DOI : https://doi.org/10.18517/ijaseit.13.1.16550

Recognition of Agricultural Land-Use Change with Machine Learning-Based for Regional Food Security Assessment in Kulon Progo Plains Area

Zulfa Khoirun Nisa (1), Ansita Gupitakingkin Pradipta (2), Liana Ni'mathus Sholikah (3), Bangkit Fatwa Pratama (4), Akram Sripandam Prihanantya (5), Ngadisih (6), Sahid Susanto (7), Sigit Supadmo Arif (8)
(1) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(2) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(3) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(4) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(5) Department of Geodetic Engineering, Universitas Gadjah Mada, Grafika Street No. 2, Sleman, 55281, Indonesia
(6) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(7) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
(8) Department of Agricultural and Biosystems Engineering, Universitas Gadjah Mada, Sleman, 55281, Indonesia
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[1]
Z. K. Nisa, “Recognition of Agricultural Land-Use Change with Machine Learning-Based for Regional Food Security Assessment in Kulon Progo Plains Area”, Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 1, pp. 54–61, Jan. 2023.
Citation Format :
High conversion of agricultural land in Kulon Progo Regency, as such the construction of the Yogyakarta International Airport (YIA) and the Bedah Menoreh road, has resulted in food production and impacted food security, including Kulon Progo plains area. This study aimed to calculate the conversion rate of agricultural land and analyze its impact on food security in the Kulon Progo plains area from 2005 to 2020. The primary materials needed are Kulon Progo administrative maps, Landsat 7 and 8 images, land productivity data, population data, and consumption per capita data. With tools used is Google Earth Engine (GEE), SPSS 25, Google Earth Pro, and ArcGIS 10.3. The method used is calculating the Normalized Difference Vegetation Index (NDVI) and machine learning-based classification through GEE to identify land-use change and analyze the state of food security. The study proved that between 2015 and 2020, there was a conversion of paddy fields, with an average rate of 126 ha/year. The existence of new paddy fields influenced this land increase. However, in 2020 there is still food insecurity in Pengasih District, thus caused by the new paddy fields not being optimally used for rice growth. The productivity of the land produced is not optimal. With the availability of agricultural land in 2020 (1382.85 ha), food self-sufficiency will be limited for the next 24.75 years if there is no effort to increase paddy fields.

M. H. Jamil, R. M. Rukka, A. N. Tenriawaru, R. Achmad, A. T. Nugraha, and Y. T. Walangadi, “The existence of rice fields in Makassar City,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 681, no. 1, doi: 10.1088/1755-1315/681/1/012091.

S. B. Khade, R. S. Khillare, and M. B. Dastagiri, “Global Livestock Development: Policies and Vision,” Indian J. Anim. Sci., vol. 91, no. 9, pp. 770-779, 2021.

S. Liu, S. Gao, W. L. Hsu, Y. C. Shiau, and H. L. Liu, “Mechanism study on the impact of china population structure change on the water use of the three main industries,” Sustain., vol. 14, no. 1, 2022, doi: 10.3390/su14010204.

K. T. Deribew, “Spatiotemporal analysis of urban growth on forest and agricultural land using geospatial techniques and Shannon entropy method in the satellite town of Ethiopia, the western fringe of Addis Ababa city,” Ecol. Process., vol. 9, no. 1, 2020, doi: 10.1186/s13717-020-00248-3.

D. A. Puspitaningrum, “System dynamic modelling of agriculture land availability,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 250, no. 1, p. 2022, doi: 10.1088/1755-1315/250/1/012087.

L. N. Sholikah et al., “Identification of agricultural land use change based on machine learning for regional food security analysis in the mountainous region of Kulon Progo regency,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 922, no. 1, p. 012060, doi: 10.1088/1755-1315/922/1/012060.

S. A. Purba, B. Slamet, and A. Rauf, “Spatial Modelling of Land Conversion Vulnerability In Padang Watersheds North Sumatera Province,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 782, no. 3, p. 2022, doi: 10.1088/1755-1315/782/3/032026.

A. Garud and B. Rao, “Understanding the Implications of the Loss of Peri-Urban Arable Land—A Case of Pune Metropolitan Region,” in Lecture Notes in Civil Engineering, 2021, vol. 121 LNCE, pp. 433-445, doi: 10.1007/978-981-33-4114-2_35.

F. Firmansyah, C. Susetyo, N. A. Pratomoatmojo, U. F. Kurniawati, and M. Yusuf, “Land Use Change Trend of Paddy Field and Its Influence on Food Security In Gerbangkertosusila Region,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 778, no. 1, doi: 10.1088/1755-1315/778/1/012023.

V. K. Nguyen, D. Dumaresq, and J. Pittock, “Impacts of rice intensification on rural households in the Mekong Delta: emerging relationships between agricultural production, wild food supply and food consumption,” Food Secur., vol. 10, no. 6, pp. 1615-1629, 2018, doi: 10.1007/s12571-018-0848-6.

A. Raj and L. K. Sharma, “Assessment of land-use dynamics of the Aravalli range (India) using integrated geospatial and CART approach,” Earth Sci. Informatics, vol. 15, no. 1, pp. 497-522, 2022, doi: 10.1007/s12145-021-00753-9.

H. Mustaqim, “Rainfall Analysis for Meteorological Drought in Kulon Progo Regency 2006-2015,” Universitas Muhamadiyah Surakarta, 2016.

M. Qonita, “Agricultural planning based on local agro-climatology assessment in Bongkot, Purwodadi, Purworejo,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 686, no. 1, doi: 10.1088/1755-1315/686/1/012052.

RPI2-JM Randal Task Force Team for DIY Creative Works, “RPI2-JM KULON PROGO REGENCY 2015-2019,” Yogyakarta, 2014.

S. E. Prasetyowati and Y. Sunaryo, “Effect of ameliorants on canopy architectures of jack bean (Canavalia ensiformis) cultivated in marginal soils,” in IOP Conference Series: Earth and Environmental Science, 2021, vol. 681, no. 1, doi: 10.1088/1755-1315/681/1/012036.

G. Gusnidar, F. Fitria, L. Maira, and Y. Yulnafatmawita, “Role of compost derived from rice straw and tithonia in improving chemical fertility of Regosol on onion cultivation,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 347, no. 1, doi: 10.1088/1755-1315/347/1/012095.

L. Mao et al., “Improved geochemical baseline establishment based on diffuse sources contribution of potential toxic elements in agricultural alluvial soils,” Geoderma, vol. 410, no. July 2021, p. 115669, 2022, doi: 10.1016/j.geoderma.2021.115669.

L. Wang, J. Wang, and F. Qin, “Feature fusion approach for temporal land use mapping in complex agricultural areas,” Remote Sens. (Multidisciplinary Digit. Publ. Institute), vol. 13, no. 13, 2021, doi: 10.3390/rs13132517.

S. Karki et al., “Mapping Spatial Distribution and Biomass of Intertidal Ulva Blooms Using Machine Learning and Earth Observation,” Front. Mar. Sci., vol. 8, no. April, pp. 1-20, 2021, doi: 10.3389/fmars.2021.633128.

Z. E. Kulenbekov, S. Z. Orunbaev, and B. D. Asanov, “Investigation of the High Mountain Vegetation Using Satellite Imagery, Kyrgyzstan,” Springer Water, pp. 151-168, 2021, doi: 10.1007/978-3-030-68337-5_15.

H. Ismanto, A. Doloksaribu, D. S. Susanti, and D. F. Septarini, “The accuracy of remote sensing image interprepation on changes in land use suitability in merauke regency papua,” in International Journal of Engineering Trends and Technology, 2020, vol. 68, no. 10, pp. 42-47, doi: 10.14445/22315381/IJETT-V68I10P207.

M. Bamdadinejad, M. J. Ketabdari, and S. M. H. Chavooshi, “Shoreline Extraction Using Image Processing of Satellite Imageries,” J. Indian Soc. Remote Sens., vol. 49, no. 10, pp. 2365-2375, 2021, doi: 10.1007/s12524-021-01398-3.

A. Hedayati, M. H. Vahidnia, and S. Behzadi, “Paddy lands detection using Landsat-8 satellite images and object-based classification in Rasht city, Iran,” Egypt. J. Remote Sens. Sp. Sci., vol. 25, no. 1, pp. 73-84, 2022, doi: 10.1016/j.ejrs.2021.12.008.

I. M. Y. Prasada, A. Dhamira, and Masyhuri, “The potential loss of rice production due to wetland conversion in East Java,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 230, no. 1, pp. 0-6, doi: 10.1088/1755-1315/230/1/012005.

D. Nofriati, N. Asni, and S. Primilestari, “The Study of Paddy Harvest Losses Determination on Tidal Land in Tanjung Jabung Timur Region Jambi Province,” in IOP Conference Series: Earth and Environmental Science, 2019, vol. 309, no. 1, pp. 0-4, doi: 10.1088/1755-1315/309/1/012017.

A. A. Adenle, K. Wedig, and H. Azadi, “Sustainable agriculture and food security in Africa: The role of innovative technologies and international organizations,” Technol. Soc., vol. 58, no. April, p. 101143, 2019, doi: 10.1016/j.techsoc.2019.05.007.

R. Martanto, Analysis of Land Use Change Patterns for Rice Self-Sufficiency Stability in Sukoharjo Regency. Yogyakarta: STPN Press, 2019.

E. F. Akmam, T. Siswantining, S. M. Soemartojo, and D. Sarwinda, “Multiple Imputation with Predictive Mean Matching Method for Numerical Missing Data,” in ICICOS 2019 - 3rd International Conference on Informatics and Computational Sciences: Accelerating Informatics and Computational Research for Smarter Society in The Era of Industry 4.0, Proceedings, 2019, p. 2022, doi: 10.1109/ICICoS48119.2019.8982510.

N. Ponganan, T. Horanont, K. Artlert, and P. Nuallaong, “Land Cover Classification using Google Earth Engine’s Object-oriented and Machine Learning Classifier,” in 2021 2nd International Conference on Big Data Analytics and Practices, IBDAP 2021, 2021, pp. 33-37, doi: 10.1109/IBDAP52511.2021.9552099.

N. H. Quang et al., “Multi-decadal changes in mangrove extent, age and species in the Red River Estuaries of Viet Nam,” Remote Sens. (Multidisciplinary Digit. Publ. Institute), vol. 12, no. 14, 2020, doi: 10.3390/rs12142289.

N. Case and A. Vitti, “Reconstruction of multi-temporal satellite imagery by coupling variational segmentation and radiometric analysis,” ISPRS Int. J. Geo-Information, vol. 10, no. 1, 2021, doi: 10.3390/ijgi10010017.

B. Irawan and S. Friyanto, “The Impact of Rice Field Conversion in Java on Rice Production and Its Control Policy,” no. 1, pp. 1-33, 2002.

J. Pan, “Improved two-stage model averaging for high-dimensional linear regression, with application to Riboflavin data analysis,” BMC Bioinformatics, vol. 22, no. 1, pp. 1-17, 2021, doi: 10.1186/s12859-021-04053-3.

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