Spatial and temporal variations of vegetation cover and its influencing factors in Shandong Province based on GEE

Dong, Hao; Liu, Yaohui; Cui, Jian; Zhu, Mingshui; Ji, Wenxin

doi:10.1007/s10661-023-11650-7

Cited by 9 publications

(2 citation statements)

References 55 publications

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“…The NDVI predictions-driven solely by climate change-were calculated imageby-element from 2000 to 2020 using the established regression equations. The NDVI observations were then subtracted from the NDVI predictions-driven solely by climate change-to obtain the NDVI residual time series from 2000 to 2020 [34]. The following formula was used to calculate the residual series:…”

Section: Multivariate Linear Residual Analysismentioning

confidence: 99%

“…It integrates multiple change analysis and attribution algorithms over 20 years to construct a set of comprehensive analysis systems for vegetation pre-restoration. These systems are used to predict future changes in vegetation trends, to thoroughly reveal through in-depth analysis the relationships and roles of vegetation, climate, and human activities, and to provide trustworthy scientific data (Figure 1) [32][33][34]. Therefore, the study's specific goals are to (1) thoroughly explore the relationship between the dynamic changes in vegetation and the spatial and temporal changes in the climate under artificial restoration and natural restoration, taking into account the context of 20 years of climate warming and aridification of severe ecological restoration projects as an entry point; (2) conduct a regional ecological assessment, evaluate the spatial and temporal changes, and forecast future NDVI trends to examine the distinct effects of artificial restoration and natural restoration on vegetation changes in the Maowusu Sandland area as an example; (3) compile and train 19 years' worth of NDVI data in the research region, as well as meteorological parameters, and propose a technique based on Informer's accurate long-term NDVI prediction, which will serve as a scientific foundation for ecological environmental conservation and sustainable development.…”

Section: Introductionmentioning

confidence: 99%

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Regional NDVI Attribution Analysis and Trend Prediction Based on the Informer Model: A Case Study of the Maowusu Sandland

Hou,

Li,

Zheng

et al. 2023

Agronomy

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Terrestrial ecosystems depend heavily on their vegetation; it is possible to forecast future growth trends of regional vegetation by keeping an eye on changes in vegetation dynamics. To circumvent the potential reduction in prediction accuracy caused by the non-stationarity of meteorological changes, we analyzed the characteristics of NDVI (Normalized Difference Vegetation Index) spatial and temporal changes and the influencing factors over the past 20 years in the Maowusu Sandland of China via attribution analysis. We also constructed a comprehensive analysis system for vegetation pre-restoration. Moreover, we combined meteorological data from 2000 to 2018 and presented a deep-learning NDVI-Informer prediction model with a self-attentive mechanism. We also used distillation operation and fusion convolutional neural network for NDVI prediction. Incorporating a probsparse self-attention method successfully overcomes Transformer weaknesses by lowering the memory use and complexity of large time series. It significantly accelerates the inference speed of long time series prediction and works well with non-smooth data. The primary findings were: (1) the Maowusu Sandland’s 20-year average showed a consistent increasing trend in the NDVI at 0.0034 a−1, which was mostly caused by climate change, with a relative contribution rate of 55.47%; (2) The Informer-based model accurately forecasted the NDVI in the research region based on meteorological elements and conducted a thorough analysis of the MAPE (mean absolute percentage error) (2.24%). This suggests that it can effectively lower the data’s volatility and increase prediction accuracy. The anticipated outcomes indicate that the trend will stabilize during the following ten years. To attain more sustainable and efficient agricultural production, the results of this study may be used to accurately estimate future crop yields and NDVI using previous data.

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Section: Multivariate Linear Residual Analysismentioning

confidence: 99%