Vegetation Dynamics under Rapid Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration during the Past Two Decades

Abstract

Detection of long-term vegetation dynamics is important for identifying vegetation improvement and degradation, especially for rapidly urbanizing regions with intensive land cover conversions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration has experienced rapid urbanization during the past decades with profound impacts on vegetation, so there is an urgent need to evaluate vegetation dynamics across land use/cover change (LUCC). Based on the normalized difference vegetation index (NDVI) during 2001–2020, we used coefficient of variation, Theil–Sen median trend analysis, and Hurst exponent to analyze the spatiotemporal change and future consistency of vegetation growth among the main LUCC in the GBA. Results demonstrated that low NDVI values with high fluctuations were mainly distributed in the central urban areas, whereas high NDVI values with low fluctuations were primarily located in the peripheral hilly mountains. The area-averaged NDVI showed an overall increasing trend at a rate of 0.0030 year−1, and areas with vegetation improvement (82.99%) were more than four times those with vegetation degradation (17.01%). The persistent forest and grassland and the regions converted from built-up to vegetation displayed the most obvious greening; NDVI in over 90% of these areas showed an increasing trend. In contrast, vegetation browning occurred in more than 60% of the regions converted from vegetation to built-up. Future vegetation change in most areas (91.37%) will continue the existing trends, and 80.06% of the GBA was predicted to develop in a benign direction, compared to 19.94% in a malignant direction. Our results contribute to in-depth understanding of vegetation dynamics during rapid urbanization in the GBA, which is crucial for vegetation conservation and land-use optimization.

Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin

“…This study employs MODIS NDVI data, DEM data, land-use data, and vector data of administrative divisions in the study area. The NDVI data were extracted from the MOD13Q1 version 6 product (Vegetation Indices 16-Day L3 Global 250 m) from 2000 to 2021, acquired from the USGS (https://lpdaac.usgs.gov/products/, accessed on 18 June 2022), and employed in the GEE platform (UIL: https://earthengine.google.com/, accessed on 18 June 2022) [7,35]. The MODIS NDVI data preprocessing in the study area, such as splicing, projection, and clipping, was performed through the GEE platform.…”

“…The MODIS NDVI data preprocessing in the study area, such as splicing, projection, and clipping, was performed through the GEE platform. The maximum value composite (MVC) method was then utilized to attain the monthly and annual maximum NDVI dataset for dynamic vegetation analyses [7]. The MVC method can remove most of the cloud and atmosphere effects.…”

“…Table 1 lists the data sources. Sen slope estimation is a robust nonparametric statistical trend calculation method [7], and Mann-Kendall can be utilized to judge the significance of Sen slope estimation results [1]. In order to judge the trend of long-time series data, the Sen slope estimation and Mann-Kendall significance test can be combined [42].…”

“…Since 1960, the Lancang-Mekong River Basin has encountered many challenges from climate change and rapid socio-economic development from human demand and the growth of urban and rural areas [3,4], which has further changed or even destroyed the ecosystems in countries along the Lancang-Mekong Economic Circle because the average basin-wide temperature increased by approximately 0.79 • C [3]. Vegetation, as an essential feature of surface habitat conditions, is not only a vital link connecting the atmosphere, soil, hydrosphere, and biosphere but also an essential indicator of global climate and ecosystem variations and crucial for the global carbon cycle [5][6][7][8]. It has become a key source of information for studying large-scale environmental changes [9].…”

“…From the research content, there were long-term NDVI temporal and spatial evolution and future sustainability analysis [20,21], the detection of driving factors [20], and the correlation analysis of NDVI, terrain, climate, and other elements [17,22], as well as mutation analysis of vegetation NDVI [23]. The commonly used research methods mainly include the linear regression analysis method [24,25], correlation analysis method [17,21,26], trend analysis method combining Theil-Sen slope and Mann-Kendall test [7,27,28], and Hurst exponent method [21,29]. Typical studies using these methods are as follows: Zhang et al detected the long-term trends and abrupt changes of urbanized area by the Theil-Sen estimator and Mann-Kendall test [30], Yan et al employed the Mann-Kendall test to test the significance of LAI [31], Tehrani et al used the Mann-Kendall test to perform a correlation analysis of two months during the epidemic of COVID-19 [32], etc.…”