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DLCD: Deep learning-based change detection approach to monitor deforestation

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Abstract

The large-scale removal of trees from forests to make way for human activities is known as deforestation, given that it may result in soil erosion, natural habitat deterioration, biodiversity loss, and water cycle disturbance, this is a major environmental problem. As a source of food, clean water, oxygen, and medicines as well as an essential component of the hydrological cycle—they supply water to the atmosphere through transpiration—forests are a contributing factor to climate change and global warming. In addition to decreasing soil fertility and rainfall, deforestation increases the likelihood of floods and droughts and has a major effect on global warming. Deforestation monitoring is an important input for forest management that helps to prepare an action plan, but monitoring is still a challenging task. Hence, there is a need for an accurate deforestation mechanism to monitor those areas that have been converted from forest to non-forest areas. Therefore, in this paper a deep learning-based forest monitoring approach has been proposed, which is implemented in two steps: (i) a machine learning-based classification technique has been applied to the Sentinel-2 images to classify the forest and non-forest areas, and (ii) a deep learning-based change detection technique is proposed to detect the changes occurred during 2017–2022 of the Kukrail forest range situated in India. The performance of the proposed algorithm is assessed by estimating the error measuring parameters like Precision, Recall, and F1 Score, and it is observed that the proposed approach is quite suitable for forest area change monitoring.

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Data availability

The images used for the development, implementation, and analysis of deep learning-based change detection may be made available by the authors upon request.

References

  1. Uddin, A., Mueen, F., Uddin, K., Gilani, H., Murthy, M.S.R., Kotru, R., Qamer, F.M.: Forest condition monitoring using very-high-resolution satellite imagery in a remote mountain watershed in Nepal forest condition monitoring using very-high- resolution satellite imagery in a remote mountain watershed in Nepal. Mt. Res. Dev. 35, 264–277 (2015)

    Article  Google Scholar 

  2. Saei, M., Abkar, A.: Forest Canopy Density Monitoring, Using Satellite Images. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 1127–1130. Beijing, China (2014)

    Google Scholar 

  3. Nguyen, T.T., Hoang, T.D., Pham, M.T., Vu, T.T., Nguyen, T.H., Huynh, Q.T., Jo, J.: Monitoring agriculture areas with satellite images and deep learning. Appl. Soft Comput. 95, 106565 (2020). https://doi.org/10.1016/J.ASOC.2020.106565

    Article  Google Scholar 

  4. Murariu, G., Hahuie, V., Murariu, A., Georgescu, L., Iticescu, C., Calin, M., Preda, C., Buruiana, D.L., Carp, G.B.: Forest monitoring method using combinations of satellite and UAV aerial images case study - Bălăbăneşti forest. Int. J. Conserv. Sci. 8, 703–714 (2017)

    Google Scholar 

  5. Torres, D.L., Turnes, J.N., Vega, P.J.S., Feitosa, R.Q., Silva, D.E., Marcato Junior, J., Almeida, C.: Deforestation detection with fully convolutional networks in the amazon forest from Landsat-8 and sentinel-2 images. Remote Sens. 13, 1–20 (2021). https://doi.org/10.3390/rs13245084

    Article  Google Scholar 

  6. Dong, L., Xing, L., Liu, T., Du, H., Mao, F., Han, N., Li, X., Zhou, G., Zhu, D., Zheng, J., Zhang, M.: Very high resolution remote sensing imagery classification using a fusion of random forest and deep learning technique-subtropical area for example. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 13, 113–128 (2020). https://doi.org/10.1109/JSTARS.2019.2953234

    Article  Google Scholar 

  7. Afaq, Y., Manocha, A.: Analysis on change detection techniques for remote sensing applications: a review. Ecol. Inform. 63, 101310 (2021). https://doi.org/10.1016/j.ecoinf.2021.101310

    Article  Google Scholar 

  8. Srivastava, S., Ahmed, T.: Feature-based image retrieval (FBIR) system for satellite image quality assessment using big data analytical technique. Educ. J. 58, 10202–10220 (2021)

    Google Scholar 

  9. Xie, B., Huang, Z.: Estimates of forest canopy height using a combination of ICESat-2/ATLAS data. Remote Sens. 12(21), 3649 (2020)

    Article  Google Scholar 

  10. Srivastava, S., Ahmed, T.: Similarity-based neural network model for FBIR system of optical satellite image quality assessment. Turkish J. Comput. Math. Educ. 12, 2391–2410 (2021)

    Google Scholar 

  11. Meghwal, L., Ahmed, T., Singh, D., Balasubramanian, R.: Classification of Land cover using different indices on MODIS imagery. In: National Conference Recent Trends on Microwave Techniques and Applications. pp. 1–4. , Jaipur, India (2012)

  12. Lakshmanan, G., Mathiyazhagan, K.: Machine learning classifiers on Sentinel-2 satellite image for the classification of banana (Musa Sp.) plantations of Theni district, Tamil Nadu, India. Machine learning classifiers on sentinel-2 satellite image for the classification of banana (Musa Sp.). Int. J. Chem. Stud. 7, 1419–1425 (2019)

    Google Scholar 

  13. Alshari, E.A., Gawali, B.W.: Analysis of Machine learning techniques for sentinel-2A satellite images. J. Electr. Comput. Eng. (2022). https://doi.org/10.1155/2022/9092299

    Article  Google Scholar 

  14. Raiyani, K., Gonçalves, T., Rato, L., Salgueiro, P., da Silva, J.R.M.: Sentinel-2 image scene classification: a comparison between sen2cor and a machine learning approach. Remote Sens. 13, 1–22 (2021). https://doi.org/10.3390/rs13020300

    Article  Google Scholar 

  15. Al-Dail, M.A.: Change detection in Urban areas using satellite data. J. King Saud Univ. Eng. Sci. 10, 217–227 (1998). https://doi.org/10.1016/S1018-3639(18)30697-4

    Article  Google Scholar 

  16. Liu, Q.J., Li, X.R., Ma, Z.Q., Takeuchi, N.: Monitoring forest dynamics using satellite imagery—a case study in the natural reserve of Changbai Mountain in China. For. Ecol. Manage. 210, 25–37 (2005). https://doi.org/10.1016/J.FORECO.2005.02.025

    Article  Google Scholar 

  17. Verma, H.C., Rajan, S., Ahmed, T.: A review on land cover classification techniques for major fruit crops in india - present scenario and future aspects. SSRN Electron. J. (2019). https://doi.org/10.2139/SSRN.3356502

    Article  Google Scholar 

  18. Mishra, S., Shrivastava, P., Dhurvey, P.: Change Detection Techniques in Remote Sensing: A Review. Int. J. Wirel. Mob. Commun. Ind. Syst 4, 1–8 (2017)

    Google Scholar 

  19. Lu, D., Mausel, P., Brondízio, E., Moran, E.: Change detection techniques. Int. j. remote sens. 25, 2365–2401 (2010). https://doi.org/10.1080/0143116031000139863

    Article  Google Scholar 

  20. Ahmed, T., Singh, D., Raman, B.: Potential application of Kanade-Lucas-Tomasi tracker on satellite images for automatic change detection. J. Appl. Remote Sens. (2016). https://doi.org/10.1117/1.JRS.10.026018

    Article  Google Scholar 

  21. Verma, H.C., Ahmed, T., Rajan, S., Hasan, M.K., Khan, A., Gohel, H., Adam, A.: Development of LR-PCA based fusion approach to detect the changes in mango fruit crop by using landsat 8 OLI images. IEEE Access. 10, 85764–85776 (2022). https://doi.org/10.1109/ACCESS.2022.3194000

    Article  Google Scholar 

  22. Waldeland, A.U., Trier, Ø.D., Salberg, A.-B.: Forest mapping and monitoring in Africa using Sentinel-2 data and deep learning. Int. J. Appl. Earth Obs. Geoinf. 111, 102840 (2022). https://doi.org/10.1016/j.jag.2022.102840

    Article  Google Scholar 

  23. Hua, J., Chen, G., Yu, L., Ye, Q., Jiao, H., Luo, X.: Improved mapping of long-term forest disturbance and recovery dynamics in the subtropical China using all available landsat time-series imagery on google earth engine platform. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 14, 2754–2768 (2021). https://doi.org/10.1109/JSTARS.2021.3058421

    Article  Google Scholar 

  24. Bai, T., Wang, L., Yin, D., Sun, K., Chen, Y., Li, W., Li, D.: Deep learning for change detection in remote sensing: a review. Geo-Spatial Inf. Sci. 00, 1–27 (2022). https://doi.org/10.1080/10095020.2022.2085633

    Article  Google Scholar 

  25. Haq, M.A.: Smotednn: a novel model for air pollution forecasting and AQI classification. Comput. Mater. Contin. 71, 1403–1425 (2022)

    Google Scholar 

  26. Shafique, A., Cao, G., Khan, Z., Asad, M., Aslam, M.: Deep Learning-Based Change Detection in Remote Sensing Images: A Review. Remote Sens. 14, 1–40 (2022). https://doi.org/10.3390/rs14040871

    Article  Google Scholar 

  27. Haq, M.A.: CDLSTM: a novel model for climate change forecasting. Comput. Mater. Contin. 71, 2363–2381 (2022)

    Google Scholar 

  28. Haq, M.A., Ahmed, A., Khan, I., Gyani, J., Mohamed, A., Attia, E.A., Mangan, P., Pandi, D.: Analysis of environmental factors using AI and ML methods. Sci. Rep. 12, 1–16 (2022). https://doi.org/10.1038/s41598-022-16665-7

    Article  Google Scholar 

  29. Jiang, J., Xing, Y., Wei, W., Yan, E., Xiang, J., Mo, D.: DSNUNet: an improved forest change detection network by combining sentinel-1 and sentinel-2 images. Remote Sens. (2022). https://doi.org/10.3390/rs14195046

    Article  Google Scholar 

  30. Haq, M.A., Jilani, A.K., Prabu, P.: Deep learning based modeling of groundwater storage change. Comput. Mater. Contin. 70, 4599–4617 (2022)

    Google Scholar 

  31. Biswas, B.: Deep learning-based multimodal fusion of sentinel-1 and sentinel-2 Data for mapping deforested areas in the Amazon Rainforest. (2022)

  32. Haq, M.A., Rahaman, G., Baral, P., Ghosh, A.: Deep learning based supervised image classification using UAV images for forest areas classification. J. Indian Soc. Remote Sens. 49, 601–606 (2021). https://doi.org/10.1007/s12524-020-01231-3

    Article  Google Scholar 

  33. Skakun, S., Vermote, E., Roger, J.-C., Franch, B.: Combined use of landsat-8 and sentinel-2A images for winter crop mapping and winter wheat yield assessment at regional scale. AIMS Geosci. 3, 163–186 (2017). https://doi.org/10.3934/geosci.2017.2.163

    Article  Google Scholar 

  34. Verma, H.C., Ahmed, T., Rajan, S.: Mapping and area estimation of mango orchards of Lucknow region by applying knowledge based decision tree to landsat 8 OLI satellite images. Int. J. Innov. Technol. Explor. Eng. 9, 3627–3645 (2020)

    Article  Google Scholar 

  35. Bentz, Y., Merunka, D.: Neural networks and the multinomial logit for brand choice modelling: a hybrid approach. J. Forecast. 19, 177–200 (2000). https://doi.org/10.1002/(SICI)1099-131X(200004)19:3%3c177::AID-FOR738%3e3.0.CO;2-6

    Article  Google Scholar 

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Authors and Affiliations

  1. Advanced Computing and Research Lab, Department of Computer Application, Integral University, Lucknow, 226026, India

    Saurabh Srivastava & Tasneem Ahmed

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  1. Saurabh Srivastava
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  2. Tasneem Ahmed
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Contributions

Research Idea Formulation, Satellite Images Collection, Conceptualization, Investigation, Experimental Setup contributed by Mr. Saurabh Srivastava; Theoretical Background, Development of A Deep Learning-Based Change Detection Model, and Results Interpretation contributed by Mr Saurabh Srivastava, Dr. Tasneem Ahmed; Research Writing, Draft Manuscript Preparation, and Proofreading contributed by Mr. Saurabh Srivastava, Dr. Tasneem Ahmed; Resources and Supervision contributed by Dr. Tasneem Ahmed All authors have reviewed and approved the final version of this manuscript.

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Correspondence to Tasneem Ahmed.

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Srivastava, S., Ahmed, T. DLCD: Deep learning-based change detection approach to monitor deforestation. SIViP (2024). https://doi.org/10.1007/s11760-024-03140-1

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