Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula.
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Barau A. S. (2017). Tension in the periphery: An analysis of spatial public and corporate views on landscape change in Iskandar Malaysia Landsc. Urban Plan. vol. 165 pp. 256–266.
Crist E. P. & R. C. Cicone (1984). A Physically-Based Transformation of Thematic Mapper Data---The TM Tasseled Cap IEEE Trans. Geosci. Remote Sens. vol. GE-22 no. 3 pp. 256–263.
De Sherbinin A. D. Carr S. Cassels and L. Jiang (2007). Population and environment Annu Rev Env. Resour. vol. 32 pp. 345–373.
Drigo R. Lasserre B. and M. Marchetti (2009). Patterns and trends in tropical forest cover” Plant Biosyst. - An Int. J. Deal. with all Asp. Plant Biol. vol. 143 no. 2 pp. 311–327 Jul.
Gobron N. Pinty B. Verstraete M. M. and J. Widlowski (2000). Advanced Vegetation Indices Optimized for Applications Contract vol. 38 no. 6 pp. 2489–2505.
Heenkenda M. K. Joyce K. E. Maier S. W. and S. De Bruin (2015). ISPRS Journal of Photogrammetry and Remote Sensing Quantifying mangrove chlorophyll from high spatial resolution imagery ISPRS J. Photogramm. Remote Sens. vol. 108 pp. 234–244.
Heenkenda M. Maier S. and K. Joyce (2016). Estimating Mangrove Biophysical Variables Using WorldView-2 Satellite Data: Rapid Creek Northern Territory Australia J. Imaging vol. 2 no. 3 p. 24.
Hmimina G. Dufrêne E. Pontailler J.-Y. Delpierre N. Aubinet M. Caquet B. de Grandcourt A. Burban B. Flechard C. Granier A. Gross P. Heinesch B. Longdoz B. Moureaux C. Ourcival J.-M. Rambal S. Saint André L. and K. Soudani (2013). Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements Remote Sens. Environ. vol. 132 pp. 145–158 May.
Hossain M. D. & A. A. Nuruddin (2016). Soil and Mangrove: A Review J. Environ. Sci. Technol. vol. 9 no. 2 pp. 198–207 Feb.
Huang C. Wylie B. Yang L. Homer C. and G. Zylstra (2002). Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance Int. J. Remote Sens. vol. 23 no. 8 pp. 1741–1748.
Jon Davies N. Y. Mathhew U. Aikanathan S. Chong Ch. and G. Chong (2010). A Quick Scan of Peatlands in Malaysia no. March p. 86.
Juliana W. A. Wan M. Razali S. and A. Latiff (2014). Mangrove Ecosystems of Asia Mangrove Ecosyst. Asia pp. 199–211.
Kamal M. Phinn S. Johansen K. and N. S. Adi (2016). Estimation of mangrove leaf area index from ALOS AVNIR-2 data (A comparison of tropical and sub-tropical mangroves) AIP Conf. Proc. vol. 1755.
Kanniah K. D. Sheikhi A. Cracknell A. P. Goh H. C. Tan K. P. Ho C. S. and F. N. Rasli (2015). Satellite images for monitoring mangrove cover changes in a fast growing economic region in southern Peninsular Malaysia Remote Sens. vol. 7 no. 11 pp. 14360–14385.
Kauth R. J. & G. S. Thomas (1976). The tasseled cap-A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat in The Symposium on Machine Processing of Remotely Sensed Data p. 4B–41–4B–50.
Kongwongjan J. Suwanprasit C. and P. Thongchumnum (2012). Comparison of vegetation indices for mangrove mapping using THEOS data Proc. Asia-Pacific Adv. Netw. vol. 33 no. Mlc pp. 56–64.
Kovacs J. M. Wang J. and F. Flores-Verdugo (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. vol. 62 no. 1–2 pp. 377–384.
Lewis R. R. Milbrandt E. C. Brown B. Krauss K. W. Rovai A. S. Beever J. W. and L. L. Flynn (2015). Stress in mangrove forests: Early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management Mar. Pollut. Bull.
Luo Z. Sun O. J. Wang E. Ren H. and H. Xu (2010). Modeling Productivity in Mangrove Forests as Impacted by Effective Soil Water Availability and Its Sensitivity to Climate Change Using Biome-BGC Ecosystems vol. 13 no. 7 pp. 949–965 Aug.
Motamedi S. Hashim R. Zakaria R. Song K. Il and B. Sofawi (2014). Long-term assessment of an innovative mangrove rehabilitation project: Case study on Carey Island Malaysia Sci. World J. vol. 2014.
RAMSAR (2003). Malaysia names three new Ramsar sites in Johor State. Retrieved September 8 2003 from https://www.ramsar.org/news/malaysia-names-three-new-ramsar-sites-in-johor-state.
Rouse J. W. Haas R. H. Schell J. A. and D. W. Deering (1974). Monitoring vegetation systems in the Great Plains with ERTS in Proceedings of the Third Earth Resource Technology Satellite-1 Symposium (pp. 3010–3017).
Sánchez-Azofeifa G. A. Castro K. L. Rivard B. Kalascka M. R. and R. C. Harriss (2003). Remote Sensing Research Priorities in Tropical Dry Forest Environments1 Biotropica vol. 35 no. 2 p. 134.
Satyanarayana B. Mohamad K. A. Idris I. F. Husain M. L. and F. Dahdouh-Guebas (2011). 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. vol. 32 no. 6 pp. 1635–1650.
Serina R. (2017). Trends in Southeast Asia: Johor’s Forest City faces critical challenges no. 3. Singapore: ISEAS Publishing.
Shah K. Mustafa Kamal A. H. Rosli Z. Hakeem K. R. and M. M. Hoque (2016). Composition and diversity of plants in Sibuti mangrove forest Sarawak Malaysia Forest Sci. Technol. vol. 12 no. 2 pp. 70–76.
Slik J. W. F. & K. a O. Eichhorn (2003). Fire survival of lowland tropical rain forest trees in relation to stem diameter and topographic position. Oecologia vol. 137 no. 3 pp. 446–55 Nov.
Smith A. M. S Kolden C. a. Tinkham W. T. Talhelm A. F. Marshall J. D. Hudak A. T. Boschetti L. Falkowski M. J. Greenberg J. a. Anderson J. W. Kliskey A. Alessa L. Keefe R. F. and J. R. Gosz (2014). Remote sensing the vulnerability of vegetation in natural terrestrial ecosystems Remote Sens. Environ. vol. 154 pp. 322–337 Jun.
Yang X. Wang F. Bento C. P. M. Meng L. van Dam R. Mol H. Liu G. Ritsema C. J. and V. Geissen (2015). Decay characteristics and erosion-related transport of glyphosate in Chinese loess soil under field conditions Sci. Total Environ. vol. 530–531 pp. 87–95.
Yarbrough L. D. Navulur K. and R. Ravi (2014). Presentation of the Kauth–Thomas transform for WorldView-2 reflectance data Remote Sens. Lett. vol. 5 no. 2 pp. 131–138.
Zhang J. & Y. Zhang (2007). Remote sensing research issues of the National Land Use Change Program of China ISPRS J. Photogramm. Remote Sens. vol. 62 no. 6 pp. 461–472 Dec.
Zhang Q. Xiao X. Braswell B. Linder E. Baret F. and B. Mooreiii (2005). Estimating light absorption by chlorophyll leaf and canopy in a deciduous broadleaf forest using MODIS data and a radiative transfer model Remote Sens. Environ. vol. 99 no. 3 pp. 357–371 Nov.