Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City

Li, Jingye; Gong, Jian; Guldmann, Jean‐Michel; Yang, Jianxin

doi:10.3390/rs13214440

Cited by 34 publications

(32 citation statements)

References 47 publications

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“…The topography notably influences the distribution and growth of vegetation, and as one of the regions with the highest average altitude worldwide, the drastic topographic fluctuations and harsh climate in Tibet result in locally obvious differences in the vegetation distribution ( Wang et al, 2019 , 2022 ). Spatial autocorrelation can reveal whether and to what extent the attribute characteristics of neighboring elements in geographic space are related and has become a common method for the study of vegetation growth, carbon cycle, heat island effect, and other changes in vegetation ecology and the environment ( Li et al, 2021 ; Yang et al, 2021 ). Global Moran’s index and local Moran’s index were used to detect the aggregation and local effects of the vegetation NDVI in Tibet, and the results revealed that the NDVI experienced high spatial agglomeration from 2001 to 2020.…”

Section: Discussionmentioning

confidence: 99%

“…The Hurst exponent method based on rescaled interval (R/S) analysis is a time-series analysis method based on fractal theory and exhibits wide applications in the fields of climate change and population migration ( Peng et al, 2012 ). R/S analysis can measure how the fluctuation range of a given time series varies with the time span, which can be used to predict the future trend of vegetation ( Jiang et al, 2015 ; Li et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

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Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China

et al. 2022

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Due to the cold climate and dramatically undulating altitude, the identification of dynamic vegetation trends and main drivers is essential to maintain the ecological balance in Tibet. The normalized difference vegetation index (NDVI), as the most commonly used greenness index, can effectively evaluate vegetation health and spatial patterns. MODIS-NDVI (Moderate-resolution Imaging Spectroradiometer-NDVI) data for Tibet from 2001 to 2020 were obtained and preprocessed on the Google Earth Engine (GEE) cloud platform. The Theil–Sen median method and Mann–Kendall test method were employed to investigate dynamic NDVI changes, and the Hurst exponent was used to predict future vegetation trends. In addition, the main drivers of NDVI changes were analyzed. The results indicated that (1) the vegetation NDVI in Tibet significantly increased from 2001 to 2020, and the annual average NDVI value fluctuated between 0.31 and 0.34 at an increase rate of 0.0007 year−1; (2) the vegetation improvement area accounted for the largest share of the study area at 56.6%, followed by stable unchanged and degraded areas, with proportions of 27.5 and 15.9%, respectively. The overall variation coefficient of the NDVI in Tibet was low, with a mean value of 0.13; (3) The mean value of the Hurst exponent was 0.53, and the area of continuously improving regions accounted for 41.2% of the study area, indicating that the vegetation change trend was continuous in most areas; (4) The NDVI in Tibet indicated a high degree of spatial agglomeration. However, there existed obvious differences in the spatial distribution of NDVI aggregation areas, and the aggregation types mainly included the high-high and low-low types; and (5) Precipitation and population growth significantly contributed to vegetation cover improvement in western Tibet. In addition, the use of the GEE to obtain remote sensing data combined with time-series data analysis provides the potential to quickly obtain large-scale vegetation change trends.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China

et al. 2022

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“…The entropy weight method has been used in recent analysis of the ecological quality of the terrestrial system using remote sensing monitoring [61]. The advantage of this method is that it is an objective multi-index evaluation to attain the relative intensity of each index solely based on the information that each index contains.…”

Section: Effectiveness Score Calculationmentioning

confidence: 99%

“…The rationale behind this weighting method is that the weight of each of the three sub-metrics mentioned above could be determined using the information provided from their observation values. Details regarding the entropy weight method are listed as follows [61]:…”

Section: Effectiveness Score Calculationmentioning

confidence: 99%

Grassland Conservation Effectiveness of National Nature Reserves in Northern China

Zhao

et al. 2022

Remote Sensing

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Grasslands are crucial ecosystem biomes for breeding livestock and combatting climate change. By 2018, the national nature reserves (NNRs) in the Inner Mongolia Autonomous Region (IMAR) had constituted 8.55% of the land area. However, there is still a knowledge gap about their effectiveness in grasslands. Based on a multiyear time series of the growing season composite from 2000 to 2020, we proposed an effectiveness score to assess the effectiveness of the NNRs, using the 250 m MOD13Q1 NDVI data with Theil–Sen and Mann–Kendall trend analysis methods. We found the following: 22 of 30 NNRs were deemed effective in protecting the Inner Mongolian grasslands. The NNRs increased pixels with a sustainable trend 19.26% and 20.55% higher than the unprotected areas and the IMAR, respectively. The pixels with a CVNDVI < 0.1 (i.e., NDVI coefficient of variation) in the NNRs increased >35.22% more than those in the unprotected areas and the IMAR. The NDVI changes within the NNRs showed that 63.64% of NNRs had a more significant trend of greening than before the change point, which suggests a general greening in NNRs. We also found that the NNRs achieved heterogeneous effectiveness scores across protection types. Forest ecology protection and wildlife animal protection types are the most efficient, whereas wildlife vegetation protection is the least effective type. This study enriches the understanding of grassland conservation and sheds light on the future direction of the sustainable management of NNRs.

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“…Since RSEI was developed, it has been applied in ecological quality assessment of cities [20], basins [21], nature reserves [22], and various regions around the world. Some research even improves the model to make it more suitable for specific regions [23]. Generally, applications proved the feasibility and efficiency of RSEI.…”

Section: Introductionmentioning

confidence: 99%

Land Use and Climate Change Altered the Ecological Quality in the Luanhe River Basin

Zhang

Song

Fan

et al. 2022

IJERPH

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Monitoring and assessing ecological quality (EQ) can help to understand the status and dynamics of the local ecosystem. Moreover, land use and climate change increase uncertainty in the ecosystem. The Luanhe River Basin (LHRB) is critical to the ecological security of the Beijing–Tianjin–Hebei region. To support ecosystem protection in the LHRB, we evaluated the EQ from 2001 to 2020 based on the Remote Sensing Ecological Index (RSEI) with the Google Earth Engine (GEE). Then, we introduced the coefficient of variation, Theil–Sen analysis, and Mann–Kendall test to quantify the variation and trend of the EQ. The results showed that the EQ in LHRB was relatively good, with 61.08% of the basin rated as ‘good’ or ‘excellent’. The spatial distribution of EQ was low in the north and high in the middle, with strong improvement in the north and serious degradation in the south. The average EQ ranged from 0.58 to 0.64, showing a significant increasing trend. Furthermore, we found that the expansion of construction land has caused degradation of the EQ, whereas climate change likely improved the EQ in the upper and middle reaches of the LHRB. The results could help in understanding the state and trend of the eco-environment in the LHRB and support decision-making in land-use management and climate change.

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Assessment of Urban Ecological Quality and Spatial Heterogeneity Based on Remote Sensing: A Case Study of the Rapid Urbanization of Wuhan City

Cited by 34 publications

References 47 publications

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China

Spatial Pattern and Dynamic Change of Vegetation Greenness From 2001 to 2020 in Tibet, China

Grassland Conservation Effectiveness of National Nature Reserves in Northern China

Land Use and Climate Change Altered the Ecological Quality in the Luanhe River Basin

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