Deep learning-based rapid recognition of oasis-desert ecotone plant communities using UAV low-altitude remote-sensing data

Ainiwaer, Mireguli; Ding, Jianli; Kasim, Nijat

doi:10.1007/s12665-020-08965-w

Cited by 15 publications

(12 citation statements)

References 33 publications

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“…Results indicate the superior capability of the VAE compared to the other models in separating real desertification cases from all other land cover changes, such as deforestation or areas undergoing seasonal phenomena of dryness of wild grasses. Also, the comparison with some existing studies demonstrated that the VAE approach outperformed the state of the art methods based on decision-tree [1], SVM [5], CNN [12], decision tree, and Isodata combination [13]. This result highlights the promising performance of the VAE algorithm in separating non-linear features even in challenging cases.…”

Section: Discussionmentioning

confidence: 65%

“…This visual evaluation confirms the performance and the robustness of each detection method and illustrates its impact on the detected surfaces. Besides, we also evaluated the performance of the VAE-based detection approach with some existing works based on decisiontree [1], SVM [5], CNN [12], decision tree, and Isodata combination [13] (Table VI). Results in Table V and Table VI indicate the promising performance of the VAE-based desertification approach.…”

Section: B Classification Results and Discussionmentioning

confidence: 99%

“…In the case of decision-tree classification (used alone in [1] or combined with Isodata in [13]), many misclassification cases can be observed, which makes the recognition capacity relatively lower (accuracy around 80%). In [12], a deep learning formalism based on CNN has been used, and the achieved accuracy is relatively high (96.73% with VGG 19). However, this classification is based on sub-image classification instead of pixels as samples.…”

Section: B Classification Results and Discussionmentioning

confidence: 99%

“…These techniques can fail due to the high degree of similarity between features of some desertification cases and like-desertification pixels. There are also several studies based on (i) object classification [5] or deep learning like CNN [12]. In these studies, each sample to be classified is composed of a region of pixel or a sub-image.…”

Section: Desertification Detection Approachmentioning

confidence: 99%

“…A new set of parameters for the Tasselled Cap transform are introduced to better fit dryland conditions and have been applied to the city of Azraq Oasis, Jordan. In [12], a deep learning framework has been applied to oasis desert recognition using UAV low altitude and remote sensing imagery. This study intends to propose a system to estimate plant communities' degradation in oasis-desert to prevent desertification and protect ecological security.…”

mentioning

confidence: 99%

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Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Zerrouki¹,

Harrou

Zerrouki

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Discussionmentioning

confidence: 65%

Section: B Classification Results and Discussionmentioning

confidence: 99%

Section: B Classification Results and Discussionmentioning

confidence: 99%

Section: Desertification Detection Approachmentioning

confidence: 99%

mentioning

confidence: 99%

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Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Zerrouki¹,

Harrou

Zerrouki

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Efficient land desertification detection using a deep learning‐driven generative adversarial network approach: A case study

Zerrouki

Dairi

Harrou

et al. 2021

Concurrency and Computation

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Summary Precisely detecting land cover changes aids in improving the analysis of the dynamics of the landscape and plays an essential role in mitigating the effects of desertification. Mainly, sensing desertification is challenging due to the high correlation between desertification and like‐desertification events (e.g., deforestation). An efficient and flexible deep learning approach is introduced to address desertification detection through Landsat imagery. Essentially, a generative adversarial network (GAN)‐based desertification detector is designed and for uncovering the pixels influenced by land cover changes. In this study, the adopted features have been derived from multi‐temporal images and incorporate multispectral information without considering image segmentation preprocessing. Furthermore, to address desertification detection challenges, the GAN‐based detector is constructed based on desertification‐free features and then employed to identify atypical events associated with desertification changes. The GAN‐detection algorithm flexibly learns relevant information from linear and nonlinear processes without prior assumption on data distribution and significantly enhances the detection's accuracy. The GAN‐based desertification detector's performance has been assessed via multi‐temporal Landsat optical images from the arid area nearby Biskra in Algeria. This region is selected in this work because desertification phenomena heavily impact it. Compared to some state‐of‐the‐art methods, including deep Boltzmann machine (DBM), deep belief network (DBN), convolutional neural network (CNN), as well as two ensemble models, namely, random forests and AdaBoost, the proposed GAN‐based detector offers superior discrimination performance of deserted regions. Results show the promising potential of the proposed GAN‐based method for the analysis and detection of desertification changes. Results also revealed that the GAN‐driven desertification detection approach outperforms the state‐of‐the‐art methods.

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Tracking landscape scale vegetation change in the arid zone by integrating ground, drone and satellite data

Francis,

Kingsford,

Moseby

et al. 2023

Remote Sens Ecol Conserv

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A combined multiscale approach using ground, drone and satellite surveys can provide accurate landscape scale spatial mapping and monitoring. We used field observations with drone collected imagery covering 70 ha annually for a 5‐year period to estimate changes in living and dead vegetation of four widespread and abundant arid zone woody shrub species. Random forest classifiers delivered high accuracy (> 95%) using object‐based detection methods, with fast repeatable and transferrable processing using Google Earth Engine. Our classifiers performed well in both dominant arid zone landscape types: dune and swale, and at extremes of dry and wet years with minimal alterations. This highlighted the flexibility of the approach, potentially delivering insights into changes in highly variable environments. We also linked this classified drone vegetation to available temporally and spatially explicit Landsat satellite imagery, training a new, more accurate fractional vegetation cover model, allowing for accurate tracking of vegetation responses at large scales in the arid zone. Our method promises considerable opportunity to track vegetation dynamics including responses to management interventions, at large geographic scales, extending inference well beyond ground surveys.

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Deep learning-based rapid recognition of oasis-desert ecotone plant communities using UAV low-altitude remote-sensing data

Cited by 15 publications

References 33 publications

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Desertification Detection Using an Improved Variational Autoencoder-Based Approach Through ETM-Landsat Satellite Data

Efficient land desertification detection using a deep learning‐driven generative adversarial network approach: A case study

Tracking landscape scale vegetation change in the arid zone by integrating ground, drone and satellite data

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