{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T18:46:19Z","timestamp":1767984379458,"version":"3.49.0"},"reference-count":72,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T00:00:00Z","timestamp":1712188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Scientific Research Fund Project of Yunnan Provincial Education Department","award":["2023Y0732"],"award-info":[{"award-number":["2023Y0732"]}]},{"name":"Education Talent of Xingdian Talent Support Program of Yunnan Province, China","award":["2023Y0732"],"award-info":[{"award-number":["2023Y0732"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 \u00d7 7 perform best for AGB sensitivity, followed by 5 \u00d7 5, with 3 \u00d7 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.<\/jats:p>","DOI":"10.3390\/rs16071276","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T06:57:34Z","timestamp":1712213854000},"page":"1276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna"],"prefix":"10.3390","volume":"16","author":[{"given":"Yong","family":"Wu","sequence":"first","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-6690","authenticated-orcid":false,"given":"Guanglong","family":"Ou","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Tengfei","family":"Lu","sequence":"additional","affiliation":[{"name":"Yunnan Institute of Forest Inventory and Planning, Kunming 650051, China"}]},{"given":"Tianbao","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Xiaoli","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Zihao","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Zhibo","family":"Yu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Binbing","family":"Guo","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Er","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Zihang","family":"Feng","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9885-3014","authenticated-orcid":false,"given":"Hongbin","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2125-6104","authenticated-orcid":false,"given":"Chi","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming 650233, China"}]},{"given":"Leiguang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650224, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9588-4931","authenticated-orcid":false,"given":"Weiheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of State Administration of Forestry and Grassland on Biodiversity Conservation in Southwest China, Southwest Forestry University, Kunming 650233, China"},{"name":"Institute of Big Data and Artificial Intelligence, Southwest Forestry University, Kunming 650224, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"120701","DOI":"10.1016\/j.foreco.2022.120701","article-title":"The size of clearings for charcoal production in miombo woodlands affects soil hydrological properties and soil organic carbon","volume":"529","author":"Lulandala","year":"2023","journal-title":"For. Ecol. Manag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1111\/geb.12364","article-title":"Diversity enhances carbon storage in tropical forests","volume":"24","author":"Poorter","year":"2015","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1080\/17538947.2014.990526","article-title":"A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems","volume":"9","author":"Lu","year":"2016","journal-title":"Int. J. Digit. Earth"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ou, G., Lv, Y., Xu, H., and Wang, G. (2019). Improving forest aboveground biomass estimation of Pinus densata forest in Yunnan of Southwest China by spatial regression using Landsat 8 images. Remote Sens., 11.","DOI":"10.3390\/rs11232750"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tang, J., Liu, Y., Li, L., Liu, Y., Wu, Y., Xu, H., and Ou, G. (2022). Enhancing aboveground biomass estimation for three pinus forests in yunnan, SW China, using landsat 8. Remote Sens., 14.","DOI":"10.3390\/rs14184589"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Vafaei, S., Soosani, J., Adeli, K., Fadaei, H., Naghavi, H., Pham, T.D., and Tien Bui, 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 Sens., 10.","DOI":"10.3390\/rs10020172"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1088","DOI":"10.1080\/10106049.2019.1568586","article-title":"Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets","volume":"35","author":"Mansaray","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_8","first-page":"303","article-title":"Potential of Landsat-8 spectral indices to estimate forest biomass","volume":"3","author":"Imran","year":"2018","journal-title":"Int. J. Hum. Cap. Urban Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, C., Li, Y., and Li, M. (2019). Improving forest aboveground biomass (AGB) estimation by incorporating crown density and using landsat 8 OLI images of a subtropical forest in Western Hunan in Central China. Forests, 10.","DOI":"10.3390\/f10020104"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bousbaa, M., Htitiou, A., Boudhar, A., Eljabiri, Y., Elyoussfi, H., Bouamri, H., Ouatiki, H., and Chehbouni, A. (2022). High-resolution monitoring of the snow cover on the Moroccan Atlas through the spatio-temporal fusion of Landsat and Sentinel-2 images. Remote Sens., 14.","DOI":"10.3390\/rs14225814"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Pandit, S., Tsuyuki, S., and Dube, T. (2018). Estimating above-ground biomass in sub-tropical buffer zone community forests, Nepal, using Sentinel 2 data. Remote Sens., 10.","DOI":"10.3390\/rs10040601"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/forestry\/cpac015","article-title":"Framework for near real-time forest inventory using multi source remote sensing data","volume":"96","author":"Coops","year":"2023","journal-title":"Forestry"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, X., Yu, B., Liu, Y., Chen, Z., Li, Q., Wang, C., and Wu, J. (2019). Estimation of poverty using random forest regression with multi-source data: A case study in Bangladesh. Remote Sens., 11.","DOI":"10.3390\/rs11040375"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2018.11.017","article-title":"Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China","volume":"221","author":"Huang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Huang, T., Ou, G., Wu, Y., Zhang, X., Liu, Z., Xu, H., Xu, X., Wang, Z., and Xu, C. (2023). Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data. Remote Sens., 15.","DOI":"10.3390\/rs15143550"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sa, R., and Fan, W. (2023). Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A. Remote Sens., 15.","DOI":"10.3390\/rs15143605"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L., and Fu, W. (2023). Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects. Forests, 14.","DOI":"10.3390\/f14061086"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chatziantoniou, A., Petropoulos, G.P., and Psomiadis, E. (2017). Co-Orbital Sentinel 1 and 2 for LULC mapping with emphasis on wetlands in a mediterranean setting based on machine learning. Remote Sens., 9.","DOI":"10.3390\/rs9121259"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4829","DOI":"10.1080\/01431160500239107","article-title":"Relating SAR image texture to the biomass of regenerating tropical forests","volume":"26","author":"Kuplich","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, C., Li, M., and Liu, Z. (2019). Influence of variable selection and forest type on forest aboveground biomass estimation using machine learning algorithms. Forests, 10.","DOI":"10.3390\/f10121073"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"10017","DOI":"10.3390\/rs70810017","article-title":"Mapping tree canopy cover and aboveground biomass in Sudano-Sahelian woodlands using Landsat 8 and random forest","volume":"7","author":"Karlson","year":"2015","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1007\/s42965-021-00140-x","article-title":"Estimation of forest aboveground biomass using combination of Landsat 8 and Sentinel-1A data with random forest regression algorithm in Himalayan Foothills","volume":"62","author":"Purohit","year":"2021","journal-title":"Trop. Ecol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1016\/j.ecolind.2019.02.023","article-title":"Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm","volume":"102","author":"Zeng","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev-ecolsys-110512-135914","article-title":"The structure, distribution, and biomass of the world\u2019s forests","volume":"44","author":"Pan","year":"2013","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ou, G., Li, C., Lv, Y., Wei, A., Xiong, H., Xu, H., and Wang, G. (2019). Improving Aboveground Biomass Estimation of Pinus densata Forests in Yunnan Using Landsat 8 Imagery by Incorporating Age Dummy Variable and Method Comparison. Remote Sens., 11.","DOI":"10.3390\/rs11070738"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, L., Lu, D., and Li, D. (2018). Exploring bamboo forest aboveground biomass estimation using Sentinel-2 data. Remote Sens., 11.","DOI":"10.3390\/rs11010007"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19","DOI":"10.2307\/3545512","article-title":"Tropical forest biodiversity: Distributional patterns and their conservational significance","volume":"63","author":"Gentry","year":"1992","journal-title":"Oikos"},{"key":"ref_30","first-page":"306","article-title":"Tropical forests of xishuangbanna, China","volume":"38","author":"Cao","year":"2006","journal-title":"Biotropica J. Biol. Conserv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, Y., Marino, J., Chen, Y., Tao, Q., Sullivan, C.D., Shi, K., and Macdonald, D.W. (2016). Predicting hotspots of human-elephant conflict to inform mitigation strategies in Xishuangbanna, Southwest China. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0162035"},{"key":"ref_32","first-page":"310","article-title":"Geological history, flora, and vegetation of Xishuangbanna, Southern Yunnan, China","volume":"38","author":"Zhu","year":"2006","journal-title":"Biotropica J. Biol. Conserv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.ecolind.2017.07.017","article-title":"Do acoustic indices correlate with bird diversity? Insights from two biodiverse regions in Yunnan Province, south China","volume":"82","author":"Mammides","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/311054a0","article-title":"Replacement of oak forest with pine in the Himalaya affects the nitrogen cycle","volume":"311","author":"Singh","year":"1984","journal-title":"Nature"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1002\/eco.1456","article-title":"Vertical patterns of soil water acquisition by non-native rubber trees (Hevea brasiliensis) in Xishuangbanna, southwest China","volume":"7","author":"Liu","year":"2014","journal-title":"Ecohydrology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.foreco.2007.06.051","article-title":"Past, present and future land-use in Xishuangbanna, China and the implications for carbon dynamics","volume":"255","author":"Li","year":"2008","journal-title":"For. Ecol. Manag."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1111\/j.1365-2486.2004.00866.x","article-title":"Carbon cycling and storage in world forests: Biome patterns related to forest age","volume":"10","author":"Pregitzer","year":"2004","journal-title":"Glob. Chang. Biol."},{"key":"ref_38","unstructured":"Wu, Z., and Zhu, Y. (1987). The Vegetation of Yunnan, Science Press."},{"key":"ref_39","first-page":"1","article-title":"Prediction models for estimating the area, volume, and age of rubber (Hevea brasiliensis) plantations in Malaysia using Landsat TM data","volume":"6","author":"Suratman","year":"2004","journal-title":"Int. For. Rev."},{"key":"ref_40","unstructured":"Jianhui, X. (2006). Forest Ecology (Revised Edition), China Forestry Publishing House."},{"key":"ref_41","unstructured":"Xu, H., Zhang, Z., Ou, G., and Shi, H. (2019). A Study on Estimation and Distribution for Forest Biomass and Carbon Storage in Yunnan Province, Yunnan Science and Technology Press."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/0034-4257(95)00193-X","article-title":"Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS","volume":"55","author":"Kaufman","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, L., Zhou, B., Liu, Y., Wu, Y., Tang, J., Xu, W., Wang, L., and Ou, G. (2023). Reduction in Uncertainty in Forest Aboveground Biomass Estimation Using Sentinel-2 Images: A Case Study of Pinus densata Forests in Shangri-La City, China. Remote Sens., 15.","DOI":"10.3390\/rs15030559"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A comparison of vegetation indices over a global set of TM images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106155","DOI":"10.1016\/j.agwat.2020.106155","article-title":"Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction","volume":"236","author":"Venancio","year":"2020","journal-title":"Agric. Water Manag."},{"key":"ref_47","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_48","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.isprsjprs.2015.06.002","article-title":"Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas","volume":"108","author":"Dube","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1080\/01431160117862","article-title":"Elevation modelling from satellite visible and infrared (VIR) data","volume":"22","author":"Toutin","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, X., Liu, Z., Lin, H., Wang, G., Sun, H., Long, J., and Zhang, M. (2020). Estimating the growing stem volume of Chinese pine and larch plantations based on fused optical data using an improved variable screening method and stacking algorithm. Remote Sens., 12.","DOI":"10.3390\/rs12050871"},{"key":"ref_52","unstructured":"Miles, J. (2014). Wiley Statsref: Statistics Reference Online, John Wiley & Sons."},{"key":"ref_53","unstructured":"Qi, Y. (2012). Ensemble Machine Learning: Methods and Applications, Springer."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","article-title":"Random forest","volume":"47","author":"Rigatti","year":"2017","journal-title":"J. Insur. Med."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shekar, B., and Dagnew, G. (2019, January 25\u201328). Grid search-based hyperparameter tuning and classification of microarray cancer data. Proceedings of the 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Sikkim, India.","DOI":"10.1109\/ICACCP.2019.8882943"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Huang, T., Ou, G., Xu, H., Zhang, X., Wu, Y., Liu, Z., Zou, F., Zhang, C., and Xu, C. (2023). Comparing Algorithms for Estimation of Aboveground Biomass in Pinus yunnanensis. Forests, 14.","DOI":"10.3390\/f14091742"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recog. Lett."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1080\/07038992.2021.1968811","article-title":"Multi-Sensor Aboveground Biomass Estimation in the Broadleaved Hyrcanian Forest of Iran","volume":"47","author":"Ronoud","year":"2021","journal-title":"Can. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1109\/TGRS.2012.2205260","article-title":"Biomass Estimation of a Temperate Deciduous Forest Using Wavelet Analysis","volume":"51","author":"Ghasemi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1752","DOI":"10.1016\/j.asr.2021.03.035","article-title":"Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India","volume":"69","author":"Malhi","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1111\/1365-2745.13261","article-title":"Effects of topography on tropical forest structure depend on climate context","volume":"108","author":"Muscarella","year":"2019","journal-title":"J. Ecol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1080\/01431161.2013.870676","article-title":"Estimating standing biomass in papyrus (Cyperus papyrus L.) swamp: Exploratory of in situ hyperspectral indices and random forest regression","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1093\/jpe\/rtn025","article-title":"Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau","volume":"1","author":"Shen","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"171442","DOI":"10.1098\/rsos.171442","article-title":"Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour","volume":"5","author":"Walton","year":"2018","journal-title":"R. Soc. Open Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"481","DOI":"10.3319\/TAO.2016.01.06.02(ISRS)","article-title":"Estimating logged-over lowland rainforest aboveground biomass in Sabah, Malaysia using airborne LiDAR data","volume":"27","author":"Phua","year":"2016","journal-title":"TAO Terr. Atmos. Ocean. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Berninger, A., Lohberger, S., St\u00e4ngel, M., and Siegert, F. (2018). SAR-based estimation of above-ground biomass and its changes in tropical forests of Kalimantan using L-and C-band. Remote Sens., 10.","DOI":"10.3390\/rs10060831"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.isprsjprs.2023.03.010","article-title":"Spectral saturation in the remote sensing of high-density vegetation traits: A systematic review of progress, challenges, and prospects","volume":"198","author":"Mutanga","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","first-page":"436537","article-title":"Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates","volume":"2012","author":"Lu","year":"2012","journal-title":"Int. J. For. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1139\/x03-274","article-title":"Range of variability in boreal aspen plant communities after wildfire and clear-cutting","volume":"34","author":"Haeussler","year":"2004","journal-title":"Can. J. For. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"275","DOI":"10.2307\/2261011","article-title":"Demography and allometry of Cecropia obtusifolia, a neotropical pioneer tree-an evaluation of the climax-pioneer paradigm for tropical rain forests","volume":"80","year":"1992","journal-title":"J. Ecol."},{"key":"ref_71","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_72","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1111\/2041-210X.12904","article-title":"Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR","volume":"9","author":"Gonzalez","year":"2017","journal-title":"Methods Ecol. 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