DOI: https://doi.org/10.14710/jil.22.2.313-325

Pemodelan Ensemble Prediksi Distribusi Ekologis Padi (Oryza sativa) di Provinsi Kalimatan Utara

1Program Studi Ilmu Pengelolaan Sumberdaya Alam dan Lingkungan, Sekolah Pascasarjana, IPB University, Bogor 16144, Indonesia, Indonesia

2Departemen Silvikultur, Fakultas Kehutanan dan Lingkungan, IPB University, Bogor 16680, Indonesia, Indonesia

3Departemen Ilmu Ekonomi, Fakultas Ekonomi dan Manajemen, IPB University, Bogor 16680, Indonesia, Indonesia

Received: 5 Aug 2022; Revised: 2 Sep 2023; Accepted: 8 Nov 2023; Available online: 4 Feb 2024; Published: 15 Feb 2024.
Editor(s): Budi Warsito

Citation Format:
Abstract

Pemerintah Provinsi Kalimantan Utara berusaha mencapai ketahanan pangan dengan prinsip kemandirian pangan melalui perluasan lahan pertanian. Penilaian kesesuaian lahan pertanian, terutama untuk padi, dilakukan menggunakan pendekatan pemodelan ensemble yang melibatkan lima algoritma pembelajaran mesin. Model-model ini dibangun menggunakan paket species distribution modeling (SDM) di RStudio dengan pembagian data pelatihan dan pengujian 70:30 serta pengaturan parameter termasuk bootstrapping dan tiga kali pengulangan. Hasil penelitian menunjukkan variasi dalam respons variabel prediktor antara algoritma. Variabel NDVI memiliki pengaruh tertinggi pada SVM dan BRT (masing-masing 48,1% dan 36,6%), sementara variabel jarak dari jalan paling berpengaruh pada GLM, MARS, dan RF (masing-masing 44,6%, 27,6%, dan 26,5%). Distribusi padi (sawah) bervariasi antara model, dengan RF memiliki persentase tertinggi (6,34%). Evaluasi kinerja model-model ini menunjukkan bahwa model RF memiliki akurasi terbaik, sementara GLM memiliki akurasi buruk dalam nilai Kappa Cohen. Model ensemble memperoleh akurasi yang dapat diterima dengan nilai masing-masing 0,96; 0,70; dan 0,71 untuk AUC, TSS, dan Kappa. Dengan demikian, pendekatan pemodelan multi-algoritma dengan model ensemble memungkinkan penilaian yang lebih baik terhadap variabilitas dalam kinerja algoritma dan menghasilkan peta kesesuaian distribusi padi yang lebih baik daripada algoritma tunggal.

Fulltext View|Download
Keywords: Ensemble; Model distribusi spesies; Regresi; Pembelajaran mesin; Kesesuaian lahan padi.

Article Metrics:

Article Info
Section: Research Article
Language : ID
Recent articles
Pemodelan Ensemble Prediksi Distribusi Ekologis Padi (Oryza sativa) di Provinsi Kalimatan Utara Perbandingan Pencemaran Pestisida dan Logam Berat di Beberapa Negara ASEAN: Systematic Review Pemanfaatan Larva Ulat Jerman (Zophobas morio) sebagai Agen Biodegradasi Stirofoam yang Ramah Lingkungan More recent articles
Most cited articles
PENGARUH EMISI UDARA PADA SENTRA PENGOLAHAN BATU KAPUR TERHADAP KAPASITAS VITAL PARU PEKERJA DAN MASYARAKAT DI DESA KARAS KECAMATAN SEDAN KABUPATEN REMBANG Analisis Tingkat Kenyamanan Di DKI Jakarta Berdasarkan Indeks THI (Temperature Humidity Index) Perencanaan Kegiatan Wisata Pendidikan Dalam Kawasan Geopark Rinjani Lombok Berbasis Daya Dukung Lingkungan (Studi Daerah Aik Berik) Evaluasi Penerapan Program Adiwiyata Untuk Membentuk Perilaku Peduli Lingkungan di Kalangan Siswa (Kasus: SMA Negeri 9 Tangerang Selatan dan MA Negeri 1 Serpong) Review: Mekanisme Akumulasi Logam Berat di Ekosistem Pascatambang Timah More cited articles
  1. Abdi, A. M., (2020), Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data, GIScience & Remote Sensing, 57(1), 1-20. doi: 10.1080/15481603.2019.1650447
  2. Abdulhafedh, A., (2017), A Novel Hybrid Method for Measuring the Spatial Autocorrelation of Vehicular Crashes: Combining Moran’s Index and Getis-Ord G i* Statistic, Open Journal of Civil Engineering, 7(02), 208. doi: 10.4236/ojce.2017.72013
  3. Ahmed, N., Atzberger, C., and Zewdie, W., (2020), Integration of remote sensing and bioclimatic data for prediction of invasive species distribution in data-poor regions: a review on challenges and opportunities, Environmental Systems Research, 9(1), 1-18. doi: 10.1186/s40068-020-00195-0
  4. Ahmed, N., Atzberger, C. and Zewdie, W., (2021), Species Distribution Modelling performance and its implication for Sentinel-2-based prediction of invasive Prosopis juliflora in lower Awash River basin, Ethiopia. Ecol Process, 10(1), 1-18. https://doi.org/10.1186/s13717-021-00285-6
  5. Akpoti K., Kabo-Bah A.T., Dossou-Yovo E.R., Groen T.A., Zwart S.J., 2020. Mapping suitability for rice production in inland valley landscapes in Benin and Togo using environmental niche modeling. Science of the total environment. 709, 136165. doi: 10.1016/j.scitotenv.2019.136165
  6. Allouche, O., Tsoar, A., and Kadmon, R. (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of applied ecology, 43(6), 1223-1232. doi: 10.1111/j.1365-2664.2006.01214.x
  7. Andriawan, R., Martanto, R., and Muryono, S., (2020), Evaluasi Kesesuaian Potensi Lahan Pertanian Pangan Berkelanjutan Terhadap Rencana Tata Ruang Wilayah, Tunas Agraria, 3(3). doi: 10.31292/jta.v3i3.126
  8. Aquino, D. D. N., Rocha Neto, O. C. D., Moreira, M. A., Teixeira, A. D. S., and Andrade, E. M. D., (2018), Use of remote sensing to identify areas at risk of degradation in the semi-arid region. Revista Ciência Agronômica, 49, 420-429. doi: 10.5935/1806-6690.20180047
  9. Austin, M. P., (2002), Spatial prediction of species distribution: an interface between ecological theory and statistical modelling, Ecological modelling, 157(2-3), 101-118. doi : 10.1016/S0304-3800(02)00205-3
  10. Badan Pusat Statistik, (2019), Konversi Gabah ke Beras Tahun 2018, Badan Pusat Statistik, Jakarta. ISBN: 978-602-438-250-6
  11. Badan Pusat Statistik Kalimantan Utara, 2020, Provinsi Kalimantan Utara dalam Angka Tahun 2020, Badan Pusat Statistik Kalimantan Utara, Tanjung Selor. ISSN: 2621-9891
  12. Bobihoe, J., Asni, N., dan Endrizal, E., (2015), Kajian Teknologi Mina Padi di Rawa Lebak di Kabupaten Batanghari Provinsi Jambi, Jurnal Lahan Suboptimal, 4(1): 47-56
  13. Byeon, D., Jung, S., Lee, and W.-H., (2018), Review of CLIMEX and MaxEnt for studying species distribution in South Korea. Journal of Asia-Pacific Biodiversity, 11(3), 325–333. doi: 10.1016/j.japb.2018.06.002
  14. Cicchetti, D. V., and Feinstein, A. R., (1990), High agreement but low kappa: II. Resolving the paradoxes, Journal of clinical epidemiology, 43(6), 551-558. doi: 0.1016/0895-4356(90)90159-m
  15. Cohen, J., (1960), A coefficient of agreement for nominal scales, Educational and psychological measurement, 20(1), 37-46. Doi: 10.1177/001316446002000104
  16. Cortes, C., and Vapnik, V., (1995), Support-vector networks. Machine learning, 20(3), 273-297. doi: 10.1109/64.163674
  17. Chhogyel, N., Kumar, L., Bajgai, Y., and Jayasinghe, L. S., (2020), Prediction of Bhutan's ecological distribution of rice (Oryza sativa L.) under the impact of climate change through maximum entropy modeling, The Journal of Agricultural Science, 158(1-2), 25-37. doi: 10.1017/S0021859620000350
  18. Dang, A. T. N., Kumar, L., and Reid, M., (2020), Modelling the Potential Impacts of Climate Change on Rice Cultivation in Mekong Delta, Vietnam., Sustainability, 12(22), 9608. doi: 10.3390/su12229608
  19. Davis, A. P., Gole, T. W., Baena, S., and Moat, J., (2012), The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities, PloS one, 7(11), e47981. doi: 10.1371/journal.pone.0047981
  20. Duan, J., and Zhou, G., (2012), Climatic suitability of double rice planting regions in China, Scientia Agricultura Sinica, 45(2), 218-227
  21. Elith, J., and Leathwick, J., (2007), Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines, Diversity and Distributions, 13(3), 265–275. doi: 10.1111/j.1472-4642.2007.00340.x
  22. Elith, J., Leathwick, J. R., and Hastie, T., (2008), A working guide to boosted regression trees, Journal of animal ecology, 77(4), 802-813. doi: 10.1111/j.13652656.2008.01390.x
  23. Engler, R., Waser, L. T., Zimmermann, N. E., Schaub, M., Berdos, S., Ginzler, C., and Psomas, A., (2013), Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution. Forest Ecology and Management, 310, 64-73. doi: 10.4236/ojce.2017.72013
  24. Fielding, A. H., and Bell, J. F., (1997), A review of methods for the assessment of prediction errors in conservation presence/ absence models, Environmental Conservation, 24(1), 38-49. doi: 10.1017/s0376892997000088
  25. Friedman, J. H., (1991), Multivariate adaptive regression splines, The annals of statistics, 19(1), 1-67. doi: 10.1214/aos/1176347963
  26. Früh, L., Kampen, H., Kerkow, A., Schaub, G. A., Walther, D., and Wieland, R., (2018), Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations, Ecological Modelling, 388, 136-144. doi: 10.1016/j.ecolmodel.2018.08.011
  27. Fourcade, Y., Engler, J. O., Rödder, D., and Secondi, J., (2014), Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PloS one, 9(5), e9712. doi: 10.1371/journal.pone.0097122
  28. Gu, H., Wang, J., Ma, L., Shang, Z., and Zhang, Q., (2019), Insights into the BRT (Boosted Regression Trees) method in the study of the climate-growth relationship of Masson pine in subtropical China, Forests, 10(3), 228. doi: 10.3390/f10030228
  29. Guisan, A., Edwards Jr, T. C., and Hastie, T., (2002), Generalized linear and generalized additive models in studies of species distributions: setting the scene, Ecological modelling, 157(2-3), 89-100. doi: 10.1016/s0304-3800(02)00204-1
  30. Guisan, A., Zimmermann, N. E., Elith, J., Graham, C. H., Phillips, S., and Peterson, A. T., (2007), What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics?, Ecological monographs, 77(4), 615-630. doi: 10.1890/06-1060.1
  31. González‐Ferreras, A. M., Barquín, J., and Peñas, F. J., (2016), Integration of habitat models to predict fish distributions in several watersheds of N orthern S pain, Journal of Applied Ichthyology, 32(1), 204-216. doi: 10.1111/jai.13024
  32. GRiSP.. (2013). Rice Almanac: Source Book for One of the Most Important Activities on Earth. Global Rice Science Partnership (GRiSP), International Rice Research Institute (IRRI), Los Banos, Philippines
  33. Harrell Jr, F. E., Lee, K. L., and Mark, D. B., (1996), Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors, Statistics in medicine, 15(4), 361-387. doi: 10.1002/0470023678.ch2b(i)
  34. He, Q., and Zhou, G., (2016), Climate-associated distribution of summer maize in China from 1961 to 2010, Agriculture, Ecosystems & Environment, 232, 326-335. doi: 10.1016/j.agee.2016.08.020
  35. Hijmans RJ, Elith J., (2019) Spatial distribution models. Di akses dari https://rspatial.org/sdm/SDM.pdf tanggal 23 Mei 2022
  36. Hossell, J. E., Ellis, N. E., Harley, M. J., and Hepburn, I. R., (2003), Climate change and nature conservation: Implications for policy and practice in Britain and Ireland, Journal for Nature Conservation, 11(1), 67-73. doi : 10.1078/1617-1381-00034
  37. Howard PL., (2019), Human adaptation to invasive species: a conceptual framework based on a case study meta-synthesis. Ambio 48:1401–1430. https://doi.org/10.1007/s13280-019-01297-5
  38. Immitzer M, Neuwirth M, Böck S, Brenner H, Vuolo F, Atzberger C., (2019), Optimal input features for tree species classification in Central Europe Based on multi- temporal Sentinel-2 data. Remote Sens 11:2599. https://doi.org/10.3390/rs11222599
  39. Jalaeian, M., Golizadeh, A., Sarafrazi, A., and Naimi, B., (2018), Inferring climatic controls of rice stem borers’ spatial distributions using maximum entropy modelling, Journal of Applied Entomology, 142(4), 388-396. doi: 10.1111/jen.12493
  40. Jensen, T., Seerup Hass, F., Seam Akbar, M., Holm Petersen, P., and Jokar Arsanjani, J., (2020), Employing machine learning for detection of invasive species using sentinel-2 and aviris data: The case of Kudzu in the United States, Sustainability, 12(9), 3544. doi: 10.3390/SU12093544
  41. Kariyawasam, C. S., Kumar, L., and Ratnayake, S. S., (2019), Invasive plant species establishment and range dynamics in Sri Lanka under climate change. Entropy, 21(6), 571. doi: 10.3390/e21060571
  42. Kementerian Negara Lingkungan Hidup, (2009), Peraturan Menteri Negara Lingkungan Hidup Nomor 17 Tahun 2009 tentang Pedoman Penentuan Daya Dukung Lingkungan Hidup Dalam Penataan Ruang Wilayah, Kementerian Negara Lingkungan Hidup, Jakarta
  43. Kementerian Pekerjaan Umum, (2007), Peraturan Menteri Pekerjaan Umum Nomor 20/PRT/M/2007 tentang Teknik Analisis Aspek Fisik dan Lingkungan, Ekonomi, Serta Sosial Budaya dalam Penyusunan Rencana Tata Ruang, Kementerian Pekerjaan Umum, Jakarta
  44. Kosicki, J. Z., (2020), Generalised Additive Models and Random Forest Approach as effective methods for predictive species density and functional species richness, Environmental and ecological statistics, 27(2), 273-292. doi: 10.1007/s10651-020-00445-5
  45. Kogo, B. K., Kumar, L., Koech, R., and Kariyawasam, C. S., (2019), Modelling climate suitability for rainfed Maize cultivation in Kenya using a Maximum Entropy (MaxENT) approach, Agronomy, 9(11), 727. doi: 10.3390/agronomy9110727
  46. Lantz, C. A., and Nebenzahl, E., (1996), Behavior and interpretation of the κ statistic: Resolution of the two paradoxes, Journal of clinical epidemiology, 49(4), 431-434. doi: 10.1016/0895-4356(95)00571-4
  47. Landmann T, Dubovyk O, Ghazaryan G, Kimani J, Abdel-Rahman E., (2020), Wide area invasive species propagation mapping is possible using phenometric trends. ISPRS J Photogrammetry Remote Sens 159:1–12. https://doi.org/10.1016/j.isprsjprs.2019.10.016
  48. Lamsal, P., Kumar, L., Aryal, A., and Atreya, K., (2018), Invasive alien plant species dynamics in the Himalayan region under climate change, Ambio, 47(6), 697-710. doi: 10.1016/S0304-3800(02)00205-3
  49. Layomi Jayasinghe, S., Kumar, L., and Sandamali, J., (2019), Assessment of potential land suitability for tea (Camellia sinensis (L.) O. Kuntze) in Sri Lanka using a GIS-based multi-criteria approach, Agriculture, 9(7), 148. Doi: 10.3390/agriculture9070148
  50. Leathwick, J. R., Elith, J., and Hastie, T., (2006), Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distributions, Ecological Modelling, 199(2), 188–196. doi: 10.1016/j.ecolmodel.2006.05.022
  51. Lima, T. A., Beuchle, R., Langner, A., Grecchi, R. C., Griess, V. C., and Achard, F., (2019), Comparing Sentinel-2 MSI and Landsat 8 OLI imagery for monitoring selective logging in the Brazilian Amazon, Remote Sensing, 11(8), 961. doi: 10.3390/rs11080961
  52. Linders T, Bekele K, Schaffner U, Allan E, Alamirew T, Choge S., (2020), The impact of invasive species on social-ecological systems: relating supply and use of selected provisioning ecosystem services. Ecosyst Serv 41:101055. https://doi.org/10.1016/j.ecoser.2019.101055
  53. Liu, C., Berry, P. M., Dawson, T. P., and Pearson, R. G., (2005), Selecting thresholds of occurrence in the prediction of species distributions, Ecography, 28(3), 385-393. doi: 10.1111/j.0906-7590.2005.03957.x
  54. Manel, S., Dias, J. M., and Ormerod, S. J., (1999), Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan River bird, Ecological modelling, 120(2-3), 337-347. doi: 10.1016/s0304-3800(99)00113-1
  55. Manel, S., Williams, H. C., and Ormerod, S. J., (2001), Evaluating presence–absence models in ecology: the need to account for prevalence, Journal of applied Ecology, 38(5), 921-931. doi: 10.1046/j.1365-2664.2001.00647.x
  56. McPherson, J. M., Jetz, W., and Rogers, D. J., (2004), The effects of species’ range sizes on the accuracy of distribution models: ecological phenomenon or statistical artefact?, Journal of applied ecology, 41(5), 811-823. doi: 10.1111/j.0021-8901.2004.00943.x
  57. Mi, C., Huettmann, F., Guo, Y., Han, X., and Wen, L., (2017), Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence, PeerJ, 5, e2849. doi: 10.7717/peerj.2849
  58. Mugo, R., and Saitoh, S.-I., (2020), Ensemble Modelling of Skipjack Tuna (Katsuwonus pelamis) Habitats in the Western North Pacific Using Satellite Remotely Sensed Data; a Comparative Analysis Using Machine-Learning Models. Remote Sensing, 12(16), 2591. doi: 10.3390/rs12162591
  59. Muthayya, S., Sugimoto, J.D., Montgomery, S. and Maberly, G.F., (2014), An overview of global rice production, supply, trade, and consumption, Annals of the New York Academy of Sciences, Vol. 1324, pp 7–14. doi: 10.1111/nyas.12540
  60. Nafi A. Y., dan Basuki Y., 2019, Penentuan kawasan sawah berkelanjutan. Jurnal Pembangunan Wilayah dan Kota, 15(3), 214-226. doi: 10.14710/pwk.v15i3.21570
  61. Naimi, B., and Araújo, M. B., (2016), SDM: a reproducible and extensible R platform for species distribution modelling. Ecography, 39(4), 368-375. doi: 10.1111/ecog.01881
  62. Ng, W. T., Meroni, M., Immitzer, M., Böck, S., Leonardi, U., Rembold, F., Gadain, H., & Atzberger, C., (2016), Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland, Int J Appl Earth Observ Geoinf, 53, 76-89. doi: 10.1016/j.jag.2016.07.019
  63. Ng, W. T., Cândido de Oliveira Silva, A., Rima, P., Atzberger, C., and Immitzer, M., (2018), Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya. Ecology and evolution, 8(23), 11921-11931. doi: 10.1002/ece3.4649
  64. Parra, J. L., Graham, C. C., and Freile, J. F., (2004), Evaluating alternative data sets for ecological niche models of birds in the Andes, Ecography, 27(3), 350-360. doi: 10.1111/j.0906-7590.2004.03822.x
  65. Paski, J. A. I., Faski, G. I. S. L., Handoyo, M. F., dan Pertiwi, D. S., (2017), Analisis neraca air lahan untuk tanaman padi dan jagung di Kota Bengkulu, Jurnal Ilmu Lingkungan, 15(2), 83-89. doi: 10.14710/jil.15.2.83-89
  66. Pearce, J., and Ferrier, S., (2000), Evaluating the predictive performance of habitat models developed using logistic regression, Ecological modelling, 133(3), 225-245. doi: 10.1016/s0304-3800(00)00322-7
  67. Pearson, R. G., Dawson, T. P., and Liu, C., (2004), Modelling species distributions in Britain: a hierarchical integration of climate and land‐cover data, Ecography, 27(3), 285-298. Doi: 10.1111/j.0906-7590.2004.03740.x
  68. Pearson, R. G., Thuiller, W., Araújo, M. B., Martinez‐Meyer, E., Brotons, L., McClean, C., Miles, L., Segurado, P., Dawson, T.E., and Lees, D. C., (2006), Model‐based uncertainty in species range prediction, Journal of biogeography, 33(10), 1704-1711. doi: 10.1111/j.1365-2699.2006.01460.x
  69. Petit, S., Chamberlain, D., Haysom, K., Pywell, R., Vickery, J., Warman, L., Allen, D., and Firbank, L., (2003), Knowledge‐based models for predicting species occurrence in arable conditions, Ecography, 26(5), 626-640. doi: 10.1034/j.1600-0587.2003.03545.x
  70. Raczko, E, and BogdanZ., (2018), Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images" Remote Sensing 10, no. 7: 1111. https://doi.org/10.3390/rs10071111
  71. Rajah, P., Odindi, J., and Mutanga, O., (2018), Evaluating the potential of freely available multispectral remotely sensed imagery in mapping American bramble (Rubus cuneifolius). South African Geographical Journal, 100(3), 291-307. https://doi.org/10.1080/03736245.2018.1461683
  72. Ratnayake, S. S., Kumar, L., and Kariyawasam, C. S., (2019), Neglected and underutilized fruit species in Sri Lanka: prioritisation and understanding the potential distribution under climate change, Agronomy, 10(1), 34. doi: 10.3390/agronomy10010034
  73. Ravindra K, Rattan P, Mor S, and Aggarwal AN (2019), Generalized additive models: building evidence of air pollution, climate change and human health. Environ Int 132:104987. https://doi.org/10.1016/j.envint.2019.104987
  74. Reese, G. C., Wilson, K. R., Hoeting, J. A., and Flather, C. H., (2005), Factors affecting species distribution predictions: a simulation modeling experiment, Ecological Applications, 15(2), 554-564. doi: 10.1890/03-5374
  75. Remya, K., Ramachandran, A., and Jayakumar, A. S., (2015), Predicting the current and future suitable habitat distribution of Myristica dactyloides Gaertn. using MaxEnt model in the Eastern Ghats, India. Ecological engineering, 82,184-188. doi: 10.1016/j.ecoleng.2015.04.053
  76. roRustan, A. S., dan Aziza, T. N., (2008), Kompleksitas Penanganan Penguatan Ketahanan Pangan. Jurnal Borneo Administrator, 4(1). doi: 10.24258/jba.v4i1.24
  77. Seoane, J., Carrascal, L. M., Alonso, C. L., and Palomino, D., (2005), Species-specific traits associated to prediction errors in bird habitat suitability modelling, Ecological Modelling, 185(2-4), 299-308. doi: 10.1016/j.ecolmodel.2004.12.012
  78. Soepraptohardjo, M., and Suhardjo, H., (1978), Rice soils of Indonesia, Soils and rice, 99-115
  79. Soultan, A., and Safi, K., (2017), The interplay of various sources of noise on reliability of species distribution models hinges on ecological specialization, PloS one, 12(11), e0187906. doi: 10.1371/journal.pone.0187906
  80. Subroto, G., dan Susetyo, C., (2016), Identifikasi Variabel-Variabel yang Mempengaruhi Penentuan Lahan Pertanian Pangan Berkelanjutan di Kabupaten Jombang, Jawa Timur. Jurnal Teknik ITS, 5(2), C129-C133. doi: 10.12962/j23373539.v5i2.18297
  81. Tarigan, S. D., dan Syumanjaya, R., (2013), Analisis Pengaruh Kualitas Infrastruktur Jalan terhadap Harga-Harga Hasil Pertanian di Kecamatan Dolok Silau, Jurnal Ekonomi dan Keuangan, 1(6), 14750
  82. Thuiller, W., Guéguen, M., Renaud, J., Karger, D. N., and Zimmermann, N. E., (2019), Uncertainty in ensembles of global biodiversity scenarios, Nature Communications, 10(1), 1-9. doi: 10.1038/s41467-019-09519-w
  83. Sabat-Tomala A, Edwin R, and Bogdan Z. (2020), Comparison of Support Vector Machine and Random Forest Algorithms for Invasive and Expansive Species Classification Using Airborne Hyperspectral Data" Remote Sensing 12, no. 3: 516. https://doi.org/10.3390/rs12030516
  84. West, A. M., Evangelista, P. H., Jarnevich, C. S., Kumar, S., Swallow, A., Luizza, M. W., & Chignell, S. M., (2017), Using multi-date satellite imagery to monitor invasive grass species distribution in post-wildfire landscapes: An iterative, adaptable approach that employs open-source data and software. Int J Appl Earth Observ Geoinf, 59, 135-146. doi: 10.1016/j.jag.2017.03.009
  85. West, A. M., Evangelista, P. H., Jarnevich, C. S., Young, N. E., Stohlgren, T. J., Talbert, C., Talbert, M., Morisette, J., and Anderson, R., (2016), Integrating remote sensing with species distribution models; mapping tamarisk invasions using the software for assisted habitat modeling (SAHM), JoVE (Journal of Visualized Experiments), (116), e54578. doi: 10.3791/54578
  86. Yoshida, S., (1981), Fundamentals of Rice Crop Science. International Rice Research Institute, Los Banos, Philippines
  87. Zheng, B. , Myint, S. W. , Thenkabail, P. S. , and Aggarwal, R. M., (2015), A support vector machine to identify irrigated crop types using time-series Landsat NDVI data. International Journal of Applied Earth Observation and Geoinformation , 34 , 103–112. doi: 10.1016/j.jag.2014.07.002

Last update:

No citation recorded.

Last update: 2024-11-20 18:32:53

No citation recorded.