一种基于指数的新型遥感建筑用地指数及其生态环境意义

城市建筑用地是一种复杂的土地利用类型,在电磁波反射光谱上表现出明显的异质性。因此,很难用简单的方法将其从遥感影像中准确地提取出来。在详细研究了城市建筑用地的光谱特征以后,创建了一种不直接采用影像的原始波段,而是采用由它们衍生的3个指数波段来构成新型建筑用地指数(IBI)。通过对ASTER和Landsat ETM+两种多光谱影像进行的实验表明,新指数除了能够有效地增强影像中的建筑用地信息外,还能和植被指数、水体指数一样,用于进行数值运算,从而实现了建筑用地对城市生态环境影响的定量研究。对厦门、福州两城市的实例分析表明,新的建筑指数与地表温度呈正相关关系,而与植被指数、水体指数呈负相关关系。研究进一步发现,建筑指数与地表温度的关系不是简单的线性关系,而是一种指数函数关系,说明高建筑用地比例地区的升温效应要明显高于低建筑用地比例地区,因此,对城市热岛的形成起着更大的作用。     

县域城镇用地信息提取研究——以福建省长汀县为例

针对如何快速、准确地获取县域城镇用地的信息问题,该文以2010年的TM影像为数据源,以典型的山地城市福建省长汀县为研究区,比较和分析常用的城镇用地提取方法的优势和不足;借鉴分层分类法的思路,综合运用归一化植被指数结合地形调节植被指数、多波段谱间关系法结合改进型归一化水体指数、归一化裸土指数结合归一化不透水面指数,对长汀城镇用地进行提取,总体精度超过85%。分析表明该方法是一种可行有效的城镇用地提取方法。     

TM 图像的城镇用地信息提取方法研究

从对城镇用地的遥感信息机理分析入手, 分析了城镇用地在Landsat TM 2、TM 3、TM 4、TM 5等各个波段上与其它地类的可分性。发现利用归一化植被指数NDVI 和归一化水体指数NDWI,设定合适的阈值, 能够有效地提取植被信息和水体信息, 得到二值化图像。由谱间结构特征可知, 城镇用地和裸地的TM 3- TM 2> 0, 城镇用地的TM 4- TM 3< 0, 而裸地的TM 4- TM 3> 0, 利用此特性可以区分城镇用地和裸地。对上述二值图像进行二值逻辑运算, 得到城镇用地专题信息。研究结果表明, 该方法的提取效率和精度都较高, 与监督分类法和屏幕数字化方法相比, 是一种行之有效的方法。     

基于2013-2016年TM影像鄱阳湖面积动态监测

基于2016年landsat-8遥感影像,采用ENVI软件分别利用归一化水体指数(NDWI)、改进的归一化差异水体指数(MNDWI)、多波段谱间关系、自动水提取指数法(AWEI)对鄱阳湖水域面积进行提取、分析对比,结果表明自动水体指数提取水体的适用性最好。利用自动水体指数法提取得到鄱阳湖区域2013年到2016年的水域面积,分析得到鄱阳湖水域面积时空变化规律,通过GRACE数据计算得到的等效水高值对变化规律进行验证,两者相关性达0.74。通过GRACE重力数据反演有效的弥补实测数据缺失的不足。     

环境减灾小卫星影像水体和湿地自动提取方法研究

本文在研究HJ星多光谱CCD相机数据地物光谱特征基础上,提出了一种基于蓝光波段的改进型归一化差异水体指数（NDWI-B）,并分别利用HJ星数据和ETM＋数据,比较分析了NDWI-B和归一化差异水体指数NDWI提取水体的效果。结果证明,应用NDWI-B除可准确提取大范围水体外,还能够提取小范围水体和湿地,为基于环境减灾小卫星数据的洪水淹没等水体分布信息快速自动提取提供了一种实用化方法。     

基于3维上下文预测的高光谱图像无损压缩

如今高光谱数据的有效压缩已成为遥感技术发展中需要迫切解决的问题,为了对高光谱数据进行有效压缩,提出了一种基于3维上下文预测的高光谱图像无损压缩算法。该算法首先根据相邻波段间的相关性大小进行波段分组,同时对各个分组重新进行波段排序;然后采用自适应波段选择算法对高光谱图像进行降维,再利用k-means算法对降维后的波段谱向矢量进行聚类;最后在参考波段和当前波段中通过定义3维上下文预测结构,在聚类结果的基础上,对各个分类分别训练其最优的预测系数。实验结果表明,该方法可显著降低压缩后图像编码的平均比特率。     

适用于多目标遥感自动解译的最佳专题指数筛选

专题指数对遥感影像自动解译至关重要,现有研究多针对单目标信息提取来筛选专题指数,无法得到适用于多目标遥感自动解译的最佳专题指数。以德州市城区及周边地区为例,采用Landsat 5TM影像提取了2个植被、3个水体和3个建筑用地专题指数,基于面向对象分类方法,分析了单个专题指数、指数组合、指数数量对同时提取植被、水体和不透水层信息的精度影响。结果表明:(1)3类地物的最小分类精度基本上随着专题指数增加而增大;(2)从单个专题指数来看,不透水层和植被提取的最佳指数分别是建筑物指数和土壤调整植被指数,而新型水体指数则能显著提高总体分类精度;(3)从专题指数的组合来看,植被分类精度随所用的植被指数数量增加而下降;建筑用地指数越多,不透水层和总体分类效果越好;随着水体指数数量增加,水体分类精度有所提高,而不透水层和总体分类精度则随之下降。     

马尾藻遥感信息提取

以Landsat数据为基础,分析马尾藻的图像和波谱特征,对比分析单波段提取法、双波段比值法、双波段差值法和归一化植被指数法对马尾藻信息的提取结果,并利用IKONOS数据来验证4种方法的提取精度.结果表明:马尾藻在Landsat真彩色(TM3、TM2、TM1)和假彩色(TM4、TM3、TM1)合成图上均呈黄色,其生长边界在假彩色合成图上更为清晰.马尾藻水体与非藻类水体在TM4的差异最大,在TM3也存在细小差异,单波段提取法(TM4)、双波段比值法(TM4/TM3)、双波段差值法(TM4-TM3)和归一化植被指数法((TM4-TM3)/(TM4+TM3))都可以从自然水体中提取出马尾藻信息,与IKONOS的提取结果相比,归一化植被指数法的提取精度最高.     

不同地表参数变化的上海市热岛效应时空分析

研究地表参数变化与热岛效应的关系对优化城市功能分区以及城市可持续发展具有重要意义。采用上海市2000、2005、2009年3个时期的Landsat ETM+卫星遥感影像,使用归一化不透水面指数(NDISI)、基于指数的植被指数(IVI)、归一化差异水体指数(MNDWI)分别从遥感影像中提取不透水面、植被和水体;然后从时间、空间角度并采用回归分析方法分析了上海市地表参数在这9 a中发生的变化及其对城市热环境造成的影响。结果表明:9 a中城市不透水面面积大幅增加,不透水面增加的代价是植被和水体大范围减少,形成了城市的热岛。上海市整体热岛强度是先增强后缓慢减弱的趋势,且热岛分布从集中型向分散型发展。     

全色影像与珞珈一号在建设用地提取中的应用

鉴于基于建筑专题指数提取城镇建筑物用地方法已经无法适应月新日异的土地利用变化的问题,提出一种将Landsat-8 OLS遥感影像和珞珈一号夜间灯光数据构建UBLI进行城镇建筑用地提取的方法。将通过UBLI提取出的城镇建筑用地与通过NDBI、IBI和夜间灯光数据提取出的城镇建筑用地进行对比分析。以南昌市为例,进行城镇建筑用地的提取。采用南昌市2018年的土地利用调查数据作为对照,并结合天地图,在NDBI、夜间灯光数据、IBI、UBLI提取结果中选取建筑物样本和非建筑样本构建混淆矩阵进行精度检验。分析结果表明,UBLI所提取的城镇建筑用地的总体精度明显优于其他方法,UBLI可有效剔除NDBI和IBI难以区分的裸土和低植被覆盖区域以及夜间灯光数据上城镇建筑周边的水体。与传统的建筑专题指数相比,该方法具有更高的精度及更优异的细节特征提取能力。     

基于多时相协同变化检测的耕地撂荒遥感监测

针对地表覆被复杂、地块破碎等原因导致的撂荒地提取精度较低问题,提出一种基于多时相协同变化检测的耕地撂荒信息提取方法。以河北省石家庄市鹿泉区为研究区,采用Sentinel?2A和Landsat 7多光谱影像,在野外样本的支持下,分析耕地各种覆盖类型的归一化植被指数（Normalized Difference Vegetation Index,NDVI）季相变化规律,以季节性撂荒、常年性撂荒、冬小麦、多年生园地为分类体系,构建多时相协同变化检测模型,开展研究区耕地撂荒状态遥感监测。研究结果表明：基于Sentinel?2A影像的季节性撂荒和常年撂荒耕地的分类精度分别为95.83%和96.55%;基于Landsat 7影像的季节性撂荒和常年撂荒耕地的分类精度分别为91.67%和93.10%;2019年鹿泉区季节性撂荒占耕地面积的4.7%,常年撂荒耕地占7.1%。利用该方法能够快速、准确地获取研究区耕地空间分布、面积等信息,对于不同分辨率的影像均具有较好的撂荒地提取精度。     

基于光谱归一化的变组分光谱混合分析(NMESMA)方法及其应用

混合像元问题在低、中分辨率遥感图像中尤为突出,混合像元的存在不仅会影响地物识别和图像分类精度,也是遥感科学向定量化发展的主要障碍之一。因此,遥感图像混合像元分解及其地表覆盖信息的定量提取是近年来研究的热点。针对城市土地覆盖信息的定量提取问题,利用中等分辨率遥感图像(Landsat TM),集成光谱归一化与变组分光谱混合分析(NMESMA)的方法,基于植被-非渗透表面-土壤(V\|I\|S)模型,定量提取研究区植被、土壤和非渗透表面3类土地覆盖的定量信息,并与固定组分的光谱混合分析(LSMA)分解结果进行对比分析。结果表明:基于光谱归一化的变组分光谱混合分析(NMESMA)方法获得的精度高于传统固定组分的光谱混合分析(LSMA)结果,可有效解决光谱异质性较高的城市区域的混合像元问题,为有效提取城市地表覆盖信息,研究城市生态环境变化和模拟分析,提供了有效的信息提取方法。     

A comparison of local variance,fractal dimension,and Moran's I as aids to multispectral image classification

The accuracy of traditional multispectral maximum‐likelihood image classification is limited by the multi‐modal statistical distributions of digital numbers from the complex, heterogenous mixture of land cover types in urban areas. This work examines the utility of local variance, fractal dimension and Moran's I index of spatial autocorrelation in segmenting multispectral satellite imagery with the goal of improving urban land cover classification accuracy. Tools available in the ERDAS Imagine^TM software package and the Image Characterization and Modeling System (ICAMS) were used to analyse Landsat ETM?+ imagery of Atlanta, Georgia. Images were created from the ETM?+ panchromatic band using the three texture indices. These texture images were added to the stack of multispectral bands and classified using a supervised, maximum likelihood technique. Although each texture band improved the classification accuracy over a multispectral only effort, the addition of fractal dimension measures is particularly effective at resolving land cover classes within urbanized areas, as compared to per‐pixel spectral classification techniques.     

基于影像融合和面向对象技术的植被信息提取研究

高分辨率影像具有丰富的光谱信息和空间信息。采用不同的图像融合技术融合GeoEye影像全色波段和多光谱波段,用建立的参考多边形和对应多边形残差法评价分割质量,以确定研究区各地物类型的最优分割参数组合,选择目标地物分类特征,建立分类规则,在此基础上实现研究区内不同地物类型的面向对象信息提取。结果表明:Gram-Schmidt(GS)融合法具有最优的融合效果,所选特征能够很好地实现目标地物信息提取,并且具有明确的地学意义,面向对象信息提取总体精度达到90.3%,Kappa系数为0.86,该研究为高精度植被信息的提取提供了有效的方法。     

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).     

基于知识发现和决策规则的遥感图像城区土地覆盖/利用分类方法

提出一种利用地类的色度信息、小波提取纹理特征、植被指数及形状知识等采用规则推理有效识别遥感土地类别的方法。采用知识发现和决策规则方法,可以充分吸收遥感专家的思想和工作经验,可以充分利用多种模糊性地学知识来提高遥感影像分类效果。最后通过实例证明了该方法的有效性。     

The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data

The relationship between land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) associated with urban land‐use type and land‐use pattern is discussed in the City of Shanghai, China using data collected by the Enhanced Thematic Mapper Plus (ETM+) and aerial photographic remote sensing system. There is an apparent correlation between LST and NDVI from the visual interpretation of LST and NDVI contrasts. Mean LST and NDVI values associated with different land‐use types are significantly different. Multiple comparisons of mean LST and NDVI values associated with pairings of each land‐use type are also shown to be significantly different. The result of a regressive analysis shows an inverse correlation relationship between LST and NDVI within all land‐use polygons, the same to each land‐use type, but correlation coefficients associated with land‐use types are different. An analysis on the relationship between LST, NDVI and Shannon Diversity Index (SHDI) shows a positive correlation between LST and SHDI and a negative correlation between NDVI and SHDI. According to the above results, LST, SHDI and NDVI can be considered to be three basic indices to study the urban ecological environment and to contribute to further validation of the applicability of relatively low cost, moderate spatial resolution satellite imagery in evaluating environmental impacts of urban land function zoning, then to examine the impact of urban land‐use on the urban environment in Shanghai City. This provides an effective tool in evaluating the environmental influences of zoning in urban ecosystems with remote sensing and geographical information systems.     

Accurate prediction of bird species richness patterns in an urban environment using Landsat‐derived NDVI and spectral unmixing

Urban landscapes are expanding rapidly and are reshaping the distribution of many animal and plant species. With these changes, the need to understand and to include urban biodiversity patterns in research and management programmes is becoming vital. Recent studies have shown that remote sensing tools can be useful in studies examining biodiversity patterns in natural landscapes. The present study aimed to explore whether remote sensing tools can be applied in biodiversity research in an urban landscape. More specifically, the study examined whether the Landsat‐derived Normalized Difference Vegetation Index (NDVI) and linear spectral unmixing of urban land cover can predict bird richness in the city of Jerusalem. Bird richness was sampled in 40 1‐ha sites over a range of urban environments in 329 surveys. NDVI and the per cent cover of built‐up area were strongly and negatively correlated with each other, and were both very successful in explaining the number of bird species in the study sites. Mean NDVI in each site was positively correlated with the site bird species richness. A hump‐shaped relationship between NDVI and species richness was observed (when calculated over increasing spatial scales), with a maximum value (Pearson's R = 0.87, p<0.001, n = 40) at a scale of 15 ha. We suggest that remote sensing approaches may provide planners and conservation biologists with an efficient and cost‐effective method to study and estimate biodiversity across urban environments that range between densely built‐up areas, residential neighbourhoods, urban parks and the peri‐urban environment.     

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。     

Monitoring Sahelian floodplains using Fourier analysis of MODIS time‐series data and artificial neural networks

Fourier analysis of Moderate Resolution Image Spectrometer (MODIS) time‐series data was applied to monitor the flooding extent of the Waza‐Logone floodplain, located in the north of Cameroon. Fourier transform (FT) enabled quantification of the temporal distribution of the MIR band and three different indices: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Water Index (NDWI), and the Enhanced Vegetation Index (EVI). The resulting amplitude, phase, and amplitude variance images for harmonics 0 to 3 were used as inputs for an artificial neural network (ANN) to differentiate between the different land cover/land use classes: flooded land, dry land, and irrigated rice cultivation. Different combinations of input variables were evaluated by calculating the Kappa Index of Agreement (KIA) of the resulting classification maps. The combinations MIR/NDVI and MIR/EVI resulted in the highest KIA values. When the ANN was trained on pixels from different years, a more robust classifier was obtained, which could consistently separate flooded land from dry land for each year.