scholarly journals Detection of County Economic Development Using LJ1-01 Nighttime Light Imagery: A Comparison with NPP-VIIRS Data

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6633
Author(s):  
Hongliang Liu ◽  
Nianxue Luo ◽  
Chunchun Hu
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Remote Sensing Data ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Regression Methods ◽  
Public Resources ◽  
The Status ◽  
Nighttime Light ◽  
Nighttime Light Imagery

Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indicators at the national and regional scales to study regional economic development. However, most previous studies only chose a single measurement indicator (such as GDP) and adopted simple regression methods to investigate the economic development of a certain area based on DMSP-OLS or NPP-VIIRS stable NTL data. The status quo shows the problems of using a single evaluation index—it has a low evaluation precision. The LJ1-01 satellite is the first dedicated NTL remote sensing satellite in the world, launched in July 2018. The data provided by LJ1-01 have a higher spatial resolution and fewer blooming phenomena. In this paper, we compared the accuracy of the LJ1-01 data and NPP-VIIRS data in detecting county-level multidimensional economic development. In three provinces in China, namely, Hubei, Hunan and Jiangxi, 20 socioeconomic parameters were selected from the following five perspectives: economic conditions, people’s livelihood, social development, public resources and natural vulnerability. Then, a County-level Economic Index (CEI) was constructed to evaluate the level of multidimensional economic development, with the spatial pattern of the multidimensional economic development also identified across the study area. The present study adopted the random forest (RF) and linear regression (LR) algorithms to establish the regression model individually, and the results were evaluated by cross-validation. The results show that the RF algorithm greatly improves the accuracy of the model compared with the LR algorithm, and thus is suitable for the study of NTL data. In addition, a better determinate coefficient (R2) based on the LJ1-01 data (0.8168) was obtained than that from the NPP-VIIRS data (0.7245) in the RF model, which reflects that the LJ1-01 data offer better potential in the evaluation of socioeconomic parameters and can be used to identify, both accurately and efficiently, multidimensional economic development at the county level.

scholarly journals Nightlight as a Proxy of Economic Indicators: Fine-Grained GDP Inference Around Mainland China via Attention-Augmented CNN from Daytime Satellite Imagery

2021 ◽  
Vol 13 (11) ◽  
pp. 2067
Author(s):  
Haoyu Liu ◽  
Xianwen He ◽  
Yanbing Bai ◽  
Xing Liu ◽  
Yilin Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Chinese Mainland ◽  
Economic Research ◽  
Official Method ◽  
County Level ◽  
Socioeconomic Indicators ◽  
Sensing Data ◽  
Fine Grained ◽  
The Chinese Mainland

The official method of collecting county-level GDP values in the Chinese Mainland relies mainly on administrative reporting data and suffers from high costs of time, money, and human labor. To date, a series of studies have been conducted to generate fine-grained maps of socioeconomic indicators from the easily accessed remote sensing data and achieved satisfactory results. This paper proposes a transfer learning framework that regards nightlight intensities as a proxy of economic activity degrees to estimate county-level GDP around the Chinese Mainland. In the framework, paired daytime satellite images and nightlight intensity levels were applied to train a VGG-16 architecture, and the output features at a specific layer, after dimensional reduction and statistics calculation, were fed into a simple regressor to estimate county-level GDP. We trained the model with data of 2017 and utilized it to predict county-level GDP of 2018, achieving an R-squared of 0.71. Furthermore, the results of gradient visualization confirmed the validity of the proposed framework qualitatively. To the best of our knowledge, this is the first time that county-level GDP values around the Chinese Mainland have been estimated from both daytime and nighttime remote sensing data relying on attention-augmented CNN. We believe that our work will shed light on both the evolution of fine-grained socioeconomic surveys and the application of remote sensing data in economic research.

scholarly journals Inferring Mixed Use of Buildings with Multisource Data Based on Tensor Decomposition

2021 ◽  
Vol 10 (3) ◽  
pp. 185
Author(s):  
Chenyang Zhang ◽  
Qingli Shi ◽  
Li Zhuo ◽  
Fang Wang ◽  
Haiyan Tao
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Remote Sensing Data ◽  
Tensor Decomposition ◽  
Decomposition Algorithm ◽  
Sensing Data ◽  
Building Recognition ◽  
Planning Decisions ◽  
Mixed Use ◽  
The Status

Information on the mixed use of buildings helps understand the status of mixed-use urban vertical land and assists in urban planning decisions. Although a few studies have focused on this topic, the methods they used are quite complex and require manual intervention in extracting different function patterns of buildings, while building recognition rates remain unsatisfying. In this paper, we propose a new method to infer the mixed use of buildings based on a tensor decomposition algorithm, which integrates information from both high-resolution remote sensing images and social sensing data. We selected the Tianhe District of Guangzhou, China to validate our method. The results show that the recognition rate of buildings can reach 98.67%, with an average recognition accuracy of 84%. Our study proves that the tensor decomposition algorithm can extract different function patterns of buildings unsupervised, while remote sensing data can provide key information for inferring building functions. The tensor decomposition-based method can serve as an effective and efficient way to infer the mixed use of buildings, which can achieve better results with simpler steps.

scholarly journals Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

2018 ◽  
Vol 7 (7) ◽  
pp. 243 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Wanchun Leng ◽  
Tengfei Long ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Protected Areas ◽  
Remote Sensing Data ◽  
Light Pollution ◽  
Sensing Data ◽  
Nighttime Light

The Dynamic Monitoring on Mining Collapsing around Xinglongzhuang Coal Mine Based on Remote Sensing Images

2013 ◽  
Vol 726-731 ◽  
pp. 4625-4630 ◽  
Author(s):  
Hai Qing Wang ◽  
Ying Jie Zhou ◽  
Ling Chen ◽  
Qing Qing Jing ◽  
Jie Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Coal Mine ◽  
Local Economic Development ◽  
Remote Sensing Data ◽  
Field Investigation ◽  
Dynamic Monitoring ◽  
Great Contribution ◽  
Remote Sensing Images ◽  
Sensing Data

A large number of old coal mine, such as Xinglongzhuang coal mine, made a great contribution to local economic development and national construction. But, serious mining collapsing was caused also, and local people's livelihood has been affected seriously. The mining collapsing could be identified on remote sensing images by some characteristics. There were 4 period remote sensing data, which was acquired respectively in June 2009, April 2010, June 2011 and July 2012, and field investigation were applied in this articles to study these mining collapsing. The research suggests that the mining collapsing could be divided into the aged type, the middle aged type and the young type. There is a suggestion that, the monitoring and prevention works should be strengthened.

Monitoring and assessing “ghost cities” in Northeast China from the view of nighttime light remote sensing data

2017 ◽  
Vol 70 ◽  
pp. 34-42 ◽  
Author(s):  
Qiming Zheng ◽  
Jingsong Deng ◽  
Ruowei Jiang ◽  
Ke Wang ◽  
Xingyu Xue ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Northeast China ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Nighttime Light
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