Please wait a minute...
 
Remote Sensing for Natural Resources    2023, Vol. 35 Issue (2) : 277-286     DOI: 10.6046/zrzyyg.2022113
|
County-level natural resource survey in western China based on both GF-6 images and the third national land resource survey results
YAN Han(), ZHANG Yi()
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Download: PDF(9818 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

The survey and change monitoring of natural resources can provide an important guarantee for the implementation of systematic policies, protection, and rational utilization of resources and are of great significance for the building of the national land space planning system, the reform of the resource management system, the modernization of space governance capacity, and the construction of national ecological civilization. Western China is characterized by a vast area, insufficient basic land data, and unreliable land change monitoring. Therefore, there is an urgent need to provide efficient and accurate survey results at a low cost for such a large area. Based on the domestic high-resolution satellite (GF-6) images and the results of the third national land survey, this study carried out a demonstration of the application of the intelligent rural land survey to the areas subject to rapid development in western China in Xuyong County. To this end, remote sensing images with high spatial resolution and hyperspectral resolution were obtained through panchromatic and multispectral image fusion. Then, the fused data were used for the basic survey of land resources in Xuyong County. Subsequently, based on the object-oriented image classification and the results of the third national land survey, supervised classification of the remote sensing images was conducted, and areas with changes in land were automatically extracted, thus forming a new efficient land survey model for the areas subject to rapid development in western China. The survey results can provide strong support in terms of basic land information for the rapid development of specialty industries in western China and have a certain value in popularization and applications.

Keywords GF-6      natural resource survey      supervised classification      change monitoring     
ZTFLH:  P237  
Issue Date: 07 July 2023
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Han YAN
Yi ZHANG
Cite this article:   
Han YAN,Yi ZHANG. County-level natural resource survey in western China based on both GF-6 images and the third national land resource survey results[J]. Remote Sensing for Natural Resources, 2023, 35(2): 277-286.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022113     OR     https://www.gtzyyg.com/EN/Y2023/V35/I2/277
Fig.1  GF-6 images of Xuyong County
Fig.2  Flow chart of natural resources survey technology
Fig.3  Flow chart of supervised classification of remote sensing image
Fig.4  Flow chart of change detection technology in natural resources survey
Fig.5  Radiometric calibration results
Fig.6  Spectral characteristics before and after atmospheric correction and remote sensing images of corresponding ground objects
Fig.7  Comparison between before and after orthophoto correction
Fig.8  Results of image fusion
Fig.9  Supervised classification results of Xuyong County
土地利用类别 图斑数目/个 面积/像素
耕地 29 504 141 978 517
建设用地 23 112 56 945 920
林地 49 546 484 540 890
水体 6 945 40 972 318
其他 6 741 18 183 683
未分类 279 63 990
Tab.1  Land use classification results of Xuyong County in 2019
土地利用类别 图斑数目/个 面积/像素
耕地 11 613 89 972 192
建设用地 21 349 108 017 154
林地 32 658 520 649 096
水体 955 4 898 657
其他 4 283 19 162 288
未分类 99 53 076
Tab.2  Land use classification results of Xuyong County in 2021
地物类别 耕地 建设用地 林地 水体 其他地类 总计
耕地 43 1 3 2 1 50
建设用地 1 30 0 1 0 32
林地 4 7 162 3 1 177
水体 2 1 2 28 0 33
其他地类 1 0 1 0 6 8
总计 51 39 168 34 8 300
Tab.3  Error matrix of land use classification in Xuyong County in 2019
地物类别 耕地 建设用地 林地 水体 其他地类 总计
耕地 40 0 1 2 0 43
建设用地 2 43 2 1 0 48
林地 2 6 175 5 1 189
水体 3 1 0 10 0 14
其他地类 0 1 0 0 5 6
总计 47 51 178 18 6 300
Tab.4  Error matrix of land use classification in Xuyong County in 2021
Fig.10  Image classification results in 2021(part)
Fig.11  Automatie detection and drawinp result of change spots
Fig.12  Comparison of change spot overlayed on image
土地利用类别 变化面积/m2 变化占比/%
耕地 104 946 393.10 -28.51
建设用地 110 355 532.21 29.98
林地 76 156 780.60 20.69
水体 74 612 165.50 -20.27
其他地类 2 044 048.60 0.55
Tab.5  Change detection results of land use types in Xuyong County
[1] 张颢骞. 高分遥感影像在第三次全国国土调查中的应用价值分析[J]. 建材与装饰, 2019(32):241-242.
[1] Zhang H Q. Application value analysis of high resolution remote sensing images in the third national land survey[J]. Construction Materials & Decoration, 2019(32):241-242.
[2] 首颗农业高分观测卫星成功发射[J]. 农业科技与信息, 2018(18):72.
[2] The first agricultural high resolution observation satellite was successfully launched[J]. Agricultural Science-Technology and Information, 2018(18):72.
[3] 袁新悦, 甘淑, 袁希平. 基于高分2号遥感影像的监督分类方法探讨——以东川区小江的河谷地带为例[J]. 地质灾害与环境保护, 2021, 32(2):78-81.
[3] Yuan X Y, Gan S, Yuan X P. Discussion on supervised classification method ased on GF-2 satellite data:The valley of Xiaojiang River in Chuandong District[J]. Journal of Geological Hazards and Environment Preservation, 2021, 32(2):78-81.
[4] 王译著, 黄亮, 陈朋弟, 等. 联合显著性和多方法差异影像融合的遥感影像变化检测[J]. 自然资源遥感, 2021, 33(3):89-96.doi:10.6046/zrzyyg.2020312.
doi: 10.6046/zrzyyg.2020312
[4] Wang Y Z, Huang L, Chen P D, et al. Change detection of remote sensing images based on the fusion of co-saliency difference images[J]. Remote Sensing for Natural Resources, 2021, 33(3):89-96.doi:10.6046/zrzyyg.2020312.
doi: 10.6046/zrzyyg.2020312
[5] 王跃峰, 武慧智, 何姝珺, 等. 河南省信阳市浉河区自然资源智能化信息提取技术方法研究[J]. 国土资源遥感, 2020, 32(4):244-250.doi:10.6046/gtzyyg.2020.04.30.
doi: 10.6046/gtzyyg.2020.04.30
[5] Wang Y F, Wu H Z, He S J, et al. Method research of intelligentized extraction of natural resources information from Shihe District,Xinyang City,Henan Province[J]. Remote Sensing for Land and Resources, 2020, 32(4):244-250.doi:10.6046/gtzyyg.2020.04.30
doi: 10.6046/gtzyyg.2020.04.30
[6] 方梦阳, 刘晓煌, 孔凡全, 等. 一种基于GEE平台制作逐年土地覆盖数据的方法——以黄河流域为例[J]. 自然资源遥感, 2022, 34(1):135-141.doi:10.6046/zrzyyg.2021088.
doi: 10.6046/zrzyyg.2021088
[6] Fang M Y, Liu X H, Kong F Q, et al. A method for creating annual land cover data based on Google Earth Engine:A case study of the Yellow River basin[J]. Remote Sensing for Natural Resources, 2022, 34(1):135-141.doi:10.6046/zrzyyg.2021088.
doi: 10.6046/zrzyyg.2021088
[7] 唐大珍, 于礼. 泸州市土地利用动态变化研究[J]. 农村经济与科技, 2011, 22(1):19-22.
[7] Tang D Z, Yu L. Study on dynamic change of land use in Luzhou City[J]. Rural Economy and Science-Technology, 2011, 22(1):19-22.
[8] 唐侨, 陈涛, 刘思源, 等. 基于3S 技术的土地利用信息动态变更调查新机制研究——以成都市为例[J]. 测绘与空间地理信息, 2015(1):77-80.
[8] Tang Q, Chen T, Liu S Y, et al. The research on new mechanism about dynamically changing of land use information based on 3S technology: Take Chengdu for example[J]. Geomatics & Spatial Information Technology, 2015(1):77-80.
[9] Matthew M W, Adler-Golden S M, Berk A, et al. Atmosphere-ic correction of spectral imagery:Evaluation of the FLAASH algorithm with AVIRIS data[J]. Applied Imagery Patten Recognition Workshop, 2002(31):157-163.
[10] 王志伟, 杨国东, 张旭晴, 等. 高分六号卫星遥感影像不同几何校正方法精度对比研究[J]. 世界地质, 2021, 40(1):125-130,139.
[10] Wang Z W, Yang G D, Zhang X Q, et al. A comparative research on the accuracty of different geometric correction methods of Gaofen-6 satellite remote sensing image[J]. Global Geology, 2021, 40(1):125-130,139.
[11] Sun W, Chen B, Messinger D W. Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images[J]. Optical Engineering, 2014, 53(1):013107.
doi: 10.1117/1.OE.53.1.013107 url: http://opticalengineering.spiedigitallibrary.org/article.aspx?doi=10.1117/1.OE.53.1.013107
[12] 尤淑撑, 张锐, 董丽娜, 等. 自然资源卫星遥感常态化监测框架设计及关键技术[J]. 地理信息世界, 2020, 27(5):115-120,128.
[12] You S C, Zhang R, Dong L N, et al. Framework and key technologies for natural resources satellites remote sensing monitoring[J]. Geomatics World, 2020, 27(5):115-120,128.
[13] 窦世卿, 宋莹莹, 徐勇, 等. 基于随机森林的高分影像分类及土地利用变化检测[J]. 无线电工程, 2021, 51(9):901-908.
[13] Dou S Q, Song Y Y, Xu Y, et al. High resolution image classification and land use change detection based on random forest[J]. Radio Engineering, 2021, 51(9):901-908.
[14] 滕玲玲, 吴武, 廖玉斌. 基于分类变化检测方法的地表覆盖影像特征数据更新处理研究[J]. 测绘与空间地理信息, 2020, 43(11):159-161,165.
[14] Teng L L, Wu W, Liao Y B. Research on updating process of land cover image feature data based on classification change detection method[J]. Geomatics & Spatial Information Technology, 2020, 43(11):159-161,165.
[15] Castellana L, D’Addabbo A, Pasquariello G. A composed super-vised / unsupervised approach to improve change detection from remote sensing[J]. Pattern Recognition Letters, 2007, 28(4) :405-413.
doi: 10.1016/j.patrec.2006.08.010 url: https://linkinghub.elsevier.com/retrieve/pii/S0167865506002200
[16] Csaba B A, Maha S, Zoltan K, et al. Multilayer Markov random field models for change detection in optical remote sensing images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 107(9):22-37.
doi: 10.1016/j.isprsjprs.2015.02.006 url: https://linkinghub.elsevier.com/retrieve/pii/S0924271615000416
[17] Qian Z, Xin H, Zhang L. An energy-driven total variation model for segmentation and classification of high spatial resolution remote-sensing imagery[J]. IEEE Geoscience & Remote Sensing Letters, 2013, 10(1):125-129.
[18] Felzenszwalb P F, Huttenlocher D P. Efficient belief propagation for early vision[J]. International Journal of Computer Vision, 2006, 70(1):41-54.
doi: 10.1007/s11263-006-7899-4 url: http://link.springer.com/10.1007/s11263-006-7899-4
[19] Kropatsch W G, Haxhimusa Y. Grouping and segmentation in a hierarchy of graphs[C]// Computational Imaging Ⅱ Proceeding of SPIE, 2004.
[20] Nielsen A A. The regularized iteratively reweighted MAD method for change detection in multi -and hyperspectral data[J]. IEEE Transactions on Image Processing:A Publication of the IEEE Signal Processing Society, 2007, 16(2) :463-478.
doi: 10.1109/TIP.2006.888195 url: http://ieeexplore.ieee.org/document/4060945/
[21] 赵展, 夏旺, 闫利. 基于多源数据的土地利用变化检测[J]. 国土资源遥感, 2018, 30(4):148-155.doi:10.6046/gtzyyg.2018.04.22.
doi: 10.6046/gtzyyg.2018.04.22
[21] Zhao Z, Xia W, Yan L. Land use change detection based on multi-source data[J]. Remote Sensing for Land and Resources, 2018, 30(4):148-155.doi:10.6046/gtzyyg.2018.04.22.
doi: 10.6046/gtzyyg.2018.04.22
[1] ZHANG Shibo, HU Wenmin, HAN Zhenying, LI Guo, WANG Zhongcheng, GAO Zhihai. Differences in rocky desertification information extracted from GF-6 and Landsat8 using the pixel unmixing method: A case study of Puding County[J]. Remote Sensing for Natural Resources, 2023, 35(3): 274-283.
[2] HU Xiaoqiang, YANG Shuwen, YAN Heng, XUE Qing, ZHANG Naixin. Time-series InSAR-based monitoring and analysis of surface deformation in the Axi mining area, Xinjiang[J]. Remote Sensing for Natural Resources, 2023, 35(1): 171-179.
[3] XU Qingyun, LI Ying, TAN Jing, ZHANG Zhe. Information extraction method of mangrove forests based on GF-6 data[J]. Remote Sensing for Natural Resources, 2023, 35(1): 41-48.
[4] KONG Ailing, ZHANG Chengming, LI Feng, HAN Yingjuan, SUN Huanying, DU Manfei. Knowledge-based remote sensing image fusion method[J]. Remote Sensing for Natural Resources, 2022, 34(2): 47-55.
[5] WANG Renjun, LI Dongying, LIU Baokang. A water body identification model for lakes in Hoh Xil based on GF-6 WFV satellite data[J]. Remote Sensing for Natural Resources, 2022, 34(2): 80-87.
[6] ZHANG Chengye, XING Jianghe, LI Jun, SANG Xiao. Recognition of the spatial scopes of tailing ponds based on U-Net and GF-6 images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 252-257.
[7] Zhaohua LIU, Chunyan ZHANG. Dynamic monitoring and driving factors analysis of urban expansion in Kaifeng[J]. Remote Sensing for Land & Resources, 2018, 30(4): 193-199.
[8] Ting WANG, Jun PAN, Lijun JIANG, Lixin XING, Yifan YU, Pengju WANG. Topographic variable analysis and lithologic classification based on DEM[J]. Remote Sensing for Land & Resources, 2018, 30(2): 231-237.
[9] LIU Baozhu, FANG Xiuqin, HE Qisheng, RONG Qiyuan. Monitoring the changes of vegetation based on MODIS data and BFAST methods[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 146-153.
[10] HE Hao, SHEN Yonglin, LIU Xiuguo, MA Li. Spatial-spectral constrained graph-based semi-supervised classification for hyperspectral image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(3): 31-36.
[11] LI Weina, YANG Jiansheng, LI Xiao, ZHANG Jilong, LI Shiwei. Extraction of urban impervious surface information from TM image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(1): 66-70.
[12] SONG Qi-Fan, WANG Shao-Jun, ZHANG Zhi, WANG Peng, AN Ping. A Water Information Extraction Method Based on WorldView II
Remote Sensing Image in Tungsten Ore Districts: A Case Study of of Dayu County in Jiangxi Province
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 33-37.
[13] LI Na, ZHAO Hui-Jie. An Improved Independent Component Analysis Method for Unsupervised Classification of Hyperspectral Data  [J]. REMOTE SENSING FOR LAND & RESOURCES, 2011, 23(2): 70-74.
[14] YOU Shu-Cheng, LIU Shun-Xi, ZHOU Lian-Fang, HE Yu-Hua, ZHANG Rong-Hui, HAN Yi. APPLICATION METHOD FOR LAND USE DYNAMIC CHANGE MONITORING BASED ON CBERS-02B DATA[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(1): 79-82.
[15] YOU Shu-Cheng, LIU Shun-Xi, ZHOU Lian-Fang, HE Yu-Hua, ZHANG Rong-Hui, HAN Yi. APPLICATION METHOD FOR LAND USE MACRO MONITORING 
BASED ON CBERS-02B CCD DATA
[J]. REMOTE SENSING FOR LAND & RESOURCES, 2009, 21(1): 83-85.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
Copyright © 2017 Remote Sensing for Natural Resources
Support by Beijing Magtech