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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Effective Chlorophyll Indicator for Forest Monitoring Using Worldview-3 Multispectral Reflectance
摘要： This paper explores the feasibility of deriving multispectral-based effective chlorophyll indicators (MECIs) for foliage chlorophyll concentration (CHLS) estimation. An average fusion method was applied to simulate the multispectral reflectance of the WorldView-3 sensor using hyperspectral data. With the experimental data of CHLS and predictors derived from multispectral reflectance, a series of linear regression analyses were carried out to derive appropriate models for CHLS estimation. Accuracy measures of RMSE and PRMSE were used to evaluate the model performance. Results showed that the coastal-band based MECI (MECIc) and the blue-band based MECI (MECIb) were able to achieve an RMSE of 0.5657 mg/g and 0.5943 mg/g as well as a PRMSE of 36% and 38% respectively. Using the Red edge and Yellow reflectance based NDVI (NDVIREY) as a predictor, the model can reduce uncertainty and achieve an estimation of 0.4089 mg/g and 26% for RMSE and PRMSE respectively. The prediction error made by the CHLS-NDVIREY model and the CHLS-MECI model were 11% and 60% larger than 0.38 mg/g the RMSE of hyperspectral-based CHLS-ECI model. In summary, NDVIREY was able to achieve a better prediction at around a level of 75% accuracy (1-PRMSE) and therefore is able to be an effective indicator of CHLS for forest monitoring.
关键词： climate change,hyperspectral remote sensing,Chlorophyll indicator,multispectral remote sensing,forest health
更新于2025-09-23 15:22:29
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Improving Super-Resolution Flood Inundation Mapping for Multispectral Remote Sensing Image by Supplying More Spectral Information
摘要： Super-resolution mapping is an effective technique in mapping ?ood inundation for multispectral remote sensing image. However, the traditional super-resolution ?ood inundation mapping (SRFIM) is unable to fully utilize the spectral information from multispectral remote sensing image band. In order to resolve this problem, a novel SRFIM by supplying more spectral information (SRFIM-MSI) is proposed to improve mapping accuracy. In the proposed SRFIM-MSI, the spectral information from the multispectral band is calculated by the normalized difference water index (NDWI). A spectral term constituted by NDWI is added into the traditional SRFIM. The proposed method is evaluated by using two Landsat 8 OLI multispectral data from the study area in Cambodia. The obtained results demonstrate that the proposed SRFIM-MSI produces better results than the traditional SRFIM methods.
关键词： super-resolution ?ood inundation mapping (SRFIM),spectral information,super-resolution mapping (SRM),Multispectral remote sensing image
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Applying an Object-Based SVM Classifier to Explore Canopy Closure of Mangrove Forest in the Mekong Delta Using Sentinel-2 Multispectral Images
摘要： This study explored the feasibility of mapping the canopy closures of mangrove forest in the Mekong Delta of Vietnam using Sentinel-2 multispectral composite image. Forest canopy closures were determined in accordance with the level of volume stocks. A method of object-based support vector machine classifier was first applied to derive LULC and then to differentiate the canopy closure over the mangrove forest. Results showed that object-based SVM classification was able to achieve an accuracy of kappa of around 0.73 which is around 0.2 higher than the kappa of a pixel-based SVM classification. However, there was a level of around 11%-24% commission rate and omission rate in the rich, medium, and poor classes of canopy closure. Further research to improve the performance of canopy closure classification is needed in order to obtain more accurate information for management planning.
关键词： forest health,Mangroves,multispectral remote sensing,canopy closure
更新于2025-09-04 15:30:14