Abstract
Tropical montane forests (TMFs) are highly valuable for their above-ground biomass (AGB) and their potential to sequester carbon, but they remain understudied. Sentinel-1, -2, biophysical data and Machine Learning were used to estimate and map the AGB and above-ground carbon (AGC) stocks in Benguet, Philippines. Non-destructive field AGB measurements were collected from 184 plots, revealing that pine forests had 33.57% less AGB than mossy forests (380.33 Mgha−1), whilst the grassland summit had 39.93 Mgha−1. In contrast to the majority of literature, AGB did not decrease linearly with elevation. NDVI, LAI, fAPAR, fCover and elevation were the most effective predictors of field-derived AGB as determined by Random Forest (RF) feature selection in R. WEKA was used to evaluate and validate 26 Machine Learning algorithms. The results show that the Machine Learning K star (K*) (r = 0.213–0.832; RMSE = 106.682 Mgha−1–224.713 Mgha−1) and RF (r = 0.391–0.822; RMSE = 108.226 Mgha−1–175.642 Mgha−1) exhibited high modelling capabilities to estimate AGB across all predictor categories. Consequently, spatially explicit models were carried out in Whitebox Runner software to map the study site’s AGB, demonstrating RF with the highest predictive performance (r = 0.982; RMSE = 53.980 Mgha−1). The study area’s carbon stock map ranged from 0 to 434.94 Mgha−1, highlighting the significance of forests at higher elevations for forest conservation and carbon sequestration. Carbon-rich mountainous regions of the county can be encouraged for carbon sequestration through REDD + interventions. Longer wavelength radar imagery, species-specific allometric equations and soil fertility should be tested in future carbon studies. The produced carbon maps can help policy makers in decision-planning, and thus contribute to conserve the natural resources of the Benguet Mountains.
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Data Availability
Sentinel-1 and -2 imagery used in this study can be downloaded in the ESA Copernicus Open Access Hub website. Appropriate clearance from the MPPL-PAMB and MPPL-PAMO shall be obtained to get the field data collected in the mossy forest and grassland summit in the Province of Benguet. Other supporting data for this study are available upon reasonable request to the corresponding author and with the consent of the other authors.
References
Ali A, Imran M, Ali A, Khan MA (2022) Evaluating Sentinel-2 red edge through hyperspectral profiles for monitoring LAI & chlorophyll content of Kinnow Mandarin orchards. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100719
Altarez RDD, Apan A, Maraseni T (2022) Spaceborne satellite remote sensing of tropical montane forests: a review of applications and future trends. Geocarto Int 0(0):1–29. https://doi.org/10.1080/10106049.2022.2060330
Altarez RDD, Apan A, Maraseni T (2023) Deep learning U-Net classification of Sentinel-1 and 2 fusions effectively demarcates tropical montane forest’s deforestation. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100887
American Society of Agronomy, Crop Science Society of America, & Soil Science Society of America (2021) Publications handbook and style manual (7th edition). ASA–CSSA–SSSA
Apan A, Suarez LA, Maraseni T, Castillo JA (2017) The rate, extent and spatial predictors of forest loss (2000–2012) in the terrestrial protected areas of the Philippines. Appl Geogr 81:32–42. https://doi.org/10.1016/j.apgeog.2017.02.007
Aryal K, Apan A, Maraseni T (2023) Comparing global and local land cover maps for ecosystem management in the Himalayas. Remote Sensi Appl Soc Environ. https://doi.org/10.1016/j.rsase.2023.100952
Asner GP, Anderson CB, Martin RE, Knapp DE, Tupayachi R, Sinca F, Malhi Y (2014) Landscape-scale changes in forest structure and functional traits along an Andes-to-Amazon elevation gradient. Biogeosciences 11(3):843–856. https://doi.org/10.5194/bg-11-843-2014
Avtar R, Tsusaka K, Herath S (2020) Assessment of forest carbon stocks for REDD+ implementation in the muyong forest system of Ifugao, Philippines. Environ Monitor Assessm. https://doi.org/10.1007/s10661-020-08531-8
Báez S, Fadrique B, Feeley K, Homeier J (2022) Changes in tree functional composition across topographic gradients and through time in a tropical montane forest. PLoS One 17:1–20. https://doi.org/10.1371/journal.pone.0263508
Baydogan E, Sarp G (2022) Urban footprint detection from night light, optical and SAR imageries: a comparison study. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100775
Beam AL, Manrai AK, Ghassemi M (2020) Challenges to the reproducibility of machine learning models in health care. JAMA J Am Med Assoc 323(4):305–306. https://doi.org/10.1001/jama.2019.20866
Belgiu M, Drăgu L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31. https://doi.org/10.1016/j.isprsjprs.2016.01.011
Berninger A, Lohberger S, Stängel M, Siegert F (2018) SAR-based estimation of above-ground biomass and its changes in tropical forests of Kalimantan using L- and C-band. Remote Sens. https://doi.org/10.3390/rs10060831
Bersamin AT, Tayaben JL, Balangcod KD, Balangcod AKD, Cendana AC, Dom-Ogen ET, Licnachan LOC, Siadto B, Wong FM, Balangcod TD (2021) Utilization of plant resources among the Kankanaeys in Kibungan, Benguet Province, Philippines. Biodiversitas 22(1):362–372. https://doi.org/10.13057/biodiv/d220144
Bouvet A, Mermoz S, Ballère M, Koleck T, Le Toan T (2018) Use of the SAR shadowing effect for deforestation detection with Sentinel-1 time series. Remote Sens 10(8):1–19. https://doi.org/10.3390/rs10081250
Carter GA (1994) Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Int J Remote Sens 15(3):517–520. https://doi.org/10.1080/01431169408954109
Castillo JAA, Apan AA, Maraseni TN, Salmo SG (2017) Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS J Photogramm Remote Sens 134:70–85. https://doi.org/10.1016/j.isprsjprs.2017.10.016
Celia M, Sonny N, Adrian D, Divina M (2017) Climate-sensitive decisions and use of climate information: Insights from selected La Trinidad and Atok, Benguet agricultural producers
Center for International Forestry Research and World Agroforestry (ICRAF) (n.d.) Tree Functional Attribute and Ecological Database. Retrieved August 17, 2023, from http://db.worldagroforestry.org/wd?fbclid=IwAR1KW8b8aFG2qexWqVL4CL0l6eIiY7rNXyF-VNeziukzYeSscv2bFw3qZvo
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, Duque A, Eid T, Fearnside PM, Goodman RC, Henry M, Martínez-Yrízar A, Mugasha WA, Muller-Landau HC, Mencuccini M, Nelson BW, Ngomanda A, Nogueira EM, Ortiz-Malavassi E, Vieilledent G (2014) Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biol 20(10):3177–3190. https://doi.org/10.1111/gcb.12629
Clerici N, Valbuena Calderón CA, Posada JM (2017) Fusion of sentinel-1a and sentinel-2A data for land cover mapping: a case study in the lower Magdalena region. Colombia J Maps 13(2):718–726. https://doi.org/10.1080/17445647.2017.1372316
Crausbay SD, Martin PH (2016) Natural disturbance, vegetation patterns and ecological dynamics in tropical montane forests. J Trop Ecol 32(5):384–403. https://doi.org/10.1017/S0266467416000328
Cuni-Sanchez A, Sullivan MJP, Platts PJ, Lewis SL, Marchant R, Imani G, Hubau W, Abiem I, Adhikari H, Albrecht T, Altman J, Amani C, Aneseyee AB, Avitabile V, Banin L, Batumike R, Bauters M, Beeckman H, Begne SK, Zibera E (2021) High aboveground carbon stock of African tropical montane forests. Nature 596:536–542. https://doi.org/10.1038/s41586-021-03728-4
Cruz MN. Medina KC, Cabriga AS, Mendoza F, Blanco AC (2019) GIS-assisted rain-included landslide susceptibility mapping of Benguet using logistic regression model. Int Archiv Photogramm Remote Sens Spatial Inform Sci ISPRS Archiv 42(4/W19):157–164. https://doi.org/10.5194/isprs-archives-XLII-4-W19-157-2019
David RM, Rosser NJ, Donoghue DNM (2022) Improving above ground biomass estimates of Southern Africa dryland forests by combining Sentinel-1 SAR and Sentinel-2 multispectral imagery. Remote Sens Environ. https://doi.org/10.1016/j.rse.2022.113232
Department of Environment and Natural Resources—Forest Management Bureau (DENR-FMB) (2011) Watershed characterization and vulnerability assessment using geographic information system and remote sensing, i–i. https://doi.org/10.1109/isqed.2008.4479675
Dionisio DJ, Agoot L (2020) Almost 900 hectares of natural forest, areas razed in Benguet | Philippine News Agency. Philippine News Agency. https://www.pna.gov.ph/articles/1094989
Doyog ND, Lumbres RIC, Lee YJ (2018) Mapping of the spatial distribution of carbon storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest in Sagada, Mt. Province, Philippines. J Sustain Forestry 37(7):661–677. https://doi.org/10.1080/10549811.2018.1450155
Doyog ND, Lumbres RIC, Baoanan ZG (2021) Monitoring of land use and land cover changes in Mt Pulag national park using landsat and sentinel imageries. Philippine J Sci 150(4):723–734. https://doi.org/10.56899/150.04.10
Dupuis C, Lejeune P, Michez A, Fayolle A (2020) How can remote sensing help monitor tropical moist forest degradation?-A systematic review. Remote Sens. https://doi.org/10.3390/rs12071087
Ezaidi S, Aydda A, Kabbachi B, Althuwaynee OF, Ezaidi A, Haddou MA, Idoumskine I, Thorpe J, Park HJ, Kim SW (2022) Multi-temporal Landsat-derived NDVI for vegetation cover degradation for the period 1984–2018 in part of the Arganeraie Biosphere Reserve (Morocco). Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100800
Fernando E, Cereno R (2010) Biodiversity and Natural Resources Management in the Mt. Pulag National Park, Philippines. In: MHS et al. Lapitan PG, Fernando ES (Ed.), Biodiversity and Natural Resources Conservation in Protected Areas of Korea and the Philippines (pp. 120–177). ASEAN-Korea Environmental Cooperation Unit, Seoul National University, Korea
Filipponi F (2019) Conferecne Paper PdF 3:2–6
Fischer R, Ensslin A, Rutten G, Fischer M, Costa DS, Kleyer M, Hemp A, Paulick S, Huth A (2015) Simulating carbon stocks and fluxes of an African tropical montane forest with an individual-based forest model. PLoS One 10(4):1–13. https://doi.org/10.1371/journal.pone.0123300
Food and Agriculture Organization of the United Nation (FAO) (2016) The State of the World’s Forests 2016. https://doi.org/10.18356/c301d13a-en
Gitelson AA, Viña A, Ciganda V, Rundquist DC, Arkebauer TJ (2005) Remote estimation of canopy chlorophyll content in crops. Geophys Res Lett 32(8):1–4. https://doi.org/10.1029/2005GL022688
Gokool S, Kunz RP, Toucher M (2022) Deriving moderate spatial resolution leaf area index estimates from coarser spatial resolution satellite products. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100743
González-Jaramillo V, Fries A, Zeilinger J, Homeier J, Paladines-Benitez J, Bendix J (2018) Estimation of above ground biomass in a tropical mountain forest in southern Ecuador using airborne LiDAR data. Remote Sens. https://doi.org/10.3390/rs10050660
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853. https://doi.org/10.1126/science.1244693
Hastie T, Tibshirani R, Friedman J (2009) Statistics the elements of statistical learning. Springer Series in Statistics, 27(2), 745. http://www.springerlink.com/index/D7X7KX6772HQ2135.pdf
Hernandez RP (2004) Assessing carbon stocks and modelling Win-win Scenarios of Carbon ..., Volume 1. January. https://books.google.co.id/books?hl=id&lr=&id=c5gS5HfBZQ4C&oi=fnd&pg=PA1&dq=Raul+Ponce-Hernandez&ots=iZ7f9PpTDA&sig=prM_inihJhj32bO7bM286M01jeA&redir_esc=y#v=onepage&q=Raul Ponce-Hernandez&f=false
IPCC (Intergovernmental Panel on Climate Change) (2006) Guidelines for national greenhouse gas inventories. In Agriculture, Ecosystems and Environment (Eggleston, Vol. 4). IGES Publishing. https://doi.org/10.1016/0167-8809(92)90023-5
Issa S, Dahy B, Ksiksi T, Saleous N (2020) A review of terrestrial carbon assessment methods using geo-spatial technologies with emphasis on arid lands. Remote Sens. https://doi.org/10.3390/rs12122008
Iverson LR, Brown S, Grainger A, Prasad A, Liu D (1993) Carbon sequestration in tropical Asia: an assessment of technically suitable forest lands using geographic information systems analysis. Climate Res 3(1–2):23–38. https://doi.org/10.3354/cr003023
Jackson RD, Slaterj PN, Pinter PJ (1983) Adjusting the tasselled-cap brightness and greenness factors for atmospheric path radiance and absorption on a pixel by pixel basis. Int J Remote Sens 4(2):313–323. https://doi.org/10.1080/01431168308948549
Japan International Cooperation Agency (JICA) (1992) Feasibility study on the restoration of rural roads
Jeyanny V, Mha H, Rasidah KW, Kumar BS (2014) Carbon stocks in different carbon pools pf a tropical lowland forest and a montane forest with vartying topography. J Trop For Sci 26(4):560–571
Jha N, Tripathi NK, Chanthorn W, Brockelman W, Nathalang A (2020) Forest aboveground biomass stock and resilience in a tropical landscape of Thailand. Biogeosciences 17:121–134
John B Lindsay (2023) WhiteboxTools v2.3 User Manual. Whitebox Geospatial Inc. https://www.whiteboxgeo.com/manual/wbt_book/print.html
Kappelle M (2004) Tropical forests | Tropical Montane Forests. Encyclopedia For Sci 1981:1782–1792. https://doi.org/10.1016/b0-12-145160-7/00175-7
Kim Y, Van Zyl JJ (2009) A time-series approach to estimate soil moisture using polarimetric radar data. IEEE Trans Geosci Remote Sens 47(8):2519–2527. https://doi.org/10.1109/TGRS.2009.2014944
Lapini A, Pettinato S, Santi E, Paloscia S, Fontanelli G, Garzelli A (2020) Comparison of machine learning methods applied to SAR images for forest classification in mediterranean areas. Remote Sens. https://doi.org/10.3390/rs12030369
Lary DJ, Alavi AH, Gandomi AH, Walker AL (2016) Machine learning in geosciences and remote sensing. Geosci Front 7(1):3–10. https://doi.org/10.1016/j.gsf.2015.07.003
Lary DJ, Zewdie GK, Liu X, Wu D, Levetin E, Allee RJ, Malakar N, Walker A, Mussa H, Mannino A, Aurin D (2018) Machine learning applications for earth observation. In Earth Observ Open Sci Innov. https://doi.org/10.1007/978-3-319-65633-5_8
Lasco RD (2002) Forest carbon budgets in Southeast Asia following harvesting and land cover change. Sci China 45:55–64
Lasco RD, Pulhin FB (2003) philippine forest ecosystems and climate change: carbon stocks, rate of sequestration and the Kyoto Protocol. Ann Trop Res 25(2):37–51
Lasco RD, Pulhin FB (2009) Carbon budgets of forest ecosystems in the Philippines. J Environ Sci Manag 12(1):1–13
Lasco RD, Pulhin FB, Cruz RVO, Pulhin JM, Roy SSN (2005) Carbon budgets of terrestrial ecosystems in the Pantabangan-Carranglan Watershed 1. Sierra 2005(10):1–23
Lasco RD, Pulhin FB, Sanchez PAJ, Villamor GB, Villegas KAL (2008) Climate change and forest ecosystems in the philippines: vulnerability, adaptation and mitigation. J Environ Sci Manag 11(1):1–14
Leventi-Peetz AM, Östreich T (2022) Deep learning reproducibility and explainable AI (XAI). Federal Office for Information Security. http://arxiv.org/abs/2202.11452
Li W, Weiss M, Waldner F, Defourny P, Demarez V, Morin D, Hagolle O, Baret F (2015) A generic algorithm to estimate LAI, FAPAR and FCOVER variables from SPOT4_HRVIR and landsat sensors: Evaluation of the consistency and comparison with ground measurements. Remote Sens 7(11):15494–15516. https://doi.org/10.3390/rs71115494
Lindsay JB (2014) The Whitebox geospatial analysis tools project and open-access GIS. Proceedings of the GIS Research UK 22nd Annual Conference, April 2014
Los SO, Street-Perrott FA, Loader NJ, Froyd CA (2021) Detection of signals linked to climate change, land-cover change and climate oscillators in Tropical Montane Cloud Forests. Remote Sens Environ. https://doi.org/10.1016/j.rse.2021.112431
Lumbres RIC, Lee YJ (2014) Aboveground biomass mapping of La Trinidad forests in Benguet, Philippines, using Landsat Thematic Mapper data and k-nearest neighbor method. For Sci Technol 10(2):104–111. https://doi.org/10.1080/21580103.2013.866171
Mandal D, Kumar V, Ratha D, Dey S, Bhattacharya A, Lopez-Sanchez JM, McNairn H, Rao YS (2020) Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data. Remote Sens Environ 247:111954. https://doi.org/10.1016/j.rse.2020.111954
Maraseni TN, Cockfield G, Apan A (2007) A comparison of greenhouse gas emissions from inputs into farm enterprises in Southeast Queensland, Australia. J Environ Sci Health Part A 42:11–19
Maxwell AE, Warner TA, Fang F (2018) Implementation of machine-learning classification in remote sensing: an applied review. Int J Remote Sens 39(9):2784–2817. https://doi.org/10.1080/01431161.2018.1433343
McGroddy ME, Daufresne T, Hedin LO (2004) Scaling of C:N: P stoichiometry in forests worldwide: implications of terrestrial redfield-type ratios. Ecology 85(9):2390–2401. https://doi.org/10.1890/03-0351
Mueller-Wilm U, Devignot O, Pessiot L (2016) Sen2Cor configuration manual. Esa, Sentinel 2
Muhe S, Argaw M (2022) Estimation of above-ground biomass in tropical afro-montane forest using Sentinel-2 derived indices. Environ Syst Res. https://doi.org/10.1186/s40068-022-00250-y
Murthy CS, Poddar MK, Choudhary KK, Srikanth P, Pandey V, Ramasubramanian S, Kumar GS (2022) Remote sensing based crop insurance for jute (Corchorus olitorius) crop in India. Remote Sens Appl Soc Environ. https://doi.org/10.1016/j.rsase.2022.100717
Napaldet JT, Gomez RA (2015) Allometric Models for Aboveground Biomass of Benguet Pine (Pinus kesiya). Int J Scient Eng Res 6(3)L182–187. http://www.ijser.org
Nasirzadehdizaji R, Sanli FB, Abdikan S, Cakir Z, Sekertekin A, Ustuner M (2019) Sensitivity analysis of multi-temporal Sentinel-1 SAR parameters to crop height and canopy coverage. Appl Sci (Switzerland). https://doi.org/10.3390/app9040655
Nuthammachot N, Askar A, Stratoulias D, Wicaksono P (2022) Combined use of Sentinel-1 and Sentinel-2 data for improving above-ground biomass estimation. Geocarto Int 37(2):366–376. https://doi.org/10.1080/10106049.2020.1726507
Ohsawa M (1991) Structural comparison of tropical montane rain forests along latitudinal and altitudinal gradients in south and east Asia. Vegetatio 97(1):1–10. https://doi.org/10.1007/BF00033897
Oo M, Shin T, Oosumi Y, Kiyono Y (2006) Biomass of planted forests and biotic climax of shrub and grass communities in the central dry zone of Myanmar. Bull For For Prod Res Inst 5(4):271–288
Paquit JC, Bulasa JMM (2021) Carbon stock of trees in the lower montane forest of Mt. Kalatungan Range Carbon stock of trees in the lower montane forest of Mt. Kalatungan Range Natural Park in Mindanao, Philippines. J Biodiver Environ Sci 19:1–6
Pearson T, Walker S, Brown S (2005) Sourcebook for land use, land-use change and forestry projects. https://www.winrock.org/wp-content/uploads/2016/03/Winrock-BioCarbon_Fund_Sourcebook-compressed.pdf
Pepe M, Costantino D, Alfio VS, Vozza G, Cartellino E (2021) A novel method based on deep learning, gis and geomatics software for building a 3d city model from vhr satellite stereo imagery. ISPRS Int J Geo-Inform. https://doi.org/10.3390/ijgi10100697
Perez GJ, Comiso JC, Aragones LV, Merida HC, Ong PS (2020) Reforestation and deforestation in Northern Luzon, Philippines: Critical Issues as Observed from Space. 1–20
Philippine Statistics Authority (2020) Regional compendium of environment statistics component 1: environmental conditions and quality, land cover, ecosystem and biodiversity.
Phillips J, Ramirez S, Wayson C, Duque A (2019) Differences in carbon stocks along an elevational gradient in tropical mountain forests of Colombia. Biotropica 51(4):490–499. https://doi.org/10.1111/btp.12675
Provincial Governor’s Office - Information Technology (2020) About the Province – Province of Benguet. Province of Be. http://benguet.gov.ph/about-the-province/
Richter M (2008) Tropical mountain forests - distribution and general features. In: J. H. and D. G. S.R. Gradstein (Ed.), Tropical Mountain Forest: Patterns and Processes in a Biodiversity Hotspot (Vol. 2, pp. 7–24). Göttingen Centre for Biodiversity and Ecology
Santoro M, Cartus O, Carvalhais N, Rozendaal D, Avitabilie V, Araza A, de Bruin S, Herold M, Quegan S, Rodríguez Veiga P, Balzter H, Carreiras J, Schepaschenko D, Korets M, Shimada M, Itoh T, Moreno Martínez Á, Cavlovic J, Cazzolla Gatti R, Willcock S (2020) The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst Science Data Discuss 5174:1–38. https://doi.org/10.5194/essd-2020-148
Santoro M, Cartus O, Carvalhais N, Rozendaal DMA, Avitabile V, Araza A, De Bruin S, Herold M, Quegan S, Rodríguez-Veiga P, Balzter H, Carreiras J, Schepaschenko D, Korets M, Shimada M, Itoh T, Moreno Martínez Á, Cavlovic J, Gatti RC, Willcock S (2021) The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst Sci Data 13(8):3927–3950. https://doi.org/10.5194/essd-13-3927-2021
Sarstedt M, Mooi E (2018) Regresion analysis. In Angewandte Chemie International Edition. https://doi.org/10.1007/978-3-662-56707-4
Sirro L, Häme T, Rauste Y, Kilpi J, Hämäläinen J, Gunia K, de Jong B, Pellat FP (2018) Potential of different optical and SAR data in forest and land cover classification to support REDD+ MRV. Remote Sens. https://doi.org/10.3390/rs10060942
Soh MCK, Mitchell NJ, Ridley AR, Butler CW, Puan CL, Peh KS-H (2019) Impacts of habitat degradation on tropical montane biodiversity and ecosystem services: a systematic map for identifying future research priorities. Front For Global Change 2:1–18. https://doi.org/10.3389/ffgc.2019.00083
Spracklen DV, Righelato R (2014) Tropical montane forests are a larger than expected global carbon store. Biogeosciences 11:2741–2754. https://doi.org/10.5194/bg-11-2741-2014
Sriwongsitanon N, Gao H, Savenije HHG, Maekan E, Saengsawang S, Thianpopirug S (2015) The Normalized Difference Infrared Index (NDII) as a proxy for soil moisture storage in hydrological modelling. Hydrol Earth Syst Sci Discuss 12(8):8419–8457. https://doi.org/10.5194/hessd-12-8419-2015
Sullivan MJP, Talbot J, Lewis SL, Phillips OL, Qie L, Begne SK, Chave J, Cuni-Sanchez A, Hubau W, Lopez-Gonzalez G, Miles L, Monteagudo-Mendoza A, Sonké B, Sunderland T, Ter Steege H, White LJT, Affum-Baffoe K, Aiba SI, De Almeida EC, Zemagho L (2017) Diversity and carbon storage across the tropical forest biome. Scient Rep 7:1–12. https://doi.org/10.1038/srep39102
Szigarski C, Jagdhuber T, Baur M, Thiel C, Parrens M, Wigneron JP, Piles M, Entekhabi D (2018) Analysis of the radar vegetation Index and potential improvements. Remote Sens 10(11):1–15. https://doi.org/10.3390/rs10111776
Thompson CN, Guo W, Sharma B, Ritchie GL (2019) Using normalized difference red edge index to assess maturity in cotton. Crop Sci 59(5):2167–2177. https://doi.org/10.2135/cropsci2019.04.0227
Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14(11):1–20. https://doi.org/10.1371/journal.pone.0224365
Varoquaux G, Colliot O (2023) Evaluating machine learning models and their diagnostic value. Machine learning for brain disorders, Springer, In Press. https://hal.science/hal-03682454
Wallis CIB, Brehm G, Donoso DA, Fiedler K, Homeier J, Paulsch D, Süßenbach D, Tiede Y, Brandl R, Farwig N, Bendix J (2017) Remote sensing improves prediction of tropical montane species diversity but performance differs among taxa. Ecol Ind 83:538–549. https://doi.org/10.1016/j.ecolind.2017.01.022
Wallis CIB, Homeier J, Peña J, Brandl R, Farwig N, Bendix J (2019) Modeling tropical montane forest biomass, productivity and canopy traits with multispectral remote sensing data. Remote Sens Environ 225:496–510. https://doi.org/10.1111/gcb.13907
Weiss M, Baret F (2016) S2ToolBox Level 2 products: LAI, FAPAR, FCOVER - Version 1.1. Sentinel2 ToolBox Level2 Products, 53. http://step.esa.int/docs/extra/ATBD_S2ToolBox_L2B_V1.1.pdf
Whitford HN (1911) Whitford_1911_Forests_of_the_Philippines.pdf.
Xie Q, Dash J, Huete A, Jiang A, Yin G, Ding Y, Peng D, Hall CC, Brown L, Shi Y, Ye H, Dong Y, Huang W (2019) Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery. Int J Appl Earth Obs Geoinf 80(May):187–195. https://doi.org/10.1016/j.jag.2019.04.019
Xu C, Ding Y, Zheng X, Wang Y, Zhang R, Zhang H, Dai Z, Xie Q (2022) A comprehensive comparison of machine learning and feature selection methods for maize biomass estimation using Sentinel-1 SAR, Sentinel-2 vegetation indices, and biophysical variables. Remote Sens. https://doi.org/10.3390/rs14164083
Zhang B, MacLean DA, Johns RC, Eveleigh ES (2018) Effects of hardwood content on balsam fir defoliation during the building phase of a spruce budworm outbreak. Forests 9(9):1–15. https://doi.org/10.3390/f9090530
Zhang S, Chen H, Fu Y, Niu H, Yang Y, Zhang B (2019) Fractional vegetation cover estimation of different vegetation types in the Qaidam Basin. Sustainability (Switzerland). https://doi.org/10.3390/su11030864
Acknowledgements
The authors are grateful for the support from the University of Southern Queensland. This study would not be possible without the assistance of the following: Mt Pulag Protected Landscape – Protected Area Management Board (MPPL-PAMB), Mt Pulag Protected Landscape – Protected Area Management Office (MPPL-PAMO), City Government of Baguio and the City Environment and Parks Management Office (CEPMO), Philippine Military Academy (PMA), Watershed and Water Resources Research, Development and Extension Center, Ecosystem Research and Development Bureau (WWRRDEC ERDB), Department of Environment and Natural Resources (Cordillera Administrative Region). RDDA would like to thank Dr. Rizza Karen Veridiano for her tenacious guidance in estimating biomass and carbon stocks; Mr Donald Apan and Ms Armina Apan for their invaluable assistance during field data gathering; Superintendent Emerita Albas for her assistance, and Forester Floro Bastian of CEPMO for his unwavering support towards the completion of data gathering in Baguio City.
Funding
The primary author was funded by the Philippine’s Department of Science and Technology Science Education Institute (DOSTSEI) through the foreign graduate scholarships in priority science and technology.
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RDDA: conceptualisation, methodology, data gathering, formal analysis, investigation, validation, writing, review and editing. AA: supervision, conceptualisation, data gathering, validation, review and editing. TM: supervision and review.
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Altarez, R.D.D., Apan, A. & Maraseni, T. Uncovering the Hidden Carbon Treasures of the Philippines’ Towering Mountains: A Synergistic Exploration Using Satellite Imagery and Machine Learning. PFG 92, 55–73 (2024). https://doi.org/10.1007/s41064-023-00264-w
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DOI: https://doi.org/10.1007/s41064-023-00264-w