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ASSESSMENT OF MANGROVE FOREST DEGRADATION THROUGH CANOPY FRACTIONAL COVER IN KARIMUNJAWA ISLAND, CENTRAL JAVA, INDONESIA

*Muhammad Kamal  -  Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
Hartono Hartono  -  Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
Pramaditya Wicaksono  -  Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia
Novi Susetyo Adi  -  Research Centre for Coastal and Maritime Resources, The Ministry of Maritime Affairs and Fisheries, Indonesia
Sanjiwana Arjasakusuma  -  Faculty of Geography, Universitas Gadjah Mada, Yogyakarta, Indonesia

Citation Format:
Abstract
The Karimunjawa Islands mangrove forest has been subjected to various direct and indirect human disturbances in the recent years. If not properly managed, this disturbance will lead to the degradation of mangrove habitat health. Assessing forest canopy fractional cover (fc) using remote sensing data is one way of measuring mangrove forest degradation. This study aims to (1) estimate the forest canopy fc using a semi-empirical method, (2) assess the accuracy of the fc estimation and (3) create mangrove forest degradation from the canopy fc results. A sample set of in-situ fc was collected using the hemispherical camera for model development and accuracy assessment purposes. We developed semi-empirical relationship models between pixel values of ALOS AVNIR-2 image (10 m pixel size) and field fc, using Enhanced Vegetation Index (EVI) as a proxy of the image spectral response. The results show that the EVI provides reasonable estimation accuracy of mangrove canopy fc in Karimunjawa Island with the values ranged from 0.17 to 0.96 (n = 69). The low fc values correspond to vegetation opening and gaps caused by human activities or mangrove dieback. The high fc values correspond to the healthy and dense mangrove stands, especially the Rhizophora sp formation at the seafront. The results of this research justify the use of simple canopy fractional cover model for assessing the mangrove forest degradation status in the study area. Further research is needed to test the applicability of this approach at different sites.
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Keywords: mangroves; degradation; canopy fractional cover; ALOS AVNIR-2; EVI

Article Metrics:

  1. Asner, G. P. (2009). Automated mapping of tropical deforestation and forest degradation: CLASlite. Journal of Applied Remote Sensing, 3(1), 33543. [http://doi.org/10.1117/1.3223675">CrossRef]

  2. Bouvet, M., et al. (2007). Preliminary radiometric calibration assessment of ALOS AVNIR-2. In 2007 IEEE International Geoscience and Remote Sensing Symposium (pp. 2673–2676).

  3. BTNK. (2008). Statistik Balai Taman Nasional Karimunjawa (BTNK) 2008 (pp. 101). Semarang: BTNK, Dirjen Perlindungan Hutan dan Konservasi Alam, Departemen Kehutanan.

  4. BTNK. (2012). Jenis-Jenis Mangrove Taman Nasional Karimunjawa. Semarang: Balai Taman Nasional Karimunjawa.

  5. Defries, R. S., et al. (2000). A new global 1-km dataset of percentage tree cover derived from remote sensing. Global Change Biology, 6(2), 247–254. [http://doi.org/10.1046/j.1365-2486.2000.00296.x">CrossRef]  

  6. Duke, N., et al. (1998). Factors influencing biodiversity and distributional gradients in mangroves. Global Ecology & Biogeography Letters, 7(1), 27–47.

  7. Franklin, S. E. (2001). Remote sensing for sustainable forest management. CRC Press.

  8. Hashim, M.,et al. (2014). Comparison of ETM+ and MODIS data for tropical forest degradation monitoring in the Peninsular Malaysia. Journal of the Indian Society of Remote Sensing, 42(2), 383–396.

  9. Huete, A., et al. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. [http://doi.org/10.1016/S0034-4257(02)00096-2">CrossRef]   

  10. Jiménez-Muñoz, J. C., et al. (2009). Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over an Agricultural Area. Sensors, 9(2), 768–793. [http://doi.org/10.3390/s90200768">CrossRef]   

  11. Kamal, M., et al. (2016). Assessment of multi-resolution image data for mangrove leaf area index mapping. Remote Sensing of Environment, 176, 242–254. [http://doi.org/10.1016/j.rse.2016.02.013">CrossRef]   

  12. Kovacs, J. M., et al. (2009). Evaluating the condition of a mangrove forest of the Mexican Pacific based on an estimated leaf area index mapping approach. Environmental Monitoring and Assessment, 157(1–4), 137–149. [http://doi.org/10.1007/s10661-008-0523-z">CrossRef]   

  13. Laongmanee, W., et al. (2013). Assessment of spatial resolution in estimating leaf area index from satellite images: A case study with Avicennia Marina Plantations in Thailand. International Journal of Geoinformatics, 9(3).

  14. Matricardi, E. A. T., et al. (2010). Assessment of tropical forest degradation by selective logging and fire using Landsat imagery. Remote Sensing of Environment, 114(5), 1117–1129. [http://doi.org/10.1016/j.rse.2010.01.001">CrossRef]   

  15. Skidmore, A. K., et al. (1997). Use of remote sensing and GIS for sustainable land management. ITC Journal, 3(4), 302–315.

  16. Souza, C. M., et al. (2005). Multitemporal analysis of degraded forests in the Southern Brazilian Amazon. Earth Interactions, 9(19), 1–25. [http://doi.org/10.1175/EI132.1">CrossRef]    

  17. Verheyden, A., et al. (2004). Growth rings, growth ring formation and age determination in the mangrove Rhizophora mucronata. Annals of Botany, 94(1), 59–66.

  18. Wang, C., Qi, J., & Cochrane, M. (2005). Assessment of tropical forest degradation with canopy fractional cover from Landsat ETM+ and IKONOS imagery. Earth Interactions, 9(22), 1–18.

  19. Weiss, M., & Baret, F. (2014). CAN-EYE V6. 313 User Manual. Available: http://www6pacainrafr/can-eye/Documentation-Publications/Documentation.

  20. Woodcock, C. E., et al. (1983). Remote sensing for land management and planning. Environmental Management, 7(3), 223–237. [http://doi.org/10.1007/BF01871537">CrossRef]

  21. Xiao, J., & Moody, A. (2005). A comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA. Remote Sensing of Environment, 98(2–3), 237–250. [http://doi.org/10.1016/j.rse.2005.07.011">CrossRef]

  22. Zeng, X., et al. (2000). Derivation and Evaluation of Global 1-km Fractional Vegetation Cover Data for Land Modeling. Journal of Applied Meteorology, 39(6), 826–839. [http://doi.org/10.1175/1520-0450(2000)039%3c0826:DAEOGK%3e2.0.CO;2">CrossRef]


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