基于MODIS-EVI2与集成学习的森林火烧迹地面积预测  

Prediction of Forest Burned Area based on MODIS-EVI2 and Ensemble Learning

作　　者：冯俊辰 董昊 韩鹏 李远斌 刘靖宇 丁云鸿 Junchen FENG;Hao DONG;Peng HAN;Yuanbin LI;Jingyu LIU;Yunhong DING(School of Computer Science and Information Engineering,Harbin Normal University,Harbin 150025,China)

机构地区：[1]哈尔滨师范大学计算机科学与信息工程学院,黑龙江哈尔滨150025

出　　处：《遥感技术与应用》2024年第5期1261-1270,共10页Remote Sensing Technology and Application

基　　金：黑龙江省自然科学基金项目(LH2020A019)。

摘　　要：在林火救援中,根据火灾早期阶段预测最终燃烧面积,可有效指导火灾救援。然而,以往研究采用归一化差值植被指数(Normalized Difference Vegetation Index,NDVI)作为输入指标,其对土壤反射率敏感,数据噪声大。因此两波段增强植被指数(Two-band Enhanced Vegetation Index,EVI2)被使用以准确预测野火过火面积。此外,针对单一机器学习预测算法抗干扰能力差的问题,一种基于堆叠泛化(Stacking)集成学习的Stacking-XRSK模型被提出。结果表明:使用EVI2使模型R^(2)较NDVI提高6.05%,MAE和MSE分别降低0.88%和0.41%。相比于单一模型,使用Stacking-XRSK模型的R^(2)最高,高出范围在2.8%~11.06%之间,MAE、MSE和AOC最低。验证了利用EVI2代替NDVI预测火烧迹地面积的可行性和准确性,同时表明Stacking模型能在充分发挥单一基模型优势的基础上提高模型的泛化能力,为森林火灾安全管理与及时扑救提供科学的参考。In forest fire rescue,predicting the final burning area based on the early stages of the fire can effectively guide fire rescue.However,previous studies have used Normalized Difference Vegetation Index(NDVI)as an input indicator,which is sensitive to soil reflectance and has high data noise.Therefore,the Two-band Enhanced Vegetation Index(EVI2)is used to accurately predict the area burned by wildfires.In addition,to address the issue of poor anti-interference ability of a single machine learning prediction algorithm,a Stacking-XRSK model based on stacking ensemble learning is proposed.The results showed that using EVI2 increased by 6.05%compared to NDVI,while reducing MAE and MSE by 0.88%and 0.41%,respectively.Compared with the single model,the Stacking-XRSK model has the highest,ranging from 2.8%to 11.06%,and MAE,MSE,and AOC are the lowest.The feasibility and accuracy of using EVI2 instead of NDVI to predict the area of burnt areas have been verified.At the same time,the Stacking model can improve its generalization ability while fully leveraging the advantages of a single base model.This study provides scientific reference for forest fire safety management and timely firefighting.

关 键 词：森林火烧迹地面积 两波段增强植被指数 预测模型 集成学习 机器学习 

分 类 号：S762[农业科学—森林保护学] TP79[农业科学—林学]