摘要
应用高分1号(GF-1)全色和多光谱(PMS)影像和k-最邻近(k-NN)方法进行县域尺度的森林蓄积量估测,探讨GF-1 PMS影像以及k-NN方法估测森林蓄积量的适用性。以北京市延庆区森林资源二类调查数据为基础数据,森林蓄积量为研究对象,基于国产GF-1 PMS影像数据提取植被指数,采用k-NN法构建森林蓄积量估测模型,并引入偏最小二乘回归法予以比较,选出最优估测方法对全区森林蓄积量进行反演。结果显示:偏最小二乘回归法估测的森林蓄积量均方根误差为21.90 m^3·hm^(-2),相对均方根误差为27.5%,偏差为17.23 m^3·hm^(-2)。基于k-NN方法的森林蓄积量估测的均方根误差为12.80 m^3·hm^(-2),相对均方根误差为16.0%,偏差为15.02 m^3·hm^(-2)。与官方公布的全区森林蓄积量进行对比,结果显示:基于k-NN法反演的全区森林蓄积量统计结果(245.98万m^3,估测精度为86.0%)要好于偏最小二乘回归法(266.22万m^3,估测精度为76.6%)。最后生成了全区森林蓄积量空间分布图。
To determine the applicability of GF-1 satellite panchromatic and multispectral(PMS)images and the k-NN(k-nearest neighbor)method for estimating forest stock volume(FSV)at the county scale,FSV data of the Forest Management Inventory in Yanqing District of Beijing was taken as the object.Vegetation indices were extracted from domestic GF-1 PMS images with the FSV estimation model then constructed first using the k-NN method and next the Partial Least Squares Regression(PLSR)method to select an optimal model.Then the optimal estimation method was used to determine FSV for the whole county.Results showed that the Root Mean Square Error of FSV estimated by the partial least squares regression method was 21.90 m^3·hm^-2,and the relative RMSE was 27.5%with a bias of 17.23 m^3·hm^-2.The RMSE for the FSV estimation based on the k-NN method was 12.80 m^3·hm^-2 having a relative RMSE of 16.0%with a bias of 15.02 m^3·hm^-2.These two sets of results,compared to the official announcement of Yanqing’s FSV,showed that the FSV estimation based on the k-NN method(2.459 8 million m^3 with an estimation precision of 86.0%)was better than the PLSR method(2.662 2 million m^3 with an estimation precision of 76.6%).Finally,a spatial distribution map of FSV for the whole area was generated.[Ch,5 fig.5 tab.37 ref.]
作者
王海宾
彭道黎
高秀会
李文芳
WANG Haibin;PENG Daoli;GAO Xiuhui;LI Wenfang(College of Forestry,Beijing Forestry University,Beijing 100083,China;Institute of Telecommunication Satellite,China Academy of Space Technology,Beijing 100094,China;Forestry Station of Daxing District,Beijing 102600,China)
出处
《浙江农林大学学报》
CAS
CSCD
北大核心
2018年第6期1070-1078,共9页
Journal of Zhejiang A&F University
基金
国家林业局引进国际先进林业科学技术项目(2015-4-32)
国家重点林业工程监测技术示范推广项目([2015]02号)
