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Research Article

The potential of optical and SAR time-series data for the improvement of aboveground biomass carbon estimation in Southwestern China’s evergreen coniferous forests

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Article: 2345438 | Received 05 Dec 2023, Accepted 16 Apr 2024, Published online: 26 Apr 2024

References

  • Adams, H. R., H. R. Barnard, and A. K. Loomis. 2014. “Topography Alters Tree Growth–Climate Relationships in a Semi-Arid Forested Catchment.” Ecosphere 5 (11): 148. https://doi.org/10.1890/es14-00296.1.
  • Askne, J. I. H., M. J. Soja, and L. M. H. Ulander. 2017. “Biomass Estimation in a Boreal Forest from TanDEM-X Data, Lidar DTM, and the Interferometric Water Cloud Model.” Remote Sensing of Environment 196:265–25. https://doi.org/10.1016/j.rse.2017.05.010.
  • Baccini, A., W. Walker, L. Carvalho, M. Farina, D. Sulla-Menashe, and R. A. Houghton. 2017. “Tropical Forests Are a Net Carbon Source Based on Aboveground Measurements of Gain and Loss.” Science 358 (6360): 230–233. https://doi.org/10.1126/science.aam5962.
  • Bai, X., B. He, X. Li, J. Zeng, X. Wang, Z. Wang, Y. Zeng, and Z. Su. 2017. “First Assessment of Sentinel-1A Data for Surface Soil Moisture Estimations Using a Coupled Water Cloud Model and Advanced Integral Equation Model Over the Tibetan Plateau.” Remote Sensing 9 (7): 714. https://doi.org/10.3390/rs9070714.
  • Beaudoin, A., T. Le Toan, S. Goze, E. Nezry, A. Lopes, E. Mougin, C. C. Hsu, H. C. Han, J. A. Kong, and R. T. Shin. 1994. “Retrieval of Forest Biomass from SAR Data.” International Journal of Remote Sensing 15 (14): 2777–2796. https://doi.org/10.1080/01431169408954284.
  • Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Rödenbeck, et al. 2010. “Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate.” Science 329 (5993): 834–838. https://doi.org/10.1126/science.1184984.
  • Belgiu, M., and L. Drăguţ. 2016. “Random Forest in Remote Sensing: A Review of Applications and Future Directions.” Isprs Journal of Photogrammetry & Remote Sensing 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011.
  • Bolton, D. K., P. Tompalski, N. C. Coops, J. C. White, M. A. Wulder, T. Hermosilla, M. Queinnec, et al. 2020. “Optimizing Landsat Time Series Length for Regional Mapping of Lidar-Derived Forest Structure.” Remote Sensing of Environment 239:111645. https://doi.org/10.1016/j.rse.2020.111645.
  • Boyd, D. S., and F. M. Danson. 2005. “Satellite Remote Sensing of Forest Resources: Three Decades of Research Development.” Progress in Physical Geography-Earth and Environment 29 (1): 1–26. https://doi.org/10.1191/0309133305pp432ra.
  • Brandes, O., J. Farley, M. Hinich, and U. Zackrisson. 1968. “The Time Domain and the Frequency Domain in Time Series Analysis.” The Swedish Journal of Economics 70 (1): 25–42. https://doi.org/10.2307/3438983.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45:5–32. https://doi.org/10.1023/A:1010933404324.
  • Bustamante, M. M. C., I. Roitman, T. M. Aide, A. Alencar, L. O. Anderson, L. Aragao, G. P. Asner, et al. 2016. “Toward an Integrated Monitoring Framework to Assess the Effects of Tropical Forest Degradation and Recovery on Carbon Stocks and Biodiversity.” Global Change Biology 22 (1): 92–109. https://doi.org/10.1111/gcb.13087.
  • Cao, M., and F. I. Woodward. 1998. “Dynamic Responses of Terrestrial Ecosystem Carbon Cycling to Global Climate Change.” Nature 393 (6682): 249–252. https://doi.org/10.1038/30460.
  • Çetin, M., and A. Meydan. 2023. “Topography and Climate of Mount Karanfil (Pozantı/Adana).” Environmental Systems Research 12 (1): 1. https://doi.org/10.1186/s40068-022-00280-6.
  • Chang, G. J. 2023. “Biodiversity Estimation by Environment Drivers Using Machine/Deep Learning for Ecological Management.” Ecological informatics 78:102319. https://doi.org/10.1016/j.ecoinf.2023.102319.
  • Chang, J., and M. Shoshany. 2017. “Radar Polarization and Ecological Pattern Properties Across Mediterranean-To-Arid Transition Zone.” Remote Sensing of Environment 200:368–377. https://doi.org/10.1016/j.rse.2017.08.032.
  • Chen, T., and C. Guestrin. 2016. “XGBoost.” In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.
  • Chen, R., B. B. He, Y. X. Li, C. Q. Fan, J. P. Yin, H. G. Zhang, and Y. R. Zhang. 2024. “Estimation of Potential Wildfire Behavior Characteristics to Assess Wildfire Danger in Southwest China Using Deep Learning Schemes.” Journal of Environmental Management 351. https://doi.org/10.1016/j.jenvman.2023.120005.
  • Chen, R., B. B. He, X. W. Quan, X. Y. Lai, and C. Q. Fan. 2023. “Improving Wildfire Probability Modeling by Integrating Dynamic-Step Weather Variables Over Northwestern Sichuan, China.” International Journal of Disaster Risk Science 14:313–325. https://doi.org/10.1007/s13753-023-00476-z.
  • Chen, J. M., W. Ju, P. Ciais, N. Viovy, R. Liu, Y. Liu, and X. Lu. 2019. “Vegetation Structural Change Since 1981 Significantly Enhanced the Terrestrial Carbon Sink.” Nature Communications 10 (1): 4259. https://doi.org/10.1038/s41467-019-12257-8.
  • China, N. F. A. O. 2014. Tree Biomass Models and Related Parameters to Carbon Accounting for Pinus Yunnanensis. Beijing: Standards Press of China.
  • Christ, M., N. Braun, J. Neuffer, and A. W. Kempa-Liehr. 2018. “Time Series FeatuRe Extraction on Basis of Scalable Hypothesis Tests (Tsfresh – a Python Package).” Neurocomputing 307:72–77. https://doi.org/10.1016/j.neucom.2018.03.067.
  • Christ, M., A. Kempa-Liehr, and M. Feindt. 2016. “Distributed and Parallel Time Series Feature Extraction for Industrial Big Data Applications.” In The 8th Asian Conference on Machine Learning (ACML 2016), Hamilton, New Zealand.
  • Conners, R. W., M. M. Trivedi, and C. A. Harlow. 1984. “Segmentation of a High-Resolution Urban Scene Using Texture Operators.” Computer Vision, Graphics and Image Processing 25 (3): 273–310. https://doi.org/10.1016/0734-189X(84)90197-X.
  • Cui, T., L. Fan, P. Ciais, R. Fensholt, F. Frappart, S. Sitch, J. Chave, et al. 2023. “First Assessment of Optical and Microwave Remotely Sensed Vegetation Proxies in Monitoring Aboveground Carbon in Tropical Asia.” Remote Sensing of Environment 293:113619. https://doi.org/10.1016/j.rse.2023.113619.
  • David, R. M., N. J. Rosser, and D. N. M. Donoghue. 2022. “Improving Above Ground Biomass Estimates of Southern Africa Dryland Forests by Combining Sentinel-1 SAR and Sentinel-2 Multispectral Imagery.” Remote Sensing of Environment 282:113232. https://doi.org/10.1016/j.rse.2022.113232.
  • De Marzo, T., M. Pratzer, M. Baumann, N. I. Gasparri, F. Pötzschner, and T. Kuemmerle. 2023. “Linking Disturbance History to Current Forest Structure to Assess the Impact of Disturbances in Tropical Dry Forests.” Forest Ecology & Management 539:120989. https://doi.org/10.1016/j.foreco.2023.120989.
  • Dixon, R. K., S. Brown, R. A. Houghton, A. M. Solomon, M. C. Trexler, and J. Wisniewski. 1994. “Carbon Pools and Flux of Global Forest Ecosystems.” Science 263 (5144): 185–190. https://doi.org/10.1126/science.263.5144.185.
  • Domingues, G. F., V. P. Soares, H. G. Leite, A. S. Ferraz, C. A. A. S. Ribeiro, A. S. Lorenzon, G. E. Marcatti, et al. 2020. “Artificial Neural Networks on Integrated Multispectral and SAR Data for High-Performance Prediction of Eucalyptus Biomass.” Computers and Electronics in Agriculture 168:105089. https://doi.org/10.1016/j.compag.2019.105089.
  • Dormann, C. F., J. Elith, S. Bacher, C. Buchmann, G. Carl, G. Carré, J. R. G. Marquéz, et al. 2013. “Collinearity: A Review of Methods to Deal with it and a Simulation Study Evaluating Their Performance.” Holarctic Ecology 36 (1): 27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x.
  • Du, L., Y. Pang, Q. Wang, C. Huang, Y. Bai, D. Chen, W. Lu, and D. Kong. 2023. “A LiDAR Biomass Index-Based Approach for Tree- and Plot-Level Biomass Mapping Over Forest Farms Using 3D Point Clouds.” Remote Sensing of Environment 290. https://doi.org/10.1016/j.rse.2023.113543.
  • Eitel, J. U. H., D. Basler, S. Braun, N. Buchmann, P. D’Odorico, S. Etzold, A. Gessler, et al. 2023. “Towards Monitoring Stem Growth Phenology from Space with High Resolution Satellite Data.” Agricultural and Forest Meteorology 339:109549. https://doi.org/10.1016/j.agrformet.2023.109549.
  • Fang, G., H. Xu, S.-I. Yang, X. Lou, and L. Fang. 2023. “Synergistic Use of Sentinel-1, Sentinel-2, and Landsat 8 in Predicting Forest Variables.” Ecological Indicators 151:110296. https://doi.org/10.1016/j.ecolind.2023.110296.
  • Forrester, D. I., I. H. H. Tachauer, P. Annighoefer, I. Barbeito, H. Pretzsch, R. Ruiz-Peinado, H. Stark, et al. 2017. “Generalized Biomass and Leaf Area Allometric Equations for European Tree Species Incorporating Stand Structure, Tree Age and Climate.” Forest Ecology & Management 396:160–175. https://doi.org/10.1016/j.foreco.2017.04.011.
  • Friedman, J. H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232. https://doi.org/10.1214/aos/1013203451.
  • Getzin, S., T. Wiegand, K. Wiegand, and F. He. 2008. “Heterogeneity Influences Spatial Patterns and Demographics in Forest Stands.” The Journal of Ecology 96 (4): 807–820. https://doi.org/10.1111/j.1365-2745.2008.01377.x.
  • Grinsztajn, L., E. Oyallon, and G. Varoquaux. 2022. “Why Do Tree-Based Models Still Outperform Deep Learning on Tabular Data?.” In 36th Conference on Neural Information Processing Sys- tems (NeurIPS 2022) Track on Datasets and Benchmarks, New Orleans, United States.
  • Guerra-Hernández, J., L. L. Narine, A. Pascual, E. Gonzalez-Ferreiro, B. Botequim, L. Malambo, A. Neuenschwander, S. C. Popescu, and S. Godinho. 2022. “Aboveground Biomass Mapping by Integrating ICESat-2, SENTINEL-1, SENTINEL-2, ALOS2/PALSAR2, and Topographic Information in Mediterranean Forests.” GIScience & Remote Sensing 59 (1): 1509–1533. https://doi.org/10.1080/15481603.2022.2115599.
  • Güner, Ş. T., M. J. Diamantopoulou, K. P. Poudel, A. Çömez, and R. Özçelik. 2022. “Employing Artificial Neural Network for Effective Biomass Prediction: An Alternative Approach.” Computers and Electronics in Agriculture 192:106596. https://doi.org/10.1016/j.compag.2021.106596.
  • Haralick, R. M., K. Shanmugam, and I. Dinstein. 1973. “Textural Features for Image Classification.” IEEE Transactions on Systems, Man, and Cybernetics SMC-3 (6): 610–621. https://doi.org/10.1109/TSMC.1973.4309314.
  • He, X., X. Lei, D. Liu, and Y. Lei. 2023. “Developing Machine Learning Models with Multiple Environmental Data to Predict Stand Biomass in Natural Coniferous-Broad Leaved Mixed Forests in Jilin Province of China.” Computers and Electronics in Agriculture 212:108162. https://doi.org/10.1016/j.compag.2023.108162.
  • Herold, M., S. Carter, V. Avitabile, A. B. Espejo, I. Jonckheere, R. Lucas, R. E. Mcroberts, et al. 2019. “The Role and Need for Space-Based Forest Biomass-Related Measurements in Environmental Management and Policy.” Surveys in Geophysics 40 (4): 757–778. https://doi.org/10.1007/s10712-019-09510-6.
  • He, N. P., D. Wen, J. X. Zhu, X. L. Tang, L. Xu, L. Zhang, H. F. Hu, M. Huang, and G. R. Yu. 2017. “Vegetation Carbon Sequestration in Chinese Forests from 2010 to 2050.” Global Change Biology 23 (4): 1575–1584. https://doi.org/10.1111/gcb.13479.
  • Hiernaux, P., H. B.-A. Issoufou, C. Igel, A. Kariryaa, M. Kourouma, J. Chave, E. Mougin, and P. Savadogo. 2023. “Allometric Equations to Estimate the Dry Mass of Sahel Woody Plants Mapped with Very-High Resolution Satellite Imagery.” Forest Ecology & Management 529:120653. https://doi.org/10.1016/j.foreco.2022.120653.
  • Houghton, R. A. 2005. “Aboveground Forest Biomass and the Global Carbon Balance.” Global Change Biology 11 (6): 945–958. https://doi.org/10.1111/j.1365-2486.2005.00955.x.
  • Huang, W., K. Dolan, A. Swatantran, K. Johnson, H. Tang, J. O’Neil-Dunne, R. Dubayah, and G. Hurtt. 2019. “High-Resolution Mapping of Aboveground Biomass for Forest Carbon Monitoring System in the Tri-State Region of Maryland, Pennsylvania and Delaware, USA.” Environmental Research Letters 14:095002. https://doi.org/10.1088/1748-9326/ab2917.
  • Huang, T., G. Ou, Y. Wu, X. Zhang, Z. Liu, H. Xu, X. Xu, Z. Wang, and C. Xu. 2023. “Estimating the Aboveground Biomass of Various Forest Types with High Heterogeneity at the Provincial Scale Based on Multi-Source Data.” Remote Sensing 15 (14): 3550. https://doi.org/10.3390/rs15143550.
  • Huang, W., L. Zhang, S. Furumi, K. Muramatsu, M. Daigo, and P. Li. 2010. “Topographic Effects on Estimating Net Primary Productivity of Green Coniferous Forest in Complex Terrain Using Landsat Data: A Case Study of Yoshino Mountain, Japan.” International Journal of Remote Sensing 31 (11): 2941–2957. https://doi.org/10.1080/01431160903140829.
  • Jensen, J. R., F. Qiu, and M. H. Ji. 1999. “Predictive Modelling of Coniferous Forest Age Using Statistical and Artificial Neural Network Approaches Applied to Remote Sensor Data.” International Journal of Remote Sensing 20 (14): 2805–2822. https://doi.org/10.1080/014311699211804.
  • Jin, H., A. Li, W. Xu, Z. Xiao, J. Jiang, and H. Xue. 2019. “Evaluation of Topographic Effects on Multiscale Leaf Area Index Estimation Using Remotely Sensed Observations from Multiple Sensors.” Isprs Journal of Photogrammetry & Remote Sensing 154:176–188. https://doi.org/10.1016/j.isprsjprs.2019.06.008.
  • Kelsey, K., and J. Neff. 2014. “Estimates of Aboveground Biomass from Texture Analysis of Landsat Imagery.” Remote Sensing 6 (7): 6407–6422. https://doi.org/10.3390/rs6076407.
  • Kenina, L., D. Elferts, E. Baders, and A. Jansons. 2018. “Carbon Pools in a Hemiboreal Over-Mature Norway Spruce Stands.” FORESTS 9 (7): 435. https://doi.org/10.3390/f9070435.
  • Khabbazan, S., S. C. Steele-Dunne, P. Vermunt, J. Judge, M. Vreugdenhil, and G. Gao. 2022. “The Influence of Surface Canopy Water on the Relationship Between L-Band Backscatter and Biophysical Variables in Agricultural Monitoring.” Remote Sensing of Environment 268:112789. https://doi.org/10.1016/j.rse.2021.112789.
  • Konya, A., and P. Nematzadeh. 2024. “Recent Applications of AI to Environmental Disciplines: A Review.” Science of the Total Environment 906:167705. https://doi.org/10.1016/j.scitotenv.2023.167705.
  • Lai, L., Y. Zhang, Z. Cao, Z. Liu, and Q. Yang. 2023. “Algal Biomass Mapping of Eutrophic Lakes Using a Machine Learning Approach with MODIS Images.” Science of the Total Environment 880:163357. https://doi.org/10.1016/j.scitotenv.2023.163357.
  • Larocque, G. R., J. S. Bhatti, R. Boutin, and O. Chertov. 2008. “Uncertainty Analysis in Carbon Cycle Models of Forest Ecosystems: Research Needs and Development of a Theoretical Framework to Estimate Error Propagation.” Ecological Modelling 219 (3): 400–412. https://doi.org/10.1016/j.ecolmodel.2008.07.024.
  • Lee, J.-S. 1981. “Refined Filtering of Image Noise Using Local Statistics.” Computer Graphics and Image Processing 15 (4): 380–389. https://doi.org/10.1016/S0146-664X(81)80018-4.
  • Lee, J. S., L. Jurkevich, P. Dewaele, P. Wambacq, and A. Oosterlinck. 1994. “Speckle Filtering of Synthetic Aperture Radar Images: A Review.” Remote sensing reviews 8 (4): 313–340. https://doi.org/10.1080/02757259409532206.
  • Li, X., L. C. R. Aguila, D. H. Wu, Z. Lie, W. F. Xu, X. L. Tang, and J. X. Liu. 2023. “Carbon Sequestration and Storage Capacity of Chinese Fir at Different Stand Ages.” Science of the Total Environment 904:166962. https://doi.org/10.1016/j.scitotenv.2023.166962.
  • Liao, Z., B. He, X. Quan, A. I. J. M. van Dijk, S. Qiu, and C. Yin. 2019. “Biomass Estimation in Dense Tropical Forest Using Multiple Information from Single-Baseline P-Band PolInSAR Data.” Remote Sensing of Environment 221:489–507. https://doi.org/10.1016/j.rse.2018.11.027.
  • Liao, Z., X. Liu, A. van Dijk, C. Yue, and B. He. 2022. “Continuous Woody Vegetation Biomass Estimation Based on Temporal Modeling of Landsat Data.” International Journal of Applied Earth Observation and Geoinformation 110:102811. https://doi.org/10.1016/j.jag.2022.102811.
  • Li, Y. X., R. Chen, B. B. He, and S. Veraverbeke. 2022. “Forest Foliage Fuel Load Estimation from Multi-Sensor Spatiotemporal Features.” International Journal of Applied Earth Observation and Geoinformation 115:103101. https://doi.org/10.1016/j.jag.2022.103101.
  • Li, X., J. Xiao, B. He, M. Altaf Arain, J. Beringer, A. R. Desai, C. Emmel, et al. 2018. “Solar-Induced Chlorophyll Fluorescence Is Strongly Correlated with Terrestrial Photosynthesis for a Wide Variety of Biomes: First Global Analysis Based on OCO-2 and Flux Tower Observations.” Glob Chang Biol 24 (9): 3990–4008. https://doi.org/10.1111/gcb.14297.
  • Lu, D. S. 2006. “The Potential and Challenge of Remote Sensing-Based Biomass Estimation.” International Journal of Remote Sensing 27 (7): 1297–1328. https://doi.org/10.1080/01431160500486732.
  • Lu, D. S., Q. Chen, G. X. Wang, L. J. Liu, G. Y. Li, and E. Moran. 2016. “A Survey of Remote Sensing-Based Aboveground Biomass Estimation Methods in Forest Ecosystems.” International Journal of Digital Earth 9 (1): 63–105. https://doi.org/10.1080/17538947.2014.990526.
  • Lu, D., and X. Jiang. 2024. “A Brief Overview and Perspective of Using Airborne Lidar Data for Forest Biomass Estimation.” International Journal of Image and Data Fusion 15 (1): 1–24. https://doi.org/10.1080/19479832.2024.2309615.
  • Lu, J. B., H. Wang, S. H. Qin, L. Cao, R. L. Pu, G. L. Li, and J. Sun. 2020. “Estimation of Aboveground Biomass of Robinia pseudoacacia Forest in the Yellow River Delta Based on UAV and Backpack LiDAR Point Clouds.” International Journal of Applied Earth Observation and Geoinformation 86. https://doi.org/10.1016/j.jag.2019.102014.
  • MacFarland, T. W. 2011. Two-Way Analysis of Variance: Statistical Tests and Graphics Using R. 1st ed. New York, NY, USA: Springer. https://doi.org/10.1007/978-1-4614-2134-4.
  • Mahdavi, S., M. Amani, and Y. Maghsoudi. 2019. “The Effects of Orbit Type on Synthetic Aperture RADAR (SAR) Backscatter.” Remote Sensing Letters 10 (2): 120–128. https://doi.org/10.1080/2150704X.2018.1530481.
  • Morecroft, M. D., S. Duffield, M. Harley, J. W. Pearce-Higgins, N. Stevens, O. Watts, and J. Whitaker. 2019. “Measuring the Success of Climate Change Adaptation and Mitigation in Terrestrial Ecosystems.” Science 366:eaaw9256. https://doi.org/10.1126/science.aaw9256.
  • Muscarella, R., S. Kolyaie, D. C. Morton, J. K. Zimmerman, and M. Uriarte. 2020. “Effects of Topography on Tropical Forest Structure Depend on Climate Context.” The Journal of Ecology 108 (1): 145–159. https://doi.org/10.1111/1365-2745.13261.
  • Natekin, A., and A. Knoll. 2013. “Gradient Boosting Machines, a Tutorial.” Frontiers in Neurorobotics 7:21. https://doi.org/10.3389/fnbot.2013.00021.
  • Ni, W., T. Yu, Y. Pang, Z. Zhang, Y. He, Z. Li, and G. Sun. 2023. “Seasonal Effects on Aboveground Biomass Estimation in Mountainous Deciduous Forests Using ZY-3 Stereoscopic Imagery.” Remote Sensing of Environment 289. https://doi.org/10.1016/j.rse.2023.113520.
  • Ogwang, B. A., H. Chen, X. Li, and C. Gao. 2014. “The Influence of Topography on East African October to December Climate: Sensitivity Experiments with RegCm4.” Advances in Meteorology 2014:143917. https://doi.org/10.1155/2014/143917.
  • Pan, Y. D., R. A. Birdsey, J. Y. Fang, R. Houghton, P. E. Kauppi, W. A. Kurz, O. L. Phillips, et al. 2011. “A Large and Persistent Carbon Sink in the world’s Forests.” Science 333 (6045): 988–993. https://doi.org/10.1126/science.1201609.
  • Peng, B., Z. Zhou, W. Cai, M. Li, L. Xu, and N. He. 2023. “Maximum Potential of Vegetation Carbon Sink in Chinese Forests.” Science of the Total Environment 905:167325. https://doi.org/10.1016/j.scitotenv.2023.167325.
  • Piao, S. L., J. Y. Fang, P. Ciais, P. Peylin, Y. Huang, S. Sitch, and T. Wang. 2009. “The Carbon Balance of Terrestrial Ecosystems in China.” Nature 458 (7241): 1009–1013. https://doi.org/10.1038/nature07944.
  • Pötzschner, F., M. Baumann, N. I. Gasparri, G. Conti, D. Loto, M. Piquer-Rodríguez, and T. Kuemmerle. 2022. “Ecoregion-Wide, Multi-Sensor Biomass Mapping Highlights a Major Underestimation of Dry Forests Carbon Stocks.” Remote Sensing of Environment 269:112849. https://doi.org/10.1016/j.rse.2021.112849.
  • Qi, Z., S. Li, Y. Pang, L. Du, H. Zhang, and Z. Li. 2023. “Monitoring Spatiotemporal Variation of Individual Tree Biomass Using Multitemporal LiDAR Data.” Remote Sensing 15 (19): 4768. https://doi.org/10.3390/rs15194768.
  • Quan, X., Y. Li, B. He, G. J. Cary, and G. Lai. 2021. “Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method.” IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing 14:5100–5110. https://doi.org/10.1109/jstars.2021.3062073.
  • Quan, X., Q. Xie, B. He, K. Luo, and X. Liu. 2021. “Integrating Remotely Sensed Fuel Variables into Wildfire Danger Assessment for China.” International Journal of Wildland Fire 30 (10): 807–821. https://doi.org/10.1071/wf20077.
  • Ramachandran, N., S. Saatchi, S. Tebaldini, M. M. D’Alessandro, and O. Dikshit. 2023. “Mapping Tropical Forest Aboveground Biomass Using Airborne SAR Tomography.” Scientific Reports 13 (1): 6233. https://doi.org/10.1038/s41598-023-33311-y.
  • Ran, F., R.-Y. Chang, Y. Yang, W.-Z. Zhu, J. Luo, and G.-X. Wang. 2017. “Allometric Equations of Select Tree Species of the Tibetan Plateau, China.” Journal of Mountain Science 14 (9): 1889–1902. https://doi.org/10.1007/s11629-016-4082-4.
  • Rodríguez-Fernández, N. J., A. Mialon, S. Mermoz, A. Bouvet, P. Richaume, A. Al Bitar, A. Al-Yaari, et al. 2018. “An Evaluation of SMOS L-Band Vegetation Optical Depth (L-VOD) Data Sets: High Sensitivity of L-VOD to Above-Ground Biomass in Africa.” Biogeosciences 15 (14): 4627–4645. https://doi.org/10.5194/bg-15-4627-2018.
  • Santi, E., S. Paloscia, S. Pettinato, G. Fontanelli, M. Mura, C. Zolli, F. Maselli, M. Chiesi, L. Bottai, and G. Chirici. 2017. “The Potential of Multifrequency SAR Images for Estimating Forest Biomass in Mediterranean Areas.” Remote Sensing of Environment 200:63–73. https://doi.org/10.1016/j.rse.2017.07.038.
  • Sarker, M. L. R., J. Nichol, B. Ahmad, I. Busu, and A. A. Rahman. 2012. “Potential of Texture Measurements of Two-Date Dual Polarization PALSAR Data for the Improvement of Forest Biomass Estimation.” Isprs Journal of Photogrammetry & Remote Sensing 69:146–166. https://doi.org/10.1016/j.isprsjprs.2012.03.002.
  • Sayedain, S. A., Y. Maghsoudi, and S. Eini-Zinab. 2020. “Assessing the Use of Cross-Orbit Sentinel-1 Images in Land Cover Classification.” International Journal of Remote Sensing 41 (20): 7801–7819. https://doi.org/10.1080/01431161.2020.1763512.
  • Seedre, M., J. Kopácek, P. Janda, R. Bace, and M. Svoboda. 2015. “Carbon Pools in a Montane Old-Growth Norway Spruce Ecosystem in Bohemian Forest: Effects of Stand Age and Elevation.” Forest Ecology & Management 346:106–113. https://doi.org/10.1016/j.foreco.2015.02.034.
  • Shang, R., J. M. Chen, M. Xu, X. Lin, P. Li, G. Yu, N. He, et al. 2023. “China’s Current Forest Age Structure Will Lead to Weakened Carbon Sinks in the Near Future.” The Innovation 4 (6): 100515. https://doi.org/10.1016/j.xinn.2023.100515.
  • Singh, C., S. K. Karan, P. Sardar, and S. R. Samadder. 2022. “Remote Sensing-Based Biomass Estimation of Dry Deciduous Tropical Forest Using Machine Learning and Ensemble Analysis.” Journal of Environmental Management 308:114639. https://doi.org/10.1016/j.jenvman.2022.114639.
  • Sinha, S., C. Jeganathan, L. K. Sharma, and M. S. Nathawat. 2015. “A Review of Radar Remote Sensing for Biomass Estimation.” International Journal of Environmental Science and Technology 12 (5): 1779–1792. https://doi.org/10.1007/s13762-015-0750-0.
  • Sinha, S., A. Santra, L. Sharma, C. Jeganathan, M. S. Nathawat, A. K. Das, and S. Mohan. 2018. “Multi-Polarized Radarsat-2 Satellite Sensor in Assessing Forest Vigor from Above Ground Biomass.” Journal of Forestry Research 29 (4): 1139–1145. https://doi.org/10.1007/s11676-017-0511-7.
  • Soja, M. J., S. Quegan, M. M. d’Alessandro, F. Banda, K. Scipal, S. Tebaldini, and L. M. H. Ulander. 2021. “Mapping Above-Ground Biomass in Tropical Forests with Ground-Cancelled P-Band SAR and Limited Reference Data.” Remote Sensing of Environment 253:112153. https://doi.org/10.1016/j.rse.2020.112153.
  • Taylor, K. E. 2001. “Summarizing Multiple Aspects of Model Performance in a Single Diagram.” Journal of Geophysical Research-Atmospheres 106 (D7): 7183–7192. https://doi.org/10.1029/2000jd900719.
  • Thurner, M., C. Beer, M. Santoro, N. Carvalhais, T. Wutzler, D. Schepaschenko, A. Shvidenko, et al. 2014. “Carbon Stock and Density of Northern Boreal and Temperate Forests.” Global Ecology & Biogeography 23 (3): 297–310. https://doi.org/10.1111/geb.12125.
  • Torrence, C., and G. P. Compo. 1998. “A Practical Guide to Wavelet Analysis.” Bulletin of the American Meteorological Society 79 (1): 61–78. https://doi.org/10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2.
  • Travers-Smith, H., N. C. Coops, C. Mulverhill, M. A. Wulder, D. Ignace, and T. C. Lantz. 2024. “Mapping Vegetation Height and Identifying the Northern Forest Limit Across Canada Using ICESat-2, Landsat Time Series and Topographic Data.” Remote Sensing of Environment 305:114097. https://doi.org/10.1016/j.rse.2024.114097.
  • Wang, Y., F. W. Davis, J. M. Melack, E. S. Kasischke, and N. L. Christensen Jr. 1995. “The Effects of Changes in Forest Biomass on Radar Backscatter from Tree Canopies.” International Journal of Remote Sensing 16 (3): 503–513. https://doi.org/10.1080/01431169508954415.
  • Wang, H., M. He, N. Ran, D. Xie, Q. Wang, M. Teng, and P. Wang. 2021. “China’s Key Forestry Ecological Development Programs: Implementation, Environmental Impact and Challenges.” FORESTS 12 (1): 101. https://doi.org/10.3390/f12010101.
  • Wang, J., F. Jiang, H. Wang, B. Qiu, M. Wu, W. He, W. Ju, Y. Zhang, J. M. Chen, and Y. Zhou. 2021. “Constraining Global Terrestrial Gross Primary Productivity in a Global Carbon Assimilation System with OCO-2 Chlorophyll Fluorescence Data.” Agricultural and Forest Meteorology 304-305:108424. https://doi.org/10.1016/j.agrformet.2021.108424.
  • West, P. W. 2015. Tree and Forest Measurement. 3rd ed. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-14708-6.
  • Wijaya, A., V. Liesenberg, A. Susanti, O. Karyanto, and L. V. Verchot. 2015. “Estimation of Biomass Carbon Stocks Over Peat Swamp Forests Using Multi-Temporal and Multi-Polratizations SAR Data.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 551–556. https://doi.org/10.5194/isprsarchives-xl-7-w3-551-2015.
  • Wooldridge, J. M. 2012. Introductory Econometrics: A Modern Approach. 5th ed. Mason, OH, USA: Cengage Learning.
  • Wu, S., J. Li, W. Zhou, B. J. Lewis, D. Yu, L. Zhou, L. Jiang, and L. Dai. 2017. “A Statistical Analysis of Spatiotemporal Variations and Determinant Factors of Forest Carbon Storage Under China’s Natural Forest Protection Program.” Journal of Forestry Research 29 (2): 415–424. https://doi.org/10.1007/s11676-017-0462-z.
  • Wu, Z., and F. Shi. 2023. “Mapping Forest Canopy Height at Large Scales Using ICESat-2 and Landsat: An Ecological Zoning Random Forest Approach.” IEEE Transactions on Geoscience & Remote Sensing 61:1–16. https://doi.org/10.1109/tgrs.2022.3231926.
  • Xiao, J., F. Chevallier, C. Gomez, L. Guanter, J. A. Hicke, A. R. Huete, K. Ichii, et al. 2019. “Remote Sensing of the Terrestrial Carbon Cycle: A Review of Advances Over 50 Years.” Remote Sensing of Environment 233:111383. https://doi.org/10.1016/j.rse.2019.111383.
  • Xu, Z., G. Huang, K. Q. Weinberger, and A. X. Zheng. 2014. “Gradient Boosted Feature Selection.” In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York, NY, USA.
  • Xu, X., A. G. Konings, M. Longo, A. Feldman, L. Xu, S. Saatchi, D. Wu, J. Wu, and P. Moorcroft. 2021. “Leaf Surface Water, Not Plant Water Stress, Drives Diurnal Variation in Tropical Forest Canopy Water Content.” The New Phytologist 231 (1): 122–136. https://doi.org/10.1111/nph.17254.
  • Xu, H., C. Yue, Y. Zhang, D. Liu, and S. Piao. 2023. “Forestation at the Right Time with the Right Species Can Generate Persistent Carbon Benefits in China.” Proceedings of the National Academy of Sciences 120 (41): e2304988120. https://doi.org/10.1073/pnas.2304988120.
  • Yang, Q., C. Niu, X. Liu, Y. Feng, Q. Ma, X. Wang, H. Tang, and Q. Guo. 2023. “Mapping High-Resolution Forest Aboveground Biomass of China Using Multisource Remote Sensing Data.” GIScience & Remote Sensing 60 (1): 2203303. https://doi.org/10.1080/15481603.2023.2203303.
  • Yommy, A. S., R. Liu, and A. S. Wu. 2015. “SAR Image Despeckling Using Refined Lee Filter.“ In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.
  • Zhang, P. 1993. “Model Selection via Multifold Cross Validation.” Annals of Statistics 21 (1): 299–313. https://doi.org/10.1214/aos/1176349027.
  • Zhang, X., L. Liu, X. Chen, Y. Gao, S. Xie, and J. Mi. 2021. “GLC_FCS30: Global Land-Cover Product with Fine Classification System at 30 m Using Time-Series Landsat Imagery.” Earth System Science Data 13 (6): 2753–2776. https://doi.org/10.5194/essd-13-2753-2021.
  • Zhang, Y., H. Li, X. Zhang, Y. Lei, J. Huang, and X. Liu. 2022. “An Approach to Estimate Individual Tree Ages Based on Time Series Diameter Data—A Test Case for Three Subtropical Tree Species in China.” FORESTS 13 (4): 614. https://doi.org/10.3390/f13040614.
  • Zhang, J., R. Shang, C. Rittenhouse, C. Witharana, and Z. Zhu. 2021. “Evaluating the Impacts of Models, Data Density and Irregularity on Reconstructing and Forecasting Dense Landsat Time Series.” Science of Remote Sensing 4:100023. https://doi.org/10.1016/j.srs.2021.100023.
  • Zhang, F. Y., X. Tian, H. B. Zhang, and M. Jiang. 2022. “Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing.” Remote Sensing 14 (13): 3022. https://doi.org/10.3390/rs14133022.
  • Zhang, Y., N. Wang, Y. Wang, and M. Li. 2023. “A New Strategy for Improving the Accuracy of Forest Aboveground Biomass Estimates in an Alpine Region Based on Multi-Source Remote Sensing.” GIScience & Remote Sensing 60 (1): 2163574. https://doi.org/10.1080/15481603.2022.2163574.
  • Zhang, W., L. Zhao, Y. Li, J. Shi, M. Yan, and Y. Ji. 2022. “Forest Above-Ground Biomass Inversion Using Optical and SAR Images Based on a Multi-Step Feature Optimized Inversion Model.” Remote Sensing 14 (7): 1608. https://doi.org/10.3390/rs14071608.
  • Zhao, P., D. Lu, G. Wang, C. Wu, Y. Huang, and S. Yu. 2016. “Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation.” Remote Sensing 8 (6). https://doi.org/10.3390/rs8060469.
  • Zhou, Q., D. Chen, Z. Hu, and X. Chen. 2021. “Decompositions of Taylor Diagram and DISO Performance Criteria.” International Journal of Climatology 41:5726–5732. https://doi.org/10.1002/joc.7149.
  • Zhou, Q., Z. Zhu, G. Xian, and C. Li. 2022. “A Novel Regression Method for Harmonic Analysis of Time Series.” Isprs Journal of Photogrammetry & Remote Sensing 185:48–61. https://doi.org/10.1016/j.isprsjprs.2022.01.006.
  • Zhu, Z., and C. E. Woodcock. 2014. “Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data.” Remote Sensing of Environment 144:152–171. https://doi.org/10.1016/j.rse.2014.01.011.
  • Zhu, Z., C. E. Woodcock, C. Holden, and Z. Q. Yang. 2015. “Generating Synthetic Landsat Images Based on All Available Landsat Data: Predicting Landsat Surface Reflectance at Any Given Time.” Remote Sensing of Environment 162:67–83. https://doi.org/10.1016/j.rse.2015.02.009.