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Integrating structure and function:mapping the hierarchical spatial heterogeneity of urban landscapes

Ecological Processes 2020年第1期9卷 698-708页

作者： Yuguo Qian Weiqi Zhou Steward T.A.Pickett Wenjuan Yu Dingpeng Xiong Weimin Wang Chuanbao Jing State Key Laboratory of Urban and Regional Ecology Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China University of Chinese Academy of Sciences Beijing 100049China Cary Institute of Ecosystem Studies Box ABMillbrookNY 12545USA PowerChina Eco-environment Group Co. LtdShenzhen 518102China Shenzhen Environmental Monitoring Center Shenzhen 518049China

Background:Cities are social-ecological systems characterized by remarkably high spatial and temporal heterogeneity,which are closely related to myriad urban ***,the tools to map and quantify this heterogeneity are *** here developed a new three-level classification scheme,by considering ecosystem types(level 1),urban function zones(level 2),and land cover elements(level 3),to map and quantify the hierarchical spatial heterogeneity of urban ***:We applied the scheme using an object-based approach for classification using very high spatial resolution imagery and a vector layer of building location and *** used a top-down classification procedure by conducting the classification in the order of ecosystem types,function zones,and land cover *** classification of the lower level was based on the results of the higher *** used an objectbased methodology to carry out the three-level ***:We found that the urban ecosystem type accounted for 45.3%of the land within the Shenzhen city administrative *** the urban ecosystem type,residential and industrial zones were the main zones,accounting for 38.4%and 33.8%,*** canopy was the dominant element in Shenzhen city,accounting for 55.6%over all ecosystem types,which includes agricultural and ***,in the urban ecosystem type,the proportion of tree canopy was only 22.6%because most trees were distributed in the forest ecosystem *** proportion of trees was 23.2% in industrial zones,2.2%higher than that in residential *** information“hidden”in the usual statistical summaries scaled to the entire administrative unit of Shenzhen has great potential for improving urban ***:This paper has taken the theoretical understanding of urban spatial heterogeneity and used it to generate a classification scheme that exploits remotely sensed imagery,infrastructural data available at a municipal level,an

关键词： Social-ecological hybridity object-based classification High-resolution imagery Ecosystem Urban function zones Land cover

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Spatial and Temporal Dynamics of Urban Wetlands in an Indian Megacity over the Past 50 Years

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REMOTE SENSING 2020年第4期12卷 662-662页

作者： Brinkmann, Katja Hoffmann, Ellen Buerkert, Andreas Univ Kassel Organ Plant Prod & Agroecosyst Res Trop & Subtrop Steinstr 19 D-37213 Witzenhausen Germany Inst Social Ecol Res ISOE Hamburger Allee 45 D-60598 Frankfurt Germany

Asian megacities have attracted much scientific attention in the context of global urbanization, but few quantitative studies analyze wetland transformation in the rural-urban interface. With its rampant growth and transformation from a tree-lined "Garden City" to a busy megalopolis with often-blocked highways and large built-up areas, Bengaluru (Karnataka, S-India) is a good example for assessing how urbanization has led to the acute degradation of wetlands. We therefore investigated long-term land cover and wetland changes from 1965 to 2018 based on an object-based classification of multi-temporal Corona and Landsat images. To quantify and compare the dynamics of open water surfaces and vegetation, we defined the potential wetland areas (PWA) along the rural-urban gradient and linked our analyses to an index describing the degree of urbanization (survey stratification index (SSI)). During the five decades studied, built-up areas in the Bengaluru Urban district increased ten-fold, with the highest growth rate from 2014 to 2018 (+ 8% annual change). Patches of lake wetlands were highly dynamic in space and time, partly reflecting highly variable annual rainfall patterns ranging from 501 mm in 1965 to 1374 mm in 2005 and monsoon-driven alterations in the hydrologic regime. While water bodies and flooded areas shrunk from 64 km(2) in 1965 to 55 km(2) in 2018, in 1965, the total rural wetland area with an SSI > 0.5 was twice as high as in 2018. The rural-urban land cover pattern within potential wetland areas changed drastically during this period. This is reflected, for example, by a four-fold increase in the wetland area with an SSI of 0.3, as compared to a decline by 43% in wetland area with an SSI of 0.8. While, in urban areas, wetlands were mostly lost to construction, in areas with a rural character, open water bodies were mainly transformed into green space. The detected changes in urban wetlands were likely accompanied by ecological regime changes, triggering d

关键词： lakes Bengaluru rural-urban gradient object-based classification land cover changes urbanization

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Does magnification of SEM image influence quantification of particulate matters deposited on vegetation foliage

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MICRON 2018年 115卷 7-16页

作者： Lin, Lin Yan, Jingli Chen, Guojian Tang, Rongli Bao, Le Ma, Keming Zhou, Weiqi Yuan, Xiu Yin, Zhe Zhou, Shuwen Chinese Acad Sci Res Ctr Ecoenvironm Sci State Key Lab Urban & Reg Ecol Beijing 100085 Peoples R China Univ Calif Riverside Dept Bot & Plant Sci 900 Univ Ave Riverside CA 92521 USA Univ Chinese Acad Sci Beijing 100049 Peoples R China Chongqing Acad Agr Sci Chongqing Peoples R China Beijing Forest Univ Coll Soil & Water Conservat Beijing 100083 Peoples R China Chinese Acad Sci Inst Sci & Dev Beijing Peoples R China

The metrics used to quantify the particles on the leaf surface can serve as indicators for the quality of the atmospheric environment and is employed to evaluate the ecosystem services that plants can provide. Magnification of an SEM image may affect the recognition of particles in various aspects, yet little research has examined the impact. In this study, images were obtained at magnifications of 500 x, 1000 x, 2000 x, and 5000 x for a widely planted vegetation fence species in Beijing: Euonymus japonicas. Particle amount per leaf area increased as magnification increased;the percentage of the leaf area covered by particles was a relatively stable index across magnifications at around 7% except for the magnification 2000 x which was around 15%;and the weight of particles per leaf area was unstable over all magnifications. The mean area of each particle decreased from 5.68 mu m(2) at magnification 500 x to 0.20 at magnification 5000 x, and shape complexity increased as magnification increased from 1.29 at magnification 500 x to 1.50 at magnification 5000 x. Particles with a diameter of less than 2.5 mu m are more sensitive to scale than particles with a diameter greater than 2.5 mu m of judged by the differences of the characteristics, diameter, area and shape index, of particles with different diameter ranges over four magnifications. Regarding the percentage characteristic, the percentage of particles with a diameter smaller than 1 pm increased as the magnification increased and accounted for 46% at magnification 500 x and 96% at magnification 5000 x. The percentage of particles with an area smaller than 1 mu m(2) also increased as the magnification increased accounting for 56% at magnification 500 x and 99% at magnification 5000 x. Particles with shape index between 1-1.5 accounted for most of the particles across all four magnifications of which the percentage was 0.66-0.80. Therefore, when comparing the results from different magnifications, three factors shou

关键词： Air pollution Magnification Urban plant Biomonitor object-based classification Ecosystem service

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Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan

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GLOBAL AND PLANETARY CHANGE 2013年第parta期110卷 88-98页

作者： Conrad, Christopher Rahmann, Maren Machwitz, Miriam Stulina, Galina Paeth, Heiko Dech, Stefan Univ Wurzburg Inst Geog & Geol Wurzburg Germany Ctr Rech Publ Gabriel Lippmann Luxembourg Luxembourg Interstate Commiss Water Coordinat Cent Asia SIC Sci Informat Ctr Tashkent Uzbekistan

This study focuses on the generation of reliable data for improving land and water use in Central Asia. An object-based remote sensing classification is applied and combined with the CropWat model developed by the Food and Agriculture Organization (FAO) to determine crop distribution and water requirements for irrigation of cotton and winter-wheat in Fergana Valley, Uzbekistan. The crop classification is conducted on RapidEye and Landsat data acquired before the onset of the main summer irrigation phases in July using a random forest algorithm. The ClimWat database of FAO is utilized for calculating crop water requirements (CWR) and crop irrigation requirements (CIR). classification reveals an overall accuracy of 86.2% and exceeds a producer's (user's) accuracy of 95% (89%) for both, cotton and wheat. In 2010, cotton and winter-wheat are planted on 66.7% of the agricultural area under investigation, whereas orchard areas amount to 15.5%. The CWR modelled for winter-wheat and cotton cultivation revealed 5443 m(3) ha(-1) and 9278 m(3) ha(-1), respectively. Subtracting effective precipitation leads to OR of 4133 m(3) ha(-1) and 8813 m(3) ha(-1). Comparisons of CWR and CIR for the area dominating crops with the total of water officially allocated for irrigation underline the pressure on the water resources in the entire Syr Darya catchment and suggest modifications of the cropping system towards more winter crops. The early season crop maps can be used for water saving as they enable modifications of water allocation plans within the different irrigation subsystems of the valley. The method for mapping spatially distributed CWR and CIR can be transferred to other irrigated areas in Central Asia and beyond. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Fergana Valley object-based classification Crop water requirement Water management Remote sensing RapidEye

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Field identification of weed species and glyphosate-resistant weeds using high resolution imagery in early growing season

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BIOSYSTEMS ENGINEERING 2020年 200卷 200-214页

作者： Shirzadifar, Alimohammad Bajwa, Sreekala Nowatzki, John Bazrafkan, Aliasghar Shiraz Univ Biosyst Engn Dept Shiraz Iran North Dakota State Univ Agr & Biosyst Engn Dept Fargo ND 58105 USA Montana State Univ Bozeman MT 59717 USA Yazd Univ Agr & Nat Resource Dept Yazd Iran

Accurate weed mapping in early growing season is an essential step in the site-specific weed management system. This study focuses on validating the potential application of high resolution multispectral and thermal UAS images in classification of weed species and glyphosate-resistant weeds at early phenological stages. A field experiment was conducted to evaluate supervised classification methods to identify three-weed species including waterhemp (Amaranthus rudis), kochia (Kochia scoparia), and ragweed (Ambrosia artemisiifolia L.). The accuracy of six classification algorithms namely Parallelepiped, Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Support Vector Machine and Decision Tree implemented at pixel and object-based levels in weed species classification were evaluated. Thermal infrared imagery was also used to assess the canopy temperature variance within the weed species to identify the glyphosate-resistance status in detected weeds. The object-based algorithms developed with mosaicked imagery effectively classified weed species with the overall accuracy and Kappa coefficient values greater than 86% and 0.77, respectively. The lowest accuracy and Kappa coefficient (67% and 0.58) were observed for pixel-based Mahalanobis Distance algorithm. The canopy temperature-based classification of susceptible and resistant weeds resulted in the discrimination accuracies of 88%, 93% and 92% in glyphosate-resistant kochia, waterhemp and ragweed, respectively. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Glyphosate-resistant weeds object-based classification UAS imagery Weed species

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Fine crop mapping by combining high spectral and high spatial resolution remote sensing data in complex heterogeneous areas

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2017年 139卷 1-9页

作者： Wu, Mingquan Huang, Wenjiang Niu, Zheng Wang, Yu Wang, Changyao Li, Wang Hao, Pengyu Yu, Bo Chinese Acad Sci Inst Remote Sensing & Digital Earth State Key Lab Remote Sensing Sci POB 9718Datun Rd Beijing 100101 Peoples R China Chinese Acad Sci Inst Remote Sensing & Digital Earth Lab Digital Earth Sci Beijing Peoples R China Beijing Twenty First Century Sci & Technol Dev Co Beijing 100083 Peoples R China

In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5 m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively. (C) 2017 Elsevier B.V. All rights reserved.

关键词： GF-1 Hyperion data Remote sensing Crop mapping Heterogeneous area object-based classification

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Comparative Assessment of Supervised Classifiers for Land Use-Land Cover classification in a Tropical Region Using Time-Series PALSAR Mosaic Data

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第4期7卷 1186-1199页

作者： Shiraishi, Tomohiro Motohka, Takeshi Thapa, Rajesh Bahadur Watanabe, Manabu Shimada, Masanobu Japan Aerosp Explorat Agcy Earth Observat Res Ctr Tsukuba Ibaraki 3058505 Japan

Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms are possible. This research investigated the accuracy and processing speeds of five supervised classifiers, including Naive Bayes, AdaBoost, multi-layer perceptron, random forest (RF), and support vector machine, for land use-land cover (LULC) classification in a tropical region using time-series Advanced Land Observing Satellite-phased array type L-band SAR (ALOS-PALSAR) 25-m mosaic data. The study area is located in central Sumatra, Indonesia, where abundant forest-related carbon stocks exist. This investigation was intended to aid the implementation of a classification algorithm for the automatic creation of LULC classification maps. We perform object-based and pixel-based analyses to investigate the ability of the classifiers and their accuracies, respectively. RF had the best classification accuracy and processing speed in which the accuracies for 10 classes and 2 classes were 64.07% and 90.22% for pixel-based and 82.94% and 86.23% for object-based evaluations, respectively. These results indicate that RF is a useful classifier for the analysis of PALSAR mosaic data and that the automatic creation of highly accurate classification maps is possible by using time-series data. The outcome of this research will be valuable resources for biodiversity and global-warming mitigation efforts in the region.

关键词： Land use-land cover (LULC) classification object-based classification Sumatra supervised classification time-series PALSAR mosaic data

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A FULLY ADAPTIVE object EXTRACTION TECHNIQUE USED FOR SPECTRAL-SPATIAL classification OF REMOTELY SENSED DATA

A FULLY ADAPTIVE OBJECT EXTRACTION TECHNIQUE USED FOR SPECTR...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Amin Zehtabian Hassan Ghassemian Faculty of Electrical and Computer Engineering Tarbiat Modares University

ISBN: (纸本)9781479979301

The effectiveness of object-based image classification approaches has been frequently addressed and discussed in literature, especially for remote sensing applications. Unlike the traditional pixel-wise methods, object-based classifiers benefit from a segmentation step before the classification process in order to generate objects. In this paper, we propose to use the Pixon concept for segmentation of the data. Meanwhile, in order to form objects which are spectrally homogenous, spatial smoothing is applied as a preprocessing step through using regularized nonlinear partial differential equations (RegAPDE). The parameters of RegAPDE as well as important thresholds used in the Pixon extraction technique are adaptively tuned using three different adaptation algorithms. We also propose to localize the smoothing process via separately applying the RegAPDE algorithm to individual partitions extracted from each layer of the hyperspectral datasets. To this end, a simple partitioning step based on Watershed transformation is used before the smoothing procedure.

关键词： object-based classification Regularized partial differential equations Adaptive parameters Watershed

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Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery

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REMOTE SENSING OF ENVIRONMENT 2008年第3期112卷 1051-1061页

作者： Stow, Douglas Hamada, Yuki Coulter, Lloyd Anguelova, ZlatIna San Diego State Univ Dept Geog San Diego CA 92182 USA

An object-based approach to generating shrub cover change maps of potential use for monitoring shrubland habitat reserves was developed and tested. A high fidelity, bi-temporal airborne image data set was generated through frame-based image acquisition, precise image-to-image registration, radiometric normalization, and selection of near-anniversary image acquisition dates with similar precipitation conditions prior to both image acquisitions. Image segmentation and classification processes were applied to the bi-temporal layer stack of very high spatial resolution visible and near infrared (V/NIR) image data, such that shrub change objects were delineated and identified directly. Image segments derived from the bi-temporal V/NIR image data set having I in spatial resolution delineated most shrub change features in a qualitatively realistic manner. A Standard Nearest Neighbor classifier with segment mean and standard deviation measures of Red, NIR, and normalized difference vegetation index (NDVI) image features yielded the shrub change map that agreed more closely with reference data than the classifier based on fuzzy membership functions. The overall accuracy and kappa statistics for the optimal shrub change map were 0.83 and 0.64, respectively, with the predominant error being associated with "over-classification" of no-change objects as some type of shrub change. No statistical difference in accuracies of three- and five-class maps was found, suggesting that changes in true shrubs and sub-shrubs within coastal sage scrub vegetation communities can be differentiated reliably. A net 5% loss of shrub cover was determined for the 1998-2005 period from the shrub change map of the study area. The greatest decrease and net loss of shrub cover occurred within the urban edge zone and within flat-lying areas. Patterns of shrub loss appear to be more related to anthropogenic disturbance than effects of the severe seven-year drought that occurred between image acquisition d

关键词： vegetation change object-based classification linage segmentation airborne multi-spectral camera

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Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 2017年 128卷 73-85页

作者： Lu, Bing He, Yuhong Univ Toronto Mississauga Dept Geog 3359 Mississauga Rd Mississauga ON L5L 1C6 Canada

Investigating spatio-temporal variations of species composition in grassland is an essential step in evaluating grassland health conditions, understanding the evolutionary processes of the local ecosystem, and developing grassland management strategies. Space-borne remote sensing images (e.g., MODIS, Landsat, and Quickbird) with spatial resolutions varying from less than 1 m to 500 m have been widely applied for vegetation species classification at spatial scales from community to regional levels. However, the spatial resolutions of these images are not fine enough to investigate grassland species composition, since grass species are generally small in size and highly mixed, and vegetation cover is greatly heterogeneous. Unmanned Aerial Vehicle (UAV) as an emerging remote sensing platform offers a unique ability to acquire imagery at very high spatial resolution (centimetres). Compared to satellites or airplanes, UAVs can be deployed quickly and repeatedly, and are less limited by weather conditions, facilitating advantageous temporal studies. In this study, we utilize an octocopter, on which we mounted a modified digital camera (with near-infrared (NIR), green, and blue bands), to investigate species composition in a tall grassland in Ontario, Canada. Seven flight missions were conducted during the growing season (April to December) in 2015 to detect seasonal variations, and four of them were selected in this study to investigate the spatio-temporal variations of species composition. To quantitatively compare images acquired at different times, we establish a processing flow of UAV-acquired imagery, focusing on imagery quality evaluation and radiometric correction. The corrected imagery is then applied to an object-based species classification. Maps of species distribution are subsequently used for a spatio-temporal change analysis. Results indicate that UAV-acquired imagery is an incomparable data source for studying fine-scale grassland species composition, owing

关键词： UAV Grassland Species composition object-based classification

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