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Spatio-temporal Evolution of Urban Expansion Based on a Novel Adjusted Index and GEE: A Case Study of Central Plains Urban Agglomeration, China

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Chinese Geographical Science 2023年第4期33卷 736-750页

作者： SU Yanlin LU Chunyan SU Yue WANG Zili HUANG Yufei YANG Nuocheng College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China Research Centre of Resource and Environment Spatial Information Statistics of Fujian Province Fujian Agriculture and Forestry UniversityFuzhou 350002China Key Laboratory of Ecology and Resources Statistics of Fujian Province Universities Fujian Agriculture and Forestry UniversityFuzhou 350002China

It is crucial to investigate the urban agglomerations spatio-temporal evolution patterns and driving factors for analyzing the urban spatial structure-functional division and promoting the coordinated development of urban agglomerations.In this study,a novel vegetation-building-nighttime light-adjusted index(VBNAI)was established for rapid and effective mapping of urban construction land(UCL)in Central Plains Urban Agglomeration(CPUA),China during 2000–2020 based on Google Earth Engine(GEE)platform.Compared with traditional indices,VBNAI can significantly decrease the blooming effect,Normalized Difference Vegetation Index(NDVI)saturation,and soil background of nighttime light data.In addition,the urban expansion indices and standard deviation ellipse model were synthetically adopted to analyze the spatio-temporal evolution pattern of urban expansion.The gravity model and the geographically weighted regression model were employed to determine the spatial interaction forces and drivers of urban expansion,respectively.The results showed that the VBNAI index has obvious advantages in efficiency and accuracy to extract UCL with the overall accuracy of more than 91%.The UCL of CPUA had increased by 4489.84 km2 during 2000–2020 with the gravity center moving towards southeast continuously.From 2000 to 2010,the urban expansion was in a‘center-hinterland’pattern which had benefit from the favorable effect of the traffic shaft belt.During 2010–2020,the urban network structure had basically established.Urban expansion had been influenced by a variety of socio-economic and demographic factors,and the impact degree varied from region to region.This study could provide scientific references for facilitating the intensive utilization of urban resources and optimizing the spatial development pattern of urban agglomeration.

关键词： urban expansion urban construction land(UCL)mapping vegetation-building-nighttime light-adjusted index(VBNAI) multi-source remote sensing Google Earth Engine(GEE) Central Plains Urban Agglomeration(CPUA),China

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Spatiotemporal distribution and influencing factors of Ulva prolifera and Sargassum and their coexistence in the South Yellow Sea,China

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Journal of Oceanology and Limnology 2022年第3期40卷 1070-1084页

作者： Longxiao ZHENG Mengquan WU Min ZHOU Lianjie ZHAO College of Resources and Environmental Engineering Ludong UniversityYantai 264039China

Marine ecological disasters occurred frequently in recent years and raised widespread concerns about the ecological health of the ocean.We analyzed the spatiotemporal distributions of Ulva prolifera and Sargassum from April to July each year between 2016 and 2020 in the South Yellow Sea using multisource(GF-1 and HJ-1A/1B)remote sensing images,combined with the MODIS sea surface temperature(SST)data,photosynthetically active radiation(PAR)data,and Quick SCAT sea surface wind(SSW)data,to explore the potential influencing factors.The results show that(1)U.prolifera and Sargassum appeared mainly from May to July and April to June,respectively;(2)U.prolifera showed an impact in larger spatial scope than that of Sargassum.U.prolifera originated in the shoal area of northern Jiangsu and finally disappeared in the sea near Haiyang-Rongcheng area.The spatial scope of the impact of Sargassum tended to expand.Sargassum was first detected in the ocean northeast of the Changjiang(Yangtze)River estuary and disappeared near 35°N;and(3)correlation analysis showed that the SST influenced the growth rate of U.prolifera and Sargassum.PAR had varied eff ects on U.prolifera and Sargassum at different times.A moderate light conditions could accelerate the growth and reproduction of U.prolifera and Sargassum.High irradiance levels of ultraviolet radiation may cause photoinhibition and damage on U.prolifera and Sargassum.The southeast monsoon and surface currents promoted the drift of U.prolifera and Sargassum from the southeast to the northwest and north.Therefore,the spatial and temporal similarities and differences between U.prolifera and Sargassum were influenced by a combination of factors during their growth processes.

关键词： Ulva prolifera Sargassum virtual-baseline floating macro algae height(VB-FAH) spatiotemporal distribution South Yellow Sea multi-source remote sensing

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Mapping China’s offshore mariculture based on dense time-series optical and radar data

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International Journal of Digital Earth 2022年第1期15卷 1326-1349页

作者： Xiaoliang Liu Zhihua Wang Xiaomei Yang Yueming Liu Bin Liu Junyao Zhang Ku Gao Dan Meng Yaxin Ding State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China

Due to the weak information about cultural targets in the complex marine environment,an omission problem exists in large-scale mariculture extraction using single-view and single-source images.To overcome this problem,we developed a mariculture extraction method that combines dense time-series Sentinel-2 and Sentinel-1 data.A high-precision Chinese mariculture distribution map for 2020 was produced with an overall accuracy of 94.00%and a kappa coefficient of 0.91.The results show that(1)the total area of mariculture was 1173249.22 ha on the national scale,which was significantly larger than the previous studies(459595.70 and 205920.28 ha,respectively),with Shandong Province(39.09%)having the largest proportion;(2)China’s mariculture presented a spatial distribution characteristic of‘Denser North and Sparser South’,and mariculture was centralized in the coastal zones of the northern provinces(60.76%)rather than the southern provinces;(3)the official production statistics and remote sensing-derived mariculture area revealed a highly corresponding trend at the provincial level,with an R2 reaching 0.78,which is much higher than the 0.07 and 0.41 values of the comparison data.The results directly provide data reference for mariculture production estimation and site selection or ideas for mariculture extraction in other regions and globally.

关键词： Aquaculture mariculture multi-source remote sensing time-series China’s coastal zone

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OBH-RSI:Object-Based Hierarchical Classification Using remote sensing Indices for Coastal Wetland

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Journal of Beijing Institute of Technology 2021年第2期30卷 159-171页

作者： Zhaoyang Lin Jianbu Wang Wei Li Xiangyang Jiang Wenbo Zhu Yuanqing Ma Andong Wang Beijing Key Laboratory of Fractional Signals and Systems Beijing Institute of TechnologyBeijing 100081China Laboratory of Marine Physics and remote sensing First Institute of OceanographyMinistry of Natural ResourcesQingdao 266061China Shandong Provincial Key Laboratory of Restoration for Marine Ecology Shandong Marine Resources and Environment Research InstituteYantai 264006China Shandong Yellow River Delta National Nature Reserve Administration Committee Dongying 257091China

With the deterioration of the environment,it is imperative to protect coastal wetlands.Using multi-source remote sensing data and object-based hierarchical classification to classify coastal wetlands is an effective method.The object-based hierarchical classification using remote sensing indices(OBH-RSI)for coastal wetland is proposed to achieve fine classification of coastal wetland.First,the original categories are divided into four groups according to the category characteristics.Second,the training and test maps of each group are extracted according to the remote sensing indices.Third,four groups are passed through the classifier in order.Finally,the results of the four groups are combined to get the final classification result map.The experimental results demonstrate that the overall accuracy,average accuracy and kappa coefficient of the proposed strategy are over 94%using the Yellow River Delta dataset.

关键词： Yellow River Delta vegetation index object-based hierarchical classification wetland multi-source remote sensing

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黄河中游1978-2010年间土壤侵蚀风险的动态监测研究（英文）

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Journal of Geographical Sciences 2018年第2期28卷 175-192页

作者：赵海根唐于渝杨胜天 School of Geography Beijing Normal University State Key Laboratory of remote sensing Science Beijing Normal University Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences Beijing Water Science and Technology Institute

Soil erosion has become a significant environmental problem that threatens eco- systems globally. The risks posed by soil erosion, the trends in the spatial distribution in soil erosion, and the status, intensity, and conservation priority level in the middle reaches of the Yellow River Basin were identified from 1978 to 2010. This study employed a multi-criteria evaluation method integrated with GIS and multi-source remote sensing data including land use, slope gradient and vegetation fractional coverage （VFC）. The erosion status in the study region improved from 1978 to 2010; areas of extremely severe, more severe, and severe soil erosion decreased from 0.05%, 0.94%, and 11.25% in 1978 to 0.04%, 0.81%, and 10.28% in 1998, respectively, and to 0.03%, 0.59%, and 6.87% in 2010, respectively. Compared to the period from 1978 to 1998, the area classed as improvement grade erosion increased by about 47,210.18 km2 from 1998 to 2010, while the area classed as deterioration grade ero- sion decreased by about 17,738.29 km2. Almost all severe erosion regions fall in the 1st and 2rid conservation priority levels, which areas accounted for 3.86% and 1.11% of the study area in the two periods, respectively. This study identified regions where soil erosion control is required and the results provide a reference for policymakers to implement soil conservation measures in the future.

关键词： dynamic identification soil erosion risk multi-criteria evaluation multi-source remote sensing Yel-low River Basin

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