基于光谱归一化的马尾松LAI遥感估算研究

通过对比不同传感器间光谱响应函数的差异,研究基于光谱响应函数的不同传感器相似波段的归一化方法,探讨归一化后植被指数在马尾松叶面积指数（LAI）估算中的应用。以某一传感器为基准,根据波段总辐射率比值关系将其他卫星传感器归一化为基准传感器,然后计算其植被指数,建立LAI反演模型。为验证方法可行性,选取永安地区2008年3月获取的BJ-1CCD、IRS-P6LISS3和MODIS数据作为研究对象,根据三者的光谱响应函数差异,将BJ-1CCD和IRS-P6的LISS3的红光和近红外波段归一化为MODIS的相应波段,并分别计算归一化前后的NDVI值。结果表明归一化后不同传感器的植被指数关系与理想的关系y=x更加接近。利用归一化后的IRS-P6影像的NDVI反演马尾松LAI,并将其应用于MODIS和BJ-1传感器,得到归一化后不同传感器的植被指数值基本相等,表明归一化以后的植被指数应用于LAI的估算具有一定的普适性,能适用于多种传感器。     

基于色匹配函数的卫星影像真彩色合成研究北大核心CSCD

色匹配函数通过构建地物反射光的光谱分布与颜色三刺激值的关系可将任意波段的信息映射到三原色基准值上,经过色彩空间的转换后重建多光谱遥感影像。针对大多数多光谱传感器在可见光内波段数量有限、波段通道较窄且波段间隔不均匀,直接在相邻波段间插值会使色匹配积分过程出现较大误差的问题,结合遥感数据的模拟和反向传播(Back Propagation,BP)神经网络,以可见光范围内通道设置丰富的近岸高光谱水色成像仪(Hyperspectral Imager for the Coastal Ocean,HICO)经色匹配函数和色彩空间转换后得到的R、G、B三刺激值作为网络输出值,其波段重建后得到的目标传感器的模拟波段作为输入值,训练得到适用于Landsat-8 OLI、Terra MODIS、Himawari-8 AHI传感器的真彩色影像合成模型。计算均值、标准差、平均梯度和信息熵这4类客观评价参数,并结合对真彩色影像和直方图的主观分析,结果表明该方法能丰富和扩展有限的波段信息,提高影像的清晰度、色彩饱和度以及包含的信息量,校正了三波段合成影像存在的色偏,解决了在原始数据波段数受限的情况下通过简单插补波段带入色匹配函数进行积分计算产成误差的问题。     

遥感图像在土地覆盖和温度分布之间对应关系研究中的应用

利用遥感手段探讨土地覆盖类型和温度之间的对应关系有新意。以日本东京地区1998年10月14日接收的TM遥感图像为主要数据,在地面实际调查的基础上,利用图像处理方法,进行了土地覆盖类型和温度分布之间关系的探讨。以该区几种代表性的土地覆盖类型作为训练区,利用TM图像的第1,2,3,4,5五个波段数据进行分类处理,获得了很好的分类结果,TM图像第6波段(10.40～12.5μm)是探测地面温度分布最佳波段,利用第6波段数据通过一系列的转换,获得了该区温度分布。温度分布和土地覆盖类型之间具有很好的对应性,如:白天的城区温度与郊区相比普遍偏高,但城区中植被覆盖率高的居民区和公园,温度偏低;城区的商业区(新宿、银座等)、机场和大型工厂等温度偏高。     

基于波段反射率模拟的遥感影像重构研究

为了增加TM遥感数据单波段信息量和视觉感知力,实现定量模拟TM遥感影像各波段反射率,提出一种TM遥感影像图像处理新方法,即在不破坏原有波段光谱特征的基础上重构TM波段反射率模拟图像。选取图像质量评价指数进行客观定量评价分析,实验结果表明:单波段信息熵值平均增加1.35,获得的均方误差系数和峰值信噪比值有明显变化,均方误差值平均减少11.2,重构的模拟单反射率TM影像灰度级离散程度集中,信息量丰富,图像增强变化和光谱信息优化程度都有显著提高。     

基于GA-BP算法的多分辨率遥感影像融合技术

由于Landsat-5唯一的热红外波段遥感影像TM6的空间分辨率不高,使得其应用与研究程度远不及其它波段广泛。为此,运用GA-BP算法来提高TM6遥感影像的空间分辨率,并进行仿真实验,结果表明:①GA-BP算法有效地避免了BP算法陷入局部最小点、收敛速度慢的问题,是一种快速、可靠的方法。它的快速算法对数据量巨大的遥感图像更具实用价值。②从提高TM6遥感影像空间分辨率的仿真结果来看,无论计算效率还是遥感影像的融合效果,GA-BP算法都优于BP算法。③GA-BP算法既保留了TM6遥感影像的基本灰度分布信息,同时也提高了其空间分辨率,可以有效地运用到提高遥感影像空间分辨率的过程中。     

BP神经网络在重建多光谱遥感影像波段中的应用

多光谱遥感影像波段之间存在着一定的相关性,发现并找出各波段之间的相关关系,并利用这种关系还原多光谱遥感影像损失的任意部分波段信息,对于深层次的影像信息提取具有重要作用.论文以Landsat TM遥感数据为例,随机选取多光谱遥感影像中六个波段任意同一位置部分影像作为神经网络的训练数据,剩余波段对应位置的数据作为神经网络的标签数据,通过BP神经网络去训练进行重建损失部分的波段研究.结果表明:1)对于重建任意影像波段的损失部分均取得相当好的效果;2)增加训练的数据量,同时适当地加深BP神经网络的深度层数,网络结构性能会变得更好,能提升重建图像质量;3)通过BP神经网络训练出的模型具有很好的稳定性,其原多光谱遥感影像波段和经BP神经网络训练出的模型所重建的波段之间的相关系数总体约可达0.99,PSNR值总体约为37.44,SSIM值总体约为0.97,MSSIM值总体约为0.97.研究表明,该BP神经网络结构及其模型在重建多光谱遥感影像波段方面具有一定的应用价值.     

沙质土壤含水率高光谱预测模型建立及分析

利用HR768型光谱仪,实地测定了古尔班通古特沙漠南缘60个样点的土壤光谱和土壤含水率。对测定的光谱数据选择土壤水分较敏感的红外波段与土壤含水率进行线性回归,结果表明：实测土壤光谱经对数变换后土壤光谱与其含水率拟合效果不理想,用去包络线且一阶微分方法对实测土壤光谱数据进行处理后,再与相应土壤含水率进行回归,其回归效果较好,决定系数R²达0.855该方法具有实用性强、易操作的特点,为沙漠区土壤含水率的反演提供新的方法和思路。     

应用Landsat TM影像估算渤海叶绿素a和总悬浮物浓度

利用23个实测样点的渤海叶绿素a和总悬浮物浓度数据及同步Landsat TM影像数据,分别分析了Landsat TM离水辐射亮度对渤海叶绿素a和总悬浮物浓度的敏感性,选择合适的波段,通过回归分析构建了基于Landsat TM离水辐射亮度的渤海叶绿素a和总悬浮物浓度反演模型。结果表明,TM1、TM2和TM3波段对叶绿素a的敏感性较高,以TM4/TM1和TM3/TM2的对数为自变量,以叶绿素a浓度的对数为因变量的线性估算模型可以有效反演渤海叶绿素a浓度,决定系数R2达到0.97;TM3波段对悬浮物的敏感性最高,以TM2、TM3和TM3/TM2为自变量,以总悬浮物浓度的以10为底的对数为因变量的多元线性模型获得的结果最佳,决定系数R2达到0.91。     

容差粗糙集卫星遥感数据分类

结合粗糙集理论和遥感数据中地物光谱特征空间分布信息,提出了一种基于光谱特征邻域的容差粗糙集分类方法,用来处理卫星遥感数据分类中的不确定性问题。利用北京地区Landsat-5 TM数据进行分类试验,对算法分类过程进行讨论及其分类结果进行验证分析;结果表明：文中方法在可理解性和稳定性上体现出比较好的性质,能够有效处理卫星遥感数据分类中存在的不确定性因素,在具有复杂光谱特征地物分类方面具有发展潜力。     

玉米叶面积指数与高光谱植被指数关系研究

探讨以不同的植被指数建立的高光谱模型对玉米叶面积指数LAI的反演精度。实测不同水肥耦合作用下，玉米冠层的高光谱反射率与叶面积指数（Leaf Area Index）数据，采用高光谱红光波段（631~760 nm）与近红外波段（760~1 074 nm）逐波段构建NDVI、RVI、DVI、TSAVI、PVI植被指数，分别找出与LAI具有最佳相关性波段组合的植被指数，建立玉米LAI估算模型。结果显示，与LAI具有佳相关性的波段组合分别是NDVI（R760，R990）、RVI（R760，R1001）、DVI（R677，R1070）、TSAVI（R 760，R 975）、PVI（R658，R966），它们反演玉米LAI的确定性系数分别：R²>0.72、R²>0.74、R²＝0.95、R²>0.79、R²>0.95。结果表明，在玉米的整个生长季的47个样本中，通过PVI和DVI方式建立的遥感估算模型能够较为准确地估算玉米LAI，TSAVI次之，NDVI、RVI稍差。     

Water body delineation using index composition and HIS transformation

The use of satellite data for mapping water bodies is important for environmental management. Previous approaches exhibit limited applicability in southeastern China, given its complex and heterogeneous landscapes as well as the difficulty to obtain cloud-free images. To overcome these problems, we proposed an approach using index composition and HIS (hue, intensity and saturation) transformation. First, a colour image was generated using three indices: the Normalized Difference Built-up Index (NDBI), the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI). Then, HIS transformation was employed to extract water bodies and remove hill shadows. Another colour image, composed by Landsat TM (Thematic Mapper) 4, NDVI, TM1 and HIS transformation, was further applied to separate water bodies from residual shadows. This approach was tested and verified with Landsat TM images in the Tiaoxi watershed, southeastern China. The results indicated the high accuracy and promising applicability of this new approach for fragmented landscapes, given its insensitivity to seasonal and subjective factors.     

Urban areas extraction from multi sensor data based on machine learning and data fusion

Accurate urban areas information is important for a variety of applications, especially city planning and natural disaster prediction and management. In recent years, extraction of urban structures from remotely sensed images has been extensively explored. The key advantages of this imaging modality are reduction of surveying expense and time. It also elevates restrictions on ground surveys. Thus far, much research typically extracts these structures from very high resolution satellite imagery, which are unfortunately of relatively poor spectral resolution, resulting in good precision yet moderate accuracy. Therefore, this paper investigates extraction of buildings from middle and high resolution satellite images by using spectral indices (Normalized Difference Building Index: NDBI, Normalized Difference Vegetation Index: NDVI, Soil Adjustment Vegetation Index: SAVI, Modified Normalized Difference Index: MNDWI, and Global Environment Monitoring Index: GEMI) by means of various Machine Learning methods (Artificial Neural Network: ANN, K-Nearest Neighbor: KNN, and Support Vector Machine: SVM) and Data Fusion (i.e., Majority Voting). Herein empirical results suggested that suitable learning methods for urban areas extraction are in preferring order Data Fusion, SVM, KNN, and ANN. Their accuracies were 85.46, 84.86, 84.66, and 84.91%, respectively.     

Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes

Global warming has obtained more and more attention because the global mean surface temperature has increased since the late 19th century. As more than 50% of the human population lives in cities, urbanization has become an important contributor for global warming. Pearl River Delta (PRD) in Guangdong Province, southern China, is one of the regions experiencing rapid urbanization that has resulted in remarkable Urban Heat Island (UHI) effect, which will be sure to influence the regional climate, environment, and socio-economic development. In this study, Landsat TM and ETM+ images from 1990 to 2000 in the PRD were selected to retrieve the brightness temperatures and land use/cover types. A new index, Normalized Difference Bareness Index (NDBaI), was proposed to extract bare land from the satellite images. Additionally, Shenzhen, which has experienced the fastest urbanization in Guangdong Province, was taken as an example to analyze the temperature distribution and changes within a large city as its size expanded in the past decade. Results show that the UHI effect has become more prominent in areas of rapid urbanization in the PRD region. The spatial distribution of heat islands has been changed from a mixed pattern, where bare land, semi-bare land and land under development were warmer than other surface types, to extensive UHI. Our analysis showed that higher temperature in the UHI was located with a scattered pattern, which was related to certain land-cover types. In order to analyze the relationship between UHI and land-cover changes, this study attempted to employ a quantitative approach in exploring the relationship between temperature and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Bareness Index (NDBaI) and Normalized Difference Build-up Index (NDBI). It was found that correlations between NDVI, NDWI, NDBaI and temperature are negative when NDVI is limited in range, but positive correlation is shown between NDBI and temperature.     

HJ-1A/B CCD与Landsat TM/ETM+植被指数的交互比较

利用5对同日过境的HJ-1A/B CCD和Landsat TM/ETM+影像对,研究了二者植被指数(NDVI,SAVI,EVI)之间的定量关系。选用其中的3对影像对作为实验影像,通过对均匀同质实验区对应的植被指数进行回归分析求出二者之间的转换方程,用未参与实验的2对影像对来验证所求转换方程的有效性,并对二者植被指数之间的差异进行了分析。结果表明：两种传感器对应的植被指数之间存在极显著的线性正相关关系,所求的转换方程具有较高的精度,可以利用转换方程将两种传感器的植被指数进行互为转换,有利于二者植被监测结果的互为补充,而两种传感器在光谱响应函数上的不同造成了二者植被指数间存在差异。     

基于决策树分层分类法的粉煤灰堆场信息提取研究

粉煤灰污染环境,危害人类健康。应用遥感方法快速、实时、准确地识别粉煤灰堆场信息,对保护环境和人类健康具有重要意义。通过分析包头市辖区内典型地物的光谱信息,基于Landsat 5 TM影像数据,采用决策树分层分类法对研究区内的粉煤灰堆场进行提取实验。首先,分析研究区内典型地物的光谱特征,对不同地物之间的关系进行比较。其次,建立决策树,利用土壤调节植被指数(SAVI)、改进归一化差异水体指数(MNDWI)、归一化建筑指数(NDBI)以及光谱阈值法对图像进行了分类。最后利用形状特征和空间位置特征等对分类图像进行后处理,分类精度达到70.7%。实验结果表明:该方法适合粉煤灰堆场信息的自动提取,结合目视解译能够达到较高的识别精度。     

An adequate band positioning to enhance NDVI contrasts among green vegetation,senescent biomass,and tropical soils

Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) imagery was used in a quantitative evaluation of the impact of band positioning and bandwidth on values of Normalized Difference Vegetation Index (NDVI) for green vegetation (GV), senescent biomass (SB), and soils (S). The results show that the band positioning needed to maximize the NDVI contrast between GV and SB or S should include a narrow or broad red band centred around the chlorophyll absorption band, between 660 and 680 nm, and a narrow near-infrared (NIR) band placed at the shortest wavelength in the 750-1100 nm range. NDVI differences between extreme NIR band positioning can reach values of 0.35 in areas of accentuated phenological contrast. Of the broad-band sensors analysed in this study, Landsat-5 Thematic Mapper (TM) and NOAA-11 Advanced Very High Resolution Radiometer (AVHRR) presented, respectively, the most favourable and unfavourable positioning.     

Monitoring the effects of Dracunculiasis remediation on agricultural productivity using satellite data

Dracunculiasis has been shown to be a major cause of agricultural work loss in many parts of West Africa. Monitoring the magnitude of agricultural loss caused by this parasite has been a problem due to the non-market nature of the agricultural output, which is often used for subsistence or barter. To test the hypothesis that ‘ Optimization of water and sanitation interventions in areas with high Dracunculiasis prevalence have a measurable socio-economic impact’ a temporal analysis of Landsat Thematic Mapper (TM) satellite data was conducted. A region in which intervention had not taken place (the control) and a region in which it had were compared to determine the change in agricultural activity. Paired comparisons were made of the difference between two Landsat TM data sets, a 31 January 1986 scene ( pre-intervention) and a 5 January 1991 scene (post-intervention) using the digital number ( DN) values in bands 5, and 7, and the Normalized Difference Vegetation Index (NDVI) calculated from bands 3 and 4. The results of the paired comparison indicated that agricultural activity increased in regions where intervention had taken place. The analysis also indicated that the Harmattan winds may have effected the utility of the NDVI because of its differential effect on atmospheric scattering in the visible and infrared parts of the spectrum.     

基于客观阈值与随机森林Gini指标的水体遥感指数对比

利用福建福州、西藏尼玛和澳大利亚弗伦奇3地代表不同水体类型的Sentinel-2A MSI和Landsat-8 OLI数据,采用客观阈值法（0阈值）和随机森林重要性评估法,比较和分析了改进型归一化差值水体指数（Modified Normalized Difference Water Index, MNDWI）、自动水体提取指数（Automated Water Extraction Index, AWEI）和水体指数2015 （Water Index 2015, WI2015） 这3种世界常用的水体指数之间的差异。从水体增强的效果来看,MNDWI增强的水体不仅具有丰富的信息还具有鲜明的对比度,AWEI和WI2015增强的水体信息的对比度相对偏弱。精度验证表明：3种指数提取的水体精度都较高,但MNDWI在3个地区的平均总精度略高于WI2015和AWEI,3者的平均总精度分别为91.83 %、91.16 %和90.07 %。在提取细小水体方面,MNDWI的能力强于其他2种指数,在阴影较为明显的高原山地区域,MNDWI提取水体的效果优于AWEI和WI2015。进一步采用随机森林的Gini指标进行的重要性评估表明,MNDWI在区分水体和非水体的分类中表现出了很强的重要性,尤其在Sentinel-2A MSI数据中表现得更为突出,而WI2015和AWEI的重要性则相对较弱。     

近岸水域悬浮物的遥感分析

利用多时相的卫星遥感影像Landsat TM／ETM＋为数据源，对厦门近岸水域悬浮物浓度分布及其变化趋势进行了研究分析。研究发现修正后的归一化水体提取指数（MNDWI）能很好地剔除非水体信息，研究应用该指数法并结合矢量层数据对研究区进行了准确提取。由于红光波段与悬浮物浓度呈现较好的线性相关性，研究将利用该波段数据来反映悬浮物浓度状况。利用建立的浓度等级划分标准对不同时相影像进行浓度等级划分，并采用分级后比较和影像差值法等变化检测技术清晰地反映了不同年份间悬浮物浓度的时空变化。研究结果显示，厦门近岸水域悬浮物污染状况总体较好，但是研究年份间悬浮物浓度均值仍呈升高趋势，升高的区域主要位于九龙江入海口和同安湾。     

Estimating local sugarcane evapotranspiration using Landsat TM image and a VITT concept

This Letter presents an approach for local evapotranspiration (ET) estimation using Landsat Thematic Mapper (TM) data for sugarcane fields based on the concept of a vegetation Index/Temperature Trapezoid (VITT). A moisture availability index ( Ma ) and, subsequently, the ET rate were computed using surface temperature ( Ts ) and Normalized Difference Vegetation Index (NDVI) derived from the TM data. The remote sensing estimates were compared with results from a water balance model. This study shows that Landsat TM data, along with the VITT concept, can provide a practical means for the ET estimation of the sugarcane field at a local scale.