scholarly journals Impact of Rapid Urban Sprawl on the Local Meteorological Observational Environment Based on Remote Sensing Images and GIS Technology

2021 ◽  
Vol 13 (13) ◽  
pp. 2624
Author(s):  
Yanhao Zhang ◽  
Guicai Ning ◽  
Shihan Chen ◽  
Yuanjian Yang
Keyword(s):  
Remote Sensing ◽  
Urban Sprawl ◽  
Rapid Expansion ◽  
Anthropogenic Heat ◽  
Spatiotemporal Variability ◽  
Remote Sensing Images ◽  
Gis Technology ◽  
Anthropogenic Heat Flux ◽  
Meteorological Observations ◽  
The Impact

Rapid increases in urban sprawl affect the observational environment around meteorological stations by changing the land use/land cover (LULC) and the anthropogenic heat flux (AHF). Based on remote sensing images and GIS technology, we investigated the impact of changes in both LULC and AHF induced by urbanization on the meteorological observational environment in the Yangtze River Delta (YRD) during 2000–2018. Our results show that the observational environments around meteorological stations were significantly affected by the rapid expansion of built-up areas and the subsequent increase in the AHF, with a clear spatiotemporal variability. A positive correlation was observed between the proportion of built-up areas and the AHF around meteorological stations. The AHF was in the order urban stations > suburban stations > rural stations, but the increases in the AHF were greater around suburban and rural stations than around urban stations. Some meteorological stations need to be relocated to address the adverse effects induced by urbanization. The proportion of built-up areas and AHF around the new stations decreased significantly after relocation, weakening the urban heat island effect on the meteorological observations and substantially improving the observational environment. As a result, the observed daily mean temperature (relative humidity) decreased (increased) around the new stations after relocation. Our study comprehensively shows the impact of rapid urban sprawl on the observational environment around meteorological stations by assessing changes in both LULC and the AHF induced by urbanization. These findings provide scientific insights for the selection and construction of networks of meteorological stations and are therefore helpful in scientifically evaluating and correcting the impact of rapid urban sprawl on meteorological observations.

scholarly journals Efficient Patch-Wise Semantic Segmentation for Large-Scale Remote Sensing Images

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3232 ◽  
Author(s):  
Yan Liu ◽  
Qirui Ren ◽  
Jiahui Geng ◽  
Meng Ding ◽  
Jiangyun Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Large Scale ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Training Data ◽  
Land Resources ◽  
Remote Sensing Images ◽  
Training Strategy ◽  
The Impact

Efficient and accurate semantic segmentation is the key technique for automatic remote sensing image analysis. While there have been many segmentation methods based on traditional hand-craft feature extractors, it is still challenging to process high-resolution and large-scale remote sensing images. In this work, a novel patch-wise semantic segmentation method with a new training strategy based on fully convolutional networks is presented to segment common land resources. First, to handle the high-resolution image, the images are split as local patches and then a patch-wise network is built. Second, training data is preprocessed in several ways to meet the specific characteristics of remote sensing images, i.e., color imbalance, object rotation variations and lens distortion. Third, a multi-scale training strategy is developed to solve the severe scale variation problem. In addition, the impact of conditional random field (CRF) is studied to improve the precision. The proposed method was evaluated on a dataset collected from a capital city in West China with the Gaofen-2 satellite. The dataset contains ten common land resources (Grassland, Road, etc.). The experimental results show that the proposed algorithm achieves 54.96% in terms of mean intersection over union (MIoU) and outperforms other state-of-the-art methods in remote sensing image segmentation.

scholarly journals ORIENTED VEHICLE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON FEATURE AMPLIFICATION AND CATEGORY BALANCE BY OVERSAMPLING DATA AUGMENTATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 153-159
Author(s):  
N. Mo ◽  
L. Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Vehicle Detection ◽  
Training Dataset ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Fine Grained ◽  
Bounding Boxes ◽  
The Impact ◽  
Oriented Bounding Boxes

Abstract. Vehicles usually lack detailed information and are difficult to be trained on the high-resolution remote sensing images because of small size. In addition, vehicles contain multiple fine-grained categories that are slightly different, randomly located and oriented. Therefore, it is difficult to locate and identify these fine categories of vehicles. Considering the above problems in high-resolution remote sensing images, this paper proposes an oriented vehicle detection approach. First of all, we propose an oversampling and stitching method to augment the training dataset by increasing the frequency of objects with fewer training samples in order to balance the number of objects in each fine-grained vehicle category. Then considering the effect of the pooling operations on representing small objects, we propose to improve the resolution of feature maps so that detailed information hidden in feature maps can be enriched and they can better distinguish the fine-grained vehicle categories. Finally, we design a joint training loss function for horizontal and oriented bounding boxes with center loss, to decrease the impact of small between-class diversity on vehicle detection. Experimental verification is performed on the VEDAI dataset consisting of 9 fine-grained vehicle categories so as to evaluate the proposed framework. The experimental results show that the proposed framework performs better than most of competitive approaches in terms of a mean average precision of 60.7% and 60.4% in detecting horizontal and oriented bounding boxes respectively.

scholarly journals EU-Net: An Efficient Fully Convolutional Network for Building Extraction from Optical Remote Sensing Images

2019 ◽  
Vol 11 (23) ◽  
pp. 2813 ◽  
Author(s):  
Wenchao Kang ◽  
Yuming Xiang ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Ground Truth ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Convolutional Network ◽  
Practical Applications ◽  
The Impact

Automatic building extraction from high-resolution remote sensing images has many practical applications, such as urban planning and supervision. However, fine details and various scales of building structures in high-resolution images bring new challenges to building extraction. An increasing number of neural network-based models have been proposed to handle these issues, while they are not efficient enough, and still suffer from the error ground truth labels. To this end, we propose an efficient end-to-end model, EU-Net, in this paper. We first design the dense spatial pyramid pooling (DSPP) to extract dense and multi-scale features simultaneously, which facilitate the extraction of buildings at all scales. Then, the focal loss is used in reverse to suppress the impact of the error labels in ground truth, making the training stage more stable. To assess the universality of the proposed model, we tested it on three public aerial remote sensing datasets: WHU aerial imagery dataset, Massachusetts buildings dataset, and Inria aerial image labeling dataset. Experimental results show that the proposed EU-Net is superior to the state-of-the-art models of all three datasets and increases the prediction efficiency by two to four times.

The Earthquake Damage Remote Sensing Automatic Recgnition Based on Texture of Roof Tiles

2014 ◽  
Vol 651-653 ◽  
pp. 1315-1319 ◽  
Author(s):  
Dong Ping Li ◽  
Jun Gong ◽  
Jing Yi Li ◽  
Shan Shan Guo
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Rapid Assessment ◽  
Texture Features ◽  
Technology Support ◽  
Remote Sensing Images ◽  
Gis Technology ◽  
Disaster Assessment ◽  
High Resolution Images ◽  
Earthquake Damages

To meet the technical demands of rapid assessment on small and medium earthquake damages, this paper presents the comprehensive disaster evaluation method of on-spot human-computer interaction survey and remote sensing image analysis based on the GIS technology support in the small and medium earthquakes. By making full use of the advantages of existing data, emphasizing on the automatical identification of the unique texture features of small earthquakes with a combination analysis on high resolution images gained from unmanned aerial vehicles (uav) and the seismic damages, the new method results in the rank distribution of earthquakes by gaining the experienced parameter of local small-medium earthquakes based on the analysis of regional characteristics of texture features of remote sensing images. It is concluded that the evaluation method is more accurate and efficient for small and medium earthquake rapid disaster assessment.

Effects of Urbanization on Black Soil: A Remote Sensing Perspective

2014 ◽  
Vol 535 ◽  
pp. 478-482 ◽  
Author(s):  
Xiao Yan Li ◽  
Hong Li ◽  
Hui Jia Liu
Keyword(s):  
Remote Sensing ◽  
Black Soil ◽  
Rapid Expansion ◽  
Soil Types ◽  
Soil Resources ◽  
Gis And Remote Sensing ◽  
Average Value ◽  
Changchun City ◽  
Impact Of Urbanization ◽  
The Impact

Urbanization is considered to be one of the most important human activities in the recent history of China. Northeast China is one of the fastest urbanizing areas in the country with its urban population proportion increasing from 23.8% in 1942 to 32.8% in 32.8%, and to about 47% which is about the same as the world average value. This is a case study conducted in Changchun city to evaluate the impact of urbanization on soil resources using Geographic Information Systems (GIS) and remote sensing. The extent of urban land in the study area and the impact of urban sprawl on soil resources at a scale of 1:500,000 were estimated. The soil types occupied by the urbanization processes were determined by overlaying the soil map on the satellite images (Landsat TM and CBERS-2) of the study area at different times (1986, 1995, 2000 and 2005). The results documented the rapid expansion of urbanization in Changchun city, as well as the soil types occupied by the urbanization process. Results showed that, the urban increased by 76.8% during the past two decades and the loss of black soil accounted more than 70% of all soil loss. Growing urbanization may threaten food security, soil diversity and sustainability.

scholarly journals Modeling the Partitioning of Turbulent Fluxes at Urban Sites with Varying Vegetation Cover

2016 ◽  
Vol 17 (10) ◽  
pp. 2537-2553 ◽  
Author(s):  
M. J. Best ◽  
C. S. B. Grimmond
Keyword(s):  
Heat Flux ◽  
Latent Heat ◽  
Water Use ◽  
Vegetation Cover ◽  
Bowen Ratio ◽  
Anthropogenic Heat ◽  
Vegetation Fraction ◽  
Anthropogenic Heat Flux ◽  
External Water ◽  
The Impact

Abstract Inclusion of vegetation is critical for urban land surface models (ULSM) to represent reasonably the turbulent sensible and latent heat flux densities in an urban environment. Here the Joint UK Land Environment Simulator (JULES), a ULSM, is used to simulate the Bowen ratio at a number of urban and rural sites with vegetation cover varying between 1% and 98%. The results show that JULES is able to represent the observed Bowen ratios, but only when the additional anthropogenic water supplied into the urban ecosystem is considered. The impact of the external water use (e.g., through irrigation or street cleaning) on the surface energy flux partitioning can be as substantial as that of the anthropogenic heat flux on the sensible and latent heat fluxes. The Bowen ratio varies from 1 to 2 when the plan area vegetation fraction is between 30% and 70%. However, when the vegetation fraction is less than 20%, the Bowen ratios increase substantially (2–10) and have greater sensitivity to assumptions about external water use. As there are few long-term observational sites with vegetation cover less than 30%, there is a clear need for more measurement studies in such environments.

scholarly journals URBAN BOUNDARY EXTRACTION AND URBAN SPRAWL MEASUREMENT USING HIGH-RESOLUTION REMOTE SENSING IMAGES: A CASE STUDY OF CHINA’S PROVINCIAL

2018 ◽  
Vol XLII-3 ◽  
pp. 1713-1719 ◽  
Author(s):  
H. Wang ◽  
X. Ning ◽  
H. Zhang ◽  
Y. Liu ◽  
F. Yu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Urban Sprawl ◽  
Urban Areas ◽  
Remote Sensing Images ◽  
Boundary Extraction ◽  
Rapid Urbanization ◽  
Important Indicator ◽  
Provincial Capital ◽  
Year 2000

Urban boundary is an important indicator for urban sprawl analysis. However, methods of urban boundary extraction were inconsistent, and construction land or urban impervious surfaces was usually used to represent urban areas with coarse-resolution images, resulting in lower precision and incomparable urban boundary products. To solve above problems, a semi-automatic method of urban boundary extraction was proposed by using high-resolution image and geographic information data. Urban landscape and form characteristics, geographical knowledge were combined to generate a series of standardized rules for urban boundary extraction. Urban boundaries of China’s 31 provincial capitals in year 2000, 2005, 2010 and 2015 were extracted with above-mentioned method. Compared with other two open urban boundary products, accuracy of urban boundary in this study was the highest. Urban boundary, together with other thematic data, were integrated to measure and analyse urban sprawl. Results showed that China’s provincial capitals had undergone a rapid urbanization from year 2000 to 2015, with the area change from 6520 square kilometres to 12398 square kilometres. Urban area of provincial capital had a remarkable region difference and a high degree of concentration. Urban land became more intensive in general. Urban sprawl rate showed inharmonious with population growth rate. About sixty percent of the new urban areas came from cultivated land. The paper provided a consistent method of urban boundary extraction and urban sprawl measurement using high-resolution remote sensing images. The result of urban sprawl of China’s provincial capital provided valuable urbanization information for government and public.

scholarly journals Dual Effects of Synoptic Weather Patterns and Urbanization on Summer Diurnal Temperature Range in an Urban Agglomeration of East China

2021 ◽  
Vol 9 ◽  
Author(s):  
Min Guo ◽  
Minxuan Zhang ◽  
Hong Wang ◽  
Linlin Wang ◽  
Shuhong Liu ◽  
...  
Keyword(s):  
Diurnal Temperature Range ◽  
Eastern China ◽  
Urban Agglomeration ◽  
Anthropogenic Heat ◽  
Diurnal Temperature ◽  
Temperature Range ◽  
Weather Patterns ◽  
Synoptic Weather ◽  
Anthropogenic Heat Flux ◽  
The Impact

Previous studies on the impact of urbanization on the diurnal temperature range (DTR) have mainly concentrated on the intra-seasonal and interannual–decadal scales, while relatively fewer studies have considered synoptic scales. In particular, the modulation of DTR by different synoptic weather patterns (SWPs) is not yet fully understood. Taking the urban agglomeration of the Yangtze River Delta region (YRDUA) in eastern China as an example, and by using random forest machine learning and objective weather classification methods, this paper analyzes the characteristics of DTR and its urban–rural differences (DTRU–R) in summer from 2013 to 2016, based on surface meteorological observations, satellite remote sensing, and reanalysis data. Ultimately, the influences of urbanization-related factors and different large-scale SWPs on DTR and DTRU–R are explored. Results show that YRDUA is controlled by four SWPs in the 850-hPa geopotential height field in summer, and the DTRs in three sub-regions are significantly different under the four SWPs, indicating that they play a role in regulating the DTR in YRDUA. In terms of the average DTR for each SWP, the southern sub-region of the YRDUA is the highest, followed by the northern sub-region, and the middle sub-region is the lowest, which is most significantly affected by high-level urbanization and high anthropogenic heat emission. The DTRU–R is negative and differs under the four different SWPs with variation in sunshine and rainfall. The difference in anthropogenic heat flux between urban and rural areas is one of the potentially important urbanization-related drivers for DTRU–R. Our findings help towards furthering our understanding of the response of DTR in urban agglomerations to different SWPs via the modulation of local meteorological conditions.

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