Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI

Yuan, Bin; Fu, Lei; Zou, Yeai; Zhang, Siqi; Chen, Xinsheng; Li, Feng; Deng, Zhong-Shan; Xie, Yonghong

doi:10.1016/j.jclepro.2021.126995

Cited by 190 publications

(131 citation statements)

References 28 publications

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“…This suggests that the contributions of wetness, dryness, and heat to urban eco-environment quality vary with the season. This finding is different from previous studies [14,17,55] that assumed a fixed contribution. The results of the correlation analysis are shown in Table 3.…”

Section: Rsei Indicatorscontrasting

confidence: 99%

“…Interestingly, the lower nWetness values in the Central Urban Area show that it was moister than most of the other areas in December (Figure 4f). As for dryness (Figure 4c) and heat (Figure 4d), they were higher in the Central Urban Area than in other areas in September, which agrees with previous studies [16,55]. However, that is not the case for December when the other areas were drier and warmer than the Central Urban Area.…”

Section: Rsei Indicatorssupporting

confidence: 90%

“…After the generation of seasonal RSEI maps, annual RSEI maps were produced by averaging four respective seasonal RSEI maps. For a comparative analysis, RSEI was classified into five levels, namely 0.0-0.2 (Poor), 0.2-0.4 (Fair), 0.4-0.6 (Moderate), 0.6-0.8 (Good) and 0.8-1.0 (Excellent) using the equal interval classification method like [14,27,55]. This method allows data produced in different years to be directly compared for a multitemporal analysis [14,27].…”

Section: Integration Of the Measuresmentioning

confidence: 99%

“…However, the remotely sensed variables (NDVI, Wetness, NDBSI, and LST) for deriving RSEI can change seasonally, monthly, and even daily and a single day's ecoenvironmental condition is insufficient for representing an entire year's. For this reason, it is advisable to avoid such a practice, which is often found in many studies [14,17,55,61], and to use as many remote-sensing images as possible to characterize a year's eco-environmental condition, which helps produce a more reliable assessment result.…”

Section: Rsei Change 431 Seasonal and Annual Rsei Changementioning

confidence: 99%

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Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Zhang

Yang

et al. 2021

IJGI

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The remote-sensing ecological index (RSEI), which is built with greenness, moisture, dryness, and heat, has become increasingly recognized for its use in urban eco-environment quality assessment. To improve the reliability of such assessment, we propose a new RSEI-based urban eco-environment quality assessment method where the impact of RSEI indicators on the eco-environment quality and the seasonal change of RSEI are examined and considered. The northern Chinese municipal city of Tianjin was selected as a case study to test the proposed method. Landsat images acquired in spring, summer, autumn, and winter were obtained and processed for three different years (1992, 2005, and 2018) for a multitemporal analysis. Results from the case study show that both the contributions of RSEI indicators to eco-environment quality and RSEI values vary with the season and that such seasonal variability should be considered by normalizing indicator measures differently and using more representative remote-sensing images, respectively. The assessed eco-environment quality of Tianjin was, overall, improving owing to governmental environmental protection measures, but the damage caused by rapid urban expansion and sea reclamation in the Binhai New Area still needs to be noted. It is concluded that our proposed urban eco-environment quality assessment method is viable and can provide a reliable assessment result that helps gain a more accurate understanding of the evolution of the urban eco-environment quality over seasons and years.

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Section: Rsei Indicatorscontrasting

confidence: 99%

Section: Rsei Indicatorssupporting

confidence: 90%

Section: Integration Of the Measuresmentioning

confidence: 99%

Section: Rsei Change 431 Seasonal and Annual Rsei Changementioning

confidence: 99%

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Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Zhang

Yang

et al. 2021

IJGI

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“…The index is composed entirely of remote sensing data, so it has the characteristics of timeliness and rapidity. The index realizes the objective and quantitative evaluation of the regional ecological environment and can invert the ecological environment quality in different regions well [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

Chang

Hou

et al. 2021

IJERPH

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Grassland ecosystems are increasingly threatened by pressures from climate change and intensified human activity, especially in the arid region of Central Asia. A comprehensive understanding of the ecological environment changes is crucial for humans to implement environmental protection measures to adapt to climate change and alleviate the contradiction between humans and land. In this study, fractional vegetation coverage (FVC), leaf area index (LAI), gross primary productivity of vegetation (GPP), land surface temperature (LST), and wetness (WET) were retrieved from Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing products in 2008 and 2018. Principal component analysis (PCA) was used to establish the MODIS data-based ecological index (MODEI) in the study area, and the spatial differentiation characteristics and driving mechanism of ecological quality in the last ten years were explored. The results showed that: (1) FVC, GPP, LAI, and WET had positive effects on the ecological environment, while LST had a negative impact on the ecological environment. FVC and GPP were more significant than other indicators. (2) The MODEI showed a spatial pattern of “excellent in the north and poor in the south” and changed from north to south in the study area. (3) From 2008 to 2018, the average MODEI of Fuyun County increased from 0.292 to 0.303, indicating that the ecological quality in Fuyun County became better overall. The improved areas were mainly located in the summer pastures at higher elevations. In comparison, the deteriorated areas were concentrated in the spring and autumn pastures and winter pastures at lower elevations. The areas where the ecological environment had obviously improved and degraded were distributed along the banks of the Irtysh River and the Ulungur River. (4) With the increase in precipitation and the decrease in grazing pressure, the MODEI of summer pasture was improved. The deterioration of ecological environment quality in spring and autumn pastures and winter pastures was related to the excessive grazing pressure. The more significant changes in the MODEI on both sides of the river were associated with implementing the herdsmen settlement project. On the one hand, the implementation of newly settled villages increased the area of construction land on both sides of the river, which led to the deterioration of ecological quality; on the other hand, due to the increase in cropland land and the planting of artificial grasses along the river, the ecological quality was improved. The study offers significant information for managers to make more targeted ecological restoration efforts in ecologically fragile areas.

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A regional assessment of ecological environment quality in Thailand special economic zone: Spatial heterogeneous influences and future prediction

Nguyen,

Kaewthongrach,

Channumsin

et al. 2023

Land Degrad Dev

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Abstract“Eastern Economic Corridor” is a development plan under the Thailand 4.0 scheme to revitalize the economy. However, it has influenced the environment through numerous infrastructural and industrial projects. The ecological environment is the most fragile component since it is susceptible to land use, land cover changes (LULCC) and rapid development. Meanwhile, there is currently a lack of comprehensive studies on spatial heterogeneity and future simulation of ecological environment quality (EEQ). Therefore, this study aims to analyze changes in EEQ and controlling factors and to predict near‐future EEQ by adopting an integrated approach of remote sensing and spatial analyses. It revealed that the regional EEQ has considerably fluctuated in the period 2013–2021. The dynamic areas, accounting for nearly half of the area, include ecologically degraded areas in coastal urban and surrounding areas and ecologically improved areas in the eastern forest and perennial plantations. Chonburi is the most vulnerable province due to its long coast with extensive urban infrastructures and industrial estates. These changes are an integrated consequence of natural and socioeconomic impacts. Particularly, LULCC, infrastructural projects, and climate are the most crucial contributors since they directly regulate the functions of the ecological environment, which will amplify the future changes of EEQ reflected via the near‐future prediction. This research identified key controlling factors and spatial heterogeneities that are useful for policymakers and local authorities to have appropriate local‐oriented plans for each area relevant to their uniqueness. Besides, regional plans should be also considered along with regional concerns such as LULCC, crop structures, water management, and green infrastructure, as well as legal interventions to promote sustainable development in this special economic zone.

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Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI

Cited by 190 publications

References 28 publications

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Assessing the Urban Eco-Environmental Quality by the Remote-Sensing Ecological Index: Application to Tianjin, North China

Assessment of Spatio-Temporal Variation and Driving Mechanism of Ecological Environment Quality in the Arid Regions of Central Asia, Xinjiang

A regional assessment of ecological environment quality in Thailand special economic zone: Spatial heterogeneous influences and future prediction

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