Remote sensing-based land surface change identification and prediction in the Aral Sea bed, Central Asia

Shen, Huajun; Abuduwaili, Jilili; Ma, Lingling; Samat, Alim

doi:10.1007/s13762-018-1801-0

Cited by 37 publications

(12 citation statements)

References 53 publications

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“…This seems to be a result of the conversion of salt soil to bare area. According to Shen, Abuduwaili, Ma, and Samat (2019) This study was conducted to find the potential vegetation establishment areas in a vast region like the Aral Sea in macroscale, using only satellite images without field survey data or meteorological observation data. Since the satellite imagery is the only material to be used, there are limitations that the analysis results may vary depending on the images or calibration method.…”

Section: Discussionmentioning

confidence: 99%

“…This seems to be a result of the conversion of salt soil to bare area. According to Shen, Abuduwaili, Ma, and Samat (2019), salt soil and bare area are mainly present in the Aral Sea dry land. Salt soil mostly tends to shift to bare area, and from 2006 to 2015, 12,430.42 km 2 of the total 19,053.31 km 2 of salt soil was converted into bare area.…”

Section: Discussionmentioning

confidence: 99%

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Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

et al. 2020

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The Aral Sea was one of the largest lakes in the world, but almost 60,000 km 2 of the waterbody has dried up due to water withdrawal for irrigation. Afforestation on the desiccated seafloor could be important in preventing soil flation, dust storms, and negative impact on human health. In this study, we aimed to delineate potential vegetation establishment areas on the dried Aral Sea bed using remote-sensed data in support of the decision-making related to afforestation. Various indices such as normalized difference vegetation index (NDVI), topsoil grain size index (TGSI), soil salinity index (SSI), and normalized multiband drought index (NMDI) were calculated from the LANDSAT-8 OLI satellite imagery. As an indicator of vegetation existence, NDVI was classified into three groups and set as a base for classifying other indices by performing statistical analyses. Based on the decision tree method, indices were combined and the potential vegetation establishment area was detected. Higher NDVI was identified in the southeast than the west of the study area. The results of statistical analyses showed that TGSI had a positive correlation with NDVI, while SSI and NMDI had a negative correlation. Overall, the potential vegetation area comprised 7,295.21 km 2 (61.34%) of the 'unsuitable' area, 2,818.64 km 2 (23.7%) of the 'intermediate' area, 1,612.15 km 2 (13.56%) of the 'suitable' area, and 166.42 km 2 (1.4%) of the 'very suitable' area. The developed map enables to identify dried seafloor area suitable for vegetation establishment thus contributing to planning the land rehabilitation efforts and preventing further land degradation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

et al. 2020

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“…According to the AOD distribution map, the highest values (> 0.5) appear over Aral Sea. It is normal to see high AOD values due to high dust activity over Aral Sea, which is becoming more dense (Shen et al, 2018). It is also seen that the AOD values are still high over the region between the Aral Sea and the Caspian Sea (including the west of Turkmenistan) even not as much as in the Aral Sea.…”

Section: Aerosol (Dust) Evaluation For Turkmenistan Regionmentioning

confidence: 94%

Analysis of Dust Event in Turkmenistan and Its Source Regions

Oğuz¹

2020

Eskişehir Teknik Üniversitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler

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Dust transports cause significant increases in atmospheric concentration and surface PM10 particulate matter. In recent years, Asian countries have experienced dust transport frequently. Dust transport has a significant negative impact on human health, economy and environment. This study includes the analysis of a specific dust transport event occured on 27-29 May 2018 over Turkmenistan and its source regions and. Firstly, a general dust situation of Turkmenistan region has been studied by using MODIS AOD satellite data in the study. After, the meteorological conditions and the pathway of this dust transport event was examined. The preliminary analysis using synoptic analyzes and satellite data showed that the dust was transported from Uzbekistan and Kazakhstan. The transport was occured with northly winds and affected almost all regions of Turkmenistan on 27 May 2018. Observed AOD data in the region increased significantly during the event and took its maximum on 27 May 2018. The effect of this transport lasted until 30 May 2018 in Turkmenistan. The global CAMS dust model has estimated dust transport event even some overestimations. Finally, MSG/RGB satellite products were examined detailly in order to analyze the source regions of this dust transport. The analyzes indicated the main source region of this dust transport as Aral Sea.

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“…• Data pre-processing Each image dataset was pre-processed to extract spectral and textural features (Table 2), i.e., indices [27][28][29][30][31][32][33] and Gray Level Co-occurrence Matrix (GLCM)-based textures of image bands [34,35]. Spectral features are known to be useful in the determination of green areas or water bodies, whereas textural features are regularly applied to identify built-up areas [36][37][38]. Textural features were extracted as the average of their values in four directions (0, 45, 90, 135) for two windows sizes: 3x3 and 5x5 pixels.…”

Section: Data Pre-processingmentioning

confidence: 99%

Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

Aguilar

Kuffer

2020

Remote Sensing

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Open spaces are essential for promoting quality of life in cities. However, accelerated urban growth, in particular in cities of the global South, is reducing the often already limited amount of open spaces with access to citizens. The importance of open spaces is promoted by SDG indicator 11.7.1; however, data on this indicator are not readily available, neither globally nor at the metropolitan scale in support of local planning, health and environmental policies. Existing global datasets on built-up areas omit many open spaces due to the coarse spatial resolution of input imagery. Our study presents a novel cloud computation-based method to map open spaces by accessing the multi-temporal high-resolution imagery repository of Planet. We illustrate the benefits of our proposed method for mapping the dynamics and spatial patterns of open spaces for the city of Kampala, Uganda, achieving a classification accuracy of up to 88% for classes used by the Global Human Settlement Layer (GHSL). Results show that open spaces in the Kampala metropolitan area are continuously decreasing, resulting in a loss of open space per capita of approximately 125 m2 within eight years.

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Remote sensing-based land surface change identification and prediction in the Aral Sea bed, Central Asia

Cited by 37 publications

References 53 publications

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

Identifying potential vegetation establishment areas on the dried Aral Sea floor using satellite images

Analysis of Dust Event in Turkmenistan and Its Source Regions

Cloud Computation Using High-Resolution Images for Improving the SDG Indicator on Open Spaces

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