Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2015, Vol. 27 Issue (2) : 196-200     DOI: 10.6046/gtzyyg.2015.02.30
|
GIS spatial modeling in mountainous land evaluation
CHEN Yingyue, GAN Shu, TIAN Yudong, ZHOU Xibing
Institute of Land and Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
Download: PDF(2905 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  Due to such factors as complex topography, thin soil and high bedrock exposure rate, the measurement of mountainous land should be adapted to local conditions for the development and utilization, and hence the suitability evaluation should be conducted first. At present, researchers in this field usually use ArcGIS spatial analysis method to do the analysis, but the process is complicated and easy to make errors. In this paper, the authors use the spatial analysis model of ArcGIS generator model to model the calculation process and the dealing process of each evaluation factor, combine every single factor to do the comprehensive factor calculation, and then get the mountain resources suitability evaluation model. These methods were applied to performing the correlation of the study area. Combined with the field work, the authors found that the evaluated result is highly identical with the suitability of land exploitation and utilization under the practical situation. The scientific character and reliability of the GIS model methods were verified. Compared with the conventional method, the methods adopted by the authors could improve the efficiency of data processing obviously. The data processing model for the sharing is also provided.
Keywords remote sensing image      texture segmentation      blue noise      forest vegetation     
:  P208  
Issue Date: 02 March 2015
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
LIU Xiaodan
YANG Shen
Cite this article:   
LIU Xiaodan,YANG Shen. GIS spatial modeling in mountainous land evaluation[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(2): 196-200.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.02.30     OR     https://www.gtzyyg.com/EN/Y2015/V27/I2/196
[1] 汤国安,杨昕.ArcGIS地理信息系统空间分析实验教程[M].北京:科学出版社,2006:16-17. Tang G A,Yang X.ArcGIS GIS Spatial Analysis Experiments Tutorial[M].Beijing:Science Press,2006:16-17.
[2] 季漩.基于Model Builder的水土流失危险性分析模型研究[J].内蒙古林业调查设计,2009,32(1):101-103. Ji X.Studies on analyzing-model of water and soil loss hazard based on Model Builder[J].Inner Mongolia Forestry Investigation and Design,2009,32(1):101-103.
[3] 杨斌,顾秀梅,刘建,等.基于ArcGIS的山地与非山地分类方法体系研究[J].国土资源遥感,2011,23(4):64-68.doi:10.6046/gtzyyg.2011.04.12. Yang B,Gu X M,Liu J,et al.A study of the classification method for mountainous and non-mountainous regions based on ArcGIS[J].Remote Sensing for Land and Resources,2011,23(4):64-68.doi:10.6046/gtzyyg.2011.04.12.
[4] 周扬,李潇丽,吴文祥,等.基于Model Builder的库区生态敏感性分析[J].安徽农业科学,2009,37(29):14272-14275,14322. Zhou Y,Li X L,Wu W X,et al.Sensitivity analysis of ecological reservoir base on Model Builder[J].Journal of Anhui Agricultural Sciences,2009,37(29):14272-14275,14322.
[5] 赵金涛,王景成.基于Geoprocessing Service的供水管网等压面的实现[J].微型电脑应用,2011,27(8):32-33. Zhao J T,Wang J C.Implementation of the isobaric surface of water supply network based on Geoprocessing Service[J].Microcomputer Applications,2011,27(8):32-33.
[6] 徐盼,张晓祥,晏王波,等.城市交通干线对盐城城市发展影响的空间分析[J].地球信息科学学报,2013,15(1):29-37. Xu P,Zhang X X,Yan W B,et al.Urban transportation infrastructure and its effects on regional development in Yancheng:A spatial analysis perspective[J].Journal of Geo-Information Science,2013,15(1):29-37.
[7] Johnston K.Using ArcGIS Geostatistical Analyst[M].Redlands:Esri Press,2004:280.
[8] Dobesova Z.Programming language python for data processing[C]//2011 International Conference on Electrical and Control Engineering(ICECE).Yichang:IEEE,2011:4866-4869.
[9] ESRI.ArcGIS Desktop Help 10,"What is ModelBuilder?"[EB/OL].[2011-09-13].http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/What-is-ModelBuilder/002w00000001000000/.
[1] ZHANG Daming, ZHANG Xueyong, LI Lu, LIU Huayong. Remote sensing image segmentation based on Parzen window density estimation of super-pixels[J]. Remote Sensing for Natural Resources, 2022, 34(1): 53-60.
[2] XUE Bai, WANG Yizhe, LIU Shuhan, YUE Mingyu, WANG Yiying, ZHAO Shihu. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1): 61-66.
[3] SONG Renbo, ZHU Yuxin, GUO Renjie, ZHAO Pengfei, ZHAO Kexin, ZHU Jie, CHEN Ying. A method for 3D modeling of urban buildings based on multi-source data integration[J]. Remote Sensing for Natural Resources, 2022, 34(1): 93-105.
[4] LI Yikun, YANG Yang, YANG Shuwen, WANG Zihao. A change vector analysis in posterior probability space combined with fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2021, 33(4): 82-88.
[5] LIU Zhizhong, SONG Yingxu, YE Runqing. An analysis of rainstorm-induced landslides in northeast Chongqing on August 31, 2014 based on interpretation of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(4): 192-199.
[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] WANG Yiuzhu, HUANG Liang, CHEN Pengdi, LI Wenguo, YU Xiaona. 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.
[8] LIU Wanjun, GAO Jiankang, QU Haicheng, JIANG Wentao. Ship detection based on multi-scale feature enhancement of remote sensing images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 97-106.
[9] SANG Xiao, ZHANG Chengye, LI Jun, ZHU Shoujie, XING Jianghe, WANG Jinyang, WANG Xingjuan, LI Jiayao, YANG Ying. Application of intensity analysis theory in the land use change in Yijin Holo Banner under the background of coal mining[J]. Remote Sensing for Natural Resources, 2021, 33(3): 148-155.
[10] LU Qi, QIN Jun, YAO Xuedong, WU Yanlan, ZHU Haochen. Buildings extraction of GF-2 remote sensing image based on multi-layer perception network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 75-84.
[11] HU Suliyang, LI Hui, GU Yansheng, HUANG Xianyu, ZHANG Zhiqi, WANG Yingchun. An analysis of land use changes and driving forces of Dajiuhu wetland in Shennongjia based on high resolution remote sensing images: Constraints from the multi-source and long-term remote sensing information[J]. Remote Sensing for Land & Resources, 2021, 33(1): 221-230.
[12] LIU Zhao, ZHAO Tong, LIAO Feifan, LI Shuai, LI Haiyang. Research and comparative analysis on urban built-up area extraction methods from high-resolution remote sensing image based on semantic segmentation network[J]. Remote Sensing for Land & Resources, 2021, 33(1): 45-53.
[13] ZHENG Zhiteng, FAN Haisheng, WANG Jie, WU Yanlan, WANG Biao, HUANG Tengjie. An improved double-branch network method for intelligently extracting marine cage culture area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 120-129.
[14] WANG Xiaobing. Denoising algorithm based on the fusion of lifting wavelet thresholding and multidirectional edge detection of remote sensing image of mining area[J]. Remote Sensing for Land & Resources, 2020, 32(4): 46-52.
[15] WEI Hongyu, ZHAO Yindi, DONG Jihong. Cooling tower detection based on the improved RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 68-73.
Viewed
Full text


Abstract

Cited

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