Economic Value and Carbon Emission Reduction Scenarios from Kon Ha Nung Forest Change, Gia Lai Province 2022-2030
Main Article Content
Abstract
This study presents a method for above-ground biomass estimation and a machine learning method to assess changes in forest status for the period 2000 - 2022. From there, the economic value obtained from conserving and developing natural forests will be determined according to scenarios for reducing deforestation and forest degradation up to 2030. The study area was selected in Kon Ha Nung plateau, Gia Lai province, where the forest area is large, the ecosystem is relatively intact, and has high biodiversity. The study uses standard plots measured in 2000, 2010, and 2022 and a linear regression equation to estimate biomass from SPOT-4 images in the form of Lin-Log with R2 = 0,72 and the root-mean-square error RMSE reached 22,87 Mg/ha while the equation applied to Sentinel-2 images in 2022 was Log-Lin with R2 = 0,759 and RMSE reached 19,50 Mg/ha. The research results showed that the forest status of the study area from 2000 to 2022 had large fluctuations within 20 years from 2000 to 2022: The total area of deforestation reached 56.159,9 ha, the area of forest degradation was 17.206,4 ha; of which the area of natural forest increased by 5.603,3 ha, the area of forest quality improvement reached 19.207,3 ha. The total area of deforestation and forest degradation in the Kon Ha Nung plateau was larger than the total area of natural forest increased and forest quality improved. The study estimates the economic value gained from participating in the carbon market with three CO2 emission reduction scenarios for the period 2022 - 2030 through forest change analysis for the two periods 2000 - 2010 and 2010 - 2022. The research results provide an overview of income sources from ecosystem services in the Kon Ha Nung plateau from forest protection and development activities.
Keywords: CO2 emissions, deforestation, forest degradation, REDD+, biomass.
References
[2] Y. Pan, R. A. Birdsey, O. L. Phillips, R. B. Jackson, The Structure, Distribution, and Biomass of the World's Forests, Annual Review of Ecology, Evolution, and Systematics, Vol. 44, 2013, pp. 593-622, https://doi.org/10.1146/annurev-ecolsys-110512-135914.
[3] N. D. Hoi, D. B. Duy, N. T. Dung, N. H. V. Hieu, A Novel Method For Estimating Biomass and Carbon Sequestration in Tropical Rainforest Areas Based on Remote Sensing Imagery: A Case Study in the Kon Ha Nung Plateau, Vietnam, Sustainability, Vol. 14, No. 24, 2022, pp. e16857, https://doi.org/10.3390/su142416857.
[4] J. S. Wright, J. S. Plant, Plant Diversity in Tropical Forests: A Review of Mechanisms of Species Coexistence, Oecologia, Vol. 130, pp. 1-14, 2002, https://doi.org/10.1007/s004420100809.
[5] C. D. Allen, A. K. Macalady, H. Chenchouni, A Global Overview of Drought and Heat-induced Tree Mortality Reveals Emerging Climate Change Risks For Forests, Forest Ecology And Management, Vol. 259, No. 4, 2010, pp. 660-684, https://doi.org/10.1016/j.foreco.2009.09.001.
[6] M. Baatz, C. Hoffmann, G. Willhauck, Progressing from Object-based to Object-oriented Image Analysis, Spatial Concepts for Knowledge-driven Remote Sensing Applications, Vol. 540, No. 3, 2008, pp. 29-42, https://doi.org/10.1007/978-3-540-77058-9_2
[7] M. O. Johnson, D. Galbraith, M. Gloor, Variation in Stem Mortality Rates Determines Patterns of Above‐ground Biomass in A Mazonian Forests: Implications for Dynamic Global Vegetation Models, Global Change Biology, Vol. 22, No. 12, 2016, pp. 3996-4013, https://doi.org/10.1111/gcb.13315.
[8] L.N. Khang , Research on Scientific and Practical Basis to Propose Solutions for Implementing the REDD+ Program in Dien Bien Province, PhD Dissertation in Vietnam National University of Forestry (VNUF), 2015, pp. 3-11 (in Vietnamese).
[9] N. D. Hoi, Features of Anthroprogenic Landscape Kon Ka Kinh National Park and Its Vicinity, Proceedings of T. I. Vyazemsky Karadag Scientific Station – Nature Reserve of the Ras, 2017, pp. 3-18.
[10] R. L. Jalbuena, R. V. Peralta, A. M. Tamondong, Object-based Image Analysis for Mangroves Extraction Using Lidar Datasets and Orthophoto, ACRS Proceedings: Asian Association on Remote Sensing, Manila, Philippines, 2015, pp. 1711-1734.
[11] T. Kavzoglu, M. Yildiz, Parameter-based Performance Analysis of Object-based Image Analysis Using Aerial and Quikbird-2 Images, ISPRS Annals of Photogrammetry: Remote Sensing & Spatial Information Sciences, Vol. 2, No. 7, 2014, pp. 31-37, https://doi.org/10.5194/isprsannals-II-7-31-2014.
[12] J. S. Griffith, Object-oriented Method to Classify the Land Use and Land Cover in San Antonio Using Ecognition Object-oriented Image Analysis, Proceedings Remote Sensing Image Process and Analysis Es-6973, 2006, pp. 1-10.
[13] S. Karakış, A.M. Marangoz, G. Büyüksalih, Analysis of Segmentation Parameters in Ecognition Software Using High Resolution Quickbird Ms Imagery, ISPRS Proceedings of Workshop on Topographic Mapping from Space, 2006, pp. 14-16.
[14] R. Genuer, J. M. Poggi, Random Forests, Random Forests with R, Vol. 978, 2020, pp. 33-55, https://doi.org/10.1007/978-3-030-56485-8_3.
[15] B. Huy, K. Kralicek, K. P. Poudel, V. T. Phuong, P. V. Khoa, N. D. Hung, H. Temesgen, Allometric Equations for Estimating Tree Aboveground Biomass in Evergreen Broadleaf Forests of Viet Nam, Forest Ecology and Management, Vol. 382, 2016, pp. 193-205, http://dx.doi.org/10.1016/j.foreco.2016.10.021.
[16] A. Field, Discovering Statistics Using SPSS, Sage Publications Ltd, London, 2009, pp. 46-52.
[17] IPCC, Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme, Published by the Intergovernmental Panel on Climate Change, 2006, pp. 3-21.
[18] L. Gao, G. Chai, X. Zhang, Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data, Remote Sensing, Vol. 14, No. 11, 2022, pp. e2568, https://doi.org/10.3390/rs14112568.
[19] D. Grebner, P. Bettinger, J. Siry, K. Boston, Forest Policies and External Pressures, 2022, pp. 365-386, https://doi.org/10.1016/b978-0-12-819002-9.00015-8.
[20] G. Matasci, T. Hermosilla, M. Wulder, J. White, N. Coops, G. Hobart, D. Bolton, P. Tompalski,
C. Bater, Three Decades of Forest Structural Dynamics Over Canada's Forested Ecosystems Using Landsat Time-series and Lidar Plots, Remote Sensing of Environment, Vol. 216, 2018, pp. 697-714, https://doi.org/10.1016/j.rse.2018.07.024.
[21] N. T. T. Huong, N. T. P. Mai, D. T. Huong, L.T. Hue Ly, Forest - Related Culture and Contribution to Sustainable Development in the Northern Mountain Region in Vietnam, Forest And Society, Vol. 5, No. 1, 2021, pp. 32-47, https://doi.org/10.24259/fs.v5i1.9834.
[22] E. Perry, K. Sheffield, D. Crawford, S. Akpa, A. Clancy, R. Clark, Spatial and Temporal Biomass and Growth for Grain Crops Using NDVI Time Series, Remote Sensing, Vol. 14, No. 13, 2022, pp. e3071, https://doi.org/10.3390/rs14133071.
[23] A. Safari, H. Sohrabi, S. Powell, Comparison of Satellite-based Estimates of Aboveground Biomass in Coppice Oak Forests Using Parametric, Semiparametric, and Nonparametric Modeling Methods, Journal of Applied Remote Sensing, Vol. 12, No. 4, 2018, pp. e046026, https://doi.org/10.1117/1.JRS.12.046026.
[24] J. V. Muñoz, J. A. A. Sánchez, M. Schoenemann , B. L. Felices, An Analysis of The Worldwide Research on the Socio-Cultural Valuation of Forest Ecosystem Services, Sustainability, Vol. 14, No. 4, 2022, pp. e2089, https://doi.org/10.3390/su14042089.
[25] V. Sati, Forest Ecosystem Services and Their Valuation in Mizoram, The Eastern Extension of the Himalaya, Vol. 4, No. 2, 2023, pp. 44-49, http://doi.org/10.26480/efcc.02.2023.44.49.
[26] S. Linser, B. Wolfslehner, National Implementation of the Forest Europe Indicators for Sustainable Forest Management, Forests, Vol. 13, No. 2, 2022, pp. 191, https://doi.org/10.3390/f13020191.