Automatic Mapping of Irrigated Areas in Mediteranean Context Using Landsat 8 Time Series Images and Random Forest Algorithm

Benbahria, Zouhair; Sebari, İmane; Hajji, Hicham; Smiej, Mohammed Faouzi

doi:10.1109/igarss.2018.8517810

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…The irrigated areas are mapped with high degree of performance (IoU >= 82%). On the same area of interest and using Random Forest algorithm (Benbahria et al, 2018) we obtained less accuracy. This could confirm the relevance of using these new approaches based on deep learning architectures.…”

Section: Discussionmentioning

confidence: 84%

“…This study was based on previous studies (Ozdogan et al 2010). In (Benbahria et al 2018), a new automatic mapping framework was proposed based on Landsat 8 (L8) time series images and using pixelwise classification Random Forest algorithm. (Zhang et al 2018) tested an approach based on well-known image classification convolutional neural networks to automatically detect only center pivot irrigation systems from Landsat 5 TM images.…”

Section: Related Workmentioning

confidence: 99%

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Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Benbahria

Sebari²,

Hajji³

et al. 2021

International Journal of Engineering and Geosciences

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The lack of reliable and up-to-date data in developing countries is a major obstacle to sustainable development. In Morocco, where groundwater withdrawals by farmers are very intensive and informal, maps describing and monitoring the extension of irrigated areas are scarce and labor-intensive to obtain. In this paper a novel transfer learning algorithm is proposed to map irrigated areas at different stages of an agricultural cycle from Landsat 8 images. The results obtained displays satisfactory performance over traditional machine learning algorithms. On a small dataset, we initially tested three well known deep learning architectures (SegNet, DenseNet and Unet). The results obtained were not satisfactory. So, to get high performance, we rely on a transfer learning architecture combining UNet with ResNet50 backbone (trained on 2012 ILSVRC ImageNet dataset) as a baseline after a phase where different configurations were tested. In the first part of this study, we compared the use of three optimization methods: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart) to find the optimal learning rate and then test the impact of data augmentation on the overall accuracies. Data augmentation had improved the overall accuracy for the three methods. Adam based method from 94% to 97% with mean IoU of 0,79 (for all land cover classes) and 0,86 for irrigated areas class. For SGD based methods, the overall accuracy had increased from 91% to 94% with mean IoU of 0,75 (for all land cover classes) and 0,82 for irrigated areas class. As we are interested in having irrigated areas maps at different key periods of the agricultural cycle, we also explored, in the second part of this study, the temporal generalization of the best model.

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Section: Discussionmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Benbahria

Sebari²,

Hajji³

et al. 2021

International Journal of Engineering and Geosciences

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“…The Landsat series of satellites, operated jointly by the National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS) [61,62], comprises Earth observation satellites equipped with multiple multispectral sensors, including a Multispectral Scanner (MSS) aboard Landsat 1-5 [63], Thematic Mapper (TM) aboard Landsat 4 and 5 [27,64], Enhanced Thematic Mapper Plus (ETM+) aboard Landsat 7 [65], Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) aboard Landsat 8 [66], and Operational Land Imager 2 (OLI-2) and TIRS-2 aboard Landsat 9 [67], as shown in Table 1. These satellites, spanning from 1972 to the present (excluding a failed launch), capture visible, near-infrared, mid-infrared, and thermal-infrared spectra, providing valuable and abundant data for lithological mapping.…”

Section: Optical Imagerymentioning

confidence: 99%

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Chen,

Wang,

Zhang

et al. 2023

Minerals

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Remote sensing (RS) technology has significantly contributed to geological exploration and mineral resource assessment. However, its effective application in vegetated areas encounters various challenges. This paper aims to provide a comprehensive overview of the challenges and opportunities associated with RS-based lithological identification in vegetated regions which includes the extensively reviewed prior research concerning the identification of lithology in vegetated regions, encompassing the utilized remote sensing data sources, and classification methodologies. Moreover, it offers a comprehensive overview of the application of remote sensing techniques in the domain of lithological mapping. Notably, hyperspectral RS and Synthetic Aperture Radar (SAR) have emerged as prominent tools in lithological identification. In addition, this paper addresses the limitations inherent in RS technology, including issues related to vegetation cover and terrain effects, which significantly impact the accuracy of lithological mapping. To propel further advancements in the field, the paper proposes promising avenues for future research and development. These include the integration of multi-source data to improve classification accuracy and the exploration of novel RS techniques and algorithms. In summary, this paper presents valuable insights and recommendations for advancing the study of RS-based lithological identification in vegetated areas.

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“…GCVI is sensitive to chlorophyll and can be used to identify agricultural areas (Huete et al, 2002). NDBI is useful for identifying built-up areas (Benbahria et al, 2018). NDWI and LSWI are vegetation indices that are highly sensitive to surface water (Jeong et al, 2012).…”

Section: Feature Extractionmentioning

confidence: 99%

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

Chen,

Dong,

Wang

et al. 2023

Front. Ecol. Evol.

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Lithological mapping in highly vegetated areas using remote sensing techniques poses a significant challenge. Inspired by the concept of “geobotany”, we attempted to distinguish lithologies indirectly using machine learning algorithms (MLAs) based on Sentinel-2 and SRTM DEM in Zhangzhou City, Fujian Province. The study area has high vegetation cover, with lithologies that are largely obscured. After preprocessing such as cloud masking, resampling, and median image synthesis, 17 spectral bands and features from Sentinel-2 and 9 terrain features from DEM were extracted. Five widely used MLAs, MD, CART, SVM, RF, and GBDT, were trained and validated for lithological mapping. The results indicate that advanced MLAs, such as GBDT and RF, are highly effective for nonlinear modeling and learning with relative increases reaching 8.18%∼11.82% for GBDT and 6.36%∼10% for RF. Compared with optical imagery or terrain data alone, combining Sentinel-2 and DEM significantly improves the accuracy of lithological mapping, as it provides more comprehensive and precise spectral characteristics and spatial information. GBDT_Sen+DEM utilizing integrated data achieved the highest classification accuracy, with an overall accuracy of 63.18%. This study provides a case study for lithological mapping of areas with high vegetation cover at the local level. This also reinforces the idea that merging remote sensing and terrain data significantly enhances the precision and reliability of the lithological mapping methods.

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Automatic Mapping of Irrigated Areas in Mediteranean Context Using Landsat 8 Time Series Images and Random Forest Algorithm

Cited by 8 publications

References 8 publications

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Intelligent mapping of irrigated areas from Landsat 8 images using transfer learning

Remote Sensing for Lithology Mapping in Vegetation-Covered Regions: Methods, Challenges, and Opportunities

Machine learning algorithms for lithological mapping using Sentinel-2 and SRTM DEM in highly vegetated areas

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