Land cover change detection in the Aralkum with multi-source satellite datasets

Löw, Fabian; Dimov, Dimo; Kenjabaev, Shavkat; Zaitov, Sherzod; Stulina, Galina; Dukhovny, Victor

doi:10.1080/15481603.2021.2009232

Cited by 9 publications

(5 citation statements)

References 58 publications

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“…Changes in the Aral Sea's underlying surface, as well as the emission and transport of dust and salt particles, have received sufficient attention and inquiry. Changes in area, water volume, and land surface have been studied in detail [10,34,35]. Models and remote sensing data were individually or jointly used to quantify the dust emission from the Aral Sea dry lakebed [29].…”

Section: Introductionmentioning

confidence: 99%

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

Abuduwaili

et al. 2022

Remote Sensing

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The dramatic shrinkage of the Aral Sea in the past decades has inevitably led to an environmental calamity. Existing knowledge on the variations and potential transport of atmospheric aerosols from the Aral Sea Basin (ASB) is limited. To bridge this knowledge gap, this study tried to identify the variations and long-range transport of atmospheric aerosols from the ASB in recent years. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data were used to gain new insight into the types, variation and long-range transport of atmospheric aerosols from the ASB. The results showed five types of tropospheric aerosols and one type of stratospheric aerosol were observed over the ASB. Polluted dust and dust were the dominant subtypes through the year. Sulfate/other was the only stratospheric aerosol detected. The occurrence frequency of aerosols over the ASB showed obvious seasonal variation. Maximum occurrence frequency of dust appeared in spring (MAM) and that of polluted dust peaked in summer (JJA). The monthly occurrence frequency of dust and polluted dust exhibited unimodal distribution. Polluted dust and dust were distributed over wide ranges from 1 km to 5 km vertically. The multi-year average thickness of polluted dust and dust layers was around 1.3 km. Their potential long-range transport in different directions mainly impacts Uzbekistan, Turkmenistan, Kazakhstan and eastern Iran, and may reach as far as the Caucasus region, part of China, Mongolia and Russia. Combining aerosol lidar, atmospheric climate models and geochemical methods is strongly suggested to gain clarity on the variations and long-range transport of atmospheric aerosols from the Aral Sea Basin.

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Section: Introductionmentioning

confidence: 99%

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

Abuduwaili

et al. 2022

Remote Sensing

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“…In the early stages, methods for LCCD relied on traditional machine learning techniques such as Maximum Likelihood [70][71][72][73][74][75][76][77][78][79], Support Vector Machine (SVM) [80], and Random Forest (RF) [81,82] for classifying land features, followed by vector change comparison to generate change maps. Fabian and Tikuye utilized satellite remote sensing data to train an RF model to classify land features into different types and subsequently analyzed the land cover change [81,82]. M.H.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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“…The number of trees was set to 300, and the other parameters to the default because, based on the tests and recommendations by previous studies, they provide higher accuracy [87][88][89][90][91].…”

Section: Random Forestmentioning

confidence: 99%

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

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Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

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Land cover change detection in the Aralkum with multi-source satellite datasets

Cited by 9 publications

References 58 publications

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

Insights into Variations and Potential Long-Range Transport of Atmospheric Aerosols from the Aral Sea Basin in Central Asia

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

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