Coupling an Intercalibration of Radiance-Calibrated Nighttime Light Images and Land Use/Cover Data for Modeling and Analyzing the Distribution of GDP in Guangdong, China

Cao, Ziyang; Wu, Zhifeng; Kuang, Yaoqiu; Huang, Ningsheng; Wang, Meng

doi:10.3390/su8020108

Cited by 31 publications

(21 citation statements)

References 41 publications

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“…Binary urban footprint images for available years Reclassify "vegetation", "bare soil", and "water" pixels as "non-urban", and "built-up" as "urban" Fill small patches in urban areas using a closing operation (i.e: a dilation followed by an erosion) Perform a two-step temporal correction The final step of the pre-processing included a two-step temporal correction, depicted in Figure 4, that was adapted from the inter-annual series correction proposed by Cao et al [58]. The temporal correction harmonizes the urban footprint image series, assuming the following: if a pixel is urban in one year, it must be urban in the following years; and if a pixel is non-urban in one year, it must have been non-urban in the previous years.…”

Section: Pre-processing Of Landsat Imagesmentioning

confidence: 99%

Spatiotemporal Modeling of Urban Growth Using Machine Learning

et al. 2019

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This paper presents a general framework for modeling the growth of three important variables for cities: population distribution, binary urban footprint, and urban footprint in color. The framework models the population distribution as a spatiotemporal regression problem using machine learning, and it obtains the binary urban footprint from the population distribution through a binary classifier plus a temporal correction for existing urban regions. The framework estimates the urban footprint in color from its previous value, as well as from past and current values of the binary urban footprint using a semantic inpainting algorithm. By combining this framework with free data from the Landsat archive and the Global Human Settlement Layer framework, interested users can get approximate growth predictions of any city in the world. These predictions can be improved with the inclusion in the framework of additional spatially distributed input variables over time subject to availability. Unlike widely used growth models based on cellular automata, there are two main advantages of using the proposed machine learning-based framework. Firstly, it does not require to define rules a priori because the model learns the dynamics of growth directly from the historical data. Secondly, it is very easy to train new machine learning models using different explanatory input variables to assess their impact. As a proof of concept, we tested the framework in Valledupar and Rionegro, two Latin American cities located in Colombia with different geomorphological characteristics, and found that the model predictions were in close agreement with the ground-truth based on performance metrics, such as the root-mean-square error, zero-mean normalized cross-correlation, Pearson's correlation coefficient for continuous variables, and a few others for discrete variables such as the intersection over union, accuracy, and the f 1 metric. In summary, our framework for modeling urban growth is flexible, allows sensitivity analyses, and can help policymakers worldwide to assess different what-if scenarios during the planning cycle of sustainable and resilient cities.cities are also beacons of hope as they provide opportunities to their inhabitants to access to basic utilities (e.g., water, electricity, gas, internet), health facilities, employment with competitive wages, financial services, entertainment, technical training, formal education, and much more [13,14]. When diverse and skilled human capital interacts, cities can boost ecosystems of research, development, innovation, and entrepreneurship [15][16][17]. It is this duality between the challenges and benefits of cities that makes the understanding of their growth so important for us.To predict how cities are going to look like in the future, urban planners rely on historical data and urban growth models [10,[18][19][20] . These models can have multiple outputs, but they commonly provide estimations of binary urban footprints (i.e., maps with urban or non-urban categories) or population distr...

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Section: Pre-processing Of Landsat Imagesmentioning

confidence: 99%

Spatiotemporal Modeling of Urban Growth Using Machine Learning

et al. 2019

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“…Located in southern China, Guangdong Province has been the largest contributor of GDP among all the provinces in China since 1989 6 . For example, Guangdong's GDP accounts for 10.6% of the total GDP of Mainland China in 2015.…”

Section: Case Studymentioning

confidence: 99%

A two-stage stochastic programming model for industrial adjustment on water and energy resources in Guangdong Province, China

Yue¹,

Cai²,

Su³

et al. 2017

Proceedings of the 2017 6th International Conference on Energy and Environmental Protection (ICEEP 2017)

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Abstract.A two-stage stochastic programming (TSP) model is effective for industrial adjustment under limited and varied capacities of water and energy resources. In this paper, industrial adjustment in the future (i.e., the 13th five-year plan) was evaluated by a eatablished TSP model under limited and uncertain supply of water and energy resources. The developed method was demonstrated in Guangdong Province of China. The results indicated that the first-stage industrial planning would not be influenced by water and energy supply capacities when water supply would be sufficient (i.e., 8938 million m 3 ). When the probability of water supply capacities would be 4491 million m 3 , economic benefits of three types of industries (i.e., manufacture of electrical machinery and equipment, textile garments, footwear and headgear, as well as food and tobacco) would reduce over 70 billion yuan.

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“…Nighttime light (NTL) images effectively depict the distribution of artificial light on the earth's surface, and have become an important indicator of the intensity of urban social and economic activities, and has been widely used in urbanization research [1][2][3][4][5][6][7]. In particular, the unique advantage of NTL collections in space-time span makes it more straightforward to carry out a global economic assessment and regional development research, compared to traditional statistical index accounting, which usually takes administrative unit as a statistical unit, lacking effective and accurate spatial location information.…”

Section: Introductionmentioning

confidence: 99%

The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis

et al. 2019

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The Operational Linescan System (OLS) carried by the National Defense Meteorological Satellite Program (DMSP) can capture the weak visible radiation emitted from earth at night and produce a series of annual cloudless nighttime light (NTL) images, effectively supporting multi-scale, long-term human activities and urbanization process research. However, the interannual instability and sensor bias of NTL time series products greatly limit further studies of lighting data in time series with OLS. Several calibration models for OLS have been proposed to implement interannual corrections to improve the continuity and consistency of time series NTL products; however, due to the subjective factors intervention and insufficient automation in the calibration process, the interannual correction study of NTL time series images is still worth being developed further. Therefore, to avoid the involvement of subjective factors and to optimize the Pseudo-Invariant Features (PIF) identification, an interannual calibration model Pixel-based PIF (PBPIF) is proposed, which identifies PIF by pixel fluctuation characteristics. Results show that a PBPIF-based model can reduce subjective interference and improve the degree of automation during the NTL interannual calibration process. The calibration performance evaluation based on Total Sum of Lights (TSOL) and Sum of the Normalized Difference Index (SNDI) shows that compared to the traditional PIF-based (tPIF-based) and Ridgeline Sampling Regression based (RSR-based) models, the PBPIF-based one achieves better performance in reducing NTL interannual turbulence and minimizing the deviation between sensors. In addition, based on the corrected NTL time series products, pixel-level linear regression analysis is implemented to maximize the potential of the NTL resolution to produce global Light Intensity Change Coefficient (LICC). The results of global LICC can be widely applied to the detailed study of the characteristics of economic development and urbanization.

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Coupling an Intercalibration of Radiance-Calibrated Nighttime Light Images and Land Use/Cover Data for Modeling and Analyzing the Distribution of GDP in Guangdong, China

Cited by 31 publications

References 41 publications

Spatiotemporal Modeling of Urban Growth Using Machine Learning

Spatiotemporal Modeling of Urban Growth Using Machine Learning

A two-stage stochastic programming model for industrial adjustment on water and energy resources in Guangdong Province, China

The Interannual Calibration and Global Nighttime Light Fluctuation Assessment Based on Pixel-Level Linear Regression Analysis

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