Versions
- 2023-05-24 (2)
- 2023-05-24 (1)
Keywords
How to Cite
Abstract
Forest biomass is the energy base and material source of forest ecosystem cycle, which is expressed by the dry matter weight or energy accumulated per unit area and time. It is also an important index to study the structure and function of forest ecosystem, and is the premise and basis of scientific management of forest ecosystem. In this paper, the concept, development history, and research status of forest biomass were reviewed. The sampling methods, model construction methods of forest biomass survey were analyzed. Finally, the research prospects and summaries of key technologies of forest biomass inventory and monitoring were put forward.
References
Austin, J. M., Mackey, B. G., & Van Niel, K. P. (2003). Estimating forest biomass using satellite radar: an exploratory study in a temperate Australian eucalyptus forest. Forest Ecology and Management, 176(1–3), 575–583. https://doi.org/10.1016/s0378-1127(02)00314-6
Bagaram, M. B., & Tóth, S. F. (2020). Multistage sample average approximation for harvest scheduling under climate uncertainty. Forests, 11(11), 1230. https://doi.org/10.3390/f11111230
Basuki, T. M., van Laake, P. E., Skidmore, A. K., & Hussin, Y. A. (2009). Allometric equations for estimating the above-ground biomass in tropical lowland dipterocarp forests. Forest Ecology and Management, 257(8), 1684–1694. https://doi.org/10.1016/j.foreco.2009.01.027
Bi, H., Turner, J., & Lambert, M. J. (2004). Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees, 18(4), 467–479. https://doi.org/10.1007/s00468-004-0333-z
Boysen, J. (1910). Studier over skovtraernes forhold til lyset Tidsskr. F Skorvaessen, 22, 11–16.
Brandeis, T. J., Delaney, M., Parresol, B. R., & Royer, L. (2006). Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume. Forest Ecology and Management, 233(1), 133–142. https://doi.org/10.1016/j.foreco.2006.06.012
Carreiras, J. M. B., Vasconcelos, M. J., & Lucas, R. M. (2012). Understanding the relationship between aboveground biomass and ALOS PALSAR data in the forests of Guinea-Bissau (West Africa). Remote Sensing of Environment, 121, 426–442. https://doi.org/10.1016/j.rse.2012.02.012
Case, B. S., & Hall, R. J. (2008). Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada. Canadian Journal of Forest Research, 38(4), 878–889. https://doi.org/10.1139/x07-212
Chirici, G., Barbati, A., Corona, P., Marchetti, M., Travaglini, D., Maselli, F., & Bertini, R. (2008). Non-parametric and parametric methods using satellite images for estimating growing stock volume in alpine and Mediterranean forest ecosystems. Remote Sensing of Environment, 112(5), 2686–2700. https://doi.org/10.1016/j.rse.2008.01.002
Chojnacky, D. C. (2002). Allometric scaling theory applied to FIA biomass estimation. U.S. Department of Agriculture, Forest Service, North Central Research Station. https://www.nrs.fs.usda.gov/pubs/gtr/gtr_nc230/gtr_nc230_096.pdf
de Wit, H. A., Palosuo, T., Hylen, G., & Liski, J. (2006). A carbon budget of forest biomass and soils in southeast Norway calculated using a widely applicable method. Forest Ecology and Management, 225(1–3), 15–26. https://doi.org/10.1016/j.foreco.2005.12.023
Dong, J., Kaufmann, R. K., Myneni, R. B., Tucker, C. J., Kauppi, P. E., Liski, J., Buermann, W., Alexeyev, V., & Hughes, M. K. (2003). Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. Remote Sensing of Environment, 84(3), 393–410. https://doi.org/10.1016/s0034-4257(02)00130-x
Dong, L., & Li, F. (2016). Comparison of three stand-level biomass estimation methods. Chinese Journal of Applied Ecology, 27(12), 3862–3870. https://doi.org/10.13287/j.1001-9332.201612.030
Ebermayer, E. (1876). Die gesammte lehre der waldstreu mit rücksicht auf die chemische statik des waldbaues. Unter zugrundlegung der in den Königl. Staatsforsten bayerns angestellten untersuchungen. Springer. https://doi.org/10.1007/978-3-642-91491-1
Edgar, C. B., Westfall, J. A., Klockow, P. A., Vogel, J. G., & Moore, G. W. (2019). Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA. Forest Ecology and Management, 437, 27–40. https://doi.org/10.1016/j.foreco.2019.01.027
Englhart, S., Keuck, V., & Siegert, F. (2011). Aboveground biomass retrieval in tropical forests—The potential of combined X- and L-band SAR data use. Remote Sensing of Environment, 115(5), 1260–1271. https://doi.org/10.1016/j.rse.2011.01.008
Fang, J., Chen, A., Peng, C., Zhao, S., & Ci, L. (2001). Changes in forest biomass carbon storage in China between 1949 and 1998. Science, 292(5525), 2320–2322. https://doi.org/10.1126/science.1058629
Fang, J., Wang, G., Liu, G., & Xu, S. (1998). Forest biomass of China: An estimate based on the biomass-volume relationship. Ecological Applications, 8(4), 1084–1091.
Fehrmann, L., Lehtonen, A., Kleinn, C., & Tomppo, E. (2008). Comparison of linear and mixed-effect regression models and a knearest neighbour approach for estimation of single-tree biomass. Canadian Journal of Forest Research, 38(1), 1–9. https://doi.org/10.1139/x07-119
Foody, G. M., Boyd, D. S., & Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions. Remote Sensing of Environment, 85(4), 463–474. https://doi.org/10.1016/s0034-4257(03)00039-7
Fu, L., Zeng, W., Tang, S., Guo, Q., Li, Z., & Cheng, X. (2012). Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales—A case study for Masson pine in Southern China. Journal of Forest Science, 58(3), 101–115. https://doi.org/10.13275/j.cnki.lykxyj.2011.03.011
Gao, H., Dong, L., & Li, F. (2017). Modeling variation in crown profile with tree status and cardinal directions for planted larix olgensis henry trees in northeast. Forests, 8(5), 139. https://doi.org/10.3390/f8050139
Gao, Y., & Gao, C. (2018). Forest biomass extraction based on ZY-3 remote sensing image. Beijing Surveying and Mapping, 32(10), 1186–1191. https://doi.org/10.19580/j.cnki.1007-3000.2018.10.017
Giese, L. A. B., Aust, W. M., Kolka, R. K., & Trettin, C. C. (2003). Biomass and carbon pools of disturbed riparian forests. Forest Ecology and Management, 180(1–3), 493–508. https://doi.org/10.1016/s0378-1127(02)00644-8
Good, N. M., Paterson, M., Brack, C., & Mengersen, K. (2001). Estimating tree component biomass using variable probability sampling methods. Journal of Agricultural Biological and Environmental Statistics, 6(2), 258–267. https://doi.org/10.1198/108571101750524599
Hawbaker, T. J., Keuler, N. S., Lesak, A. A., Gobakken, T., Contrucci, K., & Radeloff, V. C. (2009). Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design. Journal of Geophysical Research-Biogeosciences, 114. https://doi.org/10.1029/2008jg000870
Hero, J. M., Castley, J. G., Butler, S. A., & Lollback, G. W. (2013). Biomass estimation within an Australian eucalypt forest: Mesoscale spatial arrangement and the influence of sampling intensity. Forest Ecology and Management, 310, 547–554. https://doi.org/10.1016/j.foreco.2013.08.062
Hetzer, J., Huth, A., Wiegand, T., Dobner, H. J., & Fischer, R. (2020). An analysis of forest biomass sampling strategies across scales. Biogeosciences, 17(6), 1673–1683. https://doi.org/10.5194/bg-17-1673-2020
Hill, A., Mandallaz, D., & Langshausen, J. (2018). A double-sampling extension of the German national forest inventory for design—based small area estimation on forest district levels. Remote Sensing, 10(7), 1052. https://doi.org/10.3390/rs10071052
Holmberg, H., & Lundevaller, E. H. (2015). A test for robust detection of residual spatial autocorrelation with application to mortality rates in Sweden. Spatial Statistics, 14, 365–381. https://doi.org/10.1016/j.spasta.2015.07.001
Hou, Z., Domke, G. M., Russell, M. B., Coulston, J. W., Nelson, M. D., Xu, Q., & McRoberts, R. E. (2021). Updating annual state- and county-level forest inventory estimates with data assimilation and FIA data. Forest Ecology and Management, 483, 118777. https://doi.org/10.1016/j.foreco.2020.118777
Hua, W., Qiu, Y., Xu, B., Jiang, X., & Qiu, T. (2014). Estimated forest carbon sequestration of the Fujian province based on growth and biomass model. Journal of Southwest Forestry University, 34(6), 35–43.
Huang, Y. (2018). Estimating forest carbon storage of talin forest farm with forest inventory data, northeast China. Journal of North-East Forestry University, 46(5), 12–16,37. https://doi.org/10.13759/j.cnki.dlxb.2018.05.003
Hyde, P., Nelson, R., Kimes, D., & Levine, E. (2007). Exploring LIDAR-RaDAR synergy - predicting aboveground biomass in a southwestern ponderosa pine forest using LiDAR, SAR and InSAR. Remote Sensing of Environment, 106(1), 28–38. https://doi.org/10.1016/j.rse.2006.07.017
Jenkins, J., Chojnacky, D. C., Heath, L. S., & Birdsey, R. A. (2003). National scale biomass estimators for United States tree species. Forest Science, 49(1), 12–35.
Jiang, C., Wang, J., & Cao, Z. (2009). A review of geo-spatial sampling theory. Acta Geographica Sinica, 64(3), 368–380.
Jin, L., & Zhao, R. (2001). Determination method of optimum quadrat size for simple random sampling of pupa population of Pinus tabulaeformis. Liaoning Forestry Science and Technology, (2), 42–44.
Kauffman, J. B., Steele, M. D., Cummings, D. L., & Jaramillo, V. J. (2003). Biomass dynamics associated with deforestation, fire, and, conversion to cattle pasture in a Mexican tropical dry forest. Forest Ecology and Management, 176(1–3), 1–12. https://doi.org/10.1016/s0378-1127(02)00227-x
Kauppi, P. E., Mielikäinen, K., & Kuusela, K. (1992). Biomass and carbon budget of European forests, 1971 to 1990. Science, 256(5053), 70–74. https://doi.org/10.1126/science.256.5053.70
Kitterge, J. (1944). Estimation of amount of foliage of trees and shrubs. J. Forest, 42, 905–912.
Kleinn, C., Magnussen, S., Nolke, N., Magdon, P., Alvarez-Gonzalez, J. G., Fehrmann, L., & Perez-Cruzado, C. (2020). A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland of field inventory estimation of aboveground biomass through an alternative view on plot biomass. Forest Ecosystems, 7(1), 57. https://doi.org/10.1186/s40663-020-00268-7
Labrecque, S., Fournier, R. A., Luther, J. E., & Piercey, D. (2006). A comparison of four methods to map biomass from Landsat-TM and inventory data in western Newfoundland. Forest Ecology and Management, 226(1–3), 129–144. https://doi.org/10.1016/j.foreco.2006.01.030
Lefsky, M. A., Harding, D. J., Keller, M., Cohen, W. B., Carabajal, C. C., Del Bom Espirito-Santo, F., Hunter, M. O., & de Oliveira, R. C., Jr. (2005). Estimates of forest canopy height and aboveground biomass using ICESat. Geophysical Research Letters, 32(22). https://doi.org/10.1029/2005gl023971
Lehtonen, A., Mäkipää, R., Heikkinen, J., Sievänen, R., & Liski, J. (2004). Application of factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests. Forest Ecology and Management, 188(1–3), 211–224.
Lei, Y., & Tang, S. (2007). Application of adaptive cluster sampling in multi-resource inventory. Scientia Silvae Sinicae, 43(11), 132–137.
Li, C., Chen, Q., & Tang, B. (2009). Large-scale forest resources monitoring by means of spatial sampling of hi-resolution remote sensing images. Forest Resources Management, 2, 106–110.
Li, J. (2000). Research on equal generalization of unequal probability problem in sampling. Statistical Research, 17(4), 54–57. https://doi.org/10.19343/j.cnki.11-1302/c.2000.04.011
Li, Y., Chen, Z., Lei, J., Chen, X., Yang, Q., & Wu, T. (2019). Study on spatial balance sampling of forest resources survey in Haikou. Forest Resources Management, (2), 47. https://doi.org/10.13466/j.cnki.lyzygl.2019.02.007
Lin, G., Wen, X., & She, G. (2009). Selection and analysis of auxiliary factors using sampling method of sub-compartment. Journal of Nanjing Forestry University (Natural Sciences Edition), 33(1), 121–123.
Liu, H. (2001). Sampling method with remote sensing for monitoring of cultivated land changes on large scale. Nongye Gongcheng Xuebao (Transactions of the Chinese Society of Agricultural Engineering), 17(2), 168–171.
Liu, L. (2016). Inversion of forest carbon density in Changning country based on GF-1 remote sensing image [Master’s thesis, Central South University of Forestry and Technology].
Lu, D. (2005). Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon. International Journal of Remote Sensing, 26(12), 2509–2525. https://doi.org/10.1080/01431160500142145
Lucas, R. M., Cronin, N., Lee, A., Moghaddam, M., Witte, C., & Tickle, P. (2006). Empirical relationships between AIRSAR backscatter and LiDAR-derived Forest biomass, Queensland, Australia. Remote Sensing of Environment, 100(3), 407–425. https://doi.org/10.1016/j.rse.2005.10.019
Luo, Y., Zhang, X., Wang, X., Zhu, J., Hou, Z., & Zhang, Z. (2009). Forest biomass estimation methods and their prospects. Scientia Silvae Sinicae, 45(8), 129–134. https://doi.org/10.3321/j.issn:1001-7488.2009.08.023
McRoberts, R. E. (2001). Imputation and model-based updating techniques for annual forest inventories. Forest Science, 47(3), 322–330.
McRoberts, R. E., Naesset, E., & Gobakken, T. (2013). Inference for lidar-assisted estimation of forest growing stock volume. Remote Sensing of Environment, 128, 268–275. https://doi.org/10.1016/j.rse.2012.10.007
Meng, F., Xu, S., & Yang, X. (1995). Application of random sampling survey in non-productive consumption survey of forest resources. Forest Resources Management, (4), 35–37.
Montes, N., Gauquelin, T., Badri, W., Bertaudiere, V., & Zaoui, E. H. (2000). A non-destructive method for estimating above-ground forest biomass in threatened woodlands. Forest Ecology and Management, 130(1–3), 37–46. https://doi.org/10.1016/s0378-1127(99)00188-7
Montesano, P. M., Rosette, J., Sun, G., North, P., Nelson, R. F., Dubayah, R. O., Ranson, K. J., & Kharuk, V. (2015). The uncertainty of biomass estimates from modeled ICESat-2 returns across a boreal forest gradient. Remote Sensing of Environment, 158, 95–109. https://doi.org/10.1016/j.rse.2014.10.029
Muukkonen, P. (2006). Forest inventory-based large-scale forest biomass and carbon budget assessment: new enhanced methods and use of remote sensing for verification. Dissertationes Forestales, 2006(30). https://doi.org/10.14214/df.30
Nascimento, H. E. M., & Laurance, W. F. (2002). Total aboveground biomass in central Amazonian rainforests: a landscape-scale study. Forest Ecology and Management, 168(1–3), 311–321. https://doi.org/10.1016/s0378-1127(01)00749-6
Ou, G., Wang, J., Xiao, Y., & Xu, H. (2014). Modeling individual biomass of Pinus Kesaya var. langbianensis natural forests by geographically weighted regression. Forest Research, 27(2), 213–218. https://doi.org/10.13275/j.cnki.lykxyj.2014.02.012
Parresol, B. R. (1999). Assessing tree and stand biomass: A review with examples and critical comparisons. Forest Science, 45(4), 573–593.
Peng, N. (1998). Discussion on sampling technology with unequal probability. Statistical Research, 15(3), 3–5.
Perez-Cruzado, C., Kleinn, C., Magdon, P., Alvarez-Gonzalez, J. G., Magnussen, S., Fehrmann, L., & Noelke, N. (2021). The horizontal distribution of branch biomass in European beech: A model based on measurements and tls based proxies. Re-mote Sensing, 13(5), 1041. https://doi.org/10.3390/rs13051041
Poudel, K. P., Temesgen, H., & Gray, A. N. (2015). Evaluation of sampling strategies to estimate crown biomass. Forest Ecosystems, 2. https://doi.org/10.1186/s40663-014-0025-0
Qin, L., Zhang, M., & Zhong, S. (2017). Model uncertainty in forest biomass estimation. Acta Ecologica Sinica, 37(23), 7912–7919. https://doi.org/10.5846/stxb201609281973
Shi, J. (2012). The study on spatial distributions and adaptive cluster sampling for Dongzhaigang mangrove in Hainan [Doctoral dissertation, Beijing Forestry University].
Shi, J., Lei, Y., & Zhao, T. (2009). Progress in sampling technology and methodology in forest inventory. Forest Research, 22(1), 101–108.
Steininger, M. K. (2000). Satellite estimation of tropical secondary forest above-ground biomass: data from Brazil and Bolivia. International Journal of Remote Sensing, 21(6–7), 1139–1157. https://doi.org/10.1080/014311600210119
Sterba, S. K. (2009). Alternative model-based and design-based frameworks for inference from samples to populations: From polarization to integration. Multivariate Behavioral Research, 44(6), 711–740. https://doi.org/10.1080/00273170903333574
Suganuma, H., Abe, Y., Taniguchi, M., Tanouchi, H., Utsugi, H., Kojima, T., & Yamada, K. (2006). Stand biomass estimation method by canopy coverage for application to remote sensing in an and area of Western Australia. Forest Ecology and Manage-ment, 222(1–3), 75–87. https://doi.org/10.1016/j.foreco.2005.10.014
Tang, S., Zhang, H., & Xu, H. (2000). Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinicae, 36(S1), 19–27.
Thompson, S. K. (1991). Adaptive cluster sampling: Designs with primary and secondary units. Biometrics, 47(3), 1103. https://doi.org/10.2307/2532662
Tuominen, S., Eerikainen, K., Schibalski, A., Haakana, M., & Lehtonen, A. (2010). Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica, 44(1), 109–119, 458. https://doi.org/10.14214/sf.458
Turner, D. A., Koerper, G. J., Harmon, M. E., & Lee, J. E. (1995). A carbon budget for forests of the conterminous United States. Ecological Applications, 5(2), 421–436. https://doi.org/10.2307/1942033
Vallet, P., Dhote, J. F., Le Moguedec, G., Ravart, M., & Pignard, G. (2006). Development of total aboveground volume equations for seven important forest tree species in France. Forest Ecology and Management, 229(1–3), 98–110. https://doi.org/10.1016/j.foreco.2006.03.013
Wang, H., Ouchi, K., Watanabe, M., Shimada, M., Tadono, T., Rosenqvist, A., Romshoo, S. A., Matsuoka, M., Moriyama, T., & Uratsuka, S. (2006). In search of the statistical properties of high-resolution polarimetric sar data for the measurements of forest biomass beyond the rcs saturation limits. IEEE Geoscience and Remote Sensing Letters, 3(4), 495–499. https://doi.org/10.1109/lgrs.2006.878299
Wirasatriya, A., Pribadi, R., Iryanthony, S. B., Maslukah, L., Sugianto, D. N., Helmi, M., Ananta, R. R., Adi, N. S., Kepel, T. L., Ati, R. N. A., Kusumaningtyas, M. A., Suwa, R., Ray, R., Nakamura, T., & Nadaoka, K. (2022). Mangrove above-ground bio-mass and carbon stock in the karimunjawa-kemujan islands estimated from unmanned aerial vehicle-imagery. Sustainabil-ity, 14(2), 706. https://doi.org/10.3390/su14020706
Wu, H., & Xu, H. (2021). A review of the application of sampling techniques in forest biomass inventory. Journal of Southwest Forestry University (Natural Science), 41(3), 183–188. https://doi.org/10.11929/j.swfu.202007059
Wu, H., Xu, H., Tian, X., Zhang, W., & Lu, C. (2023). Multistage sampling and optimization for forest volume inventory based on spatial autocorrelation analysis. Forests, 14(2), 250. https://doi.org/10.3390/f14020250
Wu, Q., Yang, B., Pei, Z., & Wang, F. (2004). Influence of small features on crop area estimation at a using remote sensing and a doubler sampling method. Transactions of the Chinese Society of Agricultural Engineering, 20(3), 130–133.
Xiao, Y. (2004). Application of “3S” technology and sampling techniques to dynamic monitoring of forest resources. Journal of Southwest Forestry University, 24(2), 60–64.
Xu, H., & Zhang, H. (2002). Study on forest biomass model. Yunnan Science and Technology Press.
Xu, X., & Cao, M. (2006). An analysis of the applications of remote sensing method to the forest biomass estimation. Geo-Information Science, 8(4), 122–128.
Xu, X., Du, H., Zhou, G., Ge, H., Shi, Y., Zhou, Y., Fan, W., & Fan, W. (2011). Estimation of aboveground carbon stock of Moso bamboo (Phyllostachys heterocycla var. pubescens) forest with a Landsat Thematic Mapper image. International Journal of Remote Sensing, 32(5), 1431–1448. https://doi.org/10.1080/01431160903551389
Yang, D. (1993). Selection of the best sampling control method for the total stock of forest resource secondary survey. Anhui Forest-ry Science and Technology, (3), 34–36.
Yu, P. (1974). Simple sampling survey method of firewood forest volume. Forest Resources Management, (3), 31–36.
Yu, Y., Pan, Y., Yang, X., & Fan, W. (2022). Spatial scale effect and correction of forest aboveground biomass estimation using re-mote sensing. Remote Sensing, 14(12), 2828. https://doi.org/10.3390/rs14122828
Zeng, P., Zhang, W., Li, Y., Shi, J., & Wang, Z. (2022). Forest total and component Above-Ground Biomass (AGB) estimation through C- and L-band polarimetric SAR Data. Forests, 13(3), 442. https://doi.org/10.3390/f13030442
Zeng, W., Chen, X., Pu, Y., Li, M., & Tang, S. (2018). Comparison of different methods for estimating forest biomass and carbon storage based on national forest inventory data. Forest Research, 31(1), 66–71. https://doi.org/10.13275/j.cnki.lykxyj.2018.01.008
Zeng, W., Luo, Q., & Peng, C. (1995). Study on efficiency of two-stage cluster sampling in forest inventory. Forest Research, (5), 483–488.
Zeng, W., & Tang, S. (2010). Using measurement error modeling method to establish compatible single-tree biomass equations sys-tem. Forest Research, 23(6), 797–803. https://doi.org/10.13275/j.cnki.lykxyj.2010.06.004
Zeng, W., & Tang, S. (2012). Modeling compatible single-tree aboveground biomass equations for masson pine (Pinus massoniana) in southern China. Journal of Forestry Research, 23(4), 593–598. https://doi.org/10.1007/s11676-012-0299-4
Zhang, X. (2000). Introduction to statistical learning theory and support vector machines. Acta Automatica Sinica, 26(1), 32–42. https://doi.org/10.16383/j.aas.2000.01.005
Zhao, J., Zhao, L., Chen, E., Li, Z., Xu, K., & Ding, X. (2022). An improved generalized hierarchical estimation framework with geostatistics for mapping forest parameters and its uncertainty: a case study of forest canopy height. Remote Sensing, 14(3), 568. https://doi.org/10.3390/rs14030568
Zhou, C., & Sun, Q. (2004). Causes and correction methods of sample structural deviation in hierarchical multi-stage unequal probability sampling. Journal of Statistics and Information, 19(6), 16–18. https://doi.org/10.3969/j.issn.1007-3116.2004.06.004
Zhu, Y., Feng, Z., Lu, J., & Liu, J. (2020). Estimation of forest biomass in Beijing (China) using multisource remote sensing and for-est inventory data. Forests, 11(2), 163. https://doi.org/10.3390/f11020163
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2023 Heng Wu, Hui Xu