920
Views
2
CrossRef citations to date
0
Altmetric
Research Article

Using multisource satellite products to estimate forest aboveground biomass in Oita prefecture: a novel approach with improved accuracy and computational efficiency

, ORCID Icon, &
Pages 1-20 | Received 28 Jun 2022, Accepted 04 Oct 2022, Published online: 20 Oct 2022

References

  • Agency JF. 2020. Annual Report on Forest and Forestry in Japan. In 15. Ministry of Agriculture, Forestry and Fisheries, Japan.
  • Ahmed OS, Franklin SE, Wulder MA, White JC. 2015. Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm. ISPRS J Photogrammetr Remote Sens. 101:89–101.
  • Amini J, Sumantyo JTS. 2009. Employing a method on SAR and optical images for forest biomass estimation. IEEE Trans Geosci Remote Sens. 47(12):4020–4026.
  • Belgiu M, Drăguţ L. 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogrammetr Remote Sens. 114:24–31.
  • Birth GS, McVey GR. 1968. Measuring the color of growing turf with a reflectance spectrophotometer 1. Agronomy J. 60(6):640–643.
  • Bojinski S, Verstraete M, Peterson TC, Richter C, Simmons A, Zemp M. 2014. The concept of essential climate variables in support of climate research, applications, and policy. Bull Am Meteorol Soc. 95(9):1431–1443.
  • Bonan GB. 2008. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science. 320(5882):1444–1449.
  • Breiman L. 2001. Random forests. Machine Learning. 45(1):5–32.
  • Brown S, Lugo AE. 1984. Biomass of tropical forests: a new estimate based on forest volumes. Science. 223(4642):1290–1293.
  • Campbell BM. 2009. Beyond Copenhagen: REDD+, agriculture, adaptation strategies and poverty. Global Environmental Chang. 19(4):397–9.
  • Chan JC-W, Paelinckx D. 2008. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ. 112(6):2999–3011.
  • Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, Duque A, Eid T, Fearnside PM, Goodman RC, et al. 2014. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 20(10):3177–3190.
  • Chojnacky DC, Heath LS, Jenkins JC. 2014. Updated generalized biomass equations for North American tree species. Forestry. 87(1):129–151.
  • Cloude S. 2007. The dual polarization entropy/alpha decomposition: A PALSAR case study. Sci Appl SAR Polarimetry and Polarimetric Interferometry. 644:2.
  • Corcoran JM, Knight JF, Gallant AL. 2013. Influence of multi-source and multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in Northern Minnesota. Remote Sens. 5(7):3212–3238.
  • Dang ATN, Nandy S, Srinet R, Luong NV, Ghosh S, Senthil Kumar A. 2019. Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecol Inform. 50:24–32.
  • Darst BF, Malecki KC, Engelman CD. 2018. Using recursive feature elimination in random forest to account for correlated variables in high dimensional data. BMC Genet. 19(Suppl 1):65.
  • Fang J, Guo Z, Hu H, Kato T, Muraoka H, Son Y. 2014. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob Chang Biol. 20(6):2019–2030.
  • Forkuor G, Benewinde Zoungrana J-B, Dimobe K, Ouattara B, Vadrevu KP, Tondoh JE. 2020. Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study. Remote Sens Environ. 236:111496.
  • Ghosh SM, Behera MD. 2018. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Appl Geogr. 96:29–40.
  • Gitelson AA, Gritz Y, Merzlyak MN. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J Plant Physiol. 160(3):271–282.
  • Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sens Environ. 58(3):289–298.
  • Gitelson AA, Merzlyak MN. 1998. Remote sensing of chlorophyll concentration in higher plant leaves. Adv Space Res. 22(5):689–692.
  • Government, Oita Prefectural. 2020. Oita prefecture forestry figures. In: Oita Prefectural Government.
  • Gregorutti B, Michel B, Saint-Pierre P. 2017. Correlation and variable importance in random forests. Stat Comput. 27(3):659–678.
  • Hall-Beyer M. 2017a. "GLCM texture: A tutorial v. 3.0 March 2017."
  • Hall-Beyer M. 2017b. Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales. Int J Remote Sens. 38(5):1312–1338.
  • Haralick RM, Shanmugam K, Dinstein IH. 1973. Textural features for image classification. IEEE Trans Syst, Man, Cybern. SMC-3(6):610–621.
  • Hayashi M, Motohka T, Sawada Y. 2019. Aboveground biomass mapping using ALOS-2/PALSAR-2 time-series images for Borneo’s forest. IEEE J Sel Top Appl Earth Observations Remote Sens. 12(12):5167–5177.
  • Huete AR. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens Environ. 25(3):295–309.
  • Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ. 83(1-2):195–213.
  • Huggannavar V, Shetty A. 2020. Biomass estimation using synergy of ALOS-PALSAR and landsat data in tropical forests of Brazil. Applications of Geomatics in Civil Engineering. Singapore: Springer; p. 593–603.
  • Imhoff ML. 1995. Radar backscatter and biomass saturation: Ramifications for global biomass inventory. IEEE Trans Geosci Remote Sensing. 33(2):511–518.
  • Institute, Forestry and Forest Products Research. 2004. "Report on program for emergent development of forest carbon stocks dataset: fiscal year 2003." In: Forestry and Forest Products Research Institute.
  • Jaiswal JK, Samikannu R. 2017. Application of random forest algorithm on feature subset selection and classification and regression. Paper presented at the 2017 world congress on computing and communication technologies (WCCCT).
  • JAXA. 2021. High-resolution land cover classification maps (HRLULC). edited by JAXA.
  • JAXA. 2022. Global 25 m Resolution PALSAR-2 Mosaic (Ver.2.1.1), 5.
  • Kim Y, Jackson T, Bindlish R, Lee H, Hong S. 2011. Radar vegetation index for estimating the vegetation water content of rice and soybean. IEEE Geosci Remote Sens Lett. 9(4):564–568.
  • Kindermann G, McCallum I, Fritz S, Obersteiner M. 2008. A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn. 42(3):387–396.
  • Lee JS, Jurkevich L, Dewaele P, Wambacq P, Oosterlinck A. 1994. Speckle filtering of synthetic aperture radar images: A review. Remote Sens Rev. 8(4):313–340.
  • Lehtonen A, Mäkipää R, Heikkinen J, Sievänen R, Liski J. 2004. Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecol Manage. 188(1–3):211–224.
  • Liao Z, He B, Quan X. 2020. Potential of texture from SAR tomographic images for forest aboveground biomass estimation. Int J Appl Earth Observ Geoinform. 88:102049.
  • Li H, Kato T, Hayashi M, Wu L. 2022. Estimation of forest aboveground biomass of two major conifers in Ibaraki Prefecture, Japan, from PALSAR-2 and sentinel-2 data. Remote Sens. 14(3):468.
  • Li C, Zhou L, Xu W. 2021. Estimating aboveground biomass using Sentinel-2 MSI data and ensemble algorithms for grassland in the Shengjin Lake Wetland, China. Remote Sens. 13(8):1595.
  • Lu D. 2006. The potential and challenge of remote sensing‐based biomass estimation. Int J Remote Sens. 27(7):1297–1328.
  • Lu D, Chen Q, Wang G, Liu L, Li G, Moran E. 2016. A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems. Int J Digital Earth. 9(1):63–105.
  • Ma J, Xiao X, Qin Y, Chen B, Hu Y, Li X, Zhao B. 2017. Estimating aboveground biomass of broadleaf, needleleaf, and mixed forests in Northeastern China through analysis of 25-m ALOS/PALSAR mosaic data. Forest Ecol Manage. 389:199–210.
  • Matsushita K, Yoshida S. 1998. Analysis of the resent situation and problems in forestry statistics in Japan. J Forest Econ. 44(3):7–13.
  • Ministry of the Environment. 2021. Japan Greenhouse Gas Inventory Office of Japan (GIO), CGER, NIES. National Greenhouse Gas Inventory Report of JAPAN.
  • Misra A, Balaji R. 2017. Simple approaches to oil spill detection using sentinel application platform (SNAP)-ocean application tools and texture analysis: a comparative study. J Indian Soc Remote Sens. 45(6):1065–1075.
  • Mutanga O, Adam E, Cho MA. 2012. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int J Appl Earth Observ Geoinform. 18:399–406.
  • PASCO Co., Ltd. 2021. Report on Oita Prefectural Aerial Laser Surveying and Forest Resource Analysis Project: Part 2. (in Japanese).
  • Peregon A, Yamagata Y. 2013. The use of ALOS/PALSAR backscatter to estimate above-ground forest biomass: A case study in Western Siberia. Remote Sens Environ. 137:139–146.
  • Pullanagari R, Kereszturi G, Yule I. 2018. Integrating airborne hyperspectral, topographic, and soil data for estimating pasture quality using recursive feature elimination with random forest regression. Remote Sens. 10(7):1117.
  • Rodríguez-Veiga P, Quegan S, Carreiras J, Persson HJ, Fransson JE, Hoscilo A, Ziółkowski D, Stereńczak K, Lohberger S, Stängel M, et al. 2019. Forest biomass retrieval approaches from earth observation in different biomes. Int J Appl Earth Observ Geoinform. 77:53–68.
  • Rouse J, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication. 351(1974):309.
  • Shimada M, Itoh T, Motooka T, Watanabe M, Shiraishi T, Thapa R, Lucas R. 2014. New global forest/non-forest maps from ALOS PALSAR data (2007–2010). Remote Sens Environ. 155:13–31.
  • Sripada RP. 2005. Determining in-season nitrogen requirements for corn using aerial color-infrared photography. North Carolina State University.
  • Sripada RP, Heiniger RW, White JG, Meijer AD. 2006. Aerial color infrared photography for determining early in‐season nitrogen requirements in corn. Agronomy J. 98(4):968–977.
  • Tang R, Zhao Y, Lin H. 2021. Spatio-temporal variation characteristics of aboveground biomass in the headwater of the Yellow River based on machine learning. Remote Sens. 13(17):3404.
  • Tariq A, Shu H, Li Q, Altan O, Khan MR, Baqa MF, Lu L. 2021. Quantitative analysis of forest fires in Southeastern Australia using SAR data. Remote Sens. 13(12):2386.
  • Tariq A, Shu H, Siddiqui S, Mousa BG, Munir I, Nasri A, Waqas H, Lu L, Baqa MF. 2021. Forest fire monitoring using spatial-statistical and Geo-spatial analysis of factors determining forest fire in Margalla Hills, Islamabad, Pakistan. Geomatics, Nat Hazards and Risk. 12(1):1212–1233.
  • Tariq A, Shu H, Siddiqui S, Munir I, Sharifi A, Li Q, Lu L. 2022. Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods. J For Res. 33(1):183–194.
  • Tian X, Su Z, Chen E, Li Z, van der Tol C, Guo J, He Q. 2012. Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area. Int J Appl Earth Observ Geoinform. 14(1):160–168.
  • Tucker CJ. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ. 8(2):127–150.
  • Urbazaev M, Thiel C, Cremer F, Dubayah R, Migliavacca M, Reichstein M, Schmullius C. 2018. Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico. Carbon Balance Manag. 13(1):5.
  • Wang Y, Zhang X, Guo Z. 2021. Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data. Ecol Indicators. 126:107645.
  • Yu Y, Saatchi S. 2016. Sensitivity of L-Band SAR backscatter to aboveground biomass of global forests. Remote Sens. 8(6):522.
  • Zhang Y, Liang S, Yang L. 2019. A review of regional and global gridded forest biomass datasets. Remote Sens. 11(23):2744.
  • Zhao P, Lu D, Wang G, Liu L, Li D, Zhu J, Yu S. 2016. Forest aboveground biomass estimation in Zhejiang Province using the integration of Landsat TM and ALOS PALSAR data. Int J Appl Earth Observ Geoinform. 53:1–15.