最佳波段组合的城市土地利用类型提取

针对Landsat 8陆地成像仪(OLI)遥感影像光谱特征利用率不高等问题,为排除波段间冗余信息的干扰,提高土地利用特征提取的精度,该文以2014年唐山市中心城区Landsat 8OLI遥感影像为主要数据源,开展了基于Landsat 8OLI影像的城市土地利用特征提取的最佳波段选择研究。根据最佳波段选取原则统计波段光谱信息,基于最佳指数因子以及不同土地利用类型的光谱特征曲线,确定波段1、5、7为最适合该遥感影像进行土地利用特征提取的最佳波段组合。     

东平湖水体透明度的遥感反演研究

针对传统水体透明度测量方法不仅耗时费力,而且在时间和空间的监测中也会受到很大的限制,只能了解研究区域内点状剖面的透明度状况,但是遥感技术的发展为解决传统采样方法的局限性问题提供了可能。该文在分析内陆湖泊水体光学特征的基础上,利用三期Landsat8 OLI影像数据及准同期的地面实测数据,寻找与东平湖水体透明度敏感的波段,对东平湖丰、平、枯水期水体透明度的进行遥感反演研究。结果表明,OLI影像B3、B4波段的反射率与实测的透明度数据之间具有良好的相关关系,并以此为基础建立了该区域水体透明度遥感反演模型。结果表明:模拟值与实测值的总体相对误差为19.77%,均方根误差为19.35cm,模型精度在可以接受的范围之内;由于受围网养殖、叶绿素a含量等因素的影响,东平湖透明度总体呈现出南低北高的分布趋势。据此绘制了东平湖丰、平、枯水期水体透明度分布图。     

基于高分一号与Landsat-8影像的水体浊度反演比较

为分析高分一号WFV传感器16 m遥感影像在水质反演方面的能力,本文选取南四湖为研究区,以高分一号卫星影像与Landsat-8卫星OLI影像为数据源,结合地面同步实测水体浊度数据,建立反演水体浊度的原始光谱反射率模型、归一化反射率模型和波段比值模型,并对各模型进行精度评价,分别比较两个传感器在浊度反演能力方面的差异。结果表明:利用高分一号WFV 16m遥感影像进行水质反演具有较高的精度,且具备更高的空间分辨率和更短的重访周期,可以替代Landsat-8多光谱数据。     

矿区水体变化遥感监测方法研究

采用遥感技术对矿区生态环境进行动态监测,对于人类健康和资源合理开发利用具有重要意义。本文以临沂苍峄矿区为研究区,采用多时相Landsat TM/ETM+/OLI遥感影像,运用单波段法、谱间关系法、水体指数法等多种方法提取水体信息。在此基础上提出一种新的水体变化信息提取流程,即运用RGB彩色合成法对不同时相影像进行合成,根据不同色调反映像元的不同变化趋势,发现并提取变化信息,实现了矿区水体信息的变化监测。     

Landsat 8 地表温度产品降尺度深度学习方法研究

以光谱指数为趋势面因子的降尺度方法被广泛用于遥感地表温度尺度转换中,但面临构建的光谱指数难以凸显地表温度分布规律、浅层的统计模型难以精准刻画趋势面因子与地表温度之间的复杂关系的不足。为此,本文以Landsat 8 ARD 地表温度产品为降尺度对象,以Landsat 8 OLI原始数据为潜在趋势面因子,构建地表温度降尺度残差网络（LSTDRN）的深度学习模型;探索适用于Landsat 8地表温度产品空间降尺度的趋势面波段或组合,并在不同季节、不同地表类型下与经典传统方法TsHARP进行定量比较。结果表明：LSTDRN方法利用Landsat 8 OLI原始单波段作为趋势面因子就能有较好的降尺度效果,增加潜在趋势面因子的组合数量并不能提高降尺度效果。不同地表覆盖类型实验中,LSTDRN方法降尺度效果整体优于经典传统方法,且以近红外波段、红光波段和归一化植被指数为趋势面因子时,近红外波段降尺度效果定量评价表现最佳;不同地表覆盖类型的LSTDRN降尺度效果排序为：植被>建筑>水体,而经典传统方法则没有表现出明显的差异。不同季节实验中,LSTDRN方法在春夏冬3季的降尺度效果的定量评价表现明显好于经典传统方法,两类方法的秋季降尺度结果相当。因此,提出的LSTDRN对Landsat 8遥感地表温度产品具有较好的降尺度效果,整体优于经典传统方法且稳定性更强。     

基于OLI数据与决策树法的去山体阴影水体信息提取研究

研究山区地表水体信息OLI遥感数据去阴影自动提取方法,设计基于数字高程模型与指数提取的决策树分类方法,提高水体自动识别的精度。该方法选取改进的归一化水体指数、归一化植被指数、比值植被指数、主成分分析前3个分量以及波段之间的组合运算,并结合DEM构建决策树分类规则。综合采用单波段阈值、谱间关系、植被指数和水体指数阈值完成山体水体的去阴影识别研究,与计算机自动识别分类方法比较,其精度明显提高。结果表明,决策树分类方法在精度上明显高于常用的计算机自动分类方法,可以很好地被利用于OLI遥感数据水体信息的海量、大范围提取。     

一种基于Landsat 8的多波段组合水体指数模型

本文通过分析水体在Landsat 8数据中可见光波段和近红外波段的波谱差异,将Landsat 8数据中可见光波段作为一组,近红外波段和中红外波段作为另一组,构建了多波段组合水体指数(MBCWI)模型。基于Landsat 8数据在合肥、安康和康定地区共3景数据5种不同场景进行水体提取试验。结果表明,该模型不仅能够抑制云层、阴影、裸土、亮色地物和建筑物等对水体提取的影响,还能较好地提取出含有大量蓝藻的水体,且阈值稳定,Kappa系数优于0.968 5,总体精度高达99.69%,总体误差小于8.92%。相较于其他水体指数而言,提取精度显著提高。     

Landsat系列卫星对地观测40年回顾及LDCM前瞻

Landsat系列卫星数据凭借其长期连续、全球覆盖、适中的时间空间分辨率和科学的数据存档与分发策略等优势,逐渐成为地表特征和地球系统科学研究中最有效的遥感数据之一,并广泛应用于生态环境、农林地矿、能源资源、教育科研和政府管理等领域。而第8代陆地卫星--陆地卫星数据连续任务卫星(LDCM)于2013年2月发射升空,该卫星携带了运行性陆地成像仪(OLI)和热红外传感器(TIRS)两种传感器。与Landsat 7/ETM+相比,OLI/TIRS在波段设置、辐射分辨性能和扫描方式上都得到很大改进,其中OLI共包括9个波段,新增海岸带(coastal)监测和卷云(cirrus)识别波段,TIRS则设置了两个热红外波段。如果LDCM能够成功升空运行,它将继续承担起长期连续对地观测的使命。     

基于Landsat 8的泰山地区植被指数与地形因子关系分析

主要研究了基于Landsat 8遥感数据的泰山地区植被指数与地形因子之间的关系。基于Landsat 8 OLI遥感数据,综合运用遥感图像处理技术和地理信息系统空间分析技术,提取泰山地区2014年7月的归一化植被指数;再利用数字高程模型(DEM)提取该地区的地形因子(海拔、坡度以及坡向);进而使用叠合分析法,分析了泰山地区植被指数与地形因子之间的关系。研究结果表明:泰山地区总体上植被指数(NDVI)值较好;植被指数随海拔的升高而增加;坡向对NDVI值影响明显,东北坡植被指数最高。数据分析的结果对于泰山地区生态环境的监测保护与可持续发展都有积极意义。     

近30年南昌城区湖泊面积变化图谱和动态监测

针对部分水体提取方法在提取城区水体时易出现漏提、错提和夹杂背景噪音的现象,本文提出一种更适合城市小型湖泊水体提取的方法,采用Landsat卫星影像的绿光波段、近红外波段和中红外波段(或短波红外波段),构建的经验型归一化差异水体指数ENDWI,相比NDWI和MNDWI,ENDWI在提取南昌城区水体时更具优势。本文以ENDWI为最佳水体提取模型,利用1989—2016年间4个不同时期的Landsat TM/OLI影像对南昌城区11个湖泊的面积变化状况进行了监测,从而制作了研究区各湖泊的面积变化图谱。随后引入气候指标和城镇化指标对南昌城区湖泊面积变化的成因进行分析。结果表明:气候对湖泊面积变化的影响有限,多年的城镇化运动,尤其是填湖造房运动的普遍存在才是湖泊面积变化的主要原因。     

Remote estimation of water quality parameters of Himalayan lake (Kashmir) using Landsat 8 OLI imagery

The overarching aim of this study was to derive simple and accurate algorithms for the retrieval of water quality parameters for the Wular Lake using Landsat 8 OLI satellite data. The water quality parameters include pH, COD, DO, alkalinity, hardness, chloride, TDS, total suspended solids (TSS), turbidity, electric conductivity and phosphate. Regression analysis was performed using atmospherically corrected true reflectance values of original OLI bands, images after applying enhancement techniques (NDVI, principal components) and the values of the water quality parameters at different sample locations to obtain the empirical relationship. Most of the parameters were well correlated with single OLI bands with R² greater than 0.5, whereas phosphate showed a good correlation with NDVI image. The parameters like pH and DO showed a good relation with the principal component I and IV, respectively. The high concentration of pH, COD, turbidity and TSS and low concentration of DO infers the anthropogenic impact on lake.     

北京地区Landsat 8 OLI高空间分辨率气溶胶光学厚度反演

卫星气溶胶光学厚度(AOD)反演中,传统暗目标方法在反射率较低的水体、浓密植被覆盖区域取得了较好效果,在反射率较高且结构复杂的高反射地表上空目前多采用深蓝算法,但存在空间分辨率较低,对细节分布描述性较差等问题。为解决这一问题,本文首先以5年(2008年—2012年)长时间序列MODIS地表反射率产品为基础,采用最小值合成法建立500 m分辨率逐月地表反射率产品数据集,然后利用地物波谱库中典型地物波谱数据,分析建立MODIS与Landsat 8 OLI传感器蓝光波段反射率转换模型,最后北京地区AERONET地基观测数据确定了气溶胶光学物理参数,并反演获取了北京地区上空500 m分辨率的AOD分布。为验证反演算法的精度,分别将反演结果同AERONET及MODIS/Terra气溶胶产品(MOD04)进行交叉对比,同时利用相关系数R,均方根误差RMSE,平均绝对误差MAE以及MODIS AOD产品预期误差EE共4个指标进行衡量。结果表明:算法反演获取的AOD与AERONET观测值具有较高的一致性,各指标分别为R=0.963,RMSE=0.156,MAE=0.097,EE=85.3%,稍优于MOD04产品(R=0.962,RMSE=0.158,MAE=0.101,EE=75.8%),并且有效的对比点数也高于MOD04。通过与地基观测相比,卫星遥感获取的高分辨率城市地区AOD精度可作为定量评估城市空气质量的有效依据。     

Evaluating metrics derived from Landsat 8 OLI imagery to map crop cover

Developing techniques are required to generate agricultural land cover maps to monitor agricultural fields. Landsat 8 Operational Land Imager (OLI) offers reflectance data over the visible to shortwave-infrared range. OLI offers several advantages, such as adequate spatial and spectral resolution, and 16 day repeat coverage, furthermore, spectral indices derived from Landsat 8 OLI possess great potential for evaluating the status of vegetation. Additionally, classification algorithms are essential for generating accurate maps. Recently, multi-Grained Cascade Forest, which is also called deep forest, was proposed, and it was shown to give highly competitive performance for classification. However, the ability of this algorithm to generate crop maps with satellite data had not yet been evaluated. In this study, the reflectance at 7 bands and 57 spectral indices calculated from Landsat 8 OLI data were evaluated for its potential for crop type identification.     

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.     

Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images

Accurate and up-to-date information on the spatial distribution of paddy rice fields is necessary for the studies of trace gas emissions, water source management, and food security. The phenology-based paddy rice mapping algorithm, which identifies the unique flooding stage of paddy rice, has been widely used. However, identification and mapping of paddy rice in rice-wetland coexistent areas is still a challenging task. In this study, we found that the flooding/transplanting periods of paddy rice and natural wetlands were different. The natural wetlands flood earlier and have a shorter duration than paddy rice in the Panjin Plain, a temperate region in China. We used this asynchronous flooding stage to extract the paddy rice planting area from the rice-wetland coexistent area. MODIS Land Surface Temperature (LST) data was used to derive the temperature-defined plant growing season. Landsat 8 OLI imagery was used to detect the flooding signal and then paddy rice was extracted using the difference in flooding stages between paddy rice and natural wetlands. The resultant paddy rice map was evaluated with in-situ ground-truth data and Google Earth images. The estimated overall accuracy and Kappa coefficient were 95% and 0.90, respectively. The spatial pattern of OLI-derived paddy rice map agrees well with the paddy rice layer from the National Land Cover Dataset from 2010 (NLCD-2010). The differences between Rice_Landsat and Rice_NLCD are in the range of ±20% for most 1-km grid cell. The results of this study demonstrate the potential of the phenology-based paddy rice mapping algorithm, via integrating MODIS and Landsat 8 OLI images, to map paddy rice fields in complex landscapes of paddy rice and natural wetland in the temperate region.     

Land cover classification using Landsat 8 Operational Land Imager data in Beijing,China

The successful launch of Landsat 8 provides a new data source for monitoring land cover, which has the potential to significantly improve the characterization of the earth’s surface. To assess data performance, Landsat 8 Operational Land Imager (OLI) data were first compared with Landsat 7 ETM + data using texture features as the indicators. Furthermore, the OLI data were investigated for land cover classification using the maximum likelihood and support vector machine classifiers in Beijing. The results indicated that (1) the OLI data quality was slightly better than the ETM + data quality in the visible bands, especially the near-infrared band of OLI the data, which had a clear improvement; clear improvement was not founded in the shortwave-infrared bands. Moreover, (2) OLI data had a satisfactory performance in terms of land cover classification. In summary, OLI data were a reliable data source for monitoring land cover and provided the continuity in the Landsat earth observation.     

Developing detailed age-specific thematic maps for coffee (Coffea arabica L.) in heterogeneous agricultural landscapes using random forests applied on Landsat 8 multispectral sensor

Coffee is a commodity of international trade significance, and its value chain can benefit from age-specific thematic maps. This study aimed to assess the potential of Landsat 8 OLI to develop these maps. Using field-collected samples with the random forest classifier, splitting coffee into three age classes (Scheme A) was compared with running the classification with one compound coffee class (Scheme B). Higher overall classification accuracy was obtained in Scheme B (90.3% for OLI and 86.8% for ETM+) than in Scheme A (86.2% for OLI and 81.0% for ETM+). The NIR band of OLI was the most important band in intra-class discrimination of coffee. Landsat 8 OLI mapped area closely matched farm records (R^2?=?0.88) compared to that of Landsat 7 ETM+ (R^2?=?0.78). It was concluded that Landsat 8 OLI data can be used to produce age-specific thematic maps in coffee production areas although disaggregating coffee classes reduces overall accuracy.     

An experimental comparison between KELM and CART for crop classification using Landsat-8 OLI data

The operational land imager (OLI) is the latest instrument in the Landsat series of satellite imagery, which officially began normal operations on 30 May 2013. The OLI includes two bands that are not on the thematic mapper series of sensors aboard Landsat-5 and 7; a cirrus band and a coastal/aerosol band. This paper compares the classification and regression tree and the kernel-based extreme learning machine (KELM) for mapping crops in Hokkaido, Japan, using OLI data, except the cirrus band and the pan band. The OLI data acquired on 8 July 2013 was used for crop classification of beans, beets, grassland, maize, potatoes and winter wheat. The KELM algorithm performed better in this study and achieved overall accuracies of 90.1%. According to the Jeffries–Matusita (J–M) distances, the short wavelength infrared band provides the greater contribution (the highest value was observed for band 6 in OLI data).     

Sentinel-2A与Landsat 8O LI逐像元辐射归一化方法研究

考虑不同传感器光谱响应函数差异及不同地物类型反射率光谱的差异，提出了一种逐像元辐射归一化方法，并以2017年7月17日内蒙古达里诺尔湖地区准同步过境的Sentinel-2A及Landsat 8数据为例，对两类数据可见-近红外波段（VNIR）地表反射率结果进行归一化。首先采用Sen2cor方法及NASA官方提供大气校正算法，分别对Sentinel-2A及Landsat 8 OLI影像进行大气校正并重采样到同一空间分辨率；然后基于光谱库计算匹配因子并构建图像与光谱库之间的匹配转换模型，实现像元尺度上从Sentinel-2影像到Landsat 8影像地表反射率相似波段之间的转换。结果表明，经逐像元归一化的影像相比原始影像及经HLS光谱归一化的影像，与Landsat 8 VNIR波段的相关性明显提高，辐射一致性增强。该转换模型为多源中高分辨率遥感图像高精度辐射归一化提供了新思路。     

Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

For three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index (LAI), and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMC_FW), and fuel moisture content per dry weight (FMC_DW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e., Landsat 8), including narrower near-infrared (NIR) wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops.