TY - JOUR
T1 - Relationship of NDVI and oak (Quercus)pollen including a predictive model in the SW Mediterranean region
AU - González-Naharro, Rocío
AU - Quirós, Elia
AU - Fernández-Rodríguez, Santiago
AU - Silva-Palacios, Inmaculada
AU - Maya-Manzano, José María
AU - Tormo-Molina, Rafael
AU - Pecero-Casimiro, Raúl
AU - Monroy-Colin, Alejandro
AU - Gonzalo-Garijo, Ángela
N1 - Publisher Copyright:
© 2019
PY - 2019/8/1
Y1 - 2019/8/1
N2 - Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI)related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus)daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx)of Spain and the Fifth National Forest Inventory (IFN5)from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN)was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77)between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.
AB - Techniques of remote sensing are being used to develop phenological studies. Our goal is to study the correlation among the Normalized Difference Vegetation Index (NDVI)related with oak trees included in three set data polygons (15, 25 and 50 km to aerobiological sampling point as NDVI-15, 25 and 50), and oak (Quercus)daily average pollen counts from 1994 to 2013. The study was developed in the SW Mediterranean region with continuous pollen recording within the mean pollen season of each studied year. These pollen concentrations were compared with NDVI values in the locations containing the vegetation under a study based on two cartographic sources: the Extremadura Forest Map (MFEx)of Spain and the Fifth National Forest Inventory (IFN5)from Portugal. The importance of this work is to propose the relationship among data related in space and time by Spearman and Granger causality tests. 9 out of 20 studied years have shown significant results with the Granger causality test between NDVI and pollen concentration, and in 12 years, significant values were obtained by Spearman test. The distances of influence on the contribution of Quercus pollen to the sampler showed statistically significant results depending on the year. Moreover, a predictive model by using Artificial Neural Network (ANN)was applied with better results in NDVI25 than for NDVI15 or NDVI50. The addition of NDVI25 with the lag of 5 days and some weather parameters in the model was applied with a RMSE of 4.26 (Spearman coefficient r = 0.77)between observed and predicted values. Based on these results, NDVI seems to be a useful parameter to predict airborne pollen.
KW - Akaike information criterion (AIC)
KW - Artificial Neural Network (ANN)
KW - Granger causality test
KW - Normalized Difference Vegetation Index (NDVI)
KW - Polygon oak trees
KW - Quercus airborne pollen
UR - http://www.scopus.com/inward/record.url?scp=85064810624&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2019.04.213
DO - 10.1016/j.scitotenv.2019.04.213
M3 - Article
C2 - 31048171
AN - SCOPUS:85064810624
SN - 0048-9697
VL - 676
SP - 407
EP - 419
JO - Science of the Total Environment
JF - Science of the Total Environment
ER -