{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T14:42:18Z","timestamp":1775486538249,"version":"3.50.1"},"reference-count":115,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:00:00Z","timestamp":1681084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Society for the Promotion of Science (JSPS) KAKENHI","award":["JP18K05742"],"award-info":[{"award-number":["JP18K05742"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate estimation of carbon stocks in natural and plantation forests is a prerequisite for the realization of carbon peaking and neutrality. In this study, the potential of optical Sentinel-2A data and a digital elevation model (DEM) to estimate the spatial variation of carbon stocks was investigated in a mountainous warm temperate region in central Japan. Four types of image preprocessing techniques and datasets were used: spectral reflectance, DEM-based topography indices, vegetation indices, and spectral band-based textures. A random forest model combined with 103 field plots as well as remote sensing image parameters was applied to predict and map the 2160 ha University of Tokyo Chiba Forest. Structural equation modeling was used to evaluate the factors driving the spatial distribution of forest carbon stocks. Our study shows that the Sentinel-2A data in combination with topography indices, vegetation indices, and shortwave-infrared (SWIR)-band-based textures resulted in the highest estimation accuracy. The spatial distribution of carbon stocks was successfully mapped, and stand-age- and forest-type-level variations were identified. The SWIR-2-band and topography indices were the most important variables for modeling, while the forest stand age and curvature were the most important determinants of the spatial distribution of carbon stock density. These findings will contribute to more accurate mapping of carbon stocks and improved quantification in different forest types and stand ages.<\/jats:p>","DOI":"10.3390\/rs15081997","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T05:59:33Z","timestamp":1681106373000},"page":"1997","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Identifying Spatial Variation of Carbon Stock in a Warm Temperate Forest in Central Japan Using Sentinel-2 and Digital Elevation Model Data"],"prefix":"10.3390","volume":"15","author":[{"given":"Huiqing","family":"Pei","sequence":"first","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9227-4177","authenticated-orcid":false,"given":"Toshiaki","family":"Owari","sequence":"additional","affiliation":[{"name":"The University of Tokyo Hokkaido Forest, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Furano 079-1563, Japan"}]},{"given":"Satoshi","family":"Tsuyuki","sequence":"additional","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]},{"given":"Takuya","family":"Hiroshima","sequence":"additional","affiliation":[{"name":"Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 113-8657, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1038\/s41558-020-00976-6","article-title":"Global maps of twenty-first century forest carbon fluxes","volume":"11","author":"Harris","year":"2021","journal-title":"Nat. Clim. Chang."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4781","DOI":"10.1038\/s41467-022-32416-8","article-title":"Process-oriented analysis of dominant sources of uncertainty in the land carbon sink","volume":"13","author":"Friedlingstein","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7895","DOI":"10.1038\/s41598-020-64851-2","article-title":"Carbon stock in Japanese forests has been greatly underestimated","volume":"10","author":"Egusa","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"014047","DOI":"10.1088\/1748-9326\/ac45b3","article-title":"Aboveground forest biomass varies across continents, ecological zones and successional stages: Refined IPCC default values for tropical and subtropical forests","volume":"17","author":"Rozendaal","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"GB2004","DOI":"10.1029\/2004GB002253","article-title":"Biomass carbon accumulation by Japan\u2019s forest from 1947 to 1995","volume":"19","author":"Fang","year":"2005","journal-title":"Global Biogeochem. Cycles"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1126\/science.aat1205","article-title":"Tropical forests are a net carbon source based on aboveground measurements of gain and loss","volume":"363","author":"Baccini","year":"2019","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.foreco.2014.04.028","article-title":"Carbon stock dynamics in different vegetation dominated community forests under REDD+: A case from Nepal","volume":"327","author":"Pandey","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_8","unstructured":"Orians, G.H., and Millar, C.I. (2006). IPCC 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use vol 4. Agric. Ecosyst. Environ., 4, Available online: https:\/\/www.ipcc-nggip.iges.or.jp\/public\/2006gl\/vol4.html."},{"key":"ref_9","first-page":"194","article-title":"IPCC 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Agriculture, Forestry and Other Land Use","volume":"4","author":"Domke","year":"2019","journal-title":"Refinement 2006 IPCC Guidel. Natl. Greenh. Gas Invent."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"988","DOI":"10.1126\/science.1201609","article-title":"A Large and Persistent Carbon Sink in the World\u2019s Forests","volume":"333","author":"Walliker","year":"2011","journal-title":"Science"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/s11104-012-1302-8","article-title":"Spatial and temporal patterns of carbon storage from 1992 to 2002 in forest ecosystems in Guangdong, Southern China","volume":"363","author":"Ren","year":"2013","journal-title":"Plant Soil"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.foreco.2018.09.059","article-title":"Spatiotemporal patterns of carbon storage in forest ecosystems in Hunan Province, China","volume":"432","author":"Chen","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.foreco.2019.06.036","article-title":"Estimation of China\u2019s forest stand biomass carbon sequestration based on the continuous biomass expansion factor model and seven forest inventories from 1977 to 2013","volume":"448","author":"Zhao","year":"2019","journal-title":"For. Ecol. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1111\/geb.12125","article-title":"Carbon stock and density of northern boreal and temperate forests","volume":"23","author":"Thurner","year":"2014","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2019","DOI":"10.1111\/gcb.12512","article-title":"Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth","volume":"20","author":"Fang","year":"2014","journal-title":"Glob. Chang. Biol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s40663-019-0179-x","article-title":"Spatiotemporal variations in productivity and water use efficiency across a temperate forest landscape of Northeast China","volume":"6","author":"Li","year":"2019","journal-title":"For. Ecosyst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/S0038-0717(00)00017-1","article-title":"Dynamics of carbon and nitrogen mineralization in relation to stand type, stand age and soil texture in the boreal mixedwood","volume":"32","author":"Brown","year":"2000","journal-title":"Soil Biol. Biochem."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"G02S04","DOI":"10.1029\/2007JG000568","article-title":"Factors influencing spatial pattern in tropical forest clearance and stand age: Implications for carbon storage and species diversity","volume":"113","author":"Helmer","year":"2008","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118252","DOI":"10.1016\/j.foreco.2020.118252","article-title":"Impacts of forest management intensity on carbon accumulation of China\u2019s forest plantations","volume":"472","author":"Yu","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1111\/gcb.14547","article-title":"Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation","volume":"25","author":"Ge","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wai, P., Su, H., and Li, M. (2022). Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms. Remote Sens., 14.","DOI":"10.3390\/rs14092146"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.5194\/isprs-annals-IV-3-29-2018","article-title":"Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices, and biophysical variables derived from opical satellite imageries:Rapideye, Planet Scope and Sentinel-2","volume":"4","author":"Baloloy","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_23","first-page":"104","article-title":"Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan","volume":"96","author":"Bhatti","year":"2023","journal-title":"For. An Int. J. For. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"113232","DOI":"10.1016\/j.rse.2022.113232","article-title":"Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery","volume":"282","author":"David","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, X., Long, J., Zhang, M., Liu, Z., and Lin, H. (2021). Coniferous plantations growing stock volume estimation using advanced remote sensing algorithms and various fused data. Remote Sens., 13.","DOI":"10.3390\/rs13173468"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1080\/17583004.2019.1686931","article-title":"Multi-sensor approach integrating optical and multi-frequency synthetic aperture radar for carbon stock estimation over a tropical deciduous forest in India","volume":"11","author":"Sinha","year":"2020","journal-title":"Carbon Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.rse.2011.12.001","article-title":"Impact of sensor degradation on the MODIS NDVI time series","volume":"119","author":"Wang","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1750-0680-8-1","article-title":"Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage","volume":"8","author":"Wilson","year":"2013","journal-title":"Carbon Balance Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106077","DOI":"10.1016\/j.catena.2022.106077","article-title":"Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon","volume":"212","author":"Minaei","year":"2022","journal-title":"Catena"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111496","DOI":"10.1016\/j.rse.2019.111496","article-title":"Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets - A case study","volume":"236","author":"Forkuor","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2021GL093799","article-title":"Mapping Forest Height and Aboveground Biomass by Integrating ICESat-2, Sentinel-1 and Sentinel-2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India","volume":"48","author":"Nandy","year":"2021","journal-title":"Geophys. Res. Lett."},{"key":"ref_32","first-page":"767","article-title":"The use of fixed\u2013wing UAV photogrammetry with LiDAR DTM to estimate merchantable volume and carbon stock in living biomass over a mixed conifer\u2013broadleaf forest","volume":"73","author":"Jayathunga","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1111\/geb.13549","article-title":"Spatial patterns and driving factors of carbon stocks in mangrove forests on Hainan Island, China","volume":"31","author":"Meng","year":"2022","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.ecoinf.2018.12.010","article-title":"Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam","volume":"50","author":"Dang","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.geoderma.2018.12.037","article-title":"Digital mapping of soil carbon fractions with machine learning","volume":"339","author":"Keskin","year":"2019","journal-title":"Geoderma"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aldrich, C. (2020). Process variable importance analysis by use of random forests in a shapley regression framework. Minerals, 10.","DOI":"10.3390\/min10050420"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pandit, S., Tsuyuki, S., and Dube, T. (2018). Landscape-scale aboveground biomass estimation in buffer zone community forests of Central Nepal: Coupling in situ measurements with Landsat 8 Satellite Data. Remote Sens., 10.","DOI":"10.3390\/rs10111848"},{"key":"ref_39","first-page":"522","article-title":"A forest activity classification method using a geographic information system","volume":"76","author":"ZHENG","year":"1994","journal-title":"J. Japanese For. Soc."},{"key":"ref_40","first-page":"135","article-title":"Geographical Distribution of Forest Types in the Tokyo University Forest in Chiba","volume":"92","author":"Tatsuhara","year":"1994","journal-title":"Bull. Tokyo Univ. For."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/BF02347848","article-title":"Successional change of forest pattern along topographical gradients in warm-temperate mixed forests in Mt Kiyosumi, central Japan","volume":"10","author":"Ozaki","year":"1995","journal-title":"Ecol. Res."},{"key":"ref_42","first-page":"319","article-title":"An integrated management planning system for multiple-use of forests","volume":"78","author":"ZHENG","year":"1967","journal-title":"J. Japanese For. Soc."},{"key":"ref_43","first-page":"1","article-title":"Current situation of natural forest resources in the University of Tokyo Chiba Forest(in Japanese)","volume":"147","author":"Owari","year":"2022","journal-title":"Bull. Tokyo Univ. For."},{"key":"ref_44","unstructured":"Shiraishi, N., Ayako, T., Keiko, I., and Makoto, S. (2004). Estimation of carbon storage and its change in the Tokyo University Forest in Chiba: Comparison between 1995 and 1909. Bull. Tokyo Univ. For., 11\u201334. (In Japanese)."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"34687","DOI":"10.1038\/srep34687","article-title":"Forest biomass carbon stocks and variation in Tibet\u2019s carbon-dense forests from 2001 to 2050","volume":"6","author":"Sun","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1111\/gcb.14484","article-title":"Natural forests exhibit higher carbon sequestration and lower water consumption than planted forests in China","volume":"25","author":"Yu","year":"2019","journal-title":"Glob. Chang. Biol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"145292","DOI":"10.1016\/j.scitotenv.2021.145292","article-title":"Patterns and driving factors of biomass carbon and soil organic carbon stock in the Indian Himalayan region","volume":"770","author":"Ahirwal","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s43247-022-00383-z","article-title":"Rapid remote monitoring reveals spatial and temporal hotspots of carbon loss in Africa\u2019s rainforests","volume":"3","author":"Csillik","year":"2022","journal-title":"Commun. Earth Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"112483","DOI":"10.1016\/j.jenvman.2021.112483","article-title":"Revealing horizontal and vertical variation of soil organic carbon, soil total nitrogen and C:N ratio in subtropical forests of southeastern China","volume":"289","author":"Dong","year":"2021","journal-title":"J. Environ. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1038\/s41586-021-03728-4","article-title":"High aboveground carbon stock of African tropical montane forests","volume":"596","author":"Sullivan","year":"2021","journal-title":"Nature"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15684\/formath.12.1","article-title":"Relationships between the Abundance of Abies sachalinensis Juveniles and Site Conditions in Selection Forests of Central Hokkaido, Japan","volume":"12","author":"Owari","year":"2013","journal-title":"Formath"},{"key":"ref_52","unstructured":"The University of Tokyo Forests, Graduate School of Agricultural and Life Sciences (2022). Education and Research Plan (2021\u20132030) of the University of Tokyo Forests: Part 2 Standing Technical Committee Plans. Misc.Inf. Univ. Tokyo For., 64, 33\u201349."},{"key":"ref_53","unstructured":"The University of Tokyo Forests, Graduate School of Agricultural and Life Sciences (2022). Education and Research Plan (2021\u20132030) of the University of Tokyo Forests Part 3 Regional Forest Plans the University of Tokyo Chiba Forest (The 14th Period). Misc.Inf. Univ. Tokyo For., 64, 53\u2013102."},{"key":"ref_54","unstructured":"Oliver, C., and Larson, B. (1996). Forest Stand Dynamics, Yale School of the Environment Other Publications."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"969","DOI":"10.2307\/2269348","article-title":"Patterns and mechanisms of plant diversity in forested ecosystems: Implications for forest management","volume":"5","author":"Roberts","year":"1995","journal-title":"Ecol. Appl."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1001","DOI":"10.3390\/rs15041001","article-title":"Application of a Novel Multiscale Global Graph Convolutional Neural Network to Improve the Accuracy of Forest Type Classification Using Aerial Photographs","volume":"15","author":"Pei","year":"2023","journal-title":"Remote Sens."},{"key":"ref_57","unstructured":"The University of Tokyo Forests, Graduate School of Agricultural and Life Sciences (1994). University Forest in Chiba. Misc.Inf. Univ. Tokyo For., 32, 9\u201335."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/BF02348413","article-title":"Topographical pattern of the forest vegetation on a river basin in a warm-temperate hilly region, central Japan","volume":"9","author":"Sakai","year":"1994","journal-title":"Ecol. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1080\/13416979.2020.1753280","article-title":"Long observation period improves growth prediction in old Sugi (Cryptomeria japonica) forest plantations","volume":"25","author":"Hiroshima","year":"2020","journal-title":"J. For. Res."},{"key":"ref_60","unstructured":"Ministry of the Environment Japan, Greenhouse Gas Inventory Office of Japan (GIO), and CGERNI (2021). National Greenhouse Gas Inventory Report of Japan in Fiscal Year 2021."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Aranha, T.R.B.T., Martinez, J.M., Souza, E.P., Barros, M.U.G., and Martins, E.S.P.R. (2022). Remote Analysis of the Chlorophyll-a Concentration Using Sentinel-2 MSI Images in a Semiarid Environment in Northeastern Brazil. Water, 14.","DOI":"10.3390\/w14030451"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.rse.2012.05.032","article-title":"An algorithm for detecting trophic status (chlorophyll-a), cyanobacterial-dominance, surface scums and floating vegetation in inland and coastal waters","volume":"124","author":"Matthews","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_63","unstructured":"European Space Agency (2015). Sentinel-2 user handbook. Stand. Doc., 1\u201364. Available online: https:\/\/sentinels.copernicus.eu\/web\/sentinel\/user-guides\/document-library\/-\/asset_publisher\/xlslt4309D5h\/content\/sentinel-2-user-handbook."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Gascon, F., Bouzinac, C., Th\u00e9paut, O., Jung, M., Francesconi, B., Louis, J., Lonjou, V., Lafrance, B., Massera, S., and Gaudel-Vacaresse, A. (2017). Copernicus Sentinel-2A calibration and products validation status. Remote Sens., 9.","DOI":"10.3390\/rs9060584"},{"key":"ref_65","first-page":"247","article-title":"Statistical image texture analysis","volume":"86","author":"Haralick","year":"1986","journal-title":"Handb. Pattern Recognit. Image Process."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105802","DOI":"10.1016\/j.ecolind.2019.105802","article-title":"Measuring continuous landscape patterns with Gray-Level Co-Occurrence Matrix (GLCM) indices: An alternative to patch metrics?","volume":"109","author":"Park","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_68","first-page":"309","article-title":"Monitoring vegetation systems in the Great Plains with ERTS","volume":"351","author":"Rouse","year":"1974","journal-title":"NASA Spec. Publ"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_71","first-page":"1183","article-title":"The Lambertian assumption and Landsat data","volume":"46","author":"Smith","year":"1980","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/esp.3290120107","article-title":"Quantitative analysis of land surface topography","volume":"12","author":"Zevenbergen","year":"1987","journal-title":"Earth Surf. Process. Landforms"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"101","DOI":"10.5194\/hess-10-101-2006","article-title":"On the calculation of the topographic wetness index: Evaluation of different methods based on field observations","volume":"10","author":"Zinko","year":"2006","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"BREIMAN","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_75","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. ofMachine Learn. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1890\/08-1034.1","article-title":"Confirmatory path analysis in a generalized multilevel context","volume":"90","author":"Shipley","year":"2009","journal-title":"Ecology"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1111\/2041-210X.12512","article-title":"piecewiseSEM: Piecewise structural equation modelling in r for ecology, evolution, and systematics","volume":"7","author":"Lefcheck","year":"2016","journal-title":"Methods Ecol. Evol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1038\/s41559-022-01756-5","article-title":"Phylotype diversity within soil fungal functional groups drives ecosystem stability","volume":"6","author":"Liu","year":"2022","journal-title":"Nat. Ecol. Evol."},{"key":"ref_79","unstructured":"Bates, S., Maechler, M., Bolker, B., Walker, S., Christensen, R.H.B., Singmann, H., Dai, B., Scheipl, F., Grothendieck, G., and Green, P. (2022, November 01). Package \u2018lme4\u2019. Available online: https:\/\/cran.r-project.org\/package=lme4."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2019.03.016","article-title":"Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery","volume":"151","author":"Liu","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Chen, L., Wang, Y., Ren, C., Zhang, B., and Wang, Z. (2019). Optimal combination of predictors and algorithms for forest above-ground biomass mapping from Sentinel and SRTM data. Remote Sens., 11.","DOI":"10.3390\/rs11040414"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"100166","DOI":"10.1016\/j.indic.2021.100166","article-title":"Satellite based integrated approaches to modelling spatial carbon stock and carbon sequestration potential of different land uses of Northeast India","volume":"13","author":"Bordoloi","year":"2022","journal-title":"Environ. Sustain. Indic."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Li, C., Zhou, L., and 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.","DOI":"10.3390\/rs13081595"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Jia, B., Guo, W., He, J., Sun, M., Chai, L., Liu, J., and Wang, X. (2022). Topography, Diversity, and Forest Structure Attributes Drive Aboveground Carbon Storage in Different Forest Types in Northeast China. Forests, 13.","DOI":"10.3390\/f13030455"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Mngadi, M., Odindi, J., and Mutanga, O. (2021). The utility of sentinel-2 spectral data in quantifying above-ground carbon stock in an urban reforested landscape. Remote Sens., 13.","DOI":"10.3390\/rs13214281"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.1080\/01431160500181044","article-title":"Nitrogen detection with hyperspectral normalized ratio indices across multiple plant species","volume":"26","author":"Ferwerda","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_88","first-page":"318","article-title":"Performance of vegetation indices from Landsat time series in deforestation monitoring","volume":"52","author":"Schultz","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Anand, A., Pandey, P.C., Petropoulos, G.P., Pavlides, A., Srivastava, P.K., Sharma, J.K., and Malhi, R.K.M. (2020). Use of hyperion for mangrove forest carbon stock assessment in bhitarkanika forest reserve: A contribution towards blue carbon initiative. Remote Sens., 12.","DOI":"10.3390\/rs12040597"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Pham, T.D., Le, N.N., Ha, N.T., Nguyen, L.V., Xia, J., Yokoya, N., To, T.T., Trinh, H.X., Kieu, L.Q., and Takeuchi, W. (2020). Estimating mangrove above-ground biomass using extreme gradient boosting decision trees algorithm with fused sentinel-2 and ALOS-2 PALSAR-2 data in can Gio biosphere reserve, Vietnam. Remote Sens., 12.","DOI":"10.3390\/rs12050777"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Zhang, L., Shao, Z., Liu, J., and Cheng, Q. (2019). Deep learning based retrieval of forest aboveground biomass from combined LiDAR and landsat 8 data. Remote Sens., 11.","DOI":"10.3390\/rs11121459"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Li, H., Kato, T., Hayashi, M., and 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.","DOI":"10.3390\/rs14030468"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"107645","DOI":"10.1016\/j.ecolind.2021.107645","article-title":"Estimation of tree height and aboveground biomass of coniferous forests in North China using stereo ZY-3, multispectral Sentinel-2, and DEM data","volume":"126","author":"Wang","year":"2021","journal-title":"Ecol. Indic."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhao, P., Lu, D., Wang, G., Wu, C., Huang, Y., and Yu, S. (2016). Examining spectral reflectance saturation in landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sens., 8.","DOI":"10.3390\/rs8060469"},{"key":"ref_95","first-page":"1","article-title":"The impact of tree age on biomass growth and carbon accumulation capacity: A retrospective analysis using tree ring data of three tropical tree species grown in natural forests of Suriname","volume":"12","author":"Neupane","year":"2017","journal-title":"PLoS ONE"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1007\/s10584-012-0666-3","article-title":"China\u2019s forest biomass carbon sink based on seven inventories from 1973 to 2008","volume":"118","author":"Zhang","year":"2013","journal-title":"Clim. Change"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2320","DOI":"10.1126\/science.1058629","article-title":"Changes in forest biomass carbon storage in China between 1949 and 1998","volume":"292","author":"Fang","year":"2001","journal-title":"Science"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11676-020-01155-1","article-title":"A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing","volume":"32","author":"Huang","year":"2021","journal-title":"J. For. Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1007\/s13762-015-0750-0","article-title":"A review of radar remote sensing for biomass estimation","volume":"12","author":"Sinha","year":"2015","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Smith, J.E., Heath, L.S., and Jenkins, J.C. (2003). Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests, US Department of Agriculture, Forest Service, Northeastern Research Station. No. 298.","DOI":"10.2737\/NE-GTR-298"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s10584-004-2799-5","article-title":"New estimates of carbon storage and sequestration in China\u2019S forests: Effects of age-class and method on inventory-based carbon estimation","volume":"67","author":"Pan","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0378-1127(03)00146-4","article-title":"Carbon stock estimates for sugi and hinoki forests in Japan","volume":"184","author":"Fukuda","year":"2003","journal-title":"For. Ecol. Manage."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1002\/rse2.203","article-title":"The real potential of current passive satellite data to map aboveground biomass in tropical forests","volume":"7","author":"Jha","year":"2021","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13021-018-0093-5","article-title":"Estimation of forest aboveground biomass and uncertainties by integration of field measurements, airborne LiDAR, and SAR and optical satellite data in Mexico","volume":"13","author":"Urbazaev","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_105","first-page":"259","article-title":"Estimating Timber Stock of Ehime Prefecture, Japan using Airborne Laser Profiling","volume":"13","author":"Tsuzuki","year":"2008","journal-title":"J. For. Plan."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"2","DOI":"10.17159\/sajs.2020\/6339","article-title":"Estimating soil organic carbon stocks under commercial forestry using topo-climate variables in KwaZulu-Natal, South Africa","volume":"116","author":"Odebiri","year":"2020","journal-title":"S. Afr. J. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.foreco.2014.07.013","article-title":"Effects of topography and thickness of organic layer on productivity of black spruce boreal forests of the canadian clay belt region","volume":"330","author":"Laamrani","year":"2014","journal-title":"For. Ecol. Manage."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.iswcr.2015.10.002","article-title":"Relationship between topography and the distribution of understory vegetation in a Pinus massoniana forest in Southern China","volume":"3","author":"Wang","year":"2015","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"119482","DOI":"10.1016\/j.foreco.2021.119482","article-title":"Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients","volume":"496","author":"Pourbabaei","year":"2021","journal-title":"For. Ecol. Manage."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.still.2014.11.008","article-title":"Influence of ridge height, row grade, and field slope on soil erosion in contour ridging systems under seepage conditions","volume":"147","author":"Liu","year":"2015","journal-title":"Soil Tillage Res."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S1474-7065(03)00004-4","article-title":"Hyperspectral remote sensing of salt marsh vegetation, morphology and soil topography","volume":"28","author":"Silvestri","year":"2003","journal-title":"Phys. Chem. Earth"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1038\/nature07771","article-title":"Increasing carbon storage in intact African tropical forests","volume":"457","author":"Lewis","year":"2009","journal-title":"Nature"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"104737","DOI":"10.1016\/j.cageo.2021.104737","article-title":"Aboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data\u2014The superiority of deep learning over a semi-empirical model","volume":"150","author":"Ghosh","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_114","first-page":"102621","article-title":"Assessing Protected Area\u2019s Carbon Stocks and Ecological Structure at Regional-Scale Using Gedi Lidar","volume":"78","author":"Liang","year":"2021","journal-title":"SSRN Electron. J."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Xu, J., Zeng, F., Liu, W., and Takahashi, T. (2022). Damage Detection and Level Classification of Roof Damage after Typhoon Faxai Based on Aerial Photos and Deep Learning. Appl. Sci., 12.","DOI":"10.20944\/preprints202204.0240.v1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1997\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:13:10Z","timestamp":1760123590000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/1997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,10]]},"references-count":115,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15081997"],"URL":"https:\/\/doi.org\/10.3390\/rs15081997","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,10]]}}}