Abstract
Medium spatial resolution biomass is a crucial link from the plot to regional and global scales. Although remote-sensing data-based methods have become a primary approach in estimating forest above ground biomass (AGB), many difficulties remain in data resources and prediction approaches. Each kind of sensor type and prediction method has its own merits and limitations. To select the proper estimation algorithm and remote-sensing data source, several forest AGB models were developed using different remote-sensing data sources (Geoscience Laser Altimeter System (GLAS) data and Thematic Mapper (TM) data) and 108 field measurements. Three modeling methods (stepwise regression (SR), support vector regression (SVR) and random forest (RF)) were used to estimate forest AGB over the Daxing’anling Mountains in northeastern China. The results of models using different datasets and three approaches were compared. The random forest AGB model using Landsat5/TM as input data was shown the acceptable modeling accuracy (R2 = 0.95 RMSE = 17.73 Mg/ha) and it was also shown to estimate AGB reliably by cross validation (R2 = 0.71 RMSE = 39.60 Mg/ha). The results also indicated that adding GLAS data significantly improved AGB predictions for the SVR and SR AGB models. In the case of the RF AGB models, including GLAS data no longer led to significant improvement. Finally, a forest biomass map with spatial resolution of 30 m over the Daxing'anling Mountains was generated using the obtained optimal model.
Highlights
Forest ecosystems, which are the largest carbon sinks on land, account for about 80% of terrestrial biosphere carbon storage and 40% of underground carbon storage [1] and play a pivotal role in mitigating climate change [2,3]
In order to calculate theProcessing waveform metrics associated with above ground biomass (AGB), it was necessary to pre-process
Table showed that the root mean square error (RMSE) and signal-to-noise ratio (SNR) of the wavelet transform were than those of the Gaussian filter, 4) showed that the RMSE and SNR of the wavelet transform were better than those of the Gaussian but that the smoothness was not significantly different
Summary
Forest ecosystems, which are the largest carbon sinks on land, account for about 80% of terrestrial biosphere carbon storage and 40% of underground carbon storage [1] and play a pivotal role in mitigating climate change [2,3]. Estimating forest biomass accurately is the basis for terrestrial carbon cycle analysis, and the spatial distribution of forest biomass at regional scale can reveal spatial variations in carbon sequestration, which can provide a basis for rational carbon reduction targets and forest management programs. Biomass consists of above ground biomass (AGB) and below ground biomass (BGB) [3,4]. Due to the difficulty of collecting and calculating BGB, researchers have focused mainly on AGB, as did this paper.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
