LAND USE CHANGE MODELING ABOUT THE ARRANGEMENT OF THE MANAGEMENT BLOCK BUKIT BARISAN GREAT FOREST PARK, NORTH SUMATRA
Keywords:
LCM Model, Land Use Change, Prediction, Bukit Barisan Great Forest ParkAbstract
Bukit Barisan Great Forest Park (THRBB) is designated as the third TAHURA in Indonesia. TAHURA functions as a source of genetics and germplasm, erosion control, information and research center, education and conservation work, recreation and tourism services. Currently, the THRBB area is experiencing many land use changes that are not by the THRBB management block plan. Land use change modeling using the Land Change Modeler (LCM) instrument was carried out to predict land use change patterns by selecting a Multi-Layer Perceptron (MLP) to evaluate several variables that influence land use change. This research aims to analyze changes in land use in 2011-2018, factors influencing changes, predictions for 2033 and develop land use directions in the THRBB area. The results of the analysis show that during the 2011-2018 period, primary forest land decreased by 710 ha (2.19%), secondary forest by 1,185 ha (43.94%), and shrubs by 139 ha (7.58%). The decrease in forest land area was followed by an increase in open land area by 448 ha (59.74%) and dry land agriculture by 1,586 ha (57.46%). The pattern of land change in the THRBB area is from forest land to bushland, open land and dry land agriculture. The most significant driving factor for land use change is distance from roads. The prediction results for land cover in 2033 show that the area of primary forest will be 30,156 ha, secondary forest 1,236 ha, shrubs 2,833 ha, open land 1,375 ha and dry land agriculture 4,074 ha. The direction of land use policy in the THRBB area is forest restoration policy, increasing protection and security activities and law enforcement.
Downloads
References
Hapsary, M. S. A., Subiyanto, S., & Firdaus, H. S. (2021). Analisis prediksi perubahan penggunaan lahan dengan pendekatan artificial neural network dan regresi logistik di kota Balikpapan. Jurnal Geodesi UNDIP, 10(2), 88–97.
Hasan, S., Shi, W., Zhu, X., Abbas, S., & Khan, H. U. A. (2020). Future simulation of land use changes in rapidly urbanizing South China based on land change modeler and remote sensing data. Sustainability, 12(11), 4350.
Indonesia, M. L. H. dan K. R. (2015). Kriteria Zona Pengelolaan Taman Nasional dan Blok Pengelolaan Cagar Alam, Suaka Margasatwa, Taman Hutan Raya dan Taman Wisata Alam.
KLHK. (2015). Peraturan Direktur Jenderal dan Planologi Kehutanan nomor: P.1/VII-IPSDH/2015 Tentang Pedoman Pemantauan Penutupan Lahan. In Kementerian Lingkungan Hidup dan Kehutanan Direktorat Jenderal Planologi Kehutanan (pp. 1–17).
Kubangun, S. H. (2015). Model spasial bahaya lahan kritis di Kabupaten Bogor, Cianjur, dan Sukabumi [tesis]. Bogor (ID): Program Pascasarjana, Institut Pertanian Bogor.
Kubangun, S. H., Haridjaja, O., & Gandasasmita, K. (2016). Model perubahan penutupan/penggunaan lahan untuk identifikasi lahan kritis di Kabupaten Bogor, Kabupaten Cianjur, dan Kabupaten Sukabumi. Majalah Ilmiah Globe, 18(1), 21–32.
Kusniawati, I., Subiyanto, S., & Amarrohman, F. J. (2019). Analisis model perubahan penggunaan lahan menggunakan artificial neural network di Kota Salatiga. Jurnal Geodesi Undip, 9(1), 1–11.
Leta, M. K., Demissie, T. A., & Tränckner, J. (2021). Modeling and prediction of land use land cover change dynamics based on land change modeler (Lcm) in nashe watershed, upper blue nile basin, Ethiopia. Sustainability, 13(7), 3740.
Liu, X., Huang, Y., Xu, X., Li, X., Li, X., Ciais, P., Lin, P., Gong, K., Ziegler, A. D., & Chen, A. (2020). High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015. Nature Sustainability, 3(7), 564–570.
Maru, R., Nasaruddin, N., Ikhsan, M., & Laka, B. M. (2015). Perubahan penggunaan lahan Kota Makassar tahun 1990-2010. SAINSMAT" Jurnal Sains, Matematika, Dan Pembelajarannya, 4(2), 113–125.
Mas, J.-F., Lemoine-Rodríguez, R., González-López, R., López-Sánchez, J., Piña-Garduño, A., & Herrera-Flores, E. (2017). Land use/land cover change detection combining automatic processing and visual interpretation. European Journal of Remote Sensing, 50(1), 626–635.
Pérez-Vega, A., Mas, J.-F., & Ligmann-Zielinska, A. (2012). Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software, 29(1), 11–23.
Petit, C. C., & Lambin, E. F. (2001). Integration of multi-source remote sensing data for land cover change detection. International Journal of Geographical Information Science, 15(8), 785–803.
Rahmah, A. N., Subiyanto, S., & Amarrohman, F. J. (2019). Pemodelan perubahan penggunaan lahan dengan Artificial Neural Network (ANN) di kota Semarang. Jurnal Geodesi UNDIP, 9(1), 197–206.
Regmi, S. R., Thapa, M. S., & Regmi, R. R. (2020). Drivers and Dynamics of Land Use Land Cover in Phewa Watershed, Kaski, Nepal. Journal of Forest and Natural Resource Management, 2(1), 19–36.
Rimal, B., Zhang, L., Keshtkar, H., Wang, N., & Lin, Y. (2017). Monitoring and modeling of spatiotemporal urban expansion and land-use/land-cover change using integrated Markov chain cellular automata model. ISPRS International Journal of Geo-Information, 6(9), 288.
SK RPJP TAHURA Bukit Barisan.PDF. (n.d.).
Wang, J., & Maduako, I. N. (2018). Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction. European Journal of Remote Sensing, 51(1), 251–265.