Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

[1]  C. Yang,et al.  Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast , 2008 .

[2]  P. Mumby,et al.  The cost-effectiveness of remote sensing for tropical coastal resources assessment and management , 1999 .

[3]  C. Vaiphasa.  Remote sensing techniques for mangrove mapping , 2006 .

[4]  Karen C. Seto,et al.  Mangrove conversion and aquaculture development in Vietnam: A remote sensing-based approach for evaluating the Ramsar Convention on Wetlands , 2007 .

[5]  Kimberly A. Novick,et al.  Capturing species-level drought responses in a temperate deciduous forest using ratios of photochemical reflectance indices between sunlit and shaded canopies , 2017 .

[6]  A. Huete,et al.  Development of a two-band enhanced vegetation index without a blue band , 2008 .

[7]  Pierre Soille,et al.  Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas , 2016, Remote. Sens..

[8]  Enrico Borgogno Mondino,et al.  Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring , 2017, J. Imaging.

[9]  Jagannath Aryal,et al.  A Statistical Framework for Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data , 2014, IEEE Geoscience and Remote Sensing Letters.

[10]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[11]  R. M. Dwivedi,et al.  Inter sensor comparison between RESOURCESAT LISS III, LISS IV and AWiFS with reference to coastal landuse/landcover studies , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[12]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[13]  Neil Flood,et al.  Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia , 2017, Remote. Sens..

[14]  P. Mumby,et al.  A review of remote sensing for the assessment and management of tropical coastal resources , 1996 .

[15]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[16]  J. R. Jensen,et al.  Remote Sensing of Mangrove Wetlands: Relating Canopy Spectra to Site-Specific Data , 1996 .

[17]  Sarah C. Goslee,et al.  Analyzing Remote Sensing Data in R: The landsat Package , 2011 .

[18]  J. Paal,et al.  Floodplain forests in Estonia: Typological diversity and growth conditions , 2007, Folia Geobotanica.

[19]  A. Čarni,et al.  Diversity of Floodplain Forests in the Igneada Region (NW Thrace - Turkey) , 2011 .

[20]  U. Y. Ozkan,et al.  Detecting treeline dynamics in response to climate warming using forest stand maps and Landsat data in a temperate forest , 2018, Forest Ecosystems.

[21]  Assessment of Ecological Units of Important Nature Conservation Area of Thrace Region , 2010 .

[22]  Gabriele Bitelli,et al.  Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use , 2016, Remote. Sens..

[23]  Christopher Williams,et al.  Cross-Scale Correlation between In Situ Measurements of Canopy Gap Fraction and Landsat-Derived Vegetation Indices with Implications for Monitoring the Seasonal Phenology in Tropical Forests Using MODIS Data , 2018, Remote. Sens..

[24]  Hankui K. Zhang,et al.  Characterization of Sentinel-2A and Landsat-8 top of atmosphere, surface, and nadir BRDF adjusted reflectance and NDVI differences , 2018, Remote Sensing of Environment.

[25]  F. Balcik,et al.  Forecasting land-cover growth using remotely sensed data: a case study of the Igneada protection area in Turkey , 2015, Environmental Monitoring and Assessment.

[26]  Jagannath Aryal,et al.  Near-Real Time Detection of Beetle Infestation in Pine Forests Using MODIS Data , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  F. Dahdouh-Guebas,et al.  Assessment of mangrove vegetation based on remote sensing and ground-truth measurements at Tumpat, Kelantan Delta, East Coast of Peninsular Malaysia , 2011 .

[28]  Chaowei Yang,et al.  Geographic Information System , 2009 .

[29]  Christophe Sannier,et al.  Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree , 2016, Remote. Sens..

[30]  Chandra Giri,et al.  Mangrove forest distributions and dynamics (1975–2005) of the tsunami‐affected region of Asia † , 2008 .

[31]  R. Pongrácz,et al.  Extremes of ground-based and satellite measurements in the vegetation period for the Carpathian Basin , 2005 .

[32]  J. Pivec.  A short-term response of floodplain and spruce forests to evaporation requirements in Moravia in different years  , 2019, Journal of Forest Science.

[33]  Dandan Xu,et al.  Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives , 2014, Sensors.

[34]  Gang Yang,et al.  A Moving Weighted Harmonic Analysis Method for Reconstructing High-Quality SPOT VEGETATION NDVI Time-Series Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[35]  P. Atkinson,et al.  Remote sensing of mangrove forest phenology and its environmental drivers , 2018 .

[36]  T. Moss,et al.  The Flooded Forest : Guidance for policy makers and river managers in Europe on the restoration of floodplain forests , 2004 .

[37]  Jinfei Wang,et al.  Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific , 2005 .

[38]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[39]  Emilio Chuvieco,et al.  Aboveground biomass assessment in Colombia: a remote sensing approach. , 2009 .

[40]  M. E. Yazgan,et al.  Planning of İğneada Longos (Flooded) Forests as a Biosphere Reserve , 2010 .

[41]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[42]  Pedro Cabral,et al.  A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area , 2017, ISPRS Int. J. Geo Inf..

[43]  David P. Roy,et al.  A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery , 2016 .

[44]  Harald van der Werff,et al.  Sentinel-2 for Mapping Iron Absorption Feature Parameters , 2015, Remote. Sens..