A REMOTE SENSING SURVEY OF LAND USE IN YUDONC PLAIN

doi:10.6046/gtzyyg.1992.02.01

Download: PDF(740 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

This paper discussed the principle of land use status survey by using infrared aerial photographs and the methods of identifying land use categories. The principle and methods were then used to analyze the characteristics of current land use in Yudong Plain, to clarify the problems existing in land use in this region. Suggestions about land use in a comprehensive way were proposed.

Keywords Object-based image analysis Classification IKONOS QuickBird High resolution SVM

Issue Date: 02 August 2011

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	YU Hai-Yang
	GAN Fu-Ping
	WU Fa-Dong
	DANG Fu-Xing
	LIN Zhen
	YAO Yong-Jian

Cite this article:

YU Hai-Yang,GAN Fu-Ping,WU Fa-Dong, et al. A REMOTE SENSING SURVEY OF LAND USE IN YUDONC PLAIN[J]. REMOTE SENSING FOR LAND & RESOURCES, 1992, 4(2): 3-11.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.1992.02.01 OR https://www.gtzyyg.com/EN/Y1992/V4/I2/3

[1]	SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images[J]. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
[2]	WU Linlin, LI Xiaoyan, MAO Dehua, WANG Zongming. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
[3]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[4]	FAN Yinglin, LOU Debo, ZHANG Changqing, WEI Yingjuan, JIA Fudong. Information extraction technologies of iron mine tailings based on object-oriented classification: A case study of Beijing-2 remote sensing images of the Qianxi Area, Hebei Province[J]. Remote Sensing for Natural Resources, 2021, 33(4): 153-161.
[5]	BAI Junlong, WANG Zhangqiong, YAN Haitao. A K-means clustering-guided threshold-based approach to classifying UAV remote sensed images[J]. Remote Sensing for Natural Resources, 2021, 33(3): 114-120.
[6]	WANG Hua, LI Weiwei, LI Zhigang, CHEN Xueye, SUN Le. Hyperspectral image classification based on multiscale superpixels[J]. Remote Sensing for Natural Resources, 2021, 33(3): 63-71.
[7]	WANG Rong, ZHAO Hongli, JIANG Yunzhong, HE Yi, DUAN Hao. Application and analyses of texture features based on GF-1 WFV images in monthly information extraction of crops[J]. Remote Sensing for Natural Resources, 2021, 33(3): 72-79.
[8]	JIANG Xiao, ZHONG Chang, LIAN Zheng, WU Liangting, SHAO Zhitao. Research progress on classification criterion of geological information products based on satellite remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 279-283.
[9]	HAN Yanling, CUI Pengxia, YANG Shuhu, LIU Yekun, WANG Jing, ZHANG Yun. Classification of hyperspectral image based on feature fusion of residual network[J]. Remote Sensing for Land & Resources, 2021, 33(2): 11-19.
[10]	LING Xiao, LIU Jiamei, WANG Tao, ZHU Yueqin, YUAN Lingling, CHEN Yangyang. Application of information value model based on symmetrical factors classification method in landslide hazard assessment[J]. Remote Sensing for Land & Resources, 2021, 33(2): 172-181.
[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]	MENG Qing, BAI Hongying, ZHAO Ting, GUO Shaozhuang, QI Guizeng. The eco-barrier effect of Qinling Mountain on aerosols[J]. Remote Sensing for Land & Resources, 2021, 33(1): 240-248.
[13]	XU Yun, XU Aiwen. Classification and detection of cloud, snow and fog in remote sensing images based on random forest[J]. Remote Sensing for Land & Resources, 2021, 33(1): 96-101.
[14]	JIANG Shan, WANG Chun, SONG Hongli, LIU Yufeng. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
[15]	WANG Xiaolong, YAN Haowen, ZHOU Liang, ZHANG Liming, DANG Xuewei. Using SVM classify Landsat image to analyze the spatial and temporal characteristics of main urban expansion analysis in Democratic People’s Republic of Korea[J]. Remote Sensing for Land & Resources, 2020, 32(4): 163-171.