Monitoring the effects of land consolidation on the ecological environmental quality based on remote sensing: A case study of Chaohu Lake Basin, China

Guo, Beibei; Fang, Y.; Jin, Xiaobin; Zhou, Yongqiang

doi:10.1016/j.landusepol.2020.104569

Cited by 78 publications

(38 citation statements)

References 42 publications

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“…The Chaohu Lake Basin is located in Jianghuai Hills of central Anhui Province, between the Huaihe and Yangtze rivers and is a typical traditional agricultural planting area. It ranges between 30 • 58'00"-32 • 58'00" N and 116 • 24'30"-118 • 30'00" E and covers an area of 14,203 km 2 (including the 770 km 2 of Chaohu Lake) [34], adjacent to the Yangtze River in the southeast, the Dabie Mountains in the west, the Jianghuai watershed in the north, and the Chuhe River valley in the northeast. Considering the availability of the data, the counties involved in the Chaohu Lake Basin were included in the study area (Yuexi County only contains one town, Yaohe town), covering five cities (Hefei, Lu'an, Anqing, Wuhu, and Ma'anshan), 19 counties and districts, and 161 towns and streets, with 33 rivers in the entire basin ( Figure 1).…”

Section: Study Areamentioning

confidence: 99%

“…In this entire process, however, it is difficult to feedback the spatiotemporal changes of sustainable development simultaneously with these methods. A remote sensing ecological index (RSEI) can be applied to environmental monitoring in urban (impervious surface) [31,32], basin [33], and agricultural engineering areas [34]. It is necessary to explore the connections between subsystems, as well as to show their spatial differences.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Sustainable Regional Development Combining Remote Sensing Data and Ecological Constraints: A Case Study of Chaohu Basin, China

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The maintenance of sustainable development in light of climate change is presenting society with a serious challenge. This study analyzes the spatial environment and its coupled relationship with socio-economic and food security in the Chaohu Lake Basin in central Anhui Province, China. Previous studies have shown that this study area has good environmental conditions. However, the ecological security, economic development, and food security in the Chaohu Lake Basin was generally poor and poorly coordinated in 2018, especially in the center of local cities and surrounding areas. Nevertheless, many towns are in a well-organized state, meaning sustainable and coordinated regional development can be achieved through resource optimization. Generally, the economic conditions in the Chaohu Lake Basin should be improved in a way that ensures that the environment is not significantly degraded. The main functional zoning requirements of the counties’ land management planning designate most of the counties as parts of ecological conservation areas. The present study provides monitoring of important basin ecosystems and serves as a guide to future sustainable regional development by providing directions and paths to follow; it can also provide a reference for eco-functional zoning that is applicable to various natural basins worldwide.

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Section: Study Areamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of Sustainable Regional Development Combining Remote Sensing Data and Ecological Constraints: A Case Study of Chaohu Basin, China

et al. 2020

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“…In recent years, the RSEI has grown in popularity and been frequently adopted for urban eco-environment quality assessment in individual cities such as Pingtan Comprehensive Pilot Zone [19] in China, regions such as the Beijing-Tianjin-Hebei region [20], the Guangdong-Hong Kong-Macau Bay area [21], the Chaohu basin [22,23], and the Beijing-Hangzhou Canal coast [24], and even at a national scale [25]. It has also been applied by international researchers, for example, to Gaomishan City in Iran [26], the Samara region of Russia [27], as well as different cities in the United States [28] and Europe [29].…”

Section: Introductionmentioning

confidence: 99%

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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“…The NDVI is closely related to vegetation growth, forest biomass, leaf area index and vegetation coverage in the study area [41]- [43]. Thus, it has long been used as the main ecosystem proxy variable due to its robustness and simplicity, and the expression is as follows [45], [46]: NDVI = (B 5 B 4 )/(B 5 +B 4 )…”

Section: Calculation Of the Rseimentioning

confidence: 99%

Eco-Environmental Quality Monitoring in Beijing, China, Using an RSEI-Based Approach Combined With Random Forest Algorithms

Gou

Zhao²

2020

IEEE Access

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The assessment of ecological environmental quality (EEQ) has provided an important knowledge base for protecting human health and realizing sustainable development. Previous studies have often used only principal component analysis (PCA) to perform the EEQ evaluation by determining the remote sensing based ecological index (RSEI) in a single year, and the assessment results are not comparable between years. Thus, a comparable and accurate method needs to be found and applied. In this paper, we applied the PCA combined with a random forest algorithm (a machine learning algorithm) to quantify the EEQ of Beijing, China, in 2014 and 2020 and analysed the relationship between the RSEI and four ecological indicators (greenness, wetness, dryness and heat). The results suggested that the RSEI and the ecological indicators of Beijing all changed substantially from 2014 to 2020, and the method of combining PCA and random forest was suitable for calculating the time-series data of RSEI in the study period. Specifically, the RSEI in Beijing increased slightly from 0.31 to 0.33 overall, the greenness of Beijing increased drastically (26.09%), the wetness decreased by 10.00%, and the dryness and heat increased by 8.62% and 2.00%, respectively. The Pearson correlation coefficient test showed that both the greenness and wetness had positive effects on the RSEI, while the dryness and heat had negative effects. Of the four ecological indicators in Beijing, the greenness contributed greatly as the main positive factor, and dryness was the most negative factor during the six years. This paper developed an improved framework for continuous EEQ monitoring, and these results provide a scientific basis for the sustainable development and ecological environmental monitoring of Beijing and other megacities. INDEX TERMSRemote sensing based ecological index (RSEI); ecological environmental quality (EEQ); principal component analysis (PCA); random forest; dynamic monitoring

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Monitoring the effects of land consolidation on the ecological environmental quality based on remote sensing: A case study of Chaohu Lake Basin, China

Cited by 78 publications

References 42 publications

Evaluation of Sustainable Regional Development Combining Remote Sensing Data and Ecological Constraints: A Case Study of Chaohu Basin, China

Evaluation of Sustainable Regional Development Combining Remote Sensing Data and Ecological Constraints: A Case Study of Chaohu Basin, China

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

Eco-Environmental Quality Monitoring in Beijing, China, Using an RSEI-Based Approach Combined With Random Forest Algorithms

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