Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Data Acquisition and Preprocessing
2.3. Theory and Method
2.3.1. Construction of the Remote Sensing Ecological Index (RSEI)
2.3.2. Mann–Kendall Mutation Test
2.3.3. Spatial–Temporal Changes Analysis
2.3.4. Spatial Autocorrelation Analysis
2.3.5. Geodetector
3. Results
3.1. Comprehensive Ecological Environment Assessment
3.2. RSEI Mutation Analysis
3.3. Spatial–Temporal Characteristics of Ecological Environmental Quality
3.3.1. Temporal Changes in Ecological Environmental Quality
3.3.2. Spatial Changes in Ecological Environmental Quality
3.4. Spatial Autocorrelation of RSEI
3.5. Analysis of Factors Affecting Ecological and Environmental Quality
3.5.1. Single-Factor Detection Analysis
3.5.2. Multiple-Factor Detection Analysis
4. Discussion
4.1. Multi-Scale RSEI Construction and Change Analysis
4.2. Spatial Autocorrelation Analysis of Ecological Environmental Quality
4.3. RSEI Driving Force Analysis
4.4. Limitations and Future Prospects
5. Conclusions
- (1)
- The integration of Landsat and Sentinel data significantly enhanced the accuracy of RSEI construction, with an average contribution rate of 84.26% for Landsat data and 77.40% for the combined Sentinel–Landsat dataset.
- (2)
- Multi-scale analysis revealed an overall upward trend in ecological quality across Fujian Province, which was evident at the provincial, municipal, and county levels, further validating the effectiveness of the multi-scale approach, particularly in areas with complex terrain.
- (3)
- Spatiotemporal analysis revealed that from 1987 to 2023, most areas in Fujian Province experienced improvements in ecological quality, while some areas underwent an initial decline followed by recovery.
- (4)
- Moran’s I analysis demonstrated significant spatial clustering of ecological quality in Fujian Province, with high-quality ecological areas concentrated in the high-altitude regions of the western part of the province, while lower-quality areas were primarily distributed in densely populated coastal cities.
- (5)
- Geodetector analysis indicated that the interaction between topographic features and natural factors played a critical role in shaping the spatial distribution of RSEI, with these interactions becoming increasingly important in areas with complex terrain, over time.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Ying, X.; Zeng, G.-M.; Chen, G.-Q.; Tang, L.; Wang, K.-L.; Huang, D.-Y. Combining AHP with GIS in synthetic evaluation of eco-environment quality—A case study of Hunan Province, China. Ecol. Model. 2007, 209, 97–109. [Google Scholar] [CrossRef]
- Nandagiri, R. 8 Billion Lives, Infinite Possibilities: The Case for Rights and Choices; State of the World Population Report; UNFPA: New York, NY, USA, 2023; 192p, Available online: https://www.unfpa.org/sites/default/files/swop23/SWOP2023-ENGLISH-230329-web.pdf (accessed on 12 January 2024).
- Lamb, W.F.; Wiedmann, T.; Pongratz, J.; Andrew, R.; Crippa, M.; Olivier, J.G.J.; Wiedenhofer, D.; Mattioli, G.; Khourdajie, A.A.; House, J.; et al. A review of trends and drivers of greenhouse gas emissions by sector from 1990 to 2018. Environ. Res. Lett. 2021, 16, 073005. [Google Scholar] [CrossRef]
- Palo, M. Population and deforestation. In The Causes of Tropical Deforestation; Routledge: London, UK, 2023; pp. 42–56. [Google Scholar]
- Jiao, Z.; Sun, G.; Zhang, A.; Jia, X.; Huang, H.; Yao, Y. Water Benefit-Based Ecological Index for Urban Ecological Environment Quality Assessments. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2021, 14, 7557–7569. [Google Scholar] [CrossRef]
- Pahl-Wostl, C. Adaptive and sustainable water management: From improved conceptual foundations to transformative change. Int. J. Water Resour. Dev. 2020, 36, 397–415. [Google Scholar] [CrossRef]
- Wang, K.; Tang, Y.; Chen, Y.; Shang, L.; Ji, X.; Yao, M.; Wang, P. The Coupling and Coordinated Development from Urban Land Using Benefits and Urbanization Level: Case Study from Fujian Province (China). Int. J. Environ. Res. Public Health 2020, 17, 5647. [Google Scholar] [CrossRef]
- Miao, L.; Pan, W.; Yang, Z. Empirical Studies on the Dynamic Relationship between Port Logistics and Marine Economic Development in Fujian Province. Math. Probl. Eng. 2022, 2022, 4086633. [Google Scholar] [CrossRef]
- Kim, H.; Jeong, H.; Jeon, J.; Bae, S. The Impact of Impervious Surface on Water Quality and Its Threshold in Korea. Water 2016, 8, 111. [Google Scholar] [CrossRef]
- Cao, Y.; Wei, J. Coupling coordination and interactive effects of new urbanization efficiency and eco-efficiency-A case study of Fujian Province. PLoS ONE 2024, 19, e0292921. [Google Scholar] [CrossRef]
- Zhou, L.; Wu, X.; Xu, Z.; Fujita, H. Emergency decision making for natural disasters: An overview. Int. J. Disaster Risk Reduct. 2018, 27, 567–576. [Google Scholar] [CrossRef]
- Turner, H.C.; Galford, G.L.; Hernandez Lopez, N.; Falcón Méndez, A.; Borroto-Escuela, D.Y.; Hernández Ramos, I.; González-Díaz, P. Extent, Severity, and Temporal Patterns of Damage to Cuba’s Ecosystems following Hurricane Irma: MODIS and Sentinel-2 Hurricane Disturbance Vegetation Anomaly (HDVA). Remote Sens. 2023, 15, 2495. [Google Scholar] [CrossRef]
- Levine, M.D.; Steele, R.V. Climate change: What we know and what is to be done. WIREs Energy Environ. 2020, 10, e388. [Google Scholar] [CrossRef]
- Feng, D.; Fu, M.; Sun, Y.; Bao, W.; Zhang, M.; Zhang, Y.; Wu, J. How Large-Scale Anthropogenic Activities Influence Vegetation Cover Change in China? A Review. Forests 2021, 12, 320. [Google Scholar] [CrossRef]
- Sergeant, C.J.; Moynahan, B.J.; Johnson, W.F. Practical advice for implementing long-term ecosystem monitoring. J. Appl. Ecol. 2012, 49, 969–973. [Google Scholar] [CrossRef]
- Pettorelli, N.; Vik, J.O.; Mysterud, A.; Gaillard, J.M.; Tucker, C.J.; Stenseth, N.C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol. Evol. 2005, 20, 503–510. [Google Scholar] [CrossRef] [PubMed]
- Guo, Z.; Wang, N.; Shen, B.; Gu, Z.; Wu, Y.; Chen, A. Recent Spatiotemporal Trends in Glacier Snowline Altitude at the End of the Melt Season in the Qilian Mountains, China. Remote Sens. 2021, 13, 4935. [Google Scholar] [CrossRef]
- Stoms, D.M.; Hargrove, W.W. Potential NDVI as a baseline for monitoring ecosystem functioning. Int. J. Remote Sens. 2010, 21, 401–407. [Google Scholar] [CrossRef]
- Baldi, G.; Nosetto, M.D.; Aragon, R.; Aversa, F.; Paruelo, J.M.; Jobbagy, E.G. Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America. Sensors 2008, 8, 5397–5425. [Google Scholar] [CrossRef]
- Fung, T.; Siu, W. Environmental quality and its changes, an analysis using NDVI. Int. J. Remote Sens. 2010, 21, 1011–1024. [Google Scholar] [CrossRef]
- Jiang, Z.; Huete, A.; Didan, K.; Miura, T. Development of a two-band enhanced vegetation index without a blue band. Remote Sens. Environ. 2008, 112, 3833–3845. [Google Scholar] [CrossRef]
- Waring, R.H.; Coops, N.C.; Fan, W.; Nightingale, J.M. MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous U.S.A. Remote Sens. Environ. 2006, 103, 218–226. [Google Scholar] [CrossRef]
- Boucher, J.; Beaudoin, A.; Hébert, C.; Guindon, L.; Bauce, É. Assessing the potential of the differenced Normalized Burn Ratio (dNBR) for estimating burn severity in eastern Canadian boreal forests. Int. J. Wildland Fire 2017, 26, 32–45. [Google Scholar] [CrossRef]
- Giddey, B.L.; Baard, J.A.; Kraaij, T. Verification of the differenced Normalised Burn Ratio (dNBR) as an index of fire severity in Afrotemperate Forest. S. Afr. J. Bot. 2022, 146, 348–353. [Google Scholar] [CrossRef]
- Sun, G.; Jiao, Z.; Zhang, A.; Li, F.; Fu, H.; Li, Z. Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research. Int. J. Appl. Earth Obs. Geoinf. 2021, 103, 102529. [Google Scholar] [CrossRef]
- Yan, Y. Research on Saihan Dam Ecological Environment Evaluation Indexes Based on Quantitative Analysis. Acad. J. Environ. Earth Sci. 2022, 4, 1–4. [Google Scholar] [CrossRef]
- Qiao, Y.; Zhao, S.; Fang, Y. Dynamic monitoring of ecological environment based on advanced information technology. In Proceedings of the 2010 International Conference on Computer Application and System Modeling, Taiyuan, China, 22–24 October 2010. [Google Scholar] [CrossRef]
- Messer, L.C.; Jagai, J.S.; Rappazzo, K.M.; Lobdell, D.T. Construction of an environmental quality index for public health research. Environ. Health 2014, 13, 39. [Google Scholar] [CrossRef]
- Sruthi Krishnan, V.; Mohammed Firoz, C. Regional urban environmental quality assessment and spatial analysis. J. Urban Manag. 2020, 9, 191–204. [Google Scholar] [CrossRef]
- Barrera-Roldán, A.; Saldívar-Valdés, A. Proposal and application of a Sustainable Development Index. Ecol. Indic. 2002, 2, 251–256. [Google Scholar] [CrossRef]
- Xu, H. A remote sensing index for assessment of regional ecological changes. China Environ. Sci. 2013, 33, 889–897. [Google Scholar]
- Xu, H.; Wang, Y.; Guan, H.; Shi, T.; Hu, X. Detecting Ecological Changes with a Remote Sensing Based Ecological Index (RSEI) Produced Time Series and Change Vector Analysis. Remote Sens. 2019, 11, 2345. [Google Scholar] [CrossRef]
- Ning, L.; Jiayao, W.; Fen, Q. The improvement of ecological environment index model RSEI. Arab. J. Geosci. 2020, 13, 403. [Google Scholar] [CrossRef]
- Yang, H.; Yu, J.; Xu, W.; Wu, Y.; Lei, X.; Ye, J.; Geng, J.; Ding, Z. Long-time series ecological environment quality monitoring and cause analysis in the Dianchi Lake Basin, China. Ecol. Indic. 2023, 148, 110084. [Google Scholar] [CrossRef]
- Xiong, Y.; Xu, W.; Lu, N.; Huang, S.; Wu, C.; Wang, L.; Dai, F.; Kou, W. Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province, China. Ecol. Indic. 2021, 125, 107518. [Google Scholar] [CrossRef]
- Wen, X.; Ming, Y.; Gao, Y.; Hu, X. Dynamic Monitoring and Analysis of Ecological Quality of Pingtan Comprehensive Experimental Zone, a New Type of Sea Island City, Based on RSEI. Sustainability 2019, 12, 21. [Google Scholar] [CrossRef]
- Yuan, B.; Fu, L.; Zou, Y.; Zhang, S.; Chen, X.; Li, F.; Deng, Z.; Xie, Y. Spatiotemporal change detection of ecological quality and the associated affecting factors in Dongting Lake Basin, based on RSEI. J. Clean. Prod. 2021, 302, 126995. [Google Scholar] [CrossRef]
- Liu, X.Y.; Zhang, X.X.; He, Y.R.; Luan, H.J. Monitoring and Assessment of Ecological Change in Coastal Cities Based on Rsei. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, XLII-3/W10, 461–470. [Google Scholar] [CrossRef]
- Gao, P.; Kasimu, A.; Zhao, Y.; Lin, B.; Chai, J.; Ruzi, T.; Zhao, H. Evaluation of the Temporal and Spatial Changes of Ecological Quality in the Hami Oasis Based on RSEI. Sustainability 2020, 12, 7716. [Google Scholar] [CrossRef]
- Shao, Z.; Fu, H.; Li, D.; Altan, O.; Cheng, T. Remote sensing monitoring of multi-scale watersheds impermeability for urban hydrological evaluation. Remote Sens. Environ. 2019, 232, 111338. [Google Scholar] [CrossRef]
- Zhang, Y.; She, J.; Long, X.; Zhang, M. Spatio-temporal evolution and driving factors of eco-environmental quality based on RSEI in Chang-Zhu-Tan metropolitan circle, central China. Ecol. Indic. 2022, 144, 109436. [Google Scholar] [CrossRef]
- Niu, X.; Li, Y. Remote Sensing Evaluation of Ecological Environment of Anqing City Based on Remote Sensing Ecological Index. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, XLIII-B3-2020, 733–737. [Google Scholar] [CrossRef]
- Ali, M.I.; Dirawan, G.D.; Hasim, A.H.; Abidin, M.R. Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods. Int. J. Adv. Sci. Eng. Inf. Technol. 2019, 9, 946–951. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 2007, 27, 3025–3033. [Google Scholar] [CrossRef]
- Crist, E.P. A TM Tasseled Cap equivalent transformation for reflectance factor data. Remote Sens. Environ. 1985, 17, 301–306. [Google Scholar] [CrossRef]
- Nedkov, R. Orthogonal Transformation of segmented images from the Satellite Sentinel-2. Comptes Rendus L’acad’emie Bulg. Sci. 2017, 70, 687–692. [Google Scholar]
- Xu, H.; Wang, M.; Shi, T.; Guan, H.; Fang, C.; Lin, Z. Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecol. Indic. 2018, 93, 730–740. [Google Scholar] [CrossRef]
- Malakar, N.K.; Hulley, G.C.; Hook, S.J.; Laraby, K.; Cook, M.; Schott, J.R. An Operational Land Surface Temperature Product for Landsat Thermal Data: Methodology and Validation. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5717–5735. [Google Scholar] [CrossRef]
- Wang, W.; Yi, Z.; Chen, D. Mann-Kendall Mutation Analysis of Temporal Variation of Apparent Stress in Qinba Mountains and Its Adjacent Areas. IOP Conf. Ser. Earth Environ. Sci. 2021, 660, 012112. [Google Scholar] [CrossRef]
- Hamed, K.H.; Ramachandra Rao, A. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 1998, 204, 182–196. [Google Scholar] [CrossRef]
- Sen, P.K. Estimates of the Regression Coefficient Based on Kendall’s Tau. J. Am. Stat. Assoc. 1968, 63, 1379–1389. [Google Scholar] [CrossRef]
- Kossack, C.F.; Kendall, M.G. Rank Correlation Methods. Am. Math. Mon. 1950, 57, 425. [Google Scholar] [CrossRef]
- Chen, Y. Spatial autocorrelation equation based on Moran’s index. Sci. Rep. 2023, 13, 19296. [Google Scholar] [CrossRef]
- Zhao, Y.; Liu, L.; Kang, S.; Ao, Y.; Han, L.; Ma, C. Quantitative Analysis of Factors Influencing Spatial Distribution of Soil Erosion Based on Geo-Detector Model under Diverse Geomorphological Types. Land 2021, 10, 604. [Google Scholar] [CrossRef]
- Zhu, L.; Meng, J.; Zhu, L. Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin. Ecol. Indic. 2020, 117, 106545. [Google Scholar] [CrossRef]
- Yue, H.; Liu, Y.; Li, Y.; Lu, Y. Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index. IEEE Access 2019, 7, 51295–51311. [Google Scholar] [CrossRef]
- Huang, C.; Zhou, Z.; Wang, D.; Dian, Y. Monitoring forest dynamics with multi-scale and time series imagery. Environ. Monit. Assess. 2016, 188, 273. [Google Scholar] [CrossRef] [PubMed]
- Liu, P.; Ren, C.; Yu, W.; Ren, H.; Xia, C. Exploring the ecological quality and its drivers based on annual remote sensing ecological index and multisource data in Northeast China. Ecol. Indic. 2023, 154, 110589. [Google Scholar] [CrossRef]
- Huang, Q.; Qiu, Y.; Chen, X.; Sun, X.; Lin, Y. Climate Change Characteristics and Future Trends of Fujian Province During 1980–2017. J. Wuyi Univ. 2020, 39, 27–32. [Google Scholar] [CrossRef]
- Wang, Y.Q.; Yang, X.L.; Ren, L.L.; Zhang, M.R. Variation trend analysis of temperature, precipitation and drought in upper Yangtze River Basin. Yangtze River 2017, 48, 48. [Google Scholar] [CrossRef]
- Li, W.; Zinda, J.A.; Zhang, Z. Does the “Returning Farmland to Forest Program” Drive Community-Level Changes in Landscape Patterns in China? Forests 2019, 10, 933. [Google Scholar] [CrossRef]
- Li, S.; Cao, Y.; Liu, J.; Wang, S.; Zhou, W. Assessing Spatiotemporal Dynamics of Land Use and Cover Change and Carbon Storage in China’s Ecological Conservation Pilot Zone: A Case Study in Fujian Province. Remote Sens. 2022, 14, 4111. [Google Scholar] [CrossRef]
- Fang, Y.-p.; Ying, B. Spatial distribution of mountainous regions and classifications of economic development in China. J. Mt. Sci. 2016, 13, 1120–1138. [Google Scholar] [CrossRef]
- Yudina, A.; Kukushkina, Y. Construction of Low-Rise Buildings in the Coastland and Water Areas. Archit. Eng. 2021, 6, 48–53. [Google Scholar] [CrossRef]
- Liao, W.; Deng, Y.; Li, M.; Sun, M.; Yang, J.; Xu, J. Extraction and Analysis of Finer Impervious Surface Classes in Urban Area. Remote Sens. 2021, 13, 459. [Google Scholar] [CrossRef]
- Hladnik, D.; Kobler, A.; Pirnat, J. Evaluation of Forest Edge Structure and Stability in Peri-Urban Forests. Forests 2020, 11, 338. [Google Scholar] [CrossRef]
- Todeschini, S. Hydrologic and Environmental Impacts of Imperviousness in an Industrial Catchment of Northern Italy. J. Hydrol. Eng. 2016, 21, 05016013. [Google Scholar] [CrossRef]
- Wang, J.F.; Xu, C.D. Geodetector: Principle and prospective (in Chinese). Acta Geogr. 2017, 72, 116–134. [Google Scholar] [CrossRef]
- Gavrilescu, M. Water, Soil, and Plants Interactions in a Threatened Environment. Water 2021, 13, 2746. [Google Scholar] [CrossRef]
- Han, S.; Tian, F.; Gao, L. Current status and recent trend of irrigation water use in China*. Irrig. Drain. 2020, 69, 25–35. [Google Scholar] [CrossRef]
- Thom, D.; Rammer, W.; Seidl, R. Disturbances catalyze the adaptation of forest ecosystems to changing climate conditions. Glob. Chang. Biol. 2017, 23, 269–282. [Google Scholar] [CrossRef]
- Hua, L.; Zhang, X.; Nie, Q.; Sun, F.; Tang, L. The Impacts of the Expansion of Urban Impervious Surfaces on Urban Heat Islands in a Coastal City in China. Sustainability 2020, 12, 475. [Google Scholar] [CrossRef]
- Uçar, Z.; Akay, A.E.; Bilici, E. Towards Green Smart Cities: Importance of Urban Forestry and Urban Vegetation. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2020, XLIV-4/W3-2020, 399–403. [Google Scholar] [CrossRef]
- Pan, A.; Wang, Q.; Yang, Q. Assessment on the coordinated development oriented to Green City in China. Ecol. Indic. 2020, 116, 106486. [Google Scholar] [CrossRef]
- Liao, W.; Jiang, W. Evaluation of the Spatiotemporal Variations in the Eco-environmental Quality in China Based on the Remote Sensing Ecological Index. Remote Sens. 2020, 12, 2462. [Google Scholar] [CrossRef]
- Nazeer, M.; Ilori, C.O.; Bilal, M.; Nichol, J.E.; Wu, W.; Qiu, Z.; Gayene, B.K. Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data. Atmos. Res. 2021, 249, 105308. [Google Scholar] [CrossRef]
- Nie, X.; Hu, Z.; Zhu, Q.; Ruan, M. Research on Temporal and Spatial Resolution and the Driving Forces of Ecological Environment Quality in Coal Mining Areas Considering Topographic Correction. Remote Sens. 2021, 13, 2815. [Google Scholar] [CrossRef]
- Ye, J.; Lin, G.; Zhang, M.; Gao, L. Hazard analysis of typhoon disaster-causing factors based on different landing paths: A case study of Fujian Province, China. Nat. Hazards 2019, 100, 811–828. [Google Scholar] [CrossRef]
- Sarkodie, S.A. Environmental performance, biocapacity, carbon & ecological footprint of nations: Drivers, trends and mitigation options. Sci. Total Environ. 2021, 751, 141912. [Google Scholar] [CrossRef]
Indicators | Calculation Formula | Spectral Bands |
---|---|---|
Modified Normalized Difference Water Index (MNDWI) | Landsat 5/7/8: Blue (0.45–0.52 µm); Green (0.52–0.60 µm); Red (0.63–0.69 µm); NIR (0.76–0.90 µm); SWIR1 (1.55–1.75 µm); SWIR2 (2.08–2.35 µm); ST (5/7: 10.40–12.50 μm; 8: 10.60–11.19 µm) Sentinel-2: Blue (0.49–0.54 µm); Green (0.54–0.58 µm); Red (0.64–0.67 µm); NIR (0.78–0.88 µm); SWIR1 (1.57–1.65 µm); SWIR2 (2.10–2.28 µm) | |
Normalized Vegetation Index (NDVI) | ||
Wetness (WET) | ||
Normalized Difference Built-up Soil Index (NDBSI) | ||
Land Surface Temperature (LST) | ||
Remote Sensing Ecological Index (RSEI) |
FujianLD | |||||||||||
Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue | Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue |
1987 | 0.88 | 0.33 | −0.55 | −0.11 | 84.54% | 2006 | 0.73 | 0.17 | −0.55 | −0.36 | 80.20% |
1988 | 0.89 | 0.12 | −0.58 | −0.19 | 87.88% | 2007 | 0.77 | 0.19 | −0.55 | −0.29 | 84.36% |
1989 | 0.73 | 0.30 | −0.62 | −0.19 | 82.01% | 2008 | 0.77 | 0.11 | −0.58 | −0.22 | 79.40% |
1990 | 0.72 | 0.11 | −0.69 | −0.28 | 86.38% | 2009 | 0.80 | 0.15 | −0.52 | −0.25 | 82.93% |
1991 | 0.75 | 0.13 | −0.50 | −0.20 | 80.46% | 2010 | 0.78 | 0.08 | −0.53 | −0.31 | 84.31% |
1992 | 0.88 | 0.19 | −0.59 | −0.38 | 84.05% | 2011 | 0.78 | 0.13 | −0.59 | −0.20 | 85.63% |
1993 | 0.88 | 0.37 | −0.69 | −0.17 | 86.97% | 2012 | 0.73 | 0.14 | −0.53 | −0.24 | 89.44% |
1994 | 0.83 | 0.16 | −0.55 | −0.31 | 89.70% | 2013 | 0.78 | 0.19 | −0.59 | −0.18 | 83.86% |
1995 | 0.73 | 0.13 | −0.58 | −0.35 | 80.57% | 2014 | 0.86 | 0.29 | −0.55 | −0.30 | 80.87% |
1996 | 0.79 | 0.11 | −0.54 | −0.26 | 80.93% | 2015 | 0.88 | 0.24 | −0.68 | −0.31 | 83.81% |
1997 | 0.80 | 0.11 | −0.56 | −0.19 | 78.96% | 2016 | 0.81 | 0.30 | −0.61 | −0.30 | 83.51% |
1998 | 0.76 | 0.19 | −0.61 | −0.11 | 88.45% | 2017 | 0.85 | 0.24 | −0.53 | −0.26 | 87.44% |
1999 | 0.83 | 0.13 | −0.53 | −0.14 | 81.29% | 2018 | 0.73 | 0.27 | −0.59 | −0.37 | 82.75% |
2000 | 0.75 | 0.13 | −0.55 | −0.29 | 90.80% | 2019 | 0.75 | 0.16 | −0.56 | −0.33 | 86.21% |
2001 | 0.81 | 0.12 | −0.57 | −0.03 | 90.60% | 2020 | 0.81 | 0.19 | −0.65 | −0.22 | 80.23% |
2002 | 0.71 | 0.31 | −0.63 | −0.08 | 89.20% | 2021 | 0.84 | 0.15 | −0.62 | −0.18 | 81.64% |
2003 | 0.75 | 0.17 | −0.63 | −0.31 | 81.58% | 2022 | 0.71 | 0.33 | −0.68 | −0.19 | 82.62% |
2004 | 0.70 | 0.20 | −0.50 | −0.20 | 86.00% | 2023 | 0.89 | 0.26 | −0.65 | −0.25 | 86.47% |
2005 | 0.68 | 0.15 | −0.59 | −0.40 | 81.68% | ||||||
Nanping LD | |||||||||||
Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue | Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue |
1987 | 0.72 | 0.12 | −0.32 | −0.16 | 66.24% | 2006 | 0.63 | 0.27 | −0.38 | −0.08 | 66.94% |
1988 | 0.64 | 0.02 | −0.34 | −0.18 | 73.76% | 2007 | 0.73 | 0.15 | −0.31 | −0.10 | 77.19% |
1989 | 0.75 | 0.26 | −0.37 | −0.15 | 71.18% | 2008 | 0.58 | 0.03 | −0.39 | −0.11 | 74.00% |
1990 | 0.57 | 0.10 | −0.31 | −0.11 | 75.21% | 2009 | 0.74 | 0.17 | −0.37 | −0.17 | 68.63% |
1991 | 0.63 | 0.27 | −0.33 | −0.10 | 66.87% | 2010 | 0.58 | 0.09 | −0.38 | −0.12 | 75.30% |
1992 | 0.77 | 0.27 | −0.31 | −0.06 | 70.12% | 2011 | 0.70 | 0.29 | −0.32 | −0.07 | 67.41% |
1993 | 0.71 | 0.29 | −0.32 | −0.15 | 78.84% | 2012 | 0.78 | 0.08 | −0.33 | −0.06 | 70.24% |
1994 | 0.75 | 0.03 | −0.39 | −0.16 | 78.11% | 2013 | 0.79 | 0.24 | −0.34 | −0.17 | 76.71% |
1995 | 0.74 | 0.19 | −0.40 | −0.13 | 69.27% | 2014 | 0.74 | 0.12 | −0.31 | −0.18 | 72.52% |
1996 | 0.62 | 0.22 | −0.33 | −0.06 | 74.94% | 2015 | 0.53 | 0.07 | −0.38 | −0.07 | 68.86% |
1997 | 0.59 | 0.13 | −0.38 | −0.18 | 66.28% | 2016 | 0.70 | 0.27 | −0.37 | −0.18 | 78.18% |
1998 | 0.78 | 0.28 | −0.40 | −0.10 | 78.00% | 2017 | 0.72 | 0.24 | −0.36 | −0.17 | 72.54% |
1999 | 0.76 | 0.29 | −0.37 | −0.19 | 77.96% | 2018 | 0.74 | 0.29 | −0.33 | −0.13 | 74.62% |
2000 | 0.74 | 0.18 | −0.39 | −0.09 | 68.55% | 2019 | 0.77 | 0.26 | −0.37 | −0.16 | 71.73% |
2001 | 0.71 | 0.13 | −0.34 | −0.14 | 72.60% | 2020 | 0.70 | 0.07 | −0.34 | −0.06 | 76.90% |
2002 | 0.76 | 0.13 | −0.35 | −0.06 | 67.60% | 2021 | 0.65 | 0.28 | −0.33 | −0.06 | 70.82% |
2003 | 0.74 | 0.27 | −0.36 | −0.19 | 71.03% | 2022 | 0.71 | 0.19 | −0.34 | −0.19 | 75.84% |
2004 | 0.69 | 0.04 | −0.39 | −0.17 | 69.30% | 2023 | 0.74 | 0.12 | −0.35 | −0.14 | 67.83% |
2005 | 0.59 | 0.20 | −0.36 | −0.19 | 76.38% | ||||||
Guangze LD | Guangze S-L | ||||||||||
Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue | Year | NDVI | Wet | NDBSI | LST | Percent Eigenvalue |
2019 | 0.89 | 0.33 | −0.28 | −0.16 | 75.01% | 2019 | 0.64 | 0.08 | −0.67 | −0.36 | 76.59% |
2020 | 0.97 | 0.21 | −0.01 | −0.13 | 85.02% | 2020 | 0.73 | 0.06 | −0.67 | −0.15 | 76.94% |
2021 | 0.96 | 0.27 | −0.02 | −0.08 | 86.07% | 2021 | 0.73 | 0.05 | −0.67 | −0.15 | 75.56% |
2022 | 0.97 | 0.20 | −0.02 | −0.17 | 85.99% | 2022 | 0.68 | 0.16 | −0.71 | −0.05 | 79.77% |
2023 | 0.96 | 0.27 | −0.04 | −0.09 | 85.58% | 2023 | 0.75 | 0.02 | −0.64 | −0.17 | 78.16% |
Factor | 1987 | 1995 | 2000 | 2010 | 2015 | 2023 | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
q Value | p Value | q Ranking | q Value | p Value | q Ranking | q Value | p Value | q Ranking | q Value | p Value | q Ranking | q Value | p Value | q Ranking | q Value | p Value | q Ranking | |
A1 | 0.044 | 0.000 | 4 | 0.081 | 0.000 | 4 | 0.099 | 0.000 | 3 | 0.106 | 0.000 | 3 | 0.007 | 0.000 | 5 | 0.083 | 0.000 | 3 |
A2 | 0.056 | 0.000 | 3 | 0.133 | 0.000 | 1 | 0.162 | 0.000 | 1 | 0.203 | 0.000 | 1 | 0.037 | 0.000 | 2 | 0.198 | 0.000 | 1 |
A3 | 0.057 | 0.000 | 2 | 0.095 | 0.000 | 2 | 0.124 | 0.000 | 2 | 0.125 | 0.000 | 2 | 0.028 | 0.000 | 3 | 0.133 | 0.000 | 2 |
A4 | 0.009 | 0.000 | 5 | 0.024 | 0.000 | 5 | 0.019 | 0.000 | 5 | 0.025 | 0.000 | 5 | 0.010 | 0.000 | 4 | 0.034 | 0.000 | 5 |
A5 | 0.278 | 0.000 | 1 | 0.089 | 0.000 | 3 | 0.070 | 0.000 | 4 | 0.083 | 0.000 | 4 | 0.313 | 0.000 | 1 | 0.045 | 0.000 | 4 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Kong, W.; Chang, W.; Xie, M.; Li, Y.; Wan, T.; Nie, X.; Mo, D. Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province. Forests 2024, 15, 1541. https://doi.org/10.3390/f15091541
Kong W, Chang W, Xie M, Li Y, Wan T, Nie X, Mo D. Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province. Forests. 2024; 15(9):1541. https://doi.org/10.3390/f15091541
Chicago/Turabian StyleKong, Weiwei, Weipeng Chang, Mingjiang Xie, Yi Li, Tianyong Wan, Xiaoli Nie, and Dengkui Mo. 2024. "Long-Term Monitoring and Analysis of Key Driving Factors in Environmental Quality: A Case Study of Fujian Province" Forests 15, no. 9: 1541. https://doi.org/10.3390/f15091541