ESTIMATION of MANGROVE FOREST ABOVEGROUND BIOMASS USING MULTISPECTRAL BANDS, VEGETATION INDICES and BIOPHYSICAL VARIABLES DERIVED from OPTICAL SATELLITE IMAGERIES: RAPIDEYE, PLANETSCOPE and SENTINEL-2

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Volumn 4, Issue 3, 2018, Pages 29-36

  Balidoy Baloloy, Alvin ^a   Conferido Blanco, Ariel ^a   Gumbao Candido, Christian ^a   Jay Labadisos Argamosa, Reginal ^a   Bart Lovern Caboboy Dumalag, John ^a   Lee Carandang DImapilis, Lady ^a   Camero Paringit, Enrico ^a  

^a UNIVERSITY OF THE PHILIPPINES   (Philippines)

Author keywords

Aboveground Biomass; Mangroves; PlanetScope; RapidEye; Sentinel 2

Indexed keywords

EID: 85046814756     PISSN: 21949042     EISSN: 21949050     Source Type: Conference Proceeding    
DOI: 10.5194/isprs-annals-IV-3-29-2018     Document Type: Conference Paper

References (37)

1. Ali, M., Montzka, C., Stadler, A., Menz, G., Thonfeld, F., Vereecken, H., 2015. Estimation and Validation of RapidEye-Based Time-Series of Leaf Area Index for Winter Wheat in the Rur Catchment (Germany). Remote Sensing. 7(3), pp2808-2831.
2. Bilgili, Volkan A., van Es, H.M., Akbas, F., Durak, A., Hively, W.D., 2010. Visible-near infrared reflectance spectroscopy for assessment of soil properties in a semi-arid area of Turkey. Journal of Arid Environments. 74, pp229-238.
3. Castillo, J.A.A., Apan, A. A., Maraseni, T.N., Salmo, S.G. III., 2017. Estimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery. ISPRS Journal of Photogrammetry and Remote Sensing. 134: pp70-85. doi: 10.1016/j.isprsjprs.2017.10.016.
4. Clerici, N., Rubiano, K., Abd-Elrahman, A., Hoestettler, J.M.P., Escobedo, F.J. 2016. Estimating Aboveground Biomass and Carbon Stocks in Periurban Andean Secondary Forests Using Very High Resolution Imagery. Forests. 7(7) 138p.
5. Clough, B. F., Dixon, P., Dalhaus. O., 1997. Allometric relationships for estimating biomass in multi-stemmed mangrove trees. Australian Journal of Botany. 45(6), pp1023.
6. Donato, D., Kauffman, J.B., Murdiyarso, D., Kurnianto, S., Stidham, M., Kanninen, M., 2011. Mangroves among the most carbon-rich forests in the tropics. Nature Geoscience. 4(5), pp.293-297
7. European Space Agency. http://www.esa.int/Our-Activities/Observing-the-Earth/Copernicus/Sentinel-2 (December 2017).
8. European Space Agency. The Copernicus Open Access Hub. Available online: https://scihub.copernicus.eu/(November 2017).
9. Friedman, J.H., 1991. Multivariate adaptive regressions splines. Annals of Statistics. 19, pp1-67.
10. Gitelson, A.A., Gritz, Y., Merzlyak, M.N., 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology. 160, pp271-282.
11. Gitelson, A., Merzlyak, M.N., 1994. Spectral reflectance changes associated with autumn senescence of A. hippocastanum L. and A. platanoides L. Leaves. Spectral features and relation to chlorophyll estimation. Journal of Plant Physiology.143, pp286.
12. Gitelson, A.A., Kaufman, Y.J., Merzlyak, M.N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment. 58, pp289-298.
13. Gleason, C.J., 2011. Im, J.H. A Review of Remote Sensing of Forest Biomass and Biofuel: Options for Small-Area Applications. GIScience & Remote Sensing. 48(2), pp141-170.
14. Hassan, R., Scholes, R., Ash, N., 2005. Millennium Ecosystem Assessment. In Ecosystems and Human Well-Being: Current State and Trends: Findings of the Condition and Trends Working Group; Island Press: Washington, DC, USA.
15. Heiskanen, J., 2006. Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. International Journal of Remote Sensing. 27, pp1135-1158.
16. Herold, M. and Johns, T., 2007. Linking requirements with capabilities for deforestation monitoring in the context of the UNFCCC-REDD process. Environmental Research Letters, 2(4), 045025.
17. Huang, S., Miao, Y., Yuan, F., Gnyp, M.L., Yao, Y., Cao, Q., Wang, H., 1, Lenz-Wiedemann, V.I.S., Bareth, G., 2017. Potential of RapidEye andWorldView-2 Satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sensing. 9(3), 227. doi:10.3390/rs9030227
18. Huete, A.R., 1988. A soil adjusted vegetation index (SAVI). Remote Sensing of Environment. 25, pp295-309.
19. Jordan, C.F., 1969. Derivation of leaf-area index from quality of light on the forest floor. Ecology. 50, pp663-666.
20. Kross, A., Mcnairn, H., Lapen, D., Sunohara, M., Champagne, C., 2015. Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int J. Appl Earth Obs Geoinf. 34, pp235-248.
21. Kumar, L., Mutanga, O., 2017. Remote Sensing of Above-Ground Biomass. Remote Sensing. 9, 935.
22. Laffoley, D.d'A., Grimsditch, G., (eds). 2009. The management of natural coastal carbon sinks. A short summary. IUCN, Gland, Switzerland. 8p.
23. Laurin, G. V., Puletti, N., Chen, Q., Piermaria, C., Papale, D., and Valentini, R., 2016. Above ground biomass and tree species richness estimation with airborne Lidar in tropical Ghana forest. International Journal of applied Earth Observation and Geoinformation. 52, pp371-379.
24. Main-Knorn, M., Pflug, B., Debaecker, V., Louis, J., 2015. Calibration and validation plan for the L2A processor and products of the Sentinel-2 Mission. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences.40, pp1249-1255.
25. McLeod, E., Chmura, G.L., Bouillon, S., Salm, R., Björ, M., Duarte, C.M., Lovelock, C.E., Schlesinger, W.H., et al. A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment. 9, pp.552-560.
26. Mina, C.D., Barrios, E.B., 2010. Profiling Poverty with Multivariate Adaptive Regression Splines. 2010. Philippine Journal of Development. 37.
27. Norman, J.M., Kustas, W.P., Humes, K.S., Source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface-temperature. Agricultural and Forest Meteorology. 77, pp263-293.
28. Perera, K.A.R.S., Amarasinghe, M.D., Somaratna, S., 2013. Vegetation structure and species distribution of mangroves along a soil salinity gradient in a micro tidal estuary on the North-Western coast of Sri Lanka. American Journal of Marine Science. 1, pp7-15.
29. Planet Team, 2017. Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. https://api.planet.com.
30. Ramoelo, A., Cho, M.A., 2014. Dry season biomass estimation as an indicator of rangeland quantity using multi-scale remote sensing data. AARSE. University of Johannesburg, 27-31.
31. Rouse, J.W., Has, R.H., Schell, J.A., Deering, D.W., 1973. Monitoring vegetation systems in the Great Plains with ERTS. In Proceedings of the Third ERTS Symposium (NASA), Washington, DC, USA.
32. Russell, W., Tompkins, R., 2005. Estimating biomass in coastal Baccharis pilularis dominated plan communities. Fire Ecology. 1, pp 20-27.
33. Weiss, M., Baret, F., 2016. S2ToolBox Level 2 products: LAI, FAPAR, FCOVER", Version 1.1. ESA. S2 Toolbox Level 2 Product algorithms v1.1. Issued 05 February 2016.
34. Widlowski, J.L., Pinty, B., Gobron, N., Verstraete, M.M., Diner, D.J, Davis, A.B., 2004. Canopy structure parameters derived from multi-angular remote sensing data for terrestrial carbon studies. Climatic Change. 67(2-3), pp403-415.
35. Zeng, X.B., Rao, P., DeFries, R.S., Hansen, M.C., 2003. Interannual variability and decadal trend of global fractional vegetation cover from 1982 to 2000. Journal of Applied Meteorology. 42, pp1525-1530.
36. Zeng, X., Dickinson, R. E., Walker, A., Shaikh, M., DeFries, R. S., Qi. J. 2000. Derivation and evaluation of global 1-km fractional vegetation cover data for land modeling. Journal of Applied Meteorology. 39, pp826-839.
37. Zho, S., L. Zhang., 2016. Estimating Forest Aboveground Biomass by Combining Optical and SAR Data: A Case Study in Genhe, Inner Mongolia, China. Sensors. 16(6), 834.