Application of Sentinel-1/2 imagery in identifying hills cutting and land reclaiming in the Beishan Mountain of Lanzhou

doi:10.6046/zrzyyg.2023284

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Abstract

Hills cutting and land reclaiming (HCLR) serves as the most direct way for cities that are constrained by terrain to overcome land scarcity and facilitate urban spatial expansion. Obtaining the range of HCLR quickly and accurately using remote sensing technology is significant for assessing the regional ecosystem and new city development planning scientifically. Based on the Google Earth Engine (GEE) cloud computing platform for remote sensing, as well as multi-temporal data from Sentinel-1 SAR and Sentinel-2 multispectral imager (MSI), this study determined the spatiotemporal distribution of the 2017—2022 HCLR range in the study area using multi-temporal change monitoring and a random forest algorithm. Using a combination of Sentinel-1 ascending and descending images and based on noise filtering and multi-temporal image synthesis, this study calculated the difference in the backscattering intensity before and after HCLR. Then, the excavation range was determined using a threshold determined using the percentile threshold method combined with sample data. The results demonstrate that this method exhibited high operability, with an overall classification accuracy and Kappa coefficient of 85% and 0.83, respectively. By monitoring multi-temporal changes in the VH polarization band of Sentinel-1 and using a combination of Sentinel-1 ascending and descending images, this study acquired the spatial distribution of excavation areas within the study area from 2017 to 2022. Before 2019, the excavation areas were primarily concentrated in Jiuzhou Development Zone, Country Garden, and Poly Lingxiu Mountain. After 2020, new excavation areas, such as Liujiagou and Shuiyuan Station, emerged, with the scope and intensity of excavation gradually increasing. By combining the spectral, texture, and topographic features of SAR and optical data and based on feature optimization combined with a random forest algorithm, this study determined the spatial distribution of yearly HCLR from 2017 to 2022. Before 2018, the HCLR scale was small, with an area of 2.655 km². After 2019, the scale increased each year, especially in 2021, when the area reached 12.607 km², accounting for 34.56% of the total land reclamation area during the monitoring period. In 2022, the reclamation area obtained through further excavation on previously reclaimed land was estimated at only 2.686 km² due to the increasing slope and excavation volume. The method developed in this study for monitoring excavation areas and extracting land reclaiming ranges enables effective monitoring and extraction of the HCLR range.

Keywords mountain cutting and land formation Sentinel imagery multi-temporal change monitoring random forests

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Issue Date: 17 February 2025

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	Quanfu NIU
	Jiaojiao LEI
	Bo LIU
	Hao WANG
	Ruizhen ZHANG
	Gang WANG

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Quanfu NIU,Jiaojiao LEI,Bo LIU, et al. Application of Sentinel-1/2 imagery in identifying hills cutting and land reclaiming in the Beishan Mountain of Lanzhou[J]. Remote Sensing for Natural Resources, 2025, 37(1): 142-151.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023284 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/142

Fig.1 Technical route for extracting excavation areas from mountain cutting and land reclamation

特征类型	特征变量	变量名称	变量描述/公式
光谱特征	波段	B2,B3,B4,B5,B8,B8a,B11	蓝光、绿光、红光、红边、近红外、窄近红外、短波红外
指数特征	比值植被指数归一化植被指数归一化水体指数归一化建筑指数改进土壤调节植被指数增强植被指数裸露土壤覆盖程度指数	RVI(ratio vegetation index) NDVI(normalized difference vegetation index) NDWI(normalized difference water index) NDBI(normalized difference built-up index) MSAVI(modified soil-adujsted vegetation index) EVI(enhancod vegetation index) BSI(bare soil index)	$B 8 / B 4$ $(B 8 - B 4) / (B 8 + B 4$ $(B 3 - B 8) / (B 3 + B 8$ $(B 11 - B 8) / (B 11 + B 8$ $[(2 B 4 + 1) - 2 B 4 + 1) 2 - 8 (B 8 B 4)] / 2$ $2.5 (B 8 - B 4) / (B 7 + 6 B 4 - 7.5 B 2 + 1)$ $[(B 4 + B 11) - (B 4 + B 2)] / [(B 4 + B 11) + (B 8 + B 2)]$
地形特征	高程坡度	DEM Slope
极化特征	VV极化后向散射系数 VH极化后向散射系数	$σ V V$ $σ V H$
纹理特征	二阶矩对比度相关性方差逆差距熵	$B 8 a s m, V V a s m, V H a s m$ $B 8 c o n, V V c o n, V H c o n$ $B 8 c o r r, V V c o r r, V H c o r r$ $B 8 v a r, V V v a r, V H v a r$ $B 8 i d m, V V i d m, V H i d m$ $B 8 e n t, V V e n t, V H e n t$

Tab.1 Characteristic variables

Fig.2 Spatial distribution of excavation area (water source station)

Fig.3 Comparison of SAR backscattering intensity before and after excavation

Tab.2 Statistical accuracy of excavation area extracted by three schemes in 2021

Tab.3 Obtaining the number of SAR images

Fig.4 Spatial distribution of excavation areas in Lanzhou Beishan from 2017 to 2022

Tab.4 Accuracy statistics of classification results for each category (%)

Fig.5 Spatio-temporal distribution of land-making in Lanzhou Beishan from 2017 to 2022

Fig.6 Area of mountain cutting and land reclamation in Lanzhou Beishan from 2017 to 2022

[1]	徐昔保, 杨桂山, 张建明. 兰州市城市土地利用优化研究[J]. 武汉大学学报(信息科学版), 2009, 34(7):878-881.
[1]	Xu X B, Yang G S, Zhang J M. Urban land use optimization in Lanzhou,China[J]. Geomatics and Information Science of Wuhan University, 2009, 34(7):878-881.
[2]	蒲小武, 王兰民, 吴志坚, 等. 兰州丘陵沟壑区挖方黄土高边坡面临的工程地质问题及稳定性分析[J]. 地震工程学报, 2016, 38(5):787-794.
[2]	Pu X W, Wang L M, Wu Z J, et al. Engineering geological problems of loess high excavation slope in loess hilly and gully region of Lanzhou and its stability analysis[J]. China Earthquake Engineering Journal, 2016, 38(5):787-794.
[3]	Niu Q, Bai J, Cheng W, et al. Mapping the dynamics of urban land creation from hilltop removing and gully filling Projects in the river-valley city of Lanzhou,China[J]. Journal of the Indian Society of Remote Sensing, 2022, 50(10):1813-1826.
[4]	Li Y, Li Y, Karácsonyi D, et al. Spatio-temporal pattern and driving forces of construction land change in a poverty-stricken county of China and implications for poverty-alleviation-oriented land use policies[J]. Land Use Policy, 2020,91:104267.
[5]	白建荣. 兰州北山区域削山造地监测技术研究[R]. 甘肃省,甘肃省地图院,2018-05-29.
[5]	Bai J R. Research on Monitoring Technology of Land-cutting and Land-making in Beishan Area,Lanzhou[R]. Gansu Province,Gansu Map Institute,2018-05-29.
[6]	白建荣, 王桂钢, 杨腊梅. 兰州周边地区削山造地遥感监测及生态效应分析[J]. 测绘地理信息, 2020, 45(4):26-28.
[6]	Bai J R, Wang G G, Yang L M. Remote sensing monitoring and ecological effect assessment of land creation in Lanzhou City[J]. Journal of Geomatics, 2020, 45(4):26-28.
[7]	何永刚, 巨擘. 基于Skyline的兰州削山造地监测三维系统设计与实现[J]. 矿山测量, 2018, 46(1):44-48,108.
[7]	He Y G, Ju B. Design and implementation of land creation monitoring 3D system in Lanzhou based on Skyline Mine Surveying[J]. 2018, 46(1):44-48,108.
[8]	赵元, 王华栋. 无人机倾斜摄影在削山造地快速监测中的应用[J]. 矿山测量, 2018, 46(3):27-30.
[8]	Zhao Y, Wang H D. Application of UAV oblique photography in rapid monitoring in shaping mountain for land creation[J]. Mine Surveying, 2018, 46(3):27-30.
[9]	赵大卫, 贾永红, 白建荣, 等. 兰州北山区削山造地扬尘浓度预测方法及应用[J]. 武汉大学学报(信息科学版), 2021, 46(7):1106-1113.
[9]	Zhao D W, Jia Y H, Bai J R, et al. Prediction method and application of dust from land creation in Lanzhou northern mountain area[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7):1106-1113.
[10]	高小龙, 白建荣, 王桂钢. 基于航空遥感数据的地形变化分析[J]. 地理空间信息, 2020, 18(3):79-81,86,7.
[10]	Gao X L, Bai J R, Wang G G. Analysis of the terrain changes based on airborne remote sensing data[J]. Geospatial Information, 2020, 18(3):79-81,86,7.
[11]	Pauleit S, Ennos R, Golding Y. Modeling the environmental impacts of urban land use and land cover change—a study in Merseyside,UK[J]. Landscape and Urban Planning, 2005, 71(2/3/4):295-310.
[12]	Sharma J, Prasad R, Mishra V N, et al. Land use and land cover classification of multispectral Landsat8 satellite imagery using discrete wavelet transform[J]. The International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2018,XLII-5:703-706.
[13]	周珂, 杨永清, 张俨娜, 等. 光学遥感影像土地利用分类方法综述[J]. 科学技术与工程, 2021, 21(32):13603-13613.
[13]	Zhou K, Yang Y Q, Zhang Y N, et al. Review of land use classification methods based on optical remote sensing images[J]. Science Technology and Engineering, 2021, 21(32):13603-13613.
[14]	Borràs J, Delegido J, Pezzola A, et al. Land use classification from Sentinel-2 imagery[J]. Revista De Teledetección, 2017(48):55.
[15]	Jiao L, Liu Y. Analyzing the shape characteristics of land use classes in remote sensing imagery[J]. ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,I-7:135-140.
[16]	张磊, 宫兆宁, 王启为, 等. Sentinel-2影像多特征优选的黄河三角洲湿地信息提取[J]. 遥感学报, 2019, 23(2):313-326.
[16]	Zhang L, Gong Z N, Wang Q W, et al. Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images[J]. Journal of Remote Sensing, 2019, 23(2):313-326.
[17]	Colesanti C, Wasowski J. Investigating landslides with space-borne synthetic aperture Radar (SAR) interferometry[J]. Engineering Geology, 2006, 88(3/4):173-199.
[18]	罗卿莉, 崔峰志, 魏钜杰, 等. SAR影像变化检测的前景特征流形排序法[J]. 测绘学报, 2022, 51(11):2365-2378. doi: 10.11947/j.AGCS.2022.20200512
[18]	Luo Q L, Cui F Z, Wei J J, et al. Foreground feature manifold ranking method for SAR image change detection[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(11):2365-2378. doi: 10.11947/j.AGCS.2022.20200512
[19]	Huang H, Chen Y, Clinton N, et al. Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine[J]. Remote Sensing of Environment, 2017,202:166-176.
[20]	罗时雨, 童玲, 陈彦. 全极化SAR图像的山地低矮植被区域土壤含水量估计[J]. 遥感学报, 2017, 21(6):907-916.
[20]	Luo S Y, Tong L, Chen Y. Soil moisture estimation for mountainous areas covered with low vegetation using fully polarimetric SAR images[J]. Journal of Remote Sensing, 2017, 21(6):907-916.
[21]	张昊, 高小红, 史飞飞, 等. 基于Sentinel-2 MSI与Sentinel-1 SAR相结合的黄土高原西部撂荒地提取——以青海民和县为例[J]. 自然资源遥感, 2022, 34(4):144-154.doi:10.6046/zrzyyg.2021385.
[21]	Zhang H, Gao X H, Shi F F, et al. Sentinel-2 MSI and Sentinel-1 SAR based information extraction of abandoned land in the western Loess Plateau:A case study of Minhe County in Qinghai[J] Remote Sensing for Natural Resources, 2022, 34(4):144-154.doi:10.6046/zrzyyg.2021385.
[22]	Jung J, Yun S H. Evaluation of coherent and incoherent landslide detection methods based on synthetic aperture radar for rapid response:A case study for the 2018 Hokkaido landslides[J]. Remote Sensing, 2020, 12(2):265.
[23]	Mondini A C, Guzzetti F, Reichenbach P, et al. Semi-automatic recognition and mapping of rainfall induced shallow landslides using optical satellite images[J]. Remote Sensing of Environment, 2011, 115(7):1743-1757.
[24]	Handwerger A L, Jones S Y, Huang M H, et al. Rapid landslide identification using synthetic aperture radar amplitude change detection on the Google Earth Engine[J]. Natural Hazards and Earth System Sciences Discussions,2020:1-24.
[25]	Van Tricht K, Gobin A, Gilliams S, et al. Synergistic use of radar Sentinel-1 and optical Sentinel-2 imagery for crop mapping:A case study for Belgium[J]. Remote Sensing, 2018, 10(10):1642.
[26]	Carrasco L, O’Neil A W, Morton R D, et al. Evaluating combinations of temporally aggregated Sentinel-1,Sentinel-2 and Landsat 8 for land cover mapping with Google Earth Engine[J]. Remote Sensing, 2019, 11(3):288.
[27]	宋宏利, 雷海梅, 尚明. 基于Sentinel 2A/B时序数据的黑龙港流域主要农作物分类[J]. 江苏农业学报, 2021, 37(1):83-92.
[27]	Song H L, Lei H M, Shang M. Crop classification based on Sentinel 2A/B time series data in Heilonggang River basin[J]. Jiangsu Journal of Agricultural Sciences, 2021, 37(1):83-92.
[28]	Le Toan T, Beaudoin A, Riom J, et al. Relating forest biomass to SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30(2):403-411.
[29]	Svoboda J, Štych P, Laštovička J, et al. Random forest classification of land use,land-use change and forestry (LULUCF) using Sentinel-2 data:A case study of Czechia[J]. Remote Sensing,2022,14(5):1189.
[30]	Lindsay E, Frauenfelder R, Rüther D, et al. Multi-temporal satellite image composites in google earth engine for improved landslide visibility:A case study of a glacial landscape[J]. Remote Sensing, 2022, 14(10):2301.
[31]	Mondini A C, Santangelo M, Rocchetti M, et al. Sentinel-1 SAR amplitude imagery for rapid landslide detection[J]., Remote Sensing, 2019, 11(7):760.
[32]	赵怡卿, 李玫洁, 吴瑕. 包头地区极端温度天气事件的百分位阈值计算与检验[J]. 农业灾害研究, 2023, 13(3):86-88.
[32]	Zhao Y Q, Li M J, Wu X. Percentile threshold calculation and test of extreme temperature weather events in Baotou area[J]. Journal of Agricultural Catastrophology, 2023, 13(3):86-88.
[33]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[34]	帅爽, 张志, 张天, 等. 特征优化结合随机森林算法的干旱区植被高光谱遥感分类方法[J]. 农业工程学报, 2023, 39(9):287-293.
[34]	Shuai S, Zhang Z, Zhang T, et al. Hyperspectral image classification method for dryland vegetation by combining feature optimization and random forest algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(9):287-293.
[35]	魏鑫, 任雨, 陈曦东, 等. 基于时序Sentinel-2数据水体动态变化监测——以河南省为例[J/OL]. 自然资源遥感, 2023:1-11.(2023-06-19)[2023-09-11].https://kns.cnki.net/kcms/detail/10.1759.P.20230619.1013.018.html. url: https://kns.cnki.net/kcms/detail/10.1759.P.20230619.1013.018.html
[35]	Wei X, Ren Y, Chen X D, ed al. Monitoring dynamic changes of water bodies based on time-series Sentinel-2 data:A case study in Henan Province[J/OL] Remote Sensing for Natural Resources, 2023:1-11.(2023-06-19) [2023-09-11].https://kns.cnki.net/kcms/detail/10.1759.P.20230619.1013.018.html. url: https://kns.cnki.net/kcms/detail/10.1759.P.20230619.1013.018.html
[36]	Stehman S V. Selecting and interpreting measures of thematic classification accuracy[J]. Remote Sensing of Environment, 1997, 62(1):77-89.
[37]	Berardino P, Fornaro G, Lanari R, et al. A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11):2375-2383.
[38]	Du W B, Ji W Q, Xu L J, et al. Deformation time series and driving-force analysis of glaciers in the eastern Tienshan Mountains using the SBAS InSAR method[J]. International Journal of Environmental Research and Public Health, 2020, 17(8):2836.
[39]	Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry[C]// IEEE 1999 International Geoscience and Remote Sensing Symposium. IEEE,2000:1528-1530.
[40]	Adriano B, Yokoya N, Miura H, et al. A semiautomatic pixel-object method for detecting landslides using multitemporal ALOS-2 intensity images[J]. Remote Sensing, 2020, 12(3):561.

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