Monitoring and Assessment of Ecological Change in Coastal Cities Based on Rsei

Liu, X. Y.; Zhang, X. X.; He, Yuanrong; Luan, Haijun

doi:10.5194/isprs-archives-xlii-3-w10-461-2020

Cited by 14 publications

(7 citation statements)

References 12 publications

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“…The RSEI model is composed of four indicators: Vegetation Index (NDVI), Humidity Index (WET), Dryness Index (NDSI), and Heat Index (LST) [40]. The selected four indicators are related to human intervention and the ecological environment [41]. These indicators were calculated in the GEE platform using image expressions following the calculations in Table 2 and further analyzed using ArcGIS Pro.…”

Section: Remote Sensing Ecological Indexmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan

Aurora,

Furuya

2023

Land

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The Japanese City Planning Act aims to control urban sprawl and promote compact urban development. Despite Japan’s aging population, urban sprawl remains a concern in shrinking sprawl situations. This impacts ecosystem services owing to the loss of natural areas. Ecological quality is regarded as a basic parameter for preventing urban sprawl. This study examined urban sprawl, ecological quality, and their relationship in Chiba Prefecture within the spatial context of the metropolitan region. Utilizing Shannon entropy and landscape metrics for urban sprawling studies, the analysis revealed a gradual shift towards compact development at the center, while the urban periphery was unevenly distributed. The remote sensing ecological index (RSEI), supported by remote sensing, assesses ecological quality. Despite some limitations, the average RSEI indicated moderate quality, offering a suitable human environment. Pearson’s calculations were used to determine the inverse correlation between urban sprawl and ecological quality. Chiba’s slight increase in sprawl was attributed to the transition from non-compact to eco-city development. The proposed plans were formulated based on similar urban sprawl and RSEI patterns in other cities for further sustainable compact development.

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Section: Remote Sensing Ecological Indexmentioning

confidence: 99%

Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan

Aurora,

Furuya

2023

Land

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“…At the local scale, Hang et al (2020) used Nan Jing, Jiang Su Province as the study area to monitor the ecological quality based the RSEI. Liu et al (2020) calculated the RSEI of Pingtan County, Fujian Province, which was used to evaluate the trend of the ecological environment in study area. At the regional scale, Xu et al (2019) calculated the RSEI from 2002 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) data to analyze ecological changes in Fujian Province, China.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal change and driving factors of ecological status in Inner Mongolia based on the modified remote sensing ecological index

Zongfan

Han

Huiqun

et al. 2023

Environ Sci Pollut Res

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Remote sensing monitoring of regional ecological quality has advanced significantly with the rapid developments of remote sensing technology. At present, remote sensing ecological index (RSEI) has been widely used in ecological status monitoring. However, RSEI was proposed for urban environments, and the rationality and accuracy of its applicability to desert-dominated arid zone ecosystems need to be demonstrated. Therefore, in this study, we incorporated desertification monitoring index (DMI) and salinity monitoring index (SMI) to RSEI and developed the modified remote sensing ecological index (MRSEI) for arid regions. Moreover, we analyzed the stability of MRSEI in ecological status monitoring for arid regions. The MRSEI was then used to evaluate the ecological quality of Inner Mongolia from 2000 to 2020 and exploring its causes. The results show that (1) Although the evaluation results of RSEI and MRSEI are more consistent in areas with high ecological status grades, the MRSEI results are more cautious and reliable in extreme conditions (e.g., desertification, salinization) than the RSEI. ( 2) Approximately 87.66% of ecological quality have improved or remain stable from 2000 to 2020, but the remaining areas (accounting for 12.34% of the whole area) are still under degraded conditions. This demonstrates that although local governments have made some progress in ecological conservation, the areas that are fluctuating or degraded still require protection or management. (3) In Inner Mongolia, the ecological quality which drove by precipitation (P) & temperature (T) accounting for 26.67% of the study area, population density (D) and GDP per capita (G) affected 13.23% of regional ecological quality. Overall, this research is crucial for evaluating spatial and temporal changes in arid region ecology and establishing conservation strategies.

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“…RESI has some obvious advantages; for example, it can be conveniently and quickly obtained or measured, and the application of RESI can help assess the eco-environmental quality in an objective, efficient and comprehensive manner. Therefore, it is not surprising that RESI has been widely used in different geographical settings such as cities [12][13][14], islands [15], mining areas [16,17], and watersheds [18].…”

Section: Introductionmentioning

confidence: 99%

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

Zhou

Zhang

et al. 2023

Sustainability

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The Chishui River Basin is located in the bordering area of Yunnan, Guizhou and Sichuan provinces, which serves as an important ecological barrier in the upper reaches of the Yangtze River, and plays a leading role in preserving natural environments, protecting water resources, and maintaining soil functions. However, the eco-environmental quality in the basin has encountered serious challenges in recent years, and the conflict between eco-environmental protection and economic development becomes increasingly prominent. Therefore, it is particularly important to quantitatively assess the extent of the eco-environmental changes in this basin. The present study acquired Landsat series remote sensing images based on the Google Earth Engine (GEE) platform, constructed a remote sensing ecological index (RSEI) as the assessment index that reflects the eco-environmental quality using principal component analysis, studied the changing trend in the eco-environmental quality using the Sen–Mann–Kendall trend test, analyzed the spatial clustering distribution patterns of the eco-environmental quality, based on spatial autocorrelation analysis, and applied the geographical detector model to determine the impacts of natural and anthropogenic factors on the eco-environmental quality. We further applied the CA–Markov model to simulate and predict the eco-environmental quality of the basin in 2025. The results showed the following: (1) between 2000 and 2020, the eco-environmental quality of the Chishui River Basin had been greatly improved. The average RSEI value increased from 0.526 in 2000 to 0.668 in 2020, and the percentage of areas belonging to the good or excellent quality category increased from 42.65% to 68.48%. (2) The main drivers of the eco-environmental quality included population density, mean annual temperature, land use type and elevation. The interactive effect between these drivers was significantly higher than that of individual drivers, and thus possessed stronger explanatory power for quality differences. (3) It is predicted that in 2025, the eco-environmental quality of the basin will continue to improve, and the proportion of land areas with good or excellent quality will continuously increase. The present study can provide reference value for local environmental protection and regional planning.

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Monitoring and Assessment of Ecological Change in Coastal Cities Based on Rsei

Cited by 14 publications

References 12 publications

Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan

Spatiotemporal Analysis of Urban Sprawl and Ecological Quality Study Case: Chiba Prefecture, Japan

Spatiotemporal change and driving factors of ecological status in Inner Mongolia based on the modified remote sensing ecological index

The Assessment of the Spatiotemporal Characteristics of the Eco-Environmental Quality in the Chishui River Basin from 2000 to 2020

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