水深数据为基础,建立多波段组合模型进行水深模拟,结合多时相Landsat影像获取的多尔索洞错边界数据获取水深变化,估算1996~2016年多尔索洞错水量变化。实验结果表明:多因子反演模型的相关系数(R^(2))均在0.90以上,平均绝对误差低至0.48 m,相比空间插值方法能更精确地模拟多尔索洞错水深在12 m以下区域的水深分布情况;近20 a来多尔索洞错不断扩张,水位升高约0.40 m/a,水量增加约0.18 km^(3)/a。

1977—2021年博斯腾湖大湖区湖岸线动态变化

湖泊岸线的动态变化可以表征湖泊的变化。以博斯腾湖大湖区为研究区,基于1977—2021年的Landsat遥感影像数据和实测水位资料,利用MNDWI法提取水体信息,运用岸线发育系数与岸线强度,分析研究区岸线动态变化,探讨影响研究区岸线动态变化的主要因素。研究结果表明:1977—2021年研究区岸线变化最明显的区域主要是研究区北岸。1977—1989年北岸岸线向南迁移的最大距离为0.93 km;1989—2003年北岸岸线向北迁移的最大距离为5.12 km;2003—2013年北岸岸线向南迁移的最大距离为5.46 km;2013—2021年北岸岸线向北迁移的最大距离为5.11 km。1977—1989年研究区岸线长度以1.55 km/a的速率逐渐缩短;1989—2002年岸线以16.34 km/a的速率持续增长;2002—2012年岸线以22.67 km/a的速率明显缩短;2012—2021年岸线以14.36 km/a的速率快速增长。年降水量、年均气温以及开都河径流量等自然要素是研究区岸线、水位和水面面积变化的主要自然因素。研究区岸线长度和曲折性受水位和水面面积的影响较为明显,1977—2021年研究区水位、水面面积和岸线长度的变化趋势基本一致。

基于高分一号卫星影像的水体信息提取方法初探

本文以山西汾河水库为研究区域,利用高分一号卫星影像,分别选择阈值分割法、归一化差异水体指数法和面向对象法对研究区水体信息进行提取,并对3种方法提取结果进行精度检查。通过试验得出如下结论:阈值分割法对水体先验知识要求低,简单易用;归一化差异水体指数法由于未能消除阴影与水体的混淆问题,使得提取水体信息精度较低;面向对象法提取水体信息结果精度较高,但易丢失人工水塘与沟渠信息。该研究结果对利用高分一号卫星影像进行其他土地利用信息的提取具有一定借鉴意义。

基于样本自动选择与SVM结合的海岸线遥感自动提取

利用卫星遥感手段自动、快速、准确地测定海岸线动态信息是遥感应用的一个重要领域,对海域管理规划具有重要意义。由于近岸水体光谱特征受区域环境影响较大,在水陆分离过程中,利用传统的归一化差值水体指数(normalized difference water index,NDWI)阈值分割法时,一部分近岸水体易被错分为陆地,严重影响了岸线提取精度。为此,在NDWI模型的基础上,提出了基于样本自动选择与支持向量机(support vector machine,SVM)的海岸线遥感自动提取算法。首先进行NDWI计算与全局阈值分割,实现水体信息的初步提取;再通过NDWI信息控制初始样本的自动选择;然后利用SVM分类器对水体再次分类,实现海陆分离;最后填充小的陆地水体单元,实现岸线自动跟踪。实验结果表明,该方法能有效增强对近岸水体的识别能力,提高海岸线遥感提取的精度和自动化程度。

新植被水分指数的冬小麦冠层水分遥感估算

水分含量是表征作物水分胁迫生理状况的重要指标,及时有效地监测作物水分含量对于评估作物水分盈亏平衡,指导农业生产灌溉具有重要意义。针对不同形式的归一化差值水分指数(NDWI)存在的饱和性问题,拟引入增强型植被指数EVI对其加以适当改进,通过构建新的植被水分指数NDWI#(即NDWI*EVI)来估算作物水分含量。首先,利用PROSAIL辐射传输模型分析了由不同水分敏感波段(1 240,1 450,1 950和2 500nm)构建的各种典型NDWIs与相应新植被水分指数NDWI#对植被冠层水分及LAI的饱和响应特征;然后,利用田间实验光谱和水分数据,开展作物水分含量的建模和验证分析。结果表明:将EVI引入后,形成的新水分指数NDWI#能够有效提高冬小麦水分含量估算精度,特别是针对NDWI1450,NDWI1940,NDWI2500这三个指数,改进后的新指数显著提高了对LAI响应的饱和点,冬小麦作物水分估算精度也得到较为的明显改善。研究表明,将含有可见光波段信息的EVI引入到NDWI中,构建的新指数NDWI#因融合可见光、近红外和短波红外更多波段的光谱信息,对估算冬小麦冠层含水量可能具有更好的优势。