Evaluation of mountain sponge city ecological construction based on landcover types

doi:10.13474/j.cnki.11-2246.2022.0146

Abstract

Abstract: The construction of sponge cities is an important measure to solve urban rain water management. However, the construction of mountain sponge cities is still in the development and exploration stage, and a comprehensive evaluation system has not been established. In view of the inadequate use of landcover type information and the lack of evaluation criteria in mountainous city construction, the combination of terrain factors, based on the types of urban surface cover and terrain factors, the paper establishes ecological evaluation indicators for mountain sponge cities. Firstly, obtain the two-temporal land type information of the study area. Then calculate the land type change detection results, and combine the elevation and slope to evaluate the regional sponge city construction. Finally, between the two time periods, the green area rate increases by 0.22%, the impervious surface rate increases by 0.46%, and the construction land developes to the middle terrain area. At the same time, the change status of the remaining land types is learned. The results show that the rate of green land in this area has increased, the protection of natural water bodies is better, the proportion of permeable surface is larger than that of impervious surface, and the development of ecological construction and construction land still conforms to the principle of mountain sponge city.

Key words: sponge city, mountain city, high-resolution remote sensing image, types of landcover

CLC Number:

P237

PAN Jianping, FU Zhanbao, CHENG Weihua, LU Shian, LI Xin, SUN Bowen, HUANG Guiping. Evaluation of mountain sponge city ecological construction based on landcover types[J]. Bulletin of Surveying and Mapping, 2022, 0(5): 84-88.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2022.0146

http://tb.chinasmp.com/EN/Y2022/V0/I5/84

References

[1] 住房和城乡建设部.海绵城市建设技术指南:低影响开发雨水系统构建[M].北京:中国建筑工业出版社,2015.
[2] 胡爱兵,任心欣,丁年.深圳市某区域"海绵城市"目标分解机制及指标体系构建[C]//中国城市规划学会、贵阳市人民政府.新常态:传承与变革——2015中国城市规划年会论文集.贵阳:中国城市规划学会,2015:9.
[3] 中华人民共和国住房和城乡建设部.海绵城市建设评价标准:GB/T 51345-2018[S].北京:中国建筑工业出版社,2018.
[4] 骆畅.山地城市绿地生态系统服务价值评估及规划策略研究:以重庆市主城区为例[D].北京:北京林业大学, 2018.
[5] 曲悠扬,邓小鹏,閤超成.基于SEM的南方多雨地区海绵城市评价研究[J].建筑经济, 2019, 40(1):112-116.
[6] 陈伟.基于海绵城市理念下的绿地系统规划研究[D].北京:北京建筑大学, 2020.
[7] ASSADI-LANGROUDI A. A conceptual model for loess in England:principles and applications[J]. Proceedings of the Geologists' Association, 2019, 130(2):115-125.
[8] 陈林,杨贤房,张安皓,等.海绵城市建设实践性策略研究:以南方多雨地区深圳市为例[J].浙江建筑, 2020, 37(3):9-12.
[9] 谭术魁,张南.中国海绵城市建设现状评估:以中国16个海绵城市为例[J].城市问题, 2016(6):98-103.
[10] 李杰.基于SWMM的湿陷性黄土地区海绵城市建设研究:以西咸新区某校园为例[D].西安:长安大学, 2018.
[11] WU L Z,ZHOU Y,SUN P,et al.Laboratory characterization of rainfall-induced loess slope failure[J]. CATENA, 2017, 150:1-8.
[12] 钱佳.城市绿地系统规划对海绵城市建设的响应探究[D].南充:西华师范大学,2018.
[13] 李晶,张莉,尹勇.绿色生态城区海绵城市建设规划设计思路探析[J].科技创新导报, 2018, 15(24):152.
[14] 张志刚.基于海绵城市理念的重庆市两江新区城市空间重构研究[D].重庆:西南大学, 2017.

[1]	WANG Jingkai, GE Xingtong, LI Zhaobo, DING Xiang, PENG Ling. Information extraction of photovoltaic panels in highway area based on improved HRNet [J]. Bulletin of Surveying and Mapping, 2025, 0(5): 74-78,99.
[2]	XIE Batu, HU Jiarui, PAN Jun. DGIONet:dual-path global information optimization network for sea-land segmentation with remote sensing images [J]. Bulletin of Surveying and Mapping, 2025, 0(3): 52-58,86.
[3]	ZHANG Yu, HU Xin, WU Hui, ZHANG Huiran, CHEN Min. Construction of high-resolution remote sensing imagery urban detailed underlying surface classification DUSC-7 dataset for urban hydrological simulation [J]. Bulletin of Surveying and Mapping, 2025, 0(10): 133-137.
[4]	LI Zhenxuan, HUANG Miner, GAO Fei, TAO Tingye, WU Zhaofu, ZHU Yongchao. Remote sensing image water body extraction based on U-Net, U-Net++ and Attention-U-Net networks [J]. Bulletin of Surveying and Mapping, 2024, 0(8): 26-30.
[5]	ZHANG Wei, ZHANG Chaolong, WANG Benlin, CAI Anning. Road extraction of UAV remote sensing image based on deep learning [J]. Bulletin of Surveying and Mapping, 2024, 0(6): 77-81.
[6]	HE Qiuhua, ZOU Juan, YU Shuchen, YU Deqing, TANG Hui, MEI Jinhua, LUO Jianqiang, ZHAO Dong, ZOU Cong. Monitoring of flood channel bank collapse in the southern section of the Jingjiang River based on high-resolution remote sensing images: taking the Dengpanzhou bend as an example [J]. Bulletin of Surveying and Mapping, 2023, 0(9): 124-128.
[7]	ZHANG Jian, GAO Ya. Application of deep learning remote sensing image interpretation technology in cultivated land protection [J]. Bulletin of Surveying and Mapping, 2023, 0(8): 142-145.
[8]	XING Ruoyun, RAN Shuhao, GAO Xianjun, YANG Yuanwei, FANG Jun. Identification method of urban villages with improved composite dictionary considering multi-resolution features [J]. Bulletin of Surveying and Mapping, 2023, 0(4): 41-48,48.
[9]	ZHANG Yue, ZHANG Jin. Frequency domain feature extraction method and application of high-resolution remote sensing image of debris flow [J]. Bulletin of Surveying and Mapping, 2023, 0(3): 111-115.
[10]	ZHAO Yubin, NI Huan, NIU Xiaonan. Quarry recognition based on improved CycleMLP from high-resolution remote sensing imagery [J]. Bulletin of Surveying and Mapping, 2023, 0(2): 84-90.
[11]	HU Chunxia, NIE Xiangyu, LIN Cong, FU Junhao, CHU Zhengwei. High-resolution remote sensing image classification based on multi-feature collaborative deep network [J]. Bulletin of Surveying and Mapping, 2023, 0(10): 74-79,104.
[12]	CHEN Zhilang, FU Zhenhua, ZHU Ziyang, WANG Huihui, LIU Qinwen, YANG Yuling, XU Gengran. HRNet-based extraction of building change information from high-resolution remote sensing images [J]. Bulletin of Surveying and Mapping, 2022, 0(5): 126-132.
[13]	GAO Ming, ZHOU Xinxin, LIU Qi, YANG Guangdi, WU Changbin. Automatic monitoring of farmland occupation by farm house based on deep learning network [J]. Bulletin of Surveying and Mapping, 2022, 0(3): 47-53.
[14]	YANG Sufang, DU Lin. High-resolution remote sensing image classification based on multi-feature popular discrimination embedding [J]. Bulletin of Surveying and Mapping, 2021, 0(9): 37-42.
[15]	GENG Wanxuan, ZHOU Weixun, JIN Shuanggen. Scene classification of high-resolution remote sensing image based on multi-kernel SVM using features extracted from LDCNN [J]. Bulletin of Surveying and Mapping, 2021, 0(8): 14-21.