Review of Forest Age Datasets and Their Estimation Methods
Forest age is an important characteristic parameter of forest ecosystem,and it is of great significance to accurately estimate carbon storage and carbon sink of forest ecosystem.However,at present,there are few reviews on the age datasets and its estimation algorithm,so this paper systematically summarizes and analyzes the existing forest age datasets,which were divided into global and regional forest age datasets according to the spatial coverage of the data.Then,we analyzed the algorithms of forest age datasets and their advantages and disadvantages.Research has shown that(1)The forest age algorithms mainly include the downscaling statistical method,relationship equations between forest age and forest structure parameters,forest disturbance detection algorithms and machine learning algorithms(such as random forest algorithm).(2)The advantage of the down-scaling algorithm is simple and easy to use,but its main disadvantage is that the saturation of normalized differ-ential vegetation index will underestimate the age of the old-growth forest.The advantage of the relationship model between forest age and forest structure parameters is that the high-precision remote sensing data of tree height or biomass data can reflect the spatial heterogeneity characteristics of forest age,and the forest growth model has a theoretical basis.But the disadvantage is that the results are restricted by the accuracy of forest or tree species distribution map,and the influence factors of forest growth are not considered comprehensively.The advantage of forest disturbance monitoring algorithm is that mature algorithms can be used to detect forest disturbance and infer the change of forest age,but the disadvantage is that the forest age must be combined with other methods to obtain the age of old forest.The advantage of random forest algorithm model is that the model is easy to build,does not need to set specific statistical assumptions and model forms,and doesn't rely on forest type map or tree species distribution map.The disadvantage of this method is that it is restricted by the numbers and the spatial distribution representation of the model training samples.(3)Forest inventory data and remote sensing data are important data for forest age estimation.Forest age has important application prospects in eco-logical model driving,forest management and carbon neutrality.In the future,the research of forest age should strengthen the ground observation of forest age,combine the advantages of remote sensing data,and use a vari-ety of machine learning algorithms to develop high spatial and temporal resolution forest age data.
万方数据
维普
