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Estimating biomass and carbon storage of mangrove forests using UAV-image-derived variables
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Mojdeh Miraki   , Hormoz Sohrabi * , Markus Immitzer |
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Abstract: (595 Views) |
Mangrove forests are known as important sea carbon ecosystems because they play an important role in carbon sequestration among coastal ecosystems. This coastal ecosystem has 10 to 50 times more carbon sequestration capacity compared to terrestrial ecosystems, and among the most productive systems, they can effectively reduce climate change. Therefore, an accurate estimation of the biomass of mangrove forests is a necessity. Meanwhile, the evaluation of the terrestrial carbon storage in mangrove forests relies on the accurate measurement of tree biomass, which is traditionally time-consuming and expensive. In this study, height and crown diameter was estimated by using UAV equipped with an RGB sensor; following sampling and measuring soil carbon in three forest sites of Sirik, Qeshm, and Khamir, the carbon storage in trees and soil was investigated. Orthophoto mosaic and dense point cloud were created based on structure from motion algorithm. Crown diameters were extracted from orthophotos. The canopy height model was extracted by subtracting the digital surface model and digital terrain model which were derived from point cloud. Tree heights were extracted from the canopy height model following imaging in November 2021. Considering that there was no significant difference between the measured variables on the ground and the extracted variables from the UAV images, the data obtained from the UAV images and allometric equations were used to estimate the aboveground carbon storage. After estimating the biomass according to the two variables of crown diameter and tree height, the carbon storage on land obtained from the information extracted from UAV images in the three sites of Sirik, Khamir, and Qeshm was obtained at 11.63, 7.97, and 9.87 t/ha respectively. The soil carbon was also measured at two depths of 0 to 15 cm and 15 to 30 cm using the Walkley-Black method, and the values were shown as 67.98, 81.9, 85 t/ha, and 187.2, 133.53, and 113.7 for Sirik, Khamir, and Qeshm sites. This research shows that UAV data has a high ability to estimate the variables related to individual trees in forest areas with difficult traffic conditions, and subsequently to estimate the height and crown diameter variables, estimate the forest stock and carbon storage based on the mentioned variables. It can be achieved in relatively homogeneous mangrove forests. Especially because these ecosystems are environments that are often inaccessible or difficult to work in.
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Article number: 1 |
| Keywords: Mangrove forest, Carbon storage, UAV, Orthomosaic, Canopy height model |
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Full-Text [PDF 1012 kb]
(228 Downloads)
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Type of Study: Research |
Subject:
Photo&RS
Received: 2023/06/10
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1. Li, Z., Zan, Q., Yang, Q., Zhu, D., Chen, Y., and Yu, S. (2019). Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System. Remote Sensing J. 11:9.1018. [ DOI:10.3390/rs11091018] 2. Sanderman, J., Hengl, T., Fiske, G., Solvik, K., Adame, M. F., Benson, L., Bukoski, J. J., Carnell, P., Cifuentes-Jara, M., Donato, D., Duncan, C., Eid, E. M., Ermgassen, P. Z., Lewis, C. J. E., Macreadie, P. I., Glass, L., Gress, S., Jardine, S. L., Jones, T. G., and Landis, E. (2018). A Global Map of Mangrove Forest Soil Carbon at 30 m Spatial Resolution. Environmental Research Letters J. 13:5.055002. [ DOI:10.1088/1748-9326/aabe1c] 3. Näsi, R., Honkavaara, E., Päivi, L.-S., Blomqvist, M., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpää, T., and Holopainen, M. (2015). Using UAV-Based Photogrammetry and Hyperspectral Imaging for Mapping Bark Beetle Damage at Tree-Level. Remote Sensing J. 7:11.15467-15493. [ DOI:10.3390/rs71115467] 4. Amini, J., and Sadeghi, Y. (2012). Optical and Radar Satellite Images in Forests Biomass Modeling in Northern Iran. Remote Sensing & GIS J. 4:4.70-82. (In Persian) 5. Zhu, Y., Liu, K., Myint, S. W., Du, Z., Li, Y., Cao, J., Liu, L., and Wu, Z. (2020). Integration of GF2 Optical, GF3 SAR, and UAV Data for Estimating Aboveground Biomass of China's Largest Artificially Planted Mangroves. Remote Sensing J. 12:12.2039. [ DOI:10.3390/rs12122039] 6. Jones, A. R., Raja Segaran, R., Clarke, K. D., Waycott, M., Goh, W. S. H., and Gillanders, B. M. (2020). Estimating Mangrove Tree Biomass and Carbon Content: A Comparison of Forest Inventory Techniques and Drone Imagery. Frontiers in Marine Science J. 6:784. [ DOI:10.3389/fmars.2019.00784] 7. Giri, C. (2016). Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges. Remote Sensing J. 8:9.783. [ DOI:10.3390/rs8090783] 8. Ibharim, N., Mustapha, M., Lihan, T., and Mazlan, A. (2015). Mapping Mangrove Changes in the Matang Mangrove Forest Using Multi Temporal Satellite Imageries. Ocean & Coastal Management J. 114:64-76. [ DOI:10.1016/j.ocecoaman.2015.06.005] 9. Fatoyinbo, T., Feliciano, E., Lagomasino, D., Lee, S., and Trettin, C. (2018). Estimating Mangrove Aboveground Biomass from Airborne LiDAR Data: A Case Study from the Zambezi River Delta. Environmental Research Letters J. 13:2. 025012. [ DOI:10.1088/1748-9326/aa9f03] 10. Motlagh, M. G., Kafaky, S. B., Mataji, A., and Akhavan, R. (2020). Estimation of Forest Above Ground Biomass in Hyrcanian Forests Using Satellite Imagery. J of Env. Sci. Tech. 22:5.1-13. 11. Hirata, Y., Tabuchi, R., Patanaponpaiboon, P., Poungparn, S., Yoneda, R., and Fujioka, Y. (2013). Estimation of Aboveground Biomass in Mangrove Forests Using High-Resolution Satellite Data. J of forest research. 19:1.34-41. [ DOI:10.1007/s10310-013-0402-5] 12. Jachowski, N., Michelle, R., Quak, S., Friess, D., Duangnamon, D., Webb, E., and Ziegler, A. (2013). Mangrove Biomass Estimation in Southwest Thailand Using Machine Learning. Applied Geography J. 45: 11-21. [ DOI:10.1016/j.apgeog.2013.09.024] 13. Pereira, F. S., Kampel, M., Soares, M. L. G., Estrada, G. C. D., Bentz, C., and Vincent, G. (2018). Reducing Uncertainty in Mapping of Mangrove Aboveground Biomass Using Airborne Discrete Return Lidar Data. Remote Sensing J. 10:4.637. [ DOI:10.3390/rs10040637] 14. Hamdan, O., Khairunnisa, M., Ammar, A., Hasmadi, I., and Aziz, H. (2013). Mangrove carbon stock assessment by optical satellite imagery on jstor. Tropical Forest Science J. 54-65. 15. Díaz-Varela, R., de la Rosa, R., León, L., and Zarco-Tejada, P. (2015). High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials. Remote Sensing J. 7:4. 4213-4232. [ DOI:10.3390/rs70404213] 16. Birdal, A., Avdan, U., and Türk, T. (2017). Estimating Tree Heights with Images from an Unmanned Aerial Vehicle. Geomatics, Natural Hazards and Risk J. 8:2.1144-1156. [ DOI:10.1080/19475705.2017.1300608] 17. Mohan, M., Silva, C. A., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A. T., and Dia, M. (2017). Individual Tree Detection from Unmanned Aerial Vehicle ( UAV ) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest. Forests J. 8:9.1-17. [ DOI:10.3390/f8090340] 18. Navarro, A., Young, M., Allan, B., Carnell, P., Macreadie, P., and Ierodiaconou, D. (2020). The Application of Unmanned Aerial Vehicles (UAVs) to Estimate above-Ground Biomass of Mangrove Ecosystems. Remote Sensing of Environment J. 242:111747. [ DOI:10.1016/j.rse.2020.111747] 19. Iglhaut, J., Cabo, C., Puliti, S., Piermattei, L., Connor, J., and Rosette, J. (2019). Structure from Motion Photogrammetry in Forestry: A Review. Current Forestry Reports J. 5:3. 55-68. [ DOI:10.1007/s40725-019-00094-3] 20. Kabiri, K., Rezai, H., and Moradi, M. (2020). A Drone-Based Method for Mapping the Coral Reefs in the Shallow Coastal Waters - Case Study: Kish Island, Persian Gulf. Earth Science Informatics J. 13:4. 65-74. [ DOI:10.1007/s12145-020-00507-z] 21. Panagiotidis, D., Abdollahnejad, A., Surový, P., and Chiteculo, V. (2016). Determining Tree Height and Crown Diameter from High-Resolution UAV Imagery. International J of Remote Sensing. 38:8.2392-2410. [ DOI:10.1080/01431161.2016.1264028] 22. Tanhuanpaa, T., Saarinen, N., Kankare, V., Nurminen, K., Vastaranta, M., Honkavaara, E., Karjalainen, M., Yu, X., Holopainen, M., Hyyppä, J., Tanhuanpää, T., Saarinen, N., Kankare, V., Nurminen, K., Vastaranta, M., Honkavaara, E., Karjalainen, M., Yu, X., Holopainen, M., and Hyyppä, J. (2016). Evaluating the Performance of High-Altitude Aerial Image-Based Digital Surface Models in Detecting Individual Tree Crowns in Mature Boreal Forests. Forests J. 7:12.143. [ DOI:10.3390/f7070143] 23. Goldbergs, G., Shaun Levick, S., Edwards, A., Goldbergs, G., Maier, S., Levick, S., and Edwards, A. (2018). Efficiency of Individual Tree Detection Approaches Based on Light-Weight and Low-Cost UAS Imagery in Australian Savannas. Remote Sensing J. 10:2.161. [ DOI:10.3390/rs10020161] 24. Fankhauser, K., Strigul, N., and Gatziolis, D. (2018). Augmentation of Traditional Forest Inventory and Airborne Laser Scanning with Unmanned Aerial Systems and Photogrammetry for Forest Monitoring. Remote Sensing J. 10:10.1-17. [ DOI:10.3390/rs10101562] 25. Daryaei, A., Sohrabi, H., Atzberger, C., and Immitzer, M. (2020). Fine-Scale Detection of Vegetation in Semi-Arid Mountainous Areas with Focus on Riparian Landscapes Using Sentinel-2 and UAV Data. Computers and Electronics in Agriculture J. 177:105686. [ DOI:10.1016/j.compag.2020.105686] 26. Miraki, M., Sohrabi, H., Fatehi, P., and Kneubuehler, M. (2021). Individual Tree Crown Delineation from High-Resolution UAV Images in Broadleaf Forest. Ecological Informatics J. 61:101207. [ DOI:10.1016/j.ecoinf.2020.101207] 27. Peña, J., I de Castro, A., Torres-Sanchez, J., Andújar, D., San Martín, C., Dorado, J., Fernández-Quintanilla, C., and López-Granados, F. (2018). Estimating Tree Height and Biomass of a Poplar Plantation with Image-Based UAV Technology. AIMS Agriculture and Food J. 3:3. 13-23. [ DOI:10.3934/agrfood.2018.3.313] 28. Otero, V., Van De Kerchove, R., Satyanarayana, B., Martínez-Espinosa, C., Fisol, M. A. Bin, Ibrahim, M. R. Bin, Sulong, I., Mohd-Lokman, H., Lucas, R., and Dahdouh-Guebas, F. (2018). Managing Mangrove Forests from the Sky: Forest Inventory Using Field Data and Unmanned Aerial Vehicle (UAV) Imagery in the Matang Mangrove Forest Reserve, Peninsular Malaysia. Forest Ecology and Management J. 411:35-45. [ DOI:10.1016/j.foreco.2017.12.049] 29. Guerra-Hernandez, C., Silva, C., Soares, B., Gonzalez-Ferreiro, S., and Varela, D. (2019). Predicting Growing Stock Volume of Eucalyptus Plantations Using 3-D Point Clouds Derived from UAV Imagery and ALS Data. Forests J. 10:10. 905. [ DOI:10.3390/f10100905] 30. Lu, J., Wang, H., Qin, S., Cao, L., Pu, R., Li, G., and Sun, J. (2020). Estimation of Aboveground Biomass of Robinia Pseudoacacia Forest in the Yellow River Delta Based on UAV and Backpack LiDAR Point Clouds. International J of Applied Earth Observation and Geoinformation. 86:102014. [ DOI:10.1016/j.jag.2019.102014] 31. Mahmoudi, M., Pourebrahim, S., Danehkar, A., and Etemadi. H. (2021). Determination of the Biomass Allometry Equation and Carbon Calculation of Avicennia Marina Shrub in the Nayband Bay. J of Aqua. Eco 1:11.26-35. (In Persian) 32. Shaltout, K. H., Ahmed, M. T., Alrumman, S. A., Ahmed, D. A., and Eid, E. M. (2021). Standing Crop Biomass and Carbon Content of Mangrove Avicennia Marina (Forssk.) Vierh. along the Red Sea Coast of Saudi Arabia. Sustainability J. 13:24.13996. [ DOI:10.3390/su132413996] 33. Walkley, A., and Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science J. 37:1. 29-38. [ DOI:10.1097/00010694-193401000-00003] 34. Abib, S., and Chandani, A. (2012). A Pilot Study for the Estimation of above Ground Biomass and Litter Production in Rhizophora Mucronata Dominated Mangrove Ecosystems in the Island of Mauritius. Coastal Develpopment J. 16:1.40-49. 35. Owers, C. J., Rogers, K., and Woodroffe, C. D. (2018). Spatial Variation of Above-Ground Carbon Storage in Temperate Coastal Wetlands. Estuarine, Coastal and Shelf Science J. 210:55-67. [ DOI:10.1016/j.ecss.2018.06.002] 36. Fu, W., and Wu, Y. (2011). Estimation of Aboveground Biomass of Different Mangrove Trees Based on Canopy Diameter and Tree Height. Procedia Environmental Sciences J. 10: 89-94. [ DOI:10.1016/j.proenv.2011.09.343] 37. Askari, M., Homaei, A., Kamrani, E., Zeinali, F., and Andreetta. A. (2021). Estimation of Carbon Pools in the Biomass and Soil of Mangrove Forests in Sirik Azini Creek, Hormozgan Province (Iran). Environmental Science and Pollution Research J. 29:16 . 23712-23720. [ DOI:10.1007/s11356-021-17512-4] |
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