The advantages of using drones over space-borne imagery in the mapping of mangrove forests

PLoS ONE

Volumn 13, Issue 7, 2018, Pages

  Ruwaimana, Monika ^a,b,c   Satyanarayana, Behara ^a,d   Otero, Viviana ^a   Muslim, Aidy M ^d   Muhammad Syafiq, A ^d   Ibrahim, Sulong ^d   Raymaekers, Dries ^e   Koedam, Nico ^b   Dahdouh Guebas, Farid ^a,b  

^a UNIVERSITÉ LIBRE DE BRUXELLES   (Belgium)

^b VRIJE UNIVERSITEIT BRUSSEL   (Belgium)

^c UNIVERSITAS ATMA JAYA YOGYAKARTA   (Indonesia)

^d UNIVERSITI MALAYSIA TERENGGANU   (Malaysia)

^e FLEMISH INSTITUTE FOR TECHNOLOGICAL RESEARCH VITO   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; AVICENNIA ALBA; CASUARINA EQUISETIFOLIA; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; COST BENEFIT ANALYSIS; FOREST STRUCTURE; GEOGRAPHIC MAPPING; IMAGE PROCESSING; IMAGE QUALITY; LAND USE; MALAYSIA; MANGROVE; NONHUMAN; NYPA FRUTICANS; REMOTE SENSING; RHIZOPHORA APICULATA; SATELLITE IMAGERY; TREE; TROPIC CLIMATE; WETLAND; AIRCRAFT; ECOSYSTEM; REPRODUCIBILITY;

AIRCRAFT; ECOSYSTEM; GEOGRAPHIC MAPPING; MALAYSIA; REPRODUCIBILITY OF RESULTS; SATELLITE IMAGERY; WETLANDS;

EID: 85050156400     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0200288     Document Type: Article

References (91)

1. Mukherjee N, Sutherland WJ, Khan NI, Berger U, Schmitz N. Using expert knowledge and modeling to define mangrove composition, functioning, and threats and estimate time frame for recovery. Ecol Evol. 2014; 4(11):2247–62. https://doi.org/10.1002/ece3.1085 PMID: 25360265
2. UNEP. Marine and coastal ecosystems and human wellbeing: A synthesis report based on the findings of the Millennium Ecosystem Assessment. 2006. 76 p.
3. Spalding M, Kainuma M, Collins L. World Atlas of Mangrove. Malta: Earthscan; 2010. 319 p.
4. Baba S, Chan HT, Aksornkoae. Useful products from mangrove and other coastal plants. ISME Mangrove Educational Book Serie No. 3. International Society for Mangrove Ecosystem (ISME) and International Tropical Timber Organization (ITTO); 2013.
5. Brander L M., Wagtendonk A J., Hussain S S., McVittie A, Verburg PH, de Groot RS, et al. Ecosystem service values for mangroves in Southeast Asia: A meta-analysis and value transfer application. Ecosyst Serv. 2012; 1(1):62–9.
6. Mukherjee N, Sutherland WJ, Dicks L, Huge J, Koedam N, Dahdouh-Guebas F. Ecosystem service valuations of mangrove ecosystems to inform decision making and future valuation exercises. PLoS One. 2014; 9(9):1–9.
7. Polidoro BA, Carpenter KE, Collins L, Duke NC, Ellison AM, Ellison JC, et al. The loss of species: mangrove extinction risk and geographic areas of global concern. PLoS One. 2010; 5(4):P.e10095. https://doi.org/10.1371/journal.pone.0010095 PMID: 20386710
8. Dahdouh-Guebas F, Van Pottelbergh I, Kairo JG, Cannicci S, Koedam N. Human-impacted mangroves in Gazi (Kenya): Predicting future vegetation based on retrospective remote sensing, social surveys, and tree distribution. Mar Ecol Prog Ser. 2004; 272:77–92.
9. Zainun I, Budidarsono S, Rinaldi Y, Adek MC. Socio-Economic Aspects of Brackish Water Aquaculture (Tambak) Production in Nanggroe Aceh Darussalam. Bogor; 2007.
10. Griffin C, Ellis D, Beavis S, Zoleta-Nantes D. Coastal resources, livelihoods and the 2004 Indian Ocean tsunami in Aceh, Indonesia. Ocean Coast Manag. 2013; 71:176–86.
11. Dahdouh-Guebas F. The use of remote sensing and GIS in the sustainable management of tropical coastal ecosystems. Environ Dev Sustain. 2002; 4:93–112.
12. Feagin RA, Mukherjee N, Shanker K, Baird AH, Cinner J, Kerr AM, et al. Shelter from the storm? Use and misuse of coastal vegetation bioshields for managing natural disasters. Conserv Lett. 2010 Feb; 3 (1):1–11.
13. Brander LM, Wagtendonk AJ, Hussain SS, Mcvittie A, Verburg PH, de Groot RS, et al. Ecosystem service values for mangroves in Southeast Asia: A meta-analysis and value transfer application. Ecosyst Serv. 2012; 1:62–9.
14. Duke NC, Meynecke JO, Ditmann S, Ellison AM, Anger K, Berger U, et al. A World Without Mangroves? Science (80-). 2007; 317:41.
15. Green EP, Mumby PJ, Edwards AJ, Clark CD. Remote sensing book for tropical coastal management. Coastal Ma. Paris: UNESCO; 2000. x + 316 p.
16. TM. Plant Ecol Evol. 2010; 143(2):225–32.
17. Satyanarayana B, Mohamad KH, Idris IF, Husain M, Dahdouh-Guebas F. Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia. Int J Remote Sens. 2011; 32(6):1635–50.
18. Kuenzer C, Bluemel A, Gebhardt S, Quoc T V., Dech S. Remote Sensing of Mangrove Ecosystems: A Review. Remote Sens. 2011; 3:878–928.
19. Satyanarayana B, Koedam N, Smet KD, Nitto DD, Bauwens M, Jayatissa LP, et al. Long-term mangrove forest development in Sri Lanka: early predictions evaluated against outcomes using VHR remote sensing and VHR ground-truth data. Mar Ecol Prog Ser. 2011; 443:51–63.
20. Leempoel K, Satyaranayana B, Bourgeois C, Zhang J, Chen M, Wang J, et al. Dynamics in mangroves assessed by high-resolution and multi-temporal satellite data: A case study in Zhanjiang Mangrove National Nature Reserve (ZMNNR), P. R. China. Biogeosciences. 2013; 10(8):5681–9.
21. Dahdouh-Guebas F, Verheyden A, De Genst W, Hettiarachchi S, Koedam N. Four decades vegetation dynamics in Sri Lankan Mangroves as detected from sequentialaerial photography: A case study in Galle. Bull Mar Sci. 2000; 67(2):741–59.
22. Dahdouh-Guebas F, Hettiarachchi S, Lo Seen D, Batelaan O, Sooriyarachchi S, Jayatissa LP, et al. Transitions in ancient inland freshwater resource management in Sri Lanka affect biota and human populations in and around coastal lagoons. Curr Biol. 2005; 15(6):579–86. https://doi.org/10.1016/j.cub.2005.01.053 PMID: 15797030
23. Van TT, Wilson N, Thanh-Tung H, Quisthoudt K, Quang-Minh V, Xuan-Tuan L, et al. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecologica. 2015; 63:71–81.
24. Dahdouh-Guebas F, Hiel E V., Chan JCW, Jayatissa LP, Koedam N. Qualitative distinction of congeneric and introgressive mangrove species in mixed patchy forest assemblages using high spatial resolution remotely sensed imagery (IKONOS). Syst Biodivers. 2005; 2(2):113–9.
25. Neukermans G, Dahdouh-Guebas F, Kairo JG, Koedam N. Mangrove species and stand mapping in gazi bay (kenya) using quickbird satellite imagery. J Spat Sci. 2008; 53(1):75–86.
26. Thi VT, Xuan ATT, Nguyen HP, Dahdouh-Guebas F, Koedam N. Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam. Biogeosciences. 2014; 11:3781–95.
27. Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, et al. Status and distribution of mangrove forests of the world using earth observation satellite data. Glob Ecol Biogeogr. 2011; 20(1):154–9.
28. Hansen MC, Loveland TR. Remote Sensing of Environment A review of large area monitoring of land cover change using Landsat data. Remote Sens Environ. 2012; 122:66–74.
29. Dahdouh-Guebas F, Zetterström T, Rönnbäck P, Troell M, Wickramasinghe A, Koedam N. Recent changes in land-use in the Pambala-Chilaw Lagoon complex (Sri Lanka) investigated using remote sensing and GIS: Conservation of mangroves vs. development of shrimp farming. Environ Dev Sustain. 2002; 4(2):185–200.
30. Dahdouh-Guebas F, Koedam N. Long-term retrospection on mangrove development using transdisci-plinary approaches: A review. Aquat Bot. 2008; 89(2):80–92.
31. Wang L, Sousa WP, Gong P, Biging GS. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama. Remote Sens Environ. 2004; 91:432–40.
32. Carleer AP, Debeir O, Wolff E. Assessment of very high spatial resolution satellite image segmenta-tions. Photogramm Eng Remote Sensing. 2005; 71(11):1285–94.
33. Zhu Z, Woodcock CE. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ. 2012; 118(April 2016):83–94.
34. Salamí E, Barrado C, Pastor E. UAV flight experiments on the remote sensing of vegetation areas. Remote Sens. 2014; 6:11051–81.
35. ITRES. CASI 1500h Specification. http://www.itres.com/. 2016. p. 2.
36. Dahdouh-Guebas F, Verheyden A, Kairo JG, Jayatissa LP, Koedam N. Capacity building in tropical coastal resource monitoring in developing countries: A re-appreciation of the oldest remote sensing method. Vol. 13, International Journal of Sustainable Development and World Ecology. 2006. p. 62–76.
37. Ford M. Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images: Wotje Atoll, Marshall Islands. 2013; 135:130–40.
38. Rodriguez W, Feller IC. Mangrove landscape characterization and change in Twin Cays, Belize using aerial photography and IKONOS satellite data. Natl Museum Nat Hist. 2004;(513).
39. Sulong I, Mohd-Lokman H, Mohd-Tarmizi K, Ismail A. Mangrove mapping using Landsat imagery and aerial photographs: Kemaman District, Terengganu, Malaysia. Environ Dev Sustain. 2002; 4(2):135–52.
40. Strecha C, Fletcher A, Lechner A, Erskine P, Fua P. Developing species specific vegetation maps using multi-spectral hyperspatial imagery from Unmanned Aerial Vehicles. ISPRS Ann Photogramm Remote Sens Spat Inf Sci. 2012; I-3(September):311–6.
41. Kaneko K, Nohara S. Review of effective vegetation mapping using the UAV (Unmanned Aerial Vehicle) method. J Geogr Inf Syst. 2014; 6(6):733–42.
42. Díaz-Varela R, de la Rosa R, León L, Zarco-Tejada P. High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens. 2015; 7(4):4213–32.
43. Otero V, Van De Kerchove R, Satyanarayana B, Martínez-Espinosa C, Bin Fisol MA, Bin Ibrahim MA, et al. Managing mangrove forests from the sky: forest inventory using field data and Unmanned Aerial Vehicle (UAV) imagery in the Matang Mangrove Forest Reserve, peninsular Malaysia. For Ecol Manage. 2018; 411:35–45.
44. Maresma Á, Ariza M, Martínez E, Lloveras J, Martinez-Casasnovas JA. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize. Remote Sens. 2016; 8(973):1–15.
45. Lucieer A, Robinson SA, Turner D. Using an unmanned aerial vehicle (UAV) for ultra- high resolution mapping of Antarctic moss beds. In: Australasian Remote Sensing & Photogrammetry Conference. Alice Springs, NT, Australia; 2010. p. 1–12.
46. Paneque-Gálvez J, McCall MK, Napoletano BM, Wich SA, Koh LP. Small drones for community-based forest monitoring: An assessment of their feasibility and potential in tropical areas. Forest. 2014; 5 (6):1481–507.
47. Zhang J, Hu J, Lian J, Fan Z, Ouyang X, Ye W. Seeing the forest from drones: Testing the potential of lightweight drones as a tool for long-term forest monitoring. Biol Conserv. 2016; 198(March):60–9.
48. Tang L, Shao G. Drone remote sensing for forestry research and practices. J For Res. 2015; 26 (4):791–7.
49. Tian J, Wang L, Li X, Gong H, Shi C, Zhong R. Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. Int J Appl Earth Obs Geoinf. 2017; 61(May):22–31.
50. Jamilah MS. Setiu wetlands state park: Challenging task for UMT. INFOKUS UMT. 2013 Sep;12–5.
51. Nakisah MA, Fauziah AH. Setiu Wetlands: Tranquility amidst plenty. Kuala Terengganu, Malaysia: Kolej Universiti Sains dan Teknologi Malaysia—KUSTEM; 2003. 105 p.
52. Lim CK. Classification and distribution of mangrove forest in the north of Setiu, Terengganu using Aerial Photo Interpretation Technique. Report of Final Year Academic Project, Bachelor of Science (Marine Science). KUSTEM; 2002.
53. Jamilah MS, Razali MS, Putri Nurainim J. Discovery of the critically endangered Bruguiera hainesii (Berus Mata Buaya) in Pulau Layat, Setiu Wetland, Kuala Terengganu. Conservation Malaysia. 2013;1–3.
54. Chong VC. Sustainable utilization and management of Mangrove ecosystems of Malaysia. Aquat Ecosyst Health Manag. 2006; 9(2):249–60.
55. Valuing The Potential Economic Value of Mangroves Resources in Setiu Wetlands, Terengganu, Malaysia: A Preliminary Findings. 2014; 2(12):487–504.
56. Bernama. Terengganu to propose gazetting of Setiu Wetlands as state park. The Star Online. 2013.
57. Brooker G, Brooker AT. Introduction to sensors for ranging and imaging. SciTech Pub. Incorporated; 2009.
58. Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012; 9(7):671–5. PMID: 22930834
59. Flytrex. Flytrex Core V2. www.flytrex.com. 2015.
60. Schneider A. GPS Visualizer: Do-It-Yourself Mapping. www.gpsvizualizer.com. 2015.
61. Schmidt F. Geosetter Version 3.4.16. www.geosetter.de. 2011.
62. Perry TS. Agisoft’s PhotoScan. Animation. 2013; 27(2).
63. Congedo L. Semi-Automatic Classification Plugin Documentation Vers 3.0.7. 2014. p. 107.
64. Mather PM. Compute processing of remotely-sensed images: an introduction. Third edit. John Wiley & Sons Ltd, England.; 2004.
65. Otero V, Quisthoudt K, Koedam N, Dahdouh-Guebas F. Mangroves at their limits: Detection and area estimation of mangroves along the Sahara Desert Coast. Remote Sens. 2016; 8(512):1–13.
66. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Jt Conf Artif Intell. 1995; 14(2).
67. CNES. The Orfeo ToolBox Cookbook, a guide for non-developers Updated for OTB-5.2.1. 2016.
68. Julien M, Youssefi D, Grizonnet M. Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images." Geoscience and Remote Sensing, IEEE Transactions. Geosci Remote Sensing, IEEE Trans. 2015; 53(2):952–64.
69. QGIS-Development-Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2018.
70. Pontius RG, Millones M. Death to Kappa: Birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens. 2011; 32(15):4407–29.
71. Pontius RG, Santacruz A. Quantity, exchange, and shift components of difference in a square contingency table. Int J Remote Sens. 2014; 35(21):7543–54.
72. RStudio-Team. RStudio: Integrated Development for R. Boston, MA: RStudio, Inc.; 2015.
73. Hammer Ø, Harper DAT, Ryan PD. PAST: Paleontological statistics software package for education and data analysis. Palaeontol Electron. 2011; 4(1):9pp.
74. CNES. Pleiades User Guide. ASTRIUM; 2012. 118 p.
75. Nijlan W, de Jong R, de Jong SM, Wulder MA, Bater CW, Coops NC. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agric For Meteorol. 2014; 184:98–106.
76. Jenoh EM, Robert EMR, Lehmann I, Kioko E, Bosire JO, Ngisiange N, et al. Wide ranging insect infestation of the pioneer mangrove Sonneratia alba by two insect species along the Kenyan coast. PLoS One. 2016; 11(5):e0154849. https://doi.org/10.1371/journal.pone.0154849 PMID: 27171470
77. Amir AA. Canopy gaps and the natural regeneration of Matang mangroves. For Ecol Manage. 2012; 269:60–7.
78. Tucker CJ. Radiometric resolution for monitoring vegetation: How many bits are needed? Int J Remote Sens. 1980; 1(3):241–54.
79. Wu S, Hsieh Y, Chen C, Chen J. A comparison of four shadow compensation techniques for land cover classification of shaded areas from high radiometric resolution aerial images. Can J Remote Sens. 2014; 40:315–26.
80. Dailymail. French police training EAGLES to attack drones over fears ISIS could use the gadgets to carry out terror attacks. 2017.
81. ABC. Birds vs drones: Wedge-tailed eagles and unmanned aerial vehicles competing for airspace in the workplace. Australian Broadcasting Coorporation. 2017.
82. Crutsinger GM, Short J, Sollenberger R. The future of UAVs in ecology: an insider perspective from the Silicon Valley drone industry. J Unmanned Veh Syst. 2016; 4(1):1–8.
83. Ventura D, Bruno M, Jona Lasinio G, Belluscio A, Ardizzone G. A low-cost drone based application for identifying and mapping of coastal fish nursery grounds. Estuar Coast Shelf Sci. 2016; 171:85–98.
84. DJI. DJI Store [Internet]. 2017 [cited 2017 Jun 30]. Available from: https://store.dji.com/85. Parrot. Datasheet Specsheet Sequoia: the multi-band sensor designed for agriculture. 2016.
85. Wang L, Sousa WP, Gong P. Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery. Int J Remote Sens. 2004; 25(24):5655–68.
86. Blaschke T. Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens. 2010; 65(1):2–16.
87. Shafri HZM, Suhaili A, Mansor S. The performance of Maximum Likelihood, Spectral Angle Mapper, Neural Network and Decision Tree Classifiers in hyperspectral image analysis. J Comput Sci. 2007; 3 (6):419–23.
88. Castillejo-gonzález IL, López-granados F, García-ferrer A, Pena-Barragan JM, Jurado-Exposito M, de la Orden MS, et al. Object and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput Electron Agric. 2009; 68:207–15.
89. Khatami R, Mountrakis G, Stehman S V. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sens Environ. 2016; 177:89–100.
90. Thomlinson JR, Bolstad P V., Cohen WB. Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products. Remote Sens Environ. 1999; 70:16–28.
91. Foody GM. Status of land cover classification accuracy assessment. Remote Sens Environ. 2002; 80:185–201.