Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
2.3. Method
2.3.1. Building of Land Degradation and Development Index (LDDI)
2.3.2. The Sen-MK Method
2.3.3. The Hot- and Cold-Spot Analysis
2.3.4. Multiple Linear Regression Model and Residual Analysis
3. Results
3.1. Spatiotemporal Dynamics of LDD from 1985 to 2015
3.2. Relationships between LDDI, Climate Factors and Anthoropogenic Factors
3.3. Relative Roles of Climate Change and Anthropogenic Factors in LDD
4. Discussion
4.1. Effects of Climate Change on LDD Processes
4.2. Effects of Anthropogenic Activities on Land Degradation and Development
4.3. Availability and Limitation of LDDI in Monitoring LDD Processes
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- UNCCD. Elaboration of an International Convention to Combat Desertification in Countries Experiencing Serious Drought and/or Desertification, Particularly in Africa; UNCCD: Paris, France, 1994. [Google Scholar]
- Bastin, J.F.; Finegold, Y.; Garcia, C.; Mollicone, D.; Rezende, M.; Routh, D.; Zohner, C.M.; Crowther, T.W. The global tree restoration potential. Science 2019, 365, 76. [Google Scholar] [CrossRef]
- Hermans, K.; McLeman, R. Climate change, drought, land degradation and migration: Exploring the linkages. Curr. Opin. Environ. Sustain. 2021, 50, 236–244. [Google Scholar] [CrossRef]
- Reith, J.; Ghazaryan, G.; Muthoni, F.; Dubovyk, O. Assessment of Land Degradation in Semiarid Tanzania—Using Multiscale Remote Sensing Datasets to Support Sustainable Development Goal 15.3. Remote Sens. 2021, 13, 1754. [Google Scholar] [CrossRef]
- Kust, G.; Andreeva, O.; Cowie, A. Land Degradation Neutrality: Concept development, practical applications and assessment. J. Environ. Manag. 2017, 195, 16–24. [Google Scholar] [CrossRef] [PubMed]
- Li, H.; Yang, X.; Zhang, K. Understanding global land degradation processes interacted with complex biophysics and socioeconomics from the perspective of the Normalized Difference Vegetation Index (1982–2015). GPC 2021, 198, 103431. [Google Scholar] [CrossRef]
- Dubovyk, O. The role of Remote Sensing in land degradation assessments: Opportunities and challenges. Eur. J. Remote Sens. 2017, 50, 601–613. [Google Scholar] [CrossRef]
- Camps-Valls, G.; Campos-Taberner, M.; Moreno, A.; Walther, S.; Duveiller, G.; Cescatti, A.; Mahecha, M.; Muñoz, J.; García-Haro, F.; Guanter, L.; et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 2021, 7, eabc7447. [Google Scholar] [CrossRef]
- Badgley, G.; Field, C.B.; Berry, J.A. Canopy near-infrared reflectance and terrestrial photosynthesis. Sci. Adv. 2017, 3, e1602244. [Google Scholar] [CrossRef] [Green Version]
- Sur, K.; Chauhan, P. Dynamic trend of land degradation/restoration along Indira Gandhi Canal command area in Jaisalmer District, Rajasthan, India: A case study. Environ. Earth Sci. 2019, 78, 472. [Google Scholar] [CrossRef]
- Wei, W.; Pang, S.; Wang, X.; Zhou, L.; Xie, B.; Zhou, J.; Li, C. Temperature Vegetation Precipitation Dryness Index (TVPDI)-based dryness-wetness monitoring in China. Remote Sens. Environ. 2020, 248, 111957. [Google Scholar] [CrossRef]
- Yin, L.; Feng, X.; Fu, B.; Chen, Y.; Wang, X.; Tao, F. Irrigation water consumption of irrigated cropland and its dominant factor in China from 1982 to 2015. Adv. Water Resour. 2020, 143, 103661. [Google Scholar] [CrossRef]
- Shao, Y.; Jiang, Q.O.; Wang, C.; Wang, M.; Xiao, L.; Qi, Y. Analysis of critical land degradation and development processes and their driving mechanism in the Heihe River Basin. Sci. Total Environ. 2020, 716, 137082. [Google Scholar] [CrossRef] [PubMed]
- Wang, J.; Wei, H.; Cheng, K.; Ochir, A.; Davaasuren, D.; Li, P.; Chan, F.K.S.; Nasanbat, E. Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia. Environ. Dev. 2020, 34, 100497. [Google Scholar] [CrossRef]
- Cao, J.; Tian, H.; Adamowski, J.F.; Zhang, X.; Cao, Z. Influences of afforestation policies on soil moisture content in China’s arid and semi-arid regions. Land Use Policy 2018, 75, 449–458. [Google Scholar] [CrossRef]
- Li, K.; Cao, J.; Adamowski, J.F.; Biswas, A.; Zhou, J.; Liu, Y.; Zhang, Y.; Liu, C.; Dong, X.; Qin, Y. Assessing the effects of ecological engineering on spatiotemporal dynamics of carbon storage from 2000 to 2016 in the Loess Plateau area using the InVEST model: A case study in Huining County, China. Environ. Dev. 2021, 100641. [Google Scholar] [CrossRef]
- Li, K.; Feng, M.; Biswas, A.; Su, H.; Niu, Y.; Cao, J. Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China. Sensors 2020, 20, 2757. [Google Scholar] [CrossRef] [PubMed]
- Tucker, C.J.; Pinzon, J.E.; Brown, M.E.; Slayback, D.A.; Pak, E.W.; Mahoney, R.; Vermote, E.F.; El Saleous, N. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. IJRS 2005, 26, 4485–4498. [Google Scholar] [CrossRef]
- Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, A.H.; Gianelle, D.; Rossi, F.; et al. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data. Remote Sens. Environ. 2010, 114, 1416–1431. [Google Scholar] [CrossRef] [Green Version]
- Zheng, Y.; Shen, R.; Wang, Y.; Li, X.; Liu, S.; Liang, S.; Chen, J.; Ju, W.; Zhang, L.; Yuan, W. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data Discuss. 2019, 1–31. [Google Scholar] [CrossRef]
- Peng, S.; Ding, D.; Liu, W. 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. Earth Syst. Sci. Data 2019, 11, 1931–1946. [Google Scholar] [CrossRef] [Green Version]
- Mesquita, D.P.P.; Gomes, J.P.P.; Souza Junior, A.H.; Nobre, J.S. Euclidean distance estimation in incomplete datasets. Neurocomputing 2017, 248, 11–18. [Google Scholar] [CrossRef]
- Jiang, W.; Yuan, L.; Wang, W.; Cao, R.; Zhang, Y.; Shen, W. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecol. Indic. 2015, 51, 117–126. [Google Scholar] [CrossRef]
- Fensholt, R.; Proud, S.R. Evaluation of Earth Observation based global long term vegetation trends—Comparing GIMMS and MODIS global NDVI time series. Remote Sens. Environ. 2012, 119, 131–147. [Google Scholar] [CrossRef]
- Tošić, I. Spatial and temporal variability of winter and summer precipitation over Serbia and Montenegro. ThApC 2004, 77, 47–56. [Google Scholar] [CrossRef]
- Getis, A.; Ord, J.K. The Analysis of Spatial Association by Use of Distance Statistics. Geogr. Anal. 1992, 24, 189–206. [Google Scholar] [CrossRef]
- Liu, Y.; Lei, H. Responses of Natural Vegetation Dynamics to Climate Drivers in China from 1982 to 2011. Remote Sens. 2015, 7, 10243–10268. [Google Scholar] [CrossRef] [Green Version]
- Tian, H.; Cao, C.; Chen, W.; Bao, S.; Yang, B.; Myneni, R.B. Response of vegetation activity dynamic to climatic change and ecological restoration programs in Inner Mongolia from 2000 to 2012. Ecol. Eng. 2015, 82, 276–289. [Google Scholar] [CrossRef]
- Mendoza-Ponce, A.; Corona-Núñez, R.; Kraxner, F.; Leduc, S.; Patrizio, P. Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico. Glob. Environ. Chang. 2018, 53, 12–23. [Google Scholar] [CrossRef] [Green Version]
- Wang, X.; Piao, S.; Ciais, P.; Li, J.; Friedlingstein, P.; Koven, C.; Chen, A. Spring temperature change and its implication in the change of vegetation growth in North America from 1982 to 2006. Proc. Natl. Acad. Sci. USA 2011, 108, 1240–1245. [Google Scholar] [CrossRef] [Green Version]
- Zhao, A.; Zhang, A.; Liu, J.; Feng, L.; Zhao, Y. Assessing the effects of drought and “Grain for Green” Program on vegetation dynamics in China’s Loess Plateau from 2000 to 2014. Catena 2019, 175, 446–455. [Google Scholar] [CrossRef]
- Xu, G.; Zhang, H.; Chen, B.; Zhang, H.; Innes, J.; Wang, G.; Yan, J.; Zheng, Y.; Zhu, Z.; Myneni, R. Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011. Remote Sens. 2014, 6, 3263. [Google Scholar] [CrossRef] [Green Version]
- Wang, Z.; Zhang, Y.; Yang, Y.; Zhou, W.; Gang, C.; Zhang, Y.; Li, J.; An, R.; Wang, K.; Odeh, I.; et al. Quantitative assess the driving forces on the grassland degradation in the Qinghai–Tibet Plateau, in China. Ecol. Inform. 2016, 33, 32–44. [Google Scholar] [CrossRef]
- Wen, Y.; Liu, X.; Bai, Y.; Sun, Y.; Yang, J.; Lin, K.; Pei, F.; Yan, Y. Determining the impacts of climate change and urban expansion on terrestrial net primary production in China. J. Environ. Manag. 2019, 240, 75–83. [Google Scholar] [CrossRef]
- Li, G.; Sun, S.; Han, J.; Yan, J.; Liu, W.; Wei, Y.; Lu, N.; Sun, Y. Impacts of Chinese Grain for Green program and climate change on vegetation in the Loess Plateau during 1982-2015. Sci. Total Environ. 2019, 660, 177–187. [Google Scholar] [CrossRef]
- Tang, W.; Zhang, Y.G.; Liu, Y.J.; Wang, X.; Kou, M.; Yan, H.; Ma, D.F.; Li, Q. Quantifying cultivation technique and growth dynamics of purple-fleshed sweetpotato (Ipomoea batatas L.) in China. Field Crops Res. 2018, 227, 41–48. [Google Scholar] [CrossRef]
- Qiu, S.; Xie, J.; Zhao, S.; Xu, X.; Hou, Y.; Wang, X.; Zhou, W.; He, P.; Johnston, A.M.; Christie, P.; et al. Long-term effects of potassium fertilization on yield, efficiency, and soil fertility status in a rain-fed maize system in northeast China. Field Crops Res. 2014, 163, 1–9. [Google Scholar] [CrossRef]
- Zeng, R.; Yao, F.; Zhang, S.; Yang, S.; Bai, Y.; Zhang, J.; Wang, J.; Wang, X. Assessing the effects of precipitation and irrigation on winter wheat yield and water productivity in North China Plain. Agric. Water Manag. 2021, 256, 107063. [Google Scholar] [CrossRef]
- Fu, Y.C.; Li, J.F.; Weng, Q.H.; Zheng, Q.M.; Li, L.; Dai, S.; Guo, B.Y. Characterizing the spatial pattern of annual urban growth by using time series Landsat imagery. Sci. Total Environ. 2019, 666, 274–284. [Google Scholar] [CrossRef]
- He, C.; Zhang, D.; Huang, Q.; Zhao, Y. Assessing the potential impacts of urban expansion on regional carbon storage by linking the LUSD-urban and InVEST models. Environ. Model Softw. 2016, 75, 44–58. [Google Scholar] [CrossRef]
- Li, C.; Liu, M.; Hu, Y.; Zhou, R.; Wu, W.; Huang, N. Evaluating the runoff storage supply-demand structure of green infrastructure for urban flood management. J. Clean. Prod. 2021, 280. [Google Scholar] [CrossRef]
- Wang, K.; Li, Z.Y.; Zhang, J.J.; Wu, X.; Jia, M.; Wu, L. Built-up land expansion and its impacts on optimizing green infrastructure networks in a resource-dependent city. Sustain. Cites Soc. 2020, 55, 10. [Google Scholar] [CrossRef]
- Li, K.; Li, C.; Cao, J.; Wang, H.; Liu, M.; Lü, J.; Hao, Z. Spatiotemporal evolution characteristics of urban green infrastructure in central Liaoning urban agglomerations during the past 20 years based on landscape ecology and morphology. Acta Ecol. Sin. 2021, 41. [Google Scholar] [CrossRef]
- Maes, J.; Barbosa, A.; Baranzelli, C.; Zulian, G.; Batista, E.S.F.; Vandecasteele, I.; Hiederer, R.; Liquete, C.; Paracchini, M.L.; Mubareka, S.; et al. More green infrastructure is required to maintain ecosystem services under current trends in land-use change in Europe. Landscape Ecol. 2015, 30, 517–534. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ramyar, R.; Saeedi, S.; Bryant, M.; Davatgar, A.; Mortaz Hedjri, G. Ecosystem services mapping for green infrastructure planning-The case of Tehran. Sci. Total Environ. 2020, 703, 135466. [Google Scholar] [CrossRef] [PubMed]
- Wang, Y.C.; Shen, J.K.; Xiang, W.N. Ecosystem service of green infrastructure for adaptation to urban growth: Function and configuration. Ecosyst. Health Sustain. 2018, 4, 132–143. [Google Scholar] [CrossRef]
Slope of LDDI | Z Statistic | LDD Types |
---|---|---|
Slope ≤ −0.0005 | Z ≤ −1.96 | Significant degradation |
Slope ≤ −0.0005 | −1.96 < Z < 1.96 | Slight degradation |
−0.0005 < Slope < 0.0005 | −1.96 < Z < 1.96 | Nonsignificant change |
Slope ≥ 0.0005 | −1.96 < Z < 1.96 | Slight development |
Slope ≥ 0.0005 | Z ≥ 1.96 | Significant development |
Slope of Residual | Significance Level | LDD Types |
---|---|---|
Slope ≤ −0.0005 | p < 0.01 | Significant decrease |
Slope ≤ −0.0005 | 0.01 ≤ p < 0.05 | Slight decrease |
−0.0005 < Slope < 0.0005 | p ≥ 0.05 | Nonsignificant change |
Slope ≥ 0.0005 | p < 0.01 | Slight increase |
Slope ≥ 0.0005 | 0.01 ≤ p < 0.05 | Significant increase |
LDDI’s Change Trend | Significance Level of Regression Model between LDDI and Climate Factors | Significant Level of Residual’s Slope | |
---|---|---|---|
Development induced by climate change | Significant increase | p < 0.05 | p > 0.05 |
Degradation induced by climate change | Significant decrease | p < 0.05 | p > 0.05 |
Development induced by human factors | Significant increase | p > 0.05 | p < 0.05 |
Degradation induced by human factors | Significant decrease | p > 0.05 | p < 0.05 |
Development induced by climate change and human factors | Significant increase | p < 0.05 | p < 0.05 |
Degradation induced by climate change and human factors | Significant decrease | p < 0.05 | p < 0.05 |
Natural development | Significant increase | p > 0.05 | p > 0.05 |
Natural degradation | Significant decrease | p > 0.05 | p > 0.05 |
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Kang, J.; Zhang, Y.; Biswas, A. Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015. Remote Sens. 2021, 13, 3516. https://doi.org/10.3390/rs13173516
Kang J, Zhang Y, Biswas A. Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015. Remote Sensing. 2021; 13(17):3516. https://doi.org/10.3390/rs13173516
Chicago/Turabian StyleKang, Jianfang, Yaonan Zhang, and Asim Biswas. 2021. "Land Degradation and Development Processes and Their Response to Climate Change and Human Activity in China from 1982 to 2015" Remote Sensing 13, no. 17: 3516. https://doi.org/10.3390/rs13173516