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Vegetation cover in relation to socioeconomic factors in a tropical city assessed from sub-meter resolution imagery

ECOLOGICAL APPLICATIONS 2018年第3期28卷 681-693页

作者： Martinuzzi, Sebastian Ramos-Gonzalez, Olga M. Munoz-Erickson, Tischa A. Locke, Dexter H. Lugo, Ariel E. Radeloff, Volker C. Univ Wisconsin Dept Forest & Wildlife Ecol SILVIS Lab 1630 Linden Dr Madison WI 53706 USA US Forest Serv USDA Int Inst Trop Forestry Jardin Bot Sur 1201 Calle Ceiba Rio Piedras PR 00926 USA Natl Socioenvironm Synth Ctr SESYNC 1 Pk PlSuite 300 Annapolis MD 21401 USA

Fine-scale information about urban vegetation and social-ecological relationships is crucial to inform both urban planning and ecological research, and high spatial resolution imagery is a valuable tool for assessing urban areas. However, urban ecology and remote sensing have largely focused on cities in temperate zones. Our goal was to characterize urban vegetation cover with sub-meter (<1 m) resolution aerial imagery, and identify social-ecological relationships of urban vegetation patterns in a tropical city, the San Juan Metropolitan Area, Puerto Rico. Our specific objectives were to (1) map vegetation cover using sub-meter spatial resolution (0.3-m) imagery, (2) quantify the amount of residential and non-residential vegetation, and (3) investigate the relationship between patterns of urban vegetation vs. socioeconomic and environmental factors. We found that 61% of the San Juan Metropolitan Area was green and that our combination of high spatial resolution imagery and object-based classification was highly successful for extracting vegetation cover in a moist tropical city (97% accuracy). In addition, simple spatial pattern analysis allowed us to separate residential from non-residential vegetation with 76% accuracy, and patterns of residential and non-residential vegetation varied greatly across the city. Both socioeconomic (e.g., population density, building age, detached homes) and environmental variables (e.g., topography) were important in explaining variations in vegetation cover in our spatial regression models. However, important socioeconomic drivers found in cities in temperate zones, such as income and home value, were not important in San Juan. Climatic and cultural differences between tropical and temperate cities may result in different social-ecological relationships. Our study provides novel information for local land use planners, highlights the value of high spatial resolution remote sensing data to advance ecological research and urban planni

关键词： airborne imagery green infrastructure land use planning object-based classification Puerto Rico residential vegetation social-ecological relationships tropical city urban ecology

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An object-based approach for mapping forest structural types based on low-density LiDAR and multispectral imagery

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GEOCARTO INTERNATIONAL 2018年第5期33卷 443-457页

作者： Angel Ruiz, Luis Abel Recio, Jorge Crespo-Peremarch, Pablo Sapena, Marta Univ Politecn Valencia Dept Ingn Cartog Geodesia & Fotogrametria Geoenvironm Cartog & Remote Sensing Grp CGAT Valencia Spain

Mapping forest structure variables provides important information for the estimation of forest biomass, carbon stocks, pasture suitability or for wildfire risk prevention and control. The optimization of the prediction models of these variables requires an adequate stratification of the forest landscape in order to create specific models for each structural type or strata. This paper aims to propose and validate the use of an object-oriented classification methodology based on low-density LiDAR data (0.5m(-2)) available at national level, WorldView-2 and Sentinel-2 multispectral imagery to categorize Mediterranean forests in generic structural types. After preprocessing the data sets, the area was segmented using a multiresolution algorithm, features describing 3D vertical structure were extracted from LiDAR data and spectral and texture features from satellite images. objects were classified after feature selection in the following structural classes: grasslands, shrubs, forest (without shrubs), mixed forest (trees and shrubs) and dense young forest. Four classification algorithms (C4.5 decision trees, random forest, k-nearest neighbour and support vector machine) were evaluated using cross-validation techniques. The results show that the integration of low-density LiDAR and multispectral imagery provide a set of complementary features that improve the results (90.75% overall accuracy), and the object-oriented classification techniques are efficient for stratification of Mediterranean forest areas in structural- and fuel-related categories. Further work will be focused on the creation and validation of a different prediction model adapted to the various strata.

关键词： Fuel strata object-based classification LiDAR WorldView-2 Sentinel-2

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Drought and Human Impacts on Land Use and Land Cover Change in a Vietnamese Coastal Area

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REMOTE SENSING 2019年第3期11卷 333-333页

作者： Hoa Thi Tran Campbell, James B. Wynne, Randolph H. Shao, Yang Son Viet Phan Virginia Polytech Inst & State Univ Virginia Tech Blacksburg VA 24061 USA Hanoi Univ Min & Geol Hanoi Vietnam Virginia Tech Dept Geog Blacksburg VA 24061 USA

Drought is a dry-weather event characterized by a deficit of water resources in a period of year due to less rainfall than normal or overexploitation. This insidious hazard tends to occur frequently and more intensively in sub-humid regions resulting in changes in the landscape, transitions in agricultural practices and other environmental-social issues. The study area is in the sub-humid region of the northern coastal zone of Binh Thuan province, Vietnam-Tuy Phong district. This area is indicated as a subject of prolonged droughts during 6-month dry seasons, which have occurred more frequently in recent years. Associated with economic transitions in agricultural practicing, urbanization, and industrialization, prolonged droughts have resulted in rapid changes in land use and land cover (LULC) in Tuy Phong, especially in three coastal communes: Binh Thanh, Lien Huong, and Phuoc The. A bi-temporal analysis using high-resolution data, the 2011 WorldView2 and the 2016 GeoEye1, was examined to assess LULC changes from observed severe droughts in those three communes. Results showed a dramatic reduction in the extent of hydrological systems (about 20%), and significant increases of tree canopies in urban areas and near the coastal areas (approximately 76.8%). Paddy fields declined by 51% in 2016;such areas transitioned to inactive status or were alternated for growing drought-tolerant plants. Shrimp farming experienced a recognizable decrease by approximately 44%. The 2014 map and field observations during summer 2016 provide references for object-based classification and validation. Overall agreement of results is about 85%.

关键词： Vietnam Binh Thuan sub-humid region drought-human impacts LULC high-resolution images object-based classification

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Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image

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ARABIAN JOURNAL OF GEOSCIENCES 2019年第22期12卷 699-699页

作者： Wang, Fang Yang, Wunian Ren, Jintong Chengdu Univ Technol Key Lab Geosci Spatial Informat Technol Minist Land & Resources Chengdu 610059 Sichuan Peoples R China Neijiang Normal Univ Coll Geog & Resources Sci Neijiang 641100 Peoples R China

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran's I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.

关键词： object features complexity Multiscale segmentation Scale selection object-based classification

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Environmental Approach in Modelling of Urban Growth: Tehran City, Iran

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ASIAN JOURNAL OF WATER ENVIRONMENT AND POLLUTION 2018年第2期15卷 47-56页

作者： Aqbelaghi, Aref Shahi Ghorbani, Mehdi Farhadi, Ebrahim Shafiee, Hadi Univ Tehran Dept Geog Tehran Iran

The rapid growth of urbanization has put heavy pressure on the land and its surrounding resources, reduction of vegetation cover, open spaces and serious social and environmental problems. Therefore, a basic step for managing and planning urban growth, as well as evaluating its cumulative effects, is to study and simulate the physical growth of the city. The purpose of this study is to understand the factors that influence the physical growth in Tehran on the basis of sustainable urban development in terms of environmental dimension and the preservation of environmental conditions in the next two decades. For this, using Landsat multi-temporal satellite imagery and object-based classification, land-use was assessed during the period from 1990 to 2015. In the next step, using the multi-criteria analysis model and the environment-based growth model, the impact of independent variables on urban growth, including 18 variables, was calculated from 1990 to 2015 and the map of urbanization potential was produced. Then the area of change for the expected year was predicted quantitatively using the Markov chain analysis. Finally, using Cellular Automata model, urban growth simulation for 2015 was performed with relative accuracy of 0.91 and Kappa coefficient of 0.87, and this model was used to estimate urban growth in 2025. The results show that urban growth will accelerate in 2025, as in the period 2003-2015, and often in the western and northeastern parts of the city. if the nature and extent of the impact of factors affecting urban growth will remain constant.

关键词： Urban growth modelling cellular automata object-based classification Tehran

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Evaluating the Potential of GF-1 Pan-multispectral Camera Imagery for Identifying the Quasi-circular Vegetation Patches in the Yellow River Delta,China

Evaluating the Potential of GF-1 Pan-multispectral Camera Im...

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2019第十一届数字图像处理国际会议

作者： Qingsheng Liu State Key of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of Sciences

Gaofen-1（GF-1） is the first high resolution optical imaging satellite of China primary civilian Earth observation project under the China High-resolution Earth Observation *** the GF-1 trio launched on March 31,2018,the combined 3+1 GF-1 constellation will offer one-day revisit coverage,which provides more available high resolution remote sensing images for monitoring decametric-scale vegetation patches and long-term pattern *** this study,a panchromatic image,the original multispectral image,and the pan-sharpened multispectral image from GF-1 Panmultispectral Camera was compared to evaluate their potential for identifying the quasi-circular vegetation patches（QVPs） in the Yellow River Delta,*** results showed that high spatial resolution was more important for identifying the QVPs than more additional spectral bands of GF-1,and the pan-sharpened multispectral image had the highest identification accuracy of the QVPs（F-measure is 64.6%）.In the future,multi-temporal pan-sharpened GF-1 images should be used to obtain higher accuracy,which is basis for studying vegetation patch pattern dynamics.

关键词： GF-1 pan-multispectral camera imagery object-based classification support vector machine quasi-circular vegetation patch Yellow River Delta

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Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2011年第1-4期183卷 409-423页

作者： Akar, Sertac Suezen, Mehmet Lutfi Kaymakci, Nuretdin Middle E Tech Univ Geol Engn Dept TR-06531 Ankara Turkey

The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases.

关键词： Synthetic aperture radar (SAR) Oil slick object-based classification Black Sea

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An object-based classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China)

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REMOTE SENSING 2011年第8期3卷 1710-1723页

作者： d'Oleire-Oltmanns, Sebastian Coenradie, Bodo Kleinschmit, Birgit Tech Univ Berlin D-10623 Berlin Germany Goethe Univ Frankfurt Dept Phys Geog Remote Sensing & GIS Res Grp D-60438 Frankfurt Germany

Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained.

关键词： urban structure types object-based classification land-use change SPOT5 urban sprawl

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Conceptual Framework of Combined Pixel and object-based Method for Delineation of Debris-Covered Glaciers

Conceptual Framework of Combined Pixel and Object-based Meth...

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ISPRS TC V Mid-Term Symposium on Geospatial Technology - Pixel to People

作者： Sahu, R. Gupta, R. D. Motilal Nehru Natl Inst Technol GIS Cell Allahabad Uttar Pradesh India Motilal Nehru Natl Inst Technol Dept Civil Engn Allahabad Uttar Pradesh India

Delineation of the glacier is an important task for understanding response of glaciers to climate. In Himalayan region, most of the glaciers are covered with debris. Supraglacial debris works as an obstacle for automatic mapping of glacier using remote sensing data. Different methods have been used to reduce this difficulty based on pixel-based and object-based approaches using optical data, thermal data and DEM. Pixel-based glacier mapping is a traditional method for delineation of the glacier but the object-based method has emerged as a new approach in cryosphere application leading to its successful application in different applications. All pixel-based methods require some degree of manual correction because these can't be delineated automatically, especially in shadow area and debris covered part of the glacier. In the majority of studies, the object-based method has provided higher accuracy to delineate the debris-covered glacier. Spatially high spatial resolution satellite data is best suited for object-based image classification. In future, a combination of pixel-based method and object-based method can be attempted for delineation of the debris-covered glacier along with its critical analysis for suitability. The present paper critically reviews pixel-based and object-based methods as well as provides a framework for combined pixel and object-based method for delineation of debris-covered glacier.

关键词： Pixel-based classification object-based classification Debris-covered glacier Conceptual framework

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Cropland Extraction from Very High Spatial Resolution Satellite Imagery by object-based classification Using Improved Mean Shift and One-Class Support Vector Machines

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SENSOR LETTERS 2011年第3期9卷 997-1005页

作者： Shen, Jing Liu, Jiping Lin, Xiangguo Zhao, Rong Xu, Shenghua Chinese Acad Surveying & Mapping Res Ctr Govt Geog Informat Syst Beijing 100830 Peoples R China Chinese Acad Surveying & Mapping Key Lab Mapping Space State Bur Surveying & Mapping Beijing 100830 Peoples R China

The issue of cropland extraction from very high spatial resolution (VHR) satellite imagery remains a great challenge. In this paper, an object-based classification method for cropland extraction from VHR satellite imagery is proposed based on the improved mean shift and one-class SVM. After the fused satellite image is transformed by nonnegative matrix factorization into three bands, the improved mean shift is employed to segment the image. Subsequently, the structure lines of each region in the segmented image are detected, and the standard deviations of the directions of the straight lines are calculated. The spectral information and the above derived texture information are selected as features for the following classification. At last, the support vector data description is utilized to recognize the croplands from the segmented image based on only some cropland samples. Three satellite images with different spatial resolutions are employed to test the algorithm, and the results show that our proposed method obtains a higher overall classification accuracy than the eCognition's method does, and its overall classification accuracy is promoted with the increasing of spatial resolution. Another merit of our method is that it needs only the cropland samples, which is time-saving and cost-saving.

关键词： Cropland Extraction Very High Spatial Resolution object-based classification Mean Shift SVM

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