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REMOTE SENSING FOR LAND & RESOURCES    1994, Vol. 6 Issue (4) : 46-53     DOI: 10.6046/gtzyyg.1994.04.08
Applied Research |
PRESENT CONDITION AND FUTURE ADJUSTMENT OF LAND USE IN HAINAN ISLAND
Cai Yunlong
Department of Geography, Peking University
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Abstract  

This article analyses some important aspects of present condition of land use in Hainan Island, including land use composition, land productivity,spatial distribution of land use, land use technology, land use rate, and land use planning and management. Then, the guiding principles of future land use are described as following: (1) The land use composition should be adjusted appropriately according to the regional development strategy;(2) Land use should more coordinated with market in economic operation; (3) Tropical crop should take an important place in land use; (4) The land should be developed and used in sustainable way; (5) The macro control of goverment should be strengthened; (6) The area of cropland should be stabilized. Finally, some measures to realize rational and effictive land use are suggested.

Keywords Remote sensing technology      Resources and environment      Land degradation      Monitoring     
Issue Date: 02 August 2011
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YANG Qing-Hua
ZENG Fu-Nian
CAO Wen-Yu
QI Jian-Wei
FAN Jing-Hui
ZHANG Guo-Hong
LI Ren-He
Cite this article:   
YANG Qing-Hua,ZENG Fu-Nian,CAO Wen-Yu, et al. PRESENT CONDITION AND FUTURE ADJUSTMENT OF LAND USE IN HAINAN ISLAND[J]. REMOTE SENSING FOR LAND & RESOURCES, 1994, 6(4): 46-53.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1994.04.08     OR     https://www.gtzyyg.com/EN/Y1994/V6/I4/46


[1] 蔡运龙.海南岛土地利用的遥感调查与机助制图.国土资源遥感, 1993(4): 17-27

[2] 温长恩.海南岛土地资源优势及其合理利用.见温长恩、杨世高等编.《海南资源环境与空间发展研究》.海口:海南人民出版社, 1989. 69

[3] 秦文清.海南岛海岸带土地资源与土地利用.见温长恩、杨世高等编, 《海南资源环境与空间发展研究》.海口:海南人民出版社, 1989. 79

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