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Mapping Two Decades of New York State Forest Aboveground Biomass Change Using Remote Sensing
| Content Provider | MDPI |
|---|---|
| Author | Tamiminia, Haifa Salehi, Bahram Mahdianpari, Masoud Beier, Colin M. Johnson, Lucas |
| Copyright Year | 2022 |
| Description | Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, which is a valuable source for historical mapping over large areas. As such, this paper proposed a machine learning-based workflow for historical AGB estimation and its change analysis from 2001 to 2019 for the New York State’s forests using Landsat historical imagery, airborne LiDAR, and forest plot data. As the object-based image analysis (OBIA) is able to incorporate spectral, contextual, and textural features into the regression model, the proposed method utilizes an OBIA approach and a random forest (RF) regression model implemented on the Google Earth Engine (GEE) cloud computing platform. Results demonstrated that there is a considerable decrease of 983.79 × $10^{6}$ Mg/ha in the AGB of deciduous forests from 2001 to 2006, followed by an increase of 618.28 × $10^{6}$ Mg/ha from 2006 to 2011, continued with an increase of 229.12 × $10^{6}$ Mg/ha of deciduous forests from 2011–2016. Finally, the results demonstrated a slight change in AGB from 2016 to 2019. The transferability of the proposed framework provides a practical solution for monitoring forests in other states or even on a national scale. |
| Starting Page | 4097 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14164097 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-21 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Object-based Image Analysis Landsat Imagery Airborne Lidar Change Detection State-wide Mapping |
| Content Type | Text |
| Resource Type | Article |
