Comparison between modified remote sensing ecological index and RSEI

Liu, Ying; Dang, Chaoya; Yue, Heng; Lyu, Chunguang; Qian, Jiechang; Zhu, Rong

doi:10.11834/jrs.20229338

Cited by 23 publications

(6 citation statements)

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“…Liu et al [117] studied the desertification change in the eastern region from 2000 to 2015 by using the MODIS data for 15 years and combining multiple time nodes and desert stations. Liu et al [118] used PCA to construct an improved remote sensing ecological index (MRSEI) that combined greenness, humidity, dryness, heat, and air quality indices. The research also used the entropy weight method to calculate the weight of each index in the pressure state response model.…”

Section: Characteristic Index Computational Formula Referencesmentioning

confidence: 99%

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

Wang,

Shi,

Zhang

2023

Remote Sensing

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Desertification seriously hinders economic development and ecological security, which has led to increased research on desertification monitoring and control. Remote sensing technology is widely used in desert research due to its large detection range and ability to obtain target feature information without touching objects. In order to better monitor and control desertification, the research methods on desert mobility and dune morphology in mobile deserts were reviewed. Among them, an important index to distinguish mobile and nonmobile deserts is desert vegetation coverage. The research progress of desert vegetation coverage based on visual interpretation, the nonlinear spectral model, normalized vegetation index (NDVI) fitting and plant community classification was reviewed. The loss of vegetation in the transitional zone of the desert is a contributing factor to desertification. The new technologies and applications of desert area monitoring, the remote sensing ecological index, and desert feature information extraction were introduced and analyzed. To combat desertification more accurately and effectively, the classification methods of moving dunes based on deep learning were also reviewed. It can be concluded that desertification monitoring methods are gradually becoming more accurate and adaptive, but they remain insufficient and less mature. Therefore, exploring how to apply desertification control technology more scientifically and rationally is an extremely valuable area for research.

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Section: Characteristic Index Computational Formula Referencesmentioning

confidence: 99%

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

Wang,

Shi,

Zhang

2023

Remote Sensing

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“…In addition, to mitigate the limitations caused by the heterogeneity of different ecosystems, various scholars have modified the RSEI according to the characteristics of the study area. For example, aerosol optical depth as an indicator reflecting urban air quality was added to the RSEI, which can comprehensively assess urban ecological quality, and this improved method is mainly applicable to urban EQ assessment [30]. The modified RSEI combines the first to third principal components to express EQ, which ignores the characteristic representativeness of each principal component in detecting temporal changes, e.g., PC1 represents common information and PC3 represents changes in information [31].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)

Li,

Samat,

Abuduwaili

et al. 2024

Land

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Considering climate change and increasing human impact, ecological quality and its assessment have also received increasing attention. Taking the Irtysh River Basin as an example, we utilize multi-period MODIS composite imagery to obtain five factors (greenness, humidity, heat, dryness, and salinity) to construct the model for the amended RSEI (ARSEI) based on the Google Earth Engine platform. We used the Otsu algorithm to generate dynamic thresholds to improve the accuracy of ARSEI results, performed spatiotemporal pattern and evolutionary trend analysis on the results, and explored the influencing factors of ecological quality. Results indicate that: (1) The ARSEI demonstrates a correlation exceeding 0.88 with each indicator, offering an efficient approach to characterizing ecological quality. The ecological quality of the Irtysh River Basin exhibits significant spatial heterogeneity, demonstrating a gradual enhancement from south to north. (2) To evaluate the ecological quality of the Irtysh River Basin, the ARSEI was utilized, exposing a stable condition with slight fluctuations. In the current research context, the ecological quality of the Irtysh River Basin watershed area is projected to continuously enhance in the future. This is due to the constant ecological protection and management initiatives carried out by countries within the basin. (3) Precipitation, soil pH, elevation, and human population are the main factors influencing ecological quality. Due to the spatial heterogeneity, the driving factors for different ecological quality classes vary. Overall, the ARSEI is an effective method for ecological quality assessment, and the research findings can provide references for watershed ecological environment protection, management, and sustainable development.

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“…The second is to choose methods other than principal component analysis to extract remote sensing indexes, Song Meijie 16 in order to retain richer information under the premise of removing noise interference, its weighted sum of the first three principal components of the principal component analysis output to obtain the final improved remote sensing ecological index. Liu Ying 17 , on the other hand, used kernel principal component analysis (KPCA) instead of PCA to obtain the principal components.…”

Section: Introductionmentioning

confidence: 99%

IRSEI-based monitoring of ecological quality and analysis of drivers in the Daling River Basin

Ge,

Qian,

Zhang

et al. 2024

Sci Rep

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The Daling River Basin is an important ecological functional area in the western region of Liaoning with outstanding environmental problems. The monitoring of ecological and environmental quality in the basin and the analysis of driving factors are of great importance for the protection of the ecological environment and the improvement of economic quality. In this paper, the three periods of Landsat remote sensing images in 1995, 2010 and 2020 are used as the basic data, and platforms and technical means such as RS and GIS are used to decipher and extract the three periods of land use information, and to construct the land use type transfer matrix. The remote sensing ecological index (RSEI) was improved, and the principal component analysis method was applied to construct the improved remote sensing ecological index (IRSEI) model based on the greenness (NDVI), moisture (WET), heat (LST) and new dryness (N-NDBSI), so as to realize the dynamic monitoring of ecological and environmental quality in the study area. Based on the land use change, combined with the trend of improved remote sensing ecological index (IRSEI) of Daling River Basin, thus achieving the purpose of rapid and efficient dynamic monitoring of ecological quality of Daling River Basin from 1995 to 2020. A geoprobe model was then used to systematically assess the drivers of ecological quality in the catchment. The results show that the improved remote sensing ecological index (IRSEI) can efficiently and accurately obtain the spatial distribution pattern and temporal variation trend of IRSEI in the study area, which is more in line with the characteristics of indicators in this study area. The IRSEI in the study area showed an increasing trend from 1995 to 2020, from 0.4794 to 0.5615, and the proportion of benign ecological classes increased year by year during the period. Among the evaluation indicators, NDVI and N-NDBSI are the main factors affecting the environmental and ecological quality of the Daling River Basin, and the increase of vegetation cover, climate regulation and human activities have obvious promoting effects on the improvement of the ecological environment of the Daling River Basin. This study provides a scientific theoretical basis for the implementation of further ecological environmental protection measures.

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Comparison between modified remote sensing ecological index and RSEI

Cited by 23 publications

References 0 publications

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

Review of Desert Mobility Assessment and Desertification Monitoring Based on Remote Sensing

Spatiotemporal Pattern, Evolutionary Trend, and Driving Forces Analysis of Ecological Quality in the Irtysh River Basin (2000–2020)

IRSEI-based monitoring of ecological quality and analysis of drivers in the Daling River Basin

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