The Traj2Vec model to quantify residents’ spatial trajectories and estimate the proportions of urban land-use types

A poluição do ar é influenciada por fatores naturais e antropogênicos. Quatro pontos de monitoramento (veicular, comercial, residencial e background urbano (BGU))da poluição do ar em São Paulo foram avaliados durante 16 anos, revelando diferenças significativas devidoao uso do solo em todas as escalas temporais. Na escala diurna, as concentrações de poluentes primários são duas vezes mais altas nos pontos veicular e residencial do que no ponto BGU, onde a concentração de ozonio (O3) é 50% mais alta. Na escala sazonal, as concentrações de monóxido de carbono(CO) variaram em 80% devido ao uso do solo, e 55% pela sazonalidade.As variações sazonais ede uso do solo exercem impactos similares nas concentrações de O3 e monóxido de nitrogênio (NO). Para o material particulado grosso (MP10) e o dióxido de nitrogênio(NO2), as variações sazonais são mais intensas do que as por uso do solo. Na série temporal de 16 anos, o ponto BGU apresentou correlações mais fortes e significativas entre a média mensal de ondas longas (ROL) e o O3 (0,48) e o MP10 (0,37), comparadas ao ponto veicular (0,33 e 0,22, respectivamente). Estes resultados confirmam que o uso do solo urbano tem um papel significativo na concentração de poluentes em todas as escalas de análise, embora a sua influência se torne menos pronunciada em escalas maiores, conforme a qualidade do ar transita de um sistema antropogênico para um sistema natural. Isto poderá auxiliar decisões sobre políticas públicas em megacidades envolvendo a modificação do uso do solo.

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Urban land use types contribute to grassland conservation: The example of Berlin

Urban Forestry & Urban Greening ◽

10.1016/j.ufug.2013.03.009 ◽

2013 ◽

Vol 12 (3) ◽

pp. 263-272 ◽

Cited By ~ 25

Author(s):

Leonie K. Fischer ◽

Moritz von der Lippe ◽

Ingo Kowarik

Keyword(s):

Land Use ◽

Urban Land ◽

Urban Land Use ◽

Grassland Conservation ◽

Land Use Types

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Inferring Urban Land Use from Multi-Source Urban Mobility Data Using Latent Multi-View Subspace Clustering

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050274 ◽

2021 ◽

Vol 10 (5) ◽

pp. 274

Author(s):

Qiliang Liu ◽

Weihua Huan ◽

Min Deng ◽

Xiaolin Zheng ◽

Haotao Yuan

Keyword(s):

Land Use ◽

Subspace Clustering ◽

Urban Land ◽

Urban Land Use ◽

Single Type ◽

Underlying Structure ◽

Urban Mobility ◽

Trajectory Data ◽

Mobility Data ◽

Land Use Types

In the era of big data, vast urban mobility data introduce new opportunities to infer urban land use from the perspective of social function. Most existing works only derive land use information from a single type of urban mobility dataset, which is typically biased and results in difficulty obtaining a comprehensive view of urban land use. It remains challenging to fuse high-dimensional and noisy multi-source urban mobility data to infer urban land use. This study aimed to infer urban land use from multi-source urban mobility data using latent multi-view subspace clustering. The variation in the number of origin/destination points over time was initially used to characterize land use types. Then, a latent multi-view representation was applied to construct the common underlying structure shared by multi-source urban mobility data and effectively deal with noise. Finally, based on the latent multi-view representation, the subspace clustering method was used to infer the land use types. Experiments on taxi trajectory data and bus smart card data in Beijing reveal that, compared with the method using a single type of urban mobility dataset and the weighted fusion method, the approach presented in this study obtains the highest detection rate of land use. The urban land use inferred in this study provides calibration and reference for urban planning.

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Relative importance of urban and non-urban land-use types for potential denitrification derived N2O: insights from a regional study

Earth and Environmental Science Transactions of the Royal Society of Edinburgh ◽

10.1017/s175569101800066x ◽

2018 ◽

Vol 109 (3-4) ◽

pp. 453-460

Author(s):

Hui-Juan XU ◽

Manuel DELGADO-BAQUERIZO ◽

Fu-Xia PAN ◽

Xin-Li AN ◽

Brajesh K. SINGH ◽

...

Keyword(s):

Heavy Metals ◽

Land Use ◽

Soil Ph ◽

Human Activities ◽

Regional Scale ◽

Urban Land ◽

Urban Land Use ◽

Relative Importance ◽

Potential Denitrification ◽

Land Use Types

ABSTRACTIdentifying the relative importance of urban and non-urban land-use types for potential denitrification derived N2O at a regional scale is critical for quantifying the impacts of human activities on nitrous oxide (N2O) emission under changing environments. In this study we used a regional dataset from China including 197 soil samples and six land-use types to evaluate the main predictors (land use, heavy metals, soil pH, soil moisture, substrate availability, functional and broad microbial abundances) of potential denitrification using multivariate and pathway analysis. Our results provide empirical evidence that soils on farms have the greatest potential denitrifying ability (PDA) (10.92±6.08ng N2O-N·g–1 dry soil·min–1) followed by urban soil (6.80±5.35ng N2O-N·g–1 dry soil·min–1). Our models indicate that land use (low vs. high human activity), followed by total nitrogen (TN) and heavy metals (Cu, Zn, Pb, Cd) was the most important driver of PDA. In addition, our path analysis suggests that at least part of the impacts of land use on potential denitrification were mediated via microbial abundance, soil pH and substrates including TN, dissolved organic carbon and nitrate. This study identifies the main predictors of denitrification at a regional scale which is needed to quantify the impact of human activities on ecosystem functionality under changing conditions.

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Exploring Impact of Spatial Unit on Urban Land Use Mapping with Multisource Data

Remote Sensing ◽

10.3390/rs12213597 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3597

Author(s):

Xuanyan Dong ◽

Yue Xu ◽

Leping Huang ◽

Zhigang Liu ◽

Yi Xu ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Urban Land ◽

Urban Land Use ◽

Remote Sensing Images ◽

Social Sensing ◽

Sensing Data ◽

Object Based ◽

Land Use Types ◽

Functional Areas

The ability to precisely map urban land use types can significantly aid urban planning and urban system understanding. In recent years, remote sensing images and social sensing data have been frequently used for urban land use mapping. However, there still remains a problem: what is the best basic unit for fusing remote sensing images with social sensing data? The aim of this study is to explore the impact of spatial units on urban land use mapping, with remote sensing images and social sensing data of Shenzhen City, China. Three different basic units were first applied to delineate urban land use types, and for each unit, a word dictionary was built by fusing natural–physical features from high spatial resolution (HSR) remote sensing images and the socioeconomic semantic features from point of interest (POI) data. The latent Dirichlet allocation (LDA) algorithm and random forest methods were then applied to map the land use of the Futian district—the core region of Shenzhen. The experiment demonstrates that: (1) No matter what kind of spatial unit, it is beneficial to fuse multisource data to improve the performance. However, when using different spatial units, the importances of features are different. (2) Using block-based spatial units results in the final map looking the best. However, a great challenge of this approach is that the scale is too coarse to handle mixed functional areas. (3) Using grid- and object-based units, the problem of mixed functional areas can be better solved. Additionally, the object-based land use map looks better from our visual interpretation. Accordingly, the results of this study could give other researchers references and advice for future studies.

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The flora of selected urban land-use types in Berlin and Potsdam with regard to nature conservation in cities

Landscape and Urban Planning ◽

10.1016/s0169-2046(99)00066-3 ◽

2000 ◽

Vol 46 (4) ◽

pp. 209-215 ◽

Cited By ~ 61

Author(s):

U Maurer ◽

T Peschel ◽

S Schmitz

Keyword(s):

Land Use ◽

Nature Conservation ◽

Urban Land ◽

Urban Land Use ◽

Land Use Types

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Quantifying the seasonal contribution of coupling urban land use types on Urban Heat Island using Land Contribution Index: A case study in Wuhan, China

Sustainable Cities and Society ◽

10.1016/j.scs.2018.10.016 ◽

2019 ◽

Vol 44 ◽

pp. 666-675 ◽

Cited By ~ 19

Author(s):

Qiuping Huang ◽

Jiejun Huang ◽

Xining Yang ◽

Chuanglin Fang ◽

Youjia Liang

Keyword(s):

Land Use ◽

Urban Heat Island ◽

Urban Land ◽

Urban Land Use ◽

Heat Island ◽

Land Use Types ◽

Urban Heat ◽

Contribution Index

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Spatiotemporal Patterns of Urban Land Use Change in Typical Cities in the Greater Mekong Subregion (GMS)

Remote Sensing ◽

10.3390/rs11070801 ◽

2019 ◽

Vol 11 (7) ◽

pp. 801 ◽

Cited By ~ 3

Author(s):

Hui Cao ◽

Jian Liu ◽

Jianglong Chen ◽

Jinlong Gao ◽

Guizhou Wang ◽

...

Keyword(s):

Land Use ◽

Land Use Change ◽

Urban Expansion ◽

Urban Land ◽

Urban Land Use ◽

Phnom Penh ◽

Land Use Types ◽

Natural Land ◽

Urban Land Use Change ◽

Greater Mekong Subregion

The Greater Mekong Subregion (GMS) has experienced rapid economic growth and urbanization. However, few studies have paid attention to urban land use dynamics, especially spatiotemporal patterns of urban expansion and land use change, in this region. This research aimed to conduct a comprehensive study of urban land use change in Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City, from 1990 to 2015. The analysis was based on land use maps derived from Landsat satellite products and employed urban expansion intensity, sector analysis, gradient-direction analysis, and landscape metrics. The results show Xishuangbanna, Yangon, Vientiane, Phnom Penh, Bangkok, and Ho Chi Minh City all experienced dramatic urban expansion and land use change since 1990, with urban expansion intensities of 15.01, 5.26, 9.15, 1.56, 11.88 and 11.91, respectively. The landscape metrics analysis indicated that urban areas were always aggregated and self-connected, while other land use types showed trends of disaggregation and fragmentation. In the process of urban expansion, paddy and natural land use types were commonly transformed to built up area. The results further reveal several common issues in urban land use, e.g. land fragmentation and loss of natural land use types. Finally, the discussion on the relationship between government policy and land use change for these cities shows land reform and attitude toward foreign direct investments played important roles in urban land use change in GMS.

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Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network

Remote Sensing ◽

10.3390/rs12233928 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3928 ◽

Cited By ~ 1

Author(s):

Shaobai He ◽

Huaqiang Du ◽

Guomo Zhou ◽

Xuejian Li ◽

Fangjie Mao ◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Urban Forest ◽

Urban Forests ◽

Urban Land ◽

Urban Land Use ◽

Classification Error ◽

Object Based ◽

Land Use Types ◽

Multiresolution Segmentation

The application of deep learning techniques, especially deep convolutional neural networks (DCNNs), in the intelligent mapping of very high spatial resolution (VHSR) remote sensing images has drawn much attention in the remote sensing community. However, the fragmented distribution of urban land use types and the complex structure of urban forests bring about a variety of challenges for urban land use mapping and the extraction of urban forests. Based on the DCNN algorithm, this study proposes a novel object-based U-net-DenseNet-coupled network (OUDN) method to realize urban land use mapping and the accurate extraction of urban forests. The proposed OUDN has three parts: the first part involves the coupling of the improved U-net and DenseNet architectures; then, the network is trained according to the labeled data sets, and the land use information in the study area is classified; the final part fuses the object boundary information obtained by object-based multiresolution segmentation into the classification layer, and a voting method is applied to optimize the classification results. The results show that (1) the classification results of the OUDN algorithm are better than those of U-net and DenseNet, and the average classification accuracy is 92.9%, an increase in approximately 3%; (2) for the U-net-DenseNet-coupled network (UDN) and OUDN, the urban forest extraction accuracies are higher than those of U-net and DenseNet, and the OUDN effectively alleviates the classification error caused by the fragmentation of urban distribution by combining object-based multiresolution segmentation features, making the overall accuracy (OA) of urban land use classification and the extraction accuracy of urban forests superior to those of the UDN algorithm; (3) based on the Spe-Texture (the spectral features combined with the texture features), the OA of the OUDN in the extraction of urban land use categories can reach 93.8%, thereby the algorithm achieved the accurate discrimination of different land use types, especially urban forests (99.7%). Therefore, this study provides a reference for feature setting for the mapping of urban land use information from VHSR imagery.

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