Estimation of Pinus densata carbon storage based on Landsat time series data and ATC filtering algorithm
Time-series remote sensing data have applications in the accurate estimation of forest carbon storage,providing data support for a deeper understanding of the carbon cycle process of forest ecosystems,scientific management,and protection of forest resources.However,considerable noise is present in remote sensing time-series data.To enhance the accuracy of estimating carbon storage,a filtering algorithm was developed to reduce the interference of noise in Landsat time-series data from high-altitude areas.The algorithm was developed based on the continuous inventory fixed plot data of the National Forest Inventory in the Shangri-La area for the years 1987,1992,1997,2002,2007,2012,and 2017,and Landsat time-series images from 1987 to 2017.In this study,the Adaptive Topography Convolution(ATC)algorithm was developed using Python.This algorithm considers the effects of terrain factors on image quality,removes noise from the image while retaining as much details as possible,and uses Savitzky-Golay filtering and median filtering to filter Landsat time-series data.By using the random forest regression(RFR)algorithm,a carbon storage estimation model for Pinus densata in Shangri La City was constructed.The optimal estimation model was selected to invert and map the carbon storage of P.densata in 1987,1992,1997,2002,2007,2012,and 2017.The results showed the following.(1)In accordance with the mean absolute error of the image quality evaluation index,image quality is best after filtering with the ATC algorithm.In addition,the peak signal-to-noise ratio value of the time-series data filtered by the ATC algorithm is relatively high,indicating an improvement in data quality.(2)When using the RFR algorithm,the filtered data showed higher fitting and prediction accuracy than the original data.(3)When using the RFR algorithm,the time-series data filtered based on the ATC algorithm exhibit the best estimation accuracy when selecting the top 10 feature factors with contribution and the feature factors with cumulative contribution reaching 70％for modeling.(4)The estimation model constructed based on the ATC-filtered time series and remote sensing features(number of features 10)and the random forest algorithm exhibits the best performance in research,with a determination coefficient(R2)of 0.867,a root mean square error(RMSE)of 15.527/(t/hm2),a prediction accuracy(P)of 73.54％,and a relative RMSE(rRMSE)of 41.14％.5)The carbon storage inversion results of Shangri La Pinus densata based on the optimal estimation model are as follows:6.77 million tons(1987),7.16 million tons(1992),7.22 million tons(1997),4.36 million tons(2002),7.20 million tons(2007),7.11 million tons(2012),and 7.53 million tons(2017).From the inversion results,the carbon storage of Shangri-La P.densata exhibited a gradually increasing trend during the period of 1987-1997.However,carbon storage exhibited a significant fluctuating trend from 2002 to 2017.The use of the ATC filtering method can effectively remove noise in the time-series images of high-altitude areas,reducing the uncertainty of time-series images and improving the accuracy of the remote sensing estimation of P.densata carbon storage.
Landsat time seriesfilterPinus densatacarbon storageATC
万方数据
维普
