A multi-source data approach to carbon stock prediction using Bayesian hierarchical geostatistical models in plantation forest ecosystems

References

• Ahmed, N., C. Atzberger, and W. Zewdie. 2022. “The Potential of Modeling Prosopis Juliflora Invasion Using Sentinel-2 Satellite Data and Environmental Variables in the Dryland Ecosystem of Ethiopia.” Ecological Informatics 68:101545. https://doi.org/10.1016/j.ecoinf.2021.101545.
   Web of Science ®Google Scholar
• Ali, A., S.-L. Lin, J.-K. He, F.-M. Kong, J.-H. Yu, and H.-S. Jiang. 2019. “Climate and Soils Determine Aboveground Biomass Indirectly via Species Diversity and Stand Structural Complexity in Tropical Forests.” Forest Ecology and Management 432:823–21. https://doi.org/10.1016/j.foreco.2018.10.024.
   Web of Science ®Google Scholar
• Álvarez-Dávila, E., L. Cayuela, S. González-Caro, A. M. Aldana, P. R. Stevenson, O. Phillips, Á. Cogollo, et al. 2017. “Forest Biomass Density Across Large Climate Gradients in Northern South America is Related to Water Availability but Not with Temperature.” PLoS One 12 (3): e0171072. Public Library of Science San Francisco, CA USA. https://doi.org/10.1371/journal.pone.0171072.
   PubMed Web of Science ®Google Scholar
• Azevedo, L. 2021. “Model Reduction in Geostatistical Seismic Inversion with Functional Data Analysis Geophysics.” Society of Exploration Geophysicists 87 (1): M1–M11. https://doi.org/10.1190/geo2021-0096.1.
   Web of Science ®Google Scholar
• Babcock, C., A. O. Finley, H.-E. Andersen, R. Pattison, B. D. Cook, D. C. Morton, M. Alonzo, et al. 2018. “Geostatistical Estimation of Forest Biomass in Interior Alaska Combining Landsat-Derived Tree Cover, Sampled Airborne Lidar and Field Observations.” Remote Sensing of Environment 212:212–230. https://doi.org/10.1016/j.rse.2018.04.044.
   Web of Science ®Google Scholar
• Babcock, C., A. O. Finley, B. D. Cook, A. Weiskittel, and C. W. Woodall. 2016. “Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and LiDAR Data.” Remote Sensing of Environment 182:1–12. https://doi.org/10.1016/j.rse.2016.04.014.
   Web of Science ®Google Scholar
• Baccini, A., M. A. Friedl, C. E. Woodcock, and R. Warbington. 2004. “Forest biomass estimation over regional scales using multisource data.” Geophysical Research Letters 31 (10). John Wiley & Sons, Ltd. https://doi.org/10.1029/2004GL019782.
   Web of Science ®Google Scholar
• Banerjee, S., B. P. Carlin, and A. E. Gelfand. 2003. Hierarchical Modeling and Analysis for Spatial Data. Chapman and Hall/CRC.
   Google Scholar
• Baugh, W. M., and D. P. Groeneveld. 2006. “Broadband Vegetation Index Performance Evaluated for a Low‐Cover Environment.” International Journal of Remote Sensing 27 (21): 4715–4730. https://doi.org/10.1080/01431160600758543.
   Web of Science ®Google Scholar
• Beloconi, A., and P. Vounatsou. 2020. “Bayesian Geostatistical Modelling of High-Resolution NO2 Exposure in Europe Combining Data from Monitors, Satellites and Chemical Transport Models.” Environment International 138:105578. https://doi.org/10.1016/j.envint.2020.105578.
   PubMed Web of Science ®Google Scholar
• Bordoloi, R., B. Das, O. P. Tripathi, U. K. Sahoo, A. J. Nath, S. Deb, D. J. Das, et al. 2022. “Satellite Based Integrated Approaches to Modelling Spatial Carbon Stock and Carbon Sequestration Potential of Different Land Uses of Northeast India.” Environmental and Sustainability Indicators 13:100166. https://doi.org/10.1016/j.indic.2021.100166.
   Google Scholar
• Box, G. E. P., and D. R. Cox. 1964. “An Analysis of Transformations (With Discussion).” Journal of the Royal Statistical Society: Series B (Methodological) 26 (2): 211–243. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x.
   Web of Science ®Google Scholar
• Box, G. E. P., and D. R. Cox. 1982. “An Analysis of Transformations Revisited, Rebutted.” Journal of the American Statistical Association 77 (377): 209–210. https://doi.org/10.1080/01621459.1982.10477788.
   Web of Science ®Google Scholar
• Brown, S. 1997. FAO Forestry Paper, 134. Rome: FAO - Food and Agriculture Organization of the United Nations.
   Google Scholar
• Brus, D. J., J. J. de Gruijter, and J. W. van Groenigen. 2006. “Chapter 14 Designing Spatial Coverage Samples Using the K-Means Clustering Algorithm.” Developments in Soil Science 31:183–192. https://doi.org/10.1016/S0166-2481(06)31014-8.
   Google Scholar
• Chinembiri, T. S., M. C. Bronsveld, D. G. Rossiter, and T. Dube. 2013. “The Precision of C Stock Estimation in the Ludhikola Watershed Using Model-Based and Design-Based Approaches.” Natural Resources Research 22 (4): 297–309. https://doi.org/10.1007/s11053-013-9216-6.
   Google Scholar
• Chinembiri, T. S., O. Mutanga, and T. Dube. 2023a. “Carbon Stock Prediction in Managed Forest Ecosystems Using Bayesian and Frequentist Geostatistical Techniques and New Generation Remote Sensing Metrics.” Remote Sensing 15 (6): 1649. https://doi.org/10.3390/rs15061649.
   Web of Science ®Google Scholar
• Chinembiri, T. S., O. Mutanga, and T. Dube. 2023b. “Hierarchical Bayesian Geostatistics for C Stock Prediction in Disturbed Plantation Forest in Zimbabwe.” Ecological Informatics 73:101934. https://doi.org/10.1016/j.ecoinf.2022.101934.
   Web of Science ®Google Scholar
• Christman, Z. J., and J. Rogan. 2012. “Error Propagation in Raster Data Integration: Impacts on Landscape Composition and Configuration.” Photogrammetric Engineering & Remote Sensing 78 (6): 617–624. https://doi.org/10.14358/PERS.78.6.617.
   Web of Science ®Google Scholar
• Cressie, N., and C. K. Wikle. 2011. Statistics for Spatio-Temporal Data. John Wiley and Sons.
   Google Scholar
• Demirhan, H., and Z. Kalaylioglu. 2015. “Joint Prior Distributions for Variance Parameters in Bayesian Analysis of Normal Hierarchical Models.” Journal of Multivariate Analysis 135:163–174. https://doi.org/10.1016/j.jmva.2014.12.013.
   Web of Science ®Google Scholar
• Do, A. N. T., H. D. Tran, M. Ashley, and A. T. Nguyen. 2022. “Monitoring Landscape Fragmentation and Aboveground Biomass Estimation in Can Gio Mangrove Biosphere Reserve Over the Past 20 Years.” Ecological Informatics 70:101743. https://doi.org/10.1016/j.ecoinf.2022.101743.
   Web of Science ®Google Scholar
• Duchene, S., D. A. Duchêne, F. Di Giallonardo, J.-S. Eden, J. L. Geoghegan, K. E. Holt, S. Y. W. Ho, et al. 2016. “Cross-Validation to Select Bayesian Hierarchical Models in Phylogenetics.” BMC Evolutionary Biology 16 (1). https://doi.org/10.1186/s12862-016-0688-y.
   PubMedGoogle Scholar
• Eamus, D. 2003. “How Does Ecosystem Water Balance Affect Net Primary Productivity of Woody Ecosystems?” Functional Plant Biology 30 (2): 187–205. https://doi.org/10.1071/FP02084.
   PubMed Web of Science ®Google Scholar
• European-Commission. 2017. Timber Trade Flows Within, to and from Eastern and Southern African Countries. Brussels: European Commission.
   Google Scholar
• Fang, J., A. Chen,C. Peng,S. Zhao, and L. Ci. 2001. “Changes in Forest Biomass Carbon Storage in China Between 1949 and 1998.” Science American Association for the Advancement of Science 292 (5525): 2320–2322. https://doi.org/10.1126/science.1058629.
   Google Scholar
• Fan, M., X. Wang, and G. Yang. 2022. “Spatial Characteristics of Vegetation Habitat Suitability and Mountainous Settlements and Their Quantitative Relationships in Upstream of Min River, Southwestern of China.” Ecological Informatics 68:101541. https://doi.org/10.1016/j.ecoinf.2021.101541.
   Web of Science ®Google Scholar
• FAO. 2003. Forestry Outlook Study for Africa. Regional Report - Opportunities and Challenges Towards 2020. Rome, Italy.
   Google Scholar
• FAO. 2016. Global Forest Resources Assessment. Howare the World’s Forests Changing? Rome, Italy: FAO - Food and Agriculture Organization of the United Nations.
   Google Scholar
• Fararoda, R., R. S. Reddy, G. Rajashekar, T. R. K. Chand, C. S. Jha, and V. K. Dadhwal. 2021. “Improving Forest Above Ground Biomass Estimates Over Indian Forests Using Multi Source Data Sets with Machine Learning Algorithm.” Ecological Informatics 65:101392. https://doi.org/10.1016/j.ecoinf.2021.101392.
   Web of Science ®Google Scholar
• Farr, T. G., P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, et al. 2007. “The Shuttle Radar Topography Mission.” Reviews of Geophysics 45 (2): John Wiley & Sons, Ltd. https://doi.org/10.1029/2005RG000183.
   Web of Science ®Google Scholar
• Fassnacht, F. E., F. Hartig, H. Latifi, C. Berger, J. Hernández, P. Corvalán, B. Koch, et al. 2014. “Importance of Sample Size, Data Type and Prediction Method for Remote Sensing-Based Estimations of Aboveground Forest Biomass.” Remote Sensing of Environment 154:102–114. https://doi.org/10.1016/j.rse.2014.07.028.
   Web of Science ®Google Scholar
• Fick, S. E., and R. J. Hijmans. 2017. “WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas.” International Journal of Climatology 37 (12): 4302–4315. John Wiley & Sons, Ltd. https://doi.org/10.1002/joc.5086.
   Web of Science ®Google Scholar
• Finley, A. O., and S. Banerjee. 2008. Bayesian Spatial Regression for Multi-Source Predictive Mapping BT - Encyclopedia of GIS. edited by, Shekhar, S. and Xiong, H. 45–52, Boston, MA:Springer US. https://doi.org/10.1007/978-0-387-35973-1_97.
   Google Scholar
• Finley, A. O., S. Banerjee, and D. W. MacFarlane. 2011. “A Hierarchical Model for Quantifying Forest Variables Over Large Heterogeneous Landscapes with Uncertain Forest Areas.” Journal of the American Statistical Association 106 (493): 31–48. https://doi.org/10.1198/jasa.2011.ap09653.
   PubMed Web of Science ®Google Scholar
• Finley, A., B. Sudipto, and B. Carlin. 2007. “spBayes: An R Package for Univariate and Multivariate Hierarchical Point-Referenced Spatial Models.” Journal of Statistical Software 19 (4). https://doi.org/10.18637/jss.v019.i04.
   PubMed Web of Science ®Google Scholar
• Forestry-Commission. 2021. Zimbabwe Land and Vegetation Cover Area Estimates. Harare: Forestry Commission.
   Google Scholar
• Gelfand, A. E. 2012. “Hierarchical modeling for spatial data problems.” Spatial Statistics 1:30–39. https://doi.org/10.1016/j.spasta.2012.02.005.
   PubMed Web of Science ®Google Scholar
• Gelman, A. 2006. “Prior Distributions for Variance Parameters in Hierarchical Models (Comment on Article by Browne and Draper).” Bayesian Analysis 1 (3): 515–534. https://doi.org/10.1214/06-BA117A.
   Web of Science ®Google Scholar
• Ge, Y., Fisher PF, Ge Y, Leung Y, Ma J, Wang J. 2005. Registration of Remote Sensing Image with Measurement Errors and Error Propagation BT - Developments in Spatial Data Handling. Fisher, P. F., edited by. Berlin, Heidelberg Berlin Heidelberg:Springer 285–297.
   Google Scholar
• Gerald, F., Dimobe, K., Serme, I, Tondoh, J.E. et al. 2017. “Lansat-8 Vs. Sentinel-2: Examining the Added Value of Sentinel-2’s Red-Edge Bands to Land-Use and Land-Cover Mapping in Burkina Faso.” GIScience & Remote Sensing (55): 1–26/ https://doi.org/10.1080/15481603.2017.1370169.
   Google Scholar
• Ghasemi, A. R. 2015. “Changes and Trends in Maximum, Minimum and Mean Temperature Series in Iran.” Atmospheric Science Letters 16 (3): 366–372. John Wiley & Sons, Ltd. https://doi.org/10.1002/asl2.569.
   Web of Science ®Google Scholar
• Gibbs, H. K., S. Brown, J. O. Niles, and J. A. Foley. 2007. “Monitoring and Estimating Tropical Forest Carbon Stocks: Making REDD a Reality.” Environmental Research Letters 2 (4): 45023. https://doi.org/10.1088/1748-9326/2/4/045023.
   Web of Science ®Google Scholar
• González-Vélez, J. C., M. C. Torres-Madronero, J. Murillo-Escobar, and J. C. Jaramillo-Fayad. 2021. “An Artificial Intelligent Framework for Prediction of Wildlife Vehicle Collision Hotspots Based on Geographic Information Systems and Multispectral Imagery.” Ecological Informatics 63:101291. https://doi.org/10.1016/j.ecoinf.2021.101291.
   Web of Science ®Google Scholar
• Gordon, C. E., E. R. Bendall, M. G. Stares, L. Collins, and R. A. Bradstock. 2018. “Aboveground Carbon Sequestration in Dry Temperate Forests Varies with Climate Not Fire Regime.” Global Change Biology 24 (9): 4280–4292. John Wiley & Sons, Ltd. https://doi.org/10.1111/gcb.14308.
   PubMed Web of Science ®Google Scholar
• Goulard, M., and M. Voltz. 1992. “Linear Coregionalization Model: Tools for Estimation and Choice of Cross-Variogram Matrix.” Mathematical Geology 24 (3): 269–286. https://doi.org/10.1007/BF00893750.
   Google Scholar
• Green, E. J., A. O. Finley, and W. E. Strawderman. 2020. Introduction to Bayesian Methods in Ecology and Natural Resources. Cham: Springer. https://doi.org/10.1007/978-3-030-60750-0.
   Google Scholar
• Guhaniyogi, R., and S. Banerjee. 2019. “Multivariate spatial meta kriging.” Statistics & Probability Letters 144:3–8. https://doi.org/10.1016/j.spl.2018.04.017.
   PubMed Web of Science ®Google Scholar
• Halkos, G., S. Antonis, M. Chrisovalantis, and J. Nikleta. 2020. “A Hierarchical Multilevel Approach in Assessing Factors Explaining Country-Level Climate Change Vulnerability.” Sustainability. doi:10.3390/su12114438.
   Web of Science ®Google Scholar
• He, Q., K. P. Chun, B. Dieppois, L. Chen, P. Y. Fan, E. Toker, O. Yetemen, and X. Pan. 2022. “Investigating and Predicting Spatiotemporal Variations in Vegetation Cover in Transitional Climate Zone: A Case Study of Gansu (China).” Theoretical and Applied Climatology 150 (1–2): 283–307. Springer. https://doi.org/10.1007/s00704-022-04140-2.
   Web of Science ®Google Scholar
• Hengl, T., D. J. J. Walvoort, A. Brown, and D. G. Rossiter. 2004. “A Double Continuous Approach to Visualization and Analysis of Categorical Maps.” International Journal of Geographical Information Science 18 (2): 183–202. https://doi.org/10.1080/13658810310001620924.
   Web of Science ®Google Scholar
• Hu, T., Y. Su, B. Xue, J. Liu, X. Zhao, J. Fang, Q. Guo, et al. 2016. “Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data.” Remote Sensing 8 (7): 565. https://doi.org/10.3390/rs8070565.
   Web of Science ®Google Scholar
• Hu, T., Y. Zhang, Y. Su, Y. Zheng, G. Lin, and Q. Guo. 2020. “Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data.” Remote Sensing 12 (10): 1690. https://doi.org/10.3390/rs12101690.
   Web of Science ®Google Scholar
• ICAT (Initiative For Climate Action Transparency). 2022. Zimbabwe on Track to Better Climate Action Transparency. Accessed December 28, 2022. https://climateactiontransparency.org/zimbabwe-on-track-to-better-climate-action-transparency/.
   Google Scholar
• IPCC. 2006. Good Practice Guidance for Land Use, Land-Use Change and Forestry. Kanagawa, Japan: Institute for Global Environmental Strategies.
   Google Scholar
• Isaaks, E. H., and R. M. Srivastava. 1989. Applied Geostatistics. Oxford University Press. https://books.google.co.zw/books?id=vC2dcXFLI3YC.
   Google Scholar
• Jackman, S. 2000. “Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo.” American Journal of Political Science 44 (2): 375–404. https://doi.org/10.2307/2669318.
   Web of Science ®Google Scholar
• Jiang, F., M. Kutia, K. Ma, S. Chen, J. Long, and H. Sun. 2021. “Estimating the Aboveground Biomass of Coniferous Forest in Northeast China Using Spectral Variables, Land Surface Temperature and Soil Moisture.” Science of the Total Environment 785:147335. https://doi.org/10.1016/j.scitotenv.2021.147335.
   PubMed Web of Science ®Google Scholar
• Jiang, F., H. Sun, E. Chen, T. Wang, Y. Cao, and Q. Liu. 2022. “Above-Ground Biomass Estimation for Coniferous Forests in Northern China Using Regression Kriging and Landsat 9 Images.” Remote Sensing 14 (22): 5734. https://doi.org/10.3390/rs14225734.
   Web of Science ®Google Scholar
• Jia, K., X. Wei, X. Gu, Y. Yao, X. Xie, and B. Li. 2014. “Land Cover Classification Using Landsat 8 Operational Land Imager Data in Beijing, China.” Geocarto International 29 (8): 941–951. https://doi.org/10.1080/10106049.2014.894586.
   Web of Science ®Google Scholar
• Jucker, T., S. R. Hardwick, S. Both, D. M. O. Elias, R. M. Ewers, D. T. Milodowski, T. Swinfield, et al. 2018. “Canopy Structure and Topography Jointly Constrain the Microclimate of Human‐Modified Tropical Landscapes.” Global Change Biology 24 (11): 5243–5258. https://doi.org/10.1111/gcb.14415.
   PubMed Web of Science ®Google Scholar
• Keith, H., B. G. Mackey, and D. B. Lindenmayer. 2009. “Re-Evaluation of Forest Biomass Carbon Stocks and Lessons from the World’s Most Carbon-Dense Forests.” Proceedings of the National Academy of Sciences 106 (28): 11635–11640. https://doi.org/10.1073/pnas.0901970106.
   PubMed Web of Science ®Google Scholar
• Larjavaara, M., and H. C. Muller-Landau. 2012. “Still Rethinking the Value of High Wood Density.” American Journal of Botany 99 (1): 165–168. https://doi.org/10.3732/ajb.1100324.
   PubMed Web of Science ®Google Scholar
• Li, C., and X. Li. 2019. “Hazard Rate and Reversed Hazard Rate Orders on Extremes of Heterogeneous and Dependent Random Variables.” Statistics & Probability Letters 146:104–111. https://doi.org/10.1016/j.spl.2018.11.005.
   Web of Science ®Google Scholar
• Liu, Y., W. Gong, Y. Xing, X. Hu, and J. Gong. 2019. “Estimation of the Forest Stand Mean Height and Aboveground Biomass in Northeast China Using SAR Sentinel-1B, Multispectral Sentinel-2A, and DEM Imagery.” Isprs Journal of Photogrammetry & Remote Sensing 151:277–289. https://doi.org/10.1016/j.isprsjprs.2019.03.016.
   Web of Science ®Google Scholar
• Liu, Y., G. Yu, Q. Wang, Y. Zhang, and Z. Xu. 2014. “Carbon Carry Capacity and Carbon Sequestration Potential in China Based on an Integrated Analysis of Mature Forest Biomass.” Science China Life Sciences 57 (12): 1218–1229. https://doi.org/10.1007/s11427-014-4776-1.
   PubMed Web of Science ®Google Scholar
• Lloyd, J., and G. D. Farquhar. 1996. “The CO 2 Dependence of Photosynthesis, Plant Growth Responses to Elevated Atmospheric CO 2 Concentrations and Their Interaction with Soil Nutrient Status. I. General Principles and Forest Ecosystems.” Functional Ecology 10 (1): 4–32. https://doi.org/10.2307/2390258.
   Web of Science ®Google Scholar
• López-Serrano, P. M., C. A. López-Sánchez, J. G. Álvarez-González, and J. García-Gutiérrez. 2016. “A Comparison of Machine Learning Techniques Applied to Landsat-5 TM Spectral Data for Biomass Estimation.” Canadian Journal of Remote Sensing 42 (6): 690–705. https://doi.org/10.1080/07038992.2016.1217485.
   Web of Science ®Google Scholar
• Malhi, Y., D. Wood, T. R. Baker, J. WrighT, O. L. Phillips, T. Cochrane, P. Meir, et al. 2006. “The Regional Variation of Aboveground Live Biomass in Old-Growth Amazonian Forests.” Global Change Biology 12 (7): 1107–1138. John Wiley & Sons, Ltd. https://doi.org/10.1111/j.1365-2486.2006.01120.x.
   Web of Science ®Google Scholar
• Matose, F. 2008. “Institutional Configurations Around Forest Reserves in Zimbabwe: The Challenge of Nested Institutions for Resource Management.” Local Environment 13 (5): 393–404. https://doi.org/10.1080/13549830701809627.
   Web of Science ®Google Scholar
• Mon, M. S., N. Mizoue, N. Z. Htun, T. Kajisa, and S. Yoshida. 2012. “Factors Affecting Deforestation Andforest Degradation in Selectively Logged Productionforest: A Case Study in Myanmar.” Forest Ecologyand Management 267:190–198. https://doi.org/10.1016/j.foreco.2011.11.036.
   Web of Science ®Google Scholar
• Pan, Y., Birdsey, R A., Phillips, O L., Jackson, R B. 2013. “The Structure, Distribution, and Biomass of the World’s Forests.” Annual Review of Ecology, Evolution, and Systematics 44 (1): 593–622. https://doi.org/10.1146/annurev-ecolsys-110512-135914.
   Web of Science ®Google Scholar
• Poorter, L., F. Bongers, T. M. Aide, A. M. Almeyda Zambrano, P. Balvanera, J. M. Becknell, V. Boukili, et al. 2016. “Biomass Resilience of Neotropical Secondary Forests.” Nature 530 (7589): 211–214. https://doi.org/10.1038/nature16512.
   PubMed Web of Science ®Google Scholar
• Puliti, S., M. Hauglin, J. Breidenbach, P. Montesano, C. S. R. Neigh, J. Rahlf, S. Solberg, et al. 2020. “Modelling Above-Ground Biomass Stock Over Norway Using National Forest Inventory Data with ArcticDEM and Sentinel-2 Data.” Remote Sensing of Environment 236:111501. https://doi.org/10.1016/j.rse.2019.111501.
   Web of Science ®Google Scholar
• Rabus, B., M. Eineder, A. Roth, and R. Bamler. 2003. “The Shuttle Radar Topography Mission—A New Class of Digital Elevation Models Acquired by Spaceborne Radar.” ISPRS Journal of Photogrammetry and Remote Sensing 57 (4): 241–262. https://doi.org/10.1016/S0924-2716(02)00124-7.
   Web of Science ®Google Scholar
• Ranghetti, L., M. Boschetti, F. Nutini, and L. Busetto. 2020. “Sen2r: An R Toolbox for Automatically Downloading and Preprocessing Sentinel-2 Satellite Data.” Computers & Geosciences 139:104473. https://doi.org/10.1016/j.cageo.2020.104473.
   Web of Science ®Google Scholar
• Ravindranath, N. H., and M. Ostwald. 2008. “Carbon Inventory Methods Handbook for Greenhouse Gas Inventory, Carbon Mitigation and Roundwood Production Projects.” Advances in Global Change Research Volume 29. https://doi.org/10.1007/978-1-4020-6547-7_1.
   Google Scholar
• R Core Development Team. 2008. A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.
   Google Scholar
• Robinson, W. S. 1950. “Ecological Correlations and the Behavior of Individuals.” American Sociological Review 15 (3): 351–357. https://doi.org/10.2307/2087176.
   Web of Science ®Google Scholar
• Saatchi, S. S., R. A. Houghton, R. C. DOs Santos Alvalá, J. V. Soares, and Y. YU. 2007. “Distribution of Aboveground Live Biomass in the Amazon Basin.” Global Change Biology 13 (4): 816–837. John Wiley & Sons, Ltd. https://doi.org/10.1111/j.1365-2486.2007.01323.x.
   Web of Science ®Google Scholar
• Sahu, S. K. 2022. Bayesian Modeling of Spatio Temporal Data with R. 1st. Highfield, Southampton, UK: Chapman and Hall/CRC. https://doi.org/10.1201/9780429318443.
   Google Scholar
• Semela, M., A. Ramoelo, and S. Adelabu. 2020. “Testing and Comparing the Applicability of Sentinel-2 and Landsat 8 Reflectance Data in Estimating Mountainous Herbaceous Biomass Before and After Fire Using Random Forest Modelling.” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 4493–4496. https://doi.org/10.1109/IGARSS39084.2020.9323446.
   Google Scholar
• Serrano, P. M. L., J. J. Corral-Rivas, C. A. López-Sánchez, E. Jiménez, C. A. López-Sánchez, and D. J. Vega-Nieva. 2019. “Modeling of Aboveground Biomass with Landsat 8 OLI and Machine Learning in Temperate Remote Sensing.” Forests 11 (1): 0.3390/f11010011. https://doi.org/10.3390/f11010011.
   Web of Science ®Google Scholar
• Shi, L., and S. Liu Tumuluru, J. S., ed. 2017. Methods of Estimating Forest Biomass: A Review. Rijeka: IntechOpen. Ch. 2: https://doi.org/10.5772/65733.
   Google Scholar
• Simard, M., L. Fatoyinbo, C. Smetanka, V. H. Rivera-Monroy, E. Castañeda-Moya, N. Thomas, T. Van der Stocken, et al. 2019. “Mangrove Canopy Height Globally Related to Precipitation, Temperature and Cyclone Frequency.” Nature Geoscience 12 (1): 40–45. https://doi.org/10.1038/s41561-018-0279-1.
   Web of Science ®Google Scholar
• Slik, J. W. F., S.-I. Aiba, F. Q. Brearley, C. H. Cannon, O. Forshed, K. Kitayama, H. Nagamasu, et al. 2010. “Environmental Correlates of Tree Biomass, Basal Area, Wood Specific Gravity and Stem Density Gradients in Borneo’s Tropical Forests.” Global Ecology & Biogeography 19 (1): 50–60. John Wiley & Sons, Ltd. https://doi.org/10.1111/j.1466-8238.2009.00489.x.
   Web of Science ®Google Scholar
• Somvanshi, S. S., and M. Kumari. 2020. “Comparative Analysis of Different Vegetation Indices with Respect to Atmospheric Particulate Pollution Using Sentinel Data.” Applied Computing and Geosciences 7:100032. https://doi.org/10.1016/j.acags.2020.100032.
   Google Scholar
• Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde. 2002. “Bayesian Measures of Model Complexity and Fit.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64 (4): 583–639. https://doi.org/10.1111/1467-9868.00353.
   Web of Science ®Google Scholar
• Su, Y., Q. Guo, B. Xue, T. Hu, O. Alvarez, S. Tao, J. Fang, et al. 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. https://doi.org/10.1016/j.rse.2015.12.002.
   Web of Science ®Google Scholar
• Surveyor General. 2018. Revised Topo Maps in Zimbabwe. Harare: The Department of Surveyor General, Zimbabwe.
   Google Scholar
• Takagi, K., Y. Yone, H. Takahashi, R. Sakai, H. Hojyo, T. Kamiura, M. Nomura, et al. 2015. “Forest Biomass and Volume Estimation Using Airborne LiDAR in a Cool-Temperate Forest of Northern Hokkaido, Japan.” Ecological Informatics 26:54–60. https://doi.org/10.1016/j.ecoinf.2015.01.005.
   Web of Science ®Google Scholar
• Timmons, D. S., T. Buchholz, and C. H. Veeneman. 2016. “Forest Biomass Energy: Assessing Atmospheric Carbon Impacts by Discounting Future Carbon flows.” GCB Bioenergy 8 (3): 631–643. John Wiley & Sons, Ltd. https://doi.org/10.1111/gcbb.12276.
   Google Scholar
• Tran, T. V., R. Reef, and X. Zhu. 2022. “A Review of Spectral Indices for Mangrove Remote Sensing.” Remote Sensing 14 (19): 4868. https://doi.org/10.3390/rs14194868.
   Web of Science ®Google Scholar
• UNFCCC. 2020. Yearbook of Global Climate Action 2020. London: Marrakech Partnership for Global Climate Action. https://unfccc.int/sites/default/files/resource/2020_Yearbook_final_0.pdf.
   Google Scholar
• Vieilledent, G., O. Gardi, C. Grinand, C. Burren, M. Andriamanjato, C. Camara, C. J. Gardner, et al. 2016. “Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar.” The Journal of Ecology 104 (3): 703–715. John Wiley & Sons, Ltd https://doi.org/10.1111/1365-2745.12548.
   Web of Science ®Google Scholar
• Walvoort, D. J. J., D. J. Brus, and J. J. de Gruijter. 2010. “An R Package for Spatial Coverage Sampling and Random Sampling from Compact Geographical Strata by K-Means.” Computers & Geosciences 36 (10): 1261–1267. https://doi.org/10.1016/j.cageo.2010.04.005.
   Web of Science ®Google Scholar
• Wang, Y., X. Shen, M. Jiang, S. Tong, and X. Lu. 2022. “Daytime and Nighttime Temperatures Exert Different Effects on Vegetation Net Primary Productivity of Marshes in the Western Songnen Plain.” Ecological Indicators 137:108789. https://doi.org/10.1016/j.ecolind.2022.108789.
   Web of Science ®Google Scholar
• Xiong, Y., and H. Wang. 2022. “Spatial Relationships Between NDVI and Topographic Factors at Multiple Scales in a Watershed of the Minjiang River, China.” Ecological Informatics 69:101617. https://doi.org/10.1016/j.ecoinf.2022.101617.
   Web of Science ®Google Scholar
• Zhang, R. 0., Zhou, J.H., Guo, J., Miao, Y.H. and Zhang, L.L. 2023. “Inversion Models of Aboveground Grassland Biomass in Xinjiang Based on Multisource data”, Frontiers in Plant Science. https://www.frontiersin.org/articles/10.3389/fpls.2023.1152432.
   Google Scholar
• Zunguze, A. X. 2012. Quantificação de Carbono Sequestrado Em Povoamentos de Eucalyptus Spp Na Floresta de Inhamacari-Manica. Universidade Eduardo Mondlane.
   Google Scholar
• Zvobgo, L., and J. Tsoka. 2021. “Deforestation Rate and Causes in Upper Manyame Sub-Catchment, Zimbabwe: Implications on Achieving National Climate Change Mitigation Targets.” Trees, Forests and People 5:100090. https://doi.org/10.1016/j.tfp.2021.100090.
   Google Scholar