scholarly journals Mapping 10-m Resolution Rural Settlements Using Multi-Source Remote Sensing Datasets with the Google Earth Engine Platform

2020 ◽  
Vol 12 (17) ◽  
pp. 2832 ◽  
Author(s):  
Hanyu Ji ◽  
Xing Li ◽  
Xinchun Wei ◽  
Wei Liu ◽  
Lianpeng Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Google Earth ◽  
Kappa Coefficient ◽  
The Yangtze River ◽  
Rural Settlements ◽  
Training Samples ◽  
Google Earth Engine ◽  
The Yangtze River Delta

Timely and accurate information on rural settlements is essential for rural development planning. Remote sensing has become an important means for accurately mapping large scale rural settlements. Nevertheless, numerous difficulties remain in accurate and efficient rural settlement extraction. In this study, by combining multi-dimensional features derived from Sentinel-1/2 images, Visible Infrared Imaging Radiometer Suite supporting a Day-Night Band (VIIRS-DNB) dataset, and Digital Elevation Model (DEM) data using the Google Earth Engine (GEE) platform, we proposed an efficient framework with good transferability for mapping rural settlements in the Yangtze River Delta. To avoid the time-consuming selection of a large number of training samples in the whole study area, we employed four random forest models obtained from the training samples in respective training municipal districts in four different regions to classify other municipal districts in their corresponding region. We found that different features play diverse vital roles in the extraction of rural settlements in various regions. Compared to results only using optical data, accuracies obtained by the proposed method were significantly improved. The average user’s accuracy, producer’s accuracy, overall accuracy, and Kappa coefficient increased by 16.75%, 17.75%, 11.50%, and 14.50% in the four training municipal administrative areas, respectively. The overall accuracy and Kappa coefficient were 96% and 0.84, respectively. By contrast, our classification results are superior to other public datasets. The final mapping results provided a detailed spatial distribution of the rural settlements in the Yangtze River Delta and revealed that the total area of rural settlements is approximately 32,121.1 km2, accounting for 17.41% of the total area. The high-density rural settlements are mainly distributed in the Northern Plain and East Coast, while the low-density rural settlements are located in the Central Hills and Southern Mountain.

scholarly journals Accuracy Improvements to Pixel-Based and Object-Based LULC Classification with Auxiliary Datasets from Google Earth Engine

2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Le’an Qu ◽  
Zhenjie Chen ◽  
Manchun Li ◽  
Junjun Zhi ◽  
Huiming Wang
Keyword(s):  
Remote Sensing ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Google Earth ◽  
Classification Model ◽  
Classification Models ◽  
Topographic Features ◽  
Object Based ◽  
Google Earth Engine ◽  
The Yangtze River Delta

The monitoring and assessment of land use/land cover (LULC) change over large areas are significantly important in numerous research areas, such as natural resource protection, sustainable development, and climate change. However, accurately extracting LULC only using the spectral features of satellite images is difficult owing to landscape heterogeneities over large areas. To improve the accuracy of LULC classification, numerous studies have introduced other auxiliary features to the classification model. The Google Earth Engine (GEE) not only provides powerful computing capabilities, but also provides a large amount of remote sensing data and various auxiliary datasets. However, the different effects of various auxiliary datasets in the GEE on the improvement of the LULC classification accuracy need to be elucidated along with methods that can optimize combinations of auxiliary datasets for pixel- and object-based classification. Herein, we comprehensively analyze the performance of different auxiliary features in improving the accuracy of pixel- and object-based LULC classification models with medium resolution. We select the Yangtze River Delta in China as the study area and Landsat-8 OLI data as the main dataset. Six types of features, including spectral features, remote sensing multi-indices, topographic features, soil features, distance to the water source, and phenological features, are derived from auxiliary open-source datasets in GEE. We then examine the effect of auxiliary datasets on the improvement of the accuracy of seven pixels-based and seven object-based random forest classification models. The results show that regardless of the types of auxiliary features, the overall accuracy of the classification can be improved. The results further show that the object-based classification achieves higher overall accuracy compared to that obtained by the pixel-based classification. The best overall accuracy from the pixel-based (object-based) classification model is 94.20% (96.01%). The topographic features play the most important role in improving the overall accuracy of classification in the pixel- and object-based models comprising all features. Although a higher accuracy is achieved when the object-based method is used with only spectral data, small objects on the ground cannot be monitored. However, combined with many types of auxiliary features, the object-based method can identify small objects while also achieving greater accuracy. Thus, when applying object-based classification models to mid-resolution remote sensing images, different types of auxiliary features are required. Our research results improve the accuracy of LULC classification in the Yangtze River Delta and further provide a benchmark for other regions with large landscape heterogeneity.

scholarly journals Exploring the Relationship between Urbanization and Ecological Environment Using Remote Sensing Images and Statistical Data: A Case Study in the Yangtze River Delta, China

2020 ◽  
Vol 12 (14) ◽  
pp. 5620 ◽  
Author(s):  
Zhenfeng Shao ◽  
Lin Ding ◽  
Deren Li ◽  
Orhan Altan ◽  
Md. Enamul Huq ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Yangtze River ◽  
Statistical Data ◽  
Yangtze River Delta ◽  
River Delta ◽  
Ecological Environment ◽  
The Yangtze River ◽  
Remote Sensing Images ◽  
The Yangtze River Delta ◽  
Coordination Degree

With the rapid urban development in China, urbanization has brought more and more pressure on the ecological environment. As one of the most dynamic, open, and innovative regions in China, the eco-environmental issues in the Yangtze River Delta have attracted much attention. This paper takes the central region of the Yangtze River Delta as the research object, through building the index system of urbanization and ecological environment based on statistical data and two new indicators (fraction of vegetation coverage and surface urban heat island intensity) extracted from remote sensing images, uses the Entropy-TOPSIS method to complete the comprehensive assessment, and then analyzes the coupling coordination degree between the urbanization and ecological environment and main obstacle factors. The results showed that the coupling coordination degree in the study region generally shows an upward trend from 0.604 in 2008 to 0.753 in 2017, generally changing from an imbalanced state towards a basically balanced state. However, regional imbalance of urbanization and ecological environment always exists, which is mainly affected by social urbanization, economic urbanization, landscape urbanization, pollution loading and resource consumption. Finally, on the basis of the obstacle factor analysis, some specific suggestions for promoting the coordinated development of the Yangtze River Delta are put forward.

scholarly journals Spatial and Temporal Pattern Analysis of Land Use in Yangtze River Delta Based on Remote Sensing and GIS in Intelligent Environment

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Hongbing Hu ◽  
Linyuan Wu ◽  
Yulan Zhan ◽  
Shuhui Zhang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
River Delta ◽  
The Yangtze River ◽  
Intelligent Environment ◽  
Delta Region ◽  
Yangtze River Delta Region ◽  
The Yangtze River Delta

Land use change plays an important role in regional socio-economic development and global environmental change. Whether the land is effectively and efficiently used is not only related to the income level of the people in the surrounding cities but also closely related to the local economy and national economy. Intelligent environment refers to the indoor environment with a variety of data acquisition equipment. Combined with related technologies, the reasoning and analysis of the data can be used to realize the functions of activity identification, data perception, and control. In addition, the Yangtze River Delta is an economically developed area in China, and its land use situation is related to the economic development in the next ten or even decades. Therefore, it is necessary to analyze the spatial and temporal pattern of land use in Yangtze River Delta region by remote sensing image technology and GIS in intelligent environment. Based on intelligent environment, this paper uses RS and GIS technology to interpret remote sensing image and map land use in multitemporal coastal zone. The land use dynamic degree model and spatial interpolation method were used to analyze and evaluate the spatial and temporal evolution characteristics of land use in the Yangtze River Delta region, and the landscape pattern changes in the Yangtze River Delta region were analyzed and evaluated. This study found that the land use types in the Yangtze River Delta have transformed each other, and the land use change speed is fast, which is inseparable from the rapid economic development. In the future, in addition to maintaining the rapid and stable development of industry, the rational use of limited land resources, the improvement of agricultural development short board, and the improvement of tourism economic benefits will make the economy of the Yangtze River Delta region to a new level.

scholarly journals High-resolution (0.05°  ×  0.05°) NO<sub><i>x</i></sub> emissions in the Yangtze River Delta inferred from OMI

2019 ◽  
Vol 19 (20) ◽  
pp. 12835-12856 ◽  
Author(s):  
Hao Kong ◽  
Jintai Lin ◽  
Ruixiong Zhang ◽  
Mengyao Liu ◽  
Hongjian Weng ◽  
...  
Keyword(s):  
High Resolution ◽  
Yangtze River ◽  
Yangtze River Delta ◽  
Google Earth ◽  
River Delta ◽  
Inversion Method ◽  
Fine Scale ◽  
The Yangtze River ◽  
Peking University ◽  
The Yangtze River Delta

Abstract. Emission datasets of nitrogen oxides (NOx) at high horizontal resolutions (e.g., 0.05∘×0.05∘) are crucial for understanding human influences at fine scales, air quality studies, and pollution control. Yet high-resolution emission data are often missing or contain large uncertainties especially for the developing regions. Taking advantage of long-term satellite measurements of nitrogen dioxide (NO2), here we develop a computationally efficient method of estimating NOx emissions in major urban areas at the 0.05∘×0.05∘ resolution. The top-down inversion method accounts for the nonlinear effects of horizontal transport, chemical loss, and deposition. We construct a two-dimensional Peking University High-resolution Lifetime-Emission-Transport model (PHLET), its adjoint model (PHLET-A), and a satellite conversion matrix approach to relate emissions, lifetimes, simulated NO2, and satellite NO2 data. The inversion method is applied to the summer months of 2012–2015 in the Yangtze River Delta (YRD; 29–34∘ N, 118–123∘ E) area, a major polluted region of China, using the NO2 vertical column density data from the Peking University Ozone Monitoring Instrument NO2 product (POMINO). A systematic analysis of inversion errors is performed, including using an independent test based on GEOS-Chem simulations. Across the YRD area, the summer average emissions obtained in this work range from 0 to 15.3 kg km−2 h−1, and the lifetimes (due to chemical loss and deposition) range from 0.6 to 3.3 h. Our emission dataset reveals fine-scale spatial information related to nighttime light, population density, road network, maritime shipping, and land use (from a Google Earth photo). We further compare our emissions with multiple inventories. Many of the fine-scale emission structures are not well represented or not included in the widely used Multi-scale Emissions Inventory of China (MEIC).

scholarly journals Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiao Zhang ◽  
Liangyun Liu ◽  
Xidong Chen ◽  
Yuan Gao ◽  
Mihang Jiang
Keyword(s):  
Yangtze River ◽  
Landsat Imagery ◽  
Impervious Surface ◽  
Yangtze River Delta ◽  
Surface Reflectance ◽  
Impervious Surfaces ◽  
The Yangtze River ◽  
Surface Dynamics ◽  
Training Samples ◽  
The Yangtze River Delta

Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.

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