Assessing vegetation stability to climate variability in Central Asia

Yuan, Y.; Bao, Anming; Li, Shipeng; Zheng, Guoxiong; Jiang, Liangliang; Guo, Hao; Jiang, Ping; Yu, Tao; Maeyer, Philippe De

doi:10.1016/j.jenvman.2021.113330

Cited by 41 publications

(20 citation statements)

References 59 publications

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“…In this study, we focus on the vegetation status during the growing season, which lasts from April to October in this region (Gessner et al, 2013;Mohammat et al, 2013;Yuan et al, 2021).…”

Section: Study Areamentioning

confidence: 99%

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia

Yuan

Bao

Jiang

et al. 2022

Journal of Environmental Management

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“…In this study, we focus on the vegetation status during the growing season, which lasts from April to October in this region (Gessner et al, 2013;Mohammat et al, 2013;Yuan et al, 2021).…”

Section: Study Areamentioning

confidence: 99%

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia

Yuan

Bao

Jiang

et al. 2022

Journal of Environmental Management

Self Cite

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“…The health of the vegetation condition can be related to several causal factors such as availability of soil moisture, climatic conditions, heat stress, and drought conditions in the region (Jiang et al, 2022;Pei et al, 2018;Ribeiro et al, 2019;Yuan et al, 2021;Yuan et al, 2022;Islam et al, 2021;Pan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Mohanasundaram

Baghel

Thakur

et al. 2022

Preprint

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Critical applications of satellite data products include monitoring vegetation dynamics using vegetation indices such as Normalized Difference Vegetation Index (NDVI) and thermal characteristics such as Land Surface Temperature (LST) variations across different spatial and temporal scales. One of the major problems with passive remote sensing satellite data products is cloud and shadow cover that void the information from a specific image. The cloud cover problems further amplified during monsoon seasons as the formation of clouds are more frequent during these seasons. The present study proposes temporal aggregation of images and developing Harmonic Analysis of Time Series (HANTS) and Pixel-wise Multiple Linear Regression (PMLR) algorithms for retrieving cloud contaminated NDVI and LST information from Landsat-8 (L8) data products, respectively. The developed algorithms were applied in the northeastern part of Thailand to recover the missing NDVI and LST values from time series of L8 images acquired in 2018. The predicted NDVI and LST values at artificially clouded locations were compared with the corresponding clear pixel values. Additionally, the model predicted LST and NDVI values were also compared with MODIS LST and NDVI data sets. The calculated root mean square (RMSE) values were ranging from 0.004 to 0.05 and 1.05°C to 3.11°C for NDVI and LST variables, respectively. The validation statistics show that these models can be satisfactorily applied to retrieve NDVI and LST values from cloud-contaminated pixels of L8 images. Furthermore, a vegetation health index (VHI) calculated at province level show that most of the western provinces showed a healthy vegetation condition than other provinces in the northeast of Thailand.

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“…Ecosystems in arid and semi-arid regions are more fragile to climate change due to harsh environmental condition, and even slight changes in the climate can have a substantial in uence on such ecosystems (Li et al 2021a; Yuan et al 2021). However, previous studies on climate change have mainly focused on the analysis of statistical distributions of climate variables, such as mean, maximum, and minimum values, while neglecting their association with ecosystems ( Zhou et al (2015) found that the warming trend in the GLRCA initially enhanced the greenness of vegetation before 1991, but then the continued warming trend inhibited further increase in greenness.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

Gao

Zhao

Liu

2022

Preprint

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Dryland ecosystems are fragile to climate change due to harsh environmental condition. Climate change affects vegetation growth primarily by altering some key bio-temperature thresholds. Key bio-temperatures are closely related to vegetation growth, and slight changes could produce substantial effects on ecosystem structure and function. Therefore, this study selected the number of days with daily mean temperature above 0 °C (DT0), 5 °C (DT5), 10 °C (DT10), 20 °C (DT20), the start of growing season (SGS), the end of growing season (EGS), and the length of growing season (LGS) as bio-temperature indicators to analyze the response of vegetation dynamics to climate change in the Great Lakes Region of Central Asia (GLRCA) for the period 1982–2014. On the regional scale, DT0, DT5, DT10, and DT20 exhibited an overall increasing trend. Spatially, most of the study area showed that the negative correlation between DT0, DT5, DT10, DT20 with annual NDVI increased with increasing bio-temperature thresholds. Especially, more than 88.3% of the study area showed a negative correlation between annual NDVI and DT20, as increased DT20 exacerbated ecosystem drought. Moreover, SGS exhibited insignificantly advanced trend, and EGS experienced a significantly delayed trend. The overall extending trend in LGS was mainly attributed to the delayed EGS. Besides, our study revealed that about 54.7% of the study area showed a negative correlation between annual NDVI and LGS, especially in the north, indicating a negative effect of climate warming on vegetation growth in the drylands. The results of this study will help assess the stability of vegetation to climate variability, and predict the response of vegetation to future climate change in the GLRCA.

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Assessing vegetation stability to climate variability in Central Asia

Cited by 41 publications

References 59 publications

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia

Probabilistic assessment of vegetation vulnerability to drought stress in Central Asia

Reconstructing NDVI and land surface temperature from cloud cover pixels of Landsat-8 images and assessing vegetation health index in the Northeast region of Thailand

Spatial and Temporal Variability of Key Bio-Temperature Indicators and their Effects on Vegetation Dynamics in the Great Lakes Region of Central Asia

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