Pendugaan Kadar Air di Lahan Tanaman Kopi Malang Selatan dengan Penginderaan Jauh
Abstract
Soil water availability is the basis for plant growth, so its availability is the main key for coffee plants growing in South Malang. Fluctuations in groundwater content, especially in the dynamics of climate change, must be managed so that plant water needs are always met, while remote sensing is able to provide aspects of estimating the condition of soil water content in coffee plantations in South Malang. This research was conducted from July 2019 to October 2019. The water content in coffee plantations can be estimated by combining the vegetation index value and soil surface temperature because coffee plants have shade. The method used in this research is to make a tentative map obtained from the overlay between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) maps, the data is then regressed with the field results. The selection of research locations is based on the area of the coffee plantation using the smallest pixel, which is in an area of 3 km square meters. Each location of the research location was carried out four times for sampling the soil moisture content. The results showed that the correlation value between water content and the estimation results had a correlation of 0.814, with a regression value of 0.663 and a probability value of 0.9226.
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Achmad, E., Hamzah, H., Albayudi, A., & Bima, B. (2019). Indeks Kelembaban Taman Nasional Bukit Tiga Puluh Menggunakan Citra Satelit Landsat 8. https://repository.unja.ac.id/id/eprint/13303
Chemura, A. (2014). The growth response of coffee (Coffea arabica L) plants to organic manure, inorganic fertilizers and integrated soil fertility management under different irrigation water supply levels. International Journal of Recycling of Organic Waste in Agriculture, 3 (2), 59. https://doi.org/10.1007/s40093-014-0059-x
Christianto, Y. B., Prasetyo, S. Y. J., & Hartomo, K. D. (2019). Analisis Data Citra Landsat 8 OLI Sebagai Indeks Prediksi Kekeringan Menggunakan Machine Learning di Wilayah Kabupaten Boyolali dan Purworejo. Indonesian Journal of Computing and Modeling, 2(2), 25-36. https://ejournal.uksw.edu/icm/article/view/2954
Evrili, N. (2020). Ta: Analisis Tingkat Produktivitas dan Kesehatan Kelapa Sawit Menggunakan Data Foto Udara Multispketral dan Lidar (Studi Kasus: Kecamatan Batin XXIV, Provinsi Jambi) (Doctoral dissertation, Institut Teknologi Nasional Bandung).
http://eprints.itenas.ac.id/id/eprint/1362.
Fadilah, S. R. (2018). Ekstraksi Data Kedalaman Menggunakan Data Citra Landsat-8. Jurnal Online Mahasiswa (JOM) Bidang Teknik Geodesi, 1(1). https://jom.unpak.ac.id/index.php/ teknikgeodesi/article/view/1109
Febrianti, N., Murtilaksono, K., & Barus, B. (2019). Analisis Model Estimasi Tinggi Muka Air Tanah Menggunakan Indek Kekeringan. Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital, 15(1). http://dx.doi.org/10.30536/j.pjpdcd.2018.v15.a2867
Hadiwijaya, Y., Kusumiyati, K., & Munawar, A. A. (2020). Penerapan Teknologi Visible-Near Infrared Spectroscopy untuk Prediksi Cepat dan Simultan Kadar Air Buah Melon (Cucumis melo L.) Golden. Agroteknika, 3(2), 67-74. https://doi.org/10.32530/agroteknika.v3i2.83.
Ihsan, F., Anwari, A., & Iswahyudi, A. (2021, October). Distribusi Temporal Kelembaban dan Suhu Tanah di Desa Blumbungan Menggunakan Sistem Informasi Geografis dan Arduino. In Seminar Nasional Humaniora dan Aplikasi Teknologi Informasi (SEHATI) (Vol. 7, No. 1, pp. 11-19). http://www.prosiding.uim.ac.id/index.php/sehati/article/view/3
Jaya, I. N. S., & Etyarsah, S. (2021). Analisis Citra Digital Perspektif Penginderaan Jauh untuk Pengelolaan Sumber Daya Alam (Vol. 1). PT Penerbit IPB Press. INS Jaya, S Etyarsah - 2021 - books.google.com
Lailia, N., Arafaha, F., Jaelania, L. M., Subehie, L., Pamungkas, A., Koenhardonoc, E. S., Sulisetyono, A., (2015). Development of Water Quality Parameter Retrieval Algorithms for Estimating Total Suspended Solids and Chlorophyll-aConcentration using Landsat-8 Imagery at Poteran Island Water ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-2/W2, Joint International Geoinformation. Kuala Lumpur. Malaysia. http://eprints.itn.ac.id/id/eprint/2852
Malakar, N. K., Hulley, G. C., Hook, S. J., Laraby, K., Cook, M., & Schott, J. R. (2018). An operational land surface temperature product for Landsat thermal data: Methodology and validation. IEEE Transactions on Geoscience and Remote Sensing, 56(10), 5717-5735. DOI: 10.1109/TGRS.2018.2824828
Mulyani, A. S. (2021). Antisipasi Terjadinya Pemanasan Global Dengan Deteksi Dini Suhu Permukaan Air Menggunakan Data Satelit. Jurnal Rekayasa Teknik Sipil dan Lingkungan-CENTECH, 2(1), 22-29. https://doi.org/10.33541/cen.v2i1.2807
Que, V. K. S., Prasetyo, S. Y. J., & Fibriani, C. (2019). Analisis Perbedaan Indeks Vegetasi Normalized Difference Vegetation Index (NDVI) dan Normalized Burn Ratio (NBR) Kabupaten Pelalawan Menggunakan Citra Satelit Landsat 8. Indonesian Journal of Computing and Modeling, 2(1), 1-7. https://ejournal.uksw.edu/icm/article/view/2534
Rajeshwari, A. and Mani, N.D. (2014) Estimation of Land Surface Temperature of Dindigul District Using Landsat 8 Data. International Journal of Research in Engineering and Technology, 3, 2319-1163. https://doi.org/10.15623/ijret
Rumondor, B. M., Wenas, D. R., & Rondonuwu, A. T. (2021). Analisis Temporal Distribusi Temperatur Permukaan Manifestasi Panas Bumi Menggunakan Citra Landsat 8 untuk Mengetahui Arah Lateral Panas di Sekitar Gunung Tampusu. Jurnal FisTa: Fisika dan Terapannya, 2(1), 14-20. https://www.eurekaunima.com/index.php/fista/article/view/100
Sagita, A. R., Margaliu, A. S. C., Rizal, F., & Mazzaluna, H. P. (2022). Analisis Korelasi Suhu Permukaan, NDVI, Elevasi dan Pola Perubahan Suhu Daerah Panas Bumi Rendingan-Ulubelu-Waypanas, Tanggamus Menggunakan Citra Landsat 8 OLI/TIRS. Jurnal Geosains dan Remote Sensing, 3(1), 43-51. https://doi.org/10.23960/jgrs.2022.v3i1.72
Situngkir, D., & Marbun, P. (2018). Pendugaan Tingkat Bahaya Erosi pada Hutan dan Lahan Kopi Arabika (Coffea arabica) di Kecamatan Sibolangit. Jurnal Pertanian Tropik, 5(1), 30-35. http://repositori.usu.ac.id/handle/123456789/11349
Song, K., Li, L., Wang, Z., Liu, D., Zhang, B., Xu, J., Du, J., Li, L., Li, S. (2012). Retrieval of Total Suspended Matter and Chlorophyll-A Concentration from Remote-Sensing Data for Drinking Water Resources, Environmental Monitoring and Assessment, Mrch, Vol. 184, issue 3, 1449 -1470. https://doi.org/10.1007/s10661-011-2053-3
Suprapto, H. (2021). Integrasi Penginderaan Jauh dan Sistem Informasi Geografi untuk Lokasi Industri Pabrik Semen. Jurnal Swarnabhumi: Jurnal Geografi dan Pembelajaran Geografi, 6(2), 143-156. http://dx.doi.org/10.31851/swarnabhumi.v6i2.5643
Utami, D. N. A. (2020). TA: Analisis Korelasi Suhu Permukaan Tanah Berbasiskan Citra Landsat 8 Tirs dengan Data Terrain Srtm di Kota Bandung Tahun 2015 dan 2019 (Doctoral dissertation, Institut Teknologi Nasional Bandung). http://eprints.itenas.ac.id/id/eprint/1354
Wood, A. J. (2005). Eco-physiological adaptations to limited water environments. Dalam: Jenks MA, Hasegawa PM (ed) Plant Abiotic Stress. Blackwell Publishing Ltd, India. p. 1-13. https://doi.org/10.1002/9780470988503.ch1
Zauhairah, S. F., Barus, B., Wahjunie, E. D., Tjahjono, B., & Murtadho, A. (2022). Penentuan Pemetaan Kadar Air Tanah Optimal pada Lahan Perkebunan Kelapa Sawit (Studi Kasus: Kebun Cikasungka, Pt Perkebunan Nusantara VIII, Cimulang, Bogor). Jurnal Tanah dan Sumberdaya Lahan, 9(2), 447-456. https://doi.org/10.21776/ub.jtsl.2022.009.2.26
DOI: https://doi.org/10.35308/jal.v9i2.6924
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