In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more tra...In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.展开更多
基金the Spanish Directorate General for Scientific and Technical Research(Ministerio de Economía y Competitividad)[grant number CGL2013-46387-C2-2-R]Ruben Valbuena’s work is supported by an H2020 Marie Sklodowska Curie Actions entitled‘Classification of forest structural types with LIDAR remote sensing applied to study tree size-density scaling theories’[grant number LORENZLIDAR-658180].
文摘In the context of predicting forest attributes using a combination of airborne LIDAR and multispectral(MS)sensors,we suggest the inclusion of normalized difference vegetation index(NDVI)metrics along with the more traditional LIDAR height metrics.Here the data fusion method consists of back-projecting LIDAR returns onto original MS images,avoiding co-registration errors.The prediction method is based on nonparametric imputation(the most similar neighbor).Predictor selection and accuracy assessment include hypothesis tests and over-fitting prevention methods.Results show improvements when using combinations of LIDAR and MS compared to using either of them alone.The MS sensor has little explanatory capacity for forest variables dependent on tree height,already well determined from LIDAR alone.However,there is potential for variables dependent on tree diameters and their density.The combination of LIDAR and MS sensors can be very beneficial for predicting variables describing forests structural heterogeneity,which are best described from synergies between LIDAR heights and NDVI dispersion.Results demonstrate the potential of NDVI metrics to increase prediction accuracy of forest attributes.Their inclusion in the predictor dataset may,however,in a few cases be detrimental to accuracy,and therefore we recommend to carefully assess the possible advantages of data fusion on a case-by-case basis.
