In Mediterranean fire-prone ecosystems, shifts in fire regime as a consequence of global change could modify the resilience of vegetation communities. In this paper, we aim to compare the efficiency of high and moderate spatial resolution satellite imagery in the evaluation of resilience in a fire-prone landscape under different fire regime categories using two pixel unmixing techniques. A time series of Landsat (ETM+ and OLI; spatial resolution of 30 m) and WorldView-2 (spatial resolution of 2 m) imagery collected between 2011 (pre-fire conditions) and 2016 were used to estimate the temporal variation of fractional vegetation cover (FVC) as a quantitative measure of forest resilience. For this time series, FVC was computed under four fire-regime categories of recurrence and severity using two approaches: dimidiate pixel model and multiple endmember spectral mixture analysis (MESMA). The dimidiate pixel model was computed using NDVI as spectral response for the case of Landsat imagery and NDVI and red-edge NDVI (RENDVI) for WorldView-2. MESMA was applied to unmix WorldView-2 and Landsat imagery into four fraction images: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), soil and shade. The PV shade normalized fraction corresponds to the FVC. In summer of 2016 we established 85 30 × 30 m field plots and 360 2 × 2 m field plots to measure the percentage of total vegetation cover in order to validate the FVC estimates made from remote sensing data. The FVC time series showed the same general pattern with both spatial scales and modeling approaches, high fire recurrence categories registering the highest resilience. The accuracy of the dimidiate pixel model was significantly higher for WorldView-2 based estimates (RMSE: 5–10%) than for Landsat (RMSE: 10–15%). The dimidiate pixel model computed from NDVI for both Landsat and WorldView-2 underestimated FVC at high field-sampled vegetation cover, while MESMA estimations were accurate for the entire range of vegetation cover for both satellites. The fraction of photosynthetic vegetation calculated using WorldView-2 had a higher performance (RMSE: 4–6%) than that quantified from Landsat (RMSE: 6–8%). The linear relationships assumed for validation purposes were statistically significant for both sensors and modeling approaches. Our study demonstrates the highest performance of very high spatial resolution satellite imagery and MESMA models in the quantitative estimation of FVC as a measure of post-fire resilience.

在地中海易火的生态系统中，由于全球变化而引起的火势变化可能会改变植被群落的复原力。在本文中，我们旨在使用两种像素分解技术，比较高和中等空间分辨率的卫星图像在不同火情类别下易火景观的复原力评估中的效率。使用2011年（火灾前的状况）至2016年之间收集的Landsat（ETM +和OLI； 30 m的空间分辨率）和WorldView-2（2 m的空间分辨率）图像的时间序列来估算植被覆盖度的时间变化（FVC）作为森林复原力的定量度量。对于此时间序列，使用两种方法根据复发和严重性的四种火灾情况类别计算FVC：二元像素模型和多端元光谱混合分析（MESMA）。对于Landsat影像，使用NDVI作为光谱响应，对WorldView-2使用NDVI和红边NDVI（RENDVI）来计算二叉像素模型。应用MESMA将WorldView-2和Landsat图像分解为四个部分图像：光合植被（PV），非光合植被（NPV），土壤和阴影。PV阴影归一化分数对应于FVC。2016年夏季，我们建立了85个30×30 m的田地图和360 2×2 m的田地图，以测量植被总覆盖率，以验证由遥感数据得出的FVC估算值。FVC时间序列在空间尺度和建模方法上显示出相同的一般模式，高火灾重复类别记录了最高的弹性。基于WorldView-2的估计（RMSE：5–10％）比使用Landsat（RMSE：10–15％）的二叉像素模型的准确性要高得多。从NDVI计算得出的Landsat和WorldView-2的二叉像素模型在高场采样植被覆盖下低估了FVC，而MESMA估算对于两颗卫星的整个植被覆盖范围都是准确的。使用WorldView-2计算出的光合植被分数具有更高的性能（RMSE：4–6％），高于从Landsat量化的分数（RMSE：6–8％）。假定用于验证目的的线性关系对于传感器和建模方法均具有统计学意义。我们的研究表明，在对FVC进行定量估算（作为对后弹力的一种度量）时，超高分辨率的卫星图像和MESMA模型具有最高的性能。