Liu Yuqing, Yan Feng, Chen Junhan, et al. Differences in Biomass Estimation in a Feldspathic Sandstone Area by Landsat 8 and Sentinel-2 Data[J]. Bulletin of Soiland Water Conservation, 2022, 42(4): 188-194.
DOI:
Liu Yuqing, Yan Feng, Chen Junhan, et al. Differences in Biomass Estimation in a Feldspathic Sandstone Area by Landsat 8 and Sentinel-2 Data[J]. Bulletin of Soiland Water Conservation, 2022, 42(4): 188-194. DOI: 10.13961/j.cnki.stbctb.2022.04.024.
Differences in Biomass Estimation in a Feldspathic Sandstone Area by Landsat 8 and Sentinel-2 Data
[Objective] Differences in above ground biomass (AGB) estimation based on remote sensing images with different spatial resolutions were analyzed
and a basis for the selection of different spatial resolution images for remote sensing estimation of AGB in desert ecosystems was provided.[Methods] Based on a ground-based AGB survey
the AGB-MSAVI statistical model was established by combining Landsat 8 and Sentinel-2 images to estimate AGB in a feldspathic sandstone area by remote sensing
and to analyze the differences between the two estimates in different vegetation coverage areas (high
medium
and low).[Results] Both Landsat 8 and Sentinel-2 images could estimate AGB well
and the spatial distributions of AGB estimation results were consistent. The mean relative errors for the AGB estimation models based on Landsat 8 and Sentinel-2 data were 13.41% and 11.42%
respectively
and the accuracy of AGB estimation based on Sentinel-2 data was relatively higher. There were some differences in AGB estimated by Sentinel-2 and Landsat 8 data in different vegetation coverage areas.[Conclusion] In the low and high vegetation coverage areas
the differences between AGB estimated by Sentinel-2 and Landsat 8 data were relatively small. In contrast
in the medium vegetation coverage area
the spatial heterogeneity was relatively significant
the remote sensing data were constrained by the spatial resolution
and the differences between AGB estimated by the two images were relatively large. The high spatial resolution remote sensing images were effective for improving AGB estimation accuracy.
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references
Wang Jing, Feng Liang, Palmer P I, et al. Large Chinese land carbon sink estimated from atmospheric carbon dioxide data[J]. Nature, 2020,586(7831):720-723.
Houghton R A, Skole D L, Nobre C A, et al. Annual fluxes of carbon from deforestation and regrowth in the Brazilian Amazon[J]. Nature, 2000,403(6767):301-304.
Fang J, Chen A, Peng C, et al. Changes in forest biomass carbon storage in China between 1949 and 1998[J]. Science, 2001,292(5525):2320-2322.
Yan Feng, Wu Bo, Wang Yanjiao. Estimating spatiotemporal patterns of aboveground biomass using Landsat TM and MODIS images in the Mu Us sandy land, China[J]. Agricultural and Forest Meteorology, 2015,200(1):119-128.
Zandler H, Brenning A, Samimi C. Quantifying dwarf shrub biomass in an arid environment:Comparing empirical methods in a high dimensional setting[J]. Remote Sensing of Environment, 2015,158(1):140-155.
Yadav B, Nandy S. Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques[J]. Environmental Monitoring and Assessment 2015,187(5):4551-4563.
Battude M, Birar B A, Morin D, et al. Estimating maize biomass and yield over large areas using high spatial and temporal resolution Sentinel-2 like remote sensing data[J]. Remote Sensing of Environment, 2016,184(1):668-681.
William J F, Jadunandan D, Gary W, et al. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,82(1):83-92.
Anderson G L, Hanson J D, Haas R H. Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands[J]. Remote Sensing of Environment, 1993,45(2):165-175.
Katja B, Uta D, Eva S, et al. Quantification of aboveground rangeland productivity and anthropogenic degradation on the Arabian Peninsula using Landsat imagery and field inventory data[J]. Remote Sensing of Environment, 2010,115(2):465-474.
Qi J, Chehbouni A, Huete A, et al. A modified soil adjusted vegetation index[J]. Remote Sensing of Environment, 1994,48(2):119-126.
Yan Feng, Wu Bo, Wang Yanjiao. Estimating above-ground biomass in Mu Us sandy land using Landsat spectral derived vegetation indices over the past 30 years[J]. Journal of Arid Land, 2013,5(4):521-530.