|Table of Contents|

Remote Sensing-based Mining of Loess Plateau Land Change Information in Recent 40 Years

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2019年08
Page:
168-179
Research Field:
Publishing date:

Info

Title:
Remote Sensing-based Mining of Loess Plateau Land Change Information in Recent 40 Years
Author(s):
GOU Ruikun1; WANG Xuechun2; ZHAO Jun3; ZHAO Pengxiang1
(1.College of Forestry,Northwest A&F University,Yangling,Shaanxi 712100;2.College of Geography Science and Tourism,Xinjiang Normal University,Urumqi,Xinjiang 830054;3.Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences,Beijing 100085)
Keywords:
Loess Plateau; land use change; CA-Markov model
PACS:
-
DOI:
10.11937/bfyy.20182191
Abstract:
In this study,the Loess Plateau region was taken as the research object,and the remote sensing data of China′s land use status in recent 40 years were used as the main information source.Land use transfer matrix method and statistical methods were used to calculate the percentage of land transfer types and their types of transfer between different types of land cover in the Loess Plateau.We distinguished the systematic and random nature of land use transfer,and used the CA-Markov model to accurately predict land use patterns in 2030，which based on the data such as population spatial distribution and farmland production potential.The results showed that,1) From 1980 to 2000,the most dominant systematic shift was occurred from grassland to cultivated land.The net increase was the largest amount of construction land (0.28%),and the greatest net reduction was unused land (0.17%).In 2000—2015,the most dominant systematic shift was occurred from cultivated land transfer to grassland.The net increase was still the largest amount of construction land (0.78%),and the greatest net reduction was cultivated land (0.69%).2) The land use prediction pattern in 2030 showed that the cultivated land,forest and grassland in this area all show a decreasing trend,while the water,construction land and unused land show an increasing trend.In addition,this study also analyzes and predicts land use changes in all provinces in 2030,thus provids more detailed reference for future land land arrangement in this area.

References:

[1]李若麟.北半球干旱区降水转化和再循环特征及其在全球变暖背景下的变化[D].兰州:兰州大学,2017.[2]何永利.北半球中高纬度季节非对称性增温机制研究[D].兰州:兰州大学,2017.[3]张喆.中亚干旱区内陆湖泊盐尘气溶胶时空演变及其间接气候效应[D].乌鲁木齐:新疆大学,2017.[4]单楠.亚洲丝绸之路经济带土地退化时空格局及驱动力[D].北京:中国林业科学研究院,2016.[5]谢宝妮.黄土高原近30年植被覆盖变化及其对气候变化的响应[D].杨凌:西北农林科技大学,2016.[6]宋敏敏,张青峰,吴发启,等.黄土沟壑区小流域景观格局演变及生态服务价值响应[J].生态学报,2018(8):2649-2659.[7]崔宏.干旱区绿洲水环境与土地利用/覆盖变化响应关系研究[D].西安:西安理工大学,2017.[8]姜宛贝.干旱区土地退化遥感监测方法研究[D].北京:中国农业大学,2017.[9]苗百岭.干旱与半干旱区植被动态及其对气候变化的响应[D].呼和浩特:内蒙古大学,2017.[10]孙福军.基于极端干旱年份遥感植被指数的半干旱地区土地评价研究[D].沈阳:沈阳农业大学,2017.[11]熊侣英,师学义.黄土山丘区土地利用变化对生态系统服务价值的影响:以长河流域为例[J].水土保持研究,2018(2):335-340.[12]ZHAO J,YANG Y,ZHAO Q,et al.Effects of ecological restoration projects on changes in land cover:A case study on the Loess Plateau in China[J].Scientific Reports,2017(7):44496.[13]汪滨,张志强.黄土高原典型流域退耕还林土地利用变化及其合理性评价[J].农业工程学报,2017,33(7):235-245.[14]何立恒,贾子瑞,王志杰.延安市土地利用/土地覆被格局变化特征[J].南京林业大学学报(自然科学版),2015,39(6):173-176.[15]马彩虹,任志远,李小燕.黄土台塬区土地利用转移流及空间集聚特征分析[J].地理学报,2013(2):257-267.[16]陈耀亮.干旱区内陆河流域土地利用/土地覆被变化及其对蒸散发的影响[D].杭州:浙江大学,2018.[17]张俊平,李净,ZHANG J P,等.基于CA-Markov模型的甘州区土地利用变化预测分析[J].中国农学通报,2017,33(4):105-110.[18]周建.基于遥感的半干旱沙区土地资源特性与保护性开发利用布局方法研究[D].北京:中国农业大学,2017.[19]李冬梅.生态脆弱区农村居民点用地演变与土地盐碱化的空间耦合关系研究[D].长春:吉林大学,2017.[20]孙斌.退化土地遥感识别与评价技术研究[D].北京:中国林业科学研究院,2016.[21]冯异星,罗格平,周德成,等.近50年土地利用变化对干旱区典型流域景观格局的影响:以新疆玛纳斯河流域为例[J].生态学报,2010(16):4295-4305.[22]RGJR P,SHUSAS E,MCEACHERN M.Detecting important categorical land changes while accounting for persistence[J].Agriculture Ecosystems & Environment,2004,101(2-3):251-268.[23]BRAIMOH A K.Random and systematic land-cover transitions in northern Ghana[J].Agriculture Ecosystems & Environment,2006,113(1):254-263.[24]魏显虎,张增祥,胡顺光,等.北京山区土地利用转移的系统性和随机性[J].国土资源遥感,2010(4):77-84.[25]张小松.西宁市土地利用景观格局演变及预测研究[D].北京:中国地质大学,2014.[26]郑青华,罗格平,朱磊,等.基于CA_Markov模型的伊犁河三角洲景观格局预测[J].应用生态学报,2010(4):873-882.[27]周熙,马智民,尹芳,等.基于CA-Markov模型的“丝绸之路经济带”沿线地区土地利用变化分析与预测:以西北五省为例[J].西北大学学报(自然科学版),2018(2):291-298.[28]李函娇.IDRISI支持的榆林多目标土地利用变化预测[D].西安:长安大学,2015.[29]魏永坤,石登荣,陶康华,等.基于IDRISI CA-Markov模型的闵行区土地利用变化及预测[J].上海师范大学学报(自然科学版),2014(1):98-104.[30]刘淑燕,余新晓,李庆云,等.基于CA-Markov模型的黄土丘陵区土地利用变化[J].农业工程学报,2010(11):297-303.

Memo

Memo:
-
Last Update: 2019-05-17