Carbon Stock Estimation From Vegetation Biomass Using Spot-7 Imagery

https://doi.org/10.22146/ijg.78690

Iklila Rahmatika(1*), Iswari Nur Hidayati(2), R Suharyadi(3), Emilya Nurjani(4)

(1) Faculty of Geography, Universitas Gadjah Mada,Yogyakarta, Indonesia
(2) Faculty of Geography, Universitas Gadjah Mada,Yogyakarta, Indonesia
(3) Faculty of Geography, Universitas Gadjah Mada,Yogyakarta, Indonesia
(4) Faculty of Geography, Universitas Gadjah Mada,Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Vegetation absorbs carbon dioxide (CO2 ) emissions during photosynthesis. Covering more areas with trees will increase the CO2 absorption capacity more substantially than other vegetation like bushes, grasses, or rice fields. Trees convert the CO2 captured during photosynthesis into organic carbon to be stored in biomass. Woody trees account for approximately 60% of the total aboveground tree biomass, and trunks, where food reserves produced in photosynthesis are stored, have relatively large biomass compared to other parts of the tree. The biomass of a vegetation stand determines the optimization of air pollutant absorption in urban areas. Yogyakarta City is the center for tourism, education, and cultural activities in Indonesia, which is vulnerable to land-use conversion, a factor of the shrinking green space. This study aimed to estimate carbon stock from vegetation biomass in Yogyakarta City using the remote sensing product SPOT-7 imagery. To calculate the vegetation biomass, the diameter at breast height (DBH) of stands was measured in the field. Then, statistical analyses were performed to determine the correlation and regression between the actual or observed biomass and the Normalized Difference Vegetation Index (NDVI) value derived from the SPOT-7 image. The regression model used was y = 1.4277x – 0.0849. The total biomass produced in Yogyakarta City was estimated at 1,399,487.1 tonnes, which contained 643,764.1 tonnes of carbon stock.

Keywords


biomass; remote sensing; SPOT-7; carbon stock; Yogyakarta

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References

Achmad, E, Nursanti, Manalu, JHB (2018). Model Spasial Pendugaan Biomassa di Atas permukaan Tanah di Hutan Nagari Padang Limau Sundai Kabupaten Solok Selatan Provinsi Sumatera Barat, Jurnal Silva Tropika, vol. 2(3), pp. 67-76.

Andriansah, R (2020). Analisis Kondisi Mangrove Berdasarkan NDVI (Normalized Difference Vegetation Index) dan Tutupan Kanopi Menggunakan Citra Sentinel-2 di Pulau Payung, Muara Sungai Musi, Banyuasin, Sumatera Selatan, Skripsi, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Brawijaya: Malang.

Apriadna, R (2018). Estimasi Volume Oksigen Ruang Terbuka Hijau di Kota Magelang dan Sekitarnya, Skripsi, Fakultas Geografi, Universitas Gadjah Mada: Yogyakarta.

Astuti, CC (2017), Analisis Korelasi untuk Mengetahui Keeratan Hubungan antara Keaktifan Mahasiswa dengan Hasil Belajar Akhir. Journal of Information and Computer Technology Education, vol. 1(1), pp. 1-7.

Ayvaz, B, Kusakci, AO, & Temur, GT (2017). Energy-related CO2 EmissionForecast for Turkey and Europe and Eurasia: a Discrete Grey Model Approach, Grey Systems: Theory and Application, vol. 7(3), pp. 436-452.

BPS (2021a). Provinsi Daerah Istimewa Yogyakarta dalam Angka 2021, Jakarta: BPS.

BPS (2021b). Kota Yogyakarta dalam Angka 2021, Jakarta: BPS.

Brown, S (1997). Estimating Biomass and Biomass Change of Tropical Forests: a Primer, FAO Forestry Paper, vol. 5(4).

Cooley, T, Anderson, GP, Felde, GW, Hoke, ML, Ratkowski, AJ, Chetwynd, JH, & Lewis, P (2002). FLAASH, a MODTRAN4-based Atmospheric Correction Algorithm, Its Application and Validation. IEEE, pp.1414-1418.

Danoedoro, P (2012), Pengantar Penginderaan Jauh Digital, ANDI, Yogyakarta.

Fibriawati, L 2016. Koreksi Atmosfer Citra SPOT-6 Menggunakan Metode MODTRAN4. Seminar Nasional Penginderaan Jauh, pp. 98-146.

Frazer, GW, Canham, CD, & Lertzman, KP (1999). Gap Light Analyzer (GLA), Version 2.0: Imaging software to extract canopy structure and gap light transmission indices from true-colour fisheye photographs, users manual and program documentation, New York: Simon Fraser University.

Handayani, CN, Sukmono, A, & Firdaus, HS (2020). Analisis Ketersediaan Ruang Terbuka Hijau Terhadap Emisi CO2 Oleh Gas Buang Kendaraan Bermotor di Kelurahan Tembalang dan Sumurboto, Jurnal Geodesi Undip, vol. 9(2), pp. 198-207.

Hung, T (2000). MODIS Application in Monitoring Surface Parameters, Institute of Industrial Science: Tokyo.

Irawan, S & Sirait, J (2018). Perubahan Kerapatan Vegetasi Menggunakan Citra Landsat 8 di Kota Batam Berbasis Web, Jurnal Kelautan, vol. 10(2), pp. 174-184.

Jensen (2014). Remote Sensing of The Environment: An Earth Resource Perspective Second Edition, Pearson Education Limited: USA.

Jiaojun, Y, Yushi, C & Ye, Z (2008). Effect on Atmospheric Correction by Inputting Parameters of Model. Remote Sensing Application.

Kish, L (1965). Survey Sampling, Michigan: John Wiley & Sons.

Krisnawati, H, Adinugroho, WC, & Imanuddin, R (2012). Monograf Model-Model Alometrik untuk Pendugaan Biomassa Pohon pada Berbagai Tipe Ekosistem Hutan di Indonesia, Bogor: Badan Penelitian dan Pengembangan Konservasi dan Rehabilitasi-Kementrian Kehutanan dan Lingkungan Hidup.

Lee, HY (2008). An Analysis on development capacity of an urbanized area for urban growth management, Journal of the Korean Urban Geographical Society, vol. 11(1), pp. 1–18.

Lee, ZH, Sethupathi, S, Lee, KT, Bhatia, S, & Mohamed, AR (2013). An Overview on Global Warming in Southeast Asia: CO2 Emission Status, Efforts Done, and Barriers, Renewable and Sustainable Energy Reviews, vol. 28, pp. 71-81.

Leprieur, C, Kerr, YH, Mastorchio, S & Meunier, JC (2000). Monitoring vegetation cover across semi-arid regions: comparison of remote observations from various scales, International Journal of Remote Sensing, vol. 21(2), pp.281-300.

Lu, D (2005). The Potential and Challenge of Remote Sensing-Based Biomass Estimation. International Journal of Remote Sensing, vol. 27(7), pp. 1297-1328.

Ma, S, He, F, Tian, D, Zou, D, Yan, Z, Yang, Y, Zhou, T, Huang, K, Shen, H, & Fang,J (2018). Variations and Determinants of Carbon Content in Plants: A Global Synthesis, Biogeosciences, vol. 15(3), pp.693–702.

Mansur, M & Pratama, BA (2014). Potensi Serapan Gas Karbondioksida (CO2) pada Jenis- Jenis Pohon Pelindung Jalan (Potential Absorption of Carbon Dioxide (CO2) in Wayside Trees), Jurnal Biologi Indonesia, vol. 10(2), pp. 149-158.

Mardiana, G, Udiansyah, & Pitri, RMN (2018). Potensi Simpanan dan Serapan Karbon di Tas Permukaan Tanah pada Kawasan Hutan Desa Sungai Bakar Kecamatan Bajuin, Jurnal Sylva Scienteae, vol. 1(1), pp.56-64.

Margaretha, EW, Danoedoro, P, & Murti, SH (2013). Estimasi Cadangan Karbon Vegetasi Tegakan di Kota Yogyakarta dan Sekitarnya Berbasis ALOS AVNIR-2, Prosiding Simposium Nasional Sains Geoinformasi, pp. 431-440.

Moriasi, DN, Arnold, JG, Liew, MWV, Bingner, RL, Harmel, RD, & Veith, TL (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Transactions of the ASABE, vol. 50(3), pp.885−900.

Mukono, J (2011). Aspek Kesehatan Pencemaran Udara, Surabaya: Airlangga University Press.

Noor, A (2018). Perbandingan Algoritma Support Vector Machine Biasa dan Support Vector Machine Berbasis Particle Swarm Optimization untuk Prediksi Gempa Bumi, Jurnal Humaniora dan Teknologi, vol. 4(1), pp. 31-37.

Nowak, DJ, Greenfield, EJ, Hoehn, RE & Lapoint, E (2013). Carbon storage and sequestration by trees in urban and community areas of the United States, Environmental pollution, vol.178, pp.229-236.

Qanitat, F (2016). Jogja One Park Rampung Akhir 2017, diakses pada 6 Maret 2022, https://ekonomi.bisnis.com/read/20160327/48/531666/jogja-one-park-rampung-akhir-2017.

Samsoedin, I, Dharmawan, IWS, & Siregar, A (2009). Potensi Biomassa Karbon Hutan Alam dan Hutan Bekas Tebangan Setelah 30 Tahun di Hutan Penelitian Malinau, Kalimantan Timur, Jurnal Penelitian Hutan dan Konservasi Alam, Vol. IV(1), pp. 47-56.

Siregar, S (2015), Statistika Parametrik Untuk Penelitian Kuantitatif Dilengkapi Dengan Perhitungan Manual dan Aplikasi SPSS Versi 17, Jakarta: Bumi Aksara.

Solima, S (2010). Electrical Load Predictioning, Elsevier Inc, United States.

SPOT 6/7 daya user’s community 2013, SPOT 6/7 Imagery – User Guide, France: Airbus Defence and Space Intelligence.

Sutaryo, D (2009). Perhitungan Biomassa, Sebuah Pengantar untuk Studi Karbon dan Perdagangan Karbon, Wetlands International Indonesia Programme: Bogor.

Sutanto (1986). Penginderaan Jauh Jilid 1, Yogyakarta: Gadjah Mada University Press.

The European Space Agency n.d., SPOT 7, Earth Online, https://earth.esa.int/eogateway/missions/spot-7.

Usman, H dan Akbar, PS (2003), Pengantar Statistika, Jakarta: PT. Bumi Aksara.

Wahrudin, U, Atikah, S, Al Habibah, A, Paramita, QP, Tampubolon, H, Sugandi, D, & Ridwana, R (2019). Pemanfaatan Citra Landsat 8 untuk Identifikasi Sebaran Kerapatan Vegetasi di Pangandaran, Jurnal Kajian Ilmu dan Pendidikan Geografi, vol. 3(2), pp. 90- 101.

Wahyuni, NI (2012). Integrasi Penginderaan Jauh dalam Perhitungan Biomassa Hutan, Info BPK Manado, vol. 2(2), pp. 115-126.

Wijayanti, T (2015). Evaluasi Potensi Biomassa Hutan Berdasarkan Nilai Indeks Vegetasi Menggunakan Data Penginderaan Jauh (Studi Kasus: KPH Bojonegoro, Jawa Timur), Tugas Akhir, Fakultas Teknik Sipil dan Perencanaan, Institut Teknologi Sepuluh Nopember.

Wiradharma, D, Sobirin, & Saraswati, R (2014), Perubahan Kemampuan Serapan Karbon Dioksida (CO2) Oleh Ruang Hijau di Kota Bogor, Depok: Universitas Indonesia.

Yanti, D, Megantara, I, Akbar, M, Meiwanda, S, Syauqi, IM, Sugandi, D, & Ridwana, R 2020, Analisis Kerapatan Vegetasi di Kecamatan Pangandaran Melalui Citra Landsat 8, Jurnal Geografi, Edukasi dan Lingkungan (JGEL), vol. 4(1), pp. 32-38.

Yudistira, R, Mecha, AI, & Prasetyo, SYJ (2019). Perubahan Konversi Lahan Menggunakan NDVI, EVI, SAVI dan PCA pada Citra Landsat 8 (Studi Kasus: Kota Salatiga), Indonesian Journal of Computing and Modeling, vol. 1, pp. 25-30.

Yunkai & Zeng, F (2012). Atmospheric Correction Modul QUAC and FLAASH User’s Guide Version 4.7. ITT Visual Information Solution Inc.

Zhao, P, Zhou, Y, Li, F, Ling, X, Deng, N, Peng, S & Man, J (2020). The Adaptability of APSIM-Wheat Model in The Middle and Lower Reaches of Yangtze River Plain of China: A Case Study of Winter Wheat in Hubei Province, Agronomy, vol. 10, pp. 1-15.



DOI: https://doi.org/10.22146/ijg.78690

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Copyright (c) 2023 Iklila Rahmatika, Iswari Nur Hidayati, R Suharyadi, Emilya Nurjani

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