Assessing post-fire forest structure recovery by combining LiDAR data and Landsat time series in Mediterranean pine forests

Understanding post-fire recovery dynamics is critical for effective management that enhance forest resilience to fire. Mediterranean pine forests have been largely affected by wildfires, but the impacts of both changes in land use and climate endanger their capacity to naturally recover. Multispectral imagery is commonly used to estimate post-fire recovery, yet changes in forest structure must be considered for a comprehensive evaluation of forest recovery. In this research, we combine Light Detection And Ranging (LiDAR) with Landsat imagery to extrapolate forest structure variables over a 30-year period (1990?2020) to provide insights on how forest structure has recovered after fire in Mediterranean pine forests. Forest recovery was evaluated attending to vegetation cover (VC), tree cover (TC), mean height (MH) and heterogeneity (CVH). Structure variables were derived from two LiDAR acquisitions from 2016 and 2009, for calibration and independent spatial and temporal validation. A Support Vector Regression model (SVR) was calibrated to extrapolate LiDAR-derived variables using a series of Landsat imagery, achieving an R2 of 0.78, 0.64, 0.70 and 0.63, and a relative RMSE of 24.4%, 30.2%, 36.5% and 27.4% for VC, TC, MH and CVH, respectively. Models showed to be consistent in the temporal validation, although a wider variability was observed, with R2 ranging from 0.51 to 0.74. A different response to fire was revealed attending to forest cover and height since vegetation cover recovered to a pre-fire state but mean height did not 26-years after fire. Less than 50% of the area completely recovered to the pre-fire structure within 26 years, and the area subjected to fire recurrence showed signs of greater difficulty in initiating the recovery. Our results provide valuable information on forest structure recovery, which can support the implementation of mitigation and adaptation strategies that enhance fire resilience.Comunidad de Madri

Fuel Type Classification Using Airborne Laser Scanning and Sentinel 2 Data in Mediterranean Forest Affected by Wildfires

Mediterranean forests are recurrently affected by fire. The recurrence of fire in such environments and the number and severity of previous fire events are directly related to fire risk. Fuel type classification is crucial for estimating ignition and fire propagation for sustainable forest management of these wildfire prone environments. The aim of this study is to classify fuel types according to Prometheus classification using low-density Airborne Laser Scanner (ALS) data, Sentinel 2 data, and 136 field plots used as ground-truth. The study encompassed three different Mediterranean forests dominated by pines (Pinus halepensis, P. pinaster y P. nigra), oaks (Quercus ilex) and quercus (Q. faginea) in areas affected by wildfires in 1994 and their surroundings. Two metric selection approaches and two non-parametric classification methods with variants were compared to classify fuel types. The best-fitted classification model was obtained using Support Vector Machine method with radial kernel. The model includes three ALS and one Sentinel-2 metrics: the 25th percentile of returns height, the percentage of all returns above mean, rumple structural diversity index and NDVI. The overall accuracy of the model after validation was 59%. The combination of data from active and passive remote sensing sensors as well as the use of adapted structural diversity indices derived from ALS data improved accuracy classification. This approach demonstrates its value for mapping fuel type spatial patterns at a regional scale under different heterogeneous and topographically complex Mediterranean forests

Extrapolating Forest Canopy Fuel Properties in the California Rim Fire by Combining Airborne LiDAR and Landsat OLI Data

Accurate, spatially explicit information about forest canopy fuel properties is essential for ecosystem management strategies for reducing the severity of forest fires. Airborne LiDAR technology has demonstrated its ability to accurately map canopy fuels. However, its geographical and temporal coverage is limited, thus making it difficult to characterize fuel properties over large regions before catastrophic events occur. This study presents a two-step methodology for integrating post-fire airborne LiDAR and pre-fire Landsat OLI (Operational Land Imager) data to estimate important pre-fire canopy fuel properties for crown fire spread, namely canopy fuel load (CFL), canopy cover (CC), and canopy bulk density (CBD). This study focused on a fire prone area affected by the large 2013 Rim fire in the Sierra Nevada Mountains, California, USA. First, LiDAR data was used to estimate CFL, CC, and CBD across an unburned 2 km buffer with similar structural characteristics to the burned area. Second, the LiDAR-based canopy fuel properties were extrapolated over the whole area using Landsat OLI data, which yielded an R2 of 0.8, 0.79, and 0.64 and RMSE of 3.76 Mg·ha−1, 0.09, and 0.02 kg·m−3 for CFL, CC, and CBD, respectively. The uncertainty of the estimates was estimated for each pixel using a bootstrapping approach, and the 95% confidence intervals are reported. The proposed methodology provides a detailed spatial estimation of forest canopy fuel properties along with their uncertainty that can be readily integrated into fire behavior and fire effects models. The methodology could be also integrated into the LANDFIRE program to improve the information on canopy fuels

The Burning Bush: Linking LiDAR-derived Shrub Architecture to Flammability

Light detection and ranging (LiDAR) and terrestrial laser scanning (TLS) sensors are powerful tools for characterizing vegetation structure and for constructing three-dimensional (3D) models of trees, also known as quantitative structural models (QSM). 3D models and structural traits derived from them provide valuable information for biodiversity conservation, forest management, and fire behavior modeling. However, vegetation studies and 3D modeling methodologies often only focus on the forest canopy, with little attention given to understory vegetation. In particular, 3D structural information of shrubs is limited or not included in fire behavior models. Yet, understory vegetation is an important component of forested ecosystems, and has an essential role in determining fire behavior. In this dissertation, I explored the use of TLS data and quantitative structure models to model shrub architecture in three related studies. In the first study, I present a semi-automated methodology for reconstructing architecturally different shrubs from TLS LiDAR. By investigating shrubs with different architectures and point cloud densities, I showed that occlusion, shrub complexity, and shape greatly affect the accuracy of shrub models. In my second study, I assessed the 3D architectural drivers of understory flammability by evaluating the use of architectural metrics derived from the TLS point cloud and 3D reconstructions of the shrubs. I focused on eight species common in the understory of the fire-prone longleaf pine forest ecosystem of the state of Florida, USA. I found a general tendency for each species to be associated with a unique combination of flammability and architectural traits. Novel shrub architectural traits were found to be complementary to the direct use of TLS data and improved flammability predictions. The inherent complexity of shrub architecture and uncertainty in the TLS point cloud make scaling up from an individual shrub to a plot level a challenging task. Therefore, in my third study, I explored the effects of lidar uncertainty on vegetation parameter prediction accuracy. I developed a practical workflow to create synthetic forest stands with varying densities, which were subsequently scanned with simulated terrestrial lidar. This provided data sets quantitatively similar to those created by real-world LiDAR measurements, but with the advantage of exact knowledge of the forest plot parameters, The results showed that the lidar scan location had a large effect on prediction accuracy. Furthermore, occlusion is strongly related to the sampling density and plot complexity. The results of this study illustrate the potential of non-destructive lidar approaches for quantifying shrub architectural traits. TLS, empirical quantitative structural models, and synthetic models provide valuable insights into shrub structure and fire behavior

Caracterización del combustible del dosel arbóreo mediante sensores remotos y evaluación del efecto de las claras sobre el comportamiento y severidad potenciales del fuego en pinares del NO de España

En la presente tesis doctoral se han desarrollado modelos para estimar la distribución vertical de la carga de combustible disponible del dosel arbóreo, tanto a partir de datos LiDAR como de variables de rodal medidas en campo. Además, se han obtenido modelos de estimación de variables del combustible de superficie y del dosel arbóreo a partir de imágenes del satélite Sentinel-2A, que permiten generar cartografía del riesgo potencial de fuego de copas. Por otro lado, se ha evaluado el efecto a medio plazo del tratamiento de clara por si sola, sin intervención en los combustibles de superficie, sobre el complejo de combustible y el comportamiento y la severidad potenciales del fuego, así como la capacidad de los rodales quemados para proteger el suelo contra la erosión post-incendio. Todos estos estudios se realizaron en pinares del noroeste de España

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector