Several dieback episodes triggered by droughts are revealing the high vulnerability of Mediterranean forests, manifested as declines in growth, increased defoliation, and rising mortality rates. Understanding forest responses to such climate extreme events is of high priority for predicting their future vegetation dynamics. We examined how remotely sensed measures of vegetation activity (NDVI, Normalized Difference Vegetation Index) and radial growth (BAI, basal area increment) responded to climate extreme events. We considered tree (Pinus sylvestris, Quercus pubescens, Quercus frainetto) and shrub (Juniperus phoenicea) populations from Italy and Spain showing recent dieback phenomena. Two components of drought, namely elevated atmospheric demand (VPD, vapor pressure deficit) and low soil moisture were analyzed in nearby stands showing or not showing dieback symptoms. Dieback stands exhibited lower NDVI values than non-dieback stands. NDVI and BAI were positively related in all sites except for the dieback stand of Q. frainetto that was negatively related. Such NDVI-BAI linkages were related to specific time windows, which could be useful for identifying when climatic conditions have the greatest influence on vegetation. Growth decline occurred in response to increasing VPD, but responses differed among species. J. phoenicea was the most negatively impacted by higher VPD, whereas oaks responded to soil moisture. A high VPD was related to stronger growth reduction in dieback P. sylvestris trees regardless of soil moisture changes. We highlighted that coupling between proxies of forest productivity (NDVI, BAI) allows better understanding and forecasting of drought-induced dieback phenomena in forests and shrublands. Scaling up from tree to stand levels might be feasible when using the maximum growing season NDVI, which can be applied for retrospective modeling of the impact of drought stress on forest productivity and tree growth.

Declines in canopy greenness and tree growth are caused by combined climate extremes during drought-induced dieback

Castellaneta M.;Rita A.;Ripullone F.
2022-01-01

Abstract

Several dieback episodes triggered by droughts are revealing the high vulnerability of Mediterranean forests, manifested as declines in growth, increased defoliation, and rising mortality rates. Understanding forest responses to such climate extreme events is of high priority for predicting their future vegetation dynamics. We examined how remotely sensed measures of vegetation activity (NDVI, Normalized Difference Vegetation Index) and radial growth (BAI, basal area increment) responded to climate extreme events. We considered tree (Pinus sylvestris, Quercus pubescens, Quercus frainetto) and shrub (Juniperus phoenicea) populations from Italy and Spain showing recent dieback phenomena. Two components of drought, namely elevated atmospheric demand (VPD, vapor pressure deficit) and low soil moisture were analyzed in nearby stands showing or not showing dieback symptoms. Dieback stands exhibited lower NDVI values than non-dieback stands. NDVI and BAI were positively related in all sites except for the dieback stand of Q. frainetto that was negatively related. Such NDVI-BAI linkages were related to specific time windows, which could be useful for identifying when climatic conditions have the greatest influence on vegetation. Growth decline occurred in response to increasing VPD, but responses differed among species. J. phoenicea was the most negatively impacted by higher VPD, whereas oaks responded to soil moisture. A high VPD was related to stronger growth reduction in dieback P. sylvestris trees regardless of soil moisture changes. We highlighted that coupling between proxies of forest productivity (NDVI, BAI) allows better understanding and forecasting of drought-induced dieback phenomena in forests and shrublands. Scaling up from tree to stand levels might be feasible when using the maximum growing season NDVI, which can be applied for retrospective modeling of the impact of drought stress on forest productivity and tree growth.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11563/154565
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