Spatiotemporal analysis of vegetation cover (1984–2017) and modelling of its change drivers, the case of Pingtan Island, China

This study aims to examine vegetation cover dynamics through quantification of its greenness index, magnitude of change, nature of transition, and fragmentation levels using Landsat images from four periods, 1984, 1996, 2007 and 2017, together with physical and socioeconomic drivers. The results show high spatiotemporal variations of vegetation cover that generally increased from plain to mountain and towards the recent decade (2007–2017). While low vegetation cover (fractional vegetation cover < 50% and NDVI < 0.3) remained to be the dominant cover type in Pingtan, spatiotemporal variations were observed during different time periods. Overall, forestland has increased by 11.43% (3682 ha), and shrub land has expanded by 0.28% (91 ha). In contrast, grassland has reduced by 5.41% (1743 ha). Considering all vegetation types, the expanded area dominated the vegetation cover change resulting in a net gain of 6.3% (2030 ha). The increase in vegetation cover was contributed mainly by farmland. The landscape pattern of each vegetation type shows variation across periods and some metrics have inconsistent trend with change in class size. Generally, patches’ shape complexity, size variability and dominance reduced, while fragmentation level increased during the study period. Slope, elevation, strong wind and policy affected vegetation cover positively. Most socio economic variables showed significant influence (variable importance in the projection > 1) on both woodland (+) and grassland (−) in first latent factor that consists the highest proportion variance in the models. Having quantitative results of vegetation change using several metrics and its change maps would provide valuable information for comprehensive understanding of vegetation cover change and valuable inputs for environmental management planners.

[1]  Dan Zhang,et al.  Spatiotemporal analyses of urban vegetation structural attributes using multitemporal Landsat TM data and field measurements , 2017, Annals of Forest Science.

[2]  Bregje K. van Wesenbeeck,et al.  How ecological engineering can serve in coastal protection , 2011 .

[3]  E. Uuemaa,et al.  Trends in the use of landscape spatial metrics as landscape indicators: A review , 2013 .

[4]  Baofeng Su,et al.  Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications , 2017, J. Sensors.

[5]  Shufang Tian,et al.  Human Activity Influences on Vegetation Cover Changes in Beijing, China, from 2000 to 2015 , 2017, Remote. Sens..

[6]  M. Ghose,et al.  Remote Sensing-Derived Spectral Vegetation Indices and Forest Carbon:Testing the Validity of Models in Mountainous Terrain Covered with High Biodiversity , 2017 .

[7]  Xinshi Zhang,et al.  Value of ecosystem services in China , 2000 .

[8]  Fei Tang,et al.  [Remote sensing analysis of ecological change caused by construction of the new island city: Pingtan Comprehensive Experimental Zone, Fujian Province]. , 2015, Ying yong sheng tai xue bao = The journal of applied ecology.

[9]  S. Naseem,et al.  Assessment of barite of Lasbela, Balochistan, Pakistan, as drilling mud and environmental impact of associated Pb, As, Hg, Cd and Sr , 2016, Environmental Earth Sciences.

[10]  L. Carrascal,et al.  Partial least squares regression as an alternative to current regression methods used in ecology , 2009 .

[11]  Hsiang-te Kung,et al.  Assessment of Land-Cover/Land-Use Change and Landscape Patterns in the Two National Nature Reserves of Ebinur Lake Watershed, Xinjiang, China , 2017 .

[12]  Shuqing Zhao,et al.  Quantifying spatiotemporal patterns of urban expansion in three capital cities in Northeast China over the past three decades using satellite data sets , 2015, Environmental Earth Sciences.

[13]  Yu-Pin Lin,et al.  Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis , 2009, Sensors.

[14]  Bradley C. Rundquist,et al.  The influence of canopy green vegetation fraction on spectral measurements over native tallgrass prairie , 2002 .

[15]  Zhihua Shi,et al.  Impacts of land use change on watershed streamflow and sediment yield: An assessment using hydrologic modelling and partial least squares regression , 2013 .

[16]  H. Abdi.  Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[18]  Xiaochen Zou,et al.  Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops , 2017, Remote. Sens..

[19]  G. Shao,et al.  China's Forest Policy for the 21st Century , 2000, Science.

[20]  R. K. Biswas,et al.  Influence of distance between residence and health facilities on non-communicable diseases: An assessment over hypertension and diabetes in Bangladesh , 2017, PloS one.

[21]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[22]  Lifu Zhang,et al.  Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types , 2015, Remote. Sens..

[23]  Na Li,et al.  Urban expansion in China and its spatial-temporal differences over the past four decades , 2016, Journal of Geographical Sciences.

[24]  K. McGarigal,et al.  FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. , 1995 .

[25]  S. Agarwal,et al.  Fractional Vegetation Cover Analysis for Understanding Vegetation Cover Dynamics in Northeast India , 2005 .

[26]  H. Long,et al.  Analysis of rural transformation development in China since the turn of the new millennium , 2011 .

[27]  Lingxue Yu,et al.  Mapping the Influence of Land Use/Land Cover Changes on the Urban Heat Island Effect—A Case Study of Changchun, China , 2017 .

[28]  A. Braimoh.  Random and systematic land-cover transitions in northern Ghana , 2006 .

[29]  Laura Nahuelhual,et al.  Combining land transitions and trajectories in assessing forest cover change , 2012 .

[30]  Uriel Jeshua Sánchez-Reyes,et al.  Assessment of Land Use-Cover Changes and Successional Stages of Vegetation in the Natural Protected Area Altas Cumbres, Northeastern Mexico, Using Landsat Satellite Imagery , 2017, Remote. Sens..

[31]  Eric J. Gustafson,et al.  Quantifying Landscape Spatial Pattern: What Is the State of the Art? , 1998, Ecosystems.

[32]  Jianxiu Qiu,et al.  The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China , 2017 .

[33]  Shenghui Cui,et al.  Potential for forest vegetation carbon storage in Fujian Province, China, determined from forest inventories , 2011, Plant and Soil.

[34]  Chenghu Zhou,et al.  A partial least-squares regression approach to land use studies in the Suzhou-Wuxi-Changzhou region , 2007 .

[35]  Hua Ma,et al.  The threshold between natural recovery and the need for artificial restoration in degraded lands in Fujian Province, China , 2013, Environmental Monitoring and Assessment.

[36]  Huadong Guo,et al.  Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring , 2015, Remote. Sens..

[37]  Jianguo Wu,et al.  Use and misuse of landscape indices , 2004, Landscape Ecology.

[38]  Ke Wang,et al.  Characterizing the spatiotemporal evolutions and impact of rapid urbanization on island sustainable development , 2016 .

[39]  Qiang Xiao,et al.  Monitoring vegetation cover in Chongqing between 2001 and 2010 using remote sensing data , 2017, Environmental Monitoring and Assessment.

[40]  Changjiang Wu,et al.  Monitoring the vegetation activity in China using vegetation health indices , 2018 .

[41]  R. G. Pontius,et al.  Detecting important categorical land changes while accounting for persistence , 2004 .

[42]  A. Labrique,et al.  Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis , 2017, PloS one.

[43]  Xiaohua Wei,et al.  Relationship between vegetation carbon storage and urbanization: A case study of Xiamen, China , 2011 .

[44]  A. Gitelson,et al.  Novel algorithms for remote estimation of vegetation fraction , 2002 .

[45]  Ge Yu,et al.  Spatio-temporal distribution of vegetation index and its influencing factors—a case study of the Jiaozhou Bay, China , 2017, Chinese Journal of Oceanology and Limnology.

[46]  F. Ding,et al.  Remote sensing monitoring of wind-preventing and sand-fixing effects of coastal protection forests: A case study in Haitan Island, Fujian, China , 2014, 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA).

[47]  Jianguo Wu.  Effects of changing scale on landscape pattern analysis: scaling relations , 2004, Landscape Ecology.

[48]  Ting Ma,et al.  Holocene vegetation, environment and anthropogenic influence in the Fuzhou Basin, southeast China , 2015 .

[49]  Mikaela Schmitt-Harsh,et al.  Landscape change in Guatemala: Driving forces of forest and coffee agroforest expansion and contraction from 1990 to 2010 , 2013 .

[50]  Li Guangdong,et al.  Quantitative Measurement of Urban Expansion and its Driving Factors in Qingdao: an Empirical Analysis Based on County Unit Data , 2015 .

[51]  Gaohuan Liu,et al.  Characterizing Spatiotemporal Pattern of Land Use Change and Its Driving Force Based on GIS and Landscape Analysis Techniques in Tianjin during 2000–2015 , 2017 .

[52]  Onur Satir,et al.  Monitoring the land use/cover changes and habitat quality using Landsat dataset and landscape metrics under the immigration effect in subalpine eastern Turkey , 2016, Environmental Earth Sciences.

[53]  Hyongki Lee,et al.  Integrated groundwater resource management in Indus Basin using satellite gravimetry and physical modeling tools , 2017, Environmental Monitoring and Assessment.

[54]  N. Ahmad,et al.  Predicting the Effects of Urban Development on Land Transition and Spatial Patterns of Land Use in Western Peninsular Malaysia , 2016 .

[55]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[56]  Y. Bao,et al.  Spatial and temporal variations of vegetation cover and the relationships with climate factors in Inner Mongolia based on GIMMS NDVI3g data , 2017, Journal of Arid Land.

[57]  Shijie Wang,et al.  Spatial-temporal changes of vegetation cover in Guizhou Province, Southern China , 2017, Chinese Geographical Science.