Application of Machine Learning and Change Vector Analysis for Monitoring Forest Cover Change in Lore Lindu National Park, Indonesia

Nabila Shaffana Zhafira Suwiji; I Nengah Surati Jaya; Nining Puspaningsih; Tatang Tiryana

doi:10.33904/ejfe.1675580

Application of Machine Learning and Change Vector Analysis for Monitoring Forest Cover Change in Lore Lindu National Park, Indonesia

Abstract

This study aimed to develop a Decision Tree (DT) algorithm to detect forest cover change and determine its most influential driven variables in Lore Lindu National Park, Indonesia. To achieve these objectives, the study employed NDVI, NDBI, and NGBLUE indices, along with advanced Change Vector Analysis (CVA), which accounted for both the magnitude and direction of changes. These assessments were based on Landsat images acquired in 1990, 2003, and 2020 to identify key patterns in land cover change caused by deforestation, forest degradation, and regrowth. The patterns observed were the transition from shrub or bush to dryland, abandoned agriculture, bare land, settlement, waterbody, paddy, cloud, and land without change. The results showed that change detection accuracies were 94.7% for the overall accuracy and 93.3% for the Kappa Accuracy for 1990–2003, as well as 96.1% and 94.9% for the 2003–2020 period, respectively. Furthermore, the most significant influential variables identified in 1990–2003 and 2003–2020 were the distance from the forest edge and delta NGBLUE, respectively. This study showed that deforestation and forest degradation covered an area of 2,720.9 ha (an average of 209.3 ha per year) and 14,512 ha in 1990–2003. From 2003 to 2020, deforestation increased significantly to 24,559.8 ha (an average of 1,444.7 ha per year) without forest degradation. In line with the results, deforestation was found to be the conversion of primary and secondary forests into agricultural land.

Keywords

Forest cover change , Machine learning , Decision Tree , Change Vector Analysis

References

Ajie, I. S., Deliar, A., Virtriana, R. 2020. Spatial Analysis of Driving Factors on Land Cover Change’s Clusters in West Java Province. IOP Conference Series: Earth and Environmental Science, 448(1). DOI: 10.1088/1755-1315/448/1/012094.
Anderson, J.R., Hardy, E.E., Roach, J.T., Witmer, R.E. 1976. A Land Use and Land Cover Classification System For Use With Remote Sensor Data. U. S. Geological Survey, Geological Survey Professional Paper.
Bаtykova, A., Vitalii, D., Aizhamal, Z., Ainura, R., and Indira, B. 2024. Сonducting Geobotanical Survey of Pastures Using Gis Technologies. BIO Web of Conferences 83. DOI: 10.1051/bioconf/20248303007.
Boussema, S.B.F., Faiza, K.A., Rania, A., Balkis, C., Abd-Alla, G. 2023. Assessing and Monitoring the Effects of Land Cover Changes in Biodiversity. Case Study: Mediterranean Coastal Region, Sousse, Tunisia. Egyptian Journal of Remote Sensing and Space Science, 26(1):185–96. DOI: 10.1016/j.ejrs.2023.01.002.
Breiman, L. 2001. Random forests. Machine Learning, 45(1): 5-32.
Dahlan, Jaya, I.N.S., Saleh, M.B., Nining, P., Muzailin, A. 2023. Developing a Spatial Mathematical Model for Assessing the Rate of Natural Forest Changes. Aceh International Journal of Science and Technology, 12(1):14–24. DOI: 10.13170/aijst.12.1.31703.
Dhanoa, M.S., Sanderson, R., Lister, S.J., Cardenas, L.M., Ellis, J.L., López, S., France, J. 2024. Decision Tree Learning with Random Forest Models Using Agricultural and Ecological Field Data Incorporating Multi-Factor Studies and Covariate Structure. Cab Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources, 19(1). DOI: https://doi.org/10.1079/cabireviews.2024.0030.
Doshi-Velez, F., Kim, B. 2017. Advancing a stringent science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Druga, M., Adam, R. 2023. Comparison of DEM-Derived Determinants for Modelling of Long-Term Land Cover Change in a Large Scale: Case Studies from Slovak Western Carpathians. Geographia Cassoviensis, 17(1):21–36. DOI: 10.33542/GC2023-1-02.
Guidotti, R., Monreale, A., Ruggieri, S., Turini, F., Giannotti, F., Pedreschi, D. 2018. A Survey of Methods for Explaining Black Box Models. ACM Computing Surveys, 51(5): 1-42.

He, C., Yuanyuan Z., Jie T., Peijun S., Qingxu, H. 2013. Improving Change Vector Analysis by Cross-Correlogram Spectral Matching for Accurate Detection of Land-Cover Conversion. International Journal of Remote Sensing, 34(4):1127–45. DOI: 10.1080/01431161.2012.718458.
Homer, C., Jon, D., Suming, J., George, X., Catherine, C., Patrick, D., Leila, G., Michelle, F., James, W., Stephen, S., Roger, A., Kurt, R. 2020. Conterminous United States Land Cover Change Patterns 2001–2016 from the 2016 National Land Cover Database. ISPRS Journal of Photogrammetry and Remote Sensing, 162:184–99. DOI: 10.1016/j.isprsjprs.2020.02.019.
Hossain, M.S., Khan, M.A.H., Oluwajuwon, T.V., Biswas, J., Abdullah, S.M.R., Tanvir, M.S.S.I., Munira, S., and Chowdhury, M.N.A. 2023. Spatiotemporal Change Detection of Land Use Land Cover (LULC) in Fashiakhali Wildlife Sanctuary (FKWS) Impact Area, Bangladesh, Employing Multispectral Images and GIS. Modeling Earth Systems and Environment, 9(3):3151–73. DOI: 10.1007/s40808-022-01653-7.
Hossain, M.D., Dongmei, C. 2024. Performance Comparison of Deep Learning (DL)-Based Tabular Models for Building Mapping Using High-Resolution Red, Green, and Blue Imagery and the Geographic Object-Based Image Analysis Framework. Remote Sensing, 16(5):878. DOI: 10.3390/rs16050878.
Hu, Y., Raza, A., Syed, N.R., Acharki, S., Ray, R.L., Hussain, S., Dehghanisanij, H., Zubair, M., Elbeltagi, A. 2023. Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province. Sustainability, 15(4):3572. DOI: 10.3390/su15043572.
Huang, C., Zan, X., Yang, X., Zhang, S. 2016. Surface Water Change Detection Using Change Vector Analysis. International Geoscience and Remote Sensing Symposium 1–23.
Jaya, I.N.S., Kobayashi, S. 1995. Detecting Changes in Forest Vegetation Using Multitemporal Landsat TM Data: A Case Study in the Shibata Forest, Niigata Prefecture. Journal of Forest Planning, 1(1):23–38. DOI:10.20659/jfp.1.1_23.
Johnson, R.D., Kasischke, E.S. 1998. Change Vector Analysis: A Technique for the Multispectral Monitoring of Land Cover and Condition. International Journal of Remote Sensing, 19(3):411–26. DOI:10.1080/014311698216062.
Júnior, O.A.C., Guimarães, R.F., Alan, R.G., Nilton, C. S., Gomes, R.A.T. 2011. A New Approach to Change Vector Analysis Using Distance and Similarity Measures. Remote Sensing, 3(11):2473–93. DOI: 10.3390/rs3112473.
Lambin, E.F., Meyfroidt, P. 2010. Land Use Transitions: Socio-Ecological Feedback versus Socio-Economic Change. Land Use Policy, 27(2):108–18. DOI: 10.1016/j.landusepol.2009.09.003.
Landis, J.R., Koch., G.G. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics, 33(1):159–74.
Lin, C., Lui, L.M. 2024. Shape Prior Segmentation Guided by Harmonic Beltrami Signature. ArXiv Preprint, 2407(04360):1–34.
Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.I. 2020. From Local Explanations to Global Understanding with Explainable AI for Trees. Nature Machine Intelligence, 2(1): 56-67.
Maertens, M., Zeller, M., Birner, R. 2006. Sustainable Agricultural Intensification in Forest Frontier Areas. Agricultural Economics, 34(2):197–206. DOI: 10.1111/j.1574-0864.2006.00118.x.
Malik, A., Santi, N.A., Misra, M., Hamka, H. 2023. Spatial-Temporal Analysis of Forest Deforestation in Lore-Lindu National Park. AGROLAND The Agricultural Sciences Journal (e-Journal), 10(1):58–69. DOI:10.22487/agroland.v10i1.1750.
Maxwell, A.E., Warner, T.A., Fang, F. 2018. Implementation of machine-learning classification in remote sensing: an applied review. International Journal of Remote Sensing, 39(9): 2784-2817.
Molnar, C. 2020. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. ISBN 978-0-244-76852-2. 320 p.
Rudin, C. 2019. Cease elucidating black box machine learning models for critical choices and employ interpretable models instead. Nature Machine Intelligence, 1(5): 206-215.
Munawir, A., June, T., Kusmana, C., and Setiawan, Y. 2019. Dynamics Factors That Affect the Land Use Change in the Lore Lindu National Park, Indonesia. Sixth International Symposium on LAPAN-IPB Satellite (December 2019):155. DOI: 10.1117/ 12.2542812.
Mwangi, N., Waithaka, H., Mundia, C., Kinyanjui, M., Mutua, F. 2020. Assessment of Drivers of Forest Changes Using Multi-Temporal Analysis and Boosted Regression Trees Model: A Case Study of Nyeri County, Central Region of Kenya. Modeling Earth Systems and Environment, 6:1657-1670. DOI: 10.1007/s40808-020-00781-2 Padial-Iglesias, M., Ninyerola, M., Serra, P., González-Guerrero, Ò., Espelta, J.M., Pino, J., Pons, X. 2022. Driving Forces of Forest Expansion Dynamics across the Iberian Peninsula (1987–2017): A Spatio-Temporal Transect. Forests, 13(3). DOI: 10.3390/f13030475
Pasquarella, V.J., Morreale, L.L., Brown, C.F., Kilbride, J.B., Thompson, J.R. 2023. “Not-so-Random Forests: Comparing Voting and Decision Tree Ensembles for Characterizing Partial Harvest Events.” International Journal of Applied Earth Observation and Geoinformation 125(June):103561. DOI: 10.1016/j. jag.2023.103561.
Precinoto, R.S., Prieto, P.V., Figueiredo, M.S.L., Lorini, M.L. 2022. Edges as Hotspots and Drivers of Forest Cover Change in a Tropical Landscape. Perspectives in Ecology and Conservation, 20(4):314–21. DOI: 10.1016/j.pecon.2022.07.001.
Rahman, S., Mesev, V. 2019. Change Vector Analysis, Tasseled Cap, and NDVI-NDMI for Measuring Land Use/Cover Changes Caused by a Sudden Short-Term Severe Drought: 2011 Texas Event. Remote Sensing, 11(19). DOI: 10.3390/rs11192217.
Richards, J.A., 1986. Remote Sensing Digital Image Analysis an Introduction. Berlin: Springer-Verlag Berlin Heidelberg.
Samardžić-Petrović, M., Dragićević, M., Bajat, B., Kovačević, M. 2015. Exploring the Decision Tree Method for Modelling Urban Land Use Change. Geomatica, 69(3):313–25. DOI: 10.5623/cig2015-305.
Santi, N.A., Jaya, I.N.S., Saleh, M.B., Syaufina, L., Kuncahyo, B. 2022. Detecting Burnt Severity and Vegetation Regrowth Classes Using a Change Vector Analysis Approach: A Case Study in the Southern Part of Sumatra, Indonesia. International Journal of Wildland Fire, 31(12):1114–28. DOI: 10.1071/wf21190.
Schwarze, S., Erasmi, S., Priess, J.A., Zeller, M. 2009. Do National Parks Reduce Deforestation? The Effectiveness of the Lore-Lindu National Park in Indonesia. Project on Stability of Rainforest Margins-STORMA. 30(30).
Shu, B., Zhang, H., Li, Y., Qu, Y., Chen, L. 2014. Spatiotemporal Variation Analysis of Driving Forces of Urban Land Spatial Expansion Using Logistic Regression: A Case Study of Port Towns in Taicang City, China. Habitat International, 43:181–90. DOI: 10.1016/j.habitatint.2014.02.004.
Suni, M.A., Muis, H., Arianingsih, I. 2023. Analysis and Spatial Modeling of Land Cover Changes in Limited Production Forests of Kulawi Subdistrict, Sigi Regency, 10(2):273–84. DOI: 10.21776/ub.jtsl.2023.010.2.11.
Tesfay, F., Tadesse, S.A., Getahun, Y.S., Lemma, E., Gebremedhn, A.Y. 2023. Evaluating the Impact of Land Use Land Cover Changes on the Values of Ecosystem Services in the Chacha Watershed, Ethiopia’s Central Highland. Environmental and Sustainability Indicators, 18:100256. DOI: 10.1016/j.indic.2023.100256.
Tuakora, M.A., Mardiatmoko, G., Lelloltery, H. 2022. Faktor-Faktor Yang Mempengaruhi Perubahan Penutupan Lahan Di Dalam Kawasan Hutan Lindung Gunung Sirimau. Makila, 16(1):60–68. DOI: 10.30598/makila.v16i1.5234.
Vieira, R.M.S.P., Tomasella, J., Barbosa, A.A., Polizel, S.P., Ometto, J.P.H.B., Santos, F.C., Ferreira, Y.C., Toledo, P.M. 2021. Land Degradation Mapping in the MATOPIBA Region (Brazil) Using Remote Sensing Data and Decision-Tree Analysis. Science of the Total Environment, 782:146900. DOI: 10.1016/j.scitotenv. 2021.146900.
Wahla, S.S., Kazmi, J.H., Tariq, A. 2023. Mapping and Monitoring of Spatio-Temporal Land Use and Land Cover Changes and Relationship with Normalized Satellite Indices and Driving Factors. Geology, Ecology, and Landscapes, 00(00):1–17. DOI: 10.1080/24749508.2023.2187567.
Wang, W., Zhang, C., Allen, J.M., Li, W., Boyer, M.A., Segerson, K., Silander, J.A. 2016. Analysis and Prediction of Land Use Changes Related to Invasive Species and Major Driving Forces in the State of Connecticut. Land, 5(3). DOI: 10.3390/land5030025.
Wu, Y., Zhao P., Wang, F., Zhou, M., Geng, S., Zhang, D. 2024. A Prior-Guided Dual Branch Multi-Feature Fusion Network for Building Segmentation in Remote Sensing Images. Buildings, 14(7). DOI: 10.3390/buildings14072006.
Xu, H., Lan, Y., Zhang, S., Tian, B., Yu, H., Wang, X., Zhao, S., Wang, Z., Yang, D., Zhao, J. 2023. Research on Vegetation Cover Extraction Method of Summer Maize Based on UAV Visible Light Image. International Journal of Precision Agricultural Aviation, 6(1):44–51. DOI: 10.33440/j.ijpaa.20230601.197.
Yasin, M.Y., Abdullah, A., Noor, N.M., Yusoff, M.M., Noor, N.M. 2022. Landsat Observation of Urban Growth and Land Use Change Using NDVI and NDBI Analysis. IOP Conference Series: Earth and Environmental Science, 1067(1). DOI: 10.1088/ 1755-1315/1067/1/012037.
Younes, A., Ahmad, A., Hanjagi, A.D., Nair, A.M. 2023. Understanding Dynamics of Land Use & Land Cover Change Using GIS & Change Detection Techniques in Tartous, Syria. European Journal of Geography, 14(3):20–41. DOI: 10.48088/EJG.A.YOU.14.3.020.041.
Yun-Hao, C., Xiao-Bing, Li., Pei-Jun, S., Feng, X. 2001. The Change of NDVI Time Series Based on Change Vector Analysis in China. 2001 International Conferences on Info-Tech and Info-Net: A Key to Better Life, ICII 2001 - Proceedings 1:127–32. DOI: 10.1109/ICII.2001.982733.
Zha, Y., Gao, J., Ni, S. 2003. Use of Normalized Difference Built-up Index in Automatically Mapping Urban Areas from TM Imagery. International Journal of Remote Sensing, 24(3):583–94. DOI: 10.1080/01431160304987.
Zhou, H.F., Zhang, J.W., Zhou, Y.Q., Guo, X.J., Ma, Y.M. 2021. A Feature Selection Algorithm of Decision Tree Based on Feature Weight. Expert Systems with Applications, 164:113842. DOI: 10.1016/j.eswa.2020.113842.
Zuo, D., Chen, G., Wang, Q., Xu, Z., Han, Y., Peng, D., Pang, B., Abbaspour, K.C., Yang, H. 2023. Assessment of Changes in Water Conservation Capacity under Land Degradation Neutrality Effects in a Typical Watershed of Yellow River Basin, China. Ecological Indicators, 148:110145. DOI: 10.1016/ j.ecolind.2023.110145.

Details

Primary Language

English

Subjects

Photogrammetry and Remote Sensing , Forestry Sciences (Other)

Journal Section

Research Article

Authors

Nabila Shaffana Zhafira Suwiji
0009-0002-0244-5460
Indonesia

I Nengah Surati Jaya ^*
0000-0002-3868-7595
Indonesia

Nining Puspaningsih
0000-0002-1561-2339
Indonesia

Tatang Tiryana
0000-0002-8430-7311
Indonesia

Early Pub Date

October 19, 2025

Publication Date

December 25, 2025

Submission Date

April 14, 2025

Acceptance Date

September 17, 2025

Published in Issue

Year 1970 Volume: 11 Number: 2

DOI

https://doi.org/10.33904/ejfe.1675580

IZ

https://izlik.org/JA23CS37GS

APA

Suwiji, N. S. Z., Jaya, I. N. S., Puspaningsih, N., & Tiryana, T. (2025). Application of Machine Learning and Change Vector Analysis for Monitoring Forest Cover Change in Lore Lindu National Park, Indonesia. European Journal of Forest Engineering, 11(2), 160-173. https://doi.org/10.33904/ejfe.1675580

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