Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Niu, Wenhui; Xia, Haoming; Wang, Ruimeng; Pan, Li; Meng, Qingmin; Qin, Yaochen; Li, Rumeng; Zhao, Xiaoyang; Bian, Xiqing; Wei, Zhao

doi:10.3390/ijgi10010005

Cited by 19 publications

(14 citation statements)

References 81 publications

(88 reference statements)

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“…The Yellow River Basin, which includes the Yellow River drainage system, the Yellow River flood disaster area, and the Yellow River agricultural irrigation area, is located between 96 •~1 19 • E, 32 •~4 2 • N, 1900 km from East to West and 1100 km from South to North, with an area of 1.4129 × 106 km 2 (Figure 1) [56][57][58]. The elevation of the Yellow River Basin is high in the West and low in the East and crosses the Qinghai-Tibet Plateau, Inner Mongolia Plateau, Loess Plateau, and the Huang-Huai-Hai Plain.…”

Section: Study Areamentioning

confidence: 99%

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

et al. 2021

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Drought is one of the most complex and least-understood environmental disasters that can trigger environmental, societal, and economic problems. To accurately assess the drought conditions in the Yellow River Basin, this study reconstructed the Land Surface Temperature (LST) using the Annual Temperature Cycle (ATC) model and the Normalized Difference Vegetation Index (NDVI). The Temperature Condition Index (TCI), Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Temperature-Vegetation Drought Index (TVDI), which are four typical remote sensing drought indices, were calculated. Then, the air temperature, precipitation, and soil moisture data were used to evaluate the applicability of each drought index to different land types. Finally, this study characterized the spatial and temporal patterns of drought in the Yellow River Basin from 2003 to 2019. The results show that: (1) Using the LST reconstructed by the ATC model to calculate the drought index can effectively improve the accuracy of drought monitoring. In most areas, the reconstructed TCI, VHI, and TVDI are more reliable for monitoring drought conditions than the unreconstructed VCI. (2) The four drought indices (TCI, VCI, VH, TVDI) represent the same temporal and spatial patterns throughout the study area. However, in some small areas, the temporal and spatial patterns represented by different drought indices are different. (3) In the Yellow River Basin, the drought level is highest in the northwest and lowest in the southwest and southeast. The dry conditions in the Yellow River Basin were stable from 2003 to 2019. The results in this paper provide a basis for better understanding and evaluating the drought conditions in the Yellow River Basin and can guide water resources management, agricultural production, and ecological protection of this area.

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Section: Study Areamentioning

confidence: 99%

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

et al. 2021

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“…[6]. This method is time-consuming and laborious, lacks accurate crop spatial distribution information, and has poor timeliness [7][8][9][10]. Remote sensing classification can accurately and rapidly classify crops, estimate crop yield and optimize crop management decisions based on remote sensing images [11].…”

Section: Introductionmentioning

confidence: 99%

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

et al. 2021

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With the increasing population and continuation of climate change, an adequate food supply is vital to economic development and social stability. Winter crops are important crop types in China. Changes in winter crops planting areas not only have a direct impact on China’s production and economy, but also potentially affects China’s food security. Therefore, it is necessary to obtain information on the planting of winter crops. In this study, we use the time series data of individual pixels, calculate the temporal statistics of spectral bands and the vegetation indices of optical data based on the phenological characteristics of specific vegetation or crops and record them in the time series data, and apply decision trees and rule-based algorithms to generate annual maps of winter crops. First, we constructed a dataset combining all the available images from Landsat 7/8 and Sentinel-2A/B. Second, we generated an annual map of land cover types to obtain the cropland mask in 2019. Third, we generated a time series of a single cropland pixel, and calculated the phenological indicators for classification by extracting the differences in phenological characteristics of different crops: these phenological indicators include SOS (start of season), SDP (start date of peak), EOS (end of season), GUS (green-up speed) and GSL (growing-season length). Finally, we identified winter crops in 2019 based on their phenological characteristics. The main advantages of the phenology-based algorithm proposed in this study include: (1) Combining multiple sensor data to construct a high spatiotemporal resolution image collection. (2) By analyzing the whole growth season of winter crops, the planting area of winter crops can be extracted more accurately, and (3) the phenological indicators of different periods are extracted, which is conducive to monitoring winter crop planting information and seasonal dynamics. The results show that the algorithm constructed in this study can accurately extract the planting area of winter crops, with user, producer, overall accuracies and Kappa coefficients of 96.61%, 94.13%, 94.56% and 0.89, respectively, indicating that the phenology-based algorithm is reliable for large area crop classification. This research will provide a point of reference for crop area extraction and monitoring.

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“…Compared with conventional statistical data, NTL data can reflect actual human activities directly at a finer resolution [33]. A few studies have applied NTL data to identify shrinking cities and provided valuable experience [34][35][36]. However, there were several noteworthy drawbacks to these studies, among which the lack of continuous long-term time series was a main problem.…”

Section: Introductionmentioning

confidence: 99%

“…However, there were several noteworthy drawbacks to these studies, among which the lack of continuous long-term time series was a main problem. Given that city shrinkage is a gradual process, previous NTL-based studies only focused on a short period of time or even a single year [27,34,37]. With data from limited time nodes, the continuous changes in city shrinkage cannot be revealed.…”

Section: Introductionmentioning

confidence: 99%

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Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

et al. 2021

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Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

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Research on Large-Scale Urban Shrinkage and Expansion in the Yellow River Affected Area Using Night Light Data

Cited by 19 publications

References 81 publications

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

Drought Monitoring over Yellow River Basin from 2003–2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine

Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7/8 Images, and Google Earth Engine

Identifying and Classifying Shrinking Cities Using Long-Term Continuous Night-Time Light Time Series

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