Studying floodplain roughness in an Upper Tisza study area

Floods slowing down due to the significant decrease of the gradient have considerable sediment accumulation capacity in the floodplain. The grade of accumulation is further increased if the width of the floodplain is not uniform as water flowing out of the narrow sections diverge and its speed is decreased. Surface roughness in a study area of 492 hectares in the Upper Tisza region was analysed based on CIR (color-infrared) orthophotos from 2007. An NDVI index layer was created first on which object-based image segmentation and threshold-based image classification were performed. The study area is dominated by land cover / land use types (grassland-shrubs, forest) with high roughness values. It was concluded that vegetation activity based analyses on their own are not enough for determining floodplain roughness.

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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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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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LAND USE TYPES AND SOIL CHARACTERISTICTS: A CASE STUDY IN HUONG VINH COMMUNE, HUONG TRA TOWN, THUA THIEN HUE PROVINCE

HUE UNIVERSITY JOURNAL OF SCIENCE EARTH SCIENCE AND ENVIRONMENT ◽

10.26459/hujos-ese.v126i4b.4121 ◽

2017 ◽

Vol 126 (4B) ◽

Author(s):

Trần Thanh Đức

Keyword(s):

Land Use ◽

Sweet Potato ◽

Soil Analysis ◽

Soil Characteristics ◽

Low Ph ◽

Clay Loam ◽

Total N ◽

Land Use Types ◽

Total P

This research carried out in Huong Vinh commune, Huong Tra town, Thua Thien Hue province aimed to identify types of land use and soil characteristics. Results showed that five crops are found in Huong Vinh commune including rice, peanut, sweet potato, cassava and vegetable. There are two major soil orders with four soil suborders classified by FAO in Huong Vinh commune including Fluvisols (Dystric Fluvisols, Gleyic Fluvisols and Cambic Fluvisols) and Arenosols (Haplic Arenosols). The results from soil analysis showed that three soil suborders including Dystric Fluvisols, Gleyic Fluvisols and Cambic Fluvisols belonging to Fluvisols were clay loam in texture, low pH, low in OC, total N, total P2O5 and total K2O. Meanwhile, the Haplic Arenosols was loamy sand in texture, poor capacity to hold OC, total N, total P2O5 and total K2O

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Bat Community Responses to Structural Habitat Complexity Resulting from Management Practices Within Different Land Use Types — A Case Study from North-Eastern Germany

Acta Chiropterologica ◽

10.3161/15081109acc2018.20.2.010 ◽

2019 ◽

Vol 20 (2) ◽

pp. 387 ◽

Cited By ~ 4

Author(s):

Nicole Starik ◽

Thomas Göttert ◽

Emanuel Heitlinger ◽

Ulrich Zeller

Keyword(s):

Land Use ◽

Habitat Complexity ◽

Management Practices ◽

Community Responses ◽

Eastern Germany ◽

Land Use Types ◽

North Eastern ◽

Structural Habitat

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THE IMPACT OF DIFFERENT URBAN LAND USE TYPES ON AIR POLLUTION IN THE MEGACITY OF SÃO PAULO

Revista Presença Geográfica ◽

10.36026/rpgeo.v7i1.5366 ◽

2020 ◽

Vol 7 (1) ◽

pp. 91

Author(s):

Júlio Barboza Chiquetto ◽

Maria Elisa Siqueira Silva ◽

Rita Yuri Ynoue ◽

Flávia Noronha Dutra Ribieiro ◽

Débora Souza Alvim ◽

...

Keyword(s):

Land Use ◽

Air Pollution ◽

Urban Land ◽

Sao Paulo ◽

Urban Land Use ◽

São Paulo ◽

Land Use Types ◽

The Impact

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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