Long-time series ecological environment quality monitoring and cause analysis in the Dianchi Lake Basin, China

Yang, Honghui; Yu, Junsheng; Xu, Wei; Wu, Yue; Lei, Xinyi; Ye, Jinni; Geng, Jianwei; Zheng, Dong

doi:10.1016/j.ecolind.2023.110084

Cited by 43 publications

(21 citation statements)

References 29 publications

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“…Reconstruction not only improves data quality but also preserves information about changes in indicators in the time series 48 . HANTS filtering method is better than SG and WS in overall performance of constructing RSEI based on MODIS data 20 . This study further verifies the adaptability of HANTS in constructing RSEI from Landsat images.…”

Section: Hants Is Capable Of Optimizing the Filling Results For Lands...mentioning

confidence: 93%

“…In Eq (19) and (20), m denotes the total of elements; 𝐷 𝑖 signifies the ecological quality value located on I; 𝐷 ̅ stands for the mean ecological quality value across all elements in the study region, and 𝑊 𝑖𝑗 for the spatial weight.…”

Section: Spatial Correlation Analysismentioning

confidence: 99%

“…Therefore, it can monitor the ecological quality objectively, lowcostly, and quantitatively, and perform spatiotemporal visualization analysis (Dai et al, 2022). Several studies utilized the RSEI model to monitor ecological quality alterations in interior cities 16 , coastal island cities 17,18 , and plateau basins 19,20 . These studies were carried out based on medium-and low-resolution satellite data.…”

Section: Introductionmentioning

confidence: 99%

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The HANTS-fitted data in the vegetation growing season can improve RSEI accuracy in revealing the spatiotemporal patterns of ecological quality in Yuxi of China

Miao,

Chen,

Kou

et al. 2024

Preprint

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Yuxi, a region of active economic development in China's central Yunnan Plateau, is subject to the ecological pressures brought about by rapid urbanization. In order to provide a scientific rationale for making decisions about environmental management, a long-term and accurate methodology for appraising and evaluating the ecological condition is required. Firstly, this study used 30 m-resolution Landsat data to reconstruct four indices, including greenness, wetness, dryness, and heat, during the vegetation growth period in Yuxi from 2000 to 2020 by using the HANTS filtering method. Secondly, we computed the annual RSEI using reconstructed indices to assess ecological quality in Yuxi. Thirdly, through Sen + MK trend analysis, spatial auto-correlation analysis, and geographical detectors on year-by-year RSEI, the spatiotemporal pattern and determinants of Yuxi's ecological quality were revealed. The results showed that: (1) vegetation seasons obviously affect the accuracy of the RSEI, and the data of the vegetation growing season is suggested to construct the RSEI model; (2) HANTS can effectively optimize the ecological indices of constructing the RSEI model, making the missing data filling smoother and more continuous. The reconstructed RSEI and the original RSEI have an absolute difference ranging between − 0.15 and 0.15; (3) the comprehensive ecological quality of Yuxi is moderate according to the RSEI mean value (0.5413), and the ecological quality of mountainous areas is better than that of valleys and plains. Yuxi's ecological quality manifests a clear center-edge pattern. (4) Between 2000 and 2020, the ecological quality of Yuxi exhibited a fluctuation and slightly rising trend as a whole, and the alteration in land utilization patterns was the primary determinant of Yuxi’s ecological quality shift, especially forestry land and impervious surface. Based on Yuxi, a city on the Central Yunnan Plateau, this study can serve as a reference for scientific decision-making regarding sustainable development and ecological protection.

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Section: Hants Is Capable Of Optimizing the Filling Results For Lands...mentioning

confidence: 93%

Section: Spatial Correlation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The HANTS-fitted data in the vegetation growing season can improve RSEI accuracy in revealing the spatiotemporal patterns of ecological quality in Yuxi of China

Miao,

Chen,

Kou

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In choosing the indices, we used the EVI as the greenness ecological component to improve the algorithm and synthesis method of the vegetation index to compensate for the deficiency of the NDVI's easy saturation [28]; in previous studies, the wetness component of the RSEI was characterized using the wet component of the Kauth Thomas (K-T) algorithm to transform multispectral images [2,30]; the MODIS surface-temperature product was used to obtain a stable surface temperature (LST) and was suitable for characterizing the ecological component of heat over a large area [29]. The bare soil index (BI) and the building index based on urban building conditions (IBI) were averaged to synthesize the NDBSI, which is a good representation of the dryness component [13,28].…”

Section: Methodology 231 Calculation Of the Rsei Simentioning

confidence: 99%

“…The RSEI SI values were further categorized into five grades spanning 0.2 intervals each, including poor (0 < RSEI SI ≤ 0.2), fair (0.2 < RSEI SI ≤ 0.4), moderate (0.4 < RSEI SI ≤ 0.6), good (0.6 < RSEI SI ≤ 0.8), and excellent (0.8 < RSEI SI ≤ 1) [2]. To further obtain the dynamics of the RSEI SI using change vector analysis [12,13,30], the RSEI SI values for different years were comparatively analyzed and divided into five categories: significantly degraded, mild degraded, unchanged, mild improved, and significantly improved.…”

Section: Methodology 231 Calculation Of the Rsei Simentioning

confidence: 99%

Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China

Chen

Wang

Ding

et al. 2023

Land

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Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.

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Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China

Zhang

Fan

Zhou

et al. 2023

Environ Sci Pollut Res

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Long-time series ecological environment quality monitoring and cause analysis in the Dianchi Lake Basin, China

Cited by 43 publications

References 29 publications

The HANTS-fitted data in the vegetation growing season can improve RSEI accuracy in revealing the spatiotemporal patterns of ecological quality in Yuxi of China

The HANTS-fitted data in the vegetation growing season can improve RSEI accuracy in revealing the spatiotemporal patterns of ecological quality in Yuxi of China

Detecting Long-Term Series Eco-Environmental Quality Changes and Driving Factors Using the Remote Sensing Ecological Index with Salinity Adaptability (RSEISI): A Case Study in the Tarim River Basin, China

Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China

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