基于GEE的Landsat-8与Sentinel-2影像在棉花种植提取中差异性分析及提取方法对比研究

棉花作为南疆地区重要的经济作物之一,在经济工作中起着至关重要的作用。及时、准确地获取棉花种植面积,对农业政策和经济发展具有重要意义。为了实现这一目标,需要综合分析不同方法和遥感数据对最终棉花种植面积制图精度的影响。本研究以新疆阿克苏地区棉花种植区为例,借助Google Earth Engine云平台,采用随机森林法(RF)、支持向量机法(SVM)、最小距离分类法(MDC)等3种机器学习方法,利用2类中分辨率影像提取棉花种植信息,充分评估使用的档案数据和官方统计数字。结果表明,采用Sentinel-2方法和RF获得了最优棉花图,随机森林法分类器的总体精度、Kappa系数和用户精度分别高达97.4%、96.7%和91.1%,分别比Landsat-8图像和RF模型的结果高出7.3百分点、0.081、2.8百分点。与官方统计数据相比,采用RF、SVM、MDC对Sentinel-2和Landsat-8图像的棉花种植面积估算图的精度分别为98.4%、95.8%、79.6%和90.3%、83.7%、72.5%。很明显,Sentinel-2和RF模型的组合与官方数据的一致性最高。对比分析结果表明,Landsat-8和Sentinel-2数据可用于大范围复杂种植结构的棉花高精度测绘。本研究结果有望为棉花大面积鉴别提供一定的理论指导和实践指导。

基于Landsat-8影像的高原湖泊水体表面温度反演

目前,在高原环境下的水体表面温度反演成果相对较少,本文结合Landsat-8影像和辐射传输方程法对纳木错湖水体表面温度进行反演并验证其有效性。首先,计算研究区的地表比辐射率;其次,计算同温度下黑体的辐射亮度;最后,利用普朗克函数计算水体表面温度并基于MODIS地表温度产品对反演结果进行验证。实验结果表明,湖泊水体表面温度反演绝对误差最小值为0.449℃、最大值为1.685℃,均方根误差最小值为1.269℃、最大值为1.781℃,反演结果与MODIS温度产品的日均温结果较接近,夏季湖泊水体表面温度变化特征基本与纳木错气温一致。该方法可为后续高原湖泊水体水表温度反演研究提供一定的参考。

Evaluation of effective spectral features for glacial lake mapping by using Landsat-8 OLI imagery

Glacial lake mapping provides the most feasible way for investigating the water resources and monitoring the flood outburst hazards in High Mountain Region.However,various types of glacial lakes with different properties bring a constraint to the rapid and accurate glacial lake mapping over a large scale.Existing spectral features to map glacial lakes are diverse but some are generally limited to the specific glaciated regions or lake types,some have unclear applicability,which hamper their application for the large areas.To this end,this study provides a solution for evaluating the most effective spectral features in glacial lake mapping using Landsat-8 imagery.The 23 frequently-used lake mapping spectral features,including single band reflectance features,Water Index features and image transformation features were selected,then the insignificant features were filtered out based on scoring calculated from two classical feature selection methods-random forest and decision tree algorithm.The result shows that the three most prominent spectral features(SF)with high scores are NDWI1,EWI,and NDWI3(renamed as SF8,SF19 and SF12 respectively).Accuracy assessment of glacial lake mapping results in five different test sites demonstrate that the selected features performed well and robustly in classifying different types of glacial lakes without any influence from the mountain shadows.SF8 and SF19 are superior for the detection of large amount of small glacial lakes,while some lake areas extracted by SF12 are incomplete.Moreover,SF8 achieved better accuracy than the other two features in terms of both Kappa Coefficient(0.8812)and Prediction(0.9025),which further indicates that SF8 has great potential for large scale glacial lake mapping in high mountainous area.

Phenotypic Image Recognition of Asparagus Stem Blight Based on Improved YOLOv8

Asparagus stem blight,also known as“asparagus cancer”,is a serious plant disease with a regional distribution.The widespread occurrence of the disease has had a negative impact on the yield and quality of asparagus and has become one of the main problems threatening asparagus production.To improve the ability to accurately identify and localize phenotypic lesions of stem blight in asparagus and to enhance the accuracy of the test,a YOLOv8-CBAM detection algorithm for asparagus stem blight based on YOLOv8 was proposed.The algorithm aims to achieve rapid detection of phenotypic images of asparagus stem blight and to provide effective assistance in the control of asparagus stem blight.To enhance the model’s capacity to capture subtle lesion features,the Convolutional Block AttentionModule(CBAM)is added after C2f in the head.Simultaneously,the original CIoU loss function in YOLOv8 was replaced with the Focal-EIoU loss function,ensuring that the updated loss function emphasizes higher-quality bounding boxes.The YOLOv8-CBAM algorithm can effectively detect asparagus stem blight phenotypic images with a mean average precision(mAP)of 95.51%,which is 0.22%,14.99%,1.77%,and 5.71%higher than the YOLOv5,YOLOv7,YOLOv8,and Mask R-CNN models,respectively.This greatly enhances the efficiency of asparagus growers in identifying asparagus stem blight,aids in improving the prevention and control of asparagus stem blight,and is crucial for the application of computer vision in agriculture.

Cryo-EM combined with image deconvolution to determine ZIF-8 crystal structure

Metal–organic frameworks(MOFs) are crystalline porous materials with tunable properties, exhibiting great potential in gas adsorption, separation and catalysis.[1,2]It is challenging to visualize MOFs with transmission electron microscopy(TEM) due to their inherent instability under electron beam irradiation. Here, we employ cryo-electron microscopy(cryoEM) to capture images of MOF ZIF-8, revealing inverted-space structural information at a resolution of up to about 1.7A and enhancing its critical electron dose to around 20 e^(-)/A^(2). In addition, it is confirmed by electron-beam irradiation experiments that the high voltage could effectively mitigate the radiolysis, and the structure of ZIF-8 is more stable along the [100] direction under electron beam irradiation. Meanwhile, since the high-resolution electron microscope images are modulated by contrast transfer function(CTF) and it is difficult to determine the positions corresponding to the atomic columns directly from the images. We employ image deconvolution to eliminate the impact of CTF and obtain the structural images of ZIF-8. As a result, the heavy atom Zn and the organic imidazole ring within the organic framework can be distinguished from structural images.

基于Landsat-8影像的抚州市地表温度遥感反演研究

基于抚州市2022年9月28日Landsat-8影像,采用单窗算法反演地表温度(LST),并选取随机点统计方法,分析LST与归一化植被指数(NDVI)、数字高程模型(DEM)、坡向之间的关系。结果表明,研究区反演的LST为22.51~45.71℃,低值区主要分布在东北部、东南部、中部偏西高程和NDVI值均较大的区域,高值区主要分布在西部、南部城镇密集、高程和NDVI值均较小的区域。LST与NDVI、DEM 2个影响因素均呈现明显负相关,不同坡向的LST与一般认知一致,阳坡高于阴坡。

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.

基于Landsat-8的湛江东海岛地物分类研究

“宝钢湛江项目”的实施对近十年湛江东海岛的地物分布产生剧烈影响,尤其是工业用地。本文基于2013年、2017年和2021年的陆地卫星8号(Landsat-8)数据对湛江东海岛进行地物分类,研究该区域近十年的用地变化趋势。以2013年数据为参照:采用归一化水体指数(Normalized Difference Water Index,NDWI)模型和谱间关系模型实现水陆分离,比对选择分离效果较优者以提取东海岛岸线;对比最大似然法、神经网络法和支持向量机法3种监督分类方法,选择提取地物效果最优者应用于其余数据。基于Google earth在线地图及无人机实测数据构建验证点集,使用混淆矩阵进行精度评价。结果表明:谱间关系模型的水陆分离效果较优,提取海岛岸线的精确度有明显提升;支持向量机法的分类总体精度和Kappa系数最高,分类结果能较好地反映研究区的真实地物分布;汇总三年数据的分类结果,发现用于发展工业的土地面积增长突出且处于持续增长趋势。谱间关系模型与支持向量机法分别实现了对东海岛岸线和地物类型的准确提取,得出近十年研究区的用地变化趋势,能为研究区的用地规划提供参考。

基于Landsat-8卫星数据的洋山深水港区近年悬沙变化特征研究

洋山深水港区的建设对周围海域水沙分布产生显著影响,深入分析建港后的水沙特征对后期港区开发具有重要意义。基于近6年的Landsat-8 OLI洋山深水港区遥感影像,对洋山深水港区的表层悬沙浓度时空变化特征进行分析。结果表明:洋山港海域冬季悬沙浓度明显大于夏季,冬、夏季悬沙浓度差异接近1倍;大潮期的悬沙浓度大于小潮期,涨潮期的悬沙浓度大于落潮期;受喇叭口地形影响,潮流作用较强,潮型变化对口外悬沙浓度影响更为明显;强风引起的波浪变化也会使悬沙浓度明显提高。

基于Landsat-8影像的农田土壤含水量反演研究

快速实时地获取大范围土壤含水量可以为科学有效地应对干旱提供强有力的数据支撑。研究选择河北省定兴县和易县的冬小麦种植区作为研究区域,基于多光谱遥感数据Landsat-8和野外实测土壤含水量数据,构建了垂直干旱指数(PDI)、改进型垂直干旱指数(MPDI)、温度植被旱情指数(TVDI)3种干旱指数模型来反演研究区土壤含水量,并分析反演结果的精度和适用性。研究结果表明:3种干旱指数计算结果显示研究区整体偏干,不同干旱指数的空间分布存在差异,其中PDI和另外2种干旱指数的差异最大;3种干旱指数均与土壤含水量实测值呈负相关,其中MPDI、TVDI和土壤含水量之间存在较为明显的线性相关关系,从拟合精度来看MPDI表现最优,因此被用于反演研究区的土壤含水量。反演结果显示研究区的含水量整体偏少,主要介于12%~15%,含水量的空间分布特征和地表覆盖特征一致。研究结果证明了MPDI指数在冬小麦冬季干旱监测方面具有较大的应用潜力。

基于Landsat-8 OLI数据的新疆昭苏南克拉克斯赛依矿区蚀变信息提取

昭苏南克拉克斯赛依矿区气候条件恶劣,高寒缺氧,交通不便,积雪覆盖深厚,区内构造复杂,矿产资源丰富。充分发挥遥感技术的独特优势,对研究区未来的找矿工作有重要意义。文章将Landsat-8 OLI作为数据源,在预处理基础上,根据研究区矿产地质情况及围岩蚀变特征,采用主成分分析法进行铁染、羟基蚀变信息提取,结果表明:研究区蚀变异常信息分布特征具有一定规律,异常信息呈带状展布,与构造走向基本一致,羟基异常与铁染异常套合较好,且强度较高,结果与研究区内已知矿化点套合较好,证明蚀变信息为良好的找矿标志,对研究区找矿工作有重要的指导意义。

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-8陆地卫星数据的火点检测方法

传统的火点检测算法通常利用高温地物在中红外波段或热红外波段的高发射率特性来提取火点,然而受制于影像空间分辨率的限制如MODIS、AVHRR等,使得很多小规模火情现象被漏检.研究发现短波红外数据也同样能被用于高温地物的识别和检测,并且相较于热红外波段数据对低温和高温地物的区分度更大,在精确识别和定位高温目标方面更加准确.文章利用空间分辨率为30米的Landsat-8 OLI传感器数据,根据高温火点在近红外及短波红外波段的波谱特性,利用改进的归一化燃烧指数(NBRS)结果自适应地确定阈值来提取疑似火点,然后再利用高温火点在短波红外的峰值关系进行误检点剔除,从而得到最终的火点产品.提出的算法能检测到所占像元面积10%左右的火点,并能够有效地排除云层及建筑物的干扰,在保证较低漏检率的同时还能达到90%左右的准确率,相比于传统算法的火点提取精度有很大的提高.

Landsat-8卫星数据应用探讨

在过去的40年里,Landsat系列卫星对启蒙和推动遥感应用技术的发展起到了重要作用,其遥感图像数据在我国得到了广泛应用。Landsat-8卫星是Landsat系列卫星的后续任务,已于2013年2月发射,目标是延续Landsat系列卫星数据的连续性,为农业、水资源管理、植被监测、灾害响应等领域继续提供高质量的图像数据。本文将首先介绍Landsat-8卫星的研制背景、卫星的基本参数和新型成像仪的数据特点,然后对比ETM+数据,介绍了Landsat-8卫星的数据产品分级以及数据的应用方向上的变化。

基于模拟Landsat-8 OLI数据的小麦秸秆覆盖度估算

田间秸秆作为农业生产过程中的重要物质,其覆盖度的遥感估算具有十分重要的意义。Landsat-8 OLI影像作为Landsat系列影像的最新数据产品,具有更精细的光谱特征,明确其在秸秆覆盖度估算中的表现具有重要的现实意义。该研究使用ASD Field Spec 4 Hi-Res地物光谱仪,以实测田间小麦秸秆光谱反射率为数据源,模拟Landsat-8 OLI、Landsat-5TM、Aster、Hyperion影像波段反射率,构建光谱指数,并建立小麦秸秆覆盖度估算模型,通过对比分析,评估Landsat-8OLI数据的估算能力。结果表明,基于Landsat-8 OLI1和OLI2波段构建的NDIOLI21指数模型估算结果最优,决定系数(coefficient of determination,R2)为0.60,均方根误差(root mean square error,RMSE)为9.56%,平均相对误差(mean relative error,MRE)为9.83%,优于Landsat-5 TM构建的光谱指数,且仅次于Aster构建的木质素-纤维素吸收指数(lignin cellulose absorption,LCA)和短波红外归一化差异秸秆指数(shortwave infrared normalized difference residue index,SINDRI)以及Hyperion构建的纤维素吸收指数(cellulose absorption index,CAI)。因此,波段更多、波段划分更加精细的Landsat-8OLI构建的光谱指数在小麦秸秆覆盖度估算方面达到了一定精度,具有良好的应用前景。

基于支持向量机的Landsat-8影像森林类型识别研究

以吉林省汪清林业局天然林区为研究区,利用Landsat-8 OLI_TIRS多光谱遥感影像,结合森林资源野外调查数据,提取森林类型纹理、光谱特征参数,作为支持向量机的输入量,利用K-折交叉验证法确定最优核函数,识别森林类型,确定最优分类结果,评价分类精度,并与仅利用波段光谱特征的SVM分类结果进行精度对比。结果表明:利用纹理和光谱特征进行分类,构造SVM进行森林识别是可行的。惩罚系数C=100.0、核函数半径σ=1.000时的径向基核函数构造的支持向量机分类精度最好,总体分类精度可达89.58%,Kappa系数为0.87,单一分类精度中,阔叶林>针叶林>针阔混交林。只利用光谱特征的分类结果精度为81.26%,结合光谱和纹理特征的规律,能够提高分类精度。

利用Landsat-8和Worldview-2数据进行协同岩性分类

提出了一种对Landsat-8和Worldview-2协同后的岩性分类方法。首先对Landsat-8和Worldview-2影像进行协同:在对Landsat-8全色波段与其多光谱进行自协同后,与Worldview-2多光谱第8波段数据协同,将协同后的Landsat-8中短波红外数据与Worldview-2数据进行叠加,得到最后协同结果。对协同后的数据进行岩性分类:利用基于最大似然法(maximum likelihood,ML)进行初始分类,由马尔科夫随机场法(Markov Random Field,MRF)对结果进行优化得到最终分类结果。采用新疆西昆仑地区遥感数据进行了实验,结果证实协同后数据的分类结果具有更高的分类精度。

Landsat-8与GF-1卫星渤海海冰探测能力对比研究

为更有效地利用Landsat-8和GF-1卫星进行渤海海冰监测,从海冰检测和海冰类型识别两方面,开展Landsat-8与GF-1卫星的渤海海冰探测能力对比分析研究。实验研究表明,在海冰检测方面,GF-1区分海冰与海水的能力强于Landsat-8;在海冰类型识别方面,Landsat-8识别海冰类型的能力要强于GF-1影像。初步讨论了综合利用Landsat-8与GF-1卫星监测渤海海冰的方案。

基于Landsat-8影像的西宁市南北山森林生物量估测模型研究

基于Landsat-8遥感影像结合同期的野外调查样地数据,建立青海省西宁市南北山森林生物量估测模型。通过对Landsat-8遥感数据6个原始波段的灰度值和4个常用植被指数(DVI、NDVI、EVI、SAVI),开展主成分分析后得到主分量PCA1、缨帽变换得到BRIGHT、GREEN、WET,分析遥感信息与生物量的相关性,运用逐步回归分析法建立研究区森林生物量回归估测模型:B=119.495+3.704E-24e DVI+0.026OLI-52-3.478OLI-5(R2=0.554,p<0.01),经检验模型的平均相对误差为13.509%,反演得到西宁市单位面积森林生物量为5.227t/hm2,总森林生物量为998 991.768t。结合西宁市遥感影像,绘制了西宁市森林生物量分布图。

基于Landsat-8数据南海近岸悬浮泥沙与叶绿素a浓度定量反演

为了对南海近岸海域海水悬浮泥沙与叶绿素a进行浓度估算,基于Landsat-8数据确定悬浮泥沙与叶绿素a的敏感波段并构建估算模型。结果表明,Landsat-8数据第一、二、四波段对悬浮泥沙浓度较为敏感,第一、二、三波段对叶绿素a浓度较为敏感,而以上单波段仍不能单独用于准确提取该区悬浮泥沙与叶绿素a,故建立多种形式的统计模型。由第一、二、四波段线性组合形成的悬浮泥沙浓度反演模型的相关系数达到0.904,平均相对误差为10.24%,反演精度为89.76%。由第一、二、三波段线性组合形成的叶绿素a浓度反演模型的相关系数达到0.886,平均相对误差为11.27%,反演精度为88.73%。