基于遥感和陆表信息集成的广东省土地覆盖分类方法研究

土地覆盖信息是估算地-气间的生物物理过程和能量交换的关键参数,也是区域和全球尺度气候和生态系统过程模型所需要的重要参量。如何高效地利用遥感数据提取土地覆盖信息是当前研究迫切需要解决的问题。面向对象的分类方法不但充分利用了遥感数据的光谱信息,同时也利用了影像的纹理结构信息和更多的地物分布信息关系,在遥感分类中具有较大的潜力。研究基于2010年多时相的环境卫星数据、TM数据以及DEM数据,并结合地表采集的4000多个样点数据,采用面向对象的分类方法对广东省土地覆盖进行分类。经采样验证,广东省土地覆盖平均精度为85%,分类结果精度远高于常规的分类算法,说明结合陆表信息的面向对象分类方法比常规的分类算法更具有优势,可以实现高精度的土地覆盖分类。     

Delineation of salt-affected soils and waterlogged areas in the Indo-Gangetic plains using IRS-1C LISS-III data

Waterlogging and subsequent salinization and/alkalinization is the major land degradation problem in irrigated commands in the arid and semi-arid regions. The Linear Imaging Self-scanning Sensor (LISS-III) digital data acquired by the Indian Remote Sensing Satellite (IRS-1C) over part of the Indo-gangetic plains are digitally analysed for delineating salt-affected and waterlogged areas on a IBM RS-6000 system. A comparison with the IRS-1B LISS-II data, with comparable spectral bands and acquired very close to LISS-III data, reveals a relatively poor overall classification accuracy achieved from this data as compared to LISS-II data. This may be attributed to higher intra-class spectral variations resulting from high spatial resolution LISS-III data. The classifiers capable of exploiting the spectral variations in spatial domain, like segmentation, textural analysis, etc., need to be used for exploiting the full potential of IRS-1C LISS-III data for mapping salt-affected soils and other degraded lands.     

Comparison of multi-temporal NOAA-AVHRR and SPOT-XS satellite data for mapping land-cover dynamics in the west African Sahel

Abstract

Multi-resolution and multi-temporal remote sensing data (SPOT-XS and AVHRR) were evaluated for mapping local land cover dynamics in the Sahel of West Africa. The aim of this research was to evaluate the agricultural information that could be derived from both high and low spatial resolution data in areas where there is very often limited ground information. A combination of raster-based image processing and vector-based geographical information system mapping was found to be effective for understanding both spatial and spectral land-cover dynamics. The SPOT data proved useful for mapping local land-cover classes in a dominantly recessive agricultural region. The AVHRR-LAC data could be used to map the dynamics of riparian vegetation, but not the changes associated with recession agriculture. In areas where there was a complex mixture of recession and irrigated agriculture, as well as riparian vegetation, the AVHRR data did not provide an accurate temporal assessment of vegetation dynamics.     

Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes.     

高光谱遥感岩矿识别填图的技术流程与主要技术方法综述

自20世纪80年代以来,伴随着多种航空和航天成像光谱仪的研制成功和投入运行,一系列的图像光谱基本处理手段和实验室岩矿光谱分析技术方法,如最小噪声分量(MNF)变换-像元纯度指数(Pixel Purity Index,PPI)-N维可视化(N-Dimensional Visualization)、岩石类型系统光谱分类、多变量统计分析、矿物光谱特征确认的概率分析、MGM技术等相继发展起来,从而为形成成像光谱岩矿填图的完整技术流程打下了坚实的技术基础。分析了多种岩矿光谱分析的技术方法,同时指出,不同方法具有各自的优、缺点,针对不同应用目标需要不同的矿物识别与岩矿填图方案;混合方法的开发应用和从可见光到微波波段的融合应用在未来更为重要。     

Advances in classification for land cover mapping using SPOT HRV imagery

Abstract

High-resolution data from the HRV (High Resolution Visible) sensors onboard the SPOT-1 satellite have been utilized for mapping semi-natural and agricultural land cover using automated digital image classification algorithms. Two methods for improving classification performance are discussed. The first technique involves the use of digital terrain information to reduce the effects of topography on spectral information while the second technique involves the classification of land-cover types using training data derived from spectral feature space. Test areas in Snowdonia and the Somerset Levels were used to evaluate the methodology and promising results were achieved. However, the low classification accuracies obtained suggest that spectral classification alone is not a suitable tool to use in the mapping of semi-natural cover types.     

雷达遥感的地质学应用及其进展

合成孔径雷达(SAR)遥感以其独有的全天时、全天候观测能力和对地表的穿透性及形态探测能力,特别是现在新型成像雷达技术的发展,使之在地质学应用中具有独特的优势。结合SAR应用技术的发展阶段,即由单波段单极化到多波段多极化,再发展到现在极化测量和干涉测量阶段,综述了成像雷达遥感在地学中的应用,特别是对新型成像雷达技术(极化雷达、干涉雷达)的地学应用作了介绍。     

Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems

Crop cover and crop rotation mapping is an important and still evolving field in remote sensing science for which robust and highly automated processing chains are required. This study presents an improved mapping procedure for crop rotations of irrigated areas in Central Asia by using classification and regression trees (CARTs) applied to transformations of 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series. The time series were divided into several temporal segments, from which metrics were derived as input features for classification. This temporal aggregation was applied to suppress within-class temporal variability. Various lengths of temporal segments were tested for their potential to increase classification accuracy. In addition, tests of enhancing the classification accuracy were done by combining different classification results using the majority rule for voting. These different processing strategies were applied to four annual time series (2004–2007) of the Khorezm region, where 270 000 ha of irrigated land is dominated by rotations of cotton, wheat and rice. Improved classification results were obtained for CARTs applied to metrics derived from a mixture of different segment lengths. The sole use of either long or short temporal segments was inferior. CART prioritized segments representing active phases of the phenological development. The best result, the optimized segment-based approach, achieved an overall accuracy between 83 and 85% for classifications between 2004 and 2007; in particular, the small range demonstrated the robustness regarding inter-annual variations. These accuracies exceeded those of the original time series without temporal segmentation by 6–7%. With some adjustments to other crops and field heterogeneity influencing the usefulness of a respective sensor, the approach can be applied to other irrigation systems in Central Asia.     

基于自编码神经网络的航空物探遥感数据分类方法研究

航空物探遥感数据的采集过程中受到电磁波辐射等外界因素的影响,导致航空物探遥感数据分类准确率较低,为此提出基于自编码神经网络的航空物探遥感数据分类方。根据航空物探对象的基本特征,设置遥感数据的分类标准。通过辐射校正、几何纠正、噪声消除等步骤,完成航空物探遥感数据的预处理。构建自编码神经网络,利用自编码神经网络算法,从光谱、形状、纹理等方面提取遥感数据特征,通过特征匹配确定航空物探遥感数据的所属类型。通过分类性能测试实验得出结论:所提方法的全局遥感数据分类成功率和错误率的平均值分别为99.8%和0.6%,局部遥感数据分类的成功率和错误率的平均值分别为99.8%和0.3%,即所提方法在分类性能方面具有明显优势。     

成像光谱技术在土地利用动态遥感监测中的应用研究

随着数字化调查技术的发展，国土资源管理对土地利用动态遥感监测提出了更高的要求，目前主要采用的多光谱数据由于受光谱分辨率限制以及“同谱异物，同物异谱”现象的影响，难以满足管理需要。成像光谱数据具有较高光谱分辨率。在类别细分方面具有一定的优势，在当前土地利用动态遥感监测中具有一定的应用潜质。该文针对成像光谱数据特点，探索了与成像光谱数据相适应的土地利用动态遥感监测方法，提出了异常光谱检测法，该方法在试验区应用中取得了良好效果。