Determining carbon storage content at different pools under various land uses in Miandoab region using remotely-sensed data and InVEST model
Document Type : Research Paper
Authors
1 Landscape Architecture Department , Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
2 Department of Soil Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.
10.22059/jne.2024.377051.2677
Abstract
Ecological spaces play a crucial role in addressing climate change and global warming with focusing on storing and capturing CO2 from atmosphere. Consequently, quantifying and monitoring carbon storage content is the main step to toward achieving sustainable development goals. The main objective of this study is to develop a precise model for calculating carbon storage content at four pools i.e., aboveground carbon (AGC), belowground carbon (BGC), soil organic carbon (SOC) and dead organic carbon (DOC) under different land uses. For this, a site with an area extent about 243204 ha was selected from Miandoab region (West Azerbaijan Province) by the fact that it represents various land uses. Since application of integrated remotely-sensed data and novel technologies facilitates predictions as well as increases the modeling accuracy, the Google Earth Engine (GEE) platform and the InVEST model were implemented in preparing land use/land cover (LULC) maps and then carbon storage content. The prediction for 2023 showed that the total carbon storage in the aforementioned region would be approximately 11.52 × 106 ton. In addition, the detailed evaluations illustrated that the highest carbon storage content related to SOC and AGC as expected. A possible reason is that living mass is located on the surface. In terms of different land-uses without paying attention to their areas in themselves, the highest carbon storage content was for pasture, followed by tree cover, agriculture, urban area, water bodies and barren. Finally, it is concluded that applying the respective remote sensing data integrating with a suitable technique uncovers the impact of LULC changes on carbon storage with success.
Keywords
Abdi, A.M., 2020. Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GIS Science & Remote Sensing 57, 1-20.
Adelisardou, F., Zhao, W., Chow, R., Mederly, P., Minkina, T., Schou, J.S., 2022. Spatiotemporal change detection of carbon storage and sequestration in an arid ecosystem by integrating Google Earth Engine and InVEST (the Jiroft plain, Iran). International Journal of Environmental Science and Technology 19, 5929-5944.
Alemohammad, S., Nasehi, S., Alemohammad, A.A., Ramezani Mehrian, M., Farhadi, R., 2024. Spatial analysis of urban sprawling with an emphasis on ecological infrastructure integrity (Case study: Miandoab City). Environmental Energy and Economic Research 8, 1-16.
Babbar, D., Areendran, G., Sahana, M., Sarma, K., Raj, K., Sivadas, A., 2021. Assessment and prediction of carbon sequestration using Markov chain and InVEST model in Sariska Tiger Reserve, India. Journal of Cleaner Production 278, e123333.
Buditama, G., Saiya, H.G., Putri, N.P., 2021. Effects of climate change on dryland agriculture vegetation index in Nangapanda, East Nusa Tenggara. In IOP Conference Series: Earth and Environmental Science 716, e012013.
Choubin, B., Rahmati, O., Soleimani, F., Alilou, H., Moradi, E., Alamdari, N., 2019. Regional groundwater potential analysis using classification and regression trees. In Spatial modeling in GIS and R for Earth and Environmental Sciences pp. 485-498.
Congalton, R.G., Green, K., 2019. Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
Deng, S., Shi, Y., Jin, Y., Wang, L., 2011. A GIS-based approach for quantifying and mapping carbon sink and stock values of forest ecosystem: A case study. Energy Procedia 5, 1535-1545.
Eggleston, H.S., Buendia, L., Miwa, K., Ngara, T., Tanabe, K., 2006. IPCC guidelines for national greenhouse gas inventories.
Feizizadeh, B., Mohammadzade Alajujeh, K., Lakes, T., Blaschke, T., Omarzadeh, D., 2021. A comparison of the integrated fuzzy object-based deep learning approach and three machine learning techniques for land use/cover change monitoring and environmental impacts assessment. GIScience & Remote Sensing 58, 1543-1570.
Feizizadeh, B., Omarzadeh, D., Kazemi Garajeh, M., Lakes, T., Blaschke, T., 2023. Machine learning data-driven approaches for land use/cover mapping and trend analysis using Google Earth Engine. Journal of Environmental Planning and Management 66, 665-697.
Floreano, I.X., de Moraes, L.A.F., 2021. Land use/land cover (LULC) analysis (2009–2019) with Google Earth Engine and 2030 prediction using Markov-CA in the Rondônia State, Brazil. Environmental Monitoring and Assessment 193, 239.
Grafius, D.R., Corstanje, R., Warren, P.H., Evans, K.L., Hancock, S., Harris, J.A., 2016. The impact of land use/land cover scale on modelling urban ecosystem services. Landscape Ecology 31, 1509-1522.
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.
Imran, M., Din, N.U., 2021. Geospatially mapping carbon stock for mountainous forest classes using InVEST model and Sentinel-2 data: a case of Bagrote valley in the Karakoram range. Arabian Journal of Geosciences 14, 756.
Islam, R., Islam, M.M., Islam, M.N., Islam, M.N., Sen, S., Faisal, R.K., 2020. Climate change adaptation strategies: a prospect toward crop modelling and food security management. Modeling Earth Systems and Environment 6, 769-777.
Jha, P., De, A., Lakaria, B.L., Biswas, A.K., Singh, M., Reddy, K.S., Rao, A.S., 2012. Soil carbon pools, mineralization and fluxes associated with land use change in Vertisols of Central India. National Academy Science Letters 35, 475-483.
Jiang, W., Deng, Y., Tang, Z., Lei, X., Chen, Z., 2017. Modelling the potential impacts of urban ecosystem changes on carbon storage under different scenarios by linking the CLUE-S and the InVEST models. Ecological Modelling 345, 30-40.
Kafy, A.A., Saha, M., Fattah, M.A., Rahman, M.T., Duti, B.M., Rahaman, Z.A., Bakshi, A., Kalaivani, S., Rahaman, S.N., Sattar, G.S., 2023. Integrating forest cover change and carbon storage dynamics: Leveraging Google Earth Engine and InVEST model to inform conservation in hilly regions. Ecological Indicators 152, e110374.
Kalacska, M., Lucanus, O., Sousa, L., Arroyo-Mora, J.P., 2020. High-resolution surface water classifications of the Xingu River, Brazil, pre and post operationalization of the Belo Monte Hydropower Complex. Data 5, 75.
Koech, G., Makokha, G.O., Mundia, C.N., 2020. Climate change vulnerability assessment using a GIS modelling approach in ASAL ecosystem: a case study of Upper Ewaso Nyiro basin, Kenya. Modeling Earth Systems and Environment 6, 479-498.
Kohestani, N., Rastgar, S., Heydari, G., Jouibary, S.S., Amirnejad, H., 2023. Spatiotemporal modeling of the value of carbon sequestration under changing land use/land cover using InVEST model: a case study of Nour-rud Watershed, Northern Iran. Environment, Development and Sustainability 26, 14477-14505.
Koko, A.F., Yue, W., Abubakar, G.A., Hamed, R., Alabsi, A.A.N., 2020. Monitoring and predicting spatio-temporal land use/land cover changes in Zaria City, Nigeria, through an integrated cellular automata and markov chain model (CA-Markov). Sustainability 12, e10452.
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.
Lal, R., 2013. Soil carbon management and climate change. Carbon Management 4, 439-462.
Lenton, T.M., Xu, C., Abrams, J.F., Ghadiali, A., Loriani, S., Sakschewski, B., Scheffer, M., 2023. Quantifying the human cost of global warming. Nature Sustainability 6, 1237-1247.
Li, G., Cheng, G., Liu, G., Chen, C., He, Y., 2023. Simulating the Land Use and Carbon Storage for Nature-Based Solutions (NbS) under Multi-Scenarios in the Three Gorges Reservoir Area: Integration of Remote Sensing Data and the RF–Markov–CA–InVEST Model. Remote Sensing 15, e5100.
Li, K., Cao, J., Adamowski, J.F., Biswas, A., Zhou, J., Liu, Y., Qin, Y., 2021. Assessing the effects of ecological engineering on spatiotemporal dynamics of carbon storage from 2000 to 2016 in the Loess Plateau area using the InVEST model: A case study in Huining County, China. Environmental Development 39, e100641.
Ma, T., Li, X., Bai, J., Ding, S., Zhou, F., Cui, B., 2019. Four decades' dynamics of coastal blue carbon storage driven by land use/land cover transformation under natural and anthropogenic processes in the Yellow River Delta, China. Science of the Total Environment 655, 741-750.
Patel, A., Vyas, D., Chaudhari, N., Patel, R., Patel, K., Mehta, D., 2024. Novel approach for the LULC change detection using GIS & Google Earth Engine through spatiotemporal analysis to evaluate the urbanization growth of Ahmedabad city. Results in Engineering 21, e101788.
Rachid, L., Elmostafa, A., Mehdi, M., Hassan, R., 2024. Assessing Carbon Storage and Sequestration Benefits of Urban Greening in Nador City, Morocco, Utilizing GIS and the InVEST Model. Sustainable Futures 7, e100171.
Sadat, M., Zoghi, M., Malekmohammadi, B., 2020. Spatiotemporal modeling of urban land cover changes and carbon storage ecosystem services: case study in Qaem Shahr County, Iran. Environment, Development and Sustainability 22, 8135-8158.
Sil, Â., Fonseca, F., Gonçalves, J., Honrado, J., Marta-Pedroso, C., Alonso, J., Ramos, M., Azevedo, J. C., 2017. Analysing carbon sequestration and storage dynamics in a changing mountain landscape in Portugal: insights for management and planning. International Journal of Biodiversity Science, Ecosystem Services & Management 13, 82-104.
Steger, K., Fiener, P., Marvin-DiPasquale, M., Viers, J.H., Smart, D.R., 2019. Human-induced and natural carbon storage in floodplains of the Central Valley of California. Science of the Total Environment 651, 851-858.
Tilahun, A., Teferie, B., 2015. Accuracy assessment of land use land cover classification using Google Earth. American Journal of Environmental Protection 4, 193-198.
Tracker, C.A., 2021. Warming projections global update. Climate Analytics and New Climate Institute: Berlin, Germany.
Von Thaden, J.J., Lithgow, D., Revollo-Fernández, D.A., Salazar-Vargas, M.D.P., Rodríguez de los Santos, A., 2024. The Effects of Implementing Three Climate-Smart Practices with an Integrated Landscape Approach on Functional Connectivity and Carbon Storage. Land 13, 389.
Waikhom, S.I., Yadav, V., Azamathulla, H.M., Solanki, N., 2023. Impact assessment of land use/land cover changes on surface runoff characteristics in the Shetrunji River Basin using the SWAT model. Water Practice & Technology 18, 1221-1232.
Wang, N., Chen, X., Zhang, Z., Pang, J., 2022. Spatiotemporal dynamics and driving factors of county-level carbon storage in the Loess Plateau: A case study in Qingcheng County, China. Ecological Indicators 144, e109460.
Wu, X., Liu, S., Zhao, S., Hou, X., Xu, J., Dong, S., Liu, G., 2019. Quantification and driving force analysis of ecosystem services supply, demand and balance in China. Science of the Total Environment 652, 1375-1386.
Yao, J., Wu, J., Xiao, C., Zhang, Z., Li, J., 2022. The classification method study of crops remote sensing with deep learning, machine learning, and Google Earth engine. Remote Sensing 14, e2758.
Zarandian, A., Badamfirouz, J., Musazadeh, R., Rahmati, A., Azimi, S.B., 2018. Scenario modeling for spatial-temporal change detection of carbon storage and sequestration in a forested landscape in Northern Iran. Environmental Monitoring and Assessment 190, e474.
Zhao, Z., Islam, F., Waseem, L.A., Tariq, A., Nawaz, M., Islam, I.U., Bibi, T., Rehman, N.U., Ahmad, W., Aslam, R.W., Reza, D., Hatamleh, W.A., 2024. Comparison of three machine learning algorithms using google earth engine for land use land cover classification. Rangeland Ecology & Management 92, 129-137.
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, e1864.
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