Monitoring deforestation in Jordan using deep semantic segmentation with satellite imagery

Alzu’bi, Ahmad; Alsmadi, Lujain

doi:10.1016/j.ecoinf.2022.101745

Cited by 20 publications

(5 citation statements)

References 29 publications

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“…As presented in Table 12 the proposed model in this study outperformed other models from related studies [ 10 , 42 , 43 , 44 , 45 , 46 ]. However, the Unet semantic segmentation in [ 47 ] segmenting the forest images and predicting any loss (deforestation) or gain (reforestation) slightly outperformed our model with 95% accuracy. The reason could be attributed to the ability of Unet to extract more features required to perform subsequent segmentation.…”

Section: Results and Discussionmentioning

confidence: 90%

“…Unet with spatial pyramid spooling [42] 86.71 75.59 Hnet with Inception as backbone [43] 68 83 Deep Convolutional Neural Networks (DCNN) [48] 91 -Unet for forest segmentation [10] 91 -SENet and MobileNet embedded in DeepLabV3+ (SMED) [44] 82.95 60 improved tuna swarm optimization (ITSO) [46] -59 Unet semantic segmentation [47] 95 -Random Forest 94 91…”

Section: Methods Accuracy Ioumentioning

confidence: 99%

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Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Kwenda,

Gwetu,

Fonou-Dombeu

2024

J. Imaging

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Forests play a pivotal role in mitigating climate change as well as contributing to the socio-economic activities of many countries. Therefore, it is of paramount importance to monitor forest cover. Traditional machine learning classifiers for segmenting images lack the ability to extract features such as the spatial relationship between pixels and texture, resulting in subpar segmentation results when used alone. To address this limitation, this study proposed a novel hybrid approach that combines deep neural networks and machine learning algorithms to segment an aerial satellite image into forest and non-forest regions. Aerial satellite forest image features were first extracted by two deep neural network models, namely, VGG16 and ResNet50. The resulting features are subsequently used by five machine learning classifiers including Random Forest (RF), Linear Support Vector Machines (LSVM), k-nearest neighbor (kNN), Linear Discriminant Analysis (LDA), and Gaussian Naive Bayes (GNB) to perform the final segmentation. The aerial satellite forest images were obtained from a deep globe challenge dataset. The performance of the proposed model was evaluated using metrics such as Accuracy, Jaccard score index, and Root Mean Square Error (RMSE). The experimental results revealed that the RF model achieved the best segmentation results with accuracy, Jaccard score, and RMSE of 94%, 0.913 and 0.245, respectively; followed by LSVM with accuracy, Jaccard score and RMSE of 89%, 0.876, 0.332, respectively. The LDA took the third position with accuracy, Jaccard score, and RMSE of 88%, 0.834, and 0.351, respectively, followed by GNB with accuracy, Jaccard score, and RMSE of 88%, 0.837, and 0.353, respectively. The kNN occupied the last position with accuracy, Jaccard score, and RMSE of 83%, 0.790, and 0.408, respectively. The experimental results also revealed that the proposed model has significantly improved the performance of the RF, LSVM, LDA, GNB and kNN models, compared to their performance when used to segment the images alone. Furthermore, the results showed that the proposed model outperformed other models from related studies, thereby, attesting its superior segmentation capability.

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Section: Results and Discussionmentioning

confidence: 90%

Section: Methods Accuracy Ioumentioning

confidence: 99%

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Kwenda,

Gwetu,

Fonou-Dombeu

2024

J. Imaging

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“…While the Convolutional Neural Network (Alzu & Alsmadi, 2022) and the VGG16 architecture (Hindarto, 2023d) have shown impressive abilities in recognizing visual patterns and analysing image data, more extensive research is still needed to compare their strengths and weaknesses, specifically in the context of forest fire identification. The existence of these two models in the field of image recognition has sparked inquiries regarding their comparative efficacy in detecting forest fires, as well as the practical applicability of their limitations and advantages in early detection situations in real-world settings.…”

Section: Introductionmentioning

confidence: 99%

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

Hindarto

2023

CNAHPC

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The current research aims to assess the precision of forest fire detection using CNN and VGG16 models, specifically in the context of fire identification. While both models have demonstrated significant promise in visual pattern recognition, a comprehensive analysis regarding their specific benefits in forest fire identification is still needed. The rationale behind this research stems from the significance of promptly identifying forest fires as a preemptive measure to mitigate their detrimental effects on the environment and society. The employed approach involves the application of transfer learning techniques on a diverse and extensive dataset encompassing different forest fire scenarios. The dataset was used to train both CNN and VGG16 models. The test results indicated that the CNN model achieved a forest fire detection accuracy of 96%, while VGG16 achieved 98% accuracy. The primary objective of this research is to enhance comprehension regarding the merits and demerits of each model in the context of forest fire identification scenarios. While VGG16 exhibits marginally superior performance in identifying forest fires, this discrepancy offers valuable insight into the practical applicability of these two models for fire detection in real-world scenarios. These findings establish a solid basis for the advancement of more dependable and efficient early detection technology in the prevention and management of forest fires in the future. This can be accomplished by capitalizing on the unique capabilities of each model to optimize their performance in practical scenarios.

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“…As a result, these days deforestation has become one of the most intractable environmental problems [6]. Generally, deforestation monitoring is usually conducted through tedious manual procedures including visual inspections, which require frequent visits to forest regions and can be costly and dangerous [7].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images

Javed,

Kim,

Lee

et al. 2023

Remote Sensing

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Urban forests globally face severe degradation due to human activities and natural disasters, making deforestation an urgent environmental challenge. Remote sensing technology and very-high-resolution (VHR) bitemporal satellite imagery enable change detection (CD) for monitoring forest changes. However, deep learning techniques for forest CD concatenate bitemporal images into a single input, limiting the extraction of informative deep features from individual raw images. Furthermore, they are developed for middle to low-resolution images focused on specific forests such as the Amazon or a single element in the urban environment. Therefore, in this study, we propose deep learning-based urban forest CD along with overall changes in the urban environment by using VHR bitemporal images. Two networks are used independently: DeepLabv3+ for generating binary forest cover masks, and a deeply supervised image fusion network (DSIFN) for the generation of a binary change mask. The results are concatenated for semantic CD focusing on forest cover changes. To carry out the experiments, full scene tests were performed using the VHR bitemporal imagery of three urban cities acquired via three different satellites. The findings reveal significant changes in forest covers alongside urban environmental changes. Based on the accuracy assessment, the networks used in the proposed study achieved the highest F1-score, kappa, IoU, and accuracy values compared with those using other techniques. This study contributes to monitoring the impacts of climate change, rapid urbanization, and natural disasters on urban environments especially urban forests, as well as relations between changes in urban environment and urban forests.

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Monitoring deforestation in Jordan using deep semantic segmentation with satellite imagery

Cited by 20 publications

References 29 publications

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Hybridizing Deep Neural Networks and Machine Learning Models for Aerial Satellite Forest Image Segmentation

Comparison Accuracy of CNN and VGG16 in Forest Fire Identification: A Case Study

Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images

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