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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (4) : 77-87     DOI: 10.6046/zrzyyg.2024002
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Analysis of high-frequency spatiotemporal evolution of patches reflecting 2020—2023 changes in coastal areas of the Chinese mainland
LI Wei1,2(), ZHAO Binru1, LIANG Jianfeng1(), ZHOU Peng2, ZHANG Feng1
1. National Marine Data and Information Service, Tianjin 300012, China
2. College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China
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Abstract  

The analysis of patches showing changes in coastal areas of the Chinese mainland tends to encounter challenges such as low image resolution, long time intervals, and limited spatial coverage. This study aims to obtain high-frequency, accurate information on changes in coastal areas nationwide. This will facilitate the dynamic monitoring of marine resources and the implementation of relevant protection policies for coastal areas in China. To this end, using domestic high-resolution remote sensing data of 15 days (i.e., one cycle), as well as the iteratively reweighted multivariate alteration detection (IR-MAD) algorithm combined with visual interpretation, this study extracted patches reflecting 2020—2023 changes along the coasts of 11 provinces and cities in the Chinese mainland. Accordingly, this study analyzed their spatiotemporal characteristics, landscape patterns, and spatial correlation. The results indicate distinct directional changes in the patches. The patches reflecting changes from sea enclosure to reclamation exhibited the largest areas across various investigated areas. Except for Hainan Province, the area of this type of patches exceeded 1 000 km2. The proportions of patches reflecting different types of changes gradually tended to be balanced. In the winter of 2022, the proportion of patches showing changes in the reclamation dropped below 50% for the first time. The aggregation degree of patches reflecting various types of changes showed increasing trends, suggesting that patches reflecting various changes will become more concentrated in the future. The centroids of these patches of various regions shifted in varying directions, and these patches exhibited significant spatial correlation within a 20 km range.
Keywords coastal area      high-frequency      high-resolution      patch reflecting changes      spatiotemporal evolution analysis     
ZTFLH:  TP79  
Issue Date: 03 September 2025
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Wei LI
Binru ZHAO
Jianfeng LIANG
Peng ZHOU
Feng ZHANG
Cite this article:   
Wei LI,Binru ZHAO,Jianfeng LIANG, et al. Analysis of high-frequency spatiotemporal evolution of patches reflecting 2020—2023 changes in coastal areas of the Chinese mainland[J]. Remote Sensing for Natural Resources, 2025, 37(4): 77-87.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024002     OR     https://www.gtzyyg.com/EN/Y2025/V37/I4/77
Fig.1  Map of the study area
参数 GF-1 GF-2 GF-6 ZY-3
光谱范
围/μm		 全色 0.45~0.90 0.45~0.90 0.45~0.90 0.45~0.80
多光谱 0.45~0.52 0.45~0.52 0.45~0.52 0.45~0.52
0.52~0.59 0.52~0.59 0.52~0.59 0.52~0.59
0.63~0.69 0.63~0.69 0.63~0.69 0.63~0.69
0.77~0.89 0.77~0.89 0.77~0.89 0.77~0.89
空间分辨
率/m		 全色 2 0.8 2 2.1
多光谱 8 3.2 8 5.8
重访周期/d 4 5 2 5
幅宽/km 60 45 90 50
Tab.1  Data source satellite parameters
类型 取值范围
干旱、非水性表面 -1≤NDWI<-0.3
中度干旱区、水面 -0.3≤NDWI< 0
洪水 0≤NDWI<0.2
水体 -0.3<NDWI≤1
Tab.2  NDWI empirical values for different types
Tab.3  Change mapping type classification and deciphering flags
影像日期 自然间断点
分级法阈值		 经验阈值 最终阈值
2020-10-27 0.202 416 0.2 0.201 208
2020-11-13 0.294 445 0.2 0.247 223
Tab.4  Experimental data thresholds
Fig.2  Spatial constraint processes
Fig.3  Example of change patch extraction workflow
Fig.4  Results of the extraction of change patches
Fig.5  Distribution of the area of change patches
Fig.6  Percentage of area by change patch type
变化图斑转换类型 辽宁省 津冀地区 山东省 苏沪地区 浙江省 福建省 广东省 广西壮族自治区 海南省
未利用-围海 45.563 6.625 466.123 6.818 326.119 4.732 185.261 15.990 8.918
未利用-填海 717.986 53.325 181.044 138.660 335.543 656.009 379.416 241.736 135.487
未利用-构筑物 426.002 824.917 1 884.672 474.307 851.167 758.629 993.499 529.517 129.003
未利用-其他变化 633.681 40.071 1 509.616 45.870 38.317 103.296 111.409 27.319 66.556
未利用-自然生态变化 5.779 0.413 9.445 37.310 15.069 162.547 0.259 3.702 6.510
围海-填海 3 055.425 2 424.417 8 295.326 6 515.486 3 965.218 2 872.597 3 570.030 1 501.415 358.201
围海-构筑物 729.380 288.740 575.847 472.987 677.733 397.950 211.111 54.658 40.354
围海-其他变化 127.838 1 225.246 2 298.705 1 230.013 85.949 191.845 248.491 151.440 26.957
围海-自然生态变化 0.093 0.062 0.599 7.369 — — — — —
填海-构筑物 13.101 0.650 7.258 29.115 5.471 0.676 0.326 1.703 —
填海-其他变化 0.907 — — — 0.641 14.588 31.722 — 0.936
填海-自然生态变化 — — — — — 1.489 — — —
构筑物-其他变化 — — — — 0.251 — 13.287 — —
Tab.5  Land use transition areas of changed patches in different regions(km2)
Fig.7  Spatial center-of-mass transfer map of change patches
景观指数 年份 围海 填海 构筑物 其他
变化		 自然生
态变化
LPI 2020年 0.983 7 4.274 1 0.486 2 0.260 1 —
2021年 0.094 2 3.984 9 1.198 2 0.848 1 0.094 2
2022年 0.228 7 3.304 0 1.558 8 2.295 8 0.330 4
2023年 0.807 1 2.402 4 0.503 3 2.165 0 0.019 0
AI 2020年 47.308 3 51.565 2 16.026 3 32.190 0 —
2021年 23.214 3 48.692 0 26.871 2 54.411 8 28.358 2
2022年 37.000 0 52.010 5 25.221 5 70.824 4 56.903 8
2023年 58.463 7 57.765 8 28.721 3 59.702 4 0
DII 2020年 2.978 5 59.165 5 7.999 2 4.548 6 —
2021年 0.198 0 44.674 1 14.408 1 1.603 2 3.160 3
2022年 1.287 2 56.646 1 23.045 0 16.125 2 9.782 3
2023年 1.700 1 39.468 4 16.660 1 23.774 2 0.569 3
Tab.6  Type level landscape pattern indexes
年份 SHDI SHEI CONTAG
2020年 0.727 6 0.524 8 67.013 0
2021年 0.718 2 0.446 2 71.151 4
2022年 1.053 3 0.587 9 64.620 8
2023年 1.096 1 0.681 1 61.421 0
Tab.7  Landscape level landscape pattern indexes
Fig.8  Global Moran’s index analysis chart
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