Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data |
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| Institution: | 1. Institute of Applied Physics, National Research Council of Italy (IFAC – CNR), Via Madonna del Piano 10, 50019 Florence, Italy;2. Department of Agricultural, Food and Forestry Systems, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Florence, Italy;3. Italian Academy of Forest Sciences, P.zza Edison 11, 50133 Florence, Italy;4. Institute of Biometeorology, National Research Council of Italy (IBIMET – CNR), via Madonna del Piano 10, 50019 Florence, Italy;5. LaMMA Consortium, Via Madonna del Piano 10, 50019 Florence, Italy;1. University of Maryland, Department of Geographical Sciences, College Park, MD 20742, USA;2. Sigma Space Corp., Lanham, MD 20706, USA;3. Code 618, Biospheric Sciences Branch, NASA/Goddard Space Flight Center, Greenbelt, MD 20771, USA;4. Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway;5. Sukachev Institute of Forest, Siberian Branch, Russian Academy of Sciences, Akademgorodok, Krasnoyarsk 660036, Russia |
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| Abstract: | In remote sensing–based forest aboveground biomass (AGB) estimation research, data saturation in Landsat and radar data is well known, but how to reduce this problem for improving AGB estimation has not been fully examined. Different vegetation types have their own species composition and stand structure, thus they have different data saturation values in Landsat or radar data. Optical and radar data also have different characteristics in representing forest stand structures, thus effective use of their features may improve AGB estimation. This research examines the effects of Landsat Thematic Mapper (TM) and ALOS PALSAR L-band data and their integrations in forest AGB estimation of Zhejiang Province, China, and the roles of textural images from both datasets. The linear regression models of AGB were conducted by using (1) Landsat TM alone, (2) ALOS PALSAR data alone, (3) their combination as extra bands, and (4) their data fusion, based on non-stratification and stratification of vegetation types, respectively. The results show that (1) overall, Landsat TM data perform better than PALSAR data, but the latter can produce more accurate estimates for bamboo and shrub, and for forests with AGB values less than 60 Mg/ha; (2) the combination of TM and PALSAR data as extra bands can greatly improve AGB estimation performance, but their fusion using the modified high-pass filter resolution-merging technique cannot; (3) textures are indeed valuable in AGB estimation, especially for forests with complex stand structures such as mixed forests and pine forests with understories of broadleaf species; (4) stratification of vegetation types can improve AGB estimation performance; and (5) the results from the linear regression models are characterized by overestimation and underestimation for the smaller and larger AGB values, respectively, and thus, selecting non-linear models or non-parametric algorithms may be needed in future research. |
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| Keywords: | Aboveground biomass Landsat TM ALOS PALSAR Data saturation Data combination and fusion Stratification |
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