检索结果-内蒙古大学图书馆

Banded vegetation-dune development during the Medieval Warm Period and 20th century, Chihuahuan Desert, New Mexico, USA

引用

ECOSPHERE 2012年第3期3卷 1页

作者： Weems, Stacey L. Monger, H. Curtis US Forest Serv Salt Lake City UT 84138 USA New Mexico State Univ Dept Plant & Environm Sci Las Cruces NM 88003 USA

With the advent of systematic high-resolution satellite photography, striking geometric shapes of banded vegetation several km(2) in size, but not apparent from the ground, have been documented for many areas of the arid and semiarid world. Banded vegetation, in which dense perennial vegetation alternates with bands of bare soil may originate from geomorphic processes, ecological self-organization, or human land use. In the Chihuahuan Desert of New Mexico prominent arc-shaped bands of vegetation and dunes occur along the contact of a piedmont slope (bajada) and basin floor. The origin and chronology of this banded vegetation-dune complex was investigated using early aerial photography (1936-1942), landscape photography (1918), vegetation and soil surveys (1858, 1918), soil stratigraphy, C-13/C-12 ratios, and C-14 dating. These methods reveal two periods of eolian deposition. The first began in the Medieval Warm Period (ca. AD 900-1300) and was followed by a period of landscape stability during the Little Ice Age (ca. AD 1500 to 1850). The second began in the late-1800s when widespread desertification occurred throughout the American Southwest. Banded vegetation was initiated after formation of erosional scarplets that functioned as obstacles upon which eolian sand accumulated, thus becoming a dam to overland flow and causing strips of vegetation to form. Banded vegetation in this study is an emergent pattern produced by a coupled ecologic-geomorphic-climatic system. The stratigraphic record produced by this system enables us to compare current ecological responses to climate change with baseline prehistorical responses to climate change.

关键词： carbon isotopes climate change desert geomorphology emergence Little Ice Age Medieval Warm Period object-based image analysis positive feedback mechanisms soil radiocarbon dating

来源：评论

学校读者我要写书评

暂无评论

OPEN SOURCE REMOTE SENSING: INTROSPECT AND PROSPECT

引用

JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2012年第6期20卷 613-617页

作者： Kang, Sanggoo Yoo, Hee Young Lee, Kiwon Hansung Univ Dept Informat Syst Engn Seoul South Korea Inha Univ Geoinformat Engn Res Inst Inchon South Korea

It is noteworthy that open source applications have been developed and studied in various fields as well as remote sensing field. Open sources for GIS have long history related to open standards over three decades, but those of remote sensing do relatively short. Moreover, there are still no summary reports for open sources for remote sensing. In this study, we summarized characteristics of open source applications for remotely sensed image processing and discussed their strengths and weaknesses at first. based on the summary, two open source applications for InSAR Processing and Geographic object-based image analysis are practically implemented to introspect and prospect open source remote sensing. The developed applications have many advantages compared with existing open source applications because they provide user friendly interface, functions for searching from database and visualizing in mobile devises, concurrent processing of GIS and RS data. The quality of open source software is as good as commercial software. Therefore, OSRS will expand widely and improve continuously due to strengths of open source. We expect this study will be a good guide for users and developers of OSRS.

关键词： object-based image analysis open source optical image processing remote sensing software InSAR processing

来源：评论

学校读者我要写书评

暂无评论

Segmentation of Landsat Thematic Mapper imagery improves buffelgrass (Pennisetum ciliare) pasture mapping in the Sonoran Desert of Mexico

引用

APPLIED GEOGRAPHY 2012年 34卷 569-575页

作者： Brenner, Jacob C. Christman, Zachary Rogan, John Ithaca Coll Dept Environm Studies & Sci Ithaca NY 14850 USA Middlebury Coll Dept Geog Middlebury VT 05753 USA Clark Univ Grad Sch Geog Worcester MA 01610 USA

The invasive exotic buffelgrass (Pennisetum ciliare) is a principal threat to the Sonoran Desert of southwestern North America. Buffelgrass is cultivated in cattle pastures throughout Mexico's northwestern state of Sonora, where it poses an invasion threat to surrounding desert lands. Automated remote sensing methods to detect buffelgrass pasture conversion at a regional scale have shown limited success, due in part to variable land-cover conditions, and in part to intrinsic heterogeneity in desertscrub land-cover. This paper discusses a novel technique for delineating and mapping buffelgrass pastures based on vector-based satellite image segmentation followed by pixel-based classification using ancillary spatial environmental data. based on quantitative accuracy metrics and visual inspection of known pasture sites, we report that segmentation considerably improved the mapping process, in particular the detection and delineation of pastures. Comparisons of paired classifications with segmented and nonsegmented imagery revealed higher overall map accuracies and higher buffelgrass class accuracies, as well as lower errors of commission and omission for buffelgrass in segmented maps. This new application of object-based image analysis has promising implications for ongoing efforts to map and monitor buffelgrass expansion region-wide and other similar changes in land-cover type and condition across human-modified landscapes. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Land-use/land-cover change image classification Invasive alien species object-based image analysis

来源：评论

学校读者我要写书评

暂无评论

A comparison of selected machine learning classifiers in mapping a South African heterogeneous coastal zone: Testing the utility of an object-based classification with WorldVeiw-2 imagery

A comparison of selected machine learning classifiers in map...

引用

Conference on Earth Resources and Environmental Remote Sensing/GIS Applications III

作者： Adam, Elhadi M. I. Ismail, Riyad Mutanga, Onisimo Univ KwaZulu Natal Discipline Geog ZA-3209 Pietermaritzburg South Africa

ISBN: (纸本)9780819492784

This study explored the utility of an object-based image classification approach for mapping land cover in a heterogeneous coastal zone using WorldView-2 imagery. Two relatively modern and robust supervised machine learning algorithms i.e. random forest (RF) and support vector machines (SVM) were also compared. image segmentation was performed, and ten broad land cover classes were identified. Subsequently, we assessed the performance of an object-based image classification and the selected machine learning algorithms in mapping the land cover classes. The validation of the thematic land cover maps derived from RF and SVM were assessed using an independent test dataset generated from field work data and aerial photography interpretation. Results showed that both the machine learning classifiers in combination with the object-based approach are useful in mapping land cover in heterogeneous coastal areas. However, SVM achieved the best overall accuracy (93.79%) and kappa statistic (0.93) while RF produced an overall accuracy of 86.94% and kappa value of 0.85. Overall, the study underlined the utility of combining an object-based image classification with machine learning classifiers for mapping land-cover in heterogeneous coastal areas - a previously challenging task with broad band satellite sensors and traditional pixel-based image classification approaches.

关键词： object-based image analysis random forest support vector machine land cover

来源：评论

学校读者我要写书评

暂无评论

An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography

引用

JOURNAL OF ARID ENVIRONMENTS 2007年第1期69卷 1-14页

作者： Laliberte, A. S. Rango, A. Herrick, J. E. Fredrickson, Ed L. Burkett, L. New Mexico State Univ USDA ARS Las Cruces NM 88003 USA

Research into automatic image processing of digital plot photography has increased in recent years. However, in most studies only overall vegetation cover is estimated. In and regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material and it is important to be able to quantify both types of vegetation. Our objectives were to develop an image analysis approach for estimating fractional cover of green and senescent vegetation using very high-resolution ground photography, and to compare image-based estimates with line-point-intercept (LPI) measures. We acquired ground photography for 50 plots using an eight megapixel digital camera. The images were transformed from the RGB (red, green, blue) color space to the IHS (intensity, hue, saturation) color space. We used an object-based image analysis approach to classify the. images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were effectively masked out by using the intensity and saturation bands, and a nearest neighbor classification was used to separate green and senescent vegetation using intensity, hue and saturation as well as visible bands. Correlation coefficients between LPI- and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively. image analysis underestimated total and senescent vegetation by approximately 5%. The object-based image-processing approach is less labor and time intensive than the LPI method, is a viable alternative to ground-based methods, and has the potential to be incorporated into rangeland monitoring protocols. Published by Elsevier Ltd.

关键词： rangeland vegetation rangeland monitoring very high-resolution images object-based image analysis

来源：评论

学校读者我要写书评

暂无评论

Building Extraction based on Multiscale Segmentation

Building Extraction Based on Multiscale Segmentation

引用

5th International Congress on image and Signal Processing (CISP)

作者： Zhang, Limin Xu, Tao Zhang, Jianting Naval Aeronaut & Astronaut Univ Dept Elect & Informat Engn Yantai Peoples R China

ISBN: (纸本)9781467309646;9781467309653

Building extraction algorithm based on multiscale segmentation using high resolution remote sensing images and LiDAR data is presented. image objects are obtained by multiscale image segmentation. Following that, extraction is fulfilled using DSM and spectrum features of building objects. Optimized scale is selected with unsupervised method, and segmentation is optimized with multiscale method. The algorithm has a simple structure and low computation complexity. Results of experiment prove it is efficient.

关键词： High resolution remote sensing image Multiscale segmentation building extraction object-based image analysis

来源：评论

学校读者我要写书评

暂无评论

URBAN object EXTRACTION from DIGITAL SURFACE MODEL and DIGITAL AERIAL imageS 22

URBAN OBJECT EXTRACTION from DIGITAL SURFACE MODEL and DIGIT...

引用

22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012

作者： Grigillo, D. Kanjir, U. Faculty of Civil and Geodetic Engineering University of Ljubljana Jamova cesta 2 Ljubljana Slovenia Institute of Anthropological and Spatial Studies ZRC SAZU Novi trg 2 Ljubljana Slovenia

The paper describes two different methods for extraction of two types of urban objects from lidar digital surface model (DSM) and digital aerial images. Within the preprocessing digital terrain model (DTM) and orthoimages for three test areas were generated from aerial images using automatic photogrammetric methods. Automatic building extraction was done using DSM and multispectral orthoimages. First, initial building mask was created from the normalized digital surface model (nDSM), then vegetation was eliminated from the building mask using multispectral orthoimages. The final building mask was produced employing several morphological operations and buildings were vectorised using Hough transform. Automatic extraction of other green urban features (trees and natural ground) started from orthoimages using iterative object-based classification. This method required careful selection of segmentation parameters;in addition to basic spectral bands also information from nDSM was included. After the segmentation of images the segments were classified based on their attributes (spatial, spectral, geometrical, texture) using rule set classificator. First iteration focused on visible (i.e. unshaded) urban features, and second iteration on objects in deep shade. Results from both iterations were merged into appropriate classes. Evaluation of the final results (completeness, correctness and quality) was carried out on a per-area level and on a per-object level by ISPRS Commission III, WG III/4.

关键词： Building extraction Digital surface model object classification object-based image analysis Segmentation

来源：评论

学校读者我要写书评

暂无评论

object based and geo-spatial image analysis: a semi-automatic pre-operational system

Object based and geo-spatial image analysis: a semi-automati...

引用

Conference on image and Signal Processing for Remote Sensing XVI

作者： Michel, Julien Inglada, Jordi Malik, Julien CNES DCT SI AP 18 Av E Belin F-31401 Toulouse 9 France CNES CESBIO F-31401 Toulouse 9 France CS IGI F-31506 Toulouse 5 France

ISBN: (纸本)9780819483478

High resolution optical remote sensing images allow to produce accurate land-cover maps. This is usually achieved using an ad-hoc mixture of image segmentation and supervised classification. The main drawback of this approach is that it does not scale for real world complete scenes. In this paper we present a framework which allows to implement this kind of image analysis without scale issues.

关键词： Land-cover maps object-based image analysis Geographical databases

来源：评论

学校读者我要写书评

暂无评论

URBAN VEGETATION DETECTION USING HIGH DENSITY FULL-WAVEFORM AIRBORNE LIDAR DATA - COMBINATION OF object-based image AND POINT CLOUD analysis

URBAN VEGETATION DETECTION USING HIGH DENSITY FULL-WAVEFORM ...

引用

ISPRS Technical Commission VII Symposium - 100 Years ISPRS - Advancing Remote Sensing Science

作者： Hoefle, B. Hollaus, M. Heidelberg Univ Dept Geog D-69120 Heidelberg Germany Vienna Univ Technol Inst Photogrammetry & Remote Sensing A-1040 Vienna Austria

In this paper, a new GIS workflow for fully automated urban vegetation and tree parameter extraction from airborne LiDAR data is presented. The strengths of both raster- and point cloud-based methods are combined, in order to derive a vegetation map layer as well as single tree parameters (e. g. tree height and crown width) in an efficient way. The workflow is implemented in GRASS GIS making use of standard GIS functionality and newly developed tools providing access to point cloud analysis. An edge-based segmentation delineates potential tree crowns, which are further aggregated to single trees or group of trees by using local topological information (e. g. percentage of outline touched by neighbors) and constraints on segment geometry (e. g. shape of segments). Furthermore, the classification makes use of segment attributes that have been extracted from the full-waveform point cloud (e. g. percentage of first echoes, echo width and signal amplitude). A representative study area in the City of Vienna is used to demonstrate the applicability of the developed object-based GIS workflow. Buildings and vegetation objects could be separated with high accuracy, where at maximum 14% of classified vegetation segments confuse with buildings (mainly building edges). Concluding, the unique high density (50 pts/m(2)) full-waveform LiDAR data open a new scale in 3D object extraction but demanding for novel strategies in object-based raster and point cloud analysis.

关键词： Airborne LiDAR Vegetation mapping Open source GIS object-based image analysis

来源：评论

学校读者我要写书评

暂无评论

Effects of spatial resolution of remotely sensed data on estimating urban impervious surfaces

引用

Journal of Environmental Sciences 2011年第8期23卷 1375-1383页

作者： Weifeng Li Zhiyun Ouyang Weiqi Zhou Qiuwen Chen State Key laboratory of Urban and Region Ecology Research Centre for Eco-Environmental Sciences Chinese Academy of Sciences Beijing1000~85 China. Department of Plant Sciences University of California Davis Mail Stop 1 1210 PES One Shields Ave. Davis CA 95616 USA

Impervious surfaces are the result of urbanization that can be explicitly quantified, managed and controlled at each stage of land development. It is a very useful environmental indicator that can be used to measure the impacts of urbanization on surface runoff, water quality, air quality, biodiversity and rnicroclimate. Therefore, accurate estimation of impervious surfaces is critical for urban environmental monitoring, land management, decision-making and urban planning. Many approaches have been developed to estimate surface imperviousness, using remotely sensed data with various spatial resolutions. However, few studies, have investigated the effects of spatial resolution on estimating surface imperviousness. We compare medium-resolution Landsat data with high-resolution SPOT images to quantify the imperviousness in Beijing, China. The results indicated that the overall 91% accuracy of estimates of imperviousness based on TM data was considerably higher than the 81% accuracy of the SPOT data. The higher resolution SPOT data did not always predict the imperviousness of the land better than the TM data. At the whole city level, the TM data better predicts the percentage cover of impervious surfaces. At the sub-city level, however, the ring belts from the central core to the urban-rural peripheral, the SPOT data may better predict the imperviousness. These results highlighted the need to combine multiple resolution data to quantify the percentage of imperviousness, as higher resolution data do not necessarily lead to more accurate estimates. The methodology and results in this study can be utilized to identify the most suitable remote sensing data to quickly and efficiently extract the pattern of the impervious land, which could provide the base for further study on many related urban environmental problems.

关键词： remote sensing impervious surface landscape pattern spatial resolution object-based image analysis urban landscape

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：