Automated Extraction for Water Bodies Using New Water Index from Landsat 8 OLI Images

The extraction of water bodies is essential for monitoring water resources,ecosystem services and the hydrological cycle,so analyzing water bodies from remote sensing images is necessary.The water index is designed to highlight water bodies in remote sensing images.We employ a new water index and digital image processing technology to extract water bodies automatically and accurately from Landsat 8 OLI images.Firstly,we preprocess Landsat 8 OLI images with radiometric calibration and atmospheric correction.Subsequently,we apply KT transformation,LBV transformation,AWEI nsh,and HIS transformation to the preprocessed image to calculate a new water index.Then,we perform linear feature enhancement and improve the local adaptive threshold segmentation method to extract small water bodies accurately.Meanwhile,we employ morphological enhancement and improve the local adaptive threshold segmentation method to extract large water bodies.Finally,we combine small and large water bodies to get complete water bodies.Compared with other traditional methods,our method has apparent advantages in water extraction,particularly in the extraction of small water bodies.

Assessment of desertification in Eritrea: land degradation based on Landsat images

Remote sensing is an effective way in monitoring desertification dynamics in arid and semi-arid regions.In this study,we used a decision tree method based on NDVI(normalized difference vegetation index),SAVI(soil adjusted vegetation index),and vegetation cover proportion to quantify and analyze the desertification in Eritrea using Landsat data of the 1970 s,1980 s and 2014.The results demonstrate that the NDVI value and the annual mean precipitation declined while the temperature increased over the past 40 a.Strongly desertified land increased from 4.82×10^4 km^2(38.5%)in the 1970 s to 8.38×10^4 km^2(66.9%)in 2014:approximately 85%of the land of the country was under serious desertification,which significantly occurred in arid and semi-arid lowlands of the country(eastern,northern,and western lowlands)with relatively scarce precipitation and high temperature.The non-desertified area,mostly located in the sub-humid eastern escarpment,also declined from approximately 2.1%to 0.5%.The study concludes that the desertification is a cause of serious land degradation in Eritrea and may link to climate changes,such as low and unpredictable precipitation,and prolonged drought.

基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法

利用遥感技术快速准确地提取耕地信息是耕地保护的关键环节。以山东省商河县为例,提出了一种基于多季相分形特征的Landsat 8 OLI影像耕地信息提取方法。首先采用毯子覆盖法计算多季相遥感影像每个像元的上分形信号和下分形信号,对比分析耕地和其他土地利用类型的分形特征,选取上分形信号的第3尺度作为特征尺度,提取商河县耕地空间分布特征;其次采用同时期的土地利用矢量数据、Esri land cover数据和统计数据进行耕地信息提取精度评价;最后分别设置多季相分形提取与单季相分形提取、现有土地利用数据产品的对比实验,并基于点位匹配度和面积匹配度进行评价。结果表明:多季相数据更能反映农作物生长的复杂性,有助于提高耕地信息的提取精度;不同土地利用类型在不同分形尺度的信号值各不相同,分形特征可以在不同尺度上清晰地刻画出不同土地利用类型的分异性;基于矢量数据和Esri land cover数据评价的多季相分形特征耕地提取点位匹配度为87.13%和89.83%,面积匹配度为99.73%和97.91%,均比单季相分形提取结果精度高;综合考虑点位匹配度、面积匹配度和空间分布特征,研发方法能有效区分耕地和其他土地利用类型,提取结果更优,且与统计数据有更高的一致性。该方法可准确提取耕地信息,为耕地的动态监测和损害评估提供技术支撑。

Characterization of land cover types in Xilin River Basin using multi-temporal Landsat images

This study conducted computer-aided image analysis of land use and land cover in Xilin River Basin, Inner Mongolia, using 4 sets of Landsat TM/ETM+ images acquired on July 31, 1987, August 11, 1991, September 27, 1997 and May 23, 2000, respectively. Primarily, 17 sub-class land cover types were recognized, including nine grassland types at community level: F.sibiricum steppe, S.baicalensis steppe, A.chinensis+ forbs steppe, A.chinensis+ bunchgrass steppe, A.chinensis+ Ar.frigida steppe, S.grandis+ A.chinensis steppe, S.grandis+ bunchgrass steppe, S.krylavii steppe, Ar.frigida steppe and eight non-grassland types: active cropland, harvested cropland, urban area, wetland, desertified land, saline and alkaline land, cloud, water body + cloud shadow. To eliminate the classification error existing among different sub-types of the same gross type, the 17 sub-class land cover types were grouped into five gross types: meadow grassland, temperate grassland, desert grassland, cropland and non-grassland. The overall classification accuracy of the five land cover types was 81.0% for 1987, 81.7% for 1991, 80.1% for 1997 and 78.2% for 2000.

基于Landsat 8和机器学习的塔城地区草地地上生物量估测模型

以新疆塔城地区为研究区,利用植被指数、气象数据、地形数据作为自变量,结合研究区样地实测生物量数据,分析并比较K近邻回归(KNN)、多元线性回归(MLR)、梯度提升决策树(GBDT)和随机森林回归(RF)和极端梯度提升(XGBoost)5种机器学习模型,进而分析并比较采用投票回归器(Voting regressor)和堆叠(Stacking)方法构建的2种集成学习模型的估测精度。结果表明,基于Stacking集成学习模型性能最优,R^(2)达0.764,RMSE和MAE分别为23.29 g/m^(2)和16.8 g/m^(2),进而利用最优模型进行草地地上生物量(Above ground biomass,AGB)反演制图。

Study of Forest Cover Change Dynamics between 2000 and 2015 in the Ikongo District of Madagascar Using Multi-Temporal Landsat Satellite Images

Satellite images are considered reliable data that preserve land cover information. In the field of remote sensing, these images allow relevant analyses of changes in space over time through the use of computer tools. In this study, we have applied the “discriminant” change detection algorithm. In this, we have verified its effectiveness in multi-temporal studies. Also, we have determined the change in forest dynamics in the Ikongo district of Madagascar between 2000 and 2015. During the treatments, we have used the Landsat TM satellite images for the years 2000, 2005 and 2010 as well as ETM+ for 2015. Thus, analyses carried out have allowed us to note that between 2000-2005, 1.4% of natural forest disappeared. And, between 2005-2010, forests degradation was 1.8%. Also, between 2010-2015, about 0.5% of the natural forest conserved in 2010 disappeared. Furthermore, we have found that the discriminant algorithm is considerably efficient in terms of monitoring the dynamics of forest cover change.

基于Landsat 8 OLI和资源3号立体像对数据的桉树森林蓄积量估测

【目的】探索Landsat8 OLI数据和立体数据在估算桉树森林蓄积量(forest stock volume,FSV)中的潜力,并且准确地估计桉树的FSV。【方法】以3幅Landsat8 OLI图像和资源3号立体数据为遥感数据源,并且结合少量地面调查数据实现了桉树FSV的遥感估计。研究中提取了三类遥感特征用于估计桉树FSV:第一类是包括植被指数和单波段反射率在内的光谱特征;第二类是基于Landsat 8 OLI图像的单波段提取的8种纹理特征;第三类是基于资源3号立体像对数据和开源的数字高程模型(digital elevation model,DEM)提取的冠层高度模型(canopy height model,CHM)。利用Boruta算法对三类遥感特征进行提取,之后建立了随机森林(random forest,RF)、K-最近邻(K-nearest neighbor,KNN)和支持向量机(support vector machine,SVM)3种机器学习模型以及传统的多源线性回归模型(multiple linear regression,MLR),并以决定系数(R2)、均方根误差(root mean square error,RMSE)和相对均方根误差(relative root mean square error,rRMSE)作为评价指标对模型结果进行评估。【结果】基于ZY-3立体像对数据和开源的DEM数据提取的CHM与桉树的FSV具有很强的相关性,Pearson相关系数达到了0.71。仅仅利用基于Landsat 8 OLI图像提取的光谱和纹理特征难以准确2地估计桉树的FSV,估测模型的R为0.29~0.38,rRMSE为35.65%~43.30%,存在严重的数据饱和问题。2当变量集中加入CHM后,模型的估测精度明显提高,R达到了0.64~0.66,rRMSE为25.74%~26.41%。【结论】使用Landsat 8 OLI数据估算桉树FSV时存在严重的数据饱和问题,并且使用空间分辨率为30 m的纹理特征难以有效地改善森林蓄积量的估计精度。利用资源3号立体像对数据和开源的DEM数据可以提取较为准确的CHM,并且所提取的CHM可以解决改善光学数据的饱和问题,从而提高桉树FSV的估计精度。

Estimation of Rice Evapotranspiration Using Reflective Images of Landsat Satellite in Sefidrood Irrigation and Drainage Network

More accurate estimation of crop evapotranspiration(ET_c)in a regional scale has always been one of the most important challenges.Temporal and spatial monitoring of ET_(c )using satellite images can help to enhance accuracy of estimations.In this study,the(ET_c)_(rice) maps were produced by using statistical/experimental methods based on crop coefficient(K_c)maps derived from vegetation index(Ⅵ).K_c was estimated using four methods,including linear relationship between K_c and Ⅵ(K_c-Ⅵ),calibrated model of K_c-Ⅵ,linear relationship between K_(cb)(the basal crop coefficient)and Ⅵ(K_(cb)-Ⅵ),and calibrated model of K_(cb)-Ⅵ.The results showed that calibrated model of K_c-Ⅵ had a better performance compared to the other methods,with normalized root mean square errors(NRMSE),mean absolute error and root mean square error being 5.7%,0.05 mm/d and 0.06mm/d,respectively.(ET_c)_(rice) maps were produced by using calibrated model of K_c-Ⅵ and reference evapotranspiration(ET_0)from FAO Penman-Monteith method.The NRMSE was 21.3%for using FAO Penman-Monteith method.Therefore,calibrated K_c-Ⅵ model in combining with ET_0 based on the Landsat 7 ETM+images could be provided a good estimation of(ET_c)_(rice) in regional scale,and can be applied to estimate water requirement due to the free and facilitate access.

Fusion of Landsat 8 OLI and PlanetScope Images for Urban Forest Management in Baton Rouge, Louisiana

In recent years image fusion method has been used widely in different studies to improve spatial resolution of multispectral images. This study aims to fuse high resolution satellite imagery with low multispectral imagery in order to assist policymakers in the effective planning and management of urban forest ecosystem in Baton Rouge. To accomplish these objectives, Landsat 8 and PlanetScope satellite images were acquired from United States Geological Survey (USGS) Earth Explorer and Planet websites with pixel resolution of 30m and 3m respectively. The reference images (observed Landsat 8 and PlanetScope imagery) were acquired on 06/08/2020 and 11/19/2020. The image processing was performed in ArcMap and used 6-5-4 band combination for Landsat 8 to visually inspect healthy vegetation and the green spaces. The near-infrared (NIR) panchromatic band for PlanetScope was merged with Landsat 8 image using the Create Pan-Sharpened raster tool in ArcMap and applied the Intensity-Hue-Saturation (IHS) method. In addition, location of urban forestry parks in the study area was picked using the handheld GPS and recorded in an excel sheet. This sheet was converted into Excel (.csv) file and imported into ESRI ArcMap to identify the spatial distribution of the green spaces in East Baton Rouge parish. Results show fused images have better contrast and improve visualization of spatial features than non-fused images. For example, roads, trees, buildings appear sharper, easily discernible, and less pixelated compared to the Landsat 8 image in the fused image. The paper concludes by outlining policy recommendations in the form of sequential measurement of urban forest over time to help track changes and allows for better informed policy and decision making with respect to urban forest management.

基于Landsat 8数据的广东石马河流域水质参数反演研究

针对河流水域水质进行高效快速监测的需要,本文以2021年2月广东石马河流域水体为研究对象,利用Landsat 8的OLI遥感影像对试验区域的环城河化学需氧量进行遥感反演研究。本文首先对实测水质数据和相应卫星影像数据波段值进行相关性分析,然后选取相关性最高的遥感波段值对水质进行回归分析,最后建立了适用于石马河流域的三次多项式反演模型。经反演预测结果与现场实测水质数据对比,试验结果表明所建立的水质反演模型能对石马河流域水质化学需氧量浓度进行高效快速监测。

基于Landsat 8影像提取豫中地区冬小麦和夏玉米分布信息的最佳时相选择

遥感技术对大尺度农业实时监测提供了一个理想的手段,遥感影像植被分类的最佳时相对作物种植面积遥感监测非常重要。本文选取2020年至2021年的6景Landsat 8影像,覆盖了夏玉米从乳熟到收获、冬小麦从越冬到成熟的生育期,以此分析不同时相的冬小麦-夏玉米与其他地类在光谱特征和NDVI上的差异,通过决策树的方法提取豫中地区冬小麦-夏玉米的空间分布情况。结果表明,冬小麦-夏玉米在不同生长发育时期,提取到的面积比有所不同,对于夏玉米而言,乳熟时期的提取效果要优于之后的时期,其在2020年8月26日的总体精度最高,为83.60%,Kappa系数为0.72,分类质量很好;对于冬小麦而言,最佳识别时期则处于冬小麦的越冬期,其在2021年1月1日的总体精度最高,为92.36%,Kappa系数为0.81,信息提取效果很好。除了作物自身生长过程的覆盖度变化,分类精度随成像时间而改变。多时相信息提取也发现,受到天气等环境条件限制,夏玉米和冬小麦的种植区域不完全重叠,山区冬季不适合冬小麦种植从而没有与夏玉米出现重叠分布。本研究有助于我们从宏观上对作物分布及生长状况作出及时有效的判断,对农业监测,特别是对轮作农田的信息管理和作物物候、种植面积等研究具有广阔的应用前景。

针对Landsat遥感影像的去云处理方法研究

遥感传感器获取的光学影像中部分区域不可避免存在云遮挡现象。本文选择的实验区位于西昆仑山的古里雅冰帽,将影像上的云区域划分为薄云与厚云,针对Landsat遥感影像进行去云处理方法研究。结果表明:针对Landsat遥感影像,直方图匹配和小波融合两种方法能够有效去除薄云;构建的归一化厚云检测模型能够较好地对厚云进行检测;匹配算法是当前比较有效的厚云处理方法。

基于影像的Landsat TM/ETM^+数据正规化技术

阐述了基于影像的Landsat TM/ETM+的数据正规化技术及其发展。该技术通过将Landsat影像的亮度值转换成传感器处的辐射值和反射率来对影像进行辐射校正。实例表明,使用正规化技术处理后的影像可以明显削弱日照和大气的影响,去除它们产生的噪声;其所求的传感器处的反射率与地面实测反射率的RMS值非常小。

基于Landsat7 ETM^+全色数据纹理和结构信息复合的城市建筑信息提取

提出了利用主成分分析方法有效地复合纹理和结构信息 ,从Landsat7ETM+ 全色数据中直接提取区域尺度的城市建筑信息的新方法 ,并在此基础上评估了Landsat7ETM+

Evaluation of Two Absolute Radiometric Normalization Algorithms for Pre-processing of Landsat Imagery

In order to evaluate radiometric normalization techniques, two image normalization algorithms for absolute radiometric correction of Landsat imagery were quantitatively compared in this paper, which are the Illumination Correction Model proposed by Markham and Irish and the Illumination and Atmospheric Correction Model developed by the Remote Sensing and GIS Laboratory of the Utah State University. Relative noise, correlation coefficient and slope value were used as the criteria for the evaluation and comparison, which were derived from pseudo-invarlant features identified from multitemporal Landsat image pairs of Xiamen （厦门） and Fuzhou （福州） areas, both located in the eastern Fujian （福建） Province of China. Compared with the unnormalized image, the radiometric differences between the normalized multitemporal images were significantly reduced when the seasons of multitemporal images were different. However, there was no significant difference between the normalized and unnorrealized images with a similar seasonal condition. Furthermore, the correction results of two algorithms are similar when the images are relatively clear with a uniform atmospheric condition. Therefore, the radiometric normalization procedures should be carried out if the multitemporal images have a significant seasonal difference.

基于à trous小波变换的Landsat 7 ETM^+图像融合研究

以胶州湾及周边海岸带为研究区,采用Landsat 7 ETM+数据,提出一种基于àtrous小波变换的全色图像和多光谱图像融合改进算法。对全色图像和多光谱图像进行适当层数的小波分解,多光谱图像的低频部分采用全色图像和其低频分量的比来调制;最高分解层外的其余分解层采用多光谱图像和全色图像在该层分解系数的加权和,加权系数由局部区域能量比来确定;最高分解层则采用绝对值最大准则。实验表明,该方法得到的图像可提高空间分辨率,对多光谱图像的光谱信息扭曲也较小,为提高海岸带地物分类和信息提取精度奠定了基础。

An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image

Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship（y =0.723 x＋0.504） between detection threshold y and subtraction x（x=λnir–λred） is found from the comparing Landsat TM/ETM plus image with the field surveys.Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image.Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows（sub-images） with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class（green tide or sea water） according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction.

应用Landsat影像数据分析岷江上游植被覆盖度时空变化及地形分异特征

以2000、2010和2020年的Landsat遥感影像为数据源,利用像元二分模型提取岷江上游3个时期的植被覆盖度,结合海拔、坡度以及坡向等地形因子,对研究区2000—2020年的植被覆盖状况变化及地形分异特征进行分析,为生态保护和土地规划利用提供数据支持。结果表明:(1)2000—2020年,研究区植被覆盖度呈先降低后升高的趋势,总体上得到改善,植被覆盖度Ⅲ级以上区域面积达到80%以上。(2)植被覆盖度在空间上呈现为“东高西低,南高北低”的分布特征,岷江上游中西部地区的植被覆盖度为Ⅳ级以上,黑水县西北部山区和松潘县的极高海拔地带植被覆盖度为Ⅰ级。(3)随海拔、坡度的上升,研究区植被覆盖度均表现为先升高后降低的特征;植被覆盖度半阳坡最大,阴坡最小,平地大于半阴坡。岷江上游作为长江上游的生态屏障,植被覆盖状况受海拔、坡度以及坡向和人类活动影响较大,因此,对岷江上游地区生态保护和土地利用应考虑地形限制,因地制宜采取措施。

A Method for Retrieving Water-leaving Radiance from Landsat TM Image in Taihu Lake, East China

The visible and infrared bands of Landsat Thematic Mapper （TM） can be used for inland water studies. A method of retrieving water-leaving radiance from TM image over Taihu Lake in Jiangsu Province of China was investigated in this article. To estimate water-leaving radiance, atmospheric correction was performed in three visible bands of 485nm, 560nm and 660rim. Rayleigh scattering was computed precisely, and the aerosol contribution was estimated by adopting the clear-water-pixels approach. The clear waters were identified by using the Landsat TM middle-infrared band （2.1 μm）, and the water-leaving radiance of clear water pixels in the green band was estimated by using field data. Aerosol scattering at green band was derived for six points, and interpolated to match the TM image. Assuming the atmospheric correction coefficient was 1.0, the aerosol scattering image at blue and red bands were derived. Based on a simplified atmospheric radiation transfer model, the water-leaving radiance for three visible bands was retrieved. The water-leaving radiance was normalized to make it comparable with that estimated from other remotely sensed data acquired at different times, and under different atmospheric conditions. Additionally, remotely sensed reflectance of water was computed. To evaluate the atmospheric correction method presented in this article, the correlation was analyzed between the corrected remotely sensed data and the measured water parameters based on the retrieval model. The results show that the atmospheric correction method based on the image itself is more effective for the retrieval of water parameters from Landsat TM data than 6S （Second Simulation of the Satellite Signal in the Solar Spectrum） code based on standard atmospheric and aerosol models.