Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed

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DOI:

10.29303/jppipa.v10i11.6818

Published:

2024-11-25

Issue:

Vol. 10 No. 11 (2024): November

Keywords:

Carbon stock, Estimation, Geospatial, Land use, Watershed

Research Articles

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Purwanto, A., & Sulha. (2024). Carbon Stocks Estimation Using the Stock Difference Method of Various Land Use Systems Based on Geospatial in Kualan Watershed. Jurnal Penelitian Pendidikan IPA, 10(11), 8602–8611. https://doi.org/10.29303/jppipa.v10i11.6818

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Abstract

Indonesia controls 75%-80% of the world's carbon stocks, so the amount of carbon stocks must be utilized optimally. This study aims to determine carbon stocks, potential emissions, and economic value of carbon stocks in each land use. The method used is secondary data analysis and field checking. The data collected were Sentinel 2A acquisitions in 2020 and 2022, Digital Elevation Model (DEM), and land use land cover in 2020-2023. Data analysis used SNAP and ArcGIS 10.8. The tool used for data analysis is spatial analysis map algebra. The results showed mixed dryland agriculture has the most extensive carbon stock, at 2,614,178 tons/ha, with potential emissions of 9,585,320 tons/ha. The most minor carbon stock is in mining land use, which is 0 tons/ha with potential emissions of 0 tons/ha. The highest C02 value in USD is the forest land use group. In the Secondary Dryland Forest, Secondary Swamp Forest, and Plantation Forest groups, it is 17,517,400.50 USD, while the lowest is mining land use, which is 0 USD. Overall, the CO2 value of land use in the study area is 34,246,314.45 USD. Integrating remote sensing data analysis and field surveys in geospatial technology is one of the new approaches to studying carbon stocks and CO2 emissions in topsoil from various land uses. By utilizing geospatial technology, efforts to estimate carbon stocks on the surface are easier and faster.

References

Agricul, E. C. I. N., & Series, T. (2012). Peatlands–guidance for climate change mitigation by conservation, rehabilitation, and sustainable use. Retrieved from https://www.uncclearn.org/wp-content/uploads/library/fao152.pdf

Ahmad, N., Ullah, S., Zhao, N., Mumtaz, F., Ali, A., Ali, A., Tariq, A., Kareem, M., Imran, A. B., & Khan, I. A. (2023). Comparative analysis of remote sensing and geo-statistical techniques to quantify forest biomass. Forests, 14(2), 379. https://doi.org/10.3390/f14020379.

Arredondo-Trapero, F. G., Guerra Leal, E. M., & Kim, J. (2023). Effectiveness of the voluntary disclosure of corporate information and its commitment to climate change. Global Journal of Environmental Science and Management, 9(4), 1033–1048. https://doi.org/10.22034/gjesm.2023.04.25

Batsaikhan, B., Lkhamjav, O., Batsaikhan, G., Batsaikhan, N., & Norovsuren, B. (2020). Carbon stock estimation using remote sensing data and field measurement in haloxylon ammodendron dominant winter cold desert region of Mongolia. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 3, 9–17. https://doi.org/10.5194/isprs-annals-V-3-2020-9-2020.

Birhane, E., Ahmed, S., Hailemariam, M., Negash, M., Rannestad, M. M., & Norgrove, L. (2020). Carbon stock and woody species diversity in home garden agroforestry along an elevation gradient in southern Ethiopia. Agroforestry Systems, 94, 1099–1110. https://doi.org/10.1007/s10457-019-00475-4

Cahyono, W. E., Joy, B., Setyawati, W., & Mahdi, R. (2022). Projection of CO2 emissions in Indonesia. Materials Today: Proceedings, 63, S438–S444. https://doi.org/10.1016/j.matpr.2022.04.091.

Carless, D., Kulessa, B., Booth, A. D., Drocourt, Y., Sinnadurai, P., Street-Perrott, F. A., & Jansson, P. (2021). An integrated geophysical and GIS-based approach improves the estimation of peatland carbon stocks. Geoderma, 402, 115176. https://doi.org/10.1016/j.geoderma.2021.115176.

Dahy, B., Issa, S., Ksiksi, T., & Saleous, N. (2020). Geospatial technology methods for carbon stock assessment: A comprehensive review. IOP Conference Series: Earth and Environmental Science, 540(1), 12036. https://doi.org/10.1088/1755-1315/540/1/012036.

Darawan, A. A., Ariyanto, D. W. I. P., Basuki, T. M., Syamsiyah, J., & Dewi, W. S. I. H. (2022). Biomass accumulation and carbon sequestration potential in varying tree species, ages, and densities in Gunung Bromo Education Forest, Central Java, Indonesia. Biodiversitas Journal of Biological Diversity, 23(10). https://doi.org/10.13057/biodiv/d231016

Dewantoro, B. E. B., & Jatmiko, R. H. (2021). Estimation of aboveground carbon stock using SAR Sentinel-1 imagery in Samarinda city. International Journal of Remote Sensing and Earth Sciences, 18(1), 103–116. https://doi.org/10.30536/j.ijreses.2021.v18.a3609

Forestry Economics and Policy Division. (2010). Global forest resources assessment 2010—main report. Food and Agriculture Organization of the United Nations, Rome.

Frimawaty, E., Ilmika, A., Sakina, N. A., & Mustabi, J. (2023). Climate change mitigation and adaptation through livestock waste management. Global Journal of Environmental Science and Management, 9(4), 691–706. Retrieved from https://www.gjesm.net/gjesm.net/article_703123.html

Gallego-Sala, A. V, Charman, D. J., Brewer, S., Page, S. E., Prentice, I. C., Friedlingstein, P., Moreton, S., Amesbury, M. J., Beilman, D. W., & Björck, S. (2018). Latitudinal limits to the predicted increase of the peatland carbon sink with warming. Nature Climate Change, 8(10), 907–913. https://doi.org/10.1038/s41558-018-0271-1.

Goswami, S., Gamon, J., Vargas, S., & Tweedie, C. (2015). Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska. PeerJ PrePrints. https://doi.org/10.7287/peerj.preprints.913v1.

Hassan, W. H., & Nile, B. K. (2021). Climate change and predicting future temperature in Iraq using CanESM2 and HadCM3 modeling. Modeling Earth Systems and Environment, 7, 737–748. https://doi.org/10.1007/s40808-020-01034-y.

Hiraishi, T., Krug, T., Tanabe, K., Srivastava, N., Baasansuren, J., Fukuda, M., & Troxler, T. G. (2014). 2013 Supplement to the 2006 IPCC guidelines for national greenhouse gas inventories: Wetlands. IPCC, Switzerland. Retrieved from https://shorturl.at/lMCCL

Hussein, A. (2022). Carbon Stock Potential across Different Land Covers in Tropical Ecosystems of Damota Natural Vegetation, Eastern Ethiopia. Applied and Environmental Soil Science, 2022. https://doi.org/10.1155/2022/8414027

IPCC. (2014). AR5 synthesis report: climate change 2014: Intergovernmental Panel for Climate Change.

Issa, S., Dahy, B., Ksiksi, T., & Saleous, N. (2020). A review of terrestrial carbon assessment methods using geo-spatial technologies with emphasis on arid lands. Remote Sensing, 12(12), 2008. https://doi.org/10.3390/rs12122008.

IUCN. (2017). Peatlands and Climate Change. IUCN.

Javaherian, M., Ebrahimi, H., & Aminnejad, B. (2021). Prediction of changes in climatic parameters using CanESM2 model based on Rcp scenarios (case study): Lar dam basin. Ain Shams Engineering Journal, 12(1), 445–454. https://doi.org/10.1016/j.asej.2020.04.012.

Jiang, H., Wu, W., Wang, J., Yang, W., Gao, Y., Duan, Y., Ma, G., Wu, C., & Shao, J. (2021). Mapping global value of terrestrial ecosystem services by countries. Ecosystem Services, 52, 101361. https://doi.org/10.1016/j.ecoser.2021.101361.

Joosten, H., Tapio-Biström, M.-L., & Tol, S. (2012). Peatlands: guidance for climate change mitigation through conservation, rehabilitation and sustainable use. Food and Agriculture Organization of the United Nations Rome.

Katkani, D., Babbar, A., Mishra, V. K., Trivedi, A., Tiwari, S., & Kumawat, R. K. (2022). A review on applications and utility of remote sensing and geographic information systems in agriculture and natural resource management. International Journal of Environment and Climate Change, 12(4), 1–18. https://doi.org/10.9734/ijecc/2022/v12i430651

KLHK. (2018). The state of Indonesia’s forests 2018. Ministry of Environment and Forestry Republic of Indonesia (KLHK), Jakarta.

Kusumaningtyas, M. A., Kepel, T. L., Solihuddin, T., Lubis, A. A., Putra, A. D. P., Sugiharto, U., Ati, R. N. A., Salim, H. L., Mustikasari, E., & Heriati, A. (2022). Carbon sequestration potential in the rehabilitated mangroves in Indonesia. Ecological Research, 37(1), 80–91. https://doi.org/10.1111/1440-1703.12279.

Lahiji, R. N., Dinan, N. M., Liaghati, H., Ghaffarzadeh, H., & Vafaeinejad, A. (2020). Scenario-based estimation of catchment carbon storage: Linking multi-objective land allocation with InVEST model in a mixed agriculture-forest landscape. Frontiers of Earth Science, 14, 637–646. https://doi.org/10.1007/s11707-020-0825-1.

Latifah, S., Muhdi, M., Purwoko, A., & Tanjung, E. (2018). Estimation of aboveground tree biomass Toona sureni and Coffea arabica in agroforestry system of Simalungun, North Sumatra, Indonesia. Biodiversitas Journal of Biological Diversity, 19(2), 620–625. https://doi.org/10.13057/biodiv/d190239

Liu, J., Sleeter, B. M., Zhu, Z., Heath, L. S., Tan, Z., Wilson, T. S., Sherba, J., & Zhou, D. (2016). Estimating carbon sequestration in the Piedmont ecoregion of the United States from 1971 to 2010. Carbon Balance and Management, 11, 1–13. https://doi.org/10.1186/s13021-016-0052-y.

Malik, A. D., Arief, M. C. W., Withaningsih, S., & Parikesit, P. (2023). Modeling regional aboveground carbon stock dynamics affected by land use and land cover changes. Global Journal of Environmental Science and Management, 10(1), 245-266. Retrieved from https://www.gjesm.net/gjesm.net/article_704982.html

Massetti, A., & Gil, A. (2020). Mapping and assessing land cover/land use and aboveground carbon stocks rapid changes in small oceanic islands’ terrestrial ecosystems: A case study of Madeira Island, Portugal (2009–2011). Remote Sensing of Environment, 239, 111625. https://doi.org/10.1016/j.rse.2019.111625.

Nguyen, H. K. L., & Nguyen, B. N. (2016). Mapping biomass and carbon stock of forest by remote sensing and GIS technology at Bach Ma National Park, Thua Thien Hue province. Journal of Vietnamese Environment, 8(2), 80–87. https://doi.org/10.13141/jve.vol8.no2.pp80-87.

Paustian, K., Lehmann, J., Ogle, S., Reay, D., Robertson, G. P., & Smith, P. (2016). Climate-smart soils. Nature, 532(7597), 49–57. https://doi.org/10.1038/nature17174

Pratiwi, Y., Rejo, A., Fariani, A., & Faizal, M. (2022). Modeling for Estimation of Carbon Stocks in Land Cover Using A System Dynamic Approach (Case Study: Prabumulih City, South Sumatera, Indonesia). Jurnal Manajemen Hutan Tropika, 28(3), 221. https://doi.org/10.7226/jtfm.28.3.221.

Samuel, M. (2020). Estimating Above Ground Carbon Stock of Acacia Decurrens In Fetam Watershed, Banja Woreda, Anhara Region, Ethiopia Based On Field Measurement, Remote Sensing And GIS Techniques. Retrieved from http://ir.bdu.edu.et/handle/123456789/11194

Situmorang, J. P., & Sugianto, S. (2016). Estimation of carbon stock stands using EVI and NDVI vegetation index in production forest of Lembah Seulawah sub-district, Aceh Indonesia. Aceh International Journal of Science and Technology, 5(3), 126–139. https://doi.org/10.13170/aijst.5.3.5836.

Suardana, A. A. M. A. P., Anggraini, N., Nandika, M. R., Aziz, K., As-syakur, A. R., Ulfa, A., Wijaya, A. D., Prasetio, W., Winarso, G., & Dewanti, R. (2023). Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia. Forest and Society, 7(1), 116–134. https://doi.org/10.24259/fs.v7i1.22062.

Trivedi, A., Rao, K. V. R., Rajwade, Y., Yadav, D., & Verma, N. S. (2022). Remote sensing and geographic information system applications for precision farming and natural resource management. Indian Journal of Ecology, 49(5), 1624–1633. http://dx.doi.org/10.55362/IJE/2022/3707

Yu, Z., Beilman, D. W., Frolking, S., MacDonald, G. M., Roulet, N. T., Camill, P., & Charman, D. J. (2011). Peatlands and their role in the global carbon cycle. Eos, Transactions American Geophysical Union, 92(12), 97–98. https://doi.org/10.1029/2011EO120001.

Zhao, Z., Liu, G., Mou, N., Xie, Y., Xu, Z., & Li, Y. (2018). Assessment of carbon storage and its influencing factors in Qinghai-Tibet Plateau. Sustainability, 10(6), 1864. https://doi.org/10.3390/su10061864.

Author Biographies

Ajun Purwanto, IKIP PGRI Pontianak

Sulha, IKIP PGRI Pontianak

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