摘要
为了揭示河北省坝上地区林地的动态变化特征,采用250 m空间分辨率的MODIS反射率和NDVI数据,利用TM遥感影像辅助选择训练样本,基于随机森林分类算法,提取2000—2015年8个时相的林地信息,并分析其空间变化情况。结果表明,与常用的最大似然法和神经网络法相比,随机森林法分类的精度更高,总体精度和Kappa系数分别为91.89%和0.88。通过二进制编码方法,快捷地揭示了8个时相的林地信息在空间上的动态变化,识别出变化幅度较大的年份和空间分布。结果显示,林地退化严重的地区集中在丰宁、围场、张北和沽源四县,时间集中在2002,2010和2013年。
In order to reveal the dynamic characteristics of the forest in Bashang area of Hebei Province, MODIS reflectivity and NDVI data with a spatial resolution of 250 m were used for forest classification, and a Thematic Mapper（TM） image in 2005 was resorted to aid training sample selection. With Random Forest Algorithm and time series of MODIS images, the forest in Bashang area was monitored from 2000 to 2015 in every two years. Compared with widely used classifiers such as maximum likelihood classifier and BP artificial neural network algorithm, Random Forest classifier showed the best performance with its overall accuracy and Kappa coefficient being 91.89% and 0.88 respectively. Binary coding was applied to the eight phases of forest distribution images, which can easily and rapidly reflect the changing trajectory from phase to phase. It showed that the severe forest changes mainly occurred in counties of Fengning, Weichang, Zhangbei and Guyuan during the years of 2000, 2010, and 2013.
作者
周佳宁
张洁
李天宏
ZHOU Jianing;ZHANG Jie;LI Tianhong(School of Environment and Energy,Peking University Shenzhen Graduate School,Shenzhen 518055;College of Environmental Sciences and Engineering,Peking University,Beijing 100871)
出处
《北京大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2018年第4期792-800,共9页
Acta Scientiarum Naturalium Universitatis Pekinensis
基金
国家自然科学基金(41071027)资助
关键词
坝上林地
MODIS
随机森林算法
动态监测
forest in Bashang area
MODIS
Random Forest Algorithm
dynamic monitoring
作者简介
通信作者:李天宏,E-mail:litianhong@iee.pku.edu.cn
