Fernández-Guisuraga José Manuel, Verrelst Jochem, Calvo Leonor, Suárez-Seoane Susana
Area of Ecology, Faculty of Biological and Environmental Sciences, University of León, 24071 León, Spain.
Image Processing Laboratory (IPL), Parc Científic, University of Valencia, 46980 Paterna, Valencia, Spain.
Remote Sens Environ. 2021 Jan 22;255. doi: 10.1016/j.rse.2021.112304. eCollection 2021 Mar 15.
In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R = 0.83; RMSE = 7.92%) than from Sentinel-2 (R = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and under-estimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.
在受火灾影响的森林景观中，利用辐射传输模型（RTM）从遥感数据估算植被覆盖度（FVC），能够在不同时空尺度上评估此类干扰对植物群落的生态影响。尽管如此，当景观高度异质时，如果像大多数业务地球观测卫星任务那样，以中等或粗空间尺度提供FVC产品，可能无法正确捕捉精细尺度的地面空间变化。本研究的目的是评估一种RTM反演方法与使用较粗分辨率图像的标准方法相比，从高空间分辨率卫星反射率数据估算FVC的潜力。该研究在地中海盆地西部发生的一场特大火灾周边的景观和植物群落层面进行。我们基于PROSAIL-D RTM模拟开发了一种混合反演方案，以创建冠层顶部光谱反射率和优势植物群落相应FVC的训练数据集。在训练数据集上学习机器学习算法高斯过程回归（GPR），以模拟冠层反射率与FVC之间的关系。然后将GPR模型应用于从WorldView-3（空间分辨率为2米）和哨兵-2（空间分辨率为20米）的地表反射率波段反演FVC。在火灾周边的重点植物群落中，按照分层方案分布了一组75个2x2米的样地和45个20x20米的样地，以验证从卫星反演得到的FVC。在景观尺度上，从WorldView-3反演FVC的精度（R = 0.83；RMSE = 7.92%）显著高于哨兵-2（R = 0.73；RMSE = 11.89%）。在群落层面，橡树林的FVC反演比石南灌丛和金雀花丛更准确。从WorldView-3反演分别最小化了低和高野外采样植被覆盖度下的高估和低估效应。这些发现强调了使用混合RTM反演方法，高空间分辨率卫星反射率数据在捕捉异质火烧区域FVC地面空间变异性方面的有效性。
