Tracking the phenology and expansion of Spartina alterniflora coastal wetland by time series MODIS and Landsat images

Accurate information on phenology of Spartina alterniflora is basic for observing its growing condition in native and invasive places. Remote sensing (RS) can be used to analyse and monitor vegetation at a large spatial and long time scale. In this study, RS is used to explore phenology of coastal wetland vegetation (Reed, Suaeda salsa and Spartina alterniflora) based on time series data of four spectral indices (SI): Normalized Difference Vegetation Index (NDVI); Enhanced Vegetation Index (EVI); Land Surface Water Index (LSWI) and Modified Normalized Difference Moisture Index (mNDWI). These SIs were calculated based on Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Satellite (Landsat) images, respectively. Phenology (including growing season and non-growing season) of Spartina alterniflora based on SIs to track its dynamic expansion since it invaded in southern Yellow Sea (Jiangsu, Yancheng), China through observing Landsat sub-pixels in per MODIS pixel. We also developed a new phenology-based algorithm to identify Spartina alterniflora from 1984 to 2015. The results show that Spartina alterniflora has a longer growth circle (around 286 days in a year) and higher productivity (about 0.6–0.8 NDVI) than the other salt marsh vegetation. The way of observing vegetation phenology on RS, and then developing a new phenology-based algorithm to track coastal wetland including invasive species—like Spartina alterniflora can support the studies of biological expansion, coastal wetland biodiversity conservation, and even global carbon cycling and climate change further.

[1]  Raymond F. Kokaly,et al.  Mapping changing distributions of dominant species in oil-contaminated salt marshes of Louisiana using imaging spectroscopy , 2016 .

[2]  Xianwen Ding,et al.  The spatial relationship between salt marsh vegetation patterns, soil elevation and tidal channels using remote sensing at Chongming Dongtan Nature Reserve, China , 2016, Acta Oceanologica Sinica.

[3]  Anatoly A. Gitelson,et al.  Long-term monitoring of biophysical characteristics of tidal wetlands in the northern Gulf of Mexico — A methodological approach using MODIS , 2016 .

[4]  Jiaping Wu,et al.  Monitoring the Invasion of Spartina alterniflora from 1993 to 2014 with Landsat TM and SPOT 6 Satellite Data in Yueqing Bay, China , 2015, PloS one.

[5]  Gregory P. Asner,et al.  Selective logging changes forest phenology in the Brazilian Amazon: Evidence from MODIS image time series analysis , 2009 .

[6]  Jianbo Lu,et al.  Spatial distribution of an invasive plant Spartina alterniflora and its potential as biofuels in China. , 2013 .

[7]  Yude Pan,et al.  Biogeochemistry: Synergy of a warm spring and dry summer , 2016, Nature.

[8]  L. Guanter,et al.  Consistency Between Sun-Induced Chlorophyll Fluorescence and Gross Primary Production of Vegetation in North America , 2016 .

[9]  Jinwei Dong,et al.  Mapping paddy rice distribution using multi-temporal Landsat imagery in the Sanjiang Plain, northeast China , 2016, Frontiers of Earth Science.

[10]  Yongxue Liu,et al.  Classification mapping and species identification of salt marshes based on a short-time interval NDVI time-series from HJ-1 optical imagery , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[11]  Li Peng,et al.  Analysis of the Network of Protected Areas in China Based on a Geographic Perspective: Current Status, Issues and Integration , 2015 .

[12]  I. Folkins,et al.  Anomaly patterns about strong convective events in the tropics and midlatitudes: Observations from radiosondes and surface weather stations , 2014 .

[13]  Guoliang Zou,et al.  Simulating the Range Expansion of Spartina alterniflora in Ecological Engineering through Constrained Cellular Automata Model and GIS , 2015 .

[14]  Xing Li,et al.  Evolution of the Jiuduansha wetland and the impact of navigation works in the Yangtze Estuary, China , 2016 .

[15]  Hao Zhang,et al.  Evaluation of ecosystem health for the coastal wetlands at the Yangtze Estuary, Shanghai , 2013, Wetlands Ecology and Management.

[16]  Peter M. J. Herman,et al.  Impacts of salt marsh plants on tidal channel initiation and inheritance , 2013 .

[17]  John P. R. O'Donnell,et al.  Examination of Abiotic Drivers and Their Influence on Spartina alterniflora Biomass over a Twenty-Eight Year Period Using Landsat 5 TM Satellite Imagery of the Central Georgia Coast , 2016, Remote. Sens..

[18]  B. Li,et al.  Monitoring rapid vegetation succession in estuarine wetland using time series MODIS-based indicators: An application in the Yangtze River Delta area , 2009 .

[19]  P. Campíns-Falcó,et al.  A new tool for direct non-invasive evaluation of chlorophyll a content from diffuse reflectance measurements. , 2017, The Science of the total environment.

[20]  Huamei Huang,et al.  A study of the population dynamics of Spartina alterniflora at Jiuduansha shoals, Shanghai, China. , 2007 .

[21]  O. Iribarne,et al.  Distribution of saltmarsh plant communities associated with environmental factors along a latitudinal gradient on the south‐west Atlantic coast , 2006 .

[22]  Tom Spencer,et al.  Physiographic Control on the Development of Spartina Marshes , 2008, Science.

[23]  Bingfang Wu,et al.  Erratum to: Impacts of human activities on the evolution of estuarine wetland in the Yangtze Delta from 2000 to 2010 , 2015, Environmental Earth Sciences.

[24]  Min Zhang,et al.  Land claim and loss of tidal flats in the Yangtze Estuary , 2016, Scientific Reports.

[25]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[26]  Yiqi Luo,et al.  Invasion of Spartina alterniflora Enhanced Ecosystem Carbon and Nitrogen Stocks in the Yangtze Estuary, China , 2007, Ecosystems.

[27]  Dan Ye,et al.  Quantized Feedback Control Design of Nonlinear Large-Scale Systems via Decentralized Adaptive Integral Sliding Mode Control , 2015 .

[28]  Narpinder Singh,et al.  Diversity in quality traits amongst Indian wheat varieties II: Paste, dough and muffin making properties. , 2016, Food chemistry.

[29]  Li-Quan Zhang,et al.  A cellular automata model for population expansion of Spartina alterniflora at Jiuduansha Shoals, Shanghai, China , 2008 .

[30]  Yun Yang,et al.  Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[31]  N. Clinton,et al.  A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform , 2017 .

[32]  Wei Li,et al.  Variability and Changes in Climate, Phenology, and Gross Primary Production of an Alpine Wetland Ecosystem , 2016, Remote. Sens..

[33]  Merryl Alber,et al.  Classification of salt marsh vegetation using edaphic and remote sensing-derived variables , 2014 .

[34]  Jiaguo Qi,et al.  Detecting and assessing Spartina invasion in coastal region of China: A case study in the Xiangshan Bay , 2016, Acta Oceanologica Sinica.

[35]  Bo Li,et al.  Effects of Invasive Cordgrass on Presence of Marsh Grassbird in an Area where It Is Not Native , 2014, Conservation biology : the journal of the Society for Conservation Biology.

[36]  Jie Wang,et al.  Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[37]  Bo Li,et al.  The extended phenology of Spartina invasion alters a native herbivorous insect’s abundance and diet in a Chinese salt marsh , 2016, Biological Invasions.

[38]  Li Wang,et al.  The Tidal Marsh Inundation Index (TMII): An inundation filter to flag flooded pixels and improve MODIS tidal marsh vegetation time-series analysis , 2017 .

[39]  J. Mustard,et al.  Cross-scalar satellite phenology from ground, Landsat, and MODIS data , 2007 .

[40]  Bo Tian,et al.  Forecasting the effects of sea-level rise at Chongming Dongtan Nature Reserve in the Yangtze Delta, Shanghai, China , 2010 .

[41]  G. Sánchez‐Azofeifa,et al.  Comparing MODIS and near-surface vegetation indexes for monitoring tropical dry forest phenology along a successional gradient using optical phenology towers , 2017 .

[42]  Jason Grabosky,et al.  Soil metal concentrations and productivity of Betula populifolia (gray birch) as measured by field spectrometry and incremental annual growth in an abandoned urban Brownfield in New Jersey. , 2008, Environmental pollution.

[43]  Mac McKee,et al.  Life on the edge: reproductive mode and rate of invasive Phragmites australis patch expansion , 2016, Biological Invasions.

[44]  Hanqiu Xu.  Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[45]  Ji Shen,et al.  A 150-year isotopic record of lead deposition in Yancheng coastal wetland, China , 2016, Chinese Geographical Science.

[46]  Xinqing Zou,et al.  Seasonal and spatial dynamics of greenhouse gas emissions under various vegetation covers in a coastal saline wetland in southeast China , 2014 .

[47]  B. Moore,et al.  Northward expansion of paddy rice in northeastern Asia during 2000–2014 , 2016, Geophysical research letters.

[48]  F. D'incao,et al.  Natural Diet of Neohelice granulata (Dana, 1851) (Crustacea, Varunidae) in Two Salt Marshes of the Estuarine Region of the Lagoa dos Patos Lagoon , 2011 .

[49]  Li Li,et al.  Mapping Oil Palm Plantations in Cameroon Using PALSAR 50-m Orthorectified Mosaic Images , 2015, Remote. Sens..

[50]  Maggi Kelly,et al.  A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh , 2015, Remote. Sens..

[51]  Yongzhao Zhan,et al.  Maximum Neighborhood Margin Discriminant Projection for Classification , 2014, TheScientificWorldJournal.

[52]  David M. Burdick,et al.  Evaluation of field-measured vertical obscuration and full waveform lidar to assess salt marsh vegetation biophysical parameters , 2015 .

[53]  Yongning Wen,et al.  The Response of Spartina alterniflora Biomass to Soil Factors in Yancheng, Jiangsu Province, P.R. China , 2016, Wetlands.

[54]  J. Weis,et al.  Metal dynamics of plant litter of Spartina alterniflora and Phragmites australis in Metal‐Contaminated salt marshes. Part 1: Patterns of decomposition and metal uptake , 2004, Environmental toxicology and chemistry.

[55]  Yubo Liang,et al.  Distribution of Spartina spp. along China's coast , 2012 .

[56]  Sorin C. Popescu,et al.  The role of elevation, relative sea-level history and vegetation transition in determining carbon distribution in Spartina alterniflora dominated salt marshes , 2015 .

[57]  Liquan Zhang,et al.  Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China , 2006 .

[58]  Zhenshan Lin,et al.  The relative importance of sexual and asexual reproduction in the spread of Spartina alterniflora using a spatially explicit individual-based model , 2014, Ecological Research.

[59]  T. Simas,et al.  Effects of global climate change on coastal salt marshes , 2001 .

[60]  Bin Zhao,et al.  Spectral Discrimination of the Invasive Plant Spartina alterniflora at Multiple Phenological Stages in a Saltmarsh Wetland , 2013, PloS one.

[61]  Huawei Wan,et al.  Monitoring the Invasion of Spartina alterniflora Using Very High Resolution Unmanned Aerial Vehicle Imagery in Beihai, Guangxi (China) , 2014, TheScientificWorldJournal.

[62]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[63]  Bo Li,et al.  Spartina alterniflora invasions in the Yangtze River estuary, China: An overview of current status and ecosystem effects , 2009 .

[64]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[65]  Hongxing Liu,et al.  Retrieval of Mangrove Aboveground Biomass at the Individual Species Level with WorldView-2 Images , 2015, Remote. Sens..

[66]  Bin Zhao,et al.  A comparison of pixel-based and object-oriented approaches to VHR imagery for mapping saltmarsh plants , 2011, Ecol. Informatics.

[67]  William J. Mitsch,et al.  Salt marsh vegetation recovery at salt hay farm wetland restoration sites on Delaware Bay , 2005 .

[68]  Shen Ji,et al.  A 150-year isotopic record of lead deposition in Yancheng coastal wetland, China , 2016 .

[69]  Jinwei Dong,et al.  Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011 , 2013, Proceedings of the National Academy of Sciences.

[70]  N. Gobron,et al.  An automatic procedure to identify key vegetation phenology events using the JRC-FAPAR products , 2008 .

[71]  Jinwei Dong,et al.  Mapping paddy rice planting area in cold temperate climate region through analysis of time series Landsat 8 (OLI), Landsat 7 (ETM+) and MODIS imagery. , 2015, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[72]  T. Sakamoto,et al.  Detecting temporal changes in the extent of annual flooding within the cambodia and the vietnamese mekong delta from MODIS time-series imagery , 2007 .

[73]  S. Brody,et al.  The Contribution of Mangrove Expansion to Salt Marsh Loss on the Texas Gulf Coast , 2015, PloS one.

[74]  Simon M. Mudd,et al.  Response of salt-marsh carbon accumulation to climate change , 2012, Nature.

[75]  Jiafang Huang,et al.  Analysis of the Expanding Process of the Spartina Alterniflora Salt Marsh in Shanyutan Wetland, Minjiang River Estuary by Remote Sensing , 2011 .

[76]  Wenquan Zhu,et al.  Remote-Sensed Monitoring of Dominant Plant Species Distribution and Dynamics at Jiuduansha Wetland in Shanghai, China , 2015, Remote. Sens..