Underload city conceptual approach extending ghost city studies

Global population growth and land development are highly imbalanced, marked by 43% of population increase but 150% of builtup area expansion from 1990 to 2018. This results in the widely concerned ghost city phenomenon and runs against the sustainable development goals. Existing studies identify ghost cities by population densities, but ignore the spatial heterogeneity of land carrying capacities (LCC). Accordingly, this study proposes a general concept termed underload city to define cities carrying fewer people and lower economic strength than their LCC. The underload city essentially describes imbalanced human-land relationship and is understood in a broader context than the usually applied ghost city. In this study, very high-resolution satellite images are analyzed to obtain land functional structures, and further combined with population and GDP data to derive LCC. We empirically identify eight underload cities among 81 major Chinese cities, differing from previous findings of ghost cities. Accordingly, the proposed underload city considers heterogeneous human-land relationships when assessing city loads and contributes to sustainable city developments

Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs

Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images

Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved astonishing performance for semantically labeling very high resolution (VHR) remote sensing images. However, it is difficult for these models to capture the precise outlines of ground objects and explore the context information that revealing relationships among image objects for optimizing segmentation results. Consequently, this study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentation model (DeepLabv3+) and object-based image analysis (OBIA), wherein DSM is employed to provide geometric information to enhance the interpretation of VHR images. The proposed method first obtains two initial probabilistic labeling predictions using a DeepLabv3+ network on spectral image and a random forest (RF) classifier on hand-crafted features, respectively. These two predictions are then integrated by Dempster-Shafer (D-S) evidence theory to be fed into an object-constrained higher-order conditional random field (CRF) framework to estimate the final semantic labeling results with the consideration of the spatial contextual information. The proposed method is applied to the ISPRS 2D semantic labeling benchmark, and competitive overall accuracies of 90.6% and 85.0% are achieved for Vaihingen and Potsdam datasets, respectively

Effects of nitrogen additions on biomass, stoichiometry and nutrient pools of moss Rhytidium rugosum in a boreal forest in Northeast China

Global nitrogen (N) deposition has been enhanced with anthropogenic N emissions, and its impacts on mosses are receiving more and more attention. This study investigates how N deposition influence the biomass and stoichiometry of moss Rhytidium rugosum, using a 3-year N enrichment experiment with 0, 2, 5 and 10 g N m(-2) yr(-1) in a boreal forest in Northeast China. Low N additions caused an N redundancy and moderate to high N additions resulted in a biomass loss. N additions reduced biomass ratios of green to brown tissues and increased N and phosphorus (P) contents, suggesting changes in photosynthetic capacity and litter decomposition. Biomass N pools showed a unimodal response to the N additions, and P pools decreased under moderate and high N additions. Our findings indicate significant stoichiometric and biomass changes caused by N deposition may lead to a substantial carbon and nutrient loss in boreal moss carpets. (C) 2014 Elsevier Ltd. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000334001000022&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesSCI(E)[email protected]

Semantic classification of heterogeneous urban scenes using intrascene feature similarity and interscene semantic dependency

10.1109/JSTARS.2015.2414178IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing852005-201

MDFF: A Method for Fine-Grained UFZ Mapping With Multimodal Geographic Data and Deep Network

As basic units of urban areas, urban functional zones (UFZs) are fundamental to urban planning, management, and renewal. UFZs are mainly determined by human activities, economic behaviors, and geographical factors, but existing methods 1) do not fully use multimodal geographic data owing to a lack of semantic modeling and feature fusion of geographic objects and 2) are composed of multiple stages, which lead to the accumulation of errors through multiple stages and increase the mapping complexity. Accordingly, this study designs a multimodal data fusion framework (MDFF) to map fine-grained UFZs end-to-end, which effectively integrates very-high-resolution remote sensing images and social sensing data. The MDFF extracts physical attributes from remote sensing images and models socioeconomic semantics of geographic objects from social sensing data, and then fuses multimodal information to classify UFZs where object semantics guide the fine-grained classification. Experimental results in Beijing and Shanghai, two major cities of China, show that the MDFF greatly improves the quality of UFZ mapping with the accuracy about 5% higher than state-of-the-art methods. The proposed method significantly reduces the complexity of UFZ mapping to complete the urban structure analysis conveniently

Large-scale urban functional zone mapping by integrating remote sensing images and open social data

Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping integrating these two types of data is still not be applied. In this study, a novel approach to mapping large-scale UFZs by integrating remote sensing images (RSIs) and open social data is proposed. First, a context-enabled image segmentation method is improved to generate UFZ units by incorporating road vectors. Second, the segmented UFZs are classified by coupling Latent Dirichlet Allocation (LDA) and Support Vector Machine (SVM). In the classification framework, physical features from RSIs and social attributes from POI (Point of Interest) data are integrated. A case study of Beijing was performed to evaluate the proposed method, and an overall accuracy of 85.9% was achieved. The experimental results demonstrate that the presented method can provide fine-grained UFZs, and the fusion strategy of RSIs and POI data can distinguish urban functions accurately. The proposed method appears to be promising and practical for large-scale UFZ mapping

Remote sensing techniques in the investigation of aeolian sand dunes: A review of recent advances

Sand dunes are one of the most abundant aeolian landforms and play an important role in understanding how aeolian environments evolve. Since the 1970s, remote sensing has enabled large-scale investigations of dunes at comparatively low costs and with temporally continuous observations, which greatly advances our knowledge of aeolian systems. In this context, we provide a review of recent progress in three research topics that have been greatly facilitated by remote sensing techniques. These topics are 1) mapping sand extent and dune types, 2) dune pattern quantification, and 3) monitoring dune dynamics. Sand dune mapping was the early focus of aeolian geomorphologists, and continued progress has been made in refining classification schemes and developing advanced classification techniques. Dune pattern quantification can be resolved in two geomorphometric approaches, and a careful design that takes into consideration the image resolution, the data quality, and the uncertainty in dune discretization is necessary. Dune dynamics typically exhibit as dune migration, dune interactions, and dune fine-scale morphodynamics. The wide application of change detection algorithms, especially COSI-Corr, provides great insights into dune migration, while the exploration of dune interactions is still in its infancy. Future directions are highlighted in four key areas: unifying classification schemes regarding dune morphology, developing methods that are capable of recognizing diverse dune forms at large spatial extents, designing modularized workflows and more complex matching rules to quantify dune migration, and improving quantitative analysis of dune interactions

Multiscale Geoscene Segmentation for Extracting Urban Functional Zones from VHR Satellite Images

Urban functional zones, such as commercial, residential, and industrial zones, are basic units of urban planning, and play an important role in monitoring urbanization. However, historical functional-zone maps are rarely available for cities in developing countries, as traditional urban investigations focus on geographic objects rather than functional zones. Recent studies have sought to extract functional zones automatically from very-high-resolution (VHR) satellite images, and they mainly concentrate on classification techniques, but ignore zone segmentation which delineates functional-zone boundaries and is fundamental to functional-zone analysis. To resolve the issue, this study presents a novel segmentation method, geoscene segmentation, which can identify functional zones at multiple scales by aggregating diverse urban objects considering their features and spatial patterns. In experiments, we applied this method to three Chinese cities—Beijing, Putian, and Zhuhai—and generated detailed functional-zone maps with diverse functional categories. These experimental results indicate our method effectively delineates urban functional zones with VHR imagery; different categories of functional zones extracted by using different scale parameters; and spatial patterns that are more important than the features of individual objects in extracting functional zones. Accordingly, the presented multiscale geoscene segmentation method is important for urban-functional-zone analysis, and can provide valuable data for city planners