INNOVATIONS IN METHODS OF LAND USE DETAILED SURVEY BASED ON 3S TECHNIQUES

doi:10.6046/gtzyyg.2002.02.02

Download: PDF(563 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Land Use Detailed Survey (LUDS) is a process for acquiring detailed information about types, distribution, quantity, application status and tenure of land on large scales according to The Chinese Technical Order of Land Use Status Survey. LUDS is characterized by rigorous land use classifications, strict precision indices, on-the-spot survey and land tenure survey. Nevertheless, the traditional methods for LUDS are based on handwork, and are hence time-wasting and need lots of manpower; In addition, the survey processes have cockamamie programs and can easily produce errors. With the rapid progress of RS, GPS and GIS, the conditions for acquiring land use information are much better than before. This paper briefly describes the concerted methods of 3S in LUDS and evaluates the precision improvement. It also points out some troubles in 3S application.

Keywords Thermal infrared remote sensing Near surface temperature TVX approach Neural network Energy balance

Issue Date: 02 August 2011

	Service

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

	XU Yong-Meng
	QIN Zhi-Hao
	WAN Hong-Xiu

Cite this article:

XU Yong-Meng,QIN Zhi-Hao,WAN Hong-Xiu. INNOVATIONS IN METHODS OF LAND USE DETAILED SURVEY BASED ON 3S TECHNIQUES[J]. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(2): 4-9.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2002.02.02 OR https://www.gtzyyg.com/EN/Y2002/V14/I2/4

[1]	YU Xinli, SONG Yan, YANG Miao, HUANG Lei, ZHANG Yanjie. Multi-model and multi-scale scene recognition of shipbuilding enterprises based on convolutional neural network with spatial constraints[J]. Remote Sensing for Natural Resources, 2021, 33(4): 72-81.
[2]	LI Teya, SONG Yan, YU Xinli, ZHOU Yuanxiu. Monthly production estimation model for steel companies based on inversion of satellite thermal infrared temperature[J]. Remote Sensing for Natural Resources, 2021, 33(4): 121-129.
[3]	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.
[4]	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.
[5]	QIU Yifan, CHAI Dengfeng. A deep learning method for Landsat image cloud detection without manually labeled data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 102-107.
[6]	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.
[7]	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.
[8]	LIU Zhao, LIAO Feifan, ZHAO Tong. Remote sensing image urban built-up area extraction and optimization method based on PSPNet[J]. Remote Sensing for Land & Resources, 2020, 32(4): 84-89.
[9]	CAI Yaotong, LIU Shutong, LIN Hui, ZHANG Meng. Extraction of paddy rice based on convolutional neural network using multi-source remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 97-104.
[10]	GAO Kaixuan, JIAO Haiming, WANG Xinchuang. Inversion model of forest canopy height based on image texture,spectral and topographic features[J]. Remote Sensing for Land & Resources, 2020, 32(3): 63-70.
[11]	CAI Zhiling, WENG Qian, YE Shaozhen, JIAN Cairen. Remote sensing image scene classification based on Inception-V3[J]. Remote Sensing for Land & Resources, 2020, 32(3): 80-89.
[12]	WU Tong, PENG Ling, HU Yuan. Informal garbage dumps detection in high resolution remote sensing images based on SU-RetinaNet[J]. Remote Sensing for Land & Resources, 2020, 32(3): 90-97.
[13]	Ru WANG, Yanfang ZHANG, Hongmin ZHANG, Yun LI. Study on the relationship between impervious surface coverage and artificial heat in new urban districts: A case study of Xixian New District, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(1): 247-254.
[14]	Zhiwei LIN, Weihao TU, Jiahang HUANG, Qilu DING, Zhengwen ZHOU, Jinfu LIU. Tree species recognition of UAV aerial images based on FC-DenseNet[J]. Remote Sensing for Land & Resources, 2019, 31(3): 225-233.
[15]	Famao YE, Wei LUO, Yanfei SU, Xuqing ZHAO, Hui XIAO, Weidong MIN. Application of convolutional neural network feature to remote sensing image registration[J]. Remote Sensing for Land & Resources, 2019, 31(2): 32-37.