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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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Remote sensing of forest health and vitality: a South African perspective
摘要： Commercial forestry plantations are an important and valuable segment of the South African economy and forest managers are required to maximise and sustain forest productivity. However, various factors such as the outbreak of damaging agents are constantly hampering forest health and thus decrease productivity. It is therefore important to detect the presence and spread of these agents within plantation forests, a task efficiently achieved using remote sensing technology. A wide assortment of sensors with varying resolutions are available and have been extensively used for this purpose. This paper reviews the current status of remote sensing of forest health in South Africa by providing insight on the latest developments on the use of the technology in forest plantations. A systematic search was executed on Google Scholar, ScienceDirect? and EBSCOhost? databases that identified 627 articles of which 29 made reference to remote sensing of forest health in South Africa. Four key results were found: (1) the latest technology is capable of detecting and monitoring forest health with great accuracy, especially with the adoption of machine learning methods; (2) studies employing remote sensing to characterise forest health have burgeoned since 2006 with even more applying hyperspectral data; (3) most studies were spatially concentrated in the KwaZulu-Natal Midlands region around Pietermaritzburg with only a few over the Western Cape; and (4) the remote detection of pest outbreaks and pathogens have received much attention followed by alien invasive plants and a few studies directed to fragmentation. Present and future partnerships may open up opportunities for exploiting remote sensing further; this should address growing expectations from government and industry for more detailed and accurate information concerning the health and condition of South Africa’s plantation forests.
关键词： fragmentation,pest and pathogens,remote sensing,alien invasive plants,forest health
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