Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment,Germany

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008–2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.     

基于最优尺度和规则的高分辨率影像分类研究

高分辨率影像的广泛应用推进面向对象影像分析(OBIA)的发展,而分割作为面向对象分类的关键步骤,其尺度的选择直接关系到地物信息的提取。空间尺度是地物的固有属性,在合适的分割尺度下可以更好地挖掘地物信息。本文结合最大面积法和分割质量评价模型对张山营镇影像进行分割实验,先通过分析对象最大面积初步得到最优尺度范围,后结合分割质量评价模型以确定最优分割尺度层次。在此基础上,综合样本提取的光谱、纹理等特征进行规则训练,最终完成面向对象的土地覆被分类研究。结果显示:基于多层次最优尺度的规则分类方法获得更好的分类结果,其总体精度为88.8%,Kappa系数为0.861,而基于单一尺度的最邻近法总体精度81.4%,Kappa系数0.773,基于单一尺度的规则分类法总体精度为83.2%,Kappa系数为0.85。     

基于图形数据差的居民地变化信息提取方法研究

作为空间数据库更新的一项关键内容,变化信息的提取直接关系着整个更新工作的效果。居民地是空间实体中变化频率较高的要素,成为空间数据库更新的关注重点。文中针对当前提取方法尚未关注空间实体具体变化的问题,提出一种基于图形数据差的居民地变化信息提取方法。根据图形数据差的分类,实现变化信息的归类,继而通过图形数据差的判别方法,达到居民地变化信息提取的目的。通过实验验证该方法的可行性和有效性。     

基于GF-1的坑塘信息精确提取方法研究

以国产高分一号2 m全色、8 m多光谱遥感影像作为数据源,选择廊坊市文安县作为研究区域,针对坑塘信息精确提取问题,采用单波段阈值法、监督分类法、面向对象方法等进行实验研究,重点研究了面向对象方法,结合运用遥感影像光谱、纹理、形状等特征,分析坑塘与其他地物之间的差异,最终实现坑塘信息的精确提取。     

基于GIS的榆林市乡镇地名分布特征研究

利用不同的网络公开地名数据源进行数据融合,获得榆林市乡镇级居民地地名数据,在此基础上,对地名数据进行统计分类,引入地名密度和地名点距离的指标,运用GIS的分析方法,对榆林市乡镇级地名数据进行景观分析。研究得出:榆林乡镇地名从东南向西北部递减,并且呈现出聚集的分布特征;榆林市的自然人文特征直接体现在其主要地名分类的分布上,山水类地名的分布反映了榆林西北部沙漠干旱,东南部黄土丘陵、河流较密的自然特点,姓氏、军事类地名的分布则反映了榆林市的移民文化、多民族混居的民族特色,以及古代军事价值高的特点。     

辽宁省低丘缓坡调查技术探讨

作为一项全新的土地管理和资源利用工作,低丘缓坡调查目前正在全国积极开展。本文结合辽宁省低丘缓坡调查项目,简要介绍了低丘缓坡调查技术流程,详细阐述了调查方法和手段,为其他各省市的低丘缓坡调查工作提供了有益的参考。     

An adaptive unimodal subclass decomposition (AUSD) learning system for land use and land cover classification using high-resolution remote sensing

Land use and land cover classification is an important application of remote-sensing images. The performances of most classification models are largely limited by the incompleteness of the calibration set and the complexity of spectral features. It is difficult for models to realize continuous learning when the study area is transferred or enlarged. This paper proposed an adaptive unimodal subclass decomposition (AUSD) learning system, which comprises two-level iterative learning controls: The inner loop separates each class into several unimodal Gaussian subclasses; the outer loop utilizes transfer learning to extend the model to adapt to supplementary calibration set collected from enlarged study areas. The proposed model can be efficiently adjusted according to the variability of spectral signatures caused by the increasingly high-resolution imagery. The classification result can be obtained using the Gaussian mixture model by Bayesian decision theory. This AUSD learning system was validated using simulated data with the Gaussian distribution and multi-area SPOT-5 high-resolution images with 2.5-m resolution. The experimental results on numerical data demonstrated the ability of continuous learning. The proposed method achieved an overall accuracy of over 90% in all the experiments, validating the effectiveness as well as its superiority over several widely used classification methods.     

Detecting shadows in multi-temporal aerial imagery to support near-real-time change detection

Multi-temporal aerial imagery captured via an approach called repeat station imaging (RSI) facilitates post-hazard assessment of damage to infrastructure. Spectral-radiometric (SR) variations caused by differences in shadowing may inhibit successful change detection based on image differencing. This study evaluates a novel approach to shadow classification based on bi-temporal imagery, which exploits SR change signatures associated with transient shadows. Changes in intensity (brightness from red–green–blue images) and intensity-normalized blue waveband values provide a basis for classifying transient shadows across a range of material types with unique reflectance properties, using thresholds that proved versatile for very different scenes. We derive classification thresholds for persistent shadows based on hue to intensity ratio (H/I) images, by exploiting statistics obtained from transient shadow areas. We assess shadow classification accuracy based on this procedure, and compare it to the more conventional approach of thresholding individual H/I images based on frequency distributions. Our efficient and semi-automated shadow classification procedure shows improved mean accuracy (93.3%) and versatility with different image sets over the conventional approach (84.7%). For proof-of-concept, we demonstrate that overlaying bi-temporal imagery also facilitates normalization of intensity values in transient shadow areas, as part of an integrated procedure to support near-real-time change detection.     

唐山市区土地利用的Landsat 8影像分层分类

针对土地利用遥感分类方法多样、分类精度高低不一等问题,该文以土地利用变化明显的唐山市路南区、路北区为研究区域,并以中分遥感影像Landsat 8OLI为信息源,在对地类样本进行可分离性分析的基础上,建立研究区土地利用分层分类体系。通过监督分类实验,选择分类效果最好、分类精度最高的最大似然分类器进行地类初分;通过绘制归一化植被指数(NDVI)、归一化建筑指数(NDBI)、两指数差值(NDVI-NDBI)的曲线及地类光谱特征曲线,建立决策树分类规则,进行地类再分。该方法可以较好地完成多种土地利用二级地类的划分,有助于提高中分影像土地利用分类效率。