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Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification

REMOTE SENSING OF ENVIRONMENT 2007年第1期110卷 29-44页

作者： Johansen, Kasper Coops, Nicholas C. Gergel, Sarah E. Stange, Yulia Univ Queensland Sch Geog Planning & Architecture Ctr Remote Sensing & Spatial Informat Sci Brisbane Qld 4072 Australia Univ British Columbia Dept Forest Resources Management Vancouver BC V6T 1Z4 Canada Univ British Columbia Dept Forest Sci Vancouver BC V6T 1Z4 Canada

Terrestrial Ecosystem Mapping provides critical information to land and resource managers by incorporating information on climate, physiography, surficial material, soil, and vegetation structure. The main objective of this research was to determine the capacity of high spatial resolution satellite image data to discriminate vegetation structural stages in riparian and adjacent forested ecosystems as defined using the British Columbia Terrestrial Ecosystem Mapping (TEM) scheme. A high spatial resolution QuickBird image, captured in June 2005, and coincident field data covering the riparian area of Lost Shoe Creek and adjacent forests on Vancouver Island, British Columbia, was used in this analysis. Semi-variograms were calculated to assess the separability of vegetation structural stages and assess which spatial scales were most appropriate for calculation of grey-level co-occurrence texture measures to maximize structural class separation. The degree of spatial autocorrelation showed that most vegetation structural types in the TEM scheme could be differentiated and that window sizes of 3 X 3 pixels and 11 X 11 pixels were most appropriate for image texture calculations. Using these window sizes, the texture analysis showed that co-occurrence contrast, dissimilarity, and homogeneity texture measures, based on the bands in the visible part of the spectrum, provided the most significant statistical differentiation between vegetation structural classes. Subsequently, an object-oriented classification algorithm was applied to spectral and textural transformations of the QuickBird image data to map the vegetation structural classes. Using both spectral and textural image bands yielded the highest classification accuracy (overall accuracy= 78.95%). The inclusion of image texture increased the classification accuracies of vegetation structure by 2-19%. The results show that information on vegetation structure can be mapped effectively from high spatial resolution satellit

关键词： forest structure QuickBird imagery semi-variograms image texture object-oriented classification

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Mapping urban and peri-urban agriculture using high spatial resolution satellite data

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JOURNAL OF APPLIED REMOTE SENSING 2009年第1期3卷

作者： Forster, Dionys Buehler, Yves Kellenberger, Tobias W. Eawag Swiss Fed Inst Aquat Sci & Technol Dept Water & Sanitat Developing Countries Sandec CH-8600 Dubendorf Switzerland Univ Rostock Fac Agr & Environm Sci Inst Land Use D-18051 Rostock Germany Univ Zurich Remote Sensing Labs CH-8057 Zurich Switzerland

In rapidly changing peri-urban environments where biophysical and socioeconomic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to achieve improved classification results in complex heterogeneous landscapes. This study assessed the usefulness of object-oriented analysis of Quickbird high spatial resolution satellite data to classify urban and peri-urban agriculture in a limited peri-urban area of Hanoi, Vietnam. The results revealed that segmentation was essential in developing the object-oriented classification approach. Accurate segmentation of shape and size of an object enhanced classification with spectral, textural, morphological, and topological features. A qualitative, visual comparison of the classification results showed successful localisation and identification of most LCLU classes. Quantitative evaluation was conducted with a classification error matrix reaching an overall accuracy of 67% and a kappa coefficient of 0.61. In general, object-oriented classification of high spatial resolution satellite data proved the promising approach for LCLU analysis at village level. Capturing small-scale urban and peri-urban agricultural diversity offers a considerable potential for environmental monitoring. Challenges remain with the delineation of field boundaries and LCLU diversity on more spatially extensive datasets.

关键词： Hanoi land cover/land use object-oriented classification Quickbird remote sensing urban and peri-urban agriculture VHR data Vietnam

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Quantifying different types of urban growth and the change dynamic in Guangzhou using multi-temporal remote sensing data

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2013年第1期21卷 409-417页

作者： Sun, Cheng Wu, Zhi-feng Lv, Zhi-qiang Yao, Na Wei, Jian-bing Chinese Acad Sci Guangzhou Inst Geochem Guangzhou 510640 Guangdong Peoples R China Chinese Acad Sci Grad Univ Beijing 100049 Peoples R China Guangdong Inst Ecoenvironm & Soil Sci Guangzhou 510650 Guangdong Peoples R China Guangzhou Univ Sch Geog Sci Guangzhou 510006 Guangdong Peoples R China Chongqing Technol & Business Univ Dept Land Resource Management Chongqing 400067 Peoples R China Wuhan Univ Sch Remote Sensing Informat Engn Wuhan 430079 Peoples R China

There is a widespread concern about urban sprawl. It has negative impacts on natural resources, economic health, and community character. Without a universal definition of urban sprawl, its quantification and modeling is difficult. Traditionally, urban sprawl was described using qualitative terms, and landscape patterns. Quantitative methods are required to help local, regional and state land use planners to better identify, understand and address it. In this study, an integrated approach of remote sensing and GIS was used to identify three urban growth types of infilling growth, outlying growth and edge-expansion growth at the city of Guangzhou, China. Spatial metrics were used to characterize long-term trends and patterns of urban growth. Result shows that the proposed method can identify and visualize different urban growth types. Infilling growth is the dominant expansion type. Edge-expansion is concentrated at suburban areas. Outlying growth mainly occurs relatively far from the urban core. The analysis shows that initially the urban area expands mainly as outlying growth, causing increased fragmentation and dispersion of urban areas. Next, growth filled in vacant non-urban area inwards, resulting into a more compact and aggregated urban pattern. The study shows an improved understanding of urban growth, and helps to provide an effective way for urban planning. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Urban growth types Change dynamic Spatial metrics object-oriented classification

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A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants

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ECOLOGICAL INFORMATICS 2011年第2期6卷 136-146页

作者： Ouyang, Zu-Tao Zhang, Mo-Qian Xie, Xiao Shen, Qi Guo, Hai-Qiang Zhao, Bin Fudan Univ Inst Biodivers Sci Minist Educ Key Lab Biodivers Sci & Ecol Engn Shanghai 200433 Peoples R China

object-oriented classification (OOC) has shown many significant advantages over other methods for classification of urban or forest ecosystems. However, it remains unclear if this technology could exhibit these advantages on mapping monospecific plant stands in herbaceous plant dominated ecosystems (e.g. saltmarshes). In this study, we compared the effectiveness of OOC and pixel-based classification (PBC) methods for mapping plants in a saltmarsh ecosystem. QuickBird was selected for very high resolution (VHR) imagery. Eleven models defined by classification types, feature spaces, classifiers, and hierarchical approaches with multi-scale segmentation were built for comparison. The results showed that the QuickBird imagery efficiently discriminated saltmarsh monospecific vegetation stands and that OOC performed better than PBC in terms of accuracy. We also found that the improvement of OOC was primarily due to employing membership functions and a hierarchical approach with multi-scale segmentation. Although texture and shape features have been deemed as two major advantages of OOC, enhanced performance was not observed in this study. The results of this study demonstrated that OOC would be superior to PBC for classifying herbaceous plant species in terms of accuracy. To improve the classification accuracy, greater concern should be given to exploration of the relationships between features of both objects and classes and to combining information from different object scales, while shape and texture features can be a minor consideration due to their intricately high spatial variability. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Saltmarsh object-oriented classification Pixel-based classification VHR imagery

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Mediterranean forest mapping using hyper-spectral satellite imagery

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ARABIAN JOURNAL OF GEOSCIENCES 2013年第12期6卷 5017-5032页

作者： Etteieb, Selma Louhaichi, Mounir Kalaitzidis, Chariton Gitas, Ioannis Z. Mediterranean Agron Inst Chania Dept Environm Management Khania 73100 Crete Greece ICARDA Amman 11195 Jordan Aristotle Univ Thessaloniki Fac Forestry & Nat Environm Lab Forest Management & Remote Sensing Thessaloniki 54124 Greece

Mediterranean forests are characterized by spatiotemporal heterogeneity that is associated with Mediterranean climate, floristic biodiversity and topographic variability. Satellite remote sensing can be an effective tool for characterizing and monitoring forest vegetation distribution within these fragmented Mediterranean landscapes. The heterogeneity of Mediterranean vegetation, however, often exceeds the resolution typical of most satellite sensors. Hyper-spectral remote sensing technology demonstrates the capacity for accurate vegetation identification. The objective of this research is to determine to what extent forest types can be discriminated using different image analysis techniques and spectral band combinations of Hyperion satellite imagery. This research mapped forest types using a pixel-based Spectral Angle Mapper (SAM), nearest neighbour and membership function classifiers of the object-oriented classification. Hyperion classification was done after reducing Hyperion data using nine selected band combinations. Results indicate that the selection of band combination while reducing the Hyperion dataset improves classification results for both the overall and the individual forest type accuracy, in particular for the selected optimum Hyperion band combination. One shortcoming is that the performance of the best selected band combination was superior in terms of both overall and individual forest type accuracy when applying the membership classifier of the object-oriented method compared to SAM and nearest neighbour classifiers. However, all techniques seemed to suffer from a number of problems, such as spectral similarity among forest types, overall low energy response of the Hyperion sensor, Hyperion medium spatial resolution and spatiotemporal and spectral heterogeneity of the Mediterranean ecosystem at multiple scales.

关键词： Mediterranean forests mapping Hyperion satellite imagery Hyperion data reduction Pixel-based Spectral Angle Mapper (SAM) object-oriented classification

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Investigating Coupled Impacts of Climate Change and Socioeconomic Transformation on Desertification by Using Multitemporal Landsat Images: A Case Study in Central Xilingol, China

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2013年第5期10卷 1244-1248页

作者： Li, Shuang Xie, Yichun Wuhan Univ Int Sch Software Wuhan 430079 Peoples R China Eastern Michigan Univ Dept Geog & Geol Ypsilanti MI 48197 USA

A case study is conducted in Xilingol Rangeland, Inner Mongolia, China, to investigate the driving factors of temporal dynamics of desertification by using time-series Landsat images. The spectral characters of sand dunes and urban lands in the arid and semiarid grassland environments are very similar, and thus, it is hard to discriminate them with traditional image classifiers. Nine available scenes of Landsat images without cloud cover from 1985 to 2010 are chosen for the case study. An object-oriented image classification (OOIC) is developed to classify sand dunes. The classification results are assessed with the ground reference points in 1985, 2004, and 2010, the land-cover maps produced from other classifiers in literature, and Google Earth historical aerial photo archives. Second, the areas of sand dunes derived from OOIC at the nine times are extrapolated into a 26-year time-series data set from 1985 to 2010 by applying several extrapolation techniques commonly used in regional geographic studies. Afterward, six climate factors and nine socioeconomic variables during the same study period along with the sand dune area are composed into a completed data set to investigate the coupled impacts of climate change and socioeconomic transformation on the temporal dynamics of desertification. Three types of regression models (climate model, economic model, and the coupled model) are explored, respectively, to examine which factors contribute more to the desertification dynamics. The findings confirm that the desertification process in Xilingol Rangeland is very complicated although it shows a strong causal relationship with several socioeconomic factors.

关键词： Climate change coupled human-nature system desertification object-oriented classification time-series analysis

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Mapping private gardens in urban areas using object-oriented techniques and very high-resolution satellite imagery

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LANDSCAPE AND URBAN PLANNING 2007年第3期81卷 179-192页

作者： Mathieu, Renaud Freeman, Claire Aryal, Jagannath Univ Otago Sch Surveying Spatial Ecol Res Facil Dunedin New Zealand Univ Otago Dept Geog Planning Programme Dunedin New Zealand

Gardens remain the least studied and least understood habitat in urban areas. With the recent exception of the URGENT funded urban domestic gardens project in the UK, there is a notable lack of research on the ecological character and contribution of gardens to the wider urban biodiversity. This is despite the fact that gardens usually comprise the largest vegetated component of the urban greenspace resource. In part this omission has been due to the difficulties inherent in obtaining ecological data on gardens and the lack of a methodology for classifying and analysing garden data. This paper presents data from a study undertaken in the city of Dunedin, New Zealand. The study developed a methodology using object-oriented classification techniques and very high-resolution multispectral Ikonos imagery to automatically map the extent, distribution and density of private gardens in the city. The focus was on the vegetated garden area which was calculated as comprising 46% of the residential area or 36% of the total urban area. Rigorous accuracy assessments were undertaken. When using the automated classification technique, a total of 90.7% of the private gardens were correctly identified. Discrimination of garden types (e.g. trees or grass dominated) was encouraging, but still requires improvement. Our results indicate the great potential that the methodology has in providing a quick method for obtaining good quality ecological data on garden habitats in urban areas. (c) 2006 Elsevier B.V. All rights reserved.

关键词： vegetation mapping urban areas object-oriented classification Ikonos New Zealand

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Exploring the effects of biophysical parameters on the spatial pattern of rare cold damage to mangrove forests

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REMOTE SENSING OF ENVIRONMENT 2014年 150卷 20-33页

作者： Liu, Kai Liu, Lin Liu, Hongxing Li, Xia Wang, Shugong Sun Yat Sen Univ Sch Geog & Planning Ctr Integrated Geog Informat Anal Guangzhou 510275 Guangdong Peoples R China Univ Cincinnati Dept Geog Cincinnati OH 45221 USA

In recognition of the role of mangrove forests as natural barriers and bio-shields in protecting coastal population and property in the aftermath of the 2004 Indian Ocean tsunami disaster, many Asian countries have launched various projects to restore and conserve mangrove trees. The growth and vitality condition of mangroves may be negatively influenced by various anthropogenic and natural disturbances and stresses, such as cold weather events. This paper represents the first attempt to map the cold damage to mangroves in the tropical zone of Southern China using high resolution multispectral satellite imagery. In this study, the spatial distribution of severity level of cold damage to mangroves imposed by a rare blizzard in Southern China during early 2008 has been mapped and analyzed using IKONOS image. An object-oriented classification applied to the images achieved an overall accuracy of 90.9% for classifying mangroves into three categories: undamaged, damaged, and dead. Of the total 287.30 ha of mapped mangroves, approximately 51.1% were damaged or died due to the cold stress of the blizzard. These results have been verified by more recent WorldView-II images. Furthermore, we explored the associations between the cold damage severity level of mangroves and climate and landscape factors, including elevation, surface slope, aspect, wind direction and velocity, wind shadow effect, and tree height. The surface topographical variables are derived from the ASTER Global DEM. The analysis results suggest that wind direction, terrain topography, and proximity to ocean are important local environmental factors controlling the vulnerability of mangroves to cold damage. Most damaged mangroves were located near the open sea, as a result of a possible edge exposure effect. The damage to the mangroves on the leeward side of a hill was also minimal. Such knowledge of climate and landscape controls can offer insight on how to better restore and protect the mangrove forest in t

关键词： Mangroves Cold damage IKONOS object-oriented classification Climate and landscape controls Biophysical parameters

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Two Specific Multiple-Level-Set Models for High-Resolution Remote-Sensing Image classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2009年第3期6卷 558-561页

作者： Ma, Hongchao Yang, Yun Wuhan Univ Sch Remote Sensing Wuhan 430079 Peoples R China

This letter adopts level-set methods in order to seek a novel classification strategy in which classification is free of segmentation. A region-driven multiple-level-set (MLS) framework is used to perform very high resolution image classification. Two specific unsupervised classification models are presented. First, from the point of view of feature fusion, an MLS model is suggested by fusing texture features and spectral information (TSMLS model). The model combines spectral information, texture features extracted from the image, and geometrical characteristics of closed curves to achieve effective classification for high-resolution imagery. Second, an alternative MLS model with quadratic image energy (GMMLS model) is presented, which can efficiently integrate the level-set method with Bayesian theory. The model benefits from both the level-set method and Bayesian theory and performs satisfactory classifications. The experiments have demonstrated that our methods can obtain better or similar classification results as compared to support vector machine and Mansouri's method.

关键词： High-resolution remote-sensing image image segmentation multiple level set (MLS) object-oriented classification

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Spatio-Temporal Dynamics of Wetland Landscape Patterns Based on Remote Sensing in Yellow River Delta, China

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WETLANDS 2014年第4期34卷 787-801页

作者： Liu, Guilin Zhang, Luocheng Zhang, Qian Musyimi, Zipporah Jiang, Qinghu Chinese Acad Sci State Key Lab Lake Sci & Environm Nanjing Inst Geog & Limnol Nanjing 210008 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Trier Dept Environm Remote Sensing & Geoinformat D-54286 Trier Germany Nanjing Forestry Univ Coll Landscape Architecture Nanjing 210037 Jiangsu Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China

The wetland landscape patterns of the Yellow River Delta have undergone an extremely significant change due to rapid economic development since 2000. The aim of this paper is to quantitatively analyze spatial and temporal changes of these wetlands in the recent decade. Three sets of wetland maps were acquired using object-oriented classification method based on remotely sensed imageries in 2000, 2005 and 2010. Then landscape indices from class and landscape level using FRAGSTATS 3.3 software were used to quantify the spatial and temporal dynamics of wetlands. The results showed that wetlands coverage decreased by approximately 2.27%. Natural wetlands decreased while constructed wetlands increased, especially prawn pools, which increased by 268.33%. Wetland landscape patches gradually became more complex and decentralized. Wetlands distribution analysis indicated that wetlands were mainly located in the area 45 km from the coastline. The study indicated a relatively high diversity at the estuary of the river and the north of the study area. However, wetlands were highly fragmented in the south of the study area and urban area. Dynamic of wetlands was mainly caused by human-induced factors, especially GDP and urban built-up area.

关键词： Wetland Landscape dynamic object-oriented classification Yellow River Delta

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