Nájera De Ferrari F, Duarte E, Smith-Ramírez C, Rendon-Funes A, Sepúlveda Gonzalez V, Sepúlveda Gonzalez N, Levio M F, Rubilar R, Stehr A, Merino C, Jofré I, Rojas C, Aburto F, Kuzyakov Y, Filimonenko E, Dörner J, Pereira P, Matus F
Laboratorio de Conservación y Dinámica de Suelos Volcánicos, Departamento de Ciencias Químicas y Recursos Naturales, Facultad de Ingeniería y Ciencias, Universidad de La Frontera, Chile.
Laboratorio de Geomicrobiología, Facultad de Ingeniería y Ciencias, Departamento de Ciencias Químicas y Recursos Naturales, Universidad de La Frontera, Chile.
MethodsX. 2024 Oct 18;13:103011. doi: 10.1016/j.mex.2024.103011. eCollection 2024 Dec.
The study aimed to develop a methodological framework to identify forest ecosystems affected by wildfires and evaluate their recovery chronologically. To do this remote sensing analysis, sites with burn scars were selected based on various criteria (fire severity, affected area, vegetation and soil type, slope, aspect, and one-time occurrence of wildfire in the last 23 years). Spectral vegetation indices (VIs) from satellite imagery were used to estimate burn severity and vegetation cover changes. Images of surface reflectance were obtained from the collection of Landsat 5 ETM, Landsat 7 ETM+, and Landsat 8 OLI/TIRS, available and processed on the Google Earth Engine Platform (GEE). Indices VIs (i) the normalized difference vegetation index (NDVI), (ii) the normalized burn ratio (NBR), and (iii) the differenced normalized burn ratio (dNBR) were calculated to classify burn severity. The one-time occurrence selection was performed using the LandTrendr algorithm to monitor changes in land cover and burned areas. To validate the selection, the chosen sites within the chronosequence were clustered on 4 seasons of soil properties and litter accumulation recovery. Our result can guide methodological comparisons and forest management practices on large surfaces by comparing parches of different time-affected ecosystems. Validation sites of the cluster chronosequence shows consistent recovery of soil properties as soil carbon, bulk density and litter accumulation through the studied years •The study developed a framework to identify wildfire-affected forest ecosystems and evaluate their recovery using remote sensing and local data.•Vegetation indices (NDVI, NBR, dNBR) from Landsat satellite imagery processed on the Google Earth Engine were used to assess burn severity and vegetation changes over time.•Selected sites were validated using the LandTrendr algorithm and monitored for seasonal changes in soil properties and litter accumulation.
该研究旨在建立一个方法框架，以识别受野火影响的森林生态系统，并按时间顺序评估其恢复情况。为此进行了遥感分析，根据各种标准（火灾严重程度、受影响面积、植被和土壤类型、坡度、坡向以及过去23年中野火的一次性发生情况）选择有烧伤痕迹的地点。利用卫星图像的光谱植被指数（VIs）来估计烧伤严重程度和植被覆盖变化。地表反射率图像来自于在谷歌地球引擎平台（GEE）上获取和处理的陆地卫星5 ETM、陆地卫星7 ETM+和陆地卫星8 OLI/TIRS的数据集。计算指数VIs（i）归一化差异植被指数（NDVI）、（ii）归一化燃烧比（NBR）和（iii）差异归一化燃烧比（dNBR）以对烧伤严重程度进行分类。使用LandTrendr算法进行一次性发生情况选择，以监测土地覆盖和燃烧面积的变化。为了验证选择结果，将年代序列内选定的地点按土壤性质和凋落物积累恢复的4个季节进行聚类。我们的结果可以通过比较不同时间受影响生态系统的斑块，指导大面积的方法比较和森林管理实践。聚类年代序列的验证地点显示，在研究年份中，土壤性质如土壤碳、容重和凋落物积累持续恢复。•该研究建立了一个框架，以识别受野火影响的森林生态系统，并利用遥感和本地数据评估其恢复情况。•利用在谷歌地球引擎上处理的陆地卫星图像的植被指数（NDVI、NBR、dNBR）来评估烧伤严重程度和植被随时间的变化。•使用LandTrendr算法对选定地点进行验证，并监测土壤性质和凋落物积累的季节性变化。
