Above-ground biomass estimation in a Mediterranean sparse coppice oak forest using Sentinel-2 data

Authors

  • Fardin Moradi Department of Forestry and Forest Economics, Faculty of Natural Resources, University of Tehran, Karaj, Iran
  • Seyed Mohamad Moein Sadeghi Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov
  • Hadi Beygi Heidarlou Department of Forestry, Faculty of Natural Resources, Urmia University, Urmia, Iran
  • Azade Deljouei Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Faculty of Silviculture and Forest Engineering, Transilvania University of Brasov
  • Erfan Boshkar Natural Resources Department, Faculty of Agriculture, Razi University, Kermanshah, Iran
  • Stelian Alexandru Borz Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements, Transilvania University of Brasov

DOI:

https://doi.org/10.15287/afr.2022.2390

Keywords:

Sentinel-2, Above-ground biomass, Optical satellite data, Machine learning, Modeling, Estimation, Accuracy, Sparse-Coppice

Abstract

Implementing a scheduled and reliable estimation of forest characteristics is important for the sustainable management of forests. This study aimed at evaluating the capability of Sentinel-2 satellite data to estimate above-ground biomass (AGB) in coppice forests of Persian oak (Quercus brantii var. persica) located in Western Iran. To estimate the AGB, field data collection was implemented in 80 square plots (40×40 m, area of 1600 m2). Two diameters of the crown were measured and used to calculate the AGB of each tree based on allometric equations. Then, the performance of satellite data in estimating the AGB was evaluated for the area of study using the field-based AGB (dependent variable) as well as the spectral band values, spectrally-derived vegetation indices (independent variables) and four machine learning (ML) algorithms: Multi-Layer Perceptron Artificial Neural Network (MLPNN), k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Regression (SVR). A five-fold cross-validation was used to verify the effectiveness of models. Examination of the Pearson’s correlation coefficient between AGB and the extracted values showed that IPVI and NDVI vegetation indices had the highest correlation with AGB (r = 0.897). The results indicated that the MLPNN algorithm was the best ML option (RMSE = 1.71 t ha-1; MAE = 1.37 t ha-1; relative RMSE = 24.75%; R2 = 0.87) in estimating the AGB, providing new insights on the capability of remotely sensed-based AGB modeling of sparse Mediterranean forest ecosystems in an area with limited number of field sample plots.

References

Ali I., Greifeneder F., Stamenkovic J., Neumann M., Notarnicola C., 2015. Review of machine learning approaches for biomass and soil moisture retrievals from remote sensing data. Remote Sensing 7(12): 16398-16421. https://doi.org/10.3390/rs71215841Altman N.S., 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician 46: 175–185. https://doi. org/10.2307/2685209Anderson K.E., Glenn N.F., Spaete L.P., Shinneman D.J., Pilliod D.S., Arkle R.S., 2018. Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning. Ecological Indicators 84: 793-802. https://doi. org/10.1016/j.ecolind.2017.09.034Attarod P., Sadeghi S., Pypker T., Bayramzadeh V., 2017. Oak trees decline; a sign of climate variability impacts in the west of Iran. Caspian Journal of Environmental Sciences 15(4): 373-384.Axelsson C., Skidmore A.K., Schlerf M., Fauzi A., Verhoef W., 2013. Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression. International Journal of Remote Sensing, 34: 1724- 1743. https://doi.org/10.1080/01431161.2012.725958Baloloy A., Blanco A., Candido C., Argamosa R., Dumalag J., Dimapilis L., 2018. Estimation of mangrove forest aboveground biomass using multispectral bands, vegetation indices and biophysical variables derived from optical satellite imageries: Rapideye, Planetscope and Sentinel-2. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences 4(3): 29-36. https://doi.org/10.5194/isprs-annals-IV-3-29-2018Barakat A., Khellouk R., El Jazouli A., Touhami F., Nadem S., 2018. Monitoring of forest cover dynamics in eastern area of Béni-Mellal Province using ASTER and Sentinel-2A multispectral data. Geology, Ecology, and Landscapes 2(3): 203-215. https://doi.org/10.1080/24749508.2018.1452478Belgiu M., Drăguţ L., 2016. Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing 114: 24-31. https://doi.org/10.1016/j. isprsjprs.2016.01.011Beygi Heidarlou H., Shafiei A.B., Erfanian M., Tayyebi A., Alijanpour A., 2019. Effects of preservation policy on land use changes in Iranian Northern Zagros forests. Land Use Policy 81: 76-90. https://doi.org/10.1016/j. landusepol.2018.10.036Breiman L., 2001. Random forests. Machine Learning 45(1): 5-32. https://doi.org/10.1023/A:1010933404324Bui D.T., Tuan T.A., Klempe H., Pradhan B., Revhaug I., 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13(2): 361-378. https://doi.org/10.1007/ s10346-015-0557-6Çakir G., Sivrikaya F., Keleş S., 2008. Forest cover change and fragmentation using Landsat data in Maçka State Forest Enterprise in Turkey. Environmental Monitoring and Assessment 137(1): 51-66. https://doi.org/10.1007/ s10661-007-9728-9Calvao T., Palmeirim J., 2004. Mapping Mediterranean scrub with satellite imagery: biomass estimation and spectral behaviour. International Journal of Remote Sensing 25(16): 3113-3126. https://doi.org/10.1080/01431160310001654978Camargo F.F., Sano E.E.,Almeida C.M., Mura J.C.,Almeida, T., 2019. A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2/PALSAR-2 polarimetric images. Remote Sensing 11(13): 1600. https://doi.org/10.3390/rs11131600Castillo J.A.A., Apan A.A., Maraseni T.N., Salmo III S.G., 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing 134: 70-85. https://doi.org/10.1016/j.isprsjprs.2017.10.016Clevers J., De Jong S., Epema G., Van Der Meer F., Bakker W., Skidmore A., 2002. Derivation of the red edge index using the MERIS standard band setting. International Journal of Remote Sensing 23(16): 3169- 3184. https://doi.org/10.1080/01431160110104647Clevers J., Van der Heijden G., Verzakov S., Schaepman M.E., 2007. Estimating grassland biomass using SVM band shaving of hyperspectral data. Photogrammetric Engineering & Remote Sensing 73(10): 1141-1148. https://doi.org/10.14358/PERS.73.10.1141Crippen R.E., 1990. Calculating the vegetation index faster. Remote Sensing of Environments 34(1): 71-73. https://doi.org/10.1016/0034-4257(90)90085-ZDalla Corte A.P., Souza D.V., Rex F.E., Sanquetta C.R., Mohan M., Silva C.A., 2020. Forest inventory with high-density UAV-Lidar: Machine learning approaches for predicting individual tree attributes. Computers and Electronics in Agriculture 179: 105815. https://doi. org/10.1016/j.compag.2020.105815Dash J., Curran, P., 2007. Evaluation of the MERIS terrestrial chlorophyll index (MTCI). Advances in Space Research 39(1): 100-104. https://doi.org/10.1016/j. asr.2006.02.034Daughtry C.S., Walthall C., Kim M., De Colstoun E.B., McMurtrey Iii J., 2000. Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sensing of Environment 74(2): 229-239. https://doi. org/10.1016/S0034-4257(00)00113-9Eisfelder C., Kuenzer C., Dech S., 2012. Derivation of biomass information for semi-arid areas using remote- sensing data. International Journal of Remote Sensing 33(9): 2937-2984. https://doi.org/10.1080/01431161.2011.620034Englhart S., Keuck V., Siegert F., 2011. Modeling aboveground biomass in tropical forests using multi- frequency SAR data—A comparison of methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 5(1): 298-306. https://doi.org/10.1109/JSTARS.2011.2176720Eskandari S., Jaafari M., Oliva P., Ghorbanzadeh O., Blaschke T., 2020. Mapping land cover and tree canopy cover in Zagros forests of Iran:Application of Sentinel-2, Google Earth, and field data. Remote Sensing 12(12): 1912. https://doi.org/10.3390/rs12121912Fathizadeh O., Sadeghi S.M.M., Pazhouhan I., Ghanbari S., Attarod P., Su L., 2021. Spatial variability and optimal number of rain gauges for sampling throughfall under single oak trees during the leafless period. Forests 12(5): 585. https://doi.org/10.3390/f12050585Favero A., Daigneault A., Sohngen B., 2020. Forests: Carbon sequestration, biomass energy, or both?. Science Advances 6(13): 6792. https://doi.org/10.1126/sciadv.aay6792FLAASH., 2009. Atmospheric correction module: QUAC and FLAASH user’s guide. Version, 4, 44.Foody G.M., Cutler M.E., McMorrow J., Pelz D., Tangki H., Boyd D.S., 2001. Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecology and Biogeography 10(4): 379-387. https://doi. or/10.1046/j.1466-822X.2001.00248.xForkuor G., Zoungrana J.B.B., Dimobe K., Ouattara B., Vadrevu K.P., Tondoh J.E., 2020. Above-ground biomass mapping in West African dryland forest using Sentinel-1 and 2 datasets-A case study. Remote Sensing of Environment 236: 111496. https://doi.org/10.1016/j.rse.2019.111496Frampton W.J., Dash J., Watmough G., Milton E.J., 2013. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS Journal of Photogrammetry and Remote Sensing 82: 83- 92. https://doi.org/10.1016/j.isprsjprs.2013.04.007GaoY., Lu D., Li G., Wan G., Chen Q., Liu L., 2018. Comparative analysis of modeling algorithms for forest aboveground biomass estimation in a subtropical region. Remote Sensing 10(4): 627. https://doi.org/10.3390/rs10040627Gasparri N.I., Parmuchi M.G., Bono J., Karszenbaum H., Montenegro C.L., 2010. Assessing multi-temporal Landsat 7 ETM+ images for estimating above-ground biomass in subtropical dry forests of Argentina. Journal of Arid Environments 74(10): 1262-1270. https://doi. org/10.1016/j.jaridenv.2010.04.007Ghosh S.M., Behera M.D., 2018. Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest. Applied Geography 96: 29-40. https://doi.org/10.1016/j. apgeog.2018.05.011Gislason P.O., Benediktsson J.A., Sveinsson J.R., 2006. Random forests for land cover classification. Pattern Recognition Letters 27(4): 294-300. https://doi. org/10.1016/j.patrec.2005.08.011Gitelson A.A., Kaufman Y.J., Merzlyak M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environments 58(3): 289-298. https://doi.org/10.1016/ S0034-4257(96)00072-7Guyot G., Baret F., 1988. Utilisation de la haute resolution spectrale pour suivre l'etat des couverts vegetaux. In Spectral Signatures of Objects in Remote Sensing (Vol. 287, p. 279).Haykin S., 1998. A comprehensive foundation. Neural Networks 2(2004): 41. https://doi.org/10.1142/ S0129065794000372Herrmann I., Pimstein A., Karnieli A., Cohen Y., Alchanatis V., Bonfil D., 2011. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands. Remote Sensing of Environment 115(8): 2141-2151. https://doi. org/10.1016/j.rse.2011.04.018Hirigoyen A., Acosta-Muñoz C., Ariza Salamanca A.J., Varo-Martinez M.Á., Rachid-Casnati C., Franco J., Navarro-Cerrillo R., 2021. A machine learning approach to model leaf area index in Eucalyptus plantations using high-resolution satellite imagery and airborne laser scanner data. Ann. For. Res. 64(2): 165-183. https://doi. org/10.15287/afr.2021.2073Holmgren J., Joyce S., Nilsson M., Olsson H., 2000. Estimating stem volume and basal area in forest compartments by combining satellite image data with field data. Scandinavian Journalof Forest Research 15(1): 103-111. https://doi.org/10.1080/02827580050160538Hu X., Weng Q., 2009. Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment 113(10): 2089-2102. https://doi.org/10.1016/j.rse.2009.05.014Huete A.R., 1988. A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment 25(3): 295- 309. https://doi.org/10.1016/0034-4257(88)90106-XIranmanesh Y., 2013. Assessment on biomass estimation methods and carbon sequestration of Quercus brantii Lindl. Chaharmahal & Bakhtiari forests. PhD Dissertation. Tehran: Tarbiat Modares University, 107.Jordan C.F., 1969. Derivation of leaf‐area index from quality of light on the forest floor. Ecology 50(4): 663- 666. https://doi.org/10.2307/1936256Karlson M., Ostwald M., Reese H., Sanou J., Tankoano B., Mattsson E., 2015. Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest. Remote Sensing 7(8): 10017-10041. https://doi.org/10.3390/rs70810017Kelsey K.C., Neff J.C., 2014. Estimates of aboveground biomass from texture analysis of Landsat imagery. Remote Sensing 6(7): 6407-6422. https://doi.org/10.3390/rs6076407Khan I.A., Khan W.R., Ali A., Nazre M., 2021. Assessment of above-ground biomass in Pakistan forest ecosystem’s carbon pool: A review. Forests 12(5): 586. https://doi. org/10.3390/f12050586Knoke T., Kindu M., Schneider T., Gobakken T., 2021. Inventory of forest attributes to support the integration of non-provisioning ecosystem services and biodiversity into forest planning—from collecting data to providing information. Current Forestry Reports 7(1): 38-58. https://doi.org/10.1007/s40725-021-00138-7Korhonen L., Packalen P., Rautiainen M., 2017. Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index. Remote Sensing of Environment 195: 259-274. https://doi.org/10.1016/j.rse.2017.03.021Kovac M., Gasparini P., Notarangelo M., Rizzo M., Cañellas I., Fernández-de-Uña L., Alberdi I., 2020. Towards a set of national forest inventory indicators to be used for assessing the conservation status of the habitats directive forest habitat types. Journal for Nature Conservation 53: 125747. https://doi.org/10.1016/j. jnc.2019.125747Lamont K., Saintilan N., Kelleway J.J., Mazumder D., Zawadzki A., 2020. Thirty-year repeat measures of mangrove above-and below-ground biomass reveals unexpectedly high carbon sequestration. Ecosystems 23(2): 370-382. https://doi.org/10.1007/s10021-019-00408-3Li 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 Sensing 13(8): 1595. https://doi. org/10.3390/rs13081595Li Y., Li M., Li C., Liu Z., 2020. Forest aboveground biomass estimation using Landsat 8 and Sentinel- 1A data with machine learning algorithms. Scientific Reports 10(1): 1-12. https://doi.org/10.1038/s41598-020-67024-3Liu K., Wang J., Zeng W., Song J., 2017. Comparison and evaluation of three methods for estimating forest above ground biomass using TM and GLAS data. Remote Sensing 9(4): 341. https://doi.org/10.3390/rs9040341Long S., Zeng S., Wang G., 2021. Developing a new model for predicting the diameterdistribution of oak forests using an artificialneural network. Ann. For. Res. 64(2): 3-20. https://doi.org/10.15287/afr.2021.2060Lovynska V., Buchavyi Y., Lakyda P., Sytnyk S., Gritzan Y., Sendziuk R., 2020. Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data. Journal of Forest Science 66(8): 339- 348. https://doi.org/10.17221/28/2020-JFSLuo X., Chen X., Xu L., Myneni R., Zhu Z., 2013. Assessing performance of NDVI and NDVI3g in monitoring leaf unfolding dates of the deciduous broadleaf forest in Northern China. Remote Sensing 5(2): 845-861. https://doi.org/10.3390/rs5020845Marabel M., Alvarez-Taboada F., 2013. Spectroscopic determination of aboveground biomass in grasslands using spectral transformations, support vector machine and partial least squares regression. Sensors 13: 10027- 10051. https://doi.org/10.3390/s130810027Mas J., 2004. Mapping land use/cover in a tropical coastal area using satellite sensor data, GIS and artificial neural networks. Estuarine, Coastal and Shelf Science 59(2): 219-230. https://doi.org/10.1016/j.ecss.2003.08.011Masjedi A., Zhao J., Thompson A.M., Yang K.W., Flatt J.E., Crawford M.M., 2018. Sorghum biomass prediction using UAV-based remote sensing data and crop model simulation. IEEE pp. 7719-7722. https:// doi.org/10.1109/IGARSS.2018.8519034McRoberts R.E., 2008. Using satellite imagery and the k-nearest neighbors technique as a bridge between strategic and management forest inventories. Remote Sensing of Environments 112(5): 2212-2221. https:// doi.org/10.1016/j.rse.2007.07.025Mohamed M., Yusup S., Bustam M.A., 2016. Synthesis of CaO-based sorbent from biomass for CO2 capture in series of calcination-carbonation cycle. Procedia Engineering 148: 78-85. https://doi.org/10.1016/j.proeng.2016.06.438Moradi F., Darvishsefat A.A., Pourrahmati M.R., Deljouei A., Borz S.A., 2022. Estimating aboveground biomass in dense Hyrcanian forests by the use of Sentinel-2 data. Forests 13(1): 104. https://doi.org/10.3390/f13010104Neumann M., Saatchi S.S., Ulander L.M., Fransson J.E., 2012. Assessing performance of L-and P-band polarimetric interferometric SAR data in estimating boreal forest above-ground biomass. IEEE Transactions on Geoscience and Remote Sensing 50(3): 714-726. https://doi.org/10.1109/TGRS.2011.2176133Nuthammachot N., Phairuang W., Wicaksono P., Sayektiningsih T., 2018. Estimating aboveground biomass on private forest using Sentinel-2 imagery. Journal of Sensors https://doi. org/10.1155/2018/6745629Özçelık R., Diamantopoulou M.J., Eker M., Gürlevık N., 2017. Artificial neural network models: an alternative approach for reliable aboveground pine tree biomass prediction. Forest Sciences 63(3): 291-302. https://doi. org/10.5849/FS-16-006Pande C.B., Moharir K.N., Singh S.K., Varade A.M., Elbeltagi A., Khadri S.F.R., Choudhari P., 2021. Estimation of crop and forest biomass resources in a semi-arid region using satellite data and GIS. Journal of the Saudi Society of Agricultural Sciences 20(5): 302- 311. https://doi.org/10.1016/j.jssas.2021.03.002Pandit S., Tsuyuki S., Dube T., 2018. Estimating above- ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel 2 data. Remote Sensing 10(4): 601. https://doi.org/10.3390/rs10040601Pazúr R., Huber N., Weber D., Ginzler C., Price B., 2021. A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data. Earth System Science Data Discussions 14: 295-305. https://doi. org/10.5194/essd-14-295-2022Pham T.D., Le N.N., Ha N.T., Nguyen L.V., Xia J., Yokoya N., 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 Sensing 12(5): 777. https://doi.org/10.3390/rs12050777Pham T.D., Xia, J., Ha N.T., Bui D.T., Le N.N., Tekeuchi W., 2019. A review of remote sensing approaches for monitoring blue carbon ecosystems: Mangroves, seagrassesand salt marshes during 2010–2018. Sensors 19(8): 1933. https://doi.org/10.3390/s19081933Pham T.D., Yoshino K., Le N.N., Bui D.T., 2018. Estimating aboveground biomass of a mangrove plantation on the Northern coast of Vietnam using machine learning techniques with an integration of ALOS-2 PALSAR-2 and Sentinel-2A data. International Journal of Remote Sensing 39(22): 7761-7788. https://doi.org/10.1080/01431161.2018.1471544Pham T.D., Yoshino, K., Bui, D.T., 2017. Biomass estimation of Sonneratia caseolaris (l.) Engler at a coastal area of Hai Phong city (Vietnam) using ALOS-2 PALSAR imagery and GIS-based multi-layer perceptron neural networks. GIScience & Remote Sensing 54(3): 329-353. https://doi.org/10.1080/15481603.2016.1269869Pirotti F., Kutchartt E., Csaplovics E., 2020. Assessment of volume and above-ground biomass in Araucaria forest through satellite images, comparing different methods in the south of Chile. IEEE pp. 579-584. https://10.1109/LAGIRS48042.2020.9165668Pourhashemi M., Sadeghi S.M.M., 2020. A review on ecological causes of oak decline phenomenon in forests of Iran. Ecology of Iranian Forest 8(16): 148-164.Puletti N., Grotti M., Ferrara C., Chianucci F., 2020. Lidar-based estimates of aboveground biomass through ground, aerial, and satellite observation: a case study in a Mediterranean forest. Journal of Applied Remote Sensing 14(4): 044501. https://doi.org/10.1117/1.JRS.14.044501Rodriguez-Galiano V.F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J.P., 2012. An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing 67: 93-104. https://doi.org/10.1016/j. isprsjprs.2011.11.002Ronoud G., Fatehi P., Darvishsefat A.A., Tomppo E., Praks J., Schaepman M.E., 2021. Multi-sensor aboveground biomass estimation in the broadleaved Hyrcanian forest of Iran. Canadian Journal of Remote Sensing 47(6): 818- 834. https://doi.org/10.1080/07038992.2021.1968811Roozitalab M.H., Siadat H., Farshad A., 2018. The soils of Iran. Springer. https://doi.org/10.1007/978-3-319- 69048-3Roujean J.L., Breon F.M., 1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environments 51(3): 375-384. https://doi.org/10.1016/0034-4257(94)00114-3Rouse R.A., 1973. Conformational analysis of saturated heterocycles substituted final ozonides. International Journal of Quantum Chemistry 7(7): 289-294. https:// doi.org/10.1021/ja00792a003Safari A., Sohrabi H., 2020. Integration of synthetic aperture radar and multispectral data for aboveground biomass retrieval in Zagros oak forests, Iran: an attempt on Sentinel imagery. International Journal of Remote Sensing 41(20): 8069-8095. https://doi.org/10.1080/01431161.2020.1771789Safari A., Sohrabi H., Powell S., Shataee S., 2017. A comparative assessment of multi-temporal Landsat 8 and machine learning algorithms for estimating aboveground carbon stock in coppice oak forests. International Journal of Remote Sensing 38(22): 6407- 6432. https://doi.org/10.1080/01431161.2017.1356488Santoro M., Cartus O., Carvalhais N., Rozendaal D., Avitabile V., Araza A., Willcock S., 2021. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth System Science Data 13(8): 3927-3950. https://doi. org/10.5194/essd-13-3927-2021Segarra J., Buchaillot M.L., Araus J.L., Kefauver S.C., 2020. Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy 10(5): 641. https://doi.org/10.3390/ agronomy10050641Shataee S., Kalbi S., Fallah A., Pelz D., 2012. Forest attribute imputation using machine-learning methods and ASTER data: comparison of k-NN, SVR and random forest regression algorithms. International Journal of Remote Sensing 33(19): 6254-6280.https:// doi.org/10.1080/01431161.2012.682661Smola A.J., Schölkopf B., 2004. Atutorial on support vector regression. Statistics and Computing 14(3): 199-222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 Stevens F.R., Gaughan A.E., Linard C., Tatem A.J., 2015. Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data. PloS one 10(2): e0107042. https://doi.org/10.1371/journal.pone.0107042Tian X., Li Z., Su Z., Chen E., van der Tol C., Li X., 2014. Estimating montane forest above-ground biomass in the upper reaches of the Heihe River Basin using Landsat-TM data. International Journal of Remote Sensing 35(21): 7339- 7362. https://doi.org/10.1080/01431161.2014.967888Torabzadeh H., Moradi M., Fatehi P., 2019. Estimating aboveground biomass in Zagors forest, Iran, using sentinel-2 data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 42: 1059-1063. https://doi. org/10.5194/isprs-archives-XLII-4-W18-1059-2019Torre-Tojal L., Bastarrika A., Boyano A., Lopez-Guede J.M., Graña M., 2022. Above-ground biomass estimation from LiDAR data using random forest algorithms. Journal of Computational Science 58: 101517. https:// doi.org/10.1016/j.jocs.2021.101517Tucker C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2): 127-150. https://doi. org/10.1016/0034-4257(79)90013-0Tuominen S., Eerikäinen K., Schibalski A., Haakana M., Lehtonen A., 2010. Mapping biomass variables with a multi-source forest inventory technique. Silva Fennica 44(1): 109-119. https://doi.org/10.14214/sf.458Vafaei S., Soosani J., Adeli K., Fadaei H., Naghavi H., Pham T.D., 2018 Improving accuracy estimation of forest aboveground biomass based on incorporation of ALOS-2 PALSAR-2 and Sentinel-2A imagery and machine learning: A case study of the Hyrcanian forest area (Iran). Remote Sensing 10(2): 172. https://doi. org/10.3390/rs10020172Vashum K.T., Jayakumar S., 2012. Methods to estimate above-ground biomass and carbon stock in natural forests-a review. Journal of Ecosystem & Ecography 2(4): 1-7. http://doi.org/10.4172/2157-7625.1000116Wu C., Shen H., Shen A., Deng J., Gan M., Zhu J., 2016. Comparison of machine-learning methods for above- ground biomass estimation based on Landsat imagery. Journal of Applied Remote Sensing 10(3): 035010. https://doi.org/10.1117/1.JRS.10.035010Wu C., Tao H., Zhai M., Lin Y., Wang K., 2018. Using nonparametric modeling approaches and remote sensing imagery to estimate ecological welfare forest biomass. Journal of Forestry Research 29(1): 151-161. https:// doi.org/10.1007/s11676-017-0404-9Xi Z., Xu H., Xing Y., Gong W., Chen G., Yang S., 2022. Forest canopy height mapping by synergizing ICESat-2, Sentinel-1, Sentinel-2 and topographic information based on machine learning methods. Remote Sensing 14(2): 364. https://doi.org/10.3390/rs14020364Yadav S., Padalia H., Sinha S.K., Srinet R., Chauhan P., 2021. Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest. Remote Sensing Applications: Society and Environment 21: 100462. https://doi.org/10.1016/j. rsase.2020.100462Yao Y., Piao S., Wang T., 2018. Future biomass carbon sequestration capacity of Chinese forests. Science Bulletin 63(17): 1108-1117. https://doi.org/10.1016/j. scib.2018.07.015Yazdani M., Jouibary S.S., Mohammadi J., Maghsoudi Y., 2020. Comparison of different machine learning and regression methods for estimation and mapping of forest stand attributes using ALOS/PALSAR data in complex Hyrcanian forests. Journal of Applied Remote Sensing 14(2): 024509. https://doi.org/10.1117/1. JRS.14.024509Zhang J., Tian H., Wang D., Li H., Mouazen A.M., 2020. A novel approach for estimation of above-ground biomass of sugar beet based on wavelength selection and optimized support vector machine. Remote Sensing 12(4): 620. https://doi.org/10.3390/rs12040620Zhang L., Shao Z., Liu J., Cheng Q., 2019. Deep learning based retrieval of forest aboveground biomass from combined LiDAR and Landsat 8 data. Remote Sensing 11(12): 1459. https://doi.org/10.3390/rs11121459Zhao P., Lu D., Wang G., Wu C., Huang Y., Yu S., 2016. Examining spectral reflectance saturation in Landsat imagery and corresponding solutions to improve forest aboveground biomass estimation. Remote Sensing 8(6): 469. https://doi.org/10.3390/rs8060469Zhu J., Huang Z., Sun H., Wang G., 2017. Mapping forest ecosystem biomass density for Xiangjiang River Basin by combining plot and remote sensing data and comparing spatial extrapolation methods. Remote Sensing 9(3): 241. https://doi.org/10.3390/rs9030241

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2022-07-01

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