Skip to main content

Advertisement

SpringerLink
Log in

Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia

  • Original Paper
  • Published:
Wetlands Ecology and Management Aims and scope Submit manuscript

Abstract

This paper explores the suitability of remotely sensed data obtained from Landsat imaging sensors, in combination with ancillary forest inventory data, for the ecological monitoring and management of a production mangrove forest in Malaysia. An assessment is presented on the capabilities and limitations of utilizing Landsat Thematic Mapper data for mapping mangrove species and stand age structure in the Matang Mangrove Forest Reserve. The cloud-free subset satellite images used in this study were acquired in May 1988, February 1995, December 1999, March 2001, December 2009 and November 2013. The December 1999 Landsat 7 ETM+ image was used for the error and accuracy assessment because it coincided with the year the reference map was produced. The image classification process included both pixel-based and object-based approaches conducted on a combination of software which included ENVI, ArcGIS 10.1 and eCognition. Results revealed that the pixel-based approach was not effective in delineating species and stand age structure due to the presence of the ‘salt and pepper effect’ in the image output. The object-based approach accurately delineated between commercial and non-commercial mangrove species. It also produced a reliable and accurate stand-age structure map based on three designated classes: clear-felled areas; young mangrove forest (less than 13 years old); and mature mangrove forest (13 years or older). The analysis highlights that the mapping and analysis of mangrove species and stand age structure with Landsat Thematic Mapper image datasets can play a useful role in achieving cost-efficient and ecologically sustainable outcomes in a production mangrove forest.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alongi D, Sasekumar A, Chong V, Pfitzner J, Trott L, Tirendi F, Dixon P, Brunskill G (2004) Sediment accumulation and organic material flux in a managed mangrove ecosystem: estimates of land–ocean–atmosphere exchange in peninsular Malaysia. Mar Geol 208:383–402

    Article  CAS  Google Scholar 

  • Azahar M, Mohd Nik, Shah NM (2003) A working plan for the Matang Mangrove Forest Reserve, Perak: the third 10-year period (2000–2009) of the second rotation. State Forestry Department of Perak, Ipoh

    Google Scholar 

  • Barbosa J, Broadbent E, Bitencourt M (2014) Remote sensing of aboveground biomass in tropical secondary forests: a review. Int J Remote Sens. Article ID 715796 doi: 10.1155/2014/715796

  • Blaschke T, Lang S, Hay G (2008) Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer, New York

    Book  Google Scholar 

  • Carpenter SR, Defries R, Dietz T, Mooney HA, Polasky S, Reid WV, Scholes RJ (2006) Millennium ecosystem assessment: research needs. Science 314:257–258

    Article  CAS  PubMed  Google Scholar 

  • Chander G, Markham BL, Helder DL (2009) Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sens Environ 113:893–903

    Article  Google Scholar 

  • Cohen WB, Goward SN (2004) Landsat’s role in ecological applications of remote sensing. BioScience 54:535–545

    Article  Google Scholar 

  • Congalton RG (2001) Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildl Fire 10:321–328

    Article  Google Scholar 

  • Congalton RG, Green K (2009) Assessing the accuracy of remotely sensed data: principles and practices. Taylor and Francis, Florida

    Google Scholar 

  • Foody GM (2002) Status of land cover classification accuracy assessment. Remote Sens Environ 80:185–201

    Article  Google Scholar 

  • Foody GM (2009) Sample size determination for image classification accuracy assessment and comparison. Int J Remote Sens 30:5273–5291

    Article  Google Scholar 

  • Friess DA, Webb EL (2014) Variability in mangrove change estimates and implications for the assessment of ecosystem service provision. Glob Ecol Biogeogr 23:715–725

    Article  Google Scholar 

  • Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, Masek J, Duke N (2011) Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr 20:154–159

    Article  Google Scholar 

  • Gong WK, Ong JE (1995) The use of demographic studies in mangrove silviculture. Hydrobiologia 295:255–261

    Article  Google Scholar 

  • Hall RJ, Skakun RS, Arsenault EJ, Case BS (2006) Modelling forest stand structure attributes using Landsat ETM+ data: application to mapping of aboveground biomass and stand volume. For Ecol Manag 225:378–390

    Article  Google Scholar 

  • Hamdan O, Khairunnisa M, Ammar A, Hasmadi I, Aziz H (2013) Mangrove carbon stock assessment by optical satellite imagery. J Trop For Sci 25:554–565

    Google Scholar 

  • Heumann BW (2011) Satellite remote sensing of mangrove forests: recent advances and future opportunities. Prog Phys Geogr 35:87–108

    Article  Google Scholar 

  • Huete AR (2012) Vegetation indices, remote sensing and forest monitoring. Geogr Compass 6:513–532

    Article  Google Scholar 

  • Jensen JR (2009) Remote sensing of the environment: An earth resource perspective 2/e. Pearson Education, Delhi

    Google Scholar 

  • Kamal M, Phinn S (2011) Hyperspectral data for mangrove species mapping: a comparison of pixel-based and object-based approach. Remote Sens 3:2222–2242

    Article  Google Scholar 

  • Kovacs JM, Wang J, Flores-Verdugo F (2005) Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuar Coast Shelf Sci 62:377–384

    Article  Google Scholar 

  • Kuenzer C, Bluemel A, Gebhardt S, Quoc TV, Dech S (2011) Remote sensing of mangrove ecosystems: a review. Remote Sens 3:878–928

    Article  Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159–174

    Article  CAS  PubMed  Google Scholar 

  • Leimgruber P, Christen CA, Laborderie A (2005) The impact of Landsat satellite monitoring on conservation biology. Environ Monit Assess 106:81–101

    Article  PubMed  Google Scholar 

  • Li M, Mao L, Shen W, Liu S, Wei A (2013) Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977–2010). Estuar Coast Shelf Sci 130:111–120

    Article  Google Scholar 

  • Lillesand TM, Kiefer RW, Chipman JW (2004) Remote sensing and image interpretation, 5th edn. Wiley, New Jersey

    Google Scholar 

  • Loveland TR, Dwyer JL (2012) Landsat: building a strong future. Remote Sens Environ 122:22–29

    Article  Google Scholar 

  • Lu D, Weng Q (2004) Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery. Photogramm Eng Remote Sens 70:1053–1062

    Article  Google Scholar 

  • Lu D, Mausel P, Brondízio E, Moran E (2004) Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin. For Ecol Manag 198:149–167

    Article  Google Scholar 

  • Nguyen HH, Mcalpine C, Pullar D, Johansen K, Duke NC (2013) The relationship of spatial-temporal changes in fringe mangrove extent and adjacent land-use: case study of Kien Giang coast, Vietnam. Ocean Coast Manag 76:12–22

    Article  Google Scholar 

  • Ong JE, Gong WK (2013) Structure, function and management of mangrove ecosystems, ISME Mangrove Educational Book Series No. 2. International Society for Mangrove Ecosystems (ISME), Okinawa

  • Proisy C, Couteron P, Fromard F (2007) Predicting and mapping mangrove biomass from canopy grain analysis using Fourier-based textural ordination of IKONOS images. Remote Sens of Environ 109:379–392

    Article  Google Scholar 

  • Riano D, Chuvieco E, Salas J, Aguado I (2003) Assessment of different topographic corrections in Landsat-TM data for mapping vegetation types (2003). Geosci Remote Sens IEEE Trans 41:1056–1061

    Article  Google Scholar 

  • Sarker LR, Nichol JE (2011) Improved forest biomass estimates using ALOS AVNIR-2 texture indices. Remote Sens Environ 115:968–977

    Article  Google Scholar 

  • 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 103:16–26

    Article  Google Scholar 

  • Song C (2013) Optical remote sensing of forest leaf area index and biomass. Prog Phys Geogr 37:98–113

    Article  CAS  Google Scholar 

  • Toutin T (2004) Review article: geometric processing of remote sensing images: models, algorithms and methods. Int J Remote Sens 25:1893–1924

    Article  Google Scholar 

  • Wang L, Sousa WP, Gong P, Biging GS (2004) Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sens Environ 91:432–440

    Article  Google Scholar 

  • Ward D, Phinn SR, Murray AT (2000) Monitoring growth in rapidly urbanizing areas using remotely sensed data. Prof Geogr 52:371–386

    Article  Google Scholar 

  • Woodcock CE, Helmer E, Nemani R, Oreopoulos L, Schott J, Thenkabail PS, Vermote EF, Vogelmann J, Wulder MA, Wynne R, Allen R, Anderson M, Belward A, Bindschadler R, Cohen W, Gao F, Goward SN, Helder D (2008) Free access to Landsat imagery. Science 320:1011

    Article  CAS  PubMed  Google Scholar 

  • Wulder MA, White JC, Goward SN, Masek JG, Irons JR, Herold M, Cohen WB, Loveland TR, Woodcock CE (2008) Landsat continuity: issues and opportunities for land cover monitoring. Remote Sens Environ 112:955–969

    Article  Google Scholar 

  • Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122:2–10

    Article  Google Scholar 

  • Yan F, Wu B, Wang Y (2013) Estimating aboveground biomass in Mu Us Sandy Land using Landsat spectral derived vegetation indices over the past 30 years. J Arid Land 5(4):521–530

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the Forest Research Institute of Malaysia for sharing the ground sample data that was used in this study. We are also very grateful to the State Forestry Department of Perak for all the assistance and information provided throughout the completion of the research.

Author information

Authors and Affiliations

  1. School of Geography Planning and Environmental Management, University of Queensland, St Lucia, Brisbane, QLD, 4072, Australia

    Ammar Abdul Aziz, Stuart Phinn, Paul Dargusch & Sanjiwana Arjasakusuma

  2. Forest Research Institute Malaysia, 52109, Kepong, Selangor Darul Ehsan, Malaysia

    Hamdan Omar

Authors
  1. Ammar Abdul Aziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stuart Phinn
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Paul Dargusch
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hamdan Omar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sanjiwana Arjasakusuma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ammar Abdul Aziz.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdul Aziz, A., Phinn, S., Dargusch, P. et al. Assessing the potential applications of Landsat image archive in the ecological monitoring and management of a production mangrove forest in Malaysia. Wetlands Ecol Manage 23, 1049–1066 (2015). https://doi.org/10.1007/s11273-015-9443-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11273-015-9443-1

Keywords

Search

Navigation