* Corresponding Author
Object-Based Mangrove Mapping Comparison on Visible and NIR UAV Sensor
https://doi.org/10.22146/ijg.50861 Nurul Khakhim(1), Muh Aris Marfai(2), Ratih Fitria Putri(3), Muhammad Dimyati(4), Muhammad Adnan Shafry Untoro(5), Raden Ramadhani Yudha Adiwijaya(6), Taufik Walinono(7), Wahyu Lazuardi(8*), Dimas Novandias Damar Pratama(9), Arief Wicaksono(10), Azis Musthofa(11), Zulfikri Isnaen(12)
(1) Faculty of Geography, Universitas Gadjah Mada
(2) Faculty of Geography, Universitas Gadjah Mada and Geospatial Information Agency (Badan Informasi Geospasial)
(3) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(4) Departement of Geography, Faculty of Mathematics and Natural Sciences, University of Indonesia
(5) Faculty of Geography, Universitas Gadjah Mada and Geospatial Information Agency (Badan Informasi Geospasial)
(6) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(7) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta Indonesia
(8) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(9) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(10) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(11) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(12) Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
(*) Corresponding Author
Abstract
Mangrove ecosystems are natural resources that have potential value for development due to their high productivity. Mapping and identification of mangroves have always played a crucial role in mangrove ecosystem conservation efforts, especially to support the sustainable development goal of coastal resources and climate change issues. Several attempts have been made using Unmanned Aerial Vehicle (UAV) techniques acquisition of high spatial resolution aerial images data with various sensors and object-based classification for image processing with various levels of success. This study aims to identify mangrove objects using UAV with true color and NIR false-color sensors using the OBIA approach. The UAV used in this study was DJI Phantom 3 Pro with a true-color sensor (default) and NIR false-color (modified Canon IXUS 160 cameras). The comparison between the two types of sensor of aerial photographs as a source for mangrove mapping proved that the latter performed better than the former because of the near-infrared band can optimally discriminate between mangrove and non-mangrove objects. This will assist future research directions in the mangrove ecosystems mapping method.
Keywords
Full Text:
PDFReferences
Ballari, D., Orellana, D., Acosia, E., Espinoza, A., and Morocho, V., (2016). UAV Monitoring for Enviromental Management in Galapagos. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XLI-B1. XXIII ISPRS Congress, Prague, Czech Republic.
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., and Bareth, G., (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation. Vol.39, 79-87.
Bhardwaj, A., Sam, L., Martín-Torres, F.J., (2016). UAVs as remote sensing platform in glaciology: present applications and future prospects. Remote Sensing Environment. Vol. 175, 196–204.
Blaschke, T., Hay, G.J., Kelly, M., Lang, S., Hofmann, P., Addink, E., Feitosa, R.Q., van der Meer, F., van der Werff, H., van Coillie, F., and Tiede. (2014). Geographic Object-Based Image Analysis – towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 87, 180–191.
Brooker, G. (2009). Introduction to Sensors for Ranging and Imaging. SciTech Publishing. Raleigh: United States.
Buditama, A., 2016. Blue carbon for reducing the impacts of climate change: An Indonesian case study, Environmental and Planning Law Journal. Vol. 33, 68-88.
Candiago, S., Remondino, F., De Giglio, M., (2015). Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing. Vol. 7, 4026–4047.
Canon Inc. (2015). Canon IXUS 170, IXUS 165, IXUS 160: Camera User Guide. Singapore: Canon Inc.
Cao, J., Leng, W., Liu, K., Liu, L., He, Z., and Zhu, Y. (2018). Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models. Remote Sensing. Vol. 10, 89.
Clinton, N., Holt A., Scarborough J., Yan L., and Gong P. (2010). Accuracy Assessment Measures for Object-based Image Segmentation Goodness. Photogrammetric Engineering & Remote Sensing. Vol 76.
DJI. (2017). Phantom 3 Professional: User Manual. Shenzhen, China: DJI.
Duncan, C., Primavera, J.H., Pettorelli, N., Thompson, J.R., Loma, R.J.A., and Koldewey, H.J., (2016). Rehabilitating mangrove ecosystem services: a case study on the relative benefits of abandoned pond reversion from Panay Island, Philippines. Marine Pollution Bulletin. Vol. 109, 772–782.
Gilbertson, J. K., Kemp, J., and van Niekerk, A. (2017). Effect of pan-sharpening multi-temporal Landsat 8 imagery for crop type differentation using different classification techniques. Computers and Electronics in Agriculture. Vol. 134, 151-159.
Green, EP, Clark, CD, Mumby, PJ, Edwards, AJ and Ellis, AC (1998), 'Remote sensing techniques for mangrove mapping'. International Journal of Remote Sensing, vol. 19, 935-956.
Ibharim, N.A., Mustapha, M.A., Lihan, T., and Mazlan, A.G.,
(2015). Mapping mangrove changes in the Matang Mangrove Forest using multi temporal satellite imageries. Ocean & Coastal Management. Vol. 114, 64-76.
Jensen, J., (2007). Remote Sensing of the Environment: An Earth Resource Prerspective. Pearson Prentice Hall. United States.
Jin, X., Liu, S., Baret, F., Hemerle, M., and Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment. Vol. 198, 105–114.
Kamal, M., Phinn, S., and Johansen, K., (2014). Characterizing the spatial structure of mangrove features for optimizing image-based mangrove mapping. Remote Sensing. Vol. 6, 984–1006.
Kamal, M, Phinn, S and Johansen, K., (2016). Assessment of multi-resolution image data for mangrove leaf area index mapping', Remote Sensing of Environment, vol. 176, 242-254.
Kannan, L., T., T., and Kumar, T. (2008). Spectral reflectance properties of mangrove species of the Muthupettai mangrove environment, Tamil Nadu. Journal of Environmental Biology. Vol. 29, 785-788.
Khakhim, N., Marfai, M.A., Putri, R.F., Wicaksono, A., Lazuardi, W., Isnaen, Z., and Walinono, T., (2020). GEOBIA for mangrove mapping using UAV-modified NIR 1 camera sensor. The Fifth International Conferences of Indonesian Society for Remote Sensing. IOP Conference Series: Earth and Environmental Science. Vol. 500, 012018
Ma, L., Li, M., Ma, X., Cheng, L., Du, P., and Liu, Y., (2017). A Review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 130, 277-293.
Matasci, G., Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., and Zald, H.S.J. (2018). Large-area mapping of canadian boreal forest cover, height, biomass and other structural attributes using landsat composites and lidar plots. Remote Sensing Environment. Vol. 209, 90–106.
Nagelkerken, I., Blaber, S.J.M., Bouillon, S., Green, P., Haywood, M., Kirton, L.G., Meynecke, J.O., Pawlik, J., Penrose, H.M., Sasekumar, A., and Somerfield, P.J., (2008). The habitat function of mangroves for terrestrial and marine faunas: a review. Aqua Botanica. Vol. 89, 155-185.
Nehren, U and, Wicaksono, P., (2018). Mapping soil carbon stocks in an oceanic mangrove ecosystem in Karimunjawa Islands, Indonesia. Estuarine, Coastal and Shelf Science. Vol. 214, 185-193.
Panagiotidis, D., Abdollahnejad, A., Surovy, P., and Choteculo, V. (2017). Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing. Vol. 38, 2392–2410.
Rosco. (2013). Dark Sky Blue. Retrieved from https:// https://us.rosco.com/en/products/filters/g890-dark-sky-blue.
Roy, P. (1989). Spectral reflectance characteristics of vegetation and their use in estimating productive potential. Proceding Indian Academic Science (Plant Science). Vol. 99, 59-81.
Tian, J., Wang, L.., Li, X., Gong, H., Shi, C., Zhong, R., and Liu, X., (2017). Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest. International Journal of Applied Earth Observation and Geoinformation. Vol. 61, 22–31.
Wang, D., Wan, B., Qiu, P., Zuo, Z., Wang, R., and Wu, X., (2019). Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling. Remote Sensing. Vol. 2156 ,1-25.
Yao, X., Wang, N., Liu, Y., Cheng, T., Tian, Y., Chen, Q., and Zhu, Y. (2017). Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery. Remote Sensing. Vol. 9, 1304.
Zimudzi, E., Sanders, I., Rollings, N., and Omlin, C., W., (2019). Remote sensing of mangroves using unmanned aerial vehicles: current state and future directions. Journal of Spatial Science. 1-18.
DOI: https://doi.org/10.22146/ijg.50861
Article Metrics
Abstract views : 2797
| 
views : 1872
Refbacks
- There are currently no refbacks.
Copyright (c) 2021 Nurul Khakhim, Arief Wicaksono, Wahyu Lazuardi, Zulfikri Isnaen
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
ISSN 2354-9114 (online), ISSN 0024-9521 (print)