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Spatial Distribution of Soil Organic Carbon in Mangroves of Arid Environment Estimated from In Situ Data and Aerial Imagery

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Abstract

The purpose of this study is to describe spatial distribution of soil organic carbon (SOC) in mangroves of arid environment and its variability related to the species of mangrove using aerial imagery, supervised classification, and Generalized Additive model (GAM). Samples of soil were analyzed to quantify SOC. Aerial images were acquired using an unmanned aerial vehicle and two cameras (RGB [Red, Green, and Blue] and GRN [Green, Red, and Near-Infrared]). Random Forest was used to classify study area into eight classes (including three mangrove species: Rhizophora mangle, Laguncularia racemosa, and Avicennia germinans). Generalized Additive Model was used to describe relationship between SOC and predictor variables (mangrove’s species, height, and distance to water), then to predict SOC in the study area using predicted distribution of mangrove species from RF and distance to water. Estimated SOC reserve in the first 15 cm of sediment ranged between 96.95 and 125.43 Mg C in the study area. The highest SOC were present between 20 and 60 m from water bodies. Rhizophora mangle had higher SOC followed by Laguncularia racemosa and Avicennia germinans. A gradient of distribution by species was related to distance to the water in the following order: R. mangle, L. racemosa, and A. germinans. Both cameras (RGB and GRN) can be used to classify the study area with a high accuracy (> 90%). The methodology proposed was effective for describing the spatial variability of SOC in mangroves of arid environment. This method can be easily implemented in other ecosystems and represents a low cost and time procedure.

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References

  • Adame MF, Reef R, Santini NS, Najera E, Turschwell MP, Hayes MA, Masque P, Lovelock CE (2020) Mangroves in arid regions: ecology, threats, and opportunities. Estuar, Coast Shelf Sci, 248(December 2019). https://doi.org/10.1016/j.ecss.2020.106796

  • Almahasheer H, Serrano O, Duarte CM, Arias-Ortiz A, Masque P, Irigoien X (2017) Low carbon sink capacity of Red Sea mangroves. Sci Rep 7(1):1–10. https://doi.org/10.1038/s41598-017-10424-9

    Article  Google Scholar 

  • Angelopoulou T, Tziolas N, Balafoutis A, Zalidis G, Bochtis D (2019) Remote sensing techniques for soil organic carbon estimation: a review. Remote Sens 11(6):1–18. https://doi.org/10.3390/rs11060676

    Article  Google Scholar 

  • Asgari N, Ayoubi S, Demattê JAM, Jafari A, Safanelli JL, Da Silveira AFD (2020) Digital map** of soil drainage using remote sensing, DEM and soil color in a semiarid region of Central Iran. Geoderma Reg 22:e00302. https://doi.org/10.1016/j.geodrs.2020.e00302

    Article  Google Scholar 

  • Barreto MB, Lo Mónaco S, Díaz R, Barreto-Pittol E, López L, do Peralba MCR (2016) Soil organic carbon of mangrove forests (Rhizophora and Avicennia) of the Venezuelan Caribbean coast. Org Geochem 100:51–61. https://doi.org/10.1016/j.orggeochem.2016.08.002

    Article  CAS  Google Scholar 

  • Bhunia GS, Shit PK, Pourghasemi HR, Edalat M (2019) Prediction of soil organic carbon and its map** using regression analyses and remote sensing data in GIS and R. Spat model GIS R earth environ sci. Elsevier Inc. https://doi.org/10.1016/b978-0-12-815226-3.00019-3

  • Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1201/9780429469275-8

    Article  Google Scholar 

  • Brown MI, Pearce T, Leon J, Sidle R, Wilson R (2018) Using remote sensing and traditional ecological knowledge (TEK) to understand mangrove change on the Maroochy River, Queensland Australia. Appl Geogr 94(March):71–83. https://doi.org/10.1016/j.apgeog.2018.03.006

    Article  Google Scholar 

  • Cabral L, Pereira de Sousa ST, Júnior GVL, Hawley E, Andreote FD, Hess M, de Oliveira VM (2018) Microbial functional responses to long-term anthropogenic impact in mangrove soils. Ecotoxicol Environ Saf 160(April):231–239. https://doi.org/10.1016/j.ecoenv.2018.04.050

    Article  CAS  PubMed  Google Scholar 

  • Campomanes F, Pada AV, Silapan J (2016) Mangrove classification using support vector machines and random forest algorithm: a comparative study. 3–6. https://doi.org/10.3990/2.385

  • Carrillo-Bastos A, Elizalde-Rendón EM, Torrescano Valle N, Flores Ortiz G(2008. Adaptacion ante disturbios naturales, manglar de Puerto Morelos, Quitana Roo, Mexico. Foresta Veracruzana 10(1):31–38

  • Castaldi F, Chabrillat S, Don A, van Wesemael B (2019) Soil organic carbon map** using LUCAS topsoil database and Sentinel-2 data: an approach to reduce soil moisture and crop residue effects. Remote Sens 11(18):1–15. https://doi.org/10.3390/rs11182121

    Article  Google Scholar 

  • Chang DH, Islam S (2000) Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sens Environ 74(3):534–544. https://doi.org/10.1016/S0034-4257(00)00144-9

    Article  Google Scholar 

  • CONABIO (2017) Manglares de México Actualización y Exploración de los datos del sistema de monitoreo 1970/1980–2015.

  • CONAGUA (2016) Atlas del agua en México.

  • Cooray PLIGM, Jayawardana DT, Gunathilake BM, Pupulewatte PGH (2021) Characteristics of tropical mangrove soils and relationships with forest structural attributes in the northern coast of Sri Lanka. Reg Stud Mar Sci 44:101741. https://doi.org/10.1016/j.rsma.2021.101741

    Article  Google Scholar 

  • Cusack M, Saderne V, Arias-Ortiz A, Masqué P, Krishnakumar PK, Rabaoui L, Qurban MA, Qasem AM, Prihartato P, Loughland RA, Elyas AA, Duarte CM (2019) Organic carbon sequestration and storage in vegetated coastal habitats along the western coast of the Arabian Gulf. Environ Res Lett, 0–31.https://doi.org/10.1088/1748-9326/aac899

  • Delavar MA, Naderi A, Ghorbani Y, Mehrpouyan A, Bakhshi A (2020) Soil salinity map** by remote sensing south of Urmia Lake Iran. Geoderma Reg 22:e00317. https://doi.org/10.1016/j.geodrs.2020.e00317

    Article  Google Scholar 

  • Dinno A (2015) Nonparametric pairwise multiple comparisons in independent groups using Dunn’s test. Stata J 15(1):292–300. https://doi.org/10.1177/1536867x1501500117

  • DOF (Diario Oficial de la Federación). “NMX-FF-109-SCFI-2008. Humus de lombriz (lombricomposta) especificaciones y métodos de prueba”, 10 de Junio, 2008, 24 p

  • Domínguez-Cadena R, Riosmena-Rodríguez R, de la Luz JLL (2016) Forest structure and species composition of mangroves in the Eastern Baja California Peninsula: the role of microtopography. Wetlands 36(3):515–523. https://doi.org/10.1007/s13157-016-0760-9

    Article  Google Scholar 

  • Drewry JJ, Cameron KC, Buchan GD (2008) Pasture yield and soil physical property responses to soil compaction from treading and grazing—a review. Aust J Soil Res 46(3):237–256. https://doi.org/10.1071/SR07125

    Article  Google Scholar 

  • Etemadi H, Smoak JM, Abbasi E (2020) Spatiotemporal pattern of degradation in arid mangrove forests of the Northern Persian Gulf. Oceanologia, 1–16.https://doi.org/10.1016/j.oceano.2020.10.003

  • Ezcurra P, Ezcurra E, Garcillán PP, Costa MT, Aburto-Oropeza O (2016) Coastal landforms and accumulation of mangrove peat increase carbon sequestration and storage. Proc Natl Acad Sci USA 113(16):4404–4409. https://doi.org/10.1073/pnas.1519774113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Félix E, Zaragoza E, Riosmena R, León J (2011) Los Manglares de la Península de Baja California. In Centro de Investigaciones Biológicas del Noroeste (Issue 1).

  • Gao Y, Zhou J, Wang L, Guo J, Feng J, Wu H, Lin G (2019) Distribution patterns and controlling factors for the soil organic carbon in four mangrove forests of China. Glob Ecol Conserv 17:e00575. https://doi.org/10.1016/j.gecco.2019.e00575

    Article  Google Scholar 

  • Gholizadeh A, Žižala D, Saberioon M, Borůvka L (2018) Soil organic carbon and texture retrieving and map** using proximal, airborne and Sentinel-2 spectral imaging. Remote Sens Environ 218(September):89–103. https://doi.org/10.1016/j.rse.2018.09.015

    Article  Google Scholar 

  • Grinand C, Maire GL, Vieilledent G, Razakamanarivo H, Razafimbelo T, Bernoux M (2017) Estimating temporal changes in soil carbon stocks at ecoregional scale in Madagascar using remote-sensing. Int J Appl Earth Obs Geoinf 54:1–14. https://doi.org/10.1016/j.jag.2016.09.002

    Article  Google Scholar 

  • Guo PT, Li MF, Luo W, Tang QF, Liu ZW, Lin ZM (2015) Digital map** of soil organic matter for rubber plantation at regional scale: An application of random forest plus residuals kriging approach. Geoderma 237–238:49-59. https://doi.org/10.1016/j.geoderma.2014.08.009

  • Gupta K, Mukhopadhyay A, Giri S, Chanda A, Datta Majumdar S, Samanta S, Mitra D, Samal RN, Pattnaik AK, Hazra S (2018) An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery. MethodsX 5(September):1129–1139. https://doi.org/10.1016/j.mex.2018.09.011

    Article  PubMed  PubMed Central  Google Scholar 

  • Hickey SM, Callow NJ, Phinn S, Lovelock CE, Duarte CM (2018) Spatial complexities in aboveground carbon stocks of a semi-arid mangrove community: a remote sensing height-biomass-carbon approach. Estuar Coast Shelf Sci 200:194–201. https://doi.org/10.1016/j.ecss.2017.11.004

    Article  CAS  Google Scholar 

  • Howard J, Hoyt S, Isensee K, Pidgeon E, Telszewski M (2018) Carbono Azul. Métodos para evaluar las existencias y los factores de emisión de carbono en manglares, marismas y pastos marinos. 15–38. www.iucn.org/es

  • ISRIC (2002) Procedures for soil analysis (p. 119).

  • Jhonnerie R, Siregar VP, Nababan B, Prasetyo LB, Wouthuyzen S (2015) Random forest classification for mangrove land cover map** using Landsat 5 TM and Alos Palsar Imageries. Procedia Environ Sci 24:215–221. https://doi.org/10.1016/j.proenv.2015.03.028

    Article  Google Scholar 

  • Kauffman BJ, Donato D, Adame MF (2013) Protocolo para la medición, monitoreo y reporte de la estructura, biomasa y reservas de carbono de los manglares. Documento de Trabajo 117. Bogor, Indonesia: CIFOR., 117. https://doi.org/10.17528/cifor/004386<br/>

  • Kuhn M (2020) caret: Classification and Regression Training. R package version 6.0–86. https://CRAN.R-project.org/package=caret

  • Leopold A, Marchand C, Renchon A, Deborde J, Quiniou T, Allenbach M (2016) Net ecosystem CO2 exchange in the “Coeur de Voh” mangrove, New Caledonia: effects of water stress on mangrove productivity in a semi-arid climate. Agric for Meteorol 223:217–232. https://doi.org/10.1016/j.agrformet.2016.04.006

    Article  Google Scholar 

  • Li W, El-Askary H, Qurban MA, Li J, ManiKandan KP, Piechota T (2019) Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast. Ecol Indic 102(October 2018):734–745. https://doi.org/10.1016/j.ecolind.2019.03.047

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and Regression by randomForest. R News 2(3):18–22

  • Lin C, Zhu A-X, Wang Z, Wang X, Ma R (2020) The refined spatiotemporal representation of soil organic matter based on remote images fusion of Sentinel-2 and Sentinel-3. Int J Appl Earth Obs Geoinf 89(February):102094. https://doi.org/10.1016/j.jag.2020.102094

    Article  Google Scholar 

  • López-Portillo J, Ezcurra E (2002) Manglares: una revisión. Madera y Bosques 8:27–51

    Article  Google Scholar 

  • Lymburner L, Bunting P, Lucas R, Scarth P, Alam I, Phillips C, Ticehurst C, Held A (2020) Map** the multi-decadal mangrove dynamics of the Australian coastline. Remote Sens Environ 238(April 2018):111185. https://doi.org/10.1016/j.rse.2019.05.004

    Article  Google Scholar 

  • Maimaitijiang M, Sagan V, Sidike P, Maimaitiyiming M, Hartling S, Peterson KT, Maw MJW, Shakoor N, Mockler T, Fritschi FB (2019) Vegetation Index Weighted Canopy Volume Model (CVM VI ) for soybean biomass estimation from Unmanned Aerial System-based RGB imagery. ISPRS J Photogramm Remote Sens 151(August 2018):27–41. https://doi.org/10.1016/j.isprsjprs.2019.03.003

    Article  Google Scholar 

  • Mutanga O, Adam E, Cho MA (2012) High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm. Int J Appl Earth Obs Geoinf 18(1):399–406. https://doi.org/10.1016/j.jag.2012.03.012

    Article  Google Scholar 

  • Ochoa-Gómez JG, Lluch-Cota SE, Rivera-Monroy VH, Lluch-Cota DB, Troyo-Diéguez E, Oechel W, Serviere-Zaragoza E (2019) Mangrove wetland productivity and carbon stocks in an arid zone of the Gulf of California (La Paz Bay, Mexico). For Ecol Manage 442(April):135–147. https://doi.org/10.1016/j.foreco.2019.03.059

    Article  Google Scholar 

  • Ochoa-Gómez JG, Lluch-Cota SE, Rivera-Monroy VH, Lluch-Cota DB, Troyo-Diéguez E, Oechel W, Serviere-Zaragoza E (2019) Mangrove wetland productivity and carbon stocks in an arid zone of the Gulf of California (La Paz Bay, Mexico). For Ecol Manage 442(January):135–147. https://doi.org/10.1016/j.foreco.2019.03.059

    Article  Google Scholar 

  • Pham LTH, Brabyn L (2017) Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J Photogramm Rem Sens 128:86–97. https://doi.org/10.1016/j.isprsjprs.2017.03.013

  • R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

  • Rahman MS, Donoghue DNM, Bracken LJ (2021) Is soil organic carbon underestimated in the largest mangrove forest ecosystems? Evidence from the Bangladesh Sundarbans. Catena, 200(January). https://doi.org/10.1016/j.catena.2021.105159

  • Rasquinha DN, Mishra DR (2020) Impact of wood harvesting on mangrove forest structure, composition and biomass dynamics in India. Estuar, Coast Shelf Sci 106974.https://doi.org/10.1016/j.ecss.2020.106974

  • Rhyma PP, Norizah K, Hamdan O, Faridah-Hanum I, Zulfa AW (2020) Integration of normalised different vegetation index and Soil-Adjusted Vegetation Index for mangrove vegetation delineation. Remote Sens Appl: Soc Environ 17(October 2019):100280. https://doi.org/10.1016/j.rsase.2019.100280

    Article  Google Scholar 

  • Saravanakumar A, Rajkumar M, Sun J, Sesh Serebiah J, Thivakaran GA (2009) Forest structure of arid zone mangroves in relation to their physical and chemical environment in the western Gulf of Kachchh, Gujarat, Northwest coast of India. J Coast Conserv 13(4):217–234. https://doi.org/10.1007/s11852-009-0070-y

    Article  Google Scholar 

  • Seifi M, Ahmadi A, Neyshabouri MR, Taghizadeh-Mehrjardi R, Bahrami HA (2020) Remote and Vis-NIR spectra sensing potential for soil salinization estimation in the eastern coast of Urmia hyper saline lake Iran. Remote Sens Appl: Soc Environ 20(September):100398. https://doi.org/10.1016/j.rsase.2020.100398

    Article  Google Scholar 

  • Shaltout KH, Ahmed MT, Alrumman SA, Ahmed DA, Eid EM (2019) Evaluation of the carbon sequestration capacity of arid mangroves along nutrient availability and salinity gradients along the Red Sea coastline of Saudi Arabia. Oceanologia. https://doi.org/10.1016/j.oceano.2019.08.002

    Article  Google Scholar 

  • Shaltout KH, Ahmed MT, Alrumman SA, Ahmed DA, Eid EM (2020) Evaluation of the carbon sequestration capacity of arid mangroves along nutrient availability and salinity gradients along the Red Sea coastline of Saudi Arabia. Oceanologia 62(1):56–69. https://doi.org/10.1016/j.oceano.2019.08.002

    Article  Google Scholar 

  • Shi D, Yang X (2016) An assessment of algorithmic parameters affecting image classification accuracy by random forests. Photogramm Eng Remote Sens 82(6):407–417. https://doi.org/10.14358/PERS.82.6.407

    Article  Google Scholar 

  • Toosi NB, Soffianian AR, Fakheran S, Pourmanafi S, Ginzler C, Waser LT (2019) Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran. Glob Ecol Conserv, 19. https://doi.org/10.1016/j.gecco.2019.e00662

  • Torres M, Qiu G (2014) Automatic habitat classification using image analysis and random forest. Eco Inform 23:126–136. https://doi.org/10.1016/j.ecoinf.2013.08.002

    Article  Google Scholar 

  • Vaiphasa C, De Boer WF, Skidmore AK, Panitchart S, Vaiphasa T, Bamrongrugsa N, Santitamnont P (2007) Impact of solid shrimp pond waste materials on mangrove growth and mortality: a case study from Pak Phanang Thailand. Hydrobiologia 591(1):47–57. https://doi.org/10.1007/s10750-007-0783-6

    Article  Google Scholar 

  • Valderrama-Landeros LH, Rodríguez-Zúñiga MT, Troche-Souza C, Velázquez-Salazar S, Villeda-Chávez E, Alcántara-Maya JA, Vázquez-Balderas B, Cruz-López MI, Ressl R (2017) Manglares de México: Actualización y exploración de los datos del sistema de monitoreo 1970/1980-2015

  • van Bijsterveldt CEJ, van Wesenbeeck BK, Ramadhani S, Raven OV, van Gool FE, Pribadi R, Bouma TJ (2021) Does plastic waste kill mangroves? A field experiment to assess the impact of macro plastics on mangrove growth, stress response and survival. Sci Total Environ 756:143826. https://doi.org/10.1016/j.scitotenv.2020.143826

    Article  CAS  PubMed  Google Scholar 

  • Vázquez-Lule A, Colditz R, Herrera-Silveira J, Guevara M, Rodríguez-Zúñiga MT, Cruz I, Ressl R, Vargas R (2019) Greenness trends and carbon stocks of mangroves across Mexico. Environ Res Lett, 14(7). https://doi.org/10.1088/1748-9326/ab246e

  • Velázquez SS, Rodríguez ZMT, Alcántara MJA, Villeda CE, Valderrama LL, Troche SC, Vázquez BB, Pérez EI, Cruz LMI, Ressl R, De la Borbolla DV, Paz O, Aguilar SV, Hruby F, Muñoa CJH (2021) Manglares de México. Actualización y análisis de los datos 2020.

  • Vujović Ž (2021) Classification Model Evaluation Metrics. International Journal of Advanced Computer Science and Applications 12(6):599–606. https://doi.org/10.14569/IJACSA.2021.0120670

  • Wood SN (2006) Generalized Additive Models: An Introduction with R (2nd edn). Chapman and Hall/CRC. https://doi.org/10.1201/9781420010404

  • Wood SN (2017) Generalized Additive Models: An Introduction with R (2nd edition). Chapman and Hall/CRC. https://doi.org/10.1201/9781315370279

  • Zhai M (2019) Inversion of organic matter content in wetland soil based on Landsat 8 remote sensing image. J vis Commun Image Represent 64:102645. https://doi.org/10.1016/j.jvcir.2019.102645

    Article  Google Scholar 

  • Zhang Y, Sui B, Shen H, Ouyang L (2019) Map** stocks of soil total nitrogen using remote sensing data: a comparison of random forest models with different predictors. Comput Electron Agric 160(March):23–30. https://doi.org/10.1016/j.compag.2019.03.015

    Article  Google Scholar 

  • Zhou T, Geng Y, Chen J, Pan J, Haase D, Lausch A (2020) High-resolution digital map** of soil organic carbon and soil total nitrogen using DEM derivatives, Sentinel-1 and Sentinel-2 data based on machine learning algorithms. Sci Total Environ 138244.https://doi.org/10.1016/j.scitotenv.2020.138244

  • Žížala D, Minarík R, Zádorová T (2019) Soil organic carbon map** using multispectral remote sensing data: prediction ability of data with different spatial and spectral resolutions. Remote Sens 11(24):1–23. https://doi.org/10.3390/rs11242947

    Article  Google Scholar 

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Acknowledgements

We would like to thank Blanca Romero López for helpful comments on the revised version of this manuscript.

Funding

First author would like to thank Consejo Nacional de Ciencia y Tecnología (CONACyT) for the scholarship provided. This study was supported by CONACyT (FONSEC CONACYT-INEGI-278789). The funder had no role in study design, data collection and analysis, decision to prepare or publish the manuscript.

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JAH: formal analysis, methodology, writing—original draft, writing—review and editing. ESZ: writing—original draft. MDCGC: writing—original draft. ATC: writing—original draft. CASZ: writing—original draft. ROMR: conceptualization, formal analysis, methodology, writing—original draft, writing—review and editing.

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Correspondence to Raúl O. Martínez-Rincón.

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Aviña-Hernández, J., Serviere-Zaragoza, E., Gutiérrez-Castorena, M. et al. Spatial Distribution of Soil Organic Carbon in Mangroves of Arid Environment Estimated from In Situ Data and Aerial Imagery. J Soil Sci Plant Nutr 22, 4928–4942 (2022). https://doi.org/10.1007/s42729-022-00971-0

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