Pemanfaataan Penginderaan Jauh dan Sistem Informasi Geografis Berbasis Transformasi Spektral Indeks Vegetasi Untuk Estimasi Produksi Tanaman Teh
-
Natzratul Zahira
*
Universitas Negeri Padang, Padang,
Indonesia
- Muhammad Ismail Universitas Negeri Padang, Padang, Indonesia
- Wikan Jaya Prihantarto Universitas Negeri Padang, Padang, Indonesia
- Triyatno Triyatno Universitas Negeri Padang, Padang, Indonesia
- Dilla Angraina Universitas Negeri Padang, Padang, Indonesia (*) Corresponding Author
Abstract
Tea is a leading plantation commodity in Indonesia, but production estimation through manual field surveys has limitations in terms of cost, time, and accuracy, and is less able to describe spatial variations between blocks representatively. This study aims to estimate tea production in Afdeling B PTPN IV Danau Kembar using PlanetScope imagery with the Transformed Vegetation Index (TVI) approach, and to test the accuracy of the estimation compared to actual production data. The methods used include image pre-processing (radiometric calibration), TVI calculation, field data collection through sample plots, simple linear regression analysis, and production estimation at the block level using zonal aggregation and Jenks Natural Breaks classification. The results show that the TVI value ranges from 0.79–1.13 with a productive land area reaching 240 ha (84.21%) of the total 285 ha. The regression analysis yielded a coefficient of determination (R²) of 0.7933 with the equation y = 1388.8x – 1458.4, while the model validation results showed an R² of 0.7005. The total estimated tea shoot production in Afdeling B reached 119.5 tons, with the highest production in block 24 (8.80 tons) and the lowest in block 46 (0.48 tons). Although the model displayed good accuracy results at the sample scale, the resulting estimate was less precise compared to company data due to differences, namely the temporality of the data. However, the approach using TVI based on PlanetScope imagery has proven to have advantages in presenting spatial information on the distribution of tea plant productivity per block that cannot be obtained from conventional methods, thus supporting more efficient and sustainable spatial data-based tea plantation management. The contribution of this research is to provide a TVI- and PlanetScope-based tea production estimation model applied to the highland tea plantations of West Sumatra, while also generating a spatial productivity distribution map per block as a basis for more practical plantation management decision-making.
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References
Anjarsari, M. Ariyanti, dan S. Rosniawaty, “Studi Ekofisiologis Tanaman Teh guna Meningkatkan Pertumbuhan, Hasil, dan Kualitas Teh,” Kultivasi, vol. 19, no. 3, Des 2020, doi: 10.24198/kultivasi.v19i3.26623.
Septyadi, E. Gracia, dan R. P. Syamsuddin, “Tea Plantation Mapping Using UV Multispectral Imagery,” Int. J. Remote Sens. Earth Sci., vol. 20, no. 1, hal. 08, Jun 2023, doi: 10.30536/j.ijreses.2023.v20.a3817.
Lin, “UAV-Based Estimation of Tea Leaf Area Index in Mountainous Terrain: Integrating Topographic Correction and Interpretable Machine Learning,” 2026.
Dini Nuraeni, “Spatial Machine Learning for Monitoring Tea Leaves and Crop Yield Estimation Using Sentinel-2 Imagery, (A Case of Gunung Mas Plantation, Bogor).”
Weiss, F. Jacob, dan G. Duveiller, “Remote Sensing for Agricultural Applications: A meta-review,” Remote Sens. Environ., vol. 236, hal. 111402, Jan 2020, doi: 10.1016/j.rse.2019.111402.
K. Amankulova, N. Farmonov, P. Akramova, I. Tursunov, dan L. Mucsi, “Comparison of Planetscope, Sentinel-2, And Landsat 8 Data in Soybean Yield Estimation Within-Field Variability with Random Forest Regression,” Heliyon, vol. 9, no. 6, hal. e17432, Jun 2023, doi: 10.1016/j.heliyon.2023.e17432.
K. Swain,. Singha, dan B. Pradhan, “Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models,” Earth Syst. Environ., vol. 8, no. 4, hal. 1713–1731, Des 2024, doi: 10.1007/s41748-024-00481-2.
C. Diao dan G. Li, “Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology,” Remote Sens., vol. 14, no. 9, hal. 1957, Apr 2022, doi: 10.3390/rs14091957.
Y. Zhao et al., “Evaluating fine-scale phenology from PlanetScope satellites with ground observations across temperate forests in eastern North America,” Remote Sens. Environ., vol. 283, hal. 113310, Des 2022, doi: 10.1016/j.rse.2022.113310.
A. Aklilu Tesfaye dan B. Gessesse Awoke, “Evaluation of The Saturation Property of Vegetation Indices Derived from Sentinel-2 In Mixed Crop-Forest Ecosystem,” Spat. Inf. Res., vol. 29, no. 1, hal. 109–121, Feb 2021, doi: 10.1007/s41324-020-00339-5.
N. G. Silleos, T. K. Alexandridis, I. Z. Gitas, and K. Perakis, "Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years," Geocarto Int., vol. 21, no. 4, pp. 21–28, 2006.
A. Bannari, D. Morin, F. Bonn, dan A. R. Huete, “A Review of Vegetation Indices,” Remote Sens. Rev., vol. 13, no. 1–2, hal. 95–120, Agu 1995, doi: 10.1080/02757259509532298.
J. Xue dan B. Su, “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications,” J. Sensors, vol. 2017, hal. 1–17, 2017, doi: 10.1155/2017/1353691.
Rama Rao, M. Kapoor, N. Sharma, dan K. Venkateswarlu, “Yield Prediction And Waterlogging Assessment for Tea Plantation Land Using Satellite Image‐Based Techniques,” Int. J. Remote Sens., vol. 28, no. 7, hal. 1561–1576, Apr 2007, doi: 10.1080/01431160600904980.
Li, Y. Wang, K. Fan, Y. Mao, Y. Shen, dan Z. Ding, “Evaluation of Important Phenotypic Parameters of Tea Plantations Using Multi-Source Remote Sensing Data,” Front. Plant Sci., vol. 13, Jul 2022, doi: 10.3389/fpls.2022.898962.
N. Xing et al., “A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index,” Remote Sens., vol. 12, no. 1, hal. 16, Des 2019, doi: 10.3390/rs12010016.
B. Zheng, J. B. Campbell, G. Serbin, dan J. M. Galbraith, “Remote Sensing of Crop Residue and Tillage Practices: Present Capabilities and Future Prospects,” Soil Tillage Res., vol. 138, hal. 26–34, Mei 2014, doi: 10.1016/j.still.2013.12.009.
Waspadi dan P. Danoedoro, “Comparing Per-Pixel and Object-Based Classification Results Using Two Different Land-Cover/Land-Use Classification Schemes: A Case Study Using Landsat-8 OLI Imagery,” in Sixth International Symposium on LAPAN-IPB Satellite, Des 2019, hal. 126, doi: 10.1117/12.2541876.
K. Yan et al., “A Global Systematic Review of The Remote Sensing Vegetation Indices,” Int. J. Appl. Earth Obs. Geoinf., vol. 139, hal. 104560, Mei 2025, doi: 10.1016/j.jag.2025.104560.
G. Galidaki et al., “Vegetation Biomass Estimation with Remote Sensing: Focus on Forest And Other Wooded Land Over The Mediterranean Ecosystem,” Int. J. Remote Sens., vol. 38, no. 7, hal. 1940–1966, Apr 2020, doi: 10.1080/01431161.2016.1266113.
Y. Zeng et al., “Optical vegetation indices for monitoring terrestrial ecosystems globally,” Nat. Rev. Earth Environ., vol. 3, no. 7, hal. 477–493, Mei 2022, doi: 10.1038/s43017-022-00298-5.
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