Integration of Landsat time-series vegetation indices improves consistency of change detection

References

• Abdullah, Nor Azliana, Nasrudin Abd Rahim, Chin Kim Gan, and Noriah Nor Adzman. 2019. “Forecasting Solar Power Using Hybrid Firefly and Particle Swarm Optimization (HFPSO) for Optimizing the Parameters in a Wavelet Transform-Adaptive Neuro Fuzzy Inference System (WT-ANFIS).” Applied Sciences (Switzerland) 9 (16): 3214. doi:10.3390/app9163214.
   Web of Science ®Google Scholar
• Assal, Timothy J., Patrick J. Anderson, and Jason Sibold. 2016. “Spatial and Temporal Trends of Drought Effects in a Heterogeneous Semi-Arid Forest Ecosystem.” Forest Ecology and Management 365: 137–151. doi:10.1016/j.foreco.2016.01.017.
   Web of Science ®Google Scholar
• Awty-Carroll, Katie, Pete Bunting, Andy Hardy, and Gemma Bell. 2019. “An Evaluation and Comparison of Four Dense Time Series Change Detection Methods Using Simulated Data.” Remote Sensing 11 (23): 2779. doi:10.3390/rs11232779.
   Web of Science ®Google Scholar
• Badgley, Grayson, Christopher B. Field, and Joseph A. Berry. 2017. “Canopy Near-Infrared Reflectance and Terrestrial Photosynthesis.” Science Advances 3 (3): e1602244. doi:10.1126/sciadv.1602244.
   PubMed Web of Science ®Google Scholar
• Berner, Logan T., and Scott J. Goetz. 2022. “Satellite Observations Document Trends Consistent with a Boreal Forest Biome Shift.” Global Change Biology, doi:10.1111/gcb.16121.
   Web of Science ®Google Scholar
• Bonan, Gordon B., David Pollard, and Starley L. Thompson. 1992. “Effects of Boreal Forest Vegetation on Global Climate.” Nature 359 (6397): 716–718. doi:10.1038/359716a0.
   Web of Science ®Google Scholar
• Bueno, Inacio T., Greg J. McDermid, Eduarda M.O. Silveira, Jennifer N. Hird, Breno I. Domingos, and Fausto W. Acerbi Júnior. 2020. “Spatial Agreement among Vegetation Disturbance Maps in Tropical Domains Using Landsat Time Series.” Remote Sensing 12), doi:10.3390/RS12182948.
   Web of Science ®Google Scholar
• Bullock, Eric L., Curtis E. Woodcock, and Christopher E. Holden. 2020. “Improved Change Monitoring Using an Ensemble of Time Series Algorithms.” Remote Sensing of Environment 238: 111165. doi:10.1016/j.rse.2019.04.018.
   Web of Science ®Google Scholar
• Cohen, Warren B., Sean P. Healey, Zhiqiang Yang, Zhe Zhu, and Noel Gorelick. 2020. “Diversity of Algorithm and Spectral Band Inputs Improves Landsat Monitoring of Forest Disturbance.” Remote Sensing 12 (10): 1673. doi:10.3390/rs12101673.
   Web of Science ®Google Scholar
• Cohen, Warren B., Zhiqiang Yang, Sean P. Healey, Robert E. Kennedy, and Noel Gorelick. 2018. “A LandTrendr Multispectral Ensemble for Forest Disturbance Detection.” Remote Sensing of Environment 205: 131–140. doi:10.1016/j.rse.2017.11.015.
   Web of Science ®Google Scholar
• Cohen, Warren B., Zhiqiang Yang, and Robert Kennedy. 2010. “Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 2. TimeSync — Tools for Calibration and Validation.” Remote Sensing of Environment 114 (12): 2911–2924. doi:10.1016/j.rse.2010.07.010.
   Web of Science ®Google Scholar
• De Jong, Rogier, Jan Verbesselt, Michael E. Schaepman, and Sytze de Bruin. 2012. “Trend Changes in Global Greening and Browning: Contribution of Short-Term Trends to Longer-Term Change.” Global Change Biology 18 (2): 642–655. doi:10.1111/j.1365-2486.2011.02578.x.
   Web of Science ®Google Scholar
• DeVries, Ben, Mathieu Decuyper, Jan Verbesselt, Achim Zeileis, Martin Herold, and Shijo Joseph. 2015a. “Tracking Disturbance-Regrowth Dynamics in Tropical Forests Using Structural Change Detection and Landsat Time Series.” Remote Sensing of Environment 169: 320–334. doi:10.1016/j.rse.2015.08.020.
   Web of Science ®Google Scholar
• DeVries, Ben, Jan Verbesselt, Lammert Kooistra, and Martin Herold. 2015b. “Robust Monitoring of Small-Scale Forest Disturbances in a Tropical Montane Forest Using Landsat Time Series.” Remote Sensing of Environment 161: 107–121. doi:10.1016/j.rse.2015.02.012.
   Web of Science ®Google Scholar
• Ding, Z., J. Peng, S. Qiu, and Y. Zhao. 2020. “Nearly Half of Global Vegetated Area Experienced Inconsistent Vegetation Growth in Terms of Greenness, Cover, and Productivity.” Earth's Future 8 (10): 1–15. doi:10.1029/2020EF001618.
   Web of Science ®Google Scholar
• Dutrieux, Loïc P., Catarina C. Jakovac, Siti H. Latifah, and Lammert Kooistra. 2016. “Reconstructing Land use History from Landsat Time-Series.” International Journal of Applied Earth Observation and Geoinformation 47: 112–124. doi:10.1016/j.jag.2015.11.018.
   Web of Science ®Google Scholar
• Esteban, Jessica, Alfredo Fernández-Landa, José Luis Tomé, Cristina Gómez, and Miguel Marchamalo. 2021. “Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of Als Data.” Remote Sensing 13 (18): 3611. doi:10.3390/rs13183611.
   Web of Science ®Google Scholar
• Fan, Mengzhuo, Kuo Liao, Dengsheng Lu, and Dengqiu Li. 2022. “Examining Vegetation Change and Associated Spatial Patterns in Wuyishan National Park at Different Protection Levels.” Remote Sensing 14 (7): 1712–1720. doi:10.3390/rs14071712.
   Web of Science ®Google Scholar
• Fang, Xiuqin, Qiuan Zhu, Liliang Ren, Huai Chen, Kai Wang, and Changhui Peng. 2018. “Large-Scale Detection of Vegetation Dynamics and Their Potential Drivers Using MODIS Images and BFAST: A Case Study in Quebec, Canada.” Remote Sensing of Environment 206 (November 2017): 391–402. doi:10.1016/j.rse.2017.11.017.
   Google Scholar
• García, M. J.López, and V. Caselles. 1991. “Mapping Burns and Natural Reforestation Using Thematic Mapper Data.” Geocarto International 6 (1): 31–37. doi:10.1080/10106049109354290.
   Google Scholar
• Goldsmith, G. R. 2013. “Changing Directions: The Atmosphere–Plant–Soil Continuum.” New Phytologist 199 (1): 4–6. doi:10.1111/nph.12332.
   PubMed Web of Science ®Google Scholar
• Grogan, Kenneth, Dirk Pflugmacher, Patrick Hostert, Jan Verbesselt, and Rasmus Fensholt. 2016. “Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from Modis Time Series: Does Forest Type Matter?” Remote Sensing 8 (8): 657. doi:10.3390/rs8080657.
   Web of Science ®Google Scholar
• Haberl, Helmut, K. Heinz Erb, Fridolin Krausmann, Veronika Gaube, Alberte Bondeau, Christoph Plutzar, Simone Gingrich, Wolfgang Lucht, and Marina Fischer-Kowalski. 2007. “Quantifying and Mapping the Human Appropriation of Net Primary Production in Earth’s Terrestrial Ecosystems.” Proceedings of the National Academy of Sciences 104 (31): 12942–12947. doi:10.1073/pnas.0704243104.
   PubMed Web of Science ®Google Scholar
• He, Siyuan, and Yang Su. 2022. “Understanding Residents’ Perceptions of the Ecosystem to Improve Park–People Relationships in Wuyishan National Park, China.” Land 11 (4): 532. doi:10.3390/land11040532.
   Web of Science ®Google Scholar
• He, Panxing, Zongjiu Sun, Zhiming Han, Yiqiang Dong, Huixia Liu, Xiaoyu Meng, and Jun Ma. 2021. “Dynamic Characteristics and Driving Factors of Vegetation Greenness Under Changing Environments in Xinjiang, China.” Environmental Science and Pollution Research 28 (31): 42516–42532. doi:10.1007/s11356-021-13721-z.
   PubMed Web of Science ®Google Scholar
• Hislop, Samuel, Simon Jones, Mariela Soto-Berelov, Andrew Skidmore, Andrew Haywood, and Trung H. Nguyen. 2018. “Using Landsat Spectral Indices in Time-Series to Assess Wildfire Disturbance and Recovery.” Remote Sensing 10 (3): 460. doi:10.3390/rs10030460.
   Web of Science ®Google Scholar
• Hislop, Samuel, Simon Jones, Mariela Soto-Berelov, Andrew Skidmore, Andrew Haywood, and Trung H. Nguyen. 2019. “A Fusion Approach to Forest Disturbance Mapping Using Time Series Ensemble Techniques.” Remote Sensing of Environment 221: 188–197. doi:10.1016/j.rse.2018.11.025.
   Web of Science ®Google Scholar
• Holden, Christopher E., and Curtis E. Woodcock. 2016. “An Analysis of Landsat 7 and Landsat 8 Underflight Data and the Implications for Time Series Investigations.” Remote Sensing of Environment 185: 16–36. doi:10.1016/j.rse.2016.02.052.
   Web of Science ®Google Scholar
• Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1–2): 195–213. doi:10.1016/S0034-4257(02)00096-2.
   Web of Science ®Google Scholar
• Kennedy, Robert E., Zhiqiang Yang, and Warren B. Cohen. 2010. “Detecting Trends in Forest Disturbance and Recovery Using Yearly Landsat Time Series: 1. LandTrendr — Temporal Segmentation Algorithms.” Remote Sensing of Environment 114 (12): 2897–2910. doi:10.1016/j.rse.2010.07.008.
   Web of Science ®Google Scholar
• Kulawardhana, Ranjani Wasantha. 2011. “Remote Sensing of Vegetation: Principles, Techniques and Applications. By Hamlyn G. Jones and Robin A Vaughan.” Journal of Vegetation Science 22 (6): 1151–1153. doi:10.1111/j.1654-1103.2011.01319.x.
   Google Scholar
• Li, Dengqiu, Dengsheng Lu, Ming Wu, Xuexin Shao, and Jinhong Wei. 2018. “Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985-2016 Using Landsat Time-Series Data.” Remote Sensing 10 (1): 32. doi:10.3390/rs10010032.
   Web of Science ®Google Scholar
• Li, Dengqiu, Dengsheng Lu, Yan Zhao, Mingxing Zhou, and Guangsheng Chen. 2021. “Spatial Patterns of Vegetation Coverage Change in Giant Panda Habitat Based on MODIS Time-Series Observations and Local Indicators of Spatial Association.” Ecological Indicators 124: 107418. doi:10.1016/j.ecolind.2021.107418.
   Web of Science ®Google Scholar
• Liang, Jianping, Aiming Wang, and Shengfa Liang. 2002. “Disturbance and Forest Regeneration.” Forest Research 15 (4): 590–498. doi:10.13275/j.cnki.lykxyj.2002.04.023. In Chinese.
   Google Scholar
• Lin, Sen, Xisheng Hu, Chengzhen Wu, and Wei Hong. 2020. “Temporal-Spatial Features of Vegetation Cover in Mount Wuyi National Park.” Journal of Forest and Environment 40 (4): 347–355. doi:10.13324/j.cnki.jfcf.2020.04.002. In Chinese.
   Google Scholar
• Liu, Feng, Hongyan Liu, Chongyang Xu, Liang Shi, Xinrong Zhu, Yang Qi, and Wenqi He. 2021. “Old-Growth Forests Show low Canopy Resilience to Droughts at the Southern Edge of the Taiga.” Global Change Biology 27 (11): 2392–2402. doi:10.1111/gcb.15605.
   PubMed Web of Science ®Google Scholar
• Liu, Yue, Yihan Pu, Yanqing Liu, Deshuai An, Dandan Xu, Jianqin Zhu, and Honghua Ruan. 2022. “Study on the Impact of Global Climate Change on the Communities of Vegetation Vertical Zone Spectrum in Wuyishan National Park Based on Landsat Imagery.” Ecological Science 41 (5): 152–162. doi:10.14108/j.cnki.1008-8873.2022.05.019. In Chinese.
   Google Scholar
• Masek, Jeffrey G., Eric F. Vermote, Nazmi E. Saleous, Robert Wolfe, Forrest G. Hall, Karl F. Huemmrich, Feng Gao, Jonathan Kutler, and Teng Kui Lim. 2006. “A Landsat Surface Reflectance Dataset for North America, 1990-2000.” IEEE Geoscience and Remote Sensing Letters 3 (1): 68–72. doi:10.1109/LGRS.2005.857030.
   Web of Science ®Google Scholar
• Matsushita, Bunkei, Wei Yang, Jin Chen, Yuyichi Onda, and Guoyu Qiu. 2007. “Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest.” Sensors 7 (11): 2636–2651. doi:10.3390/s7112636.
   PubMed Web of Science ®Google Scholar
• Mayer, D. G., and D. G. Butler. 1993. “Statistical Validation.” Ecological Modelling 68 (1–2): 21–32. doi:10.1016/0304-3800(93)90105-2.
   Web of Science ®Google Scholar
• Moreno-Fernández, Daniel, Alba Viana-Soto, Julio Jesús Camarero, Miguel A. Zavala, Julián Tijerín, and Mariano García. 2021. “Using Spectral Indices as Early Warning Signals of Forest Dieback: The Case of Drought-Prone Pinus Pinaster Forests.” Science of the Total Environment 793: 148578. doi:10.1016/j.scitotenv.2021.148578.
   PubMed Web of Science ®Google Scholar
• Murillo-Sandoval, Paulo J., Thomas Hilker, Meg A. Krawchuk, and Jamon Van Den Hoek. 2018. “Detecting and Attributing Drivers of Forest Disturbance in the Colombian Andes Using Landsat Time-Series.” Forests 9 (5): 269. doi:10.3390/f9050269.
   Web of Science ®Google Scholar
• Nitze, Ingmar, and Guido Grosse. 2016. “Detection of Landscape Dynamics in the Arctic Lena Delta with Temporally Dense Landsat Time-Series Stacks.” Remote Sensing of Environment 181: 27–41. doi:10.1016/j.rse.2016.03.038.
   Web of Science ®Google Scholar
• Pastor-Guzman, J., Jadunandan Dash, and Peter M. Atkinson. 2018. “Remote Sensing of Mangrove Forest Phenology and Its Environmental Drivers.” Remote Sensing of Environment 205: 71–84. doi:10.1016/j.rse.2017.11.009.
   Web of Science ®Google Scholar
• Piragnolo, Marco, Francesco Pirotti, Carlo Zanrosso, Emanuele Lingua, and Stefano Grigolato. 2021. “Responding to Large-Scale Forest Damage in an Alpine Environment with Remote Sensing, Machine Learning, and Web-GIS.” Remote Sensing 13, doi:10.3390/rs13081541.
   Google Scholar
• Querejeta, José, Héctor Ignacio, Michael F. Estrada-Medina, and Juan José Jiménez-Osornio. Allen. 2007. “Water Source Partitioning among Trees Growing on Shallow Karst Soils in a Seasonally Dry Tropical Climate.” Oecologia 152 (1): 26–36. doi:10.1007/s00442-006-0629-3.
   PubMed Web of Science ®Google Scholar
• Rouse, J. W., R. H. Hass, J. A. Schell, and D. W. Deering. 1976. “Metabolism of Limonoids. Limonin D-Ring Lactone Hydrolase Activity in Pseudomonas.” Journal of Agricultural and Food Chemistry 24 (1): 24–26. doi:10.1021/jf60203a024.
   PubMed Web of Science ®Google Scholar
• Saxena, Rishu, Layne T. Watson, Valerie A. Thomas, and Randolph H. Wynne. 2017. “Scaling Constituent Algorithms of a Trend and Change Detection Polyalgorithm.” Simulation Series 49 (3): 59–70. doi:10.22360/springsim.2017.hpc.020.
   Google Scholar
• Schroeder, Todd A., Michael A. Wulder, Sean P. Healey, and Gretchen G. Moisen. 2011. “Mapping Wildfire and Clearcut Harvest Disturbances in Boreal Forests with Landsat Time Series Data.” Remote Sensing of Environment 115 (6): 1421–1433. doi:10.1016/j.rse.2011.01.022.
   Web of Science ®Google Scholar
• Schultz, Michael, Jan G.P.W. Clevers, Sarah Carter, Jan Verbesselt, Valerio Avitabile, Hien Vu Quang, and Martin Herold. 2016. “Performance of Vegetation Indices from Landsat Time Series in Deforestation Monitoring.” International Journal of Applied Earth Observation and Geoinformation 52: 318–327. doi:10.1016/j.jag.2016.06.020.
   Web of Science ®Google Scholar
• Schultz, Michael, Aurélie Shapiro, Jan G.P.W. Clevers, Craig Beech, and Martin Herold. 2018. “Forest Cover and Vegetation Degradation Detection in the Kavango Zambezi Transfrontier Conservation Area Using BFAST Monitor.” Remote Sensing 10 (11): 1850. doi:10.3390/rs10111850.
   Web of Science ®Google Scholar
• Shimizu, Katsuto, Tetsuji Ota, Nobuya Mizoue, and Shigejiro Yoshida. 2019. “A Comprehensive Evaluation of Disturbance Agent Classification Approaches: Strengths of Ensemble Classification, Multiple Indices, Spatio-Temporal Variables, and Direct Prediction.” ISPRS Journal of Photogrammetry and Remote Sensing 158: 99–112. doi:10.1016/j.isprsjprs.2019.10.004.
   Web of Science ®Google Scholar
• Smith, Vaughn, Carlos Portillo-Quintero, Arturo Sanchez-Azofeifa, and Jose L. Hernandez-Stefanoni. 2019. “Assessing the Accuracy of Detected Breaks in Landsat Time Series as Predictors of Small Scale Deforestation in Tropical Dry Forests of Mexico and Costa Rica.” Remote Sensing of Environment 221: 707–721. doi:10.1016/j.rse.2018.12.020.
   Web of Science ®Google Scholar
• Tucker, Compton J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. doi:10.1016/0034-4257(79)90013-0.
   Web of Science ®Google Scholar
• Verbesselt, Jan, Rob Hyndman, Glenn Newnham, and Darius Culvenor. 2010. “Detecting Trend and Seasonal Changes in Satellite Image Time Series.” Remote Sensing of Environment 114 (1): 106–115. doi:10.1016/j.rse.2009.08.014.
   Web of Science ®Google Scholar
• Verbesselt, Jan, Rob Hyndman, Achim Zeileis, and Darius Culvenor. 2010. “Phenological Change Detection While Accounting for Abrupt and Gradual Trends in Satellite Image Time Series.” Remote Sensing of Environment 114 (12): 2970–2980. doi:10.1016/j.rse.2010.08.003.
   Web of Science ®Google Scholar
• Verma, Manish, Mark A. Friedl, Adrien Finzi, and Nathan Phillips. 2016. “Multi-Criteria Evaluation of the Suitability of Growth Functions for Modeling Remotely Sensed Phenology.” Ecological Modelling 323: 123–132. doi:10.1016/j.ecolmodel.2015.12.005.
   Web of Science ®Google Scholar
• Vermote, Eric, Chris Justice, Martin Claverie, and Belen Franch. 2016. “Preliminary Analysis of the Performance of the Landsat 8/OLI Land Surface Reflectance Product.” Remote Sensing of Environment, 46–56. doi:10.1016/j.rse.2016.04.008.
   PubMed Web of Science ®Google Scholar
• Vogelmann, James E., and Barrett N. Rock. 1988. “Assessing Forest Damage in High-Elevation Coniferous Forests in Vermont and New Hampshire Using Thematic Mapper Data.” Remote Sensing of Environment 24 (2): 227–246. doi:10.1016/0034-4257(88)90027-2.
   Web of Science ®Google Scholar
• Wang, Xugao, Xiuzhen Li, Fanhua Kong, Yuehui Li, Binglu Shi, and Zhenling Gao. 2003. “Model of Vegetation Restoration Under Natural Regeneration and Human Interference in the Burned Area of Northern Daxinganling.” Chinese Journal of Ecology 22 (5): 30–34. doi:10.13292/j.1000－4890.2003.0101. In Chinese.
   Google Scholar
• Willmott, Cort J. 1981. “On the Validation of Models.” Physical Geography 2 (2): 184–194. doi:10.1080/02723646.1981.10642213.
   Google Scholar
• Wu, Ling, Zhaoliang Li, Xiangnan Liu, Lihong Zhu, Yibo Tang, Biyao Zhang, Boliang Xu, Meiling Liu, Yuanyuan Meng, and Boyuan Liu. 2020. “Multi-Type Forest Change Detection Using BFAST and Monthly Landsat Time Series for Monitoring Spatiotemporal Dynamics of Forests in Subtropical Wetland.” Remote Sensing 12 (2): 341. doi:10.3390/rs12020341.
   Web of Science ®Google Scholar
• Xin, Qinchuan, Jing Li, Ziming Li, Yaoming Li, and Xuewen Zhou. 2020. “Evaluations and Comparisons of Rule-Based and Machine-Learning-Based Methods to Retrieve Satellite-Based Vegetation Phenology Using MODIS and USA National Phenology Network Data.” International Journal of Applied Earth Observation and Geoinformation 93: 102189. doi:10.1016/j.jag.2020.102189.
   Web of Science ®Google Scholar
• Yang, Huiting, and Hanqiu Xu. 2020. “Assessing Fractional Vegetation Cover Changes and Ecological Quality of the Wuyi Mountain National Nature Reserve Based on Remote Sensing Spatial Information.” Chinese Journal of Applied Ecology 31 (2): 533–542. doi:10.13287/j.1001-9332.202002.014. In Chinese.
   Google Scholar
• Zeileis, Achim, Friedrich Leisch, Kurt Hornik, and Christian Kleiber. 2002. “Strucchange: An R Package for Testing for Structural Change in Linear Regression Models.” Journal of Statistical Software 7: 1–38. doi:10.18637/jss.v007.i02.
   Google Scholar
• Zeng, Yelu, Dalei Hao, Alfredo Huete, Benjamin Dechant, Joe Berry, Jing M. Chen, Joanna Joiner, et al. 2022. “Optical Vegetation Indices for Monitoring Terrestrial Ecosystems Globally.” Nature Reviews Earth & Environment 3: 477–493. doi:10.1038/s43017-022-00298-5.
   Google Scholar
• Zhu, Zhe, Yingchun Fu, Curtis E. Woodcock, Pontus Olofsson, James E. Vogelmann, Christopher Holden, Min Wang, Shu Dai, and Yang Yu. 2016. “Including Land Cover Change in Analysis of Greenness Trends Using All Available Landsat 5, 7, and 8 Images: A Case Study from Guangzhou, China (2000–2014).” Remote Sensing of Environment 185: 243–257. doi:10.1016/j.rse.2016.03.036.
   Web of Science ®Google Scholar
• Zhu, Zhe, and Curtis E. Woodcock. 2012. “Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery.” Remote Sensing of Environment 118: 83–94. doi:10.1016/j.rse.2011.10.028.
   Web of Science ®Google Scholar
• Zhu, Zhe, and Curtis E. Woodcock. 2014. “Continuous Change Detection and Classification of Land Cover Using All Available Landsat Data.” Remote Sensing of Environment 144: 152–171. doi:10.1016/j.rse.2014.01.011.
   Web of Science ®Google Scholar
• Zhu, Yaohui, Chunjiang Zhao, Hao Yang, Guijun Yang, Liang Han, Zhenhai Li, Haikuan Feng, Bo Xu, Jintao Wu, and Lei Lei. 2019. “Estimation of Maize Above-Ground Biomass Based on Stem-Leaf Separation Strategy Integrated with LiDAR and Optical Remote Sensing Data.” PeerJ 2019 (9): e7593. doi:10.7717/peerj.7593.
   Google Scholar
• Zou, Weicheng. 2014. “Spatial Heterogeneity Relationship of Vegetation and Environmental Factors in Wuyi Mountain.” Master's Thesis, Fuzhou University. (In Chinese).
   Google Scholar