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Utility of Landsat Image in the Study of Land Cover and Land Surface Temperature Change

Procedia Environmental Sciences 2011年 10卷 1287-1292页

作者： Zhi-qiang Lv Qi-gang Zhou Tourism and Land Department Chongqing Technology and Business University Chongqing China Chongqing Engineering Technology Research Center for Information Mangement in Developent Chongqing Technology and Business University Chongqing China

Scientists have aimed at exploring land use and land cover (LULC) and their environmental influence in order to improve our understanding of the causes and consequences of these phenomena. This study addresses and use and land cover change (LUCC) in the upper reaches of Pearl River Delta, China, from 1990 to 2008. based on remotely sensed images of Landsat, LUCC and land surface temperature were assessed. Results showed that land surface temperature was highly influenced by the LUCC from 1990 to 2008. An examination of the relationship between the LULC and LST maps was finally conducted to comprehension their interactions. Results indicated that Landsat image was effective for quantifying the heat environment and providing reliable measurements of land use change. LST was found to be positively correlated with impervious surface but negatively correlated with vegetated land. Each temperature zone was associated with a dominant LULC category.

关键词： Land use land surface temperature object-based classification TM/ETM+ Fast developing region

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object classification of Aerial Images With Bag-of-Visual Words

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2010年第2期7卷 366-370页

作者： Xu, Sheng Fang, Tao Li, Deren Wang, Shiwei Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200240 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China

This letter presents a Bag-of-Visual Words (BOV) representation for object-based classification in land-use/cover mapping of high spatial resolution aerial photograph. The method is introduced to handle the special characteristics of aerial images, i.e., variability of spectral and spatial content. Specifically, patch detection and description are used to divide and represent various subregions of objects comprising multiple homogeneous components. Moreover, the BOV representation is constructed with the statistics of the occurrence of visual words, which are learned from the training data set. A combination of spectral and texture features is verified to be a satisfactory choice through the evaluations of various patch descriptors. Furthermore, a threshold-based method is employed to reduce the impact of outliers on classification in test data. Experiments based on aerial-image data set show that the proposed BOV representation yields better classification performance than the low-level features, such as the spectral and texture features.

关键词： Bag-of-Visual Words (BOV) composite object low-level features object-based classification

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Pixel, object and hybrid classification comparisons

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JOURNAL OF SPATIAL SCIENCE 2010年第1期55卷 43-54页

作者： Bernardini, A. Frontoni, E. Malinverni, E. S. Mancini, A. Tassetti, A. N. Zingaretti, P. Polytech Univ Marche Fac Engn DARDUS Ancona Italy Polytech Univ Marche Fac Engn DIIGA Ancona Italy

The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit The generalized GIS-ready pi ducts derived from the results of pixel-based approaches and the availability of higher-resolution imagery have directed research towards object-based classification approaches In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover in derived from a pixel classification by means of the Winner Takes All (WTA) algorithm Land use and land cover results obtained though this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach

关键词： pixel-based classification object-based classification thematic map Winner Takes All (WTA) algorithm One Against All (OAA) algorithm stability map land use/land cover (LULC)

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Utility of landsat Image in the Study of Land Cover and Land Surface Temperature Change

Utility of landsat Image in the Study of Land Cover and Land...

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2011 3rd International Conference on Environmental Science and Information Application Technology(ESIAT 2011)

作者： Zhi-qiang Lv Qi-gang Zhou Tourism and Land Department Chongqing Technology and Business University Chongqing Engineering Technology Research Center for Information Mangement in Developent Chongqing Technology and Business University

Scientists have aimed at exploring land use and land cover(LULC) and their environmental influence in order to improve our understanding of the causes and consequences of these phenomena. This study addresses and use and land cover change(LUCC) in the upper reaches of Pearl River Delta, China, from 1990 to 2008. based on remotely sensed images of Landsat, LUCC and land surface temperature were assessed. Results showed that land surface temperature was highly influenced by the LUCC from 1990 to 2008. An examination of the relationship between the LULC and LST maps was finally conducted to comprehension their interactions. Results indicated that Landsat image was effective for quantifying the heat environment and providing reliable measurements of land use change. LST was found to be positively correlated with impervious surface but negatively correlated with vegetated land. Each temperature zone was associated with a dominant LULC category.

关键词： Land use land surface temperature object-based classification TM/ETM+ Fast developing region

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Per-Field Irrigated Crop classification in Arid Central Asia Using SPOT and ASTER Data

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REMOTE SENSING 2010年第4期2卷 1035-1056页

作者： Conrad, Christopher Fritsch, Sebastian Zeidler, Julian Ruecker, Gerd Dech, Stefan Univ Wurzburg Remote Sensing Unit D-97074 Wurzburg Germany German Remote Sensing Data Ctr DFD German Aerosp Ctr DLR D-82234 Oberpfaffenhofen Wessling Germany

The overarching goal of this research was to explore accurate methods of mapping irrigated crops, where digital cadastre information is unavailable: (a) Boundary separation by object-oriented image segmentation using very high spatial resolution (2.5-5 m) data was followed by (b) identification of crops and crop rotations by means of phenology, tasselled cap, and rule-based classification using high resolution (15-30 m) bi-temporal data. The extensive irrigated cotton production system of the Khorezm province in Uzbekistan, Central Asia, was selected as a study region. Image segmentation was carried out on pan-sharpened SPOT data. Varying combinations of segmentation parameters (shape, compactness, and color) were tested for optimized boundary separation. The resulting geometry was validated against polygons digitized from the data and cadastre maps, analysing similarity (size, shape) and congruence. The parameters shape and compactness were decisive for segmentation accuracy. Differences between crop phenologies were analyzed at field level using bi-temporal ASTER data. A rule set based on the tasselled cap indices greenness and brightness allowed for classifying crop rotations of cotton, winter-wheat and rice, resulting in an overall accuracy of 80 %. The proposed field-based crop classification method can be an important tool for use in water demand estimations, crop yield simulations, or economic models in agricultural systems similar to Khorezm.

关键词： object-based classification segmentation tasselled cap Uzbekistan irrigated agriculture multi-sensor

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Mapping Postfire Vegetation Recovery Using EO-1 Hyperion Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2010年第3期48卷 1613-1618页

作者： Mitri, George H. Gitas, Ioannis Z. Univ Trieste Dept Biol I-34127 Trieste Italy Aristotle Univ Thessaloniki Sch Forestry & Nat Environm Lab Forest Management & Remote Sensing Thessaloniki 54124 Greece

The aim of this paper is to investigate whether it is possible to accurately map postfire vegetation recovery on the Mediterranean island of Thasos by employing Earth Observing- 1 (EO-1) Hyperion imagery and object-based classification. Specific objectives include the following: 1) locating and mapping areas of forest regeneration and other vegetation recovery and distinguishing among them;2) distinguishing between Pinus brutia regeneration and Pinus nigra regeneration within the area of forest regeneration;and 3) examining whether it is possible to distinguish between areas of forest regeneration (Pinus brutia, Pinus nigra) and mature forest. The data used in this study consist of satellite images, field-spectroradiometry measurements, and field observations of the homogenous revegetated areas. The methodology comprised four consecutive steps. The first step involved preprocessing of the Hyperion image and field data. Subsequently, an object-oriented model was developed, which involved three steps, namely, image segmentation, object training, and object classification. The process resulted in the separation of five classes ("brutia mature," "nigra mature," "brutia regeneration," "nigra regeneration," and "other vegetation"). The accuracy assessment revealed very promising results (approximately 75.81% overall accuracy, with a Kappa Index of Agreement of 0.689). Some classification confusion involving the classes of Pinus brutia regeneration and Pinus nigra regeneration was recorded. This could be attributed to the absence of large homogenous areas of regenerated pine trees. The main conclusion drawn in this paper was that object-based classification can be used to accurately map postfire vegetation recovery using EO-1 Hyperion imagery.

关键词： Hyperspectral remote sensing object-based classification vegetation recovery

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Use of textural measurements to map invasive wetland plants in the Hudson River National Estuarine Research Reserve with IKONOS satellite imagery

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REMOTE SENSING OF ENVIRONMENT 2010年第4期114卷 876-886页

作者： Laba, Magdeline Blair, Betsy Downs, Roger Monger, Bruce Philpot, William Smith, Stephen Sullivan, Patrick Baveye, Philippe C. Cornell Univ Dept Civil & Environm Engn Ithaca NY 14853 USA Bard Coll Field Stn Hudson River NERR NYSDEC Annandale On Hudson NY 12504 USA Cornell Univ Inst Resource Informat Sci Ithaca NY 14853 USA Cornell Univ Dept Earth & Atmospher Sci Ithaca NY 14853 USA Cornell Univ Dept Nat Resources Ithaca NY 14853 USA Abertay Univ SIMBIOS Ctr Dundee DD1 1HG Scotland

At this point, models, and accompanying field data, that could be used to predict the likely response of estuaries and tidal marshes to future environmental change are lacking. To improve this situation, monitoring efforts in these complex ecosystems need to be intensified, and new, efficient monitoring techniques should be developed. In this context, our research assessed the use of IKONOS satellite imagery to map plant communities at Tivoli Bays, in the Hudson River National Estuarine Research Reserve (HRNERR). Tivoli Bays, a freshwater tidal wetland, contains a unique assemblage of plant communities, including three invasive plants (Trapa natans, Phragmites australis, and Lythrum salicaria). To study the effects of textural information on the accuracy of land cover maps produced for the HRNERR, seven different 11-class land cover maps were produced using a maximum-likelihood classification on seven combinations of spectral and textural data derived from an IKONOS image. Conventional contingency tables served as a basis for an accuracy assessment of these maps. The overall classification accuracies, as assessed by the contingency tables, ranged from 45% to 77.7%. The maximum-likelihood classification relying on four spectral and four 5-by-5 filter textural bands (created by superposing a textural filter separately on each band of the IKONOS image) had the lowest overall accuracy, whereas the one based on four spectral and four 3-by-3 filter textural bands associated with all segments, identified by an object-based classification of the IKONOS image, had the highest accuracy. Results suggest that a combination of per-pixel classification and incorporation of texture for segments generated through an object-based classification slightly increases classification accuracy from 76.2% for the maximum-likelihood classification of the four spectral bands of the IKONOS image to 77.7% for the combination of spectral and textural information produced for selected segments. F

关键词： NERR Ikonos object-based classification Texture Image segmentation Invasive Wetlands Estuarine National Estuarine Research Reserve

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object-based feature extraction using high spatial resolution satellite data of urban areas

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JOURNAL OF SPATIAL SCIENCE 2010年第1期55卷 117-132页

作者： Taubenboeck, H. Esch, T. Wurm, M. Roth, A. Dech, S. German Aerosp Ctr DLR German Remote Sensing Data Ctr DFD D-82234 Oberpfaffenhofen Wessling Germany Univ Wurzburg Inst Geog D-97074 Wurzburg Germany

Urban morphology is characterized by a complex and variable coexistence of diverse, spatially and spectrally heterogeneous objects Built-up areas are among the most rapidly changing and expanding elements of the landscape Thus, remote sensing becomes an essential field for up-to-date and area-wide data acquisition, especially in explosively sprawling cities of developing countries The urban heterogeneity requires high spatial resolution image data for an accurate geometric differentiation of the small-scale physical features This study proposes an object-based, multi-level, hierarchical classification framework combining shape, spectral, hierarchical and contextual information for the extraction of in ban features The particular focus is on high class accuracies and stable transferability by fast and easy adjustments on varying urban structures or sensor characteristics The framework is based on a modular concept following a chronological workflow from a bottom-up segmentation optimization to a hierarchical, fuzzy-based decision fusion top-down classification The workflow has been developed on IKONOS data for the megacity Istanbul, Turkey Transferability is tested based on Quickbird data from the various urban structures of the incipient megacity Hyderabad, India The validation of both land-cover classifications shows an overall accuracy of more than 81 percent

关键词： urban remote sensing object-based classification multi-level structure detection fuzzy logic decision fusion transferability

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Mapping urban landscape heterogeneity: agreement between visual interpretation and digital classification approaches

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LANDSCAPE ECOLOGY 2010年第1期25卷 53-67页

作者： Zhou, Weiqi Schwarz, Kirsten Cadenasso, M. L. Univ Calif Davis Dept Plant Sci Davis CA 95616 USA Cary Inst Ecosyst Studies Millbrook NY 12545 USA Rutgers State Univ Dept Ecol Evolut & Nat Resources New Brunswick NJ 08901 USA

Visual interpretation of remotely sensed imagery has long been used for landscape pattern analysis. Few studies, however, have investigated human variation in estimates of within-patch composition for classification of those patches, particularly in urban settings. This paper compares the agreement of two approaches-visual interpretation and object-based-to estimate the proportion cover of landscape features within delineated patches, and investigates the spatial patterns of patches with large disagreement between the two approaches. The two approaches were compared for the Gwynns Falls watershed, Maryland, USA. Three methods were used to assess agreement: a traditional error matrix based procedure and two fuzzy methods, a plus-one modification of the traditional procedure, and a fuzzy set theory method. We found that while visual interpretation does not work effectively when patches contain a mix of different types of features, accuracy increases with patches that are either dominated by a specific feature, or do not contain a specific feature. The overall accuracies of estimates by visual interpretation also vary by features, ranging from 63.3% for pavement to 93.8% for bare soil. Patches with large disagreement between the two approaches cluster spatially at locations where the urban landscape is more structurally complex, suggesting the accuracy of visual interpretation may be affected by patch shape complexity, and the spatial configuration of the landscape features within the patches. These results provide important insights into the accuracy of thematic maps based on visual interpretation, not only for ecologists and managers who are using the maps, but also for those who produce the maps.

关键词： Visual interpretation object-based classification Patch classification Accuracy assessment Fuzzy set theory Spatial heterogeneity Urban landscape Land cover

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An automatic region-based image segmentation algorithm for remote sensing applications

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ENVIRONMENTAL MODELLING & SOFTWARE 2010年第10期25卷 1149-1165页

作者： Wang, Zhongwu Jensen, John R. Im, Jungho SUNY Syracuse Dept Environm Resources & Forest Engn Coll Environm Sci & Forestry Syracuse NY 13210 USA Sanborn Map Co Ann Arbor MI 48103 USA Univ S Carolina Dept Geog Columbia SC 29208 USA

object-based image analysis has proven its potentials for remote sensing applications, especially when using high-spatial resolution data. One of the first steps of object-based image analysis is to generate homogeneous regions from a pixel-based image, which is typically called the image segmentation process. This paper introduces a new automatic Region-based Image Segmentation Algorithm based on k-means clustering (RISA), specifically designed for remote sensing applications. The algorithm includes five steps: k-means clustering, segment initialization, seed generation, region growing, and region merging. RISA was evaluated using a case study focusing on land-cover classification for two sites: an agricultural area in the Republic of South Africa and a residential area in Fresno, CA. High spatial resolution SPOT 5 and QuickBird satellite imagery were used in the case study. RISA generated highly homogeneous regions based on visual inspection. The land-cover classification using the RISA-derived image segments resulted in higher accuracy than the classifications using the image segments derived from the Definiens software (eCognition) and original image pixels in combination with a minimum-distance classifier. Quantitative segmentation quality assessment using two object metrics showed RISA-derived segments successfully represented the reference objects. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Image segmentation k-means clustering Region growing Region merging object-based classification

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