Classifying tree species in a northern mixed forest using high-resolution IKONOS data

The high-spatial-resolution IKONOS satellite is now operating as a resource and disaster monitor, after a successful launch in September 1999. The ground resolution of the IKONOS panchromatic band is about 1m, the greatest of any satellite. The objectives of this study were to verify the extent to which high-resolution IKONOS data can be used to classify tree species. A field survey and image analysis study used IKONOS imagery to classify 21 species in mixed stands of deciduous and conifer species with the following results: (1) The panchromatic and multi-spectral bands 4, 3, and 2 were useful for classifying tree species owing to the great difference in the reflectance values between tree species. (2) Some groups, for which there were significant differences among species, were identified using Tukeys multiple comparison test; conifers and some broadleaved trees were identified correctly more often than other species. (3) A random selection of validation pixels showed that the overall classification accuracy was 62%. The classification accuracy of broadleaved trees was a little low, ranging from 40% to 63%, while that of conifers exceeded 70%. (4) The overall accuracy of the classification at the genus level improved by 4% more than the species level. The misclassification of broadleaved trees was due to the similar spectral characteristics of species in the same genus.     

Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation

Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using object-specific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1) multiscale dataset generation using an object-specific analysis and upscaling technique, 2) marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3) landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.This revised version was published online in May 2005 with corrections to the Cover Date.     

IKONOS数据在植被景观制图和景观空间格局分析中的应用——以丘陵沟壑区准格尔五分地沟小流域为例

本文利用高分辨率 IKONOS数据 ,绘制了内蒙古准格尔旗五分地沟小流域植被景观图 ,并分析不同尺度的景观格局。结果表明 :IKONOS遥感数据在大比例尺植被景观制图方面具有较大的应用潜力。格局分析还表明 ,研究区植被景观为典型的人工生态、天然草原和耕地植被的高度镶嵌体 ,人工乔木林是各景观类型中面积最大的一类。整个研究区景观破碎度较大。     

基于互补相关模型和IKONOS数据的农田蒸散时空特征分析

获取田块内高分辨率农田实际蒸散信息对于精准农业中制定灌溉计划、变量处方实施及评价水分利用效率等具有重要参考价值,将传统方法与遥感结合并生成精细田块尺度的农田蒸散成为当前研究热点方向。本文基于互补相关模型和北京2011年3－6月份间内气象观测数据进行了冬小麦实际蒸散估算,并利用大型蒸渗仪对结果进行了验证和分析。最后将互补相关模型与高空间分辨率遥感数据结合实现了田块尺度农田瞬时蒸散估算,并结合蒸发比率不变法实现了日尺度蒸散扩展。结果表明：在2011年3－6月间试验区内冬小麦总耗水量达到469.12 mm,其中在灌浆期5月份耗水比重最大,占到总量近二分之一;互补相关模型估算精度整体较高,其中在5月份估算精度最高（R2=0.863,RMSE=0.103 mm）;扩展后的日尺度蒸散量与实测结果非常一致（R2=0.937,RMSE=0.668 mm）。上述结果表明在没有土壤温、湿度数据及高分辨率热红外遥感数据条件下,仅利用互补相关模型,并结合气象观测数据和高分辨率遥感数据即可估算出精细尺度农田蒸散。     

2007年郑州市经济技术开发区土地利用动态遥感监测分析

摘要：为了加强郑州市经济技术开发区土地利用监管,突出经济技术开发区的产业优势,利用2004年的IKONOS影像图、2007年的QuickBird影像图为数据源,应用ERDAS IMAGINE等遥感图像处理软件,采用人机交互信息提取方法获取开发区的土地利用现状及动态变化信息,并对获取信息进行分析。分析结果表明：经济技术开发区土地利用方面存在建设用地规模增长过快、后备土地资源不足、土地集约利用程度不高、土地绿化率低等问题。针对发现的问题,本文提出了相应的对策和建议。     

IKONOS卫星影像在小流域水土保持成果调查中的应用

利用官兴岔流域2001年IKONOS卫星数据资料，对流域的土地利用和土地覆盖进行普查，并与1982年进行小流域综合治理初的土地利用和土地覆盖资料进行对比分析，客观评价了小流域治理的成果，评估流域土地利用的合理性，为“3S”新技术在水土保持成果调查工作中的应用做了进一步探索。     

Image-based reflectance conversion of ASTER and IKONOS imagery as precursor to structural assessment of plantation forests in KwaZulu-Natal,South Africa

Reflectance-converted imagery is a requirement for establishing temporally robust remote sensing algorithms, given the reduction of time-specific atmospheric effects. Thus, in this study image-based atmospheric correction methods for ASTER and IKONOS imagery for retrieving surface reflectance of plantation forests in KwaZulu-Natal, South Africa were evaluated. This effort formed part of a larger initiative that focused on retrieval of forest structural attributes from resultant reflectance imagery. Atmospheric correction methods in this study included the apparent reflectance model (AR), dark object subtraction model (DOS), and the cosine approximation model (COST). Spectral signatures derived from different image-based models for ASTER and IKONOS were inspected visually as first departure. This was followed by comparison of the total accuracy and Kappa index computed from supervised classification of images that were derived from different image-based atmospheric correction of ASTER and IKONOS imagery. The classification accuracy of DOS images derived from ASTER and IKONOS imagery exhibited percentages of 93.3% and 94.7%, respectively. Classification accuracies for images from AR and COST, on the other hand, resulted in lower accuracy values of 87.9% and 83.6% for ASTER and 90.5% and 92.8% for IKONOS, respectively. We concluded that the image-based DOS model was better suited to atmospheric correction for ASTER and IKONOS imagery in this study area and for the purpose of forest structural assessment. This has important implications for the operational use of similar imagery types for forest inventory approaches.     

Detection and interpretation of landslides using satellite images

The severity of the landslide hazard in Hong Kong has resulted in the establishment of a comprehensive landslide database, the Natural Terrain Landslide Inventory (NTLI). It is derived mainly from the interpretation of medium to large‐scale aerial photographs, and describes the location of all landslides. In view of the labour‐intensive nature of air photo interpretation, as well as the lack of regular air photo cover in many countries, satellite images were examined for their ability to monitor landslides at a similarly detailed level, using the NTLI database as a reference. Using automated change detection with SPOT XS® images it was possible to identify 70% of landslides, the main omissions being due to those less than 10 m in width, and many of those identified were of sub‐pixel width. The study also examined different techniques of image fusion for the enhancement of IKONOS images, and demonstrated that landslides on fused images are of similar detail to those on air photos. A methodology for regional scale monitoring is proposed which combines the efficiency of automated techniques for large area monitoring using SPOT® with the qualitative detail obtained from Pan‐sharpened IKONOS images. Copyright © 2005 John Wiley & Sons, Ltd.     

基于地表覆盖分类的IKONOS影像融合算法分析与评价

不同的遥感影像融合算法有不同的优点和局限性,因此难以单纯评价某种算法的优劣,融合算法的选择与研究对象和应用目的有着密切的关系。在概略介绍IHS变换、Brovey变换、PCA变换、SFIM变换及Gram-Schmidt变换5种图像融合算法原理的基础上,对IKONOS全色和多光谱数据进行像元级融合,并对融合效果进行了定性和定量评价。在此基础上,对融合影像进行最大似然法分类,利用混淆矩阵对分类结果进行精度分析,以期找出适合于地表覆盖分类的IKONOS影像融合算法。结果表明,在图像空间信息提高和光谱信息保真方面,以SFIM变换和Gram-Schmidt变换相对较好,其中Gram-Schmidt变换对图像微小细节反差的表达能力优于SFIM变换。在上述5种变换中,SFIM及Gram-Schmidt变换后融合影像地表覆盖分类精度较高,总体精度均超过98%,Gram-Schmidt变换的分类精度略高于SFIM变换,IHS变换后融合影像的分类精度最低,其总体精度和Kappa系数分别为83.14%和0.76。因此,利用Gram-Schmidt变换和SFIM变换得到的IKONOS融合影像更有利于提高地表覆盖分类精度。     

云南省耕地需求量预测研究*

 耕地保护关系到粮食安全、经济发展和社会稳定,而耕地需求量预测是耕地保护前期工作的重中之重。本文采用Excel,SPSS等数据分析软件,对云南省1997~2005年的人口数、人均粮食占有量、粮食净调入或调出量、粮食平均单产、复种指数以及粮播比等数据进行处理,并建立相应的耕地需求量数学预测模型,预测出云南省近期（2010年）和远期（2020年）的耕地需求量,这对预测云南省耕地变化趋势,制定合理的耕地保护、开发对策,保障粮食安全具有较大的现实意义。