Suchergebnisse

Abstract. A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.

Abstract. Remote sensed information on vegetation and soil moisture, namely the Normalised Difference Vegetation Index (NDVI) and the Soil Water Index (SWI), is employed to monitor the spatial extent, severity and persistence of drought episodes over Continental Portugal, from 1999 to 2006. The severity of a given drought episode is assessed by evaluating the cumulative impact over time of drought conditions on vegetation. Special attention is given to the drought episodes that have occurred in the last decade, i.e., 1999, 2002 and particularly the major event of 2005. During both the 1999 and 2005 drought episodes negative anomalies of NDVI are observed over large sectors of Southern Portugal for up to nine months (out of eleven) of the vegetative cycle. On the contrary, the 2002 event was characterized by negative anomalies in the northern half of Portugal and for a shorter period (eight out of eleven months). The impact of soil moisture on vegetation dynamics is evaluated by analyzing monthly anomalies of SWI and by studying the annual cycle of SWI vs. NDVI. While in the case of the drought episode of 1999 the scarcity of water in the soil persisted until spring, in the recent episode of 2005 the deficit in greenness was already apparent at the end of summer. The impact of dry periods on vegetation is clearly observed in both arable land and forest, and it is found that arable land presents a higher sensitivity. From an operational point of view, obtained results reveal the possibility of using the developed methodology to monitor, in quasi real-time, vegetation stress and droughts in Mediterranean ecosystems.

Abstract. The present work aims to study the combined effect of drought and large wildfires in the Iberian Peninsula relying on remotely sensed data of vegetation dynamics and leaf moisture content, in particular monthly NDVI, NDWI and NDDI time series from 1999–2009, derived from VEGETATION dataset. The impact of the exceptional 2004/2005 drought on vegetation was assessed for vegetation recovering from the extraordinary fire season of 2003 and on the conditions that contributed to the onsetting of the fire season of 2005. Drought severity was estimated by the cumulative negative effect on photosynthetic activity (NDVI) and vegetation dryness (NDDI), with about 2/3 of Iberian Peninsula presenting vegetative stress and low water availability conditions, in spring and early summer of 2005. Furthermore, NDDI has shown to be very useful to assess drought, since it combines information on vegetation and water conditions. Moreover, we show that besides looking at the inter-annual variability of NDVI and NDDI, it is useful to evaluate intra-annual changes (δNDVI and δNDDI), as indicators of change in vegetation greenness, allowing a detailed picture of the ability of the different land-cover types to resist to short-term dry conditions. In order to assess drought impact on post-fire regeneration, recovery times were evaluated by a mono-parametric model based on NDVI data and values corresponding to drought months were set to no value. Drought has shown to delay recovery times for several months in all the selected scars from 2003. The analysis of vegetation dynamics and fire selectivity in 2005 suggests that fires tended to occur in pixels presenting lower vegetative and water stress conditions during spring and early summer months. Additionally, pre-fire vegetation dynamics, in particular vegetation density and water availability during spring and early summer, has shown to influence significantly the levels of fire damage. These results stress the role of fuel availability in fire occurrence and impact on the Iberian Peninsula.

Abstract. Due to its economic and nutritional value, the world production of chestnuts is increasing as new stands are being planted in various regions of the world. This work focuses on the relation between weather and annual chestnut production to model the role of weather, to assess the impacts of climate change and to identify appropriate locations for new groves. The exploratory analysis of chestnut production time series and the striking increase of production area have motivated the use for chestnut productivity. A large set of meteorological variables and remote sensing indices were computed and their role on chestnut productivity evaluated with composite and correlation analyses. These results allow for the identification of the variables cluster with a high correlation and impact on chestnut production. Then, different selection methods were used to develop multiple regression models able to explain a considerable fraction of productivity variance: (i) a simulation model (R2-value = 87%) based on the winter and summer temperature and on spring and summer precipitation variables; and, (ii) a model to predict yearly chestnut productivity (R2-value of 63%) with five months in advance, combining meteorological variables and NDVI. Goodness of fit statistic, cross validation and residual analysis demonstrate the model's quality, usefulness and consistency of obtained results.