在过去的几年中,野火对全球森林和地中海地区的影响越来越严重,气候变化导致降水减少和温度升高。为了评估野火对环境的影响,烧毁面积测绘变得越来越重要。最初是通过野外草图进行的,卫星遥感的出现开辟了新的可能性,降低了以前技术的成本不确定性和安全性。在本研究中,采用了一种实验方法来测试先进的遥感技术的潜力,例如多光谱Sentinel-2,PRISMA高光谱卫星,和无人机(无人机)遥感数据,用于通过葡萄牙和意大利两个试验场的土壤植被恢复分析,对被烧毁地区进行多时段测绘。在案例研究一,利用Sentinel-2RBR(相对燃烧比)火灾严重程度等级与现场高光谱特征之间的相关性,进行了创新的多平台数据分类,通过逐像素比较执行,从而实现收敛分类。在采用的方法中,根据生物物理植被参数(LAI,fCover,和fAPAR)。在案例研究2中,采用无人机感知NDVI指数进行高分辨率测绘数据收集。在大范围内,Sentinel-2RBR指数被证明是有效的烧伤面积分析,从火灾严重程度和植被恢复现象的角度来看。尽管事件和采集之间经过了一段时间,基于Sentinel-2的PRISMA高光谱会聚分类能够检测和区分对应于不同火灾严重程度等级的不同光谱特征。在斜率上,无人机平台被证明是绘制和表征烧毁区域的有效工具,在现场GPS测绘方面具有明显的优势。结果强调,无人机平台,如果配备高光谱传感器并与PRISMA协同使用,将为卫星采集的数据场景分类创建一个有用的工具,允许获得地面真相。
Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil-vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2
RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology,
RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2
RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.