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Mapping mangrove forest using Landsat 8 to support estimation of land-based emissions in Kenya

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Abstract

The Kenyan coast is constantly under persistent cloud cover which hinders mapping using optical images. Up-to-date land-cover information in such areas is sometimes missing from national mapping initiatives. This study uses a computed composite image based on a mean of cloud and shadow free Function of Mask masked multi-temporal Landsat 8 images acquired during long-dry season in a pilot area. We test the effectiveness of the composite to map mangrove forest using random forest (RF) and support vector machines (SVM) machine learning algorithms integrated with context from Markov random fields (MRF(s)). MRFs was chosen because it is computationally efficient hence can be scaled out nationally. The MRF frameworks are compared to pixel-based classification using threefold independent validation samples. SVM–MRFs and RF–MRFs methods have the highest overall accuracy compared to pixel-based classification. However, visual assessment of predicted land-cover using aerial photograghs established that SVM–MRFs framework corresponded well to land-cover in the study area. This framework also managed to map classes with limited ground reference data better than RF–MRFs. Generally, context in both techniques played a discriminative role especially in heterogeneous regions. Therefore, scaling out this approaches would go a long way in generating mangrove forest map inventory in persistent cloud cover regions which is useful for land-based emission estimation.

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References

  • Anderson JR, Hardy EE, Roach JT, Witmer RE (1976) A land use and land cover classification system for use with remote sensor data. Development 2001(964):28

    Google Scholar 

  • Baetens L, Desjardins C, Hagolle O (2019) Validation of Copernicus Sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sens 11:433. https://doi.org/10.3390/rs11040433

    Article  Google Scholar 

  • Barbier EB, Koch EW, Silliman BR, Hacker SD, Wolanski E, Primavera J, Granek EF, Polasky S, Aswani S, Cramer LA, Stoms DM, Kennedy CJ, Bael D, Kappel CV, Perillo GME, Reed DJ (2008) Coastal ecosystem-based management with nonlinear ecological functions and values. Science 319(5861):321–323. https://doi.org/10.1126/science.1150349

    Article  Google Scholar 

  • Besag J (1986) On the statistical analysis of dirty pictures. J R Stat Soc Ser B (Methodol) 48(3):259–302. https://doi.org/10.2307/2345426

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  Google Scholar 

  • Bruzzone L, Persello C (2009) A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples. IEEE Trans Geosci Remote Sens 47(7):2142–2154. https://doi.org/10.1109/TGRS.2008.2011983

    Article  Google Scholar 

  • Buja K, Menza C (2013) Sampling Design Tool for ArcGIS - Instruction Manual. In: NOAA, Silver Spring, MD

  • Bunting P, Rosenqvist A, Lucas RM, Rebelo L, Hilarides L, Thomas N, Hardy A, Itoh T, Shimada M, Finlayson CM (2018) The global mangrove watch—a new 2010 global baseline of mangrove extent. Remote Sens 10(10):1669. https://doi.org/10.3390/rs10101669

    Article  Google Scholar 

  • Candra DS, Phinn S, Scarth P (2017) Cloud and cloud shadow removal of Landsat 8 images using Multitemporal Cloud Removal method. In: 2017 6th international conference on agro-geoinformatics, pp 1–5. https://doi.org/10.1109/Agro-Geoinformatics.2017.8047007

  • Cao X, Zhou F, Xu L, Meng D, Xu Z, Paisley J (2018) Hyperspectral image classification with Markov random fields and a convolutional neural network. IEEE Trans Image Process 27(5):2354–2367. https://doi.org/10.1109/TIP.2018.2799324

    Article  Google Scholar 

  • Cao Y, Luo Y, Yang S (2011) Image denoising based on hierarchical Markov random field. Pattern Recognit Lett 32(2):368–374. https://doi.org/10.1016/j.patrec.2010.09.017

    Article  Google Scholar 

  • Chavez PS (1996) Image-based atmospheric corrections-revisited and improved. Photogramm Eng Remote Sens 62(9):1025–1035

    Google Scholar 

  • Congalton RG, Green K (2008) Assessing the accuracy of remotely sensed data: principles and practices, 2nd edn. CRC Press, Cambridge

    Book  Google Scholar 

  • Donato DC, Kauffman JB, Murdiyarso D, Kurnianto S, Stidham M, Kanninen M (2011) Mangroves among the most carbon-rich forests in the tropics. Nat Geosci 4:293

    Article  Google Scholar 

  • DRSRS (2016) The land cover change mapping program—technical manual. In: Technical report. Directorate of Resource Surveys and Remote Sensing (DRSRS)

  • Eggleston S, Buendia L, Miwa K, Ngara T, Tanabe K (2006) 2006 IPCC guidelines for national greenhouse gas inventories. In: Technical report. Intergovernmental Panel on Climate Change (IPCC)

  • FAO (2007) The world’s mangrove forest 1980–2005: a thematic study prepared in the framework of the global forest resources assessment 2005. In: FAO forestry paper, pp 1–77

  • Foga S, Scaramuzza PL, Guo S, Zhu Z, Dilley RD, Beckmann T, Schmidt GL, Dwyer JL, Hughes MJ, Laue B (2017) Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens Environ 194:379–390. https://doi.org/10.1016/j.rse.2017.03.026

    Article  Google Scholar 

  • Gang PO, Agatsiva JL (1992) The current status of mangroves along the Kenyan coast: a case study of Mida Creek mangroves based on remote sensing. Hydrobiologia 247(1):29–36

    Article  Google Scholar 

  • Geman S, Geman D (1984) Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell PAMI 6(6):721–741. https://doi.org/10.1109/TPAMI.1984.4767596

    Article  Google Scholar 

  • Gómez-Chova L, Amorós-López J, Mateo-García G, Muñoz-Marí J, Camps-Valls G (2017) Cloud masking and removal in remote sensing image time series. J Appl Remote Sens 11(1):1–15. https://doi.org/10.1117/1.JRS.11.015005

    Article  Google Scholar 

  • Hagolle O, Huc M, Pascual DV, Dedieu G (2010) A multi-temporal method for cloud detection, applied to FORMOSAT-2, VEN\(\mu \)S, LANDSAT and SENTINEL-2 images. Remote Sens Environ 114(8):1747–1755. https://doi.org/10.1016/j.rse.2010.03.002

    Article  Google Scholar 

  • Hallahan N, Prepperneau C (2013) Cloud detection and removal techniques for Landsat 8 imagery. In: Technical report. Oregon State University

  • Hastie T, Tibshirani R, Friedman J (2011) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer. https://doi.org/10.1007/978-0-387-84858-7

  • Irish RR (2000) Landsat 7 automatic cloud cover assessment. In: Algorithms for multispectral, hyperspectral, and ultraspectral imagery VI. International Society for Optics and Photonics, vol 4049, pp 348–355

  • Irish RR, Barker JL, Goward SN, Arvidson T (2006) Characterization of the Landsat-7 ETM+ automated cloud-cover assessment (ACCA) algorithm. Photogramm Eng Remote Sens 72(10):1179–1188

    Article  Google Scholar 

  • Jeon B, Landgrebe DA (1992) Classification with spatio-temporal interpixel class dependency contexts. IEEE Trans Geosci Remote Sens 30(4):663–672. https://doi.org/10.1109/36.158859

    Article  Google Scholar 

  • Jin S, Homer C, Yang L, Xian G, Fry J, Danielson P, Townsend PA (2013) Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA. Int J Remote Sens 34(5):1540–1560

    Article  Google Scholar 

  • Kairo JG, Kivyatu B, Koedam N (2002) Application of remote sensing and GIS in the management of mangrove forests within and adjacent to Kiunga Marine Protected Area, Lamu, Kenya. Environ Dev Sustain 4(2):153–166

    Article  Google Scholar 

  • Karatzoglou A, Smola A, Hornik K, Zeileis A (2004) kernlab-an S4 package for kernel methods in R. J Stat Softw 11(9):1–20. https://doi.org/10.18637/jss.v011.i09

    Article  Google Scholar 

  • Kasetkasem T, Arora MK, Varshney PK (2005) Super-resolution land cover mapping using a markov random field based approach. Remote Sens Environ 96(3):302–314. https://doi.org/10.1016/j.rse.2005.02.006

    Article  Google Scholar 

  • Kenduiywo BK, Tolpekin VA, Stein A (2014) Detection of built-up area in optical and synthetic aperture radar images using conditional random fields. J Appl Remote Sens 8(1):83618–83672. https://doi.org/10.1117/1.JRS.8.083672

    Article  Google Scholar 

  • Kirui KB, Kairo JG, Bosire J, Viergever KM, Rudra S, Huxham M, Briers RA (2013) Mapping of mangrove forest land cover change along the Kenya coastline using Landsat imagery. Ocean Coast Manag 83:19–24. https://doi.org/10.1016/J.OCECOAMAN.2011.12.004

    Article  Google Scholar 

  • Lafferty JD, McCallum A, Pereira F (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the eighteenth international conference on machine learning, Morgan Kaufmann, San Francisco, CA, USA, pp 282–289

  • Li X, Du Y, Ling F (2014) Super-resolution mapping of forests with bitemporal different spatial resolution images based on the spatial-temporal Markov random field. IEEE J Sel Top Appl Earth Observ Remote Sens 7(1):29–39

    Article  Google Scholar 

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

    Google Scholar 

  • Liu D, Kelly M, Gong P (2006) A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote Sens Environ 101(2):167–180. https://doi.org/10.1016/j.rse.2005.12.012

    Article  Google Scholar 

  • Liu D, Song K, Townshend JRG, Gong P (2008) Using local transition probability models in Markov random fields for forest change detection. Remote Sens Environ 112(5):2222–2231. https://doi.org/10.1016/j.rse.2007.10.002

    Article  Google Scholar 

  • Lonjou V, Desjardins C, Hagolle O, Petrucci B, Tremas T, Dejus M, Makarau A, Auer S (2016) MACCS-ATCOR joint algorithm (MAJA). In: SPIE 10001, remote sensing of clouds and the atmosphere XXI, Edinburgh, United Kingdom, vol 10001. https://doi.org/10.1117/12.2240935

  • Loveland TR, Dwyer JL (2012) Landsat: building a strong future. Remote Sens Environ 122:22–29. https://doi.org/10.1016/j.rse.2011.09.022

    Article  Google Scholar 

  • Mateo-García G, Gómez-Chova L, Amorós-López J, Muñoz-Marí J, Camps-Valls G (2018) Multitemporal cloud masking in the Google earth engine. Remote Sens 10(7):1079. https://doi.org/10.3390/rs10071079

    Article  Google Scholar 

  • Melgani F, Serpico S (2003) A Markov random field approach to spatio-temporal contextual image classification. IEEE Trans Geosci Remote Sens 41(11):2478–2487. https://doi.org/10.1109/TGRS.2003.817269

    Article  Google Scholar 

  • Millard K, Richardson M (2015) On the importance of training data sample selection in random forest image classification: a case study in peatland ecosystem mapping. Remote Sens 7(7):8489–8515

    Article  Google Scholar 

  • Moser G, Serpico SB (2011) Multitemporal region-based classification of high-resolution images by Markov random fields and multiscale segmentation. In: IEEE international geoscience and remote sensing symposium, Vancouver, BC, Canada, pp 102–105. https://doi.org/10.1109/IGARSS.2011.6048908

  • Neukermans G, Dahdouh-Guebasand F, Kairo JG, Koedam N (2008) Mangrove species and stand mapping in Gazi Bay (Kenya) using Quickbird satellite imagery. J Spatial Sci 53(1):75–86. https://doi.org/10.1080/14498596.2008.9635137

    Article  Google Scholar 

  • Platt JC (1999) Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers. MIT Press, pp 61–74

  • Qiu S, He B, Zhu Z, Liao Z, Quan X (2017) Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images. Remote Sens Environ 199:107–119. https://doi.org/10.1016/J.RSE.2017.07.002

    Article  Google Scholar 

  • Richter R, Louis J, Müller-Wilm U (2012) Sentinel-2 MSI—Level 2A products algorithm theoretical basis document. In: Technical reports, vol 49. European Space Agency, Paris

  • Sanpayao M, Kasetkasem T, Isshiki, Rakwatin P, Chanwimaluang T (2017) A super-resolution land cover mapping based on a random forest and Markov random field model. In: 14th international conference on ECTI-CON, pp 553–556. https://doi.org/10.1109/ECTICon.2017.8096297

  • Schroeder TA, Cohen WB, Song C, Canty MJ, Yang Z (2006) Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in western Oregon. Remote Sens Environ 103(1):16–26. https://doi.org/10.1016/j.rse.2006.03.008

    Article  Google Scholar 

  • SLEEK (2019) System for land-based emissions estimation in Kenya. http://www.sleek.environment.go.ke/. Accessed 1 April 2019

  • Sokolova M, Japkowicz N, Szpakowicz S (2006) Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar A, Kang BH (eds) AI 2006: Advances in artificial intelligence, Lecture notes in computer science, vol 4304. Springer, Berlin, pp 1015–1021

    Chapter  Google Scholar 

  • Solberg AHS (1999) Contextual data fusion applied to forest map revision. IEEE Trans Geosci Remote Sens 37(3):1234–1243

    Article  Google Scholar 

  • Solberg AHS, Taxt T, Jain AK (1996) A Markov random field model for classification of multisource satellite imagery. IEEE Trans Geosci Remote Sens 34(1):100–113. https://doi.org/10.1109/36.481897

    Article  Google Scholar 

  • Stehman SV (2009) Sampling designs for accuracy assessment of land cover. Int J Remote Sens 30(20):5243–5272. https://doi.org/10.1080/01431160903131000

    Article  Google Scholar 

  • Thomlinson JR, Bolstad PV, Cohen WB (1999) Coordinating methodologies for scaling landcover classifications from site-specific to global: steps toward validating global map products. Remote Sens Environ 70(1):16–28

    Article  Google Scholar 

  • Tiwari LK, Sinha SK, Saran S, Tolpekin VA, Raju PLN (2016) Markov random field-based method for super-resolution mapping of forest encroachment from remotely sensed ASTER image. Geocarto Int 31(4):428–445. https://doi.org/10.1080/10106049.2015.1054441

    Article  Google Scholar 

  • Tso B, Mather PM (2009) Classification methods for remotely sensed data, 2nd edn. CRC Press, Boca Raton

    Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory. Springer, New York. https://doi.org/10.1007/978-1-4757-3264-1

    Book  Google Scholar 

  • Young NE, Anderson RS, Chignell SM, Vorster AG, Lawrence R, Evangelista PH (2017) A survival guide to landsat preprocessing. Ecology 98(4):920–932. https://doi.org/10.1002/ecy.1730

    Article  Google Scholar 

  • Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ 118:83–94. https://doi.org/10.1016/J.RSE.2011.10.028

    Article  Google Scholar 

  • Zhu Z, Woodcock CE (2014) Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sens Environ 152:217–234. https://doi.org/10.1016/j.rse.2014.06.012

    Article  Google Scholar 

  • Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8 and Sentinel 2 images. Remote Sens Environ 159:269–277. https://doi.org/10.1016/j.rse.2014.12.014

    Article  Google Scholar 

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Acknowledgements

We wish to thank SLEEK under the Department of Resource Surveys and Remote Sensing (DRSRS) for aerial imagery and Forest2020 under Kenya Forest Service (KFS) for ground reference data. This research was funded by the Australian government through the SLEEK programme facilitated by the Clinton Change Initiative(CCI).

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Authors and Affiliations

  1. Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

    Benson Kipkemboi Kenduiywo, Felix Nzive Mutua, Thomas Gathungu Ngigi & Edward Hunja Waithaka

  2. Environmental Science and Policy, University of California, Davis, USA

    Benson Kipkemboi Kenduiywo

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  1. Benson Kipkemboi Kenduiywo
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Correspondence to Benson Kipkemboi Kenduiywo.

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Kenduiywo, B.K., Mutua, F.N., Ngigi, T.G. et al. Mapping mangrove forest using Landsat 8 to support estimation of land-based emissions in Kenya. Model. Earth Syst. Environ. 6, 1619–1632 (2020). https://doi.org/10.1007/s40808-020-00778-x

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