Seagrass ecosystem biodiversity mapping in part of Rote Island using multi-generation PlanetScope imagery

Abstract

Remote sensing offers an effective and efficient solution to provide information on the biodiversity of seagrass ecosystems, which is currently lacking in most parts of the world. Therefore, this study aimed to map the biodiversity of seagrass ecosystems in parts of Rote Island, which is one of the seagrass biodiversity hotspots, using multi-generation PlanetScope magery to see how they compare. The most frequently used biodiversity indicators were identified, including the major benthic habitat (coral, seagrass, macroalgae, bare substrate) and the composition of seagrass species based on life forms. We also aim to understand the actual biodiversity indicators of seagrass ecosystems captured by PlanetScope imagery. To achieve this, field data was integrated with the resulting ISODATA classification results to assess what ISODATA class clusters represent in the field, and new classification schemes are developed accordingly. The random forest algorithm was used to carry out the classification, with seagrass field data serving as training data. Independent field data was subsequently used to assess the accuracy. The results showed that the accuracy of benthic habitat and seagrass mapping ranged from 60%-70%. However, through the use of a classification scheme built on ISODATA clustering, the spatial distribution of classes and accuracy of all PlanetScope images was significantly improved to > 90%. This highlighted the importance of understanding which indicators of seagrass biodiversity were effectively captured by PlanetScope images to achieve higher mapping accuracy. Overall, this approach optimized the ability of PlanetScope images to map seagrass biodiversity while obtaining a higher number of biodiversity indicator classes and mapping accuracy than the commonly used biodiversity indicator classification scheme.

Keywords

Seagrass / PlanetScope / mapping / biodiversity

References

[1] Planet Labs. PlanetScope. 2023. Available from: https://developers.planet.com/docs/data/planetscope/ [Last accessed on 22 Sep 2023].

[2] Planet Labs Inc. PLANET IMAGERY PRODUCT SPECIFICATIONS. 2019. Available from: https://assets.planet.com/docs/combined-imagery-product-spec-final-may-2019.pdf [Last accessed on 18 Sep 2023]

[3] Maroneze MM,Vieira JG,Jacob-Lopes E.A tecnologia de remoção de fósforo: gerenciamento do elemento em resíduos industriais.Rev Ambiente e Agua2014;9:445-58

[4] Wicaksono P.Assessment of PlanetScope images for benthic habitat and seagrass species mapping in a complex optically shallow water environment.Int J Remote Sens2018;39:5739-65

[5] Wicaksono P,Lazuardi W.Consistency assessment of multi-date PlanetScope imagery for seagrass percent cover mapping in different seagrass meadows.Geocarto Int2022;37:15161-86

[6] Lee CB,Martin L,Reinartz P. Nationwide seagrass mapping using analysis-ready Sentinel-2 and PlanetScope data to support the nationally determined contributions of seychelles. 2022. Available from: https://elib.dlr.de/188097/1/ISBW14_seychelles_seagrass.pdf [Last accessed on 18 Sep 2023]

[7] Hedley JD,Brando V.Coral reef applications of Sentinel-2: coverage, characteristics, bathymetry and benthic mapping with comparison to Landsat 8.Remote Sens Environ2018;216:598-614

[8] Lyons MB,Kennedy EV.Mapping the world's coral reefs using a global multiscale earth observation framework.Remote Sens Ecol Conserv2020;6:557-68

[9] Kennedy EV,Lyons MB.Reef cover, a coral reef classification for global habitat mapping from remote sensing.Sci Data2021;8:196 PMCID:PMC8329285

[10] Roelfsema CM,Kovacs EM.Multi-temporal mapping of seagrass cover, species and biomass: a semi-automated object based image analysis approach.Remote Sens Environ2014;150:172-87

[11] Astuty IS.Seagrass species composition and above-ground carbon stock mapping in Parang Island using Planetscope image. Proceedings of the 6th Geoinformation Science Symposium; 2019 Aug 26-27; Yogyakarta, Indonesia.

[12] Ariasari A.Random forest classification and regression for seagrass mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara. Proceedings of the 6th International Symposium on LAPAN-IPB Satellite; 2019 Sep 17-18; Bogor, Indonesia.

[13] Munir M.Support vector machine for seagrass percent cover mapping using PlanetScope image in Labuan Bajo, East Nusa Tenggara. Proceedings of the 6th International Symposium on LAPAN-IPB Satellite; 2019 Sep 17-18; Bogor, Indonesia.

[14] Isnaen Z,Utami AA.Carbon stock estimation of seagrass species thalassia hempricii using planet imagery with band ratio transformation in nirwana beach, padang city. Proceedings of the 4th International Conference of Indonesian Society for Remote Sensing; 2018 Oct 30; Makassar, Indonesia.

[15] Li J,Knapp DE.Object-based mapping of coral reef habitats using planet dove satellites.Remote Sens2019;11:1445

[16] Houborg R.High-resolution NDVI from planet’s constellation of earth observing nano-satellites: a new data source for precision agriculture.Remote Sens2016;8:768

[17] Chénier R,Ahola R.Satellite-derived bathymetry for improving canadian hydrographic service charts.IJGI2018;7:306

[18] Poursanidis D,Reinartz P.On the use of Sentinel-2 for coastal habitat mapping and satellite-derived bathymetry estimation using downscaled coastal aerosol band.Int J Appl Earth Obs Geoinf2019;80:58-70

[19] Wicaksono P,Lazuardi W.Sentinel-2 images deliver possibilities for accurate and consistent multi-temporal benthic habitat maps in optically shallow water.Remote Sens Appl Soc Environ2021;23:100572

[20] Planet Labs. PlanetScope. 2023. Available from: https://developers.planet.com/docs/data/planetscope/ [Last accessed on 18 Sep 2023].

[21] Planet Labs. Planet imagery product specifications. 2022. Available from: https://assets.planet.com/docs/Planet_Combined_Imagery_Product_Specs_letter_screen.pdf [Last accessed on 22 Sep 2023].

[22] Green E,Edwards A. Remote sensing handbook for tropical coastal management. Paris: The United Nations Educational, Scientific and Cultural Organization; 2000.

[23] Goodman JA,Phinn S.Coral reef remote sensing: a guide for mapping, monitoring and management. London: Springer; 2013.

[24] Planet Labs Inc. Planet fusion monitoring technical specification calibration, Analysis Ready Data, and InterOperability (CARDIO) operations. 2021. Available from: https://assets.planet.com/docs/Planet_fusion_specification_March_2021.pdf [Last accessed on 22 Sep 2023].

[25] Simpson J,Davies KP.A blueprint for the estimation of seagrass carbon stock using remote sensing-enabled proxies.Remote Sens2022;14:3572

[26] Supriyadi IH,Wouthuyzen S.Seagrass condition at some small islands in the taka bonerate national marine park, south sulawesi indonesia.J Segara2021;17:83-96

[27] Roelfsema C. A manual for conducting georeferenced photo transects surveys to assess the benthos of coral reef and seagrass habitats. 2009. Available from: https://sees-rsrc.science.uq.edu.au/Publications/GPS_Photo_Transects_for_Benthic_Cover_Manual.pdf [Last accessed on 18 Sep 2023]

[28] Kohler KE.Coral point count with excel extensions (CPCe): a visual basic program for the determination of coral and substrate coverage using random point count methodology.Comput Geosci2006;32:1259-69

[29] Jensen JR. Remote sensing of the environment: an earth resource perspective. England: Pearson Education Limited; 2014. p. 614.

[30] Jensen JR. Introductory digital image processing. Hoboken: Pearson Education; 2015.

[31] Lilesand TM,Chipman JW. Remote sensing and image interperation. 2015. Available from: https://www.wiley.com/en-us/Remote+Sensing+and+Image+Interpretation%2C+7th+Edition-p-9781118343289 [Last accessed on 18 Sep 2023]

[32] Mumby PJ.Development of a systematic classification scheme of marine habitats to facilitate regional management and mapping of Caribbean coral reefs.Biol Conserv1999;88:155-63

[33] Phinn S,Dekker A,Anstee J.Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia).Remote Sens Environ2008;112:3413-25

[34] Wicaksono P.Mapping seagrass from space: addressing the complexity of seagrass LAI mapping.Eur J Remote Sens2013;46:18-39

[35] Wicaksono P,Lazuardi W.Benthic habitat mapping model and cross validation using machine-learning classification algorithms.Remote Sens2019;11:1279

[36] Yuwono DM.Development of a systematic reef habitat classification scheme using multi-scale remotely sensed data for Indonesia’s coastal management. The University of Queensland. 2022. Available from: https://espace.library.uq.edu.au/view/UQ:4d3b2e7 [Last accessed on 18 Sep 2023]

[37] Green A,Switzer P.A transformation for ordering multispectral data in terms of image quality with implications for noise removal.IEEE Trans Geosci Remote Sens1988;26:65-74

[38] Hotelling H.Analysis of a complex of statistical variables into principal components.J Educ Psychol1933;24:417-41

[39] Pearson K.LIII. On lines and planes of closest fit to systems of points in space..Lond Edinb Dublin Philos Mag J Sci1901;2:559-72

[40] Luo G,Tian L,Qian S.Minimum noise fraction versus principal component analysis as a preprocessing step for hyperspectral imagery denoising.Can J Remote Sens2016;42:106-16

[41] Hedley JD,Mumby PJ.Technical note: simple and robust removal of sun glint for mapping shallow-water benthos.Int J Remote Sens2005;26:2107-12

[42] Breiman L.Random forests.Mach Learn2001;45:5-32

[43] Pal M.Random forests for land cover classification.IEEE Int Geosci Remote Sens Symp2003;6:3510-2

[44] Mutanga O,Cho MA.High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm.Int J Appl Earth Obs Geoinf2012;18:399-406

[45] Senf C,Pflugmacher D,Hostert P.Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery.Remote Sens Environ2015;156:527-36

[46] Traganos D,Lee CB.Earth observation for ecosystem accounting: spatially explicit national seagrass extent and carbon stock in Kenya, Tanzania, Mozambique and Madagascar.Remote Sens Ecol Conserv2022;8:778-92

[47] Blume A,Lee CB.Bahamian seagrass extent and blue carbon accounting using Earth Observation.Front Mar Sci2023;10:1058460

[48] Zhang C,Xie Z,Cooper H.Object-based benthic habitat mapping in the florida keys from hyperspectral imagery.Estuar Coast Shelf Sci2013;134:88-97

[49] Congalton RG. Green, assessing the accuracy of remotely sensed data: principles and practices. CRC Press/Taylor & Francis, 2009. Available: https://books.google.com/books/about/Assessing_the_Accuracy_of_Remotely_Sense.html?id=T4zj2bnGldEC

[50] Kutser T,Skirving W.Modeling spectral discrimination of great barrier reef benthic communities by remote sensing instruments.Limnol Oceanogr2003;48:497-510

[51] Hedley J,Chollett I.Remote sensing of coral reefs for monitoring and management: a review.Remote Sens2016;8:118

[52] Thorhaug A,Berlyn GP.Spectral reflectance of the seagrasses: thalassia testudinum, halodule wrightii, syringodium filiforme and five marine algae.Int J Remote Sens2007;28:1487-501

[53] Wicaksono P.Spectral response of healthy and damaged leaves of tropical seagrass Enhalus acoroides, Thalassia hemprichii, and Cymodocea rotundata. Proceedings of SPIE Remote Sensing; 2017 Sep 11-14 . Warsaw, Poland.

[54] Manessa M,Sekine M,Widagti N.Shallow-water benthic identification using multispectral satellite imagery: investigation on the effects of improving noise correction method and spectral cover.Remote Sens2014;6:4454-72

[55] Zoffoli ML,Kampel M.Water column correction for coral reef studies by remote sensing.Sensors2014;14:16881-931 PMCID:PMC4208206

[56] Sagawa T.Simulation of seagrass bed mapping by satellite images based on the radiative transfer model.Ocean Sci J2015;50:335-42

[57] Pittman SJ,Thapa B. Outlining a methodological pathway to improve the global seagrass map. The Pew Charitable Trusts. 2021. Available from: https://www.researchgate.net/publication/359513011 [Last accessed on 18 Sep 2023]

[58] Hossain M,Zakaria M.The application of remote sensing to seagrass ecosystems: an overview and future research prospects.Int J Remote Sens2015;36:61-114

[59] Ha N,Pham H,Hawes I.Total organic carbon estimation in seagrass beds in Tauranga Harbour, New Zealand using multi-sensors imagery and grey wolf optimization.Geocarto Int2023;38:2160832

[60] Bakirman T,Tuney I.Mapping of the seagrass cover along the mediterranean coast of turkey using landsat 8 OLI images.Int Arch Photogramm Remote Sens Spatial Inf Sci2016;41:1103-5

[61] Phinn SR,Mumby PJ.Multi-scale, object-based image analysis for mapping geomorphic and ecological zones on coral reefs.Int J Remote Sens2012;33:3768-97

[62] Wicaksono P.Improving the accuracy of multispectral-based benthic habitats mapping using image rotations: the application of principle component analysis and independent component analysis.Eur J Remote Sens2016;49:433-63

[63] Wicaksono P,Wahyudi AJ.Multitemporal seagrass carbon assimilation and aboveground carbon stock mapping using Sentinel-2 in Labuan Bajo 2019-2020.Remote Sens Appl Soc Environ2022;27:100803

[64] Houborg R.A cubesat enabled spatio-temporal enhancement method (CESTEM) utilizing planet, Landsat and MODIS data.Remote Sens Environ2018;209:211-26

[65] Houborg R.Daily Retrieval of NDVI and LAI at 3 m Resolution via the fusion of CubeSat, Landsat, and MODIS Data.Remote Sens2018;10:890