Please wait a minute...
 
REMOTE SENSING FOR LAND & RESOURCES    2010, Vol. 22 Issue (2) : 113-117     DOI: 10.6046/gtzyyg.2010.02.24
Technology Application |
The Monitoring of Changes of Land Vegetation Covers by Remote Sensing in Farming-pastoral Mixed Zones of North China:A Case Study in Guyuan County, Hebei Province
LI Pan 1,2,3, HU De-yong 1,2,3, ZHAO Wen-ji 1,2,3
1. College of Resource Environment and Tourism, Capital Normal University, Beijing 100048,China;2.Key Laboratory of 3D Information Acquisition and application of Ministry, Beijing 100048, China;3.Resources,Environment and GIS Key Laboratory of Beijing, Beijing 100048, China
Download: PDF(1134 KB)   HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks    
Abstract  

Based on the TM remote sensing images obtained in Guyuan County of Hebei Province in 1998, 2003 and 2008

and the landuse map as well as field data of the study area , the authors analyzed the spatial and temporal

patterns for dynamics changes of the vegetation cover in Guoyuan County in the past ten years by using the

dimidiate pixel model and expert classifier and consulting the statistical data with the purpose of understanding

the situation of ecological control The results show that most areas were in the moderate and relatively high cover

in the three monitoring periods, with the vegetation cover being improved in general. The condition of vegetation

cover was stable between 1998 and 2003, and was improved between 2003 and 2008. The areas with slightly increased

vegetation cover was mainly distributed in the plateau farmingpasture area, and the stable areas were mainly

distributed in the southern low mountain areas. The effect of the regulation project is remarkable.

Keywords Planning of Olympic Sites      Remote sensing technology      DOM     
Issue Date: 29 June 2010
Service
E-mail this article
E-mail Alert
RSS
Articles by authors
Cite this article:   
LI Pan, HU De-Yong, ZHAO Wen-Ji. The Monitoring of Changes of Land Vegetation Covers by Remote Sensing in Farming-pastoral Mixed Zones of North China:A Case Study in Guyuan County, Hebei Province[J]. REMOTE SENSING FOR LAND & RESOURCES,2010, 22(2): 113-117.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2010.02.24     OR     https://www.gtzyyg.com/EN/Y2010/V22/I2/113
[1] 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.
[2] LIU Mingxing, LIU Jianhong, MA Minfei, JIANG Ya, ZENG Jingchao. Monitoring of Zanthoxylum bungeanum Maxim planting using GF-2 PMS images and the random forest algorithm: A case study of Linxia, Gansu Province[J]. Remote Sensing for Natural Resources, 2022, 34(1): 218-229.
[3] GUO Xiaozheng, YAO Yunjun, JIA Kun, ZHANG Xiaotong, ZHAO Xiang. Information extraction of Mars dunes based on U-Net[J]. Remote Sensing for Natural Resources, 2021, 33(4): 130-135.
[4] ZHOU Chaofan, GONG Huili, CHEN Beibei, LEI Kunchao, SHI Liyuan, ZHAO Yu. Prediction of land subsidence along Tianjin-Baoding high-speed railway using WT-RF method[J]. Remote Sensing for Natural Resources, 2021, 33(4): 34-42.
[5] 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.
[6] ZHENG Xiongwei, PENG Bei, SHANG Kun. Assessment of the interpretation ability of domestic satellites in geological remote sensing[J]. Remote Sensing for Natural Resources, 2021, 33(3): 1-10.
[7] LIU Chunting, FENG Quanlong, JIN Dingjian, SHI Tongguang, LIU Jiantao, ZHU Mingshui. Application of random forest and Sentinel-1/2 in the information extraction of impervious layers in Dongying City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 253-261.
[8] WU Yu, ZHANG Jun, LI Yixu, HUANG Kangyu. Research on building cluster identification based on improved U-Net[J]. Remote Sensing for Land & Resources, 2021, 33(2): 48-54.
[9] WU Qian, JIANG Qigang, SHI Pengfei, ZHANG Lili. The estimation of soil calcium carbonate content based on Hyperspectral data[J]. Remote Sensing for Land & Resources, 2021, 33(1): 138-144.
[10] JIANG Xiao, LU Yunge, SUN Ang, LI Yongzhi, LIAN Zheng. The application of domestic GF-1 satellite data to geological and mineral resources survey abroad: A case study of Faryab area, Iran[J]. Remote Sensing for Land & Resources, 2021, 33(1): 199-204.
[11] 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.
[12] YANG Lijuan. Estimating PM2.5 concentrations in eastern coastal area of China using a two-stage random forest model[J]. Remote Sensing for Land & Resources, 2020, 32(4): 137-144.
[13] WANG Dejun, JIANG Qigang, LI Yuanhua, GUAN Haitao, ZHAO Pengfei, XI Jing. Land use classification of farming areas based on time series Sentinel-2A/B data and random forest algorithm[J]. Remote Sensing for Land & Resources, 2020, 32(4): 236-243.
[14] LI Guoqing, HUANG Jinghua, LIU Guan, LI Jie, ZHAI Bochao, DU Sheng. A study of the landscape fragmentations of land cover structure based on Landsat8 remote sensing image: A case study of Mata watershed in Yan’an, Shaanxi Province[J]. Remote Sensing for Land & Resources, 2020, 32(3): 121-128.
[15] LI Yu, XIAO Chunjiao, ZHANG Hongqun, LI Xiangjuan, CHEN Jun. Remote sensing image semantic segmentation using deep fusion convolutional networks and conditional random field[J]. Remote Sensing for Land & Resources, 2020, 32(3): 15-22.
Viewed
Full text


Abstract

Cited

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