Detecting Land Degradation in Eastern China Grasslands with Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and GIMMS NDVI3g Data

Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint detection to identify pixels with abrupt ecosystem changes which violate the assumptions of RESTREND. With TSS-RESTREND, in Xilingol (111◦59′–120◦00′E and 42◦32′–46◦41′E) and Hulunbuir (115◦30′–122◦E and 47◦10′–51◦23′N) grassland, 5.5% and 3.3% of the area experienced a decrease in greenness between 1984 and 2009, 80.2% and 73.2% had no significant change, 4.9% and 2.6% increased in greenness, and 9.4% and 20.9% were undetermined, respectively. RESTREND may underestimate the greening trend in Xilingol, but both TSS-RESTREND and RESTREND revealed no significant differences in Hulunbuir. The proposed TSS-RESTREND methodology captured both the time and magnitude of vegetation changes.

[1]  Xuehong Chen,et al.  Estimating the age and population structure of encroaching shrubs in arid/semiarid grasslands using high spatial resolution remote sensing imagery , 2018, Remote Sensing of Environment.

[2]  Jiaguo Qi,et al.  Differentiating anthropogenic modification and precipitation-driven change on vegetation productivity on the Mongolian Plateau , 2015, Landscape Ecology.

[3]  Martin Brandt,et al.  Towards improved remote sensing based monitoring of dryland ecosystem functioning using sequential linear regression slopes (SeRGS) , 2019, Remote Sensing of Environment.

[4]  Na Li,et al.  Analyzing vegetation dynamic trend on the Mongolian Plateau based on the Hurst exponent and influencing factors from 1982–2013 , 2018, Journal of Geographical Sciences.

[5]  Dirk Pflugmacher,et al.  Land use and land cover change in Inner Mongolia - understanding the effects of China's re-vegetation programs , 2018 .

[6]  J. Ardö,et al.  Detecting changes in vegetation trends using time series segmentation , 2015 .

[7]  C. Tucker,et al.  Increased plant growth in the northern high latitudes from 1981 to 1991 , 1997, Nature.

[8]  Jennifer Small,et al.  Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa , 2007 .

[9]  Lijuan Miao,et al.  Future Climate Impact on the Desertification in the Dry Land Asia Using AVHRR GIMMS NDVI3g Data , 2015, Remote. Sens..

[10]  F. van den Bergh,et al.  Limits to detectability of land degradation by trend analysis of vegetation index data , 2012 .

[11]  中国科学院中国植被图编辑委员会 中国植被图集 = Vegetation atlas of China , 2001 .

[12]  Chunyang He,et al.  Monitoring vegetation dynamics by coupling linear trend analysis with change vector analysis: a case study in the Xilingol steppe in northern China , 2012 .

[13]  R. Myneni,et al.  Satellite-indicated long-term vegetation changes and their drivers on the Mongolian Plateau , 2014, Landscape Ecology.

[14]  Qiao-yun Zhang,et al.  Understanding grassland rental markets and their determinants in eastern inner Mongolia, PR China , 2017 .

[15]  C. Tucker,et al.  Assessing Drivers of Vegetation Changes in Drylands from Time Series of Earth Observation Data , 2015 .

[16]  Yuqi Bai,et al.  Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine , 2017 .

[17]  J. Evans,et al.  Discrimination between climate and human-induced dryland degradation. , 2004 .

[18]  P. Gong,et al.  Monitoring dynamic changes of global land cover types: fluctuations of major lakes in China every 8 days during 2000–2010 , 2014 .

[19]  Compton J. Tucker,et al.  A Non-Stationary 1981-2012 AVHRR NDVI3g Time Series , 2014, Remote. Sens..

[20]  Zheng-xiang Zhang,et al.  Using GIS spatial analysis and logistic regression to predict the probabilities of human-caused grassland fires. , 2010 .

[21]  Caixia Liu,et al.  Integration of multi-resource remotely sensed data and allometric models for forest aboveground biomass estimation in China , 2019, Remote Sensing of Environment.

[22]  Anja Linstädter,et al.  Quantifying drylands' drought resistance and recovery: the importance of drought intensity, dominant life history and grazing regime , 2015, Global change biology.

[23]  B. He,et al.  Climate impact on vegetation and animal husbandry on the Mongolian plateau: a comparative analysis , 2015, Natural Hazards.

[24]  Zhiqiu Gao,et al.  Effects of precipitation on grassland ecosystem restoration under grazing exclusion in Inner Mongolia, China , 2014, Landscape Ecology.

[25]  M. F. Hutchinson,et al.  Interpolating Mean Rainfall Using Thin Plate Smoothing Splines , 1995, Int. J. Geogr. Inf. Sci..

[26]  Claas Nendel,et al.  Land‐use change and land degradation on the Mongolian Plateau from 1975 to 2015—A case study from Xilingol, China , 2018 .

[27]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[28]  Shawn W. Laffan,et al.  Effectiveness of the BFAST algorithm for detecting vegetation response patterns in a semi-arid region , 2014 .

[29]  Yi Y. Liu,et al.  Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) , 2017 .

[30]  Dirk Pflugmacher,et al.  Remote Sensing How Normalized Difference Vegetation Index (ndvi) Trends from Advanced Very High Resolution Radiometer (avhrr) and Système Probatoire D'observation De La Terre Vegetation (spot Vgt) Time Series Differ in Agricultural Areas: an Inner Mongolian Case Study , 2012 .

[31]  C. Liang,et al.  Historical landscape dynamics of Inner Mongolia: patterns, drivers, and impacts , 2015, Landscape Ecology.

[32]  Rob J Hyndman,et al.  Phenological change detection while accounting for abrupt and gradual trends in satellite image time series , 2010 .

[33]  Yi Y. Liu,et al.  Changing Climate and Overgrazing Are Decimating Mongolian Steppes , 2013, PloS one.

[34]  Jianhui Huang,et al.  Distinguishing between human-induced and climate-driven vegetation changes: a critical application of RESTREND in inner Mongolia , 2012, Landscape Ecology.

[35]  Jonas Ardö,et al.  Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel , 2014 .

[36]  Changhui Peng,et al.  Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada , 2018 .

[37]  Xiaoping Zhou,et al.  Distinguishing the vegetation dynamics induced by anthropogenic factors using vegetation optical depth and AVHRR NDVI: A cross-border study on the Mongolian Plateau. , 2018, The Science of the total environment.

[38]  Christopher E. Holden,et al.  Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000–2014) , 2016 .

[39]  Yoshiki Yamagata,et al.  Analysis of spatiotemporal land cover changes in Inner Mongolia using self-organizing map neural network and grid cells method. , 2018, The Science of the total environment.

[40]  Jason P. Evans,et al.  The impact of dataset selection on land degradation assessment , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[41]  C. Woodcock,et al.  Continuous change detection and classification of land cover using all available Landsat data , 2014 .

[42]  R. Reid,et al.  Mongolian rangelands at a tipping point? Biomass and cover are stable but composition shifts and richness declines after 20 years of grazing and increasing temperatures. , 2015 .

[43]  Hong Wang,et al.  Human-induced vegetation degradation and response of soil nitrogen storage in typical steppes in Inner Mongolia, China , 2016 .

[44]  R. Fensholt,et al.  Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent , 2006 .

[45]  Jiaguo Qi,et al.  Understanding the coupled natural and human systems in Dryland East Asia , 2012 .

[46]  Hurtado Abril,et al.  Generación de un índice espectro-temporal para la identificación de zonas afectadas por deforestación usando imágenes Landsat. , 2020 .

[47]  F. Fisher Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note , 1970 .

[48]  Xuehua Liu,et al.  Causal relationship in the interaction between land cover change and underlying surface climate in the grassland ecosystems in China. , 2019, The Science of the total environment.

[49]  Jingyun Fang,et al.  Long-term vegetation changes in the four mega-sandy lands in Inner Mongolia, China , 2015, Landscape Ecology.

[50]  Haihua Shen,et al.  Rapid loss of lakes on the Mongolian Plateau , 2015, Proceedings of the National Academy of Sciences.

[51]  Elias Symeonakis,et al.  Remote Sensing Assessing Land Degradation and Desertification Using Vegetation Index Data: Current Frameworks and Future Directions , 2022 .

[52]  P. Jiang,et al.  Outlook of coal-fired power plant development and the regional ecosystem and environmental protection in China , 2017 .

[53]  Zhiqiang Yang,et al.  A LandTrendr multispectral ensemble for forest disturbance detection , 2018 .

[54]  Jiaguo Qi,et al.  Grassland canopy cover and aboveground biomass in Mongolia and Inner Mongolia: Spatiotemporal estimates and controlling factors , 2018, Remote Sensing of Environment.

[55]  Guoqiang Wang,et al.  Evaluation of semiarid grassland degradation in North China from multiple perspectives , 2018 .

[56]  C. Peng,et al.  Multiple afforestation programs accelerate the greenness in the 'Three North' region of China from 1982 to 2013 , 2016 .