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The Spatial Model of Paddy Productivity Based on Environmental Vulnerability in Each Phase of Paddy Planting

Rahmatia Susanti  -  Indonesia Geospatial Agency, Indonesia
S. Supriatna  -  Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
R. Rokhmatuloh  -  Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
*Masita Dwi Mandini Manessa  -  Department of Geography, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Indonesia
Aris Poniman  -  Department of Geography, Faculty of Mathematics and Natural Sciences, Indonesia University, Indonesia, Indonesia
Yoniar Hufan Ramadhani  -  Geospatial Information Agency, Indonesia

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Abstract

The national primary always growth and increase in line with the increase in population, such as the rise of rice consumption in Indonesia.  Paddy productivity influenced by the physical condition of the land and the declining of those factors can detected from the environmental vulnerability parameters. Purpose of this study was to compile a spatial model of paddy productivity based on environmental vulnerability in each planting phase using the remote sensing and GIS technology approaches. This spatial model is compiled based on the results of the application of two models, namely spatial model of paddy planting phase and paddy productivity. The spatial model of paddy planting phase obtained from the analysis of vegetation index from Sentinel-2A imagery using the random forest classification model. The variables for building the spatial model of the paddy planting phase are a combination of NDVI vegetation index, EVI, SAVI, NDWI, and time variables. The overall accuracy of the paddy planting phase model is 0.92 which divides the paddy planting phase into the initial phase of planting, vegetative phase, generative phase, and fallow phase. The paddy productivity model obtained from environmental vulnerability analysis with GIS using the linear regression method. The variables used are environmental vulnerability variables which consist of hazards from floods, droughts, landslides, and rainfall. Estimation of paddy productivity based on the influence of environmental vulnerability has the best accuracy done at the vegetative phase of 0.63 and the generative phase of 0.61 while in the initial phase of planting cannot be used because it has a weak relationship with an accuracy of 0.35.

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Keywords: Environmental Vulnerability, Paddy Productivity, Paddy Planting Phase, Random Forest Classification Model, Regression Model, Sentinel-2A

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  1. Danoedoro, P. (2012). Pengantar penginderaan jauh digital. Penerbit Andi, Yogyakarta.

  2. Dong, J., Xiao, X., Menarguez, M. A., Zhang, G., Qin, Y., Thau, D., … Moore, B. (2016). Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. Remote Sensing of Environment, 185, 142–154. [https://doi.org/10.1016/j.rse.2016.02.016">Crossref]

  3. Frost, J. (2018). Regression analysis: An intuitive guide for using and interpreting linear models. Statisics By Jim Publishing.

  4. Gao, B. (1996). NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58(3), 257–266. [https://doi.org/10.1016/s0034-4257(96)00067-3">Crossref]

  5. Geospatial Information Agency. (2017). Data of Flood-prone Map.

  6. Grover, D. K., & Upadhya, D. (2014). Research Note: Changing Climate Pattern and Its Impact on Paddy Productivity in Ludhiana District of Punjab. Indian Journal of Agricultural Economics, 69(902-2016–67962), 150–162.

  7. He, Z., Li, S., Wang, Y., Dai, L., & Lin, S. (2018). Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal {RADARSAT}-2 Datasets. Remote Sensing, 10(2), 340. [https://doi.org/10.3390/rs10020340">Crossref]

  8. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1–2), 195–213. [https://doi.org/10.1016/s0034-4257(02)00096-2">Crossref]

  9. Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. [https://doi.org/10.1016/0034-4257(88)90106-x">Crossref]

  10. Lembaga Penerbangan dan Antariksa Nasional. (2015). Pedoman Pemantauan Fase Pertumbuhan Padi Menggunakan Data Satelit Penginderaan Jauh. Jakarta: Pusat Pemanfaatan Penginderaan Jauh, LAPAN.

  11. Liu, M., Wang, T., Skidmore, A. K., & Liu, X. (2018). Heavy metal-induced stress in rice crops detected using multi-temporal Sentinel-2 satellite images. Science of The Total Environment, 637638, 18–29. [https://doi.org/10.1016/j.scitotenv.2018.04.415">Crossref]

  12. McLean, L. D., Guilford, J. P., & Fruchter, B. (1980). Fundamental Statistics in Psychology and Education. Educational Researcher, 8(3), 22. [https://doi.org/10.2307/1174363">Crossref]

  13. Ministry of Energy and Mineral Resources. (2017). Data of Landslide-prone Map.

  14. Muslim, M., Romshoo, S. A., & Rather, A. Q. (2015). Paddy crop yield estimation in Kashmir Himalayan rice bowl using remote sensing and simulation model. Environmental Monitoring and Assessment, 187(6). [https://doi.org/10.1007/s10661-015-4564-9">Crossref]

  15. National Disaster Management Agency. (2012). Regulation of the Head of the National Disaster Management Agency Number 2 of 2012 concerning General Guidelines for Disaster Risk Assessment. Jakarta.

  16. National Disaster Management Agency. (2017). Data of Drought-prone map.

  17. Onojeghuo, A. O., Blackburn, G. A., Wang, Q., Atkinson, P. M., Kindred, D., & Miao, Y. (2017). Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data. International Journal of Remote Sensing, 39(4), 1042–1067. [https://doi.org/10.1080/01431161.2017.1395969">Crossref]

  18. Park, S., Im, J., Park, S., Yoo, C., Han, H., & Rhee, J. (2018). Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data. Remote Sensing, 10(3), 447. [https://doi.org/10.3390/rs10030447">Crossref]

  19. Song, P., Mansaray, L. R., Huang, J., & Huang, W. (2018). Mapping paddy rice agriculture over China using {AMSR}-E time series data. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 469–482. [https://doi.org/10.1016/j.isprsjprs.2018.08.015">Crossref]

  20. Thakur, A. K., Mohanty, R. K., Patil, D. U., & Kumar, A. (2013). Impact of water management on yield and water productivity with system of rice intensification (SRI) and conventional transplanting system in rice. Paddy and Water Environment, 12(4), 413–424. [https://doi.org/10.1007/s10333-013-0397-8">Crossref]

  21. Widiatmaka, Ambarwulan, W., Santoso, P. B. K., Sabiham, S., Machfud, & Hikmat, M. (2016). Remote Sensing and Land Suitability Analysis to Establish Local Specific Inputs for Paddy Fields in Subang, West Java. Procedia Environmental Sciences, 33, 94–107. [https://doi.org/10.1016/j.proenv.2016.03.061">Crossref]


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