Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

Wang, Luyao; Fan, Hong; Wang, Yankun

doi:10.3390/rs11161900

Cited by 22 publications

(17 citation statements)

References 65 publications

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“…RF is an integrated learning algorithm of bagging algorithm and decision tree algorithm, which can fit the complex nonlinear relationship between independent variables and dependent variables (Wang, Fan & Wang, 2019). The major parameters in this algorithm were set as follows: the scale was set as "TRUE", and the number of trees (ntree) was set as 500, the number of features tried at each node (mtry) depends on the lowest out-of-bag error.…”

Section: Model Construction and Validationmentioning

confidence: 99%

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Wei

Liu

Zhang

et al. 2020

PeerJ

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Soil salinization is a global problem closely related to the sustainable development of social economy. Compared with frequently-used satellite-borne sensors, unmanned aerial vehicles (UAVs) equipped with multispectral sensors provide an opportunity to monitor soil salinization with on-demand high spatial and temporal resolution. This study aims to quantitatively estimate soil salt content (SSC) using UAV-borne multispectral imagery, and explore the deep mining of multispectral data. For this purpose, a total of 60 soil samples (0–20 cm) were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. Meanwhile, from the UAV sensor we obtained the multispectral data, based on which 22 spectral covariates (6 spectral bands and 16 spectral indices) were constructed. The sensitive spectral covariates were selected by means of gray relational analysis (GRA), successive projections algorithm (SPA) and variable importance in projection (VIP), and from these selected covariates estimation models were built using back propagation neural network (BPNN) regression, support vector regression (SVR) and random forest (RF) regression, respectively. The performance of the models was assessed by coefficient of determination (R2), root mean squared error (RMSE) and ratio of performance to deviation (RPD). The results showed that the estimation accuracy of the models had been improved markedly using three variable selection methods, and VIP outperformed GRA and GRA outperformed SPA. However, the model accuracy with the three machine learning algorithms turned out to be significantly different: RF > SVR > BPNN. All the 12 SSC estimation models could be used to quantitatively estimate SSC (RPD > 1.4) while the VIP-RF model achieved the highest accuracy (Rc2 = 0.835, RP2 = 0.812, RPD = 2.299). The result of this study proved that UAV-borne multispectral sensor is a feasible instrument for SSC estimation, and provided a reference for further similar research.

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Section: Model Construction and Validationmentioning

confidence: 99%

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Wei

Liu

Zhang

et al. 2020

PeerJ

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“…With the increase in the availability of geospatial big data that are highly correlated with human activities, detailed estimations of coastal population exposure are possible. For example, previous studies showed the considerable potential of point-of-interest (POI) [9,10] and Sina Weibo check-in data [11] in high-resolution population mapping.…”

Section: Introductionmentioning

confidence: 99%

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

Yang

Yao

Chen

et al. 2019

IJERPH

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With sea level predicted to rise and the frequency and intensity of coastal flooding expected to increase due to climate change, high-resolution gridded population datasets have been extensively used to estimate the size of vulnerable populations in low-elevation coastal zones (LECZ). China is the most populous country, and populations in its LECZ grew rapidly due to urbanization and remarkable economic growth in coastal areas. In assessing the potential impacts of coastal hazards, the spatial distribution of population exposure in China’s LECZ should be examined. In this study, we propose a combination of multisource remote sensing images, point-of-interest data, and machine learning methods to improve the performance of population disaggregation in coastal China. The resulting population grid map of coastal China for the reference year 2010, with a spatial resolution of 100 × 100 m, is presented and validated. Then, we analyze the distribution of population in LECZ by overlaying the new gridded population data and LECZ footprints. Results showed that the total population exposed in China’s LECZ in 2010 was 158.2 million (random forest prediction) and 160.6 million (Cubist prediction), which account for 12.17% and 12.36% of the national population, respectively. This study also showed the considerable potential in combining geospatial big data for high-resolution population estimation.

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“…To some extent, night-time light data can compensate for the lack of panel statistical data in urban space research. Moreover, night-time light data can be applied in the study of urban space [11] because urban activities are closely correlated with electricity consumption [12], and many studies have proven that the intensity of urban light has a high correlation with urban population distribution [13]. Therefore, night-time light data are currently mainly used in urban expansion [14], urban morphology and structure [15], and social and economic status decisions [16].…”

Section: A Research On Night-time Light Datamentioning

confidence: 99%

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

2021

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With the urban built-up area becoming one of the most prominent forms of urban expansion, accurately extracting the urban built-up area is becoming more and more important to judge the urbanization process and evaluate the urban environment. Since it is difficult to significantly improve the accuracy of single satellite data in the extraction of urban built-up areas, this study proposes a method integrating POI (Point of Interest) data and Luojia-1A data to improve the extraction accuracy of urban built-up areas. In this study, integrated Density Graph, OSTU and geometric mean are used to extract urban built-up areas respectively. The highest precision of urban built-up areas extracted before data integration is 74.3% with the highest Kappa value of 0.54; while the highest precision of urban built-up area extracted after data integration is 91.4%, with the highest Kappa value of 0.91. Therefore, it can be concluded that compared with the existing widely used night-light-based methods, this method can integrate the advantages of POI data and Luojia-1A data, that is, it can not only solve the long-term oversaturation phenomenon of night light data, but also can extract urban built-up areas in a more refined way. The method used in this study, which integrates POI data and Luojia-1A data, can not only provide a new method for urban built-up area extraction, but also can play an active guiding role in urban planning and construction.

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Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

Cited by 22 publications

References 65 publications

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms

Improved Estimates of Population Exposure in Low-Elevation Coastal Zones of China

The Extraction of Urban Built-Up Areas by Integrating Night-Time Light and POI Data—A Case Study of Kunming, China

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