A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

Mengmeng, Hu; Xia, Beicheng

doi:10.1002/ldr.3221

Cited by 69 publications

(42 citation statements)

References 55 publications

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“…This means that urban land change leads to both vegetation decreases and vegetation increases. The vegetation increase in urban areas can lead to a better environment for humans, and economic development and human activities can promote vegetation increase, as found in a previous study [41].…”

Section: Limitations Of Mod-tr Applied To Small Areassupporting

confidence: 65%

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Zhu

Kuang

et al. 2020

Remote Sensing

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Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) can detect an abrupt change that was undetected by Residual Trend analysis (RESTREND), but it is usually combined with the Global Inventory for Mapping and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI), which cannot detect detailed vegetation changes in small areas. Hence, we used Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD-TR) to analyze the vegetation dynamic of the Pearl River Delta region (PRD) in this study. To choose the most suitable MODIS NDVI from MOD13Q1 (250 m), MOD13A1 (500 m), and MOD13A2 (1 km), whole and local comparison of results of the break year and MOD-TR were used. Meanwhile, a comparison of vegetation change at the city-scale was also implemented. Moreover, to reduce insignificant trend pixels in TSS-RESTREND, a combination method of TSS-RESTREND and RESTREND (CTSS-RESTREND) was proposed. We found that: (1) MOD13Q1 and MOD13A1 two NDVI were suitable for combination with TSS-RESTREND to detect vegetation change in PRD, but MOD13Q1 was a better choice when considering the accuracy of local detailed vegetation change; (2) CTSS-RESTREND could detect more pixels with a significant change (i.e., significant increase and significant decrease) than those of TSS-RESTREND and RESTREND. Also, its effectiveness could be verified by Landsat data; (3) at the city-scale, the CTSS-RESTREND detected that only vegetation decreases in Shenzhen, Foshan, Dongguan, and Zhongshan were higher than vegetation increases, but, significant vegetation changes (i.e., decreases and increases) were mainly concentrated in Huizhou, Jiangmen, Zhaoqing, and Guangzhou.

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Section: Limitations Of Mod-tr Applied To Small Areassupporting

confidence: 65%

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Zhu

Kuang

et al. 2020

Remote Sensing

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“…According to the Guangdong Statistical Yearbook (2019), the GDP of Guangdong reached approximately 9.73 trillion yuan (RMB) (USD 1.47 trillion, 2018), and its GDP per capita was 86,412 yuan (or USD 13,058). Guangdong is home to the Pearl River Delta (PRD), which is one of the most densely populated and industrialized areas in China and consists of 21 municipalities in different states of economic development (Hu and Xia, 2019). Guangdong Statistical Yearbook.…”

Section: Pvar Modelmentioning

confidence: 99%

Energy consumption and economic development in Guangdong, China: Distribution, relationship and causes

Hu¹,

Wang²,

Bei-cheng³

et al. 2021

Preprint

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Analysing the relationship between energy consumption and economic growth is essential to achieve the goal of sustainable development. We employ hot spot analysis to discover the spatial agglomeration of GDP per capita and energy intensity in Guangdong, China, from 2005–2018. Furthermore, panel vector autoregression coupled with a system generalized method of moments is performed to examine the dynamic causal relationship between energy consumption and economic growth under the framework of the Cobb-Douglas production function. Using a multivariate model and grouped studies based on the differences in regional economic development, we show that the GDP per capita of the Pearl River Delta (PRD) is significantly higher than that of the peripheral municipalities. However, energy intensity shows an entirely different spatial distribution. The development of the regional economy depends on its own “assembling effect”. GDP explains approximately 68.3% of the total variation in energy consumption in the PRD and only approximately 34.5% of that in the peripheral municipalities. We do not confirm Granger causality between energy consumption and economic development. Guangdong can decrease its energy consumption growth without substantially sacrificing its economic growth. The analysis framework of this paper has significant implications for regions in balancing economic development and energy consumption.

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“…Vegetation plays a central role in stabilizing the Earth ecosystem and maintaining human environments (Zhu and Southworth 2013, Hu and Xia 2019, Chu et al 2019. Vegetation dynamics constitute a sensitive indicator for the status of ecosystems and are closely related to climate change, hydrological regime, and human activities (John et al 2013, Guo et al 2017.…”

Section: Introductionmentioning

confidence: 99%

Regime shift of the hydroclimate–vegetation system in the Yellow River Delta of China from 1982 through 2015

Niu

Zhang

et al. 2020

Environ. Res. Lett.

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The Yellow River Delta (YRD) has been experiencing substantial climatic, hydrological, and anthropogenic stresses, and a sound understanding of the regime shift in its hydroclimate-vegetation system is of fundamental importance for maintaining the health and stability of its regional ecosystems. This study constructs and analyzes a 34-year-dataset (1982-2015) of hydro-climatic variables and satellite-based Normalized Difference Vegetation Index (NDVI) in the YRD. A seasonaltrend decomposition technique based on loess (STL), and a structural change analysis were coupled to detect regime shifts of regional hydroclimate and vegetation in the YRD from 1982 through to 2015. During this period the YRD exhibited a significant warmer-drier-greening trend and experienced four regime shifts of its hydroclimate-vegetation system, with the four shift periods roughly centered in 1989, 1998, 2004, and 2012. Partial correlation analysis revealed that temperature was the dominant factor promoting vegetative growth in spring and autumn (all P NDVI-TEM greater than 0.65), and streamflow impacted the NDVI mainly in summer. Temperature and precipitation were the dominant controls of vegetative growth during the growing season prior to 2002, and thereafter precipitation and streamflow alternately became the main moisture-influencing factors of vegetative growth. Streamflow played an important complementary role on vegetative growth, particularly in near riverine areas when drought exceeds a certain threshold. Additionally, climate shifts determined the changing trend of NDVI across the region, while the effect of land use change is localized and predominant in the northeastern part of the study region. These findings offer an insight into appropriate water regulation of the Yellow River and on climatic adaptation within the YRD.

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A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

Cited by 69 publications

References 55 publications

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI

Energy consumption and economic development in Guangdong, China: Distribution, relationship and causes

Regime shift of the hydroclimate–vegetation system in the Yellow River Delta of China from 1982 through 2015

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