Evaluation of Remote Sensing Ecological Index Based on Soil and Water Conservation on the Effectiveness of Management of Abandoned Mine Landscaping Transformation

Lian, Zeke; Hao, Huichao; Zhao, Jing; Cao, Kaizhong; Wang, Hesong; He, Zhechen

doi:10.3390/ijerph19159750

Cited by 13 publications

(7 citation statements)

References 34 publications

(35 reference statements)

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“…The mean value of NDVI was 0.6069, and the mean value of WET was 0.4603. It can be concluded that the eigenvalue of greenness was greater than that of wetness [79]. NDVI had a maximum value of 0.8025 in 2005, while WET had a maximum value of 0.5272 in 2010.…”

Section: Rsei Model Resultsmentioning

confidence: 93%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

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The Ulan Mulun River Basin is an essential ecological protective screen of the Mu Us Desert and a necessary energy base in Ordos City. With the acceleration of industrialization and urbanization, human activities have caused enormous challenges to the local ecological environment. To achieve the region’s economic sustainability and make local development plans more objective, it is necessary to evaluate the basin’s ecological environment quality over a period of time. First, in the Landsat historical images, we selected 5 years of data to investigate the changes in this time-period (2000–2020). Second, based on the opened remote sensing database on Google Earth Engine, we calculated the remote-sensing ecological index (RSEI) distribution map. RSEI includes greenness, temperature, humidity, and dryness. Thirdly, we assessed the ecological-environmental distribution and change characteristics in the Ulan Mulun River Basin. Finally, we analyzed the RSEI spatial auto-correlation distribution characteristics in the study area. The mean values of RSEI in 2000, 2005, 2010, 2015, and 2020 were 0.418, 0.421, 0.443, 0.456, and 0.507, respectively, which indicated that the ecological environment quality had gradually improved. The ecological environment quality from 2000 to 2005 had the biggest change, as the area with drastically changed water levels accounted for 78.98% of the total basin. It showed a downward trend in the central and western regions. It showed an upward trend in the eastern region. For 20 years, the area of deterioration decreased by 24.37%, and the slight change area increased by 45.84%. The Global Moran’s I value ranged from 0.324 to 0.568. The results demonstrated that the Ulan Mulun River Basin ecological environment quality spatial distribution was positively correlated, and the clustering degree decreased gradually. Local spatial auto-correlation of RSEI showed that high-high(H-H) was mainly distributed in the basin’s eastern and southern regions, where the population density was low and the vegetation was in good condition. Low-low(L-L) was mainly distributed in the basin’s central regions and western regions, where the population density was high, and the industrial and mining enterprises were concentrated. This study provided a theoretical basis for the sustainable development of the Ulan Mulun River Basin, which is crucial for the local ecological environment and economic development.

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Section: Rsei Model Resultsmentioning

confidence: 93%

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

et al. 2022

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“…WET showed the lowest contribution, indicating a less significant effect on ecological quality. Additionally, the sum of the eigenvalues for LST and NDSI surpassed the total value of the eigenvalues of NDVI and WET, indicating weaker ecological enhancement from vegetation and humidity relative to the adverse influence of dryness and heat [48]. The WET indicator had the highest average values in 1990 and 2021, whereas LST consistently had the lowest average value.…”

Section: Rsei Quality In Chiba Prefecturementioning

confidence: 95%

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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“…The ecological index based on remote sensing is developed to quantify ecological quality by integrating four ecological factors: SAVI, NDBI, NDSI, and NDMI. These factors were selected based on a review of existing research (Jiang et al 2020;Lian et al 2022). Firstly, the dynamics of land use change, particularly in urban settings, alter the conditions and procedures of ecological study.…”

Section: Development Of Ecological Index Remote Sensing Datamentioning

confidence: 99%

Assessment Of Remote Sensing Approach For Urban Ecological Quality Evaluation In Pekanbaru City, Riau Province Indonesia

Giofandi,

Syahzaqi,

Sekarjati

et al. 2024

GES

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There are obstacles in estimating environmental dynamics behind its convenience, beginning with the development of effective policies for sustainable urban development. The objectives of this research were to comprehend the ability and performance of ecological indices integration and to identify the spatial distribution of changes from 2018 to 2021 in Pekanbaru City, Riau province, Indonesia. This study employed remote sensing data to create ecological parameters including the build-up index, vegetation index, soil index, and moisture index, as well as principal component analysis to generate ecological index integration. The findings indicate a correlation of over 90% among these parameters from 2018 to 2021. Overall, there has been a significant decrease in the ecological quality index’s high-quality categories, such as good and excellent, covering a total of 19.6% over 127 km². Conversely, the poor ecological quality category increased to 2.2%, encompassing an area of 15 km², up from the initial 21.2% covering 122 km². Additionally, the fair and moderate categories also experienced increases of 4% and 13.4%, respectively, reaching 28 km² and 84 km². The study area’s ecological quality is largely affected by increased anthropogenic activities, leading to a drastic decrease in the presence of ecological quality in the good and excellent categories. The importance of spatial planning is emphasized to incorporate aspects of ecological assessment rather than solely focusing on increasing economic activity. This outcome can be used to respond to the concept of sustainable development by caring for the ecological environment, particularly in urban areas, and mitigating ecological damage.

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Evaluation of Remote Sensing Ecological Index Based on Soil and Water Conservation on the Effectiveness of Management of Abandoned Mine Landscaping Transformation

Cited by 13 publications

References 34 publications

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

Temporal and Spatial Changes of Ecological Environment Quality Based on RSEI: A Case Study in Ulan Mulun River Basin, China

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

Assessment Of Remote Sensing Approach For Urban Ecological Quality Evaluation In Pekanbaru City, Riau Province Indonesia

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