Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road

Tan, Chao; Guo, Bo; Kuang, Honghai; Yang, Hong; Ma, Mingguo

doi:10.3390/rs10040595

Cited by 40 publications

(43 citation statements)

References 39 publications

(35 reference statements)

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“…Therefore, there are only two drought periods in our research, and future studies with more data can provide a better understanding of the relationship between seasonal indicators and drought periodicity in longer period. Secondly, insufficient effects of human activities on surface water and phenology were examined in this study, but agricultural activities such as irrigation and multiple growth seasons were excluded, especially in drought periods [85,86]. For example, the dryer the condition, the more water is pumped from rivers and lakes for irrigation, which reduces SWA from observation.…”

Section: Limitation and Future Researchmentioning

confidence: 99%

Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Lai

Zhang

et al. 2020

Remote Sensing

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Significant impact of extreme droughts on human society and ecosystem has occurred in many places of the world, for example, Southwest China (SWC). Considerable research concentrated on analyzing causes and effects of droughts in SWC, but few studies have examined seasonal indicators, such as variations of surface water and vegetation phenology. With the ongoing satellite missions, more and more earth observation data become available to environmental studies. Exploring the responses of seasonal indicators from satellite data to drought is helpful for the future drought forecast and management. This study analyzed the seasonal responses of surface water and vegetation phenology to drought in SWC using the multi-source data including Seasonal Water Area (SWA), Permanent Water Area (PWA), Start of Season (SOS), End of Season (EOS), Length of Season (LOS), precipitation, temperature, solar radiation, evapotranspiration, the Palmer Drought Severity Index (PDSI), the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and data from water conservancy construction. The results showed that SWA and LOS effectively revealed the development and recovery of droughts. There were two obvious drought periods from 2000 to 2017. In the first period (from August 2003 to June 2007), SWA decreased by 11.81% and LOS shortened by 5 days. They reduced by 21.04% and 9 days respectively in the second period (from September 2009 to June 2014), which indicated that there are more severe droughts in the second period. The SOS during two drought periods delayed by 3~6 days in spring, while the EOS advanced 1~3 days in autumn. All of PDSI, SWA and LOS could reflect the period of droughts in SWC, but the LOS and PDSI were very sensitive to the meteorological events, such as precipitation and temperature, while the SWA performed a more stable reaction to drought and could be a good indicator for the drought periodicity. This made it possible for using SWA in drought forecast because of the strong correlation between SWA and drought. Our results improved the understanding of seasonal responses to extreme droughts in SWC, which will be helpful to the drought monitoring and mitigation for different seasons in this ecologically fragile region.

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Section: Limitation and Future Researchmentioning

confidence: 99%

Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Lai

Zhang

et al. 2020

Remote Sensing

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“…In addition, a rapid reduction was noticed in the surface area of the lake between 1972 and 1984. Tan et al (2018) analysed areal changes of 24 lakes along the Silk Road from China to Europe. Authors indicated that surface areas of 15 lakes had decreased while areas of 9 lakes had increased from 2001 to 2016.…”

Section: Figure 2 Temporal Change In Water Surface Areamentioning

confidence: 99%

“…Monitoring of spatial and temporal changes using satellite images has globally become crucial for better understanding and explaining of environmental changes in the reservoirs. Spatial and temporal changes of water surface in lakes and reservoirs were monitored by several scientists (Sidle et al, 2007;El-Asmar et al, 2013;Abu-Faraj et al, 2014;Hossen et al, 2018;Ji et al, 2018;Tan et al, 2018;Yapiyev et al, 2019). There are few researches on change monitoring of water surface in lakes and reservoirs in Turkey (Akar et al, 2002;Reis and Yılmaz, 2008;Durduran, 2010;Avdan et al, 2013;Bahadır, 2013;Battal et al, 2016;Erener et al, 2016;Yücel and Turan, 2016;Sarp and Ozcelik, 2017;Topuz and Karabulut, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Change Monitoring in Water Surface Area of Atikhisar Reservoir (Çanakkale, Turkey) by using Remote Sensing and Geographic Information System Techniques

Kale¹,

Acarlı²

2019

Alınteri Zirai Bilimler Dergisi

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Spatial and temporal changes in surface area of Atikhisar Reservoir were monitored by using remote sensing and geographic information system techniques from 1975 to 2017. Satellite images were processed, analysed and manually digitized to reveal the changes in surface area of the reservoir. The results showed that total surface area of the reservoir was ranged between 1.72km 2 and 3.84km 2 during the monitoring period. Maximum increase in the surface area has been observed with 74.6% while maximum decrease has been observed with 31.8%. These fluctuations could be related with the climatic changes, natural and man-made processes such as sediment transportation, water leakages, excessive water exploitation for drinking, domestic or agricultural purposes, and human interventions along the reservoir. Therefore, surface area should be monitored continuously and all factors influencing the variation in surface area should be considered in decision making processes to support water sharing policy toward the management of water resources.

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“…Change rate and change range were used to estimate regional tendency based on the linear regression and appeared to be unaffected by possible constant biases present in the data [20,27,[31][32][33]. Therefore, the slope and range were used to assess the interannual variations of LST and SINDVI [20,31,33]. The slope represents the change rate of each pixel calculated by the ordinary least-squares estimation via linear regression from 2001 to 2016.…”

Section: Time-series Analysismentioning

confidence: 99%

Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016

Islam

2018

IJGI

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Land surface temperature (LST) can significantly alter seasonal vegetation phenology which in turn affects the global and regional energy balance. These are the most important parameters of surface–atmosphere interactions and climate change. Methods for retrieving LSTs from satellite remote-sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on the Earth’s surface. This paper assesses the geospatial patterns of LST using correlations of the seasonally integrated normalized difference vegetation index (SINDVI) in the southeastern region of Bangladesh from 2001 to 2016. Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets for LST and SINDVI were used for estimations in the study. From 2001 to 2016, the MODIS-based land surface temperature in the southeastern region of Bangladesh was found to have gently increased by 0.2 °C (R2 = 0.030), while the seasonally integrated normalized difference vegetation index also increased by 0.43 (R2 = 0.268). The interannual average LSTs mostly increased across the study areas, except in some coastal plain and tidal floodplain areas of the study. However, the SINDVI increased in the floodplain and coastal plain regions, except for in hilly areas. Physiographically, the study area is a combination of low lying alluvial floodplains, river basin wetlands, tidal floodplains, tertiary hills, terraced lands and coastal plains in nature. The hilly areas are mostly covered by dense forests, with the exception of agricultural areas. The impacts of increased LSTs were inversely correlated for the hilly areas and areas with forest coverage; LSTs were conversely correlated for the floodplain region, and tree cover outside of the forest and agricultural crops. This study will be very helpful for the protection and restoration of the natural environment.

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Lake Area Changes and Their Influence on Factors in Arid and Semi-Arid Regions along the Silk Road

Cited by 40 publications

References 39 publications

Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Responses of Seasonal Indicators to Extreme Droughts in Southwest China

Spatial and Temporal Change Monitoring in Water Surface Area of Atikhisar Reservoir (Çanakkale, Turkey) by using Remote Sensing and Geographic Information System Techniques

Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016

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