Assessing spatial dynamics of GEDI biomass prediction in managed versus unmanaged tropical forest ecosystems in Kenya
DOI:
https://doi.org/10.15287/afr.2025.4245Keywords:
GEDI, forest monitoring, remote sensing, tropical forest, carbon storageAbstract
Above ground biomass (AGB) estimation is vital for monitoring carbon storage and ecosystem fluxes, especially in tropical forests for climate mitigation in the Global South. The Global Ecosystem Dynamics Investigation (GEDI) instrument offers high resolution forest monitoring; however, field measurements are crucial to enhance spatial accuracy. This study assessed the limitations of using machine learning models trained on GEDI data to estimate AGB for two distinct forest ecosystems in Kenya: Kakamega National Forest Reserve (KNFR) and Karura Forest Reserve (KFR). Our specific objectives were (i) developing Random Forest (RF) machine learning model using GEDI data for training, (ii) comparing GEDI estimates with field measurements, and (iii) quantifying the limitations of using integrated GEDI-RF model. Plots were coincided with GEDI beams for comparison to assess variability and bias in AGB estimates. The p-value of 0.005 for heteroskedasticity in KNFR indicated high variability and bias of the GEDI-RF model relative to the field measured model. In contrast, p-value of 0.195 for heteroskedasticity in KFR indicated low variability and bias of the GEDI-RF model relative to the field measured model. 55% of plots which coincided with GEDI had less than 10% relative difference in AGB estimates between models. Plots outside of GEDI had a relative difference in AGB estimates between models greater than 10%. Relative to the field measured model, the GEDI-RF model overestimated AGB values less than 100 Mg ha⁻¹ and underestimated greater than 200 Mg ha⁻¹. This study contributes to effective forest monitoring, carbon accounting, and conservation in heterogeneous forests.
References
Adinugroho W. C., Krisnawati H., Imanuddin R., Siregar C. A., Weston C. J., & Volkova L. (2023). Developing biomass allometric equations for small trees in mixed-species forests of tropical rainforest ecozone. Trees, Forests and People, 13, 100425. https://doi.org/10.1016/j.tfp.2023.100425
Adoyo B., Kehbila A., Lutta A., Opiyo R. O., Onyango S. A., Mungo C., & Osano P. (2024). Land cover scenarios in four Kenyan arid and semi-arid regions by 2050. SEI Working Paper. Stockholm Environment Institute. https://doi.org/10.51414/sei2024.029
Alados C. L., Sánchez-Granero M. A., Errea P., Castillo-García M., & Pueyo Y. (2022). Two dimensional searching paths exhibit fractal distribution that change with food availability (Normalized Difference Infrared Index, NDII). Ecological Indicators, 139, 108940. https://doi.org/10.1016/j.ecolind.2022.108940
Asrat Z., Eid T., Gobakken T., & Negash M. (2020). Aboveground tree biomass prediction options for the Dry Afromontane forests in south-central Ethiopia. Forest Ecology and Management, 473, 118335. https://doi.org/10.1016/j.foreco.2020.118335
Augusto L., & Boča A. (2022). Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon. Nature Communications, 13(1), 1097. https://doi.org/10.1038/s41467-022-28748-0
Baban G., & Niţă M.D. (2023). Measuring forest height from space. Opportunities and limitations observed in natural forests. Measurement, 211, 112593. https://doi.org/10.1016/j.measurement.2023.112593
Barinas G., Good S. P., & Tullos D. (2024). Continental Scale Assessment of Variation in Floodplain Roughness With Vegetation and Flow Characteristics. Geophysical Research Letters, 51(1), e2023GL105588. https://doi.org/10.1029/2023GL105588
Basak D., Bose A., Roy S., & Chowdhury I. R. (2023). Understanding the forest cover dynamics and its health status using GIS-based analytical hierarchy process: a study from Alipurduar district, West Bengal, India. Water, Land, and Forest Susceptibility and Sustainability: Geospatial Approaches and Modeling, 1, 475–508. https://doi.org/10.1016/B978-0-323-91880-0.00014-3
Bischl B., Binder M., Lang M., Pielok T., Richter J., ..., & Lindauer, M. (2023). Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges. WIREs Data Mining and Knowledge Discovery, 13(2), e1484. https://doi.org/10.1002/widm.1484
Biswas P. L., & Biswas S. R. (2019). Mangrove Forests: Ecology, Management, and Threats. In: Leal Filho W., Azul A., Brandli L., Özuyar P., Wall T. (eds) Life on Land. Encyclopedia of the UN Sustainable Development Goals (pp. 1–14). Springer, Cham. https://doi.org/10.1007/978-3-319-71065-5_26-1
Bleher B., Uster D., & Bergsdorf T. (2006). Assessment of threat status and management effectiveness in Kakamega Forest, Kenya. Biodiversity and Conservation, 15(4), 1159–1177. https://doi.org/10.1007/s10531-004-3509-3
Boali A., Asgari H. R., Mohammadian Behbahani A., Salmanmahiny A., & Naimi B. (2024). Remotely sensed desertification modeling using ensemble of machine learning algorithms. Remote Sensing Applications: Society and Environment, 34, 101149. https://doi.org/10.1016/j.rsase.2024.101149
Borz S. A., Toaza J. M. M., & Proto A. R. (2024). Accuracy of two LiDAR-based augmented reality apps in breast height diameter measurement. Ecological Informatics, 81, 102550. https://doi.org/10.1016/j.ecoinf.2024.102550
Breiman L. (2001). Random Forests. Machine Learning. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Brodie J. F., Mohd-Azlan J., Chen C., Wearn O. R., Deith M. C. M., Ball J. G. C., … Luskin M. S. (2023). Landscape-scale benefits of protected areas for tropical biodiversity. Nature, 620(7975), 807–812. https://doi.org/10.1038/s41586-023-06410-z
Bruening J., May P., Armston J., & Dubayah R. (2023). Precise and unbiased biomass estimation from GEDI data and the US Forest Inventory. Frontiers in Forests and Global Change, 6. https://doi.org/10.3389/ffgc.2023.1149153
Calders K., Brede B., Newnham G., Culvenor D., Armston J., Bartholomeus H., … & Herold M. (2023). StrucNet: a global network for automated vegetation structure monitoring. Remote Sensing in Ecology and Conservation, 9(5), 587–598. https://doi.org/10.1002/rse2.333
Cha G.-W., Moon H.-J., & Kim Y.-C. (2021). Comparison of Random Forest and Gradient Boosting Machine Models for Predicting Demolition Waste Based on Small Datasets and Categorical Variables. International Journal of Environmental Research and Public Health, 18(16), 8530. https://doi.org/10.3390/ijerph18168530
Cheng F., Tian J., He J., He H., Bao G., Yang Y., Liu G., & Zhang Z. (2024). China’s future forest carbon sequestration potential under different management scenarios. Trees, Forests and People, 17, 100621. https://doi.org/10.1016/j.tfp.2024.100621
Chere Z., Zewdie W., & Biru D. (2023). Machine learning for modeling forest canopy height and cover from multi-sensor data in Northwestern Ethiopia. Environmental Monitoring and Assessment, 195(12), 1452. https://doi.org/10.1007/s10661-023-12066-z
Chirici G., Chiesi M., Fibbi L., Giannetti F., Corona P., & Maselli F. (2022). High spatial resolution modelling of net forest carbon fluxes based on ground and remote sensing data. Agricultural and Forest Meteorology, 316, 108866. https://doi.org/10.1016/j.agrformet.2022.108866
Clarke L., Wei Y.-M. (eds.) (2023). Energy Systems. In ICPP 2022: Shukla P.R. et al. (Ed.), Climate Change 2022: Mitigation of Climate Change (pp. 613–746). Cambridge University Press. https://doi.org/10.1017/9781009157926.008
Cushman, K. C., Armston, J., Dubayah, R., Duncanson, L., Hancock, S., Janík, D., … & Kellner, J. R. (2023). Impact of leaf phenology on estimates of aboveground biomass density in a deciduous broadleaf forest from simulated GEDI lidar. Environmental Research Letters, 18(6), 065009. https://doi.org/10.1088/1748-9326/acd2ec
Daba, D. E., & Soromessa, T. (2019a). The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: Case study of Albizia grandibracteata and Trichilia dregeana. Carbon Balance and Management, 14(1), 1–13. https://doi.org/10.1186/s13021-019-0134-8
Das K. R., & Imon A. H. M. R. (2016). A Brief Review of Tests for Normality. American Journal of Theoretical and Applied Statistics, 5(1), 5. https://doi.org/10.11648/j.ajtas.20160501.12
Demarchi G., Subervie J., Catry T., & Tritsch I. (2023). Using publicly available remote sensing products to evaluate REDD + projects in Brazil. Global Environmental Change, 80, 102653. https://doi.org/10.1016/j.gloenvcha.2023.102653
Divasón J., Pernia-Espinoza A., & Martinez-de-Pison F. J. (2023). HYB-PARSIMONY: A hybrid approach combining Particle Swarm Optimization and Genetic Algorithms to find parsimonious models in high-dimensional datasets. Neurocomputing, 560, 126840. https://doi.org/10.1016/j.neucom.2023.126840
Dorado-Roda I., Pascual A., Godinho S., Silva C., Botequim B., Rodríguez-Gonzálvez P., González-Ferreiro E., & Guerra-Hernández J. (2021). Assessing the Accuracy of GEDI Data for Canopy Height and Aboveground Biomass Estimates in Mediterranean Forests. Remote Sensing, 13(12), 2279. https://doi.org/10.3390/rs13122279
Dubayah R., Blair J. B., Goetz S., Fatoyinbo L., Hansen M., Healey S., Hofton M., Hurtt G., Kellner J., …, & Silva, C. (2020). The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth’s forests and topography. Science of Remote Sensing, 1, 100002. https://doi.org/10.1016/j.srs.2020.100002
Dubayah R., Tang H., Armston J., Luthcke S., Hofton M., & Blair J. (2021). GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002 [Data set]. NASA EOSDIS Land Processes Distributed Active Archive Center. https://doi.org/10.5067/GEDI/GEDI02_B.002
Dutca I., McRoberts R. E., Næsset E., & Blujdea V. N. B. (2019). A practical measure for determining if diameter (D) and height (H) should be combined into D2H in allometric biomass models. Forestry, 92(5), 627–634. https://doi.org/10.1093/forestry/cpz041
Dutcă I., McRoberts R. E., Næsset E., & Blujdea V. N. B. (2022). Accommodating heteroscedasticity in allometric biomass models. Forest Ecology and Management, 505, 119865. https://doi.org/10.1016/j.foreco.2021.119865
Ekoungoulou R., Liu X., Loumeto J. J., & Ifo S. A. (2014). Tree Above-And Below-Ground Biomass Allometries for Carbon Stocks Estimation in Secondary Forest of Congo. IOSR Journal of Environmental Science, Toxicology and Food Technology, 8(4), 09–20. https://doi.org/10.9790/2402-08420920
EU Directorate-General for Climate Action (2024, February). International carbon pricing and markets diplomacy. The Call to Action for Paris-Aligned Carbon Markets. https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/international-carbon-pricing-and-markets-diplomacy_en
Fadl M. E., AbdelRahman M. A. E., El-Desoky A. I., & Sayed Y. A. (2024). Assessing soil productivity potential in arid region using remote sensing vegetation indices. Journal of Arid Environments, 222, 105166. https://doi.org/10.1016/J.JARIDENV.2024.105166
Fagua J. C., Jantz P., Burns P., Massey R., Buitrago J. Y., Saatchi S., Hakkenberg C., & Goetz S. J. (2021). Mapping tree diversity in the tropical forest region of Chocó-Colombia. Environmental Research Letters, 16(5), 054024. https://doi.org/10.1088/1748-9326/abf58a
Fassnacht F. E., White J. C., Wulder M. A., & Næsset E. (2024). Remote sensing in forestry: current challenges, considerations and directions. Forestry: An International Journal of Forest Research, 97(1), 11–37. https://doi.org/10.1093/forestry/cpad024
Fayad I., Baghdadi N., & Lahssini K. (2022). An Assessment of the GEDI Lasers’ Capabilities in Detecting Canopy Tops and Their Penetration in a Densely Vegetated, Tropical Area. Remote Sensing, 14(13), 2969. https://doi.org/10.3390/rs14132969
Feitosa T. B., Fernandes M. M., Santos C. A. G., Silva R. M. da, Garcia J. R., ... & Cunha, E. R. da. (2023). Assessing economic and ecological impacts of carbon stock and land use changes in Brazil’s Amazon Forest: A 2050 projection. Sustainable Production and Consumption, 41, 64–74. https://doi.org/10.1016/j.spc.2023.07.009
Feukeng S. S. K., Maffo L. N., Nguetsop V. F., Rossi V., Chimi C. D., Wouokoue B. J. T., & Kengne C. O.. (2020). Single-species allometric equations for above-ground biomass of most abundant long-lived pioneer species in semi-deciduous rain forests of the central region of Cameroon. World Journal of Advanced Research and Reviews, 7(2), 336–348. https://doi.org/10.30574/wjarr.2020.7.2.0288
Ferreira M. P., Martins G. B., de Almeida T. M. H., da Silva Ribeiro R., da Veiga Júnior V. F., … & Kurtz B. C. (2024). Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data. Urban Forestry & Urban Greening, 96, 128362. https://doi.org/10.1016/j.ufug.2024.128362
Francini S., Cavalli A., D’Amico G., McRoberts R. E., Maesano M., Munafò M., Scarascia Mugnozza G., & Chirici G. (2023). Reusing Remote Sensing-Based Validation Data: Comparing Direct and Indirect Approaches for Afforestation Monitoring. Remote Sensing, 15(6), 1638. https://doi.org/10.3390/rs15061638
Gelabert P. J., Rodrigues M., Coll L., Vega-Garcia C., & Ameztegui A. (2024). Maximum tree height in European Mountains decreases above a climate-related elevation threshold. Communications Earth & Environment, 5(1), 84. https://doi.org/10.1038/s43247-024-01246-5
Giavarina D. (2015). Understanding Bland Altman analysis. Biochemia Medica, 25(2), 141–151. https://doi.org/10.11613/BM.2015.015
Gonçalves F., Treuhaft R., Law B., Almeida A., Walker W., Baccini A., Dos Santos J., & Graça P. (2017). Estimating Aboveground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations. Remote Sensing, 9(1), 47. https://doi.org/10.3390/rs9010047
Hakkenberg C. R., Tang H., Burns P., & Goetz S. J. (2023). Canopy structure from space using GEDI lidar. Frontiers in Ecology and the Environment, 21(1), 55–56. https://doi.org/10.1002/fee.2585
Hancock S., Armston J., Hofton M., Sun X., Tang H., Duncanson L. I., Kellner J. R., & Dubayah R. (2019a). The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions. Earth and Space Science, 6(2), 294–310. https://doi.org/10.1029/2018EA000506
Hancock S., Armston J., Hofton M., Sun X., Tang H., Duncanson L. I., Kellner J. R., & Dubayah R. (2019b). The GEDI Simulator: A Large‐Footprint Waveform Lidar Simulator for Calibration and Validation of Spaceborne Missions. Earth and Space Science, 6(2), 294–310. https://doi.org/10.1029/2018EA000506
Hansen M. C., Potapov P. V., Moore R., Hancher M., Turubanova S. A., Tyukavina A., Thau D., … & Townshend J. R. G. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853. https://doi.org/10.1126/science.1244693
Hemingway H., & Opalach D. (2024). Integrating Lidar Canopy Height Models with Satellite-Assisted Inventory Methods: A Comparison of Inventory Estimates. Forest Science, 70(1), 2–13. https://doi.org/10.1093/forsci/fxad047
Hunka N., Duncanson L., Armston J., Dubayah R., Healey S. P., Santoro M., May P., Araza A., … Melo J. (2024). Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation. Scientific Data, 11(1), 1127. https://doi.org/10.1038/s41597-024-03930-9
Jia D., Wang C., Hakkenberg C. R., Numata I., Elmore A. J., & Cochrane M. A. (2024). Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States. GIScience & Remote Sensing, 61(1). https://doi.org/10.1080/15481603.2023.2292374
Joshi N., Mitchard E. T. A., Brolly M., Schumacher J., Fernández-Landa A., Johannsen V. K., Marchamalo M., & Fensholt R. (2017). Understanding ‘saturation’ of radar signals over forests. Scientific Reports, 7(1), 3505. https://doi.org/10.1038/s41598-017-03469-3
Keige E. W. (2019). Impact of Benefit Sharing Arrangements on Sustainable Management of Public Forests: a Case Study of Karura Forest in Kenya. PhD Thesis. University of Nairobi.
Kellner J. R., Armston J., & Duncanson L. (2023). Algorithm Theoretical Basis Document for GEDI Footprint Aboveground Biomass Density. Earth and Space Science, 10(4). https://doi.org/10.1029/2022EA002516
Khajavi H., & Rastgoo A. (2023). Predicting the carbon dioxide emission caused by road transport using a Random Forest (RF) model combined by Meta-Heuristic Algorithms. Sustainable Cities and Society, 93, 104503. https://doi.org/10.1016/j.scs.2023.104503
Kikuchi Y., Ouchida K., Kanematsu Y., & Okubo T. (2018). Design Support of Smart Energy Systems based on Locally Available Resources: A Case Study in Isolated Islands in Japan (pp. 2515–2520). https://doi.org/10.1016/B978-0-444-64241-7.50414-6
Kipkorir T. G. (2017). Modelling impacts of climate change on tree biomass and distribution in Arabuko Sokoke forest reserve, Kenya. University of Nairobi.
Krause P., Forbes B., Barajas-Ritchie A., Clark M., Disney M., Wilkes P., & Bentley L. P. (2023). Using terrestrial laser scanning to evaluate non-destructive aboveground biomass allometries in diverse Northern California forests. Frontiers in Remote Sensing, 4. https://doi.org/10.3389/frsen.2023.1132208
Krzywinski M., & Altman N. (2014). Comparing samples—part I. Nature Methods, 11(3), 215–216. https://doi.org/10.1038/nmeth.2858
Kuyah S., Dietz J., Muthuri C., Jamnadass R., Mwangi P., Coe R., & Neufeldt H. (2012). Allometric equations for estimating biomass in agricultural landscapes: I. Aboveground biomass. Agriculture, Ecosystems & Environment, 158, 216–224. https://doi.org/10.1016/j.agee.2012.05.011
Lahssini K., Baghdadi N., Le Maire G., Dupuy S., & Fayad I. (2024). Use of GEDI Signal and Environmental Parameters to Improve Canopy Height Estimation over Tropical Forest Ecosystems in Mayotte Island. Canadian Journal of Remote Sensing, 50(1). https://doi.org/10.1080/07038992.2024.2351004
Lee J., Kim J., Hahn S., Han H., Shin G., Kim W.-C., & Yoon S.-W. (2024). Data-driven disruption prediction using random forest in KSTAR. Fusion Engineering and Design, 199, 114128. https://doi.org/10.1016/j.fusengdes.2023.114128
Lei Q., Yu H., & Lin Z. (2024). Understanding China’s CO2 emission drivers: Insights from random forest analysis and remote sensing data. Heliyon, 10(7), e29086. https://doi.org/10.1016/j.heliyon.2024.e29086
Li H., Li X., Kato T., Hayashi M., Fu J., & Hiroshima T. (2024). Accuracy assessment of GEDI terrain elevation, canopy height, and aboveground biomass density estimates in Japanese artificial forests. Science of Remote Sensing, 10, 100144. https://doi.org/10.1016/j.srs.2024.100144
Lindberg L. (2020). Forest data acquisition with the application Arboreal Forest – A study about measurement precision, accuracy and efficiency. Umeå: SLU, Department of Forest Biomaterials and Technology. https://stud.epsilon.slu.se/15456/
Lisboa S. N., Guedes B. S., Ribeiro N., & Sitoe A. (2018). Biomass allometric equation and expansion factor for a mountain moist evergreen forest in Mozambique. Carbon Balance and Management, 13(1), 23. https://doi.org/10.1186/s13021-018-0111-7
Liu A., Chen Y., & Cheng X. (2024). Evaluating ICESat-2 and GEDI with Integrated Landsat-8 and PALSAR-2 for Mapping Tropical Forest Canopy Height. Remote Sensing, 16(20), 3798. https://doi.org/10.3390/rs16203798
Ma T., Zhang C., Ji L., Zuo Z., Beckline M., Hu Y., Li X., & Xiao X. (2024). Development of forest aboveground biomass estimation, its problems and future solutions: A review. Ecological Indicators, 159, 111653. https://doi.org/10.1016/j.ecolind.2024.111653
Magruder L. A., Farrell S. L., Neuenschwander A., Duncanson L., Csatho B., Kacimi S., & Fricker H. A. (2024). Monitoring Earth’s climate variables with satellite laser altimetry. Nature Reviews Earth & Environment, 5(2), 120–136. https://doi.org/10.1038/s43017-023-00508-8
Malimbwi R. E., & Chamshama S. (2016). Allometric volume and biomass models in Tanzania. https://doi.org/10.13140/RG.2.1.1891.5445
Manji A. (2017). Property, conservation, and enclosure in Karura Forest, Nairobi. African Affairs, 116(463), 186–205. https://doi.org/10.1093/afraf/adx006
Mbuvi M. T. E., Ndalilo L., & Hussein A. (2018). Applying Sustainability and Ethics in Forest Management and Community Livelihoods: A Case Study from Arabuko Sokoke Forest, Kenya. Open Journal of Forestry, 08(04), 532–552. https://doi.org/10.4236/ojf.2018.84033
Mellor A., Boukir S., Haywood A., & Jones S. (2015). Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 155–168. https://doi.org/10.1016/j.isprsjprs.2015.03.014
Mohite J., Sawant S., Pandit A., Sakkan M., Pappula S., & Parmar A. (2024). Forest aboveground biomass estimation by GEDI and multi-source EO data fusion over Indian forest. International Journal of Remote Sensing, 45(4), 1304–1338. https://doi.org/10.1080/01431161.2024.2307944
Morgan E. A., Bush G., Mandea J. Z., & Maraseni T. (2023). Community evaluation of forest and REDD+ governance quality in the Democratic Republic of the Congo. Journal of Environmental Management, 328, 116891. https://doi.org/10.1016/j.jenvman.2022.116891
Moundounga Mavouroulou Q., Ngomanda A., Engone Obiang N. L., Lebamba J., Gomat H., … & Picard N. (2014). How to improve allometric equations to estimate forest biomass stocks? Some hints from a central African forest. Canadian Journal of Forest Research, 44(7), 685–691. https://doi.org/10.1139/cjfr-2013-0520
Mulatu A., Negash M., & Asrat Z. (2024). Species-specific allometric models for reducing uncertainty in estimating above ground biomass at Moist Evergreen Afromontane Forest of Ethiopia. Scientific Reports, 14(1), 1147. https://doi.org/10.1038/s41598-023-51002-6
Munteanu C., Kraemer B. M., Hansen H. H., Miguel S., Milner-Gulland E. J., ... & Kuemmerle T. (2024). The potential of historical spy-satellite imagery to support research in ecology and conservation. BioScience. https://doi.org/10.1093/biosci/biae002
Murrins Misiukas J., Carter S., & Herold M. (2021). Tropical Forest Monitoring: Challenges and Recent Progress in Research. Remote Sensing, 13(12), 2252. https://doi.org/10.3390/rs13122252
Mwendwa Mugambi, J., Kagendo J., Kweyu M., & Mbuvi M. T. E. (2020). Influence of Community Forest Association Activities on Dryland Resources Management: Case of Kibwezi Forest in Kenya. International Journal of Natural Resource Ecology and Management, 5(3), 119. https://doi.org/10.11648/j.ijnrem.20200503.16
Nandy S., Srinet R., & Padalia H. (2021). Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India. Geophysical Research Letters, 48(14). https://doi.org/10.1029/2021GL093799
Narin O. G., Abdikan S., Gullu M., Lindenbergh R., Balik Sanli F., & Yilmaz I. (2024). Improving global digital elevation models using space-borne GEDI and ICESat-2 LiDAR altimetry data. International Journal of Digital Earth, 17(1). https://doi.org/10.1080/17538947.2024.2316113
NASA Jet Propulsion Laboratory (2024). NASA Jet Propulsion Laboratory (JPL) - Robotic Space Exploration. https://www.jpl.nasa.gov/
Navarro J. A., Algeet N., Fernández-Landa A., Esteban J., Rodríguez-Noriega P., & Guillén-Climent M. L. (2019). Integration of UAV, Sentinel-1, and Sentinel-2 Data for Mangrove Plantation Aboveground Biomass Monitoring in Senegal. Remote Sensing, 11(1), 77. https://doi.org/10.3390/rs11010077
Nesha K., Herold M., De Sy V., Duchelle A. E., Martius C., Branthomme A., Garzuglia M., Jonsson O., & Pekkarinen A. (2021). An assessment of data sources, data quality and changes in national forest monitoring capacities in the Global Forest Resources Assessment 2005–2020. Environmental Research Letters, 16(5), 054029. https://doi.org/10.1088/1748-9326/abd81b
Nieto P. J. G., Gonzalo E. G., García L. A. M., Prado L. Á., & Sánchez A. B. (2024). Predicting the critical superconducting temperature using the random forest, MLP neural network, M5 model tree and multivariate linear regression. Alexandria Engineering Journal, 86, 144–156. https://doi.org/10.1016/j.aej.2023.11.034
Niu W., Ha Y., & Chi N. (2020). Support vector machine based machine learning method for GS 8QAM constellation classification in seamless integrated fiber and visible light communication system. Science China Information Sciences, 63(10), 202306. https://doi.org/10.1007/s11432-019-2850-3
Nyamukuru A., Whitney C., Tabuti, J. R. S., Esaete J., & Low M. (2023). Allometric models for aboveground biomass estimation of small trees and shrubs in African savanna ecosystems. Trees, Forests and People, 11, 100377. https://doi.org/10.1016/j.tfp.2023.100377
Obonyo O. A., Agevi H., & Tsingalia M. H. (2023). Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya. Scientific Reports, 13(1), 20921. https://doi.org/10.1038/s41598-023-47871-6
Osewe E. O., & Dutcă I. (2021). The Effects of Combining the Variables in Allometric Biomass Models on Biomass Estimates over Large Forest Areas: A European Beech Case Study. Forests, 12(10), 1428. https://doi.org/10.3390/f12101428
Osewe E. O., Niţă M. D., & Abrudan I. V. (2022). Assessing the Fragmentation, Canopy Loss and Spatial Distribution of Forest Cover in Kakamega National Forest Reserve, Western Kenya. Forests, 13(12). https://doi.org/10.3390/f13122127
Pascual A., Guerra-Hernández J., Armston J., Minor D. M., Duncanson L. I., May P. B., Kellner J. R., & Dubayah R. (2023). Assessing the performance of NASA’s GEDI L4A footprint aboveground biomass density models using National Forest Inventory and airborne laser scanning data in Mediterranean forest ecosystems. Forest Ecology and Management, 538, 120975. https://doi.org/10.1016/j.foreco.2023.120975
Pascual A., May P. B., Cárdenas-Martínez A., Guerra-Hernández J., Hunka N., …. & Dubayah R. O. (2025). Calibration of GEDI footprint aboveground biomass models in Mediterranean forests with NFI plots: A comparison of approaches. Journal of Environmental Management, 375, 124313. https://doi.org/10.1016/j.jenvman.2025.124313
Pati P. K., Kaushik P., Khan M. L., & Khare P. K. (2022). Allometric equations for biomass and carbon stock estimation of small diameter woody species from tropical dry deciduous forests: Support to REDD+. Trees, Forests and People, 9, 100289. https://doi.org/10.1016/j.tfp.2022.100289
Picard N., Saint-André L., & Henry M. (2012). Manual for building tree volume and biomass allometric equations: from field measurement to prediction. FAO & CIRAD.
Potapov P., Li X., Hernandez-Serna A., Tyukavina A., Hansen M. C., Kommareddy A., ... & Hofton M. (2021). Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, 112165. https://doi.org/10.1016/j.rse.2020.112165
Prayogo, C., Muthahar C., & Ishaq R. M. (2021). Allometric equation of local bamboo for estimating carbon sequestration of bamboo riparian forest. IOP Conference Series: Earth and Environmental Science, 905(1), 012002. https://doi.org/10.1088/1755-1315/905/1/012002
Pretzsch H., Rötzer T., & Forrester D. I. (2017). Modelling Mixed-Species Forest Stands. In Mixed-Species Forests (pp. 383–431). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54553-9_8
Probst P., Wright M. N., & Boulesteix A. (2019). Hyperparameters and tuning strategies for random forest. WIREs Data Mining and Knowledge Discovery, 9(3). https://doi.org/10.1002/widm.1301
QGIS Project (2023). Gentle GIS Introduction QGIS Project. https://docs.qgis.org/3.34/en/docs/index.html
Quiros E., Polo, M.-E., & Fragoso-Campon, L. (2021). GEDI Elevation Accuracy Assessment: A Case Study of Southwest Spain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 5285–5299. https://doi.org/10.1109/JSTARS.2021.3080711
Ramachandran N., Saatchi S., Tebaldini S., d’Alessandro M. M., & Dikshit O. (2023). Mapping tropical forest aboveground biomass using airborne SAR tomography. Scientific Reports, 13(1), 6233. https://doi.org/10.1038/s41598-023-33311-y
Chollet Ramampiandra E. C., Scheidegger A., Wydler J., & Schuwirth N. (2023). A comparison of machine learning and statistical species distribution models: Quantifying overfitting supports model interpretation. Ecological Modelling, 481, 110353. https://doi.org/10.1016/j.ecolmodel.2023.110353
Republic of Kenya (2016). Forest Conservation and Management Act. Forest Conservation and Management Act, 155(34), 677–736. http://www.ilo.org/dyn/travail/docs/505/Employment Act 2007.pdf
Rey D., & Neuhäuser M. (2011). Wilcoxon-Signed-Rank Test. In International Encyclopedia of Statistical Science (pp. 1658–1659). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-04898-2_616
Rodrigues de Moura Fernandes M., Fernandes da Silva G., Quintão de Almeida A., Marques Fernandes M., Ribeiro de Mendonça A., ... & Pereira Martins Silva J. (2023). Aboveground biomass estimation in dry forest in northeastern Brazil using metrics extracted from sentinel-2 data: Comparing parametric and non-parametric estimation methods. Advances in Space Research, 72(2), 361–377. https://doi.org/10.1016/j.asr.2023.03.010
Rojas-García F., De Jong B. H. J., Martínez-Zurimendí P., & Paz-Pellat F. (2015). Database of 478 allometric equations to estimate biomass for Mexican trees and forests. Annals of Forest Science, 72(6), 835–864. https://doi.org/10.1007/s13595-015-0456-y
Rozendaal D. M. A., Requena Suarez D., De Sy V., Avitabile V., Carter S., Adou Yao C. Y., … Herold M. (2022). Aboveground forest biomass varies across continents, ecological zones and successional stages: refined IPCC default values for tropical and subtropical forests. Environmental Research Letters, 17(1), 014047. https://doi.org/10.1088/1748-9326/ac45b3
Sandim A., Amaro M., Silva M. E., Cunha J., Morais S., Marques A., Ferreira A., Lousada J. L., & Fonseca T. (2023). New Technologies for Expedited Forest Inventory Using Smartphone Applications. Forests, 14(8), 1553. https://doi.org/10.3390/f14081553
Sarkar D. P., Uma Shankar B., & Ranjan Parida B. (2024). A novel approach for retrieving GPP of evergreen forest regions of India using random forest regression. Remote Sensing Applications: Society and Environment, 33, 101116. https://doi.org/10.1016/j.rsase.2023.101116
Schneider A., Blick T., Pauls S. U., & Dorow W. H. O. (2021). The list of forest affinities for animals in Central Europe – A valuable resource for ecological analysis and monitoring in forest animal communities? Forest Ecology and Management, 479, 118542. https://doi.org/10.1016/j.foreco.2020.118542
Sebrala H., Abich A., Negash M., Asrat Z., & Lojka B. (2022a). Tree allometric equations for estimating biomass and volume of Ethiopian forests and establishing a database: Review. Trees, Forests and People, 9, 100314. https://doi.org/10.1016/j.tfp.2022.100314
Sebrala H., Abich A., Negash M., Asrat Z., & Lojka B. (2022b). Tree allometric equations for estimating biomass and volume of Ethiopian forests and establishing a database: Review. Trees, Forests and People, 9, 100314. https://doi.org/10.1016/j.tfp.2022.100314
Shannon E. S., Finley A. O., Hayes D. J., Noralez S. N., Weiskittel A. R., Cook B. D., & Babcock C. (2022). Quantifying and correcting geolocation error in sampling LiDAR forest canopy observations using high spatial accuracy ALS: A case study involving GEDI. https://doi.org/10.1002/env.2840
Shi Y., Gao J., Li X., Li J., dela Torre D. M. G., & Brierley G. J. (2021). Improved Estimation of Aboveground Biomass of Disturbed Grassland through Including Bare Ground and Grazing Intensity. Remote Sensing, 13(11), 2105. https://doi.org/10.3390/rs13112105
Silva C. A., Duncanson L., Hancock S., Neuenschwander A., Thomas N., Hofton M., ..., & Dubayah R. (2021). Fusing simulated GEDI, ICESat-2 and NISAR data for regional aboveground biomass mapping. Remote Sensing of Environment, 253, 112234. https://doi.org/10.1016/j.rse.2020.112234
Silva C. A., Saatchi S., Garcia M., Labriere N., Klauberg C., Ferraz A., … & Hudak A. T. (2018). Comparison of Small- and Large-Footprint Lidar Characterization of Tropical Forest Aboveground Structure and Biomass: A Case Study from Central Gabon. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(10), 3512–3526. https://doi.org/10.1109/JSTARS.2018.2816962
Singha C., & Sahoo S. (2024). Predicting Forest Canopy Height Using GEDI LiDAR Based Machine Learning Technique Over Similipal Biosphere, India. In: Pradhan B., Mukhopadhyay S. (eds) IoT Sensors, ML, AI and XAI: Empowering A Smarter World. Smart Sensors, Measurement and Instrumentation (vol. 50, pp. 363–374). Springer, Cham. https://doi.org/10.1007/978-3-031-68602-3_18
Stan K. D., Sanchez-Azofeifa, A., & Hamann, H. F. (2024). Widespread degradation and limited protection of forests in global tropical dry ecosystems. Biological Conservation, 289, 110425. https://doi.org/10.1016/j.biocon.2023.110425
Sun Z., Wang, G., Li, P., Wang, H., Zhang, M., & Liang, X. (2024). An improved random forest based on the classification accuracy and correlation measurement of decision trees. Expert Systems with Applications, 237, 121549. https://doi.org/10.1016/j.eswa.2023.121549
Tadese S., Soromessa T., Aneseye A. B., Gebeyehu G., Noszczyk T., & Kindu M. (2023). The impact of land cover change on the carbon stock of moist afromontane forests in the Majang Forest Biosphere Reserve. Carbon Balance and Management, 18(1), 24. https://doi.org/10.1186/s13021-023-00243-z
Tarus G. K., & Nadir S. W. (2020). Effect of Forest Management Types on Soil Carbon Stocks in Montane Forests: A Case Study of Eastern Mau Forest in Kenya. International Journal of Forestry Research, 2020, 1–10. https://doi.org/10.1155/2020/8862813
Tetere V., & Zeverte-Rivza S. (2023). Closing Data Gaps to Measure the Bioeconomy in the EU. Biomass, 3(2), 108–122. https://doi.org/10.3390/biomass3020008
Tian L., Wu X., Tao Y., Li M., Qian C., Liao L., & Fu W. (2023). Review of Remote Sensing-Based Methods for Forest Aboveground Biomass Estimation: Progress, Challenges, and Prospects. Forests, 14(6), 1086. https://doi.org/10.3390/f14061086
Tom Dieck M. C., Jung T. H., & Loureiro S. M. C. (Eds.) (2021). Augmented Reality and Virtual Reality. New Trends in Immersive Technology. Springer International Publishing. https://doi.org/10.1007/978-3-030-68086-2
Tomppo E. (2004). Resource Assessment | Forest Resources. In Encyclopedia of Forest Sciences (pp. 965–973). Elsevier. https://doi.org/10.1016/B0-12-145160-7/00156-3
Tumwebaze S. B., Bevilacqua E., Briggs R., & Volk T. (2013). Allometric biomass equations for tree species used in agroforestry systems in Uganda. Agroforestry Systems, 87(4), 781–795. https://doi.org/10.1007/s10457-013-9596-y
Ullah F., Gilani H., Sanaei A., Hussain K., & Ali A. (2021). Stand structure determines aboveground biomass across temperate forest types and species mixture along a local-scale elevational gradient. Forest Ecology and Management, 486, 118984. https://doi.org/10.1016/j.foreco.2021.118984
Urbazaev M., Hess L. L., Hancock S., Sato L. Y., Ometto J. P., .... & Schmullius C. (2022). Assessment of terrain elevation estimates from ICESat-2 and GEDI spaceborne LiDAR missions across different land cover and forest types. Science of Remote Sensing, 6, 100067. https://doi.org/10.1016/j.srs.2022.100067
Vrtač T., Ocepek D., Česnik M., Čepon G., & Boltežar M. (2024). A hybrid modeling strategy for training data generation in machine learning-based structural health monitoring. Mechanical Systems and Signal Processing, 207, 110937. https://doi.org/10.1016/j.ymssp.2023.110937
Vuillod B., Zani M., Hallo L., & Montemurro M. (2024). Handling noise and overfitting in surrogate models based on non-uniform rational basis spline entities. Computer Methods in Applied Mechanics and Engineering, 425, 116913. https://doi.org/10.1016/j.cma.2024.116913
Wang C., Zhang W., Ji Y., Marino A., Li C., Wang L., Zhao H., & Wang M. (2024a). Correction: Wang et al. Estimation of Aboveground Biomass for Different Forest Types Using Data from Sentinel-1, Sentinel-2, ALOS PALSAR-2, and GEDI. Forests 2024, 15, 215. Forests, 15(3), 401. https://doi.org/10.3390/f15030401
Wang W., Han F., Kong Z., Ling H., & Hao X. (2024b). The maximum threshold of vegetation restoration (EVI-Area) in typical watersheds of arid regions under water constraints. Ecological Indicators, 158, 111580. https://doi.org/10.1016/j.ecolind.2024.111580
Wen D., Zhu S., Tian Y., Guan X., & Lu Y. (2024). Generating 10-Meter Resolution Land Use and Land Cover Products Using Historical Landsat Archive Based on Super Resolution Guided Semantic Segmentation Network. Remote Sensing, 16(12), 2248. https://doi.org/10.3390/rs16122248
Yao Z., Xin Y., Yang L., Zhao L., & Ali A. (2022). Precipitation and temperature regulate species diversity, plant coverage and aboveground biomass through opposing mechanisms in large-scale grasslands. Frontiers in Plant Science, 13. https://doi.org/10.3389/fpls.2022.999636
Yohannes H., & Soromessa T. (2015). Carbon Stock Analysis along Slope and Slope Aspect Gradient in Gedo Forest: Implications for Climate Change Mitigation. Journal of Earth Science & Climatic Change, 06(09). https://doi.org/10.4172/2157-7617.1000305
Zadbagher E., Marangoz A., & Becek K. (2024). Estimation of above-ground biomass using machine learning approaches with InSAR and LiDAR data in tropical peat swamp forest of Brunei Darussalam. IForest - Biogeosciences and Forestry, 17(3), 172–179. https://doi.org/10.3832/ifor4434-017
Zhang S., Vega C., Deleuze C., Durrieu S., Barbillon P., Bouriaud O., & Renaud J.-P. (2022). Modelling forest volume with small area estimation of forest inventory using GEDI footprints as auxiliary information. International Journal of Applied Earth Observation and Geoinformation, 114, 103072. https://doi.org/10.1016/j.jag.2022.103072
Zhang X., Shen H., Huang T., Wu Y., Guo B., Liu Z., Luo H., Tang J., Zhou H., Wang L., Xu W., & Ou G. (2024). Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery. Ecological Indicators, 159, 111752. https://doi.org/10.1016/j.ecolind.2024.111752
Zhao P., Lu D., Wang G., Wu C., Huang Y., & Yu S. (2016). Examining Spectral Reflectance Saturation in Landsat Imagery and Corresponding Solutions to Improve Forest Aboveground Biomass Estimation. Remote Sensing, 8(6), 469. https://doi.org/10.3390/rs8060469
Zhu X., Wang C., Nie S., Pan F., Xi X., & Hu Z. (2020). Mapping forest height using photon-counting LiDAR data and Landsat 8 OLI data: A case study in Virginia and North Carolina, USA. Ecological Indicators, 114, 106287. https://doi.org/10.1016/j.ecolind.2020.106287
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