Enhancing the estimation accuracy of above-ground carbon storage in Eucalyptus urophylla plantation on Timor Island, Indonesia, through higher spatial-resolution satellite imagery

Authors

  • Ronggo Sadono Department of Forest Management, Faculty of Forestry, Universitas Gadjah Mada, Jl. Agro No. 1 Bulaksumur, Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia
  • Emma Soraya Department of Forest Management, Faculty of Forestry, Universitas Gadjah Mada, Jl. Agro No. 1 Bulaksumur, Sleman, Daerah Istimewa Yogyakarta 55281, Indonesia

DOI:

https://doi.org/10.15243/jdmlm.2024.113.5623

Keywords:

detailed object detection, Eucalyptus tree density, Pléiades imagery, remote sensing technology , very high spatial resolution

Abstract

Eucalyptus urophylla plantation is an important contributor to carbon storage in climate change mitigation, established due to a land rehabilitation program in the semi-arid ecosystem in Timor Island. To ensure an accurate estimate of the above-ground carbon storage of these plantations, it is important to continuously combine ground measurement with remote sensing technology. Therefore, this study aimed to compare the above-ground carbon storage estimation of two very high spatial resolution images, namely Pleiades-1B 2021 and Pléiades Neo 2022 with pixel sizes of 2 x 2 m and 1.2 x 1.2 m, respectively. The normalized difference vegetation index was employed to identify the eucalyptus trees and classify the density into low, moderate, and high. The results showed that Pléiades Neo imagery provided superior eucalyptus tree identification to Pleiades-1B imagery and was more accurate in estimating above-ground carbon storage. However, there is a trade-off between increasing this accuracy and incurring a higher cost to achieve the highest spatial resolution image. 

References

Abad-Segura, E., González-Zamar, M.D., Vázquez-Cano, E. and López-Meneses, E. 2020. Remote sensing applied in forest management to optimize ecosystem services: advances in research. Forests 11(9):969. https://doi.org/10.3390/f11090969

Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M.A.Al., Hassan, Q.K. and Dewan, A. 2019. Spatio-temporal patterns of land use/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sensing 11:790. https://doi.org/10.3390/rs11070790

Ahady, A.B. and Kaplan, G. 2022. Classification comparison of Landsat-8 and Sentinel-2 data in Google Earth Engine, study case of the city of Kabul. International Journal of Engineering and Geosciences 7(1):24-31. https://doi.org/10.26833/ijeg.860077

Alemu, B. 2014. The role of forest and soil carbon sequestrations on climate change mitigation. Research Journal of Agriculture and Environmental Management 3(10):492-505.

Arini, D., Guvil, Q. and Wahidah, N. 2020. Land cover identification using Pleiades satellite imagery by comparison of NDVI and BI method in Jatinangor, West Java. IOP Conference Series: Earth and Environmental Science 500:012007. https://doi.org/10.1088/1755-1315/500/1/012007

Aryal, J., Sitaula, C. and Aryal, S. 2022. NDVI Threshold-based urban green space mapping from Sentinel-2A at the local governmental area (LGA) level of Victoria, Australia. Land 11:351. https://doi.org/10.3390/land11030351

Barbierato, E., Bernetti, I., Capecchi, I. and Saragosa, C. 2020. Integrating remote sensing and street view images to quantify urban forest ecosystem services. Remote Sensing 12(2):329. https://doi.org/10.3390/rs12020329

Chen, M., Qiu, X., Zeng, W. and Peng, D. 2022. Combining sample plot stratification and machine learning algorithms to improve forest above-ground carbon density estimation in Northeast China Using Airborne LiDAR Data. Remote Sensing 14:1477. https://doi.org/10.3390/rs14061477

Costa, A.S. and Lameira, O.A. 2022. The use of NDVI derived from Pléiades images in the analysis of the vegetation structure in two forest fragments. Research, Society and Development [S. l.] 11(1):e54711124170. https://doi.org/10.33448/rsd-v11i1.24170

Devkota, S., Mandal, R.A. and Khadka, A. 2023. Assessing the correlation between above-ground carbon stock, NDVI and tree species diversity (A study of Kailali and Kanchanpur District). International Journal of World Policy and Development Studies 9(1):11- 18. https://doi.org/10.32861/ijwpds.91.11.18

Duan, T., Chapman, S.C., Guo, Y. and Zheng, B. 2017. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crops Research 210:71-80. https://doi.org/10.1016/j.fcr.2017.05.025

Ekoungoulou, R., Liu, X., Ifo, S.A., Loumeto, J.J. and Folega, F. 2014. Carbon stock estimation in secondary forest and gallery forest of Congo using allometric equations. International Journal of Scientific & Technology Research 3(3):465-474.

Flenniken, J.M., Stuglik, S. and Iannone, B.V. 2020. Quantum GIS (QGIS): An introduction to a free alternative to more costly GIS platforms: FOR359/FR428, 2/2020. EDIS 2020(2):7-7. https://doi.org/10.32473/edis-fr428-2020

Gashu, E.G. and Marelign, M.A. 2022. Estimation of carbon stock using ground inventory and remote sensing imagery in the case of Tiru-Selam Forest, North-western Ethiopia. Computational Ecology and Software 12(3):141- 153.

Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W., Al-Ansari, N., Geertsema, M., Amiri, M.P., Gholamnia, M., Dou, J. and Ahmad, A. 2021. Performance evaluation of Sentinel-2 and Landsat 8 OLI data for land cover/use classification using a comparison between machine learning algorithms. Remote Sensing 13:1349. https://doi.org/10.3390/rs13071349

Hamimeche, M., Niculescu, S., Billey, A. and Moulaï, R. 2021. Identification and mapping of Algerian island vegetation using high resolution images (Pléiades and SPOT 6/7) and random forest modeling. Environmental Monitoring and Assessment 193:617. https://doi.org/10.1007/s10661-021-09429-9

Han, X. and Xu, H. 2022. Extraction Method of Stand Density Based on High-Resolution Remote Sensing Imagery. Journal of Physics: Conference Series 2410 012014. https://doi.org/10.1088/1742-6596/2410/1/012014

Hartoyo, A.P.P., Prasetyo, L.B., Siregar, I.Z., Supriyanto, Theilade, I. and Siregar, U.J. 2019. Carbon stock assessment using forest canopy density mapper in agroforestry land in Berau, East Kalimantan, Indonesia. Biodiversitas 20(9):2661-2676. https://doi.org/10.13057/biodiv/d200931

Hashim, H., Latif, A.Z. and Adnan, N.A. 2019. Urban vegetation classification with NDVI threshold value method with very high resolution (VHR) Pleiades imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42(4/W16):237-240. https://doi.org/10.5194/isprs-archives-XLII-4-W16-237-2019

Hestrio, Y.F., Soleh, M., Hidayat, A., Afida, H., Gunawan, H. and Maryanto, A. 2021. Satellite data receiving antenna system for Pleiades neo observation satellite. Journal of Physics: Conference Series 1763:012019. https://doi.org/10.1088/1742-6596/1763/1/012019

Huang, S., Tang, L., Hupy, J.P., Wang, Y. and Shao, G. 2021. A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research 32(1):1-6. https://doi.org/10.1007/s11676-020-01155-1

Jaelani, L.M. and Putri, K. 2019. Analisis kemampuan Citra Satelit Pleiades-1B dalam mengestimasi kedalaman perairan Gili Iyang dengan menerapkan Geographically Weighted Regression (GWR). Geoid, Journal of Geodesy and Geomatics 14(2):28-34. https://doi.org/10.12962/j24423998.v14i2.3877

Khan, K., Listyanto, T. and Soraya, E. 2022. Moisture content, density, and allometric model for estimating above-ground biomass of Peronema canescens trees in the private forest. Biodiversitas 23(2):1132-1139. https://doi.org/10.13057/biodiv/d230258

Kusuma, A.F., Sadono, R. and Wardhana, W. 2022. Ten years assessment of shifting cultivation on land cover and carbon storage in Timor Island, Indonesia. Floresta e Ambient 29 (4):e20220016. https://doi.org/10.1590/2179-8087-floram-2022-0016

Larekeng, S.H., Nursaputra, M., Nasri, N., Hamzah, A.S., Mustari, A.S., Arif, A.R., Ambodo, A.P., Lawang, Y. and Ardiansyah, A. 2022. A diversity index model based on spatial analysis to estimate high conservation value in a mining area. Forest and Society 6(1):142- 156. https://doi.org/10.24259/fs.v6i1.12919

Le Maire, G., Marsden, C., Nouvellon, Y., Grinand, C., Hakamada, R., Stape, J. L. and Laclau, J.P. 2011. MODIS NDVI time-series allow the monitoring of Eucalyptus plantation biomass. Remote Sensing of Environment 115(10):2613-2625. https://doi.org/10.1016/j.rse.2011.05.017

Marelign, M.A. and Mekonen, D.T. 2022. Estimating and mapping woodland biomass and carbon using Landsat 8 vegetation index: A case study in Dirmaga Watershed, Ethiopia. Computational Ecology and Software 12(2):67-79.

Marimpan, L.S., Purwanto, R.H., Wardhana, W. and Sumardi, 2022. Carbon storage potential of Eucalyptus urophylla at several density levels and forest management types in dry land ecosystems. Biodiversitas 23(6):2830-2837. https://doi.org/10.13057/biodiv/d230607

Marimpan, L.S. 2023. Analysis of Carbon Stocks and Factors That Influence the Ampupu (Eucalyptus urophylla) Natural Forest in the Mutis Timau Area, East Nusa Tenggara Province. Faculty of Foretry, Univesitas Gadjah Mada. Disertation (in Indonesian).

Mitchell, A.L., Rosenqvist, A. and Mora, B. 2017. Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD+. Carbon Balance and Management 12:9. https://doi.org/10.1186/s13021-017-0078-9

Pang, Z., Zhang, G., Tan, S., Yang, Z. and Wu, X. 2022. Improving the Accuracy of Estimating Forest Carbon Density Using the Tree Species Classification Method. Forests 13:2004. https://doi.org/10.3390/f13122004

Pu, R., Landry, S. and Yu, Q. 2018. Assessing the potential of multi-seasonal high resolution Pléiades satellite imagery for mapping urban tree species. International Journal of Applied Earth Observation and Geoinformation 71:144- 158. https://doi.org/10.1016/j.jag.2018.05.005

Qin, H., Zhou, W., Yao, Y. and Wang, W. 2021. Estimating aboveground carbon stock at the scale of individual trees in subtropical forests using UAV LiDAR and hyperspectral data. Remote Sensing 13:4969. https://doi.org/10.3390/rs13244969

Sadono, R., Pujiono, E. and Lestari, L. 2020b. Land cover changes and carbon storage before and after community forestry program in Bleberan village, Gunungkidul, Indonesia, 1999-2018. Forest Science and Technology 16(3):134-144.

https://doi.org/10.1080/21580103.2020.1801523

Sadono, R., Wardhana, W., Idris, F. and Wirabuana, P.Y.A.P. 2023a. Estimating carbon storage of Eucalyptus urophylla vegetation in Mutis Timau Nature Reserve, East Nusa Tenggara, Indonesia using remote sensing analysis. Biodiversitas 24(4):1946-1952. https://doi.org/10.13057/biodiv/d240402

Sadono, R., Wardhana, W., Idris, F. and Wirabuana, P.Y.A.P. 2023b. Developing energy production from Eucalyptus urophylla plantation in dryland ecosystem at East Nusa Tenggara, Indonesia. Journal of Degraded and Mining Lands Management 10(4):4673-4681. https://doi.org/10.15243/jdmlm.2023.104.4673

Sadono, R., Wardhana, W., Wirabuana, P.Y.A.P. and Idris, F. 2020a. Productivity evaluation of Eucalyptus urophylla plantation established in dryland ecosystems, East Nusa Tenggara. Journal of Degraded and Mining Lands Management 8(1):2502-2458. https://doi.org/10.15243/jdmlm.2020.081.2471

Samsuri, Zaitunah, A., Meliani, S., Syahputra, O.K., Budiharta, S., Susilowati, A., Rambe, R., Ulfa, M., Harahap, M.M., Arinah, H., Elfiati, D., Rangkuti, A.B., Sucipto, T., Hakim, L., Iswanto, A.H., Manurung, H. and Azhar I. 2021. Mapping of mangrove forest tree density using SENTINEL 2A satelit image in remained natural mangrove forest of Sumatra eastern coastal. IOP Conference Series: Earth and Environmental Science 912 012001. https://doi.org/10.1088/1755-1315/912/1/012001

Shah, S. and Sharma, D.P. 2023. Monitoring carbon stock changes in Solan Forest Division of Indian Western Himalayas. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03040-3

Sousa, A.M., Gonçalves, A.C., Mesquita, P. and da Silva, J.R.M. 2015. Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia. ISPRS Journal of Photogrammetry and Remote Sensing 101:69-79. https://doi.org/10.1016/j.isprsjprs.2014.12.004

Sukarna, R.M., Birawa, C. and Junaedi, A. 2021. Mapping above-ground carbon stock of secondary peat swamp forest using forest canopy density model Landsat 8 OLI-TIRS: A case study in Central Kalimantan Indonesia. Environment and Natural Resources Journal 19(2):165- 175. https://doi.org/10.32526/ennrj/19/2020209

Tian, L., Wu, X., Tao, Y., Li, M., Qian, C., Liao, L. and Fu, W. 2023. Review of remote sensing-based methods for forest aboveground biomass estimation: progress, challenges, and prospects. Forests 14(6):1-31. https://doi.org/10.3390/f14061086

Tosiani, A. 2015. Carbon Absorption and Emission Activity Book. Directorate of Forest Resources Inventory and Monitoring Directorate General of Forestry Planning and Environmental Management Ministry of the Environment. Jakarta (in Indonesian).

Trigozo, J.P.R., Oré Cierto, L.E., Aliaga, W.C.L. and Oré Cierto, J.D. 2021. Carbon stored in forest plantations in The Mariano Dámaso Beraún District, Huánuco - Perú. Revista Científica Yotantsipanko 1(1):32-43. https://doi.org/10.54288/yotantsipanko.v1i1.6

Vega-Puga, M., Romo-Leon, J.R., Castellanos, A.E., Castillo-Gámez, R.A., Garatuza-Payán, J. and Ángeles-Pérez, G. 2023. High resolution images for change detection on aboveground carbon storage in semiarid communities, after the introduction of exotic species Cenchrus ciliaris. Botanical Sciences [S.l.] 101(1):41- 56. https://doi.org/10.17129/botsci.3026

Viana, H., Aranha, J., Lopes, D. and Cohen, W.B. 2012. Estimation of crown biomass of Pinus pinaster stands and shrubland above-ground biomass using forest inventory data, remotely sensed imagery and spatial prediction models. Ecological Modelling 226:22-35. https://doi.org/10.1016/j.ecolmodel.2011.11.027

Wang, D., Wan, B., Qiu, P., Su, Y., Guo, Q., Wang, R., Sun, F. and Wu, X. 2018. Evaluating the performance of Sentinel-2, Landsat 8 and Pléiades-1 in mapping mangrove extent and species. Remote Sensing 10:1468. https://doi.org/10.3390/rs10091468

Wang, S., Zhang, X., Hassan, M.A., Chen, Q., Li, C., Tang, Z. and Wang, Y. 2018. QuickBird image-based estimation of tree stand density using local maxima filtering method: A case study in a Beijing forest. PLoS ONE 13(12):e0208256. https://doi.org/10.1371/journal.pone.0208256

Zhang, F., Tian, X., Zhang, H. and Jiang, M. 2022. Estimation of aboveground carbon density of forests using deep learning and multisource remote sensing. Remote Sensing 14(13):3022. https://doi.org/10.3390/rs14133022

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Submitted

10-10-2023

Accepted

27-01-2024

Published

01-04-2024

How to Cite

Sadono, R., & Soraya, E. (2024). Enhancing the estimation accuracy of above-ground carbon storage in Eucalyptus urophylla plantation on Timor Island, Indonesia, through higher spatial-resolution satellite imagery . Journal of Degraded and Mining Lands Management, 11(3), 5623–5634. https://doi.org/10.15243/jdmlm.2024.113.5623

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Research Article