Abstract
Deforestation is an important environmental issue that involves the removal of forests on a large scale, resulting in ecological imbalance and biodiversity loss. Synthetic Aperture Radar (SAR) images are widely used as a valuable tool to detect deforestation effectively. The SAR technology allows capturing high-resolution images irrespective of weather conditions or daylight, making it helpful to monitor remote and densely vegetated areas. Recently, deep learning techniques used on SAR images have showcased promising results in the automation of deforestation detection and mapping processes. By leveraging neural networks (NNs) and machine learning (ML) systems, these approaches examine SAR data to recognize deforestation patterns and estimate deforestation rates over time. Therefore, this study develops a cross-breed multilayer convolutional neural network (CNN) for deforestation rate estimation in the Amazon. The proposed model initially preprocesses the input SAR data to remove the speckle noise using a box car mean squared sparse coding filter (BCMSSCF). Besides, crossbreed multilayer CNN (CM_CNN) is used for mapping and segmentation of the deforested area. To determine the pace of deforestation in the Amazon region, a widespread experimental analysis was performed on the LBA-ECO LC-14 dataset. A detailed comparative result analysis of the proposed model is made with recent approaches. The experimental results stated that the proposed model shows promising results in terms of different performance measures.
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References
Daiyoub A, Gelabert P, Saura-Mas S, Vega-Garcia C (2023) War and deforestation: using remote sensing and machine learning to identify the war-induced deforestation in Syria 2010–2019. Land 12(8):1509
Masolele RN, Marcos D, De Sy V, Abu IO, Verbesselt J, Reiche J, Herold M (2024) Mapping the diversity of land uses following deforestation across Africa. Sci Rep 14(1):1681
Solórzano JV, Mas JF, Gallardo-Cruz JA, Gao Y, de Oca AFM (2023) Deforestation detection using a spatio-temporal deep learning approach with synthetic aperture radar and multispectral images. ISPRS J Photogramm Remote Sens 199:87–101
Kalaiyarasi M, Saravanan S, Karthi S, Rao DNM, Sireesha DNV (2023) Estimation of deforestation rate and forest land use land cover change detection, 2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), pp 1–4. https://doi.org/10.1109/ICECONF57129.2023.10083928
Altarez RDD, Apan A, Maraseni T (2023) Deep learning U-Net classification of Sentinel-1 and 2 fusions effectively demarcates tropical montane forest deforestation. Remote Sens Appl: Soc Environ 29:100887
Vorotyntsev P, Gordienko Y, Alienin O, Rokovyi O, Stirenko S (2021) Satellite image segmentation using deep learning for deforestation detection. IEEE 3rd Ukraine Conference on Electrical and Computer Engineering (UKRCON), Lviv, Ukraine, pp 226–231. https://doi.org/10.1109/UKRCON53503.2021.9575783
Chitra NT, Anusha R, Kumar SH, Chandana DS, Harika C, Kumar VU (2021) Satellite Imagery for Deforestation Prediction using Deep Learning. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), pp. 522–525.
John D, Zhang C (2022) An attention-based U-Net for detecting deforestation within satellite sensor imagery. Int J Appl Earth Obs Geoinf 107:102685
Naughton-Rockwell M (2022) Using Deep Learning with Satellite Imagery to Estimate Deforestation Rates
Tovar P, Adarme M, Feitosa R (2021) Deforestation detection in the amazon rainforest with spatial and channel attention mechanisms. Int Arch Photogramm Remote Sens Spat Inf Sci 43(B3–2021):851–858
Adarme MO, Feitosa RQ, Happ PN, De Almeida CA, Gomes AR (2020) Evaluation of Deep Learning Techniques for Deforestation Detection in the Brazilian Amazon and Cerrado Biomes From Remote Sensing Imagery. Remote Sens 12:910
Chen J, Yuan Z, Peng J, Chen L, Huang H, Zhu J et al (2020) DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images. IEEE J Sel Top Appl Earth Obs Remote Sens 14:1194–1206
De Bem PP, de Carvalho Junior OA, FontesGuimarães R, Trancoso Gomes RA (2020) Change detection of deforestation in the Brazilian Amazon using landsat data and convolutional neural networks. Remote Sens 12:901
Kotharkar A, Chavan O, Jadhav S, Chandran D (2021) Predicting the lost green cover in deforestation using a neural network. Electronic copy available at: https://ssrn.com/abstract=3867995
Shumilo L, Lavreniuk M, Kussul N, Shevchuk B (2021) Automatic deforestation detection based on the deep learning in Ukraine. 11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Cracow, Poland, pp 337–342. https://doi.org/10.1109/IDAACS53288.2021.9661008
P. Guilherme B. A., D. Fernanda B. J. R., Á. Fazenda and F. A. Faria, "Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests," 2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), Natal, Brazil, 2022, pp. 13-18, https://doi.org/10.1109/SIBGRAPI55357.2022.9991798
Kalwar A, Mathur R, Chavan S, Narvekar C (2022) Forest Cover Change Detection Using Satellite Images. In: Vieira V, Coelho Rodrigues JJP (eds) Khanna K. Cyber Security and Digital Forensics, Springer, pp 565–573
Zhang J, Wang Z, Bai L, Song G, Tao J, Chen L (2021) Deforestation detection based on U-Net and LSTM in optical satellite remote sensing images. IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp 3753–3756. https://doi.org/10.1109/IGARSS47720.2021.9554689
Ortega MX, Feitosa RQ, Bermudez JD, Happ PN, De Almeida CA (2021) Comparison of optical and SAR data for deforestation mapping in the amazon rainforest with fully convolutional networks. IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, pp 3769–3772. https://doi.org/10.1109/IGARSS47720.2021.9554970
Pugliese A, Yordanov V, Delipetrev B, Brovelli M (2021) Amazon forest monitoring using the U-Net fully convolutional neural network. POLITesi - Digital archive of degree and doctoral theses
Boaro JMC, dos Santos PTC, Serra A, Rego VG, Martins CV, Júnior GB (2021) Satellite Image Segmentation of Gold Exploration Areas in the Amazon Rainforest Using U-Net. In IEEE Int Humanitarian Technol Conf (IHTC) 2021:1–8
Koguchi C, Ketagoda N, Pathak N, Sunarjo S (n.d.) Understanding the Amazon Rainforest from Space using Neural Networks
Andrade R, Costa G, Mota G, Ortega M, Feitosa R, Soto P et al (2020) Evaluation of semantic segmentation methods for deforestation detection in the amazon. ISPRS Archives 43:1497–1505
Kuck TN, Sano EE, Bispo PdC, Shiguemori EH, Silva Filho PFF, Matricardi EAT (2021) A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images. Remote Sens 13:3341
Zhang S, Ma Z, Zhang G, Lei T, Zhang R, Cui Y (2020) Semantic image segmentation with deep convolutional neural networks and quick shift. Symmetry 12:427
Qu L, Zhang H, Li D, Yu X, Tang D, He L (2022) Imbalanced Image Classification by An Enhanced Depthwise Separable Convolutions Network. In 2022 5th International Conference on Artificial Intelligence and Big Data (ICAIBD), pp. 261–266.
Dominguez D, del Villar LdJ, Pantoja O, González-Rodríguez M (2022) Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model. Sustainability 14:691
Matosak BM, Fonseca LMG, Taquary EC, Maretto RV, Bendini HdN, Adami M (2022) Mapping Deforestation in Cerrado Based on Hybrid Deep Learning Architecture and Medium Spatial Resolution Satellite Time Series. Remote Sens 14:209
Ball JGC, Petrova K, Coomes D, Flaxman S (2021) Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation. bioRxiv
Gao S (2019) Deforestation Prediction Using Time Series and LSTM. Int Conf Inf Technol Comput Appl (ITCA) 2019:95–99
Wade CM, Austin KG, Cajka J, Lapidus D, Everett KH, Galperin D et al (2020) What is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018. Forests 11:539
Austin KG, Schwantes A, Gu Y, Kasibhatla PS (2019) What causes deforestation in Indonesia? Environ Res Lett 14:024007
Austin K, Mosnier A, Pirker J, McCallum I, Fritz S, Kasibhatla P (2017) Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69:41–48
Irvin J, Sheng H, Ramachandran N, Johnson-Yu S, Zhou S, Story K et al (2020) Forestnet: Classifying drivers of deforestation in indonesia using deep learning on satellite imagery. arXiv preprint arXiv:2011.05479
Gaveau DL, Santos L, Locatelli B, Salim MA, Husnayaen H, Meijaard E et al (2021) Forest loss in Indonesian New Guinea: trends, drivers, and outlook. BioRxiv
Kayet N, Pathak K, Kumar S, Singh C, Chowdary V, Chakrabarty A et al (2021) Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region. J Environ Manage 289:112504
Xu L, Bondi E, Fang F, Perrault A, Wang K, Tambe M (2021) Dual-mandate patrols: Multi-armed bandits for green security. In Proc AAAI Conf Artif Intell 35(17):14974–14982
Mc Carthy SM, Tambe M, Kiekintveld C, Gore M, Killion A (2016) Preventing illegal logging: Simultaneous optimization of resource teams and tactics for security. In Proc AAAI Conf Artif Intell
Johnson M, Fang F, Tambe M (2012) Patrol strategies to maximize pristine forest area. In Proc AAAI Conf Artif Intell 26(1):295–301
Santika T, Meijaard E, Budiharta S, Law EA, Kusworo A, Hutabarat JA et al (2017) Community forest management in Indonesia: Avoided deforestation in the context of anthropogenic and climate complexities. Glob Environ Chang 46:60–71
Kumar CV, Suhasini A (2016) Improved secure three-tier architecture for WSN using hopfield chaotic neural network with two stage encryption. In 2016 International Conference on Computer, Electrical & Communication Engineering (ICCECE), pp. 1–4.
Venkatesh K, Parthiban S, Kumar PS, Kumar CV (2021) IoT based Unified approach for Women safety alert using GSM. In Third Int Conf Intell Commun Technol Virtual Mobile Netw (ICICV) 2021:388–392
Kumar R (2021) Detection of Cyberbullying using Machine Learning. Turk J Comp Math Educ (TURCOMAT) 12:656–661
Pan Y, Meng Y, Zhu L (2021) SAR image despeckling method based on improved Frost filtering. SIViP 15:843–850
Murugesan K, Balasubramani P, Murugan PR, Sankaranarayanan S (2021) Color-based SAR image segmentation using HSV+ FKM clustering for estimating the deforestation rate of LBA-ECO LC-14 modeled deforestation scenarios, Amazon basin: 2002–2050. Arab J Geosci 14:1–15
Sun R, Zhao F, Huang C, Huang H, Lu Z, Zhao P, Ni X, Meng R (2023) Integration of deep learning algorithms with a Bayesian method for improved characterization of tropical deforestation frontiers using Sentinel-1 SAR imagery. Remote Sens Environ 298:113821
Kang J, Zhang B, Dang A (2024) A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics. Int J Appl Earth Obs Geoinf 128:103712
Chen S, Wei X, Zheng W (2023) ASA-DRNet: An Improved Deeplabv3+ Framework for SAR Image Segmentation. Electronics 12(6):1300
Li Q, Kong Y (2023) An Improved SAR Image Semantic Segmentation Deeplabv3+ Network Based on the Feature Post-Processing Module. Remote Sensing 15(8):2153
Chen Z, Li D, Fan W, Guan H, Wang C, Li J (2021) Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images. Remote Sens 13:2524. https://doi.org/10.3390/rs13132524
Sboui T, Saidi S, Lakti A (2023) A Machine-Learning-Based Approach to Predict Deforestation Related to Oil Palm: Conceptual Framework and Experimental Evaluation. Appl Sci 13(3):1772
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Subhahan, D., Kumar, C.N.S.V. Deforestation rate estimation using crossbreed multilayer convolutional neural networks. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19319-0
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DOI: https://doi.org/10.1007/s11042-024-19319-0