Estimation Model of Mangrove Carbon Stock Using LDCM Imagery

Firman Farid Muhsoni

doi:10.21107/ijseit.v5i1.9116

Estimation Model of Mangrove Carbon Stock Using LDCM Imagery

Firman Farid Muhsoni

Abstract

Mangroves are one of the forest ecosystems with the capacity to reduce greenhouse effect. However, there is limited data on thecarbon absorbent properties, and, a fast as well as accurate method of estimating the stock in mangrove is needed. The objective of this research, therefore, was to obtain an estimation model of mangrove carbon stocks, using LDCM satellite imagery. Thisdevelopment involved a hybrid method,where information obtainedfrom LDCM satellite imagery were combined with the field data. The result of this studyidentified the best model to estimate carbon stock. This involvedthe combination of total vegetation stock, using the VARI vegetation index (power regression/ geometry) and soil composition, basedon six variables multiple regression.The%RMSE test result was determined to be 9.58%. In addition, field data was not required in modelsinvolving two variables (MSAVI vegetation index and average sediment depth 100.6 cm), and the % RMSE determined was 34.18%.

Keywords

Mangrove carbon stock, Hybrid, LDCM Imagery

Full Text:

PDF

References

Alan, J., Castillo, A., Apan, A. A., Narayan, T., & Iii, S. G. S. (2017). Geoderma Soil C quantities of mangrove forests , their competing land uses , and their spatial distribution in the coast of Honda Bay , Philippines. Geoderma, 293, 82–90. https://doi.org/10.1016/j.geoderma.2017.01.025

Alongi, D., & Mukhopadhyay, S. (2015). Contribution of mangroves to coastal carbon cycling in low latitude seas. Agricultural and Forest Meteorology, 213, 266–272. https://doi.org/10.1016/j.agrformet.2014.10.005

Alparone, L., Aiazzi, B., Baronti, S., & Garzelli, A. (2015). Remote Sensing Image Fusion.

Broge, N. H., & Leblanc, E. (2001). Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76(2), 156–172. https://doi.org/10.1016/S0034-4257(00)00197-8

Cartus, O., Kellndorfer, J., Walker, W., Franco, C., Bishop, J., Santos, L., & Fuentes, J. (2014). A national, detailed map of forest aboveground carbon stocks in Mexico. Remote Sensing, 6(6), 5559–5588. https://doi.org/10.3390/rs6065559

Chen, J. M. (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canadian Journal of Remote Sensing, 22(3), 229–242. https://doi.org/10.1080/07038992.1996.10855178

Chuvieco, E., Li, J., & Yang, X. (2010). Advances in Earth Observation of Global Change. Springer. https://doi.org/DOI 10.1007/978-90-481-9085-0

Clark, M. L., Roberts, D. A., Ewel, J. J., & Clark, D. B. (2011). Estimation of tropical rain forest aboveground biomass with small-footprint lidar and hyperspectral sensors. Remote Sensing of Environment, 115(11), 2931–2942. https://doi.org/10.1016/j.rse.2010.08.029

Feller, I. C., Lovelock, C. E., Berger, U., McKee, K. L., Joye, S. B., & Ball, M. C. (2010). Biocomplexity in mangrove ecosystems. Annual Review of Marine Science, 2, 395–417. https://doi.org/10.1146/annurev.marine.010908.163809

Frananda, H., Hartono, & Jatmiko, R. H. (2015). Komparasi Indeks Vegetasi untuk Estimasi Stok Karbon Hutan Mangrove Kawasan Segoro Anak pada Kawasan Taman Nasional Alas Purwo Banyuwangi , Jawa Timur. Majalah Ilmiah Globë, 17(2), 113–123.

Gao, B. C. (1996). NDWI - A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves. International Journal of Remote Sensing, 18(12), 37–41. https://doi.org/10.1080/014311697217558

Gitelson, A., Kaufman, Y., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9

Gumbricht, T. (2015). Hybrid Mapping of Pantropical Wetlands from Optical Satellite Images, Hydrology, and Geomorphology. In R. W. Tiner, M. W. Lang, & V. V. Klemas (Eds.), Remote sensing of wetlands (pp. 435–454). CRC Press is an imprint of Taylor & Francis Group, an Informa business. http://www.taylorandfrancis.com

Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352. https://doi.org/10.1016/j.rse.2003.12.013

Hamdan, O., Khairunnisa, M., Ammar, A., Mohd Hasmadi, I., & Khali Aziz, H. (2013). Mangrove Carbon Stock Assessment By Optical Satellite Imagery. Journal of Tropical Forest Science, 25(4), 554–565.

Hooijer, A., Page, S., Canadell, J. G., Silvius, M., Kwadijk, J., Wösten, H., & Jauhiainen, J. (2010). Current and future CO2 emissions from drained peatlands in Southeast Asia. Biogeosciences, 7(5), 1505–1514. https://doi.org/10.5194/bg-7-1505-2010

Hu, T., Su, Y., Xue, B., Liu, J., Zhao, X., Fang, J., & Guo, Q. (2016). Mapping Global Forest Aboveground Biomass with Spaceborne LiDAR, Optical Imagery, and Forest Inventory Data. Remote Sensing, 8(7), 565. https://doi.org/10.3390/rs8070565

Kamal, M. (2015). Remote Sensing for Multi-scale Mangrove Mapping. In School of Geography. https://doi.org/10.14264/uql.2015.847

Kauffman, J., & Donato, D. (2012). Protocols for the measurement, monitoring and reporting of structure, biomass and carbon stocks in mangrove forests. In Center for International Forestry: Vol. Working pa. https://doi.org/10.17528/cifor/003749

Klinkhamer, P. G. L. (1995). Plant allometry: The scaling of form and process. Trends in Ecology & Evolution, 10(3), 134. https://doi.org/10.1016/S0169-5347(00)89015-1

Köhl, M., Magnussen, S., & Marchetti, M. (2006). Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. In Springer-Verlag Berlin Heidelberg New York (Vol. 3, Issues 2–3). https://doi.org/10.1300/J091v03n02_06

Komiyama, A., Ong, J. E., & Poungparn, S. (2008). Allometry, biomass, and productivity of mangrove forests: A review. Aquatic Botany, 89(2), 128–137. https://doi.org/10.1016/j.aquabot.2007.12.006

Kulawardhana, R. W., Popescu, S. C., & Feagin, R. A. (2014). Fusion of lidar and multispectral data to quantify salt marsh carbon stocks. Remote Sensing of Environment, 154, 345–357. https://doi.org/10.1016/j.rse.2013.10.036

Muhsoni, F. F., Sambah, A. B., Mahmudi, M., & Wiadnya, D. G. P. (2018). Estimation of mangrove carbon stock with hybrid method using image Sentinel-2. International Journal of GEOMATE, 15(49). https://doi.org/10.21660/2018.49.52661

Muhsoni, F. F., Sambah, A. B., Mahmudi, M., & Wiadnya, D. G. R. (2020). Comparative study of carbon stock in estuarine and oceanic mangroves. Malayan Nature Journal, 72(9), 189–199. https://www.mnj.my/

Ng, W.-T., Rima, P., Einzmann, K., Immitzer, M., Atzberger, C., & Eckert, S. (2017). Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya. Remote Sensing, 9(1), 74. https://doi.org/10.3390/rs9010074

Noor, Y. R., Khazali, M., & Suryadiputra, I. N. N. (1999). Panduan Pengenalan Mangrove di Indonesia. Wetlands International – Indonesia Programme.

Page, S. E., Rieley, J. O., & Banks, C. J. (2011). Global and regional importance of the tropical peatland carbon pool. Global Change Biology, 17(2), 798–818. https://doi.org/10.1111/j.1365-2486.2010.02279.x

Pettorelli, N., Ryan, S., Mueller, T., Bunnefeld, N., Jedrzejewska, B., Lima, M., & Kausrud, K. (2011). The Normalized Difference Vegetation Index (NDVI): Unforeseen successes in animal ecology. Climate Research, 46(1), 15–27. https://doi.org/10.3354/cr00936

Polidoro, B. A., Carpenter, K. E., Collins, L., Duke, N. C., Ellison, A. M., Ellison, J. C., Farnsworth, E. J., Fernando, E. S., Kathiresan, K., Koedam, N. E., Livingstone, S. R., Miyagi, T., Moore, G. E., Nam, V. N., Ong, J. E., Primavera, J. H., Salmo, S. G., Sanciangco, J. C., Sukardjo, S., … Yong, J. W. H. (2010). The loss of species: Mangrove extinction risk and geographic areas of global concern. Plos One, 5(4), 1–10. https://doi.org/10.1371/journal.pone.0010095

Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment, 48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1

Roujean, J. L., & Breon, F. M. (1995). Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51(3), 375–384. https://doi.org/10.1016/0034-4257(94)00114-3

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. Progress Report RSC 1978-1, 112. https://doi.org/19740008955

Sripada, R. P., Heiniger, R. W., White, J. G., & Weisz, R. (2005). Aerial Color Infrared Photography for Determining Late-Season Nitrogen Requirements in Corn. Agronomy Journal, 97, 1443–1451. https://doi.org/10.2134/agronj2004.0314

Thenkabail, P. S. (2016). Remotely Sensed Data Characterization, Classification, and Accuracies. Taylor &Francis Group, LLC. http://www.taylorandfrancis.com

Vicharnakorn, P., Shrestha, R. P., Nagai, M., Salam, A. P., & Kiratiprayoon, S. (2014). Carbon stock assessment using remote sensing and forest inventory data in Savannakhet, Lao PDR. Remote Sensing, 6(6), 5452–5479. https://doi.org/10.3390/rs6065452

Waser, L. T., Küchler, M., Jütte, K., & Stampfer, T. (2014). Evaluating the potential of worldview-2 data to classify tree species and different levels of ash mortality. Remote Sensing, 6(5), 4515–4545. https://doi.org/10.3390/rs6054515

Weng, Q. (2010). Remote Sensing and GIS Integration Theories, Methods, and Applications. Mc Graw Hill. https://doi.org/10.1191/0309133304pp413pr

Wibowo, A. (2010). Measurable, Reportable dan Verifyable (MRV) Untuk Emisi Gas Rumah Kaca dari Kegiatan Kehutanan. In N. Masripatin & C. Wulandari (Eds.), REDD and Forest Governance (pp. 3–22). Pusat Penelitian Sosial Ekonomi dan Kebijakan Kehutanan.