在重要生态功能区、生态环境敏感区/脆弱区等区域划定生态红线,控制人类活动强度,对于维护区域生态完整性和生态服务功能的可持续性,解决生态环境退化和资源枯竭问题,减轻异常自然灾害不利影响具有重要意义.虽然不少省市已开展生态红线划分和管理试点工作,但生态红线划分的理论和技术尚不完善。生态红线区划和管理急需理论指导和技术支持。对生态红线的内涵进行了分析,指出了空间红线、面积红线和管理红线之间的有机联系,强调了生态安全空间格局和区域生态服务需求在生态红线划分中的重要性;在生态红线划分技术研究综述基础上剖析了生态红线的研究中存在的问题,如划分方法简单粗放、对景观和区域尺度上的空间过程和空间联系考虑不足以及由于部门和学科分割带来的(海)水陆缺乏统筹等;论述了生态适宜性评价、景观/区域安全格局理论、海陆统筹理论、干扰生态学理论、生态系统管理和适应性理论以及驱动力-压力-状态-影响-响应(DPSIR)模型框架等技术方法和理论以及它们在在生态红线划分中的潜在应用;最后提出了基于生态安全格局和区域生态服务需求的生态红线划分的技术路线,并对今后生态红线划分研究进行了展望。提出的生态红线划分技术和理论方法可为今后生态红线的研究提供有益参考。

植被是陆地生态系统的主体，分析长时期植被覆盖变化，有助于揭示陆地生态环境的演变规律。研究以京津风沙源重点治理区的MODIS02B产品为数据源，通过数据处理获得2000-2012年的NDVI（Normalized difference vegetation index）时序数据集，采用线性趋势分析、标准差、Hurst指数和相关系数等方法，分析京津风沙源重点治理区植被覆盖的时空变化特征及影响因子。结果显示：（1）近13年来，治理区植被覆盖总体呈上升趋势（R2=0.70），2012 年NDVI值达最大值0.324，比2000年增加了135.62%。但增加速率和幅度各异：北部干旱区（Bbghq）增加速度最快，浑善达克区（Hsdkq）次之，农牧交错区（Nmjcq）植被变化相对稳定。（2）工程区地表植被覆盖改善区域的面积明显大于退化区域，其中得到改善且通过显著性检验（P =0.10）的区域约占总面积的94.31%；Hurst指数分析表明，工程区植被变化整体呈中强持续性特征，面积合计约占64.48%；综合分析表明，工程区植被变化以良性发展为主，特别是强持续性的退化区和弱持续性的改善区值得关注，二者合计占35.27%。（3）人类活动是京津风沙地区植被覆盖上升的重要驱动因素；自然因素中，降水是控制工程区植被生长的主要因子，温度的影响相对较弱。

水土流失是世界面临的一个严峻问题，它已给全球的生态造成了严重的威胁，及时快速地监测水土流失区的生态变化已显得尤为重要。为此，该文提出遥感生态指数(RSEI)来监测水土流失区的生态变化。该指数选取了绿度、湿度、热度和干度作为四大生态指标，并分别以遥感植被指数、湿度分量、地表温度和土壤-建筑指数为代表。同时采用主成分变换技术来集成各个指标，使各指标的权重是由数据本身的性质来决定，而不是人为的设定。将RSEI应用于福建长汀水土流失区的结果表明，RSEI指数可以定量地评价水土流失区生态修复的效果。数据显示，该区经过20多年的水土流失治理，RSEI生态指数值上升了17%，生态为优良的等级所占的面积比例从33.9%上升到52.3%，总体反映了该区的生态质量有了较明显的提高。

Severe soil loss has caused ecological degradation for the global ecosystem, thus it is a major problem facing the world today. Timely and fast monitoring ecological changes in soil loss regions has become an increasing concern. This paper develops a remote sensing assessment method of soil erosion-induced changes in regional ecological quality based ecological index (RSEI). The proposed index combines four indicators from existing remote-sensing indices/components to represent greenness, dryness, wetness and heat, which are the important ecological indicators frequently used in assessing regional ecological status. The four remote-sensing indices/components are the normalized difference vegetation index (NDVI), soil index (SI), wetness component of the tasseled cap transformation (Wet), and land surface temperature (LST). The principal component analysis (PCA) was utilized to compress the four indicators into one index - RSEI, in order to assess overall ecological status. The new index, RSEI, was thus constructed using the first component as it was proved to have effectively combined the most information of the four indicators. The application of the RSEI in Hetian basin area in Changting county of Fujian province, one of the most serious reddish soil erosion areas in southern China, showed that the RSEI can quantitatively assess the ecological effects of soil loss treatment in the area and easily detect spatial and temporal changes of the ecological quality through a time period from 1988 to 2010. The application utilized three Landsat TM images of 1988, 2004 and 2010. The four indicators (NDVI, SI, Wet and LST) of each year were retrieved from the images and then combined through the PCA transform to form the RSEIs for the study years. The RSEI-based analysis indicated that after a more than 20 years fight for soil loss in the area by the local people and government, the ecological quality of the area has been significantly improved. This is suggested by an increase in the mean RSEI value from 0.5 in 1988 to 0.59 in 2010, accompanied by a decrease in low level RSEI area from 66.1% to 47.7%, and an increase of high level RSEI area from 33.9% to 52.3% in this duration. Quantitative analysis reveals that the greenness indicator represented by NDVI contributes most to the RSEI change among the four indicators used for generating the index. This suggests that the biological restore of soil erosion areas by planting tree and grass is an effective way to soil-erosion treatment for Hetian basin.

8元/a,突出了保护区重要的生态地位。在保护区11项生态服务功能中,水文调节和水资源供给生态服务功能价值最高,食物和原料生产生态服务功能价值较低。③ 1989-2014年生态服务总价值量降低31.49%,其中冰川和草地的总价值量持续增长27.63%和35.70%,水体和荒地的持续减少55.43%和13.81%,林地的先减22.13%,后增15.75%。在价值构成比例上,1989年水体占主要地位,2014年冰川占主要地位。]]>

随着国产高分卫星的陆续发射,高分卫星数据在区域地质调查中的应用能力显得越来越突出。采用资源一号02C(ZY-1 02C)和高分一号(GF-1)卫星数据,在对原始数据质量进行评价的基础上,开展其在高海拔地区地质调查应用中的潜力分析。研究结果表明: ZY-1 02C和GF-1卫星数据已基本满足大比例尺地质制图的要求; 在区域构造解译和岩性信息提取方面已达到同等空间分辨率国外卫星数据的水平,在高海拔地区地质调查中有很大的应用潜力。

为了推进北京一号小卫星遥感数据在城市景观生态研究中的应用,针对其全色影像4m分辨率、多光谱影像32m分辨率的特点,试验分析北京一号小卫星影像在城市景观格局变化中的应用效果和特点。通过对不同分类器的比较,选择支持向量机分类器对多光谱影像、全色影像、全色与多光谱融合影像三个数据集进行景观组分分类,结果表明,全色与多光谱融合影像的分类精度最高。利用多时相、多光谱遥感数据统计分析了城市景观组分与格局变化,表明32m空间分辨率的多光谱影像可以用于城市景观格局变化和土地覆盖变化分析。本文全面试验和分析评价了北京一号多分辨率数据在城市景观格局研究中的应用效果。通过对同一年份全色影像和多光谱影像计算的景观格局指标的分析表明,全色数据能更有效地描述景观详细信息,多光谱数据可展现城市景观的整体格局,而融合后对景观格局分析能够获得优于单一数据的效果。试验和分析表明,北京一号小卫星4m全色高分辨率影像和32m多光谱数据的波段组合,能从不同尺度揭示城市景观格局和变化过程。

鄱阳湖南矶湿地国家级自然保护区内时常有烧荒事件发生，这对保护区生物多样性和区域生态环境造成一定影响，快速、便捷、准确地获取烧荒地信息是该区域环境管理的紧迫需求。本文针对2012年1月初鄱阳湖南矶山湿地发生的烧荒事件，采用环境与灾害监测预报小卫星影像数据，以鄱阳湖南矶湿地国家级自然保护区所在的新建县为研究区。以面向对象的遥感影像分析方法，通过对影像的多尺度分割，根据影像的光谱信息、空间特征、纹理特征等，构建烧荒地提取的规则集，对影像中的烧荒地信息进行提取，并利用混淆矩阵对信息提取结果进行精度检验。精度检验结果表明，烧荒信息提取的总体精度为99.38%，Kappa系数为0.89，证明面向对象的遥感信息提取方法可以有效、快速地提取烧荒地信息，避免了传统方法中的椒盐状破碎图斑的现象。

城市受人类活动影响比较大,结构组成比较复杂,对该区域进行分类研究存在一些问题。甚高分辨率遥感影像,以其丰富的细节信息为城市土地覆被分类研究提供了可能。本文结合使用甚高分辨率QuickBird遥感影像和激光扫描LIDAR数据,论述了利用多尺度、多变量影像分割的面向对象的分类技术对马来西亚基隆坡市城市中心区的土地覆被分类研究。针对特定地物选择合适的影像分割特征和分割尺度、按照合理的提取顺序逐步进行城市土地覆被信息提取。在建筑物的提取过程中构建了归一化数字表面模型nDSM,使用成员函数将建筑物信息提取出来。精度评价结果表明,利用该方法得到了理想的城市土地覆被分类结果,其分类总精度从常规面向对象分类方法的83.04%上升到88.52%,其中建筑物生产精度从60.27%增加到93.91%。

Urban land cover classification is not easy for its inner complexity resulting from human's impact.The emergency of very high resolution remote sensing images makes it possible to classify the urban land cover for its ample details.The paper is about urban land cover classification method using multi-scale and multi-variable image segmentation method.The innovation of this method lies in selection of proper scale parameter resulting from proper image data and certain classification order.Normalized Digital Surface Model(nDSM) is constructed in the process of building extraction;the threshold of elevation difference is used to extract building.Accuracy assessment result indicates that the ideal urban land cover classification results have been obtained using this method.The total accuracy has increased from 83.04% to 88.52% compared with traditional object oriented classification result.The product accuracy of building increases from 60.27% to 93.91%,which is the highest among all the six objects.

AbstractRemote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.]]>

鄱阳湖南矶湿地是亚热带典型过水性湿地,由于该区域水文情况复杂,且泥滩、沼泽和疫水（血吸虫）分布较广,导致野外考察验证工作困难,使用传统的遥感信息提取方法很难保证该地区湿地景观的提取精度。本文以高分一号影像为数据源,综合运用数字高程模型（DEM）、归一化植被指数（NDVI）、归一化水体指数（NDWI）等辅助数据,采用面向对象分类方法,对鄱阳湖南矶湿地景观信息进行提取研究,并取得了较好的分类效果。研究结果表明：（1）基于国产高分辨率影像的面向对象分类,既兼顾了国产高分辨率影像光谱、空间、结构、纹理信息,又综合利用多源辅助数据参与到分类计算中,分类精度得到明显的提升;（2）基于面向对象与多源数据分类方法对湿地混合像元有较好地识别能力,可获得较高的总体分类精度（94.3275%）和Kappa系数（0.9324）,说明利用多源数据的面向对象方法提取湿地信息是可行的,其分类结果具有较高的准确性和可信度,较好地解决了过水性湿地景观分类问题;（3）该分类方法弥补了单一遥感影像分类方法的不足,对研究国产高分卫星在提取过水性湿地景观信息方面具有重要的参考和实际意义。最后,分析了多源数据面向对象分类尚待解决的问题和下一步的研究方向。

In using traditional digital classification algorithms, a researcher typically encounters serious issues in identifying urban land cover classes employing high resolution data. A normal approach is to use spectral information alone and ignore spatial information and a group of pixels that need to be considered together as an object We used QuickBird image data over a central region in the city of Phoenix, Arizona to examine if an object-based classifier can accurately identify urban classes. To demonstrate if spectral information alone is practical in urban classification, we used spectra of the selected classes from randomly selected points to examine if they can be effectively discriminated. The overall accuracy based on spectral information alone reached only about 63.33%. We employed five different classification procedures with the object-based paradigm that separates spatially and spectrally similar pixels at different scales. The classifiers to assign land covers to segmented objects used in the study include membership functions and the nearest neighbor classifier. The object-based classifier achieved a high overall accuracy (90.40%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (67.60%). This study demonstrates that the object-based classifier is a significantly better approach than the classical per-pixel classifiers. Further, this study reviews application of different parameters for segmentation and classification, combined use of composite and original bands, selection of different scale levels, and choice of classifiers. Strengths and weaknesses of the object-based prototype are presented and we provide suggestions to avoid or minimize uncertainties and limitations associated with the approach. (C) 2011 Elsevier Inc.

Pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM). Overall classification accuracies between pixel-based and object-based classifications were not statistically significant (p > 0.05) when the same machine learning algorithms were applied. Using object-based image analysis, there was a statistically significant difference in classification accuracy between maps produced using the DT algorithm compared to maps produced using either RF (p = 0.0116) or SVM algorithms (p = 0.0067). Using pixel-based image analysis. there was no statistically significant difference (p > 0.05) between results produced using different classification algorithms. Classifications based on RF and SVM algorithms provided a more visually adequate depiction of wetland, riparian, and crop land cover types when compared to DT based classifications, using either object-based or pixel-based image analysis. In this study, pixel-based classifications utilized fewer variables (15 vs. 300), achieved similar classification accuracies, and required less time to produce than object-based classifications. Object-based classifications produced a visually appealing generalized appearance of land cover classes. Based exclusively on overall accuracy reports, there was no advantage to preferring one image analysis approach over another for the purposes of mapping broad land cover types in agricultural environments using medium spatial resolution earth observation imagery. (C) 2011 Elsevier Inc.

提出了一种基于区域生长方法的分割参数选择方案,从各个类别的训练样区中提取分割参数信息。通过一系列的影像区域分割,计算得出一个最大的目标函数值,为每个类别推演出最佳分割参数;在单个类别参数影像分割和分类的基础上,融合所有处理结果,最后完成影像分类。实验验证了所提出方法的有效性。

Image segmentation is prerequisite for object-oriented image analysis.Most image segmentation algorithms need the user to provide parameters to control the quality of the resulting segmentation.Selecting suitable parameters is a challenging task in using such algorithms.We proposed a method of parameters selection for region-growing image segmentation.Information about segmentation parameters was extracted from training sample areas of each class in the image.By multiple-segmentation of the training sample area,a maximum of objective function was found to deduce the suitable parameters for a class.Using the obtained parameters,n(the number of classes) resulting segmentations and subsequent resulting classifications were achieved.Then the n resulting classifications were fused to complete the final image classification.We tested the parameters selection for image segmentation in an object-oriented classification of remote sensing image.

在利用遥感图像进行土地利用/覆盖分类过程中,可采用以下两种途径来提高分类精度:一是通过增加有利于分类的数据源,引入地理辅助数据和归一化植被指数(NDVI)来进行多源信息融合;二是选择更好的分类方法,例如支持向量机(SVM)学习方法,由于该方法克服了最大似然法和神经网络的弱点,非常适合高维、复杂的小样本多源数据的分类。为了提高多源遥感图像分类的精度,还研究了支持向量机在遥感图像分类中模型的选择,包括多类模型和核函数的选择。分类结果表明,支持向量机比传统的分类方法具有更高的精度,尤其是基于径向基核函数和一对一多类方法的支持向量机模型更适合多源遥感图像分类,因此,基于支持向量机的多源土地利用/覆盖分类能大大提高分类精度。

There are two ways to improve the performance of land cover classification with remote sensing images.The first way is to apply new data source including GIS data and normalized difference vegetation index(NDVI) to multi-source information fusion.The second one is to use methods with higher accuracy.Support vector machines(SVM) overcome the defects of maximum-likelihood and neural networks classifiers.SVMs are suitable to process complex data of high dimension and small number of training data.In this paper,selection of SVM models including kernel functions and multi-class methods is studied in order to improve the accuracy of multi-source remote sensing images classification.Experimental results show that the SVMs have higher accuracy than other traditional classifiers for the classification of multi-source remote sensing data.The SVM with a RBF kernel function and One-against-one multi-class method is the best classifier in this study.SVM methods could greatly improve the multi-source land cover classification.