Super-resolution mapping of wetland inundation from remote sensing imagery based on integration of back-propagation neural network and genetic algorithm

Mapping the spatio-temporal characteristics of wetland inundation has an important significance to the study of wetland environment and associated flora and fauna. High temporal remote sensing imagery is widely used for this purpose with the limitations of relatively low spatial resolutions. In this study, a novel method based on integration of back-propagation neural network (BP) and genetic algorithm (GA), so-called IBPGA, is proposed for super-resolution mapping of wetland inundation (SMWI) from multispectral remote sensing imagery. The IBPGA-SMWI algorithm is developed, including the fitness function and integration search strategy. IBPGA-SMWI was evaluated using Landsat TM/ETM + imagery from the Poyanghu wetland in China and the Macquarie Marshes in Australia. Compared with traditional SMWI methods, IBPGA-SMWI consistently achieved more accurate super-resolution mapping results in terms of visual and quantitative evaluations. In comparison with GA-SMWI, IBPGA-SMWI not only improved the accuracy of SMWI, but also accelerated the convergence speed of the algorithm. The sensitivity analysis of IBPGA-SMWI in relation to standard crossover rate, BP crossover rate and mutation rate was also carried out to discuss the algorithm performance. It is hoped that the results of this study will enhance the application of median-low resolution remote sensing imagery in wetland inundation mapping and monitoring, and ultimately support the studies of wetland environment.This paper was supported by the National Natural Science Foundation of China (Grant No. 41371343 and Grant No. 41001255) and the scholarship provided by the China Scholarship Council (Grant No. 201308420290)

Identification of polycentric cities in China based on NPP-VIIRS nighttime light data

Nighttime light data play an important role in the research on cities, while the urban centers over a large spatial scale are still far from clearly understood. Aiming at the current challenges in monitoring the spatial structure of cities using nighttime light data, this paper proposes a new method for identifying urban centers for massive cities at the large spatial scale based on the brightness information captured by the Suomi National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) sensor. Based on the method for extracting the peak point based on digital elevation model (DEM) data in terrain analysis, the maximum neighborhood and di_erence algorithms were applied to the NPP-VIIRS data to extract the pixels with the peak nighttime light intensity to identify the potential locations of urban centers. The results show 7239 urban centers in 2200 cities in China in 2017, with an average of 3.3 urban centers per city. Approximately 68% of the cities had significant polycentric structures. The developed method in this paper is useful for identifying the urban centers and can provide the reference to the city planning and construction

Urban expansion and agricultural land loss in China: A multiscale perspective

China’s rapid urbanization has contributed to a massive agricultural land loss that could threaten its food security. Timely and accurate mapping of urban expansion and urbanization-related agricultural land loss can provide viable measures to be taken for urban planning and agricultural land protection. In this study, urban expansion in China from 2001 to 2013 was mapped using the nighttime stable light (NSL), normalized difference vegetation index (NDVI), and water body data. Urbanization-related agricultural land loss during this time period was then evaluated at national, regional, and metropolitan scales by integrating multiple sources of geographic data. The results revealed that China’s total urban area increased from 31,076 km2 in 2001 to 80,887 km2 in 2013, with an average annual growth rate of 13.36%. This widespread urban expansion consumed 33,080 km2 of agricultural land during this period. At a regional scale, the eastern region lost 18,542 km2 or 1.2% of its total agricultural land area. At a metropolitan scale, the Shanghai–Nanjing–Hangzhou (SNH) and Pearl River Delta (PRD) areas underwent high levels of agricultural land loss with a decrease of 6.12% (4728 km2) and 6.05% (2702 km2) of their total agricultural land areas, respectively. Special attention should be paid to the PRD, with a decline of 13.30% (1843 km2) of its cropland. Effective policies and strategies should be implemented to mitigate urbanization-related agricultural land loss in the context of China’s rapid urbanization

Are Karst Rocky Desertification Areas Affected by Increasing Human Activity in Southern China? An Empirical Analysis from Nighttime Light Data

Due to remarkable socioeconomic development, an increasing number of karst rocky desertification areas have been severely affected by human activities in southern China. Effectively analyzing human activities in karst rocky desertification areas is a critical prerequisite for managing and restoring areas with tremendous negative impacts from desertification. At present, a timely and accurate way of quantifying the spatiotemporal variations of human activities in karst rocky desertification areas is still lacking. In this communication, we attempted to quantify human activities from the corrected Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) nighttime light composite data from 2012 to 2018 based on statistical analysis. The results show that a significant increase of night lights could be clearly identified during the study period. The total nighttime lights (TL) related to severe karst rocky desertification (S) were particularly concentrated in Guizhou and Yunnan. The nighttime light intensity (LI) related to the S areas in Chongqing was the strongest due to its rapid socioeconomic development. The annual growth rate of nighttime lights (GL) has been slow or even negative in Guangdong because of its various karst rocky desertification restoration programs. This communication could provide an effective approach for quantifying human activities and provide useful information about where prompt attention is required for policy-making on the restoration of the karst rocky desertification areas

Does China's City-Size Distribution Present a Flat Distribution Trend? A Socioeconomic and Spatial Size Analysis From DMSP-OLS Nighttime Light Data

Inconsistent measurements of city-size and a lack of time-series information on urban socioeconomic development have hindered determining whether China's city-size distribution (CD) follows a Pareto distribution according to multiple perspectives. This article has attempted to evaluate China's CD based on the defense meteorological satellite program- operational line-scan system (DMSP-OLS) nighttime light data in terms of socioeconomic size (SS) and spatial size (SC). First, city size was defined from the DMSP-OLS data. Then, whether China's CD followed a Pareto distribution was evaluated from different perspectives. The results show that China's CD from 1995 to 2015 presents a flat distribution trend; the flat distribution trend of the SC is more obvious than that of the SS; “borrowed size” has become an important reason for the flat trend of China's CD; and residential suburbanization, transportation cost reductions, local government policies, and land finances could effectively explain the CD differences in the flat trends between the SS and SC. This article offers an effective means for quantifying and comparing CD in long time series at a large scale (e.g., national scale or regional scale) and provides a scientific decision basis for governments to build a reasonable CD system in China

Does Fallowing Cultivated Land Threaten Food Security? Empirical Evidence from Chinese Pilot Provinces

Facing worsening problems, including the decreasing amount, quality, and deterioration of land ecosystems, cultivated land needs protective measures. China has been conducting an experimental fallow policy to deter these problems in five pilot provinces since 2016. However, inadequate and inconclusive studies of the impacts of fallow policy on food security have motivated the authors to fill this knowledge gap and to provide evidence for policy-making. Using the modified cultivated land pressure model, this study explores the cultivated land pressure at three scales (nation, province, and prefecture) to determine the capacity of feeding people using cultivated land, and examines the impact of fallowing cultivated land. There are three main findings. First, the cultivated land pressure in China continually decreased during the period of 2000−2016, and would remain in a decreasing trend during 2017−2020 even if the measures implemented doubled the fallowing scale every year. Second, the spatial patterns of the cultivated land pressure between the provincial and prefectural scale show a similar overview, with some nuanced disparities. Finally, the five pilot provinces show various amplitudes of variation in cultivated land pressure, ranging from 0.017% to 9.027% under three fallow scale scenarios. Thus, the results of this research support the argument that fallow policy will not threaten food security at a national and provincial scale, based on the current fallow scale and enlargement pace. The deeper understanding of the impact of fallow policy provides a scientific reference for policymaking and calls for further studies focusing on a more comprehensive measurement of cultivated land pressure and optimization fallow scale

Estimating and Interpreting Fine-Scale Gridded Population Using Random Forest Regression and Multisource Data

Gridded population results at a fine resolution are important for optimizing the allocation of resources and researching population migration. For example, the data are crucial for epidemic control and natural disaster relief. In this study, the random forest model was applied to multisource data to estimate the population distribution in impervious areas at a 30 m spatial resolution in Chongqing, Southwest China. The community population data from the Chinese government were used to validate the estimation accuracy. Compared with the other regression techniques, the random forest regression method produced more accurate results (R2 = 0.7469, RMSE = 2785.04 and p < 0.01). The points of interest (POIs) data played a more important role in the population estimation than the nighttime light images and natural topographical data, particularly in urban settings. Our results support the wide application of our method in mapping densely populated cities in China and other countries with similar characteristics

The Effects of Urban Forms on the PM2.5 Concentration in China: A Hierarchical Multiscale Analysis

For a better environment and sustainable development of China, it is indispensable to unravel how urban forms (UF) affect the fine particulate matter (PM2.5) concentration. However, research in this area have not been updated consider multiscale and spatial heterogeneities, thus providing insufficient or incomplete results and analyses. In this study, UF at different scales were extracted and calculated from remote sensing land-use/cover data, and panel data models were then applied to analyze the connections between UF and PM2.5 concentration at the city and provincial scales. Our comparison and evaluation results showed that the PM2.5 concentration could be affected by the UF designations, with the largest patch index (LPI) and landscape shape index (LSI) the most influential at the provincial and city scales, respectively. The number of patches (NP) has a strong negative influence (−0.033) on the PM2.5 concentration at the provincial scale, but it was not statistically significant at the city scale. No significant impact of urban compactness on the PM2.5 concentration was found at the city scale. In terms of the eastern and central provinces, LPI imposed a weighty positive influence on PM2.5 concentration, but it did not exert a significant effect in the western provinces. In the western cities, if the urban layout were either irregular or scattered, exposure to high PM2.5 pollution levels would increase. This study reveals distinct ties of the different UF and PM2.5 concentration at the various scales and helps to determine the reasonable UF in different locations, aimed at reducing the PM2.5 concentration

Carbon dioxide (CO2) emissions from the service industry, traffic, and secondary industry as revealed by the remotely sensed nighttime light data

Exploring carbon dioxide (CO2) emissions from human activities is essential for urban energy conservation and resource management. Remotely sensed nighttime lights from the Suomi NPP-VIIRS provide spatial consistency in and a low-cost way of revealing CO2 emissions. Although many researches have documented the feasibility of the Suomi NPP-VIIRS data for assessing CO2 emissions, few have systematically revealed the ability of nighttime lights for evaluating CO2 emissions from different industries, such as service industry CO2 emissions (SC), traffic CO2 emissions (TC), and secondary industry CO2 emissions (IC). Here, China was selected as the experimental subject, and we comprehensively explored the relationships between the nighttime lights and SC, TC, and IC, and investigated the factors mediating these relationships. We found that without considering other factors, the nighttime lights only revealed up to 51.2% of TC, followed by 41.7% of IC and 22.7% of SC. When controlling for city characteristic variables, the models showed that there were positive correlations between the Suomi NPP-VIIRS data and SC, IC, and TC, and that nighttime lights have an Inverted-U relationship with SC. The Suomi NPP-VIIRS data are more suitable for revealing SC, TC, and IC in medium-sized and large-sized cities than in small-sized cities and megacities