A Comparison of Gaofen-2 and Sentinel-2 Imagery for Mapping Mangrove Forests Using Object-Oriented Analysis and Random Forest

Mangrove forest (MF) extents and distributions are fundamental for conservation and restoration efforts. According to previous studies, both the commercial Gaofen-2 (GF-2) imagery (0.8 m spatial resolution and 4 spectral bands) and freely accessed Sentinel-2 (S2) imagery (10 m spatial resolution and 13 spectral bands) have been successfully used to map MFs. However, the efficiency and accuracy of MF mapping based on these two data is not clear, especially for large-scale applications. To address this issue, first, we developed a robust classification approach by integrating object-based image analysis (OBIA) and random forest (RF) algorithm; and then, applied this approach to GF-2 and S2 images to map the extents of MF along the entire coasts of Guangxi, China, respectively; at last, compared the efficiency and accuracy of GF-2 and S2 imagery in MF mapping. Results showed that: first, based on OBIA and RF integrated classification approach both MF maps derived from GF-2 and S2 obtained high mapping accuracies (the overall accuracy was 96% and 94%, respectively); second, areal extent of MFs in Guangxi extracted from GF-2 and S2 images was 8182 and 8040 ha, respectively; third, GF-2 imagery has extraordinary abilities in detecting fragmented MF patches located along landward and seaward edges; and finally, S2 imagery performed better in detecting seaward submerged MFs and separating MF from terrestrial vegetation. Results and conclusions of this study can provide basic considerations for selecting appropriate data source in MF or wetland vegetation mapping tasks.

[1]  Ruben Van De Kerchove,et al.  An Analysis of the Early Regeneration of Mangrove Forests using Landsat Time Series in the Matang Mangrove Forest Reserve, Peninsular Malaysia , 2019, Remote. Sens..

[2]  Mingming Jia,et al.  Dynamic Analysis of Mangrove Forests Based on an Optimal Segmentation Scale Model and Multi-Seasonal Images in Quanzhou Bay, China , 2018, Remote. Sens..

[3]  Rick L. Lawrence,et al.  Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) , 2006 .

[4]  R. Lucas,et al.  Aberystwyth University Distribution and Drivers of Global Mangrove Forest Change, 1996-2010 , 2017 .

[5]  M. Clark.  Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California , 2020 .

[6]  Geoffrey J. Hay,et al.  How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains , 2012 .

[7]  Le Wang,et al.  Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges , 2019, Remote Sensing of Environment.

[8]  Xiyong Hou,et al.  Characteristics of coastline changes in mainland China since the early 1940s , 2016, Science China Earth Sciences.

[9]  Steffen Gebhardt,et al.  Remote Sensing of Mangrove Ecosystems: A Review , 2011, Remote. Sens..

[10]  Zongming Wang,et al.  A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery , 2019, Remote. Sens..

[11]  Chandra P. Giri,et al.  Observation and Monitoring of Mangrove Forests Using Remote Sensing: Opportunities and Challenges , 2016, Remote. Sens..

[12]  Yun Zhang,et al.  DEVELOPMENT OF A SUPERVISED SOFTWARE TOOL FOR AUTOMATED DETERMINATION OF OPTIMAL SEGMENTATION PARAMETERS FOR ECOGNITION , 2010 .

[13]  Chao Wang,et al.  Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[14]  T. Danaher,et al.  A remote sensing approach to mapping fire severity in south-eastern Australia using sentinel 2 and random forest , 2020 .

[15]  Kaishan Song,et al.  Landsat-Based Estimation of Mangrove Forest Loss and Restoration in Guangxi Province, China, Influenced by Human and Natural Factors , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[16]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[17]  Mariana Belgiu,et al.  Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis , 2018 .

[18]  Min Zhang,et al.  Scale parameter selection by spatial statistics for GeOBIA: Using mean-shift based multi-scale segmentation as an example , 2015 .

[19]  Zongming Wang,et al.  Tracking long-term floodplain wetland changes: A case study in the China side of the Amur River Basin , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[20]  Aniruddha Ghosh,et al.  A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Chen Shi,et al.  Comparison of UAV and WorldView-2 imagery for mapping leaf area index of mangrove forest , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Le Wang,et al.  A review of remote sensing for mangrove forests: 1956–2018 , 2019, Remote Sensing of Environment.

[23]  Richard M. Lucas,et al.  Mapping Mangrove Extent and Change: A Globally Applicable Approach , 2018, Remote. Sens..

[24]  J. Kovacs,et al.  An assessment of commonly employed satellite-based remote sensors for mapping mangrove species in Mexico using an NDVI-based classification scheme , 2017, Environmental Monitoring and Assessment.

[25]  Stuart R. Phinn,et al.  Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach , 2011, Remote. Sens..

[26]  Mingming Jia,et al.  Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI Images , 2018, Remote. Sens..

[27]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[28]  Xi Chen,et al.  Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM , 2019, ISPRS Int. J. Geo Inf..

[29]  Hui Lin,et al.  Textural-Spectral Feature-Based Species Classification of Mangroves in Mai Po Nature Reserve from Worldview-3 Imagery , 2015, Remote. Sens..

[30]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[31]  Ashbindu Singh,et al.  Status and distribution of mangrove forests of the world using earth observation satellite data , 2011 .

[32]  Xin Wen,et al.  Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform , 2019, Remote. Sens..

[33]  Naoto Yokoya,et al.  Remote Sensing Approaches for Monitoring Mangrove Species, Structure, and Biomass: Opportunities and Challenges , 2019, Remote. Sens..

[34]  María-Teresa Sebastiá-Frasquet,et al.  Comparison of Sentinel-2 and High-Resolution Imagery for Mapping Land Abandonment in Fragmented Areas , 2020, Remote. Sens..

[35]  Qingsheng Liu,et al.  Comparison of CBERS-04, GF-1, and GF-2 Satellite Panchromatic Images for Mapping Quasi-Circular Vegetation Patches in the Yellow River Delta, China , 2018, Sensors.

[36]  Huili Gong,et al.  Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion , 2020, Remote Sensing of Environment.

[37]  Clement Atzberger,et al.  How much does multi-temporal Sentinel-2 data improve crop type classification? , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[38]  Stuart R. Phinn,et al.  Object-Based Approach for Multi-Scale Mangrove Composition Mapping Using Multi-Resolution Image Datasets , 2015, Remote. Sens..

[39]  S. Phinn,et al.  Assessment of multi-resolution image data for mangrove leaf area index mapping , 2016 .

[40]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[41]  P. Gong,et al.  Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of Panama , 2004 .

[42]  Yun Zhang,et al.  A Supervised and Fuzzy-based Approach to Determine Optimal Multi-resolution Image Segmentation Parameters , 2012 .

[43]  Jungho Im,et al.  Detection of Convective Initiation Using Meteorological Imager Onboard Communication, Ocean, and Meteorological Satellite Based on Machine Learning Approaches , 2015, Remote. Sens..

[44]  Run Wang,et al.  Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species , 2018, Remote. Sens..

[45]  Dongmei Chen,et al.  Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[46]  He Li,et al.  Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery , 2018, Remote. Sens..

[47]  Bruce W. Pengra,et al.  Distribution and dynamics of mangrove forests of South Asia. , 2015, Journal of environmental management.

[48]  Johannes R. Sveinsson,et al.  Random Forests for land cover classification , 2006, Pattern Recognit. Lett..

[49]  D. Mao,et al.  Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine , 2021 .

[50]  Lei Huang,et al.  Mapping Thermokarst Lakes on the Qinghai–Tibet Plateau Using Nonlocal Active Contours in Chinese GaoFen-2 Multispectral Imagery , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Bing Xu,et al.  Monitoring mangrove forest change in China from 1990 to 2015 using Landsat-derived spectral-temporal variability metrics , 2018, Int. J. Appl. Earth Obs. Geoinformation.