机器学习方法在九寨沟县自然保护地植被解译中的应用  

Application of Machine Learning in Vegetation Interpretation of Natural Protected Areas in Jiuzhaigou County

作　　者：付小丽 周文佐[1] 周新尧 李凤[1] FU Xiaoli;ZHOU Wenzuo;ZHOU Xinyao;LI Feng(School of Geographical Sciences,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学地理科学学院,重庆400715

出　　处：《自然保护地》2023年第2期53-65,共13页Natural Protected Areas

基　　金：中国南北过渡带综合科学考察(2017FY100900)。

摘　　要：明确自然保护地的植被类型和空间分布,在改善生态环境质量与维护国家生态安全等方面具有重要参考意义。本文基于Sentinel-2A遥感影像数据,利用随机森林算法和旋转森林算法对九寨沟县自然保护地植被进行分类,并结合植被类型图与坡度、坡向以及高程数据对研究区内各植被类型的空间分布特征进行分析。研究结果表明:1)旋转森林算法分类效果优于随机森林算法,总体精度为85.73%,Kappa系数为0.83;2)九寨沟县植被在阴坡和半阴坡朝向生长较好,各植被类型集中分布于坡度16°~45°范围内,且区域内植被分布垂直地带性明显,海拔由低到高陆续出现的主要植被类型为栽培植被、灌丛、阔叶林、针阔混交林、针叶林以及草地。The clarification of vegetation types and spatial distribution of nature reserves show important reference significance in improving ecological environment quality and maintaining national ecological security.In this paper,based on sentinel-2A remote sensing image data,the random forest algorithm and rotation forest algorithm were used to classify the vegetation in the natural protected areas in Jiuzhaigou county.The spatial distribution characteristics of vegetation types in the study area were analyzed by combining the vegetation type map with slope,aspect,and elevation zones.The results showed that:1)The classification performance of the rotation forest algorithm was better than the random forest algorithm,which overall accuracy is 85.73%and Kappa coefficient is 0.83.2)The vegetation in Jiuzhaigou county grew well on shady slopes and semi-shady slopes,and all vegetation types are concentrated in the slope range from 16°to 45°.The vertical zonal distribution of vegetation in the region is obvious.The main vegetation types that appear successively are cultivated vegetation,shrub,broad-leaved forest,coniferous and broad-leaved mixed forest,coniferous forest,and grassland from low elevation to high elevation.

关 键 词：植被解译 自然保护地 随机森林 旋转森林 植被类型 空间分布 九寨沟县 

分 类 号：TP79[自动化与计算机技术—检测技术与自动化装置] TP181[自动化与计算机技术—控制科学与工程] S719[农业科学—林学]