Evaluation of machine learning methods and multi-source remote sensing data combinations to construct forest above-ground biomass models

References

• Breiman, Leo. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Breiman, Leo. 2017. Classification and Regression Trees. New York, USA: Routledge.
   Google Scholar
• Brovkina, Olga, Jan Novotny, Emil Cienciala, Frantisek Zemek, and Radek Russ. 2017. “Mapping forest aboveground biomass using airborne hyperspectral and LiDAR data in the mountainous conditions of Central Europe.” Ecological Engineering 100: 219–230. http://dx.doi.org/10.1016/j.ecoleng.2016.12.004.
   Web of Science ®Google Scholar
• Bulut, Sinan. 2023. “Machine Learning Prediction of Above-Ground Biomass in Pure Calabrian Pine (Pinus Brutia Ten.) Stands of the Mediterranean Region, Türkiye.” Ecological Informatics 74: 101951. https://doi.org/10.1016/j.ecoinf.2022.101951
   Web of Science ®Google Scholar
• Chen, Lin, Chunying Ren, Bai Zhang, Zongming Wang, and Yanbiao Xi. 2018. “Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery.” Forests 9 (10): 582. https://doi.org/10.3390/f9100582.
   Web of Science ®Google Scholar
• Cohen, Israel, Yiteng Huang, Jingdong Chen, Jacob Benesty, Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009. “Pearson Correlation Coefficient.” In Noise Reduction in Speech Processing. Springer Topics in Signal Processing. Vol. 2, 1–4. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-00296-0_5.
   Google Scholar
• Cutler, Adele, D Richard Cutler, and John R Stevens. 2012. “Random forests.” In Ensemble machine learning: Methods and applications, 157–175. New York, USA: Springer. https://doi.org/10.1007/978-1-4419-9326-7_5.
   Google Scholar
• Fang, Jing-Yun, and Zhang Ming Wang. 2001. “Forest Biomass Estimation at Regional and Global Levels, with Special Reference to China’s Forest Biomass.” Ecological Research 16 (3): 587–592. https://doi.org/10.1046/j.1440-1703.2001.00419.x
   Web of Science ®Google Scholar
• Friedman, Jerome H. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232.
   Web of Science ®Google Scholar
• Frolking, Stephen, Michael W Palace, D. B. Clark, Jeffrey Q Chambers, H. H. Shugart, and George C Hurtt. 2009. “Forest Disturbance and Recovery: A General Review in the Context of Spaceborne Remote Sensing of Impacts on Aboveground Biomass and Canopy Structure.” Journal of Geophysical Research: Biogeosciences 114 (G2).
   Google Scholar
• Gamon, John A, Ran Wang, and Sabrina E Russo. 2023. “Contrasting Photoprotective Responses of Forest Trees Revealed Using PRI Light Responses Sampled with Airborne Imaging Spectrometry.” New Phytologist 238 (3): 1318–1332. https://doi.org/10.1111/nph.18754.
   PubMed Web of Science ®Google Scholar
• Gómez, Cristina, Michael A. Wulder, Fernando Montes, and José A. Delgado. 2012. “Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain Using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART).” Remote Sensing 4 (1): 135–159. https://doi.org/10.3390/rs4010135.
   Web of Science ®Google Scholar
• Gorelick, Noel, Matt Hancher, Mike Dixon, Simon Ilyushchenko, David Thau, and Rebecca Moore. 2017. “Google Earth Engine: Planetary-Scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. https://doi.org/10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Han, Haoshuang, Rongrong Wan, and Bing Li. 2022. “Estimating Forest Aboveground Biomass Using Gaofen-1 Images, Sentinel-1 Images, and Machine Learning Algorithms: A Case Study of the Dabie Mountain Region, China.” Remote Sensing 14 (1): 176. https://doi.org/10.3390/rs14010176.
   Web of Science ®Google Scholar
• He, Kai, Chenjing Fan, Mingchuan Zhong, Fuliang Cao, Guibin Wang, and Lin Cao. 2023. “Evaluation of Habitat Suitability for Asian Elephants in Sipsongpanna Under Climate Change by Coupling Multi-Source Remote Sensing Products with MaxEnt Model.” Remote Sensing 15 (4): 1047. https://doi.org/10.3390/rs15041047
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Hyde, Peter, Ross Nelson, Dan Kimes, and Elissa Levine. 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.
   Web of Science ®Google Scholar
• Isbaex, Crismeire, and Ana Margarida Coelho. 2021. “The potential of Sentinel-2 satellite images for land-cover/land-use and forest biomass estimation: A review..” Forest Biomass-From Trees to Energy. doi: 10.5772/intechopen.90324.
   Google Scholar
• Jordan, Carl F. 1969. “Derivation of Leaf-Area Index from Quality of Light on the Forest Floor.” Ecology 50 (4): 663–666. http://dx.doi.org/10.2307/1936256.
   Web of Science ®Google Scholar
• Jordan, M. I, and T. M Mitchell. 2015. “Machine learning: Trends, perspectives, and prospects.” Science 349 (6245): 255–260. http://dx.doi.org/10.1126/science.aaa8415.
   PubMed Web of Science ®Google Scholar
• Lechner, Alex M., Giles M. Foody, and Doreen S. Boyd. 2020. “Applications in Remote Sensing to Forest Ecology and Management.” One Earth 2 (5): 405–412. https://doi.org/10.1016/j.oneear.2020.05.001.
   Google Scholar
• Le Toan, T., S. Quegan, M.W.J Davidson, H. Balzter, P Paillou, K. Papathanassiou, S Plummer, etal. 2011. “The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle.” Remote Sensing of Environment 115 (11): 2850–2860. http://dx.doi.org/10.1016/j.rse.2011.03.020.
   Web of Science ®Google Scholar
• Li, Yingchang, Mingyang Li, Chao Li, and Zhenzhen Liu. 2020. “Forest Aboveground Biomass Estimation Using Landsat 8 and Sentinel-1A Data with Machine Learning Algorithms.” Scientific Reports 10 (1): 9952. https://doi.org/10.1038/s41598-020-67024-3.
   PubMed Web of Science ®Google Scholar
• Li, Xiao, Yu Wang, Sumanta Basu, Karl Kumbier, and Bin Yu. 2019. “A Debiased MDI Feature Importance Measure for Random Forests.” Advances in Neural Information Processing Systems 32.
   Google Scholar
• Li, Deren, Changwei Wang, Yueming Hu, and Shuguang Liu. 2012. “General Review on Remote Sensing-Based Biomass Estimation.” Geomatics and Information, Science of Wuhan University 37 (6): 631–635.
   Google Scholar
• Loh, Wei-Yin. 2008. “Classification and Regression Tree Methods.” Encyclopedia of Statistics in Quality and Reliability 1: 315–323.
   Google Scholar
• Loh, Wei-Yin. 2011. “Classification and regression trees.” WIREs Data Mining and Knowledge Discovery 1 (1): 14–23. http://dx.doi.org/10.1002/widm.v1.1.
   Web of Science ®Google Scholar
• Lu, Dengsheng. 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.
   Web of Science ®Google Scholar
• Lu, Dengsheng, Qi Chen, Guangxing Wang, Lijuan Liu, Guiying Li, and Emilio 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.
   Web of Science ®Google Scholar
• Luo, Weixue, Hyun Seok Kim, Xiuhai Zhao, Daun Ryu, Ilbin Jung, Hyunkook Cho, Nancy Harris, Sayon Ghosh, Chunyu Zhang, and Jingjing Liang. 2020. “New Forest Biomass Carbon Stock Estimates in Northeast Asia Based on Multisource Data.” Global Change Biology 26 (12): 7045–7066. https://doi.org/10.1111/gcb.15376
   PubMed Web of Science ®Google Scholar
• Mahdianpari, M., H. Jafarzadeh, J. E. Granger, F. Mohammadimanesh, B. Brisco, B. Salehi, S. Homayouni, and Q. Weng. 2020. “A Large-Scale Change Monitoring of Wetlands Using Time Series Landsat Imagery on Google Earth Engine: A Case Study in Newfoundland.” GIScience & Remote Sensing 57 (8): 1102–1124. https://doi.org/10.1080/15481603.2020.1846948.
   Web of Science ®Google Scholar
• McFEETERS, S. K. 1996. “The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features.” International Journal of Remote Sensing 17 (7): 1425–1432. http://dx.doi.org/10.1080/01431169608948714.
   Web of Science ®Google Scholar
• Menze, Bjoern H, B Michael Kelm, Ralf Masuch, Uwe Himmelreich, Peter Bachert, Wolfgang Petrich, and Fred A Hamprecht. 2009. “A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data.” BMC Bioinformatics 10 (1): 1157. http://dx.doi.org/10.1186/1471-2105-10-213.
   Google Scholar
• Mountrakis, Giorgos, Jungho Im, and Caesar Ogole. 2011. “Support Vector Machines in Remote Sensing: A Review.” ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001.
   Web of Science ®Google Scholar
• Olaode, Abass, Golshah Naghdy, and Catherine Todd. 2014. “Unsupervised Classification of Images: A Review.” International Journal of Image Processing 8 (5): 325–342. https://doi.org/10.1016/j.isprsjprs.2010.11.001.
   Google Scholar
• Pham, Tien Dat, Nga Nhu Le, Nam Thang Ha, Luong Viet Nguyen, Junshi Xia, Naoto Yokoya, Tu Trong To, Hong Xuan Trinh, Lap Quoc Kieu, and Wataru Takeuchi. 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: 777. https://doi.org/10.3390/rs12050777.
   Web of Science ®Google Scholar
• Rahman, M Mahmudur, and Josaphat Tetuko Sri Sumantyo. 2013. “Retrieval of Tropical Forest Biomass Information from ALOS PALSAR Data.” Geocarto International 28 (5): 382–403. https://doi.org/10.1080/10106049.2012.710652.
   Web of Science ®Google Scholar
• Rodríguez-Veiga, Pedro, Shaun Quegan, Joao Carreiras, Henrik J. Persson, Johan E. S. Fransson, Agata Hoscilo, Dariusz Ziółkowski, et al. 2019. “Forest Biomass Retrieval Approaches from Earth Observation in Different Biomes.” International Journal of Applied Earth Observation and Geoinformation 77: 53–68. https://doi.org/10.1016/j.jag.2018.12.008.
   Web of Science ®Google Scholar
• Shaharum, Nur Shafira Nisa, Helmi Zulhaidi Mohd Shafri, Wan Azlina Wan Ab Karim Ghani, Sheila Samsatli, Mohammed Mustafa Abdulrahman Al-Habshi, and Badronnisa Yusuf. 2020. “Oil Palm Mapping Over Peninsular Malaysia Using Google Earth Engine and Machine Learning Algorithms.” Remote Sensing Applications: Society and Environment 17: 100287. https://doi.org/10.1016/j.rsase.2020.100287.
   Google Scholar
• Shao, Zhenfeng, Linjing Zhang, and Lei Wang. 2017. “Stacked Sparse Autoencoder Modeling Using the Synergy of Airborne LiDAR and Satellite Optical and SAR Data to Map Forest Above-Ground Biomass.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (12): 5569–5582. https://doi.org/10.1109/JSTARS.2017.2748341.
   Web of Science ®Google Scholar
• Sinha, Suman, C Jeganathan, L K Sharma, M S Nathawat, Anup K Das, and Shiv Mohan. 2016. “Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass.” Journal of Earth System Science 125 (4): 725–735. http://dx.doi.org/10.1007/s12040-016-0692-z.
   Web of Science ®Google Scholar
• Speiser, Jaime Lynn, Michael E Miller, Janet Tooze, and Edward Ip. 2019. “A Comparison of Random Forest Variable Selection Methods for Classification Prediction Modeling.” Expert Systems with Applications 134: 93–101. https://doi.org/10.1016/j.eswa.2019.05.028.
   PubMed Web of Science ®Google Scholar
• Su, Yanjun, Qinghua Guo, Baolin Xue, Tianyu Hu, Otto Alvarez, Shengli Tao, and Jingyun Fang. 2016. “Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data.” Remote Sensing of Environment 173: 187–199. http://dx.doi.org/10.1016/j.rse.2015.12.002.
   Web of Science ®Google Scholar
• Sun, Guoqing, K. Jon Ranson, Z Guo, Z. Zhang, P Montesano, and D. Kimes. 2011. “Forest biomass mapping from lidar and radar synergies.” Remote Sensing of Environment 115 (11): 2906–2916. http://dx.doi.org/10.1016/j.rse.2011.03.021.
   Web of Science ®Google Scholar
• Tamiminia, Haifa, Bahram Salehi, Masoud Mahdianpari, Colin M Beier, Lucas Johnson, Daniel B Phoenix, and Michael Mahoney. 2022. “Decision Tree-Based Machine Learning Models for Above-Ground Biomass Estimation Using Multi-Source Remote Sensing Data and Object-Based Image Analysis.” Geocarto International 37 (26): 12763–12791. https://doi.org/10.1080/10106049.2022.2071475.
   Web of Science ®Google Scholar
• Tamiminia, Haifa, Bahram Salehi, Masoud Mahdianpari, Lindi Quackenbush, Sarina Adeli, and Brian Brisco. 2020. “Google Earth Engine for geo-big Data Applications: A Meta-Analysis and Systematic Review.” ISPRS Journal of Photogrammetry and Remote Sensing 164: 152–170. https://doi.org/10.1016/j.isprsjprs.2020.04.001.
   Web of Science ®Google Scholar
• Tian, Xin, Min Yan, Christiaan van der Tol, Zengyuan Li, Zhongbo Su, Erxue Chen, Xin Li, et al. 2017. “Modeling Forest Above-Ground Biomass Dynamics Using Multi-Source Data and Incorporated Models: A Case Study Over the Qilian Mountains.” Agricultural and Forest Meteorology 246: 1–14. https://doi.org/10.1016/j.agrformet.2017.05.026.
   Web of Science ®Google Scholar
• Tsui, Olivier W, Nicholas C Coops, Michael A Wulder, and Peter L Marshall. 2013. “Integrating Airborne LiDAR and Space-Borne Radar via Multivariate Kriging to Estimate Above-Ground Biomass.” Remote Sensing of Environment 139: 340–352. https://doi.org/10.1016/j.rse.2013.08.012.
   Web of Science ®Google Scholar
• Vafaei, Sasan, Javad Soosani, Kamran Adeli, Hadi Fadaei, Hamed Naghavi, Tien Dat Pham, and Dieu Tien Bui. 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/rs10020172
   Web of Science ®Google Scholar
• Vashum, Kuimi T, and S. Jayakumar. 2012. “Methods to Estimate Above-Ground Biomass and Carbon Stock in Natural Forests-a Review.” Journal of Ecosystem & Ecography 2 (4): 1–7.
   Google Scholar
• Velasco Pereira, Edward A, María A Varo Martínez, Francisco J Ruiz Gómez, and Rafael M Navarro-Cerrillo. 2023. “Temporal Changes in Mediterranean Pine Forest Biomass Using Synergy Models of ALOS PALSAR-Sentinel 1-Landsat 8 Sensors.” Remote Sensing 15 (13): 3430. https://doi.org/10.3390/rs15133430.
   Web of Science ®Google Scholar
• Wang, Dezhi, Bo Wan, Jing Liu, Yanjun Su, Qinghua Guo, Penghua Qiu, and Xincai Wu. 2020. “Estimating Aboveground Biomass of the Mangrove Forests on Northeast Hainan Island in China Using an Upscaling Method from Field Plots, UAV-LiDAR Data and Sentinel-2 Imagery.” International Journal of Applied Earth Observation and Geoinformation 85: 101986. https://doi.org/10.1016/j.jag.2019.101986
   Web of Science ®Google Scholar
• Wolfowitz, Jacob. 1957. “The Minimum Distance Method.” The Annals of Mathematical Statistics 28 (1): 75–88. https://doi.org/10.1214/aoms/1177707038
   Google Scholar
• Wongchai, Warakhom, Thossaporn Onsree, Natthida Sukkam, Anucha Promwungkwa, and Nakorn Tippayawong. 2022. “Machine Learning Models for Estimating Above Ground Biomass of Fast Growing Trees.” Expert Systems with Applications 199: 117186. https://doi.org/10.1016/j.eswa.2022.117186
   Web of Science ®Google Scholar
• Wulder, Michael A, Joanne C White, Ross F Nelson, Erik Næsset, Hans Ole Ørka, Nicholas C Coops, Thomas Hilker, Christopher W Bater, and Terje Gobakken. 2012. “Lidar Sampling for Large-Area Forest Characterization: A Review.” Remote Sensing of Environment 121: 196–209. https://doi.org/10.1016/j.rse.2012.02.001
   Web of Science ®Google Scholar
• Yan, Xingguang, Di Yang Jing Li, Jiwei Li, Tianyue Ma, Yiting Su, Jiahao Shao, and Rui Zhang. 2022. “A Random Forest Algorithm for Landsat Image Chromatic Aberration Restoration Based on GEE Cloud Platform—A Case Study of Yucatán Peninsula, Mexico.” Remote Sensing 14 (20): 5154. https://doi.org/10.3390/rs14205154.
   Web of Science ®Google Scholar
• Yang, Zelong, Wenwen Li, Qi Chen, Sheng Wu, Shanjun Liu, and Jianya Gong. 2018. “A Scalable Cyberinfrastructure and Cloud Computing Platform for Forest Aboveground Biomass Estimation Based on the Google Earth Engine.” International Journal of Digital Earth 12 (9): 995–1012. https://doi.org/10.1080/17538947.2018.1494761.
   Web of Science ®Google Scholar
• Yang, Lu, Shunlin Liang, and Yuzhen Zhang. 2020. “A New Method for Generating a Global Forest Aboveground Biomass Map from Multiple High-Level Satellite Products and Ancillary Information.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13: 2587–2597. https://doi.org/10.1109/JSTARS.2020.2987951.
   Web of Science ®Google Scholar
• Yang, Qiuli, Chunyue Niu, Xiaoqiang Liu, Yuhao Feng, Qin Ma, Xuejing Wang, Hao Tang, and Qinghua Guo. 2023. “Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data.” GIScience & Remote Sensing 60 (1). http://dx.doi.org/10.1080/15481603.2023.2203303.
   Web of Science ®Google Scholar
• Zeng, Yelu, Dalei Hao, Alfredo Huete, Benjamin Dechant, Joe Berry, Jing M Chen, Joanna Joiner, etal. 2022. “Optical vegetation indices for monitoring terrestrial ecosystems globally.” Nature Reviews Earth & Environment 3 (7): 477–493. http://dx.doi.org/10.1038/s43017-022-00298-5.
   Google Scholar
• Zhang, Xiang, Lexin Li, Hua Zhou, Yeqing Zhou, and Dinggang Shen. 2019. “Tensor Generalized Estimating Equations for Longitudinal Imaging Analysis.” Statistica Sinica 29 (4): 1977.
   PubMed Web of Science ®Google Scholar
• Zhang, Yuzhen, Jun Ma, Shunlin Liang, Xisheng Li, and Manyao Li. 2020. “An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products.” Remote Sensing 12 (24): 4015. https://doi.org/10.3390/rs12244015.
   Web of Science ®Google Scholar
• Zhang, Yuzhen, Jun Ma, Shunlin Liang, Xisheng Li, and Manyao Li. 2020. “An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products.” Remote Sensing 12 (24): 4015. http://dx.doi.org/10.3390/rs12244015.
   Web of Science ®Google Scholar
• Zhang, Yali, Ni Wang, Yuliang Wang, and Mingshi Li. 2023a. “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.
   Web of Science ®Google Scholar
• Zhang, Zheyuan, Jia Wang, Nina Xiong, Boyi Liang, and Zong Wang. 2023b. “Air Pollution Exposure Based on Nighttime Light Remote Sensing and Multi-Source Geographic Data in Beijing.” Chinese Geographical Science 33 (2): 320–332. https://doi.org/10.1007/s11769-023-1339-z
   Google Scholar
• Zhang, Linjing, Xiaoxue Zhang, Zhenfeng Shao, Wenhao Jiang, and Huimin Gao. 2023c. “Integrating Sentinel-1 and 2 with LiDAR Data to Estimate Aboveground Biomass of Subtropical Forests in Northeast Guangdong, China.” International Journal of Digital Earth 16 (1): 158–182. https://doi.org/10.1080/17538947.2023.2165180.
   Web of Science ®Google Scholar
• Zhao, Yifan, Weiwei Zhu, Panpan Wei, Peng Fang, Xiwang Zhang, Nana Yan, Wenjun Liu, Hao Zhao, and Qirui Wu. 2022. “Classification of Zambian grasslands using random forest feature importance selection during the optimal phenological period.” Ecological Indicators 135: 108529. http://dx.doi.org/10.1016/j.ecolind.2021.108529.
   Web of Science ®Google Scholar