Sentinel-1/2影像在兰州北山削山造地范围识别中的应用

doi:10.6046/zrzyyg.2023284

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摘要

城市空间发展易受地形所限,削山造地能克服土地资源稀缺,成为解决城市空间拓展最为直接的途径。该方法利用遥感技术快速准确获取削山造地范围信息,对区域生态环境科学评估和新城发展规划具有十分重要的意义。本文基于GEE遥感云计算平台,利用Sentinel-1 合成孔径雷达(synthetic aperture Rader,SAR)数据,采用组合升、降轨影像,在噪声滤除和多时相影像合成的基础上,计算削山造地前后后向散射强度的差值,并采用百分位阈值法结合样本数据确定阈值,提取研究区2017—2022年削山造地开挖区时空分布;然后联合SAR和光学数据的光谱特征、纹理特征和地形特征,在特征优化的基础上结合随机森林算法获取了2017—2022年逐年削山造地范围时空分布。研究结果表明:①提取的开挖区范围总体分类精度和Kappa系数分别达85%和0.83。②研究期间,发现2019年前开挖区主要集中在九州开发区、碧桂园和保利领秀山,2020年以后新增加了刘家沟、水源站等开挖区,开挖范围和强度逐渐增大。③2018年前造地规模较小,面积为2.655 km²;2019年以后造地规模逐年增大,特别是2021年,其造地面积达12.607 km²,占监测期间总造地面积的34.56%,2022年在原造地基础上开挖,因坡度和开挖量逐渐增大,造地面积仅2.686 km²。本文构建的削山造地开挖区监测和造地范围提取方法可有效获取削山和造地范围快速监测与提取。

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	牛全福
	雷姣姣
	刘博
	王浩
	张瑞珍
	王刚

关键词 ：削山造地, Sentinel影像, 多时相, 变化监测, 随机森林

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.

Key words： mountain cutting and land formation Sentinel imagery multi-temporal change monitoring random forests

收稿日期: 2023-09-14 出版日期: 2025-02-17

ZTFLH:

TP79

基金资助:国家自然科学基金项目“面向生态工程驱动的甘肃黄土高原植被恢复遥感监测与成效评估”(42261069)

作者简介: 牛全福(1973-),男,博士,教授,研究方向为3S与地质灾害监测。Email: Niuqf@lut.edu.cn。

引用本文:

牛全福, 雷姣姣, 刘博, 王浩, 张瑞珍, 王刚. Sentinel-1/2影像在兰州北山削山造地范围识别中的应用[J]. 自然资源遥感, 2025, 37(1): 142-151.
NIU Quanfu, LEI Jiaojiao, LIU Bo, WANG Hao, ZHANG Ruizhen, WANG Gang. Application of Sentinel-1/2 imagery in identifying hills cutting and land reclaiming in the Beishan Mountain of Lanzhou. Remote Sensing for Natural Resources, 2025, 37(1): 142-151.

链接本文:

https://www.gtzyyg.com/CN/10.6046/zrzyyg.2023284 或 https://www.gtzyyg.com/CN/Y2025/V37/I1/142

Fig.1 削山造地开挖区提取技术路线

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 特征变量

Fig.2 开挖区空间分布(水源站)

Fig.3 开挖前后的SAR后向散射强度对比

Tab.2 2021年3种方案提取的开挖区统计精度

Tab.3 获取SAR影像数

Fig.4 2017—2022年兰州北山削山造地开挖区空间分布

Tab.4 分类结果精度统计

Fig.5 2017—2022年兰州北山削山造地时空分布

Fig.6 2017—2022年兰州北山削山和造地面积

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