Estimation Method Of Forest Canopy Density Based On High Resolution Remote Sensing Data

Forest canopy density is an important parameter for studying forest ecosystem and understanding forest resources.Traditional field measurement methods are inefficient and can only obtain some representative data in a small range,which is not conducive to the study of spatial distribution and change of forest canopy density in a large area or region.With the rapid development of high resolution image technology,it can realize all-day,all-weather and real-time observation in earth observation.Therefore,the use of remote sensing technology provides a powerful support and effective way for remote sensing estimation of forest canopy density in a large area or region,which has attracted much attention in recent years and is the development trend of forest canopy density estimation.In order to estimate forest canopy density and analyze its correlation with remote sensing image factors,this paper takes Bakeshiying Town and Changshanyu Town in Luanping County of Hebei Province as the research area,and uses high resolution GF-1 data,combined with SRTM DEM data topographic factors,to invert forest canopy density in this area.Firstly,four commonly used fusion algorithms are used to fuse the preprocessed multi-band and panchromatic images.The results show that NNDiffuse Pan Sharpening fusion is the best.Then,on the basis of systematically analyzing and evaluating the related literatures of forest canopy density at home and abroad,14 factors,such as red band,near infrared band,brightness,greenness and yellowness,were selected as independent variables to participate in the construction of three models: multi-variable stepwise regression(MSR),Random Forest(RF)and Cubist.Estimate.The experimental results show that the results of random forest and Cubist algorithm based on machine learning are better than those of traditional multiple stepwise regression algorithm.The evaluation indexes show that Cubist regression algorithm has the best fitting effect in this research area.Multivariate stepwise regression(MSR)algorithm is mature and simple,and widely used,but the model is unstable,the inversion accuracy is not high,and it is not suitable for estimating canopy density in large areas;Random Forest(RF)can process large data quickly,but the situation of overestimation and underestimation is serious,which increases the estimation error of canopy density;Cubist is very successful in predicting continuous values,and uses the nearest neighbor sample to adjust the rules.The prediction results show that the model is stable and can get more accurate prediction values,but it takes a long time to calculate.