Dynamic monitoring and analysis of factors influencing ecological quality in rapidly urbanizing areas based on the Google Earth Engine DOI Creative Commons

Fuxianmei Zhang,

Zhongfa Zhou, Denghong Huang

et al.

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: Sept. 7, 2023

Rapid urbanization poses significant challenges to ecological preservation in karst ecologically fragile regions. Systematically monitor and evaluate of its urban pattern change driving factors are the basis for achieving regional sustainable development. Taking Gui'an New Area(GNA) China as object, using Google Earth Engine(GEE) cloud platform, Remote Sensing Ecological Index(RSEI) method Geodetector study quality(EQ) changes between 2010 2020. The results show that: (1) An overall increase RSEI (0.12), with concentrated +1 0 range, revealing spatial autocorrelation. (2) Comparing LU/LC types, forest showed highest RSEI, followed by shrub, cropland, impervious, grassland, barren areas. (3) Among considered, interaction greenness had most influence, was primary external factor affecting EQ. result provides a reference decision makers formulate protection policies implement coordinated development strategies.

Language: Английский

Detection of spatiotemporal changes in ecological quality in the Chinese mainland: Trends and attributes DOI
Yang Li, Haifeng Tian, Jingfei Zhang

et al.

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 884, P. 163791 - 163791

Published: May 2, 2023

Language: Английский

Citations

28
Assessment of optimal seasonal selection for RSEI construction: a case study of ecological environment quality assessment in the Beijing-Tianjin-Hebei region from 2001 to 2020 DOI Creative Commons
Shaodong Huang, Yujie Li, Haowen Hu

et al.

Geocarto International, Journal Year: 2024, Volume and Issue: 39(1)

Published: Jan. 1, 2024

Timely and objective assessment of the optimal season for construction remote sensing ecological index (RSEI) is great significance accurate effective environment quality. We manipulated RSEI in to monitor seasonal variations quality (EEQ) Beijing-Tianjin-Hebei (JJJ) region from 2001 2020. First, we evaluated image across all four seasons filled missing observations through liner interpolation. Second, Seasonal was constructed using MODIS compared different years. Third, temporal spatial within same EEQ. Additionally, Moran's I utilized evaluate autocorrelation EEQ, stability correlation between indicators compared. The results showed that: 1) PC1 component concentrates most characteristics indicators, especially summer (over 71%); 2) Moran' 2001, 2006, 2011, 2016 2020 are 0.909, 0.898, 0.917, 0.921 0.892, respectively, which indicated that EEQ has a strong positive correlation. 3) high years, standard deviation fluctuated slightly summer, std NDVI, WET, LST and, NDBSI were 0.005, 0.052, 0.026 0.017, respectively. This study theoretically demonstrates constructing RSEI, filling research gap previous studies regarding rationale selecting images periods vigorous vegetation growth construction, can provide reference optimum monitoring urban future.

Language: Английский

Citations

10
Remote sensing perspective in exploring the spatiotemporal variation characteristics and post-disaster recovery of ecological environment quality, a case study of the 2010 Ms7.1 Yushu earthquake DOI Creative Commons
Yaohui Liu, Yu Lin, Wenyi Liu

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: March 14, 2024

Natural hazards usually cause heavy casualties and vast economic losses, as well severe damage to the ecological environment. Quantitative scientific evaluations of environment quality (EEQ) its recovery trend after can provide valuable insights for disaster risk reductions. This study takes 2010 Ms7.1 Yushu earthquake an example explore spatiotemporal changes driving mechanisms EEQ before using remote sensing GIScience. First, Moderate-resolution Imaging Spectroradiometer (MODIS) data was selected establish based index (RSEI). Then, we analyzed characteristics Yushu's from 2001 2020 explored spatial autocorrelation relationships. Last, mechanism in GeoDetector model. The main conclusions are follows: (1) From perspective RSEI time series, County strongly negatively affected during recovered earthquake. (2) Based on a distribution analysis, it be observed that regions with relatively high primarily concentrated central southern areas. Conversely, northwestern southeastern areas display lower quality. Moreover, has strong correlation clustering, evidenced by Moran's I value exceeding 0.7 over years. (3) results, elevation population were found key factors affecting post-disaster EEQ. interaction between slope plays most critical role process recovery. provides theoretical basis evolution helps decision-makers better balance relationship social development environmental protection management urban planning. It also useful reference guidance future under similar disasters.

Language: Английский

Citations

9
Spatiotemporal change in ecological quality and its influencing factors in the Dongjiangyuan region, China DOI
Xinmin Zhang, Houbao Fan,

Caihua Zhou

et al.

Environmental Science and Pollution Research, Journal Year: 2023, Volume and Issue: 30(26), P. 69533 - 69549

Published: May 4, 2023

Language: Английский

Citations

17
Detecting Spatial-Temporal Changes of Urban Environment Quality by Remote Sensing-Based Ecological Indices: A Case Study in Panzhihua City, Sichuan Province, China DOI Creative Commons
Yunfeng Shan,

Xiaoai Dai,

Weile Li

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(17), P. 4137 - 4137

Published: Aug. 23, 2022

Panzhihua City is a typical agricultural-forestry-pastoral and ecologically sensitive city in China. It also an important ecological defense the upper Yangtze River. has abundant mineral resources, including vanadium, titanium, water supplies. However, environmental problems emerge due to excessive development of mining, agriculture, animal husbandry, other non-natural urban economies. Therefore, scientific understanding spatio-temporal changes eco-environment critical for protection, planning, construction. To objectively evaluate eco-environmental status Panzhihua, remote sensing-based index (RSEI) was first applied resource-based city, its quality (EEQ) quantitatively assessed from 1990 2020. This study explored effects mining activities policies on EEQ used change detection reveal spatial-temporal over past three decades. In addition, this verified suitability RSEI evaluating using spatial autocorrelation, revealed heterogeneity optimized hot spot analysis, showed different clustering by analysis at two scales areas. According results: (1) From 2020, general condition improving, but there are still regional differences. (2) The Moran’s I value ranges 0.436 (1990) 0.700 (2020), indicating that autocorrelation distribution quality. (3) At mine, mean dropped 20–40%, decreased significantly activities. (4) A series restoration can buffer negative impact ecosystem, resulting slight improvement environment. evaluates constructed Google Earth Engine (GEE) platform, which provide theoretical support conditions monitoring, protection policy-making city.

Language: Английский

Citations

27
Impact of Land Use/Land Cover Change on Ecological Quality during Urbanization in the Lower Yellow River Basin: A Case Study of Jinan City DOI Creative Commons

Guangting Yu,

Tongwen Liu,

Qi Wang

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(24), P. 6273 - 6273

Published: Dec. 11, 2022

Rapid urbanization in the lower Yellow River basin has greatly contributed to socio-economic development of Northern China, but it also exacerbated land use/land cover change, with significant impacts on ecology. Ecological quality is a comprehensive spatial and temporal measure an ecosystem’s elements, structure function, reflecting ecological state under external pressures. However, how change affects during rarely been explored. In this study, Jinan, megacity basin, was taken as typical region, response 2000, 2010 2020 retrieved using remote sensing index. For mixed types, type-decomposition heterogeneity quantification method based abundance index proposed, impact mechanisms were revealed by coupling GeoDetector. The results show that: (1) Farmland built-up areas, dominant primary factors controlling pattern quality. (2) Urban expansion farmland protection policies resulted transfer woodland areas well grassland farmland, which intensified degradation (3) prompted main cause for improvement (4) Although urban implemented parallel, uneven changes 1.4 times expanded area poorer increasingly serious agglomeration effects. This study can provide scientific references conservation high-quality, sustainable cities basin.

Language: Английский

Citations

25
Dynamic Monitoring of Environmental Quality in the Loess Plateau from 2000 to 2020 Using the Google Earth Engine Platform and the Remote Sensing Ecological Index DOI Creative Commons
Zhang Jing, Guijun Yang, Liping Yang

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(20), P. 5094 - 5094

Published: Oct. 12, 2022

The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it necessary to develop tools monitor environmental quality in LP quickly accurately. To reveal spatio-temporal changes from 2000 2020, we used Moderate-Resolution Imaging Spectroradiometer (MODIS) products on Google Earth Engine platform constructed remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied determine changing trend LP. Finally, natural anthropogenic factors affecting probed using geographical detector model. results showed that: (1) average RSEI values 2000, 2010 2020 0.396, 0.468 0.511, respectively, displaying an upward with growth rate 0.005 year−1. overall environment was moderate (0.4–0.6). (2) In terms spatial distribution, excellent southeast poor northwest areas improved (84.51%) located all counties, whereas degraded (8.11%) occurred north study area. (3) Greenness, heat, wetness, dryness land use types prominent throughout period; additionally, total industrial gross domestic product growing influence. contribution multi-factor interaction stronger than single factors. will provide reference new research perspective for local protection regional planning.

Language: Английский

Citations

24
Spatial Simulation and Prediction of Land Use/Land Cover in the Transnational Ili-Balkhash Basin DOI Creative Commons

敬二 樋口,

Jinjie Wang,

Jianli Ding

et al.

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(12), P. 3059 - 3059

Published: June 11, 2023

Exploring the future trends of land use/land cover (LULC) changes is significant for sustainable development a region. The simulation and prediction LULC in large-scale basin an arid zone can help management planning rational allocation resources this ecologically fragile Using whole Ili-Balkhash Basin as study area, patch-generating use (PLUS) model combination PLUS Markov predictions (PLUS–Markov) were used to simulate predict 2020 based on assessment accuracy classification global dataset. simulations using measured data covering different time periods. Model settings with better results selected simulating predicting possible conditions basin. 2025 2030, which are historical change characteristics, indicate that overall spatial pattern remains relatively stable general without influence other external factors. Over scale five years, expansion croplands barren areas primarily stems from loss grasslands. Approximately 48% converted grassland transformed into croplands, while around 40% areas. In longer decade, conversion grasslands also evident. However, phenomenon urban built-up lands at expense more significant, approximately 774.2 km2 developing lands. This work provides effective new approach data-deficient basins large regions, thereby establishing foundation research impact human activities hydrology related studies.

Language: Английский

Citations

15
Instability of remote sensing ecological index and its optimisation for time frequency and scale DOI
Xinyue Yang, Fei Meng, Pingjie Fu

et al.

Ecological Informatics, Journal Year: 2022, Volume and Issue: 72, P. 101870 - 101870

Published: Oct. 20, 2022

Language: Английский

Citations

22
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 DOI Creative Commons
Wen Chen,

Jinjie Wang,

Jianli Ding

et al.

Land, Journal Year: 2023, Volume and Issue: 12(7), P. 1309 - 1309

Published: June 28, 2023

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

Language: Английский

Citations

12