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Automatic image segmentation model for indirect land use change with deep convolutional neural network

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Abstract

Inherent limitations of Landsat images restrict the accuracy of land categorization efforts for a better comprehension involved in transforming raw image data into relevant land cover information. Managing mixed pixels and complex spectrum responses, the introduction of advanced algorithmic approaches is essential. Hybrid classification methods that include spectral, spatial, and contextual data can increase the precision of assigning class labels to pixels with confusing features. This paper demonstrate the construction of automatic images segmentation based on deep convolution neural networks with object-oriented integration. Harnessing machine learning approaches in remote sensing images, an exper imental phase showed the best fit model that can be implemented further into larger areas with a Kappa validation value of 99.466% and errors of 0.015 on average. The model used to classify land use to see land degrada tion in Air Bengkulu watershed, the main source of annual flood disaster in Bengkulu area. We found that the area has been reduced by 20%, 1.9%, 48%, and 7.9% for forest, bare land, plantations, and rice fields, respectively, from its initial area of year 2000. Furthermore, increase value of palm oil plantations (7.6%) over the area showed indirectly reason for replacement of agriculture allocation with correlation value of 97.12%.

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References

  1. Webber, J. L., Gibson, M. J., Chen, A. S., Savic, D., Fu, G., & Butler, D. (2018). Rapid assessment of surface-water flood-management options in urban catchments. Urban Water Journal, 15, 210–217. https://doi.org/10.1080/1573062X.2018.1424212

    Article  Google Scholar 

  2. Wingfield, T., Macdonald, N., Peters, K., Spees, J., & Potter, K. (2019). Natural flood management: Beyond the evidence debate. Area, 51(4), 743–751. https://doi.org/10.1111/area.12535

    Article  Google Scholar 

  3. Sulistyo, B., Suhartoyo, H., Adiprasetyo, T., & Hindarto, K. S. (2021). Noviyanti: Accuracy of the level of critical water catchment area for flood mitigation around Bengkulu city Indonesia. Indonesian Journal of Geography, 53, 226–235.

    Article  Google Scholar 

  4. Gunawan, G. (2021). Flood modelling of air Bengkulu watershed. Indonesia, using SUH and HEC-HMS. IOP Conference Series: Earth and Environmental Science, 871, 1–7.

    Google Scholar 

  5. Mase, L. Z. (2020). Slope stability and erosion-sedimentation analyses along sub-watershed of Muara Bangkahulu River in Bengkulu City, Indonesia. In E3S Web of Conferences (Vol. 148). https://doi.org/10.1051/e3sconf/202014803002

  6. BNPB. Infografis Bencana Banjir Dan Longsor Bengkulu. https://bnpb.go.id/infografis/infografis-bencana-banjir-dan-longsor-bengkulu

  7. Parinduri, R.T., Gunawan, G., & Amri, K. (2019). Evaluasi kinerja das air bengkulu dengan menggunakan metode hss nakayasu dan program hec ras versi 5.0.1 (studi kasus das air bengkulu). In Civil engineering and built environment conference 2019.

  8. Rassarandi, F. D., & Tambunan, B. R. S. (2019). Penerapan fuzzy logic Dalam Pembuatan Peta element at risk Bencana Luapan Banjir Sangai air Bengkulu Kota Bengkulu. Jurnal Integrasi, 11(2), 135–139.

    Article  Google Scholar 

  9. Finkbeiner, M. (2014). Indirect land use change - Help beyond the hype? Biomass and Bioenergy, 62, 218–221. https://doi.org/10.1016/j.biombioe.2014.01.024

    Article  Google Scholar 

  10. Brinkman, M. L. J., van der Hilst, F., Faaij, A. P. C., & Wicke, B. (2021). Low-ILUC- risk rapeseed biodiesel: potential and indirect ghg emission effects in eastern Romania. Biofuels. https://doi.org/10.1080/17597269.2018.1464873

    Article  Google Scholar 

  11. Srisunthon, P., & Chawchai, S. (2020). Land-use changes and the effects of oil palm expansion on a peatland in southern Thailand. Frontiers in Earth Science, 8, 559868. https://doi.org/10.3389/feart.2020.559868

    Article  Google Scholar 

  12. Azhar, B., Nobilly, F., Lechner, A. M., Tohiran, K. A., Maxwell, T. M. R., Zulkifli, R., Kamel, M. F., & Oon, A. (2021). Mitigating the risks of indirect land use change (ILUC) related deforestation from industrial palm oil expansion by sharing land access with displaced crop and cattle farmers. Land Use Policy, 107, 105498. https://doi.org/10.1016/j.landusepol.2021.105498

    Article  Google Scholar 

  13. Wicke, B., Sikkema, R., Dornburg, V., & Faaij, A. (2011). Exploring land use changes and the role of palm oil production in Indonesia and Malaysia. Land Use Policy, 28, 193–206. https://doi.org/10.1016/j.landusepol.2010.06.001

    Article  Google Scholar 

  14. Zhou, D. X. (2020). Universality of deep convolutional neural networks. Applied and Computational Harmonic Analysis, 48, 787–794.

    Article  Google Scholar 

  15. Sun, J., He, W.-T., Wang, L., Lai, A., Ji, X., Zhai, X., Li, G., Suchard, M. A., Tian, J., Zhou, J., Veit, M., & Su, S. (2020). Covid-19: Epidemiology, evolution, and cross-disciplinary perspectives. Trends in Molecular Medicine. https://doi.org/10.1016/J.MOLMED.2020.02.008

    Article  Google Scholar 

  16. Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial intelligence review, 53, 5455–5516.

    Article  Google Scholar 

  17. Hansen, S. B., Padfield, R., Syayuti, K., Evers, S., Zakariah, Z., & Mastura, S. (2015). Trends in global palm oil sustainability research. Journal of Cleaner Production, 100, 140–149. https://doi.org/10.1016/j.jclepro.2015.03.051

    Article  Google Scholar 

  18. Taheripour, F., & Tyner, W. E. (2020). Us biofuel production and policy: Implications for land use changes in Malaysia and Indonesia. Biotechnology for Biofuels, 13(1), 1–17. https://doi.org/10.1186/s13068-020-1650-1

    Article  CAS  Google Scholar 

  19. Seydi, S. T., Hasanlou, M., & Amani, M. (2020). A new end-to-end multi- dimensional CNN framework for land cover/land use change detection in multi-source remote sensing datasets. Remote Sensing, 12, 2010.

    Article  Google Scholar 

  20. Zhang, Y., et al. (2021). An integrated CNN model for reconstructing and predicting land use/cover change: A case study of the Baicheng area northeast china. Remote Sensing, 13, 4846.

    Article  Google Scholar 

  21. Amini, S., Saber, M., Rabiei-Dastjerdi, H., & Homayouni, S. (2022). Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sensing, 14, 1–23.

    Article  Google Scholar 

  22. Chaudhury, A., Ward, C., Talasaz, A., Ivanov, A. G., Brophy, M., Grodzin-ski, B., Huner, N. P. A., Patel, R. V., & Barron, J. L. (2019). Machine vision system for 3d plant phenotyping. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16, 2009–2022.

    Article  Google Scholar 

  23. Kolagati, S., Priyadharshini, T., & Rajam, V. M. A. (2022). Exposing deepfakes using a deep multilayer perceptron—convolutional neural network model. International Journal of Information Management Data Insights, 2, 100054. https://doi.org/10.1016/j.jjimei.2021.100054

    Article  Google Scholar 

  24. Nazarova, T., Martin, P., & Giuliani, G. (2020). Monitoring vegetation change in the presence of high cloud cover with sentinel-2 in a lowland tropical forest region in brazil. Remote Sensing, 12, 1829. https://doi.org/10.3390/rs12111829

    Article  Google Scholar 

  25. Lang, L., Xu, K., Zhang, Q., & Wang, D. (2021). Fast and accurate object detection in remote sensing images based on lightweight deep neural network. Sensors, 21, 5460. https://doi.org/10.3390/s21165460

    Article  Google Scholar 

  26. Jin, L., & Liu, G. (2021). An approach on image processing of deep learning based on improved ssd. Symmetry, 13, 495. https://doi.org/10.3390/sym13030495

    Article  Google Scholar 

  27. Deng, Z., Sun, H., Zhou, S., Zhao, J., Lei, L., & Zou, H. (2018). Multi-scale object detection in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 3–22.

    Article  Google Scholar 

  28. Tu, F., Yin, S., Ouyang, P., Tang, S., Liu, L., & Wei, S. (2017). Deep convolutional neural network architecture with reconfigurable computation patterns. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 25, 2220–2233.

    Article  Google Scholar 

  29. Fernandes, F. E., & Yen, G. G. (2021). Pruning deep convolutional neural networks architectures with evolution strategy. Information Sciences, 552, 29–47. https://doi.org/10.1016/j.ins.2020.11.009

    Article  Google Scholar 

  30. Redmon, J., & Farhadi, A. (2017). Yolo v2.0. Cvpr2017.

  31. Bhosle, K., & Musande, V. (2019). Evaluation of deep learning cnn model for land use land cover classification and crop identification using hyperspectral remote sensing images. Journal of the Indian Society of Remote Sensing, 47, 1949–1958.

    Article  Google Scholar 

  32. Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2012). A spatial-spectral kernel- based approach for the classification of remote-sensing images. Pattern Recognition, 45, 381–392. https://doi.org/10.1016/j.patcog.2011.03.035

    Article  Google Scholar 

  33. Decuyper, M., Chavez, R. O., Lohbeck, M., Lastra, J. A., Tsendbazar, N., Hacklander, J., Herold, M., & Vagen, T. G. (2022). Continuous monitoring of forest change dynamics with satellite time series. Remote Sensing of Environment, 269, 112829. https://doi.org/10.1016/j.rse.2021.112829

    Article  Google Scholar 

  34. Ayele, W. Y. (2020). Adapting crisp-dm for idea mining. International Journal of Advanced Computer Science and Applications, 11, 20–32.

    Article  Google Scholar 

  35. Su, G. (2019). Analysis of optimisation method for online education data mining based on big data assessment technology. International Journal of Continuing Engineering Education and Life-Long Learning, 29, 321–335. https://doi.org/10.1504/IJCEELL.2019.102768

    Article  Google Scholar 

  36. Xiong, Y., Guo, S., Chen, J., Deng, X., Sun, L., Zheng, X., & Xu, W. (2020). Improved srgan for remote sensing image super-resolution across locations and sensors. Remote Sensing, 12, 1263. https://doi.org/10.3390/RS12081263

    Article  Google Scholar 

  37. Qin, M., Mavromatis, S., Hu, L., Zhang, F., Liu, R., Sequeira, J., & Du, Z. (2020). Remote sensing single-image resolution improvement using a deep gradient-aware network with image-specific enhancement. Remote Sensing, 12, 758. https://doi.org/10.3390/rs12050758

    Article  Google Scholar 

  38. Heydarian, M., Doyle, T. E., & Samavi, R. (2022). Mlcm: Multi-label confusion matrix. IEEE Access, 10, 19083–19095. https://doi.org/10.1109/ACCESS.2022.3151048

    Article  Google Scholar 

  39. Xu, J., Zhang, Y., & Miao, D. (2020). Three-way confusion matrix for classification: A measure driven view. Information Sciences, 507, 772–794. https://doi.org/10.1016/j.ins.2019.06.064

    Article  Google Scholar 

  40. Sun, Z., Qi, M., Lian, J., Jia, W., Zou, W., He, Y., Liu, H., & Zheng, Y. (2018). Image segmentation by searching for image feature density peaks. Applied Sciences (Switzerland), 8, 969. https://doi.org/10.3390/app8060969

    Article  Google Scholar 

  41. Chen, Y., Fan, R., Bilal, M., Yang, X., Wang, J., & Li, W. (2018). Multilevel cloud detection for high-resolution remote sensing imagery using multiple convo lutional neural networks. ISPRS International Journal of Geo-Information, 7, 181. https://doi.org/10.3390/ijgi7050181

    Article  Google Scholar 

  42. Xiao, P., Zhang, X., Wang, D., Yuan, M., Feng, X., & Kelly, M. (2016). Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition. ISPRS Journal of Photogrammetry and Remote Sensing, 119, 402–414. https://doi.org/10.1016/j.isprsjprs.2016.07.003

    Article  Google Scholar 

  43. Pedersen, E. J., Miller, D. L., Simpson, G. L., & Ross, N. (2019). Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ, 7, e6876. https://doi.org/10.7717/peerj.6876

    Article  Google Scholar 

  44. Wood, S. N. (2020). Mixed gam computation vehicle with automatic smoothness estimation. Generalized Additive Models: An Introduction with R. 2nd Edition, (pp. 1.8–33).

  45. Miculescu, R., Mihail, A., & Urziceanu, S. A. (2020). A new algorithm that generates the image of the attractor of a generalized iterated func tion system. Numerical Algorithms, 83, 1399–1413. https://doi.org/10.1007/s11075-019-00730-w

    Article  Google Scholar 

  46. Ilahude, D. (2010). Heavy metal contents in marine sediments and seawater at Totok bay area, North Sulawesi. Bulletin of the Marine Geology, 25(1), 39–52. https://doi.org/10.32693/bomg.25.1.2010.24

    Article  Google Scholar 

  47. Khalik, I., Sapei, A., Hariyadi, S., & Anggraeni, E. (2022). The water quality characteristics and quality status of Bengkulu river and Nelas river, Bengkulu province: Conditions for the last six years. (vol. 950). https://doi.org/10.1088/1755-1315/950/1/012038

  48. WanMohdJaafar, W. S., Said, N. F. S., Abdul Maulud, K. N., Uning, R., Latif, M. T., Muhmad Kamarulzaman, A. M., Mohan, M., Pradhan, B., Saad, S. N. M., Broadbent, E. N., Cardil, A., Silva, C. A., & Takriff, M. S. (2020). Carbon emissions from oil palm induced forest and peatland conversion in Sabah and Sarawak Malaysia. Forests, 11(12), 1285.

    Article  Google Scholar 

  49. Pambudi, A. S. (2020). The development of social forestry in Indonesia. The Journal of Indonesia Sustainable Development Planning, 1(1), 57–66. https://doi.org/10.46456/jisdep.v1i1.11

    Article  Google Scholar 

  50. Runtuboi, Y. Y., Permadi, D. B., Sahide, M. A. K., & Maryudi, A. (2021). Oil palm plantations, forest conservation and indigenous peoples in west Papua province: What lies ahead? Forest and Society, 5, 2. https://doi.org/10.24259/fs.v5i1.11343

    Article  Google Scholar 

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Acknowledgements

This study is supported by the funding from The Ministry of Education, Culture, Research, and Technology under the contract number 211/E5/PG.02.00.PT/2022, 30 May 2022 and 1946/UN30.15/PP/2022, 16 June 2022 by the Research and Community Service of University of Bengkulu.

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Author notes

  1. Ferzha Utama, Nanang Sugianto, Astri Widyastiti, Rendra Rais and Rido Ismanto have contributed equally to this work.

Authors and Affiliations

  1. Informatics, Universitas Bengkulu, Jalan W.R. Supratman, Bengkulu, 38371, Indonesia

    Arie Vatresia & Astri Widyastiti

  2. Information System, Universitas Bengkulu, Jalan W.R. Supratman, Bengkulu, 38371, Indonesia

    Ferzha Utama

  3. Geophysics, Universitas Bengkulu, Jalan W.R. Supratman, Bengkulu, 38371, Indonesia

    Nanang Sugianto

  4. Conservation and Natural Resource Body, The Ministry of Environmental and Forestry, Jalan Mahoni, Bengkulu, 38225, Indonesia

    Rendra Rais

  5. Research Center for Computing, The National Research and Innovation Agency, Cibinong, Bogor, 16915, Indonesia

    Arie Vatresia & Rido Ismanto

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  1. Arie Vatresia
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Vatresia, A., Utama, F., Sugianto, N. et al. Automatic image segmentation model for indirect land use change with deep convolutional neural network. Spat. Inf. Res. 32, 327–337 (2024). https://doi.org/10.1007/s41324-023-00560-y

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