基于TM影像的北京城乡结合部土地利用变化分析

利用遥感分类技术能够快速获取土地利用变化信息。基于1996年和2006年两时相的北京城乡结合部地区TM卫星影像数据,采用监督分类和分类后处理方法,对研究区10年间的土地利用变化情况进行了详细分析,得到如下结论:10年间北京城南地区城乡结合部的各种土地利用类型之间相互转化,并以耕地,林地和建设用地相互转化最为显著;耕地和大范围水域面积较大幅度减少,城市居民点及工矿用地和未利用土地面积大幅度地增加,城乡结合部的范围在10年间从北向南进行了大范围地移动。     

多种融合方法支持下的Landsat7-ETM+影像自动解译精度评价

对ETM+影像进行多种算法融合实验,除应用ERDAS软件现有的融合方法(PCA、MLT)外,还利用IDL语言编程实现了SFIM、HPF、MB等融合算法,通过多次修改SFIM、HPF、MB融合算法中滤波器窗口的大小、滤波算子的实验,达到既不产生噪声又增强了图像纹理信息的融合效果。对融合后的影像进行了相同地物样本、不同分类方法的监督分类。以2002年内蒙古土地利用遥感调查数据为评价标准(内蒙古自治区遥感与地理信息系统重点实验室提供),用总体精度(overall accuracy)、kappa系数两种评价指数综合反映各种融合算法与各种分类方法结合的分类精度,并对各种分类方法及融合算法予以评价。     

基于TM影像的城市绿地信息提取方法研究

基于TM遥感影像,运用ERDAS,对某地城市绿地专题信息进行了提取。实验过程中首先对图像进行预处理,然后通过四种绿地信息提取方案进行比较分析,这四种方案分别为:原始波段合成法、主成份分析法、归一化植被指数(NDVI)法和实验波段组合法。将以上几种方案的图像进行反复比较,根据研究对象的实际情况,植被景观目视效果最好的是NDVI植被指数法。对以上四种方法的彩色合成图像进行监督分类,利用目视判读的方法对TM影像的分类结果进行精度检验,由此可以看出实验波段组合法的精度最高,该方法是一种有效的绿地提取的方法。     

基于TM影像的果园空间信息提取技术研究

山东半岛是我国重要的果园基地,素有水果之乡的美誉。快速、准确地获取其面积及分布状况,是果园业健康发展和科学管理的客观需求。以山东省半岛北部沿海城市龙口市为例,以2005.6.13的TM影像为主要信息源,在深入分析各主要地物光谱特征的基础上,通过DEM数据建立地理控制区,把研究区分为海滨平原区和丘陵区。在海滨平原区,主要通过NDVI方法,建立果园提取模型;在地物构成相对复杂的丘陵区,建立基于光谱知识和地学知识相结合的果园提取模型,均取得了很好的效果。研究表明,龙口市2005年的果园地面积为316001.2Ha,集中分布于龙口东部的石良、兰高等乡镇。     

Evaluation of multisource data for glacier terrain mapping: a neural net approach

Spectrally similar nature of land covers in a glacierized terrain hampers their automated mapping from multispectral satellite data, which may be overcome by using multisource data. In the present study, an artificial neural network (ANN)-based information extraction approach was applied for mapping the Kolahoi glacier and adjoining areas, using Landsat TM (Thematic Mapper) data and several ancillary layers such as image transformations and topographic attributes. Results reveal that ANN (highest overall accuracy (OA): 83.74%) outperforms maximum likelihood classifier (highest OA: 66.90%) and the incorporation of ancillary data into the classification process significantly enhances the mapping accuracy (>9%), particularly the addition of Near Infrared Red/Short Wave Infrared (NIR/SWIR) data to the spectral data. A nine-band combination dataset (spectral data, slope, Red/NIR and decorrelation stretch) was found to be the best multisource dataset. Results of the Z-tests (at 95% confidence level) also corroborate and statistically validate the above findings.     

Application of spectral decomposition algorithm for mapping water quality in a turbid lake (Lake Kasumigaura,Japan) from Landsat TM data

The remote sensing of Case 2 water has been far less successful than that of Case 1 water, due mainly to the complex interactions among optically active substances (e.g., phytoplankton, suspended sediments, colored dissolved organic matter, and water) in the former. To address this problem, we developed a spectral decomposition algorithm (SDA), based on a spectral linear mixture modeling approach. Through a tank experiment, we found that the SDA-based models were superior to conventional empirical models (e.g. using single band, band ratio, or arithmetic calculation of band) for accurate estimates of water quality parameters. In this paper, we develop a method for applying the SDA to Landsat-5 TM data on Lake Kasumigaura, a eutrophic lake in Japan characterized by high concentrations of suspended sediment, for mapping chlorophyll-a (Chl-a) and non-phytoplankton suspended sediment (NPSS) distributions. The results show that the SDA-based estimation model can be obtained by a tank experiment. Moreover, by combining this estimation model with satellite-SRSs (standard reflectance spectra: i.e., spectral end-members) derived from bio-optical modeling, we can directly apply the model to a satellite image. The same SDA-based estimation model for Chl-a concentration was applied to two Landsat-5 TM images, one acquired in April 1994 and the other in February 2006. The average Chl-a estimation error between the two was 9.9%, a result that indicates the potential robustness of the SDA-based estimation model. The average estimation error of NPSS concentration from the 2006 Landsat-5 TM image was 15.9%. The key point for successfully applying the SDA-based estimation model to satellite data is the method used to obtain a suitable satellite-SRS for each end-member.     

Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images

Image compositing is a multi-objective optimization process. Its goal is to produce a seamless cloud and artefact-free artificial image. This is achieved by aggregating image observations and by replacing poor and cloudy data with good observations from imagery acquired within the timeframe of interest. This compositing process aims to minimise the visual artefacts which could result from different radiometric properties, caused by atmospheric conditions, phenologic patterns and land cover changes. It has the following requirements: (1) image compositing must be cloud free, which requires the detection of clouds and shadows, and (2) the image composite must be seamless, minimizing artefacts and visible across inter image seams. This study proposes a new rule-based compositing technique (RBC) that combines the strengths of several existing methods. A quantitative and qualitative evaluation is made of the RBC technique by comparing it to the maximum NDVI (MaxNDVI), minimum red (MinRed) and maximum ratio (MaxRatio) compositing techniques. A total of 174 Landsat TM and ETM+ images, covering three study sites and three different timeframes for each site, are used in the evaluation. A new set of quantitative/qualitative evaluation techniques for compositing quality measurement was developed and showed that the RBC technique outperformed all other techniques, with MaxRatio, MaxNDVI, and MinRed techniques in order of performance from best to worst.     

遥感影像与POI数据相结合的城市建成区提取适用性研究——以沈阳市为例

城市建成区的发展状况是地理国情监测的重要内容,本文基于遥感影像数据和POI数据对城市建成区进行提取,针对二者的适用性问题进行了研究。试验以沈阳市为研究区域,在研究区域内选择2016年遥感影像数据和POI数据作为数据源进行对比分析。首先,对遥感影像数据和POI数据进行预处理;其次,通过监督分类的方法对遥感影像进行建成区的提取;然后,采用核密度估计法分析POI数据并提取出建成区;最后,利用叠加分析法对比分析这两种数据的适用性。试验结果表明：使用遥感影像数据作为数据源可以较为全面客观地反映城市建成区的发展现状;利用POI数据提取出的城市建成区具有较强的经济属性,能够很好地反映出城市中的经济活跃区。     

Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models

Land cover monitoring using digital Earth data requires robust classification methods that allow the accurate mapping of complex land cover categories. This paper discusses the crucial issues related to the application of different up-to-date machine learning classifiers: classification trees (CT), artificial neural networks (ANN), support vector machines (SVM) and random forest (RF). The analysis of the statistical significance of the differences between the performance of these algorithms, as well as sensitivity to data set size reduction and noise were also analysed. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land cover categories in south Spain. Overall, statistically similar accuracies of over 91% were obtained for ANN, SVM and RF. However, the findings of this study show differences in the accuracy of the classifiers, being RF the most accurate classifier with a very simple parameterization. SVM, followed by RF, was the most robust classifier to noise and data reduction. Significant differences in their performances were only reached for thresholds of noise and data reduction greater than 20% (noise, SVM) and 25% (noise, RF), and 80% (reduction, SVM) and 50% (reduction, RF), respectively.     

利用Landsat 5 TM影像估算沉水植物地上生物量的研究——以江西省鄱阳湖国家自然保护区为例

阐述了利用Landsat 5 TM影像在鄱阳湖国家自然保护区进行沉水植物地上生物量估算的方法和过程。研究结果显示,采用该影像,结合传统的采样策略和估算方法进行生物量的估算,在此研究区域中具有一定的局限性。分析了产生这一问题的原因,并对后续的研究工作提出了具体的建议。