A previously developed procedure that aims at monitoring the process of vegetation recovery in areas affected by major fire episodes is revisited and assessed in terms of consistency and robustness. The procedure is based on 10-day fields of Maximum Value Composites of the Normalised Difference Vegetation Index (MVC-NDVI). The identification of fire scars is first achieved based on cluster analysis of persistent NDVI anomalies during the year following the fire event. Post-fire vegetation behaviour is then characterised based on maps of recovery rates as estimated by fitting a mono-parametric model of vegetation recovery to NDVI data over each burned scar. Results obtained indicate that reliable estimates of vegetation recovery times may be achieved using time series of NDVI of moderate length. It is also shown that consistent results are obtained when time series are derived either from 1-km spatial resolution data retrieved by the VEGETATION sensor on-board SPOT or from 250-m spatial resolution data from the MODIS instrument on-board Aqua and Terra. The regeneration model is also applied to estimate recovery rates in the case of recurrent fires. Overall results point out that the proposed methodology may play an important role in studying vegetation recovery and species succession after recurrent fires, namely when one vegetation type is replaced by another that regenerates faster, despite being more flammable and therefore increasing the risk of severe and large fires. The robustness of the proposed model highlights its adequacy to assess post-fire vegetation dynamics and therefore the procedure reveals as a promising tool for planning and implementing of better fire management practices before and after fire events.
Part of the book: Forest Fire
We describe a methodology to discriminate burned areas and date burning events that use a burn-sensitive (V, W) index system defined in near-/mid-infrared space. Discrimination of burned areas relies on a monthly composite of minimum of W and on the difference between this composite and that of the previous month. The rationale is to identify pixels with high confidence of having burned and aggregate new burned pixels on a contextual basis. Dating of burning events is based on the analysis of time series of W, and searching for the day before maximum temporal separability is achieved. The procedure is applied to the fire of Monchique, a large event that took place in the southwest of Portugal in August 2018. When the obtained pattern of burned pixels is compared against a reference map, the overall accuracy is larger than 99%; the commission and omission errors are lower than 5 and 10%, respectively; and the bias and the Dice coefficient are above 0.95 and 0.9, respectively. Differences between estimated dates of burning and reference dates derived from remote-sensed observations of active fires show a bias of 0.03 day and a root mean square difference of 0.24 day.
Part of the book: Satellite Information Classification and Interpretation