Academic Journal
Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula
| العنوان: | Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula |
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| المؤلفون: | Tomé Morán, Jose Luis, Marino del Amo, Eva, González Romero, Javier, Botella Bou, Raúl, Díaz Montero, Asunción, Peña Molina, Esther, Moya Navarro, Daniel, Fajardo Cantos, Álvaro, Lucas Borja, Manuel Esteban, Heras Ibáñez, Jorge Antonio de las |
| بيانات النشر: | Elsevier |
| سنة النشر: | 2024 |
| المجموعة: | Universidad de Castilla-La Mancha: Repositorio Universitario Institucional de Recursos Abiertos (RUIdeRA) |
| مصطلحات موضوعية: | Forest management, LiDAR, Neural networks, Remote sensing, Wildfires |
| الوصف: | Wildfires represent one of the major ecological disasters with significant repercussions for terrestrial ecosystems (loss of biodiversity, material damages, erosion, etc). Using LiDAR (Light Detection and Ranging) technology allows large amounts of forest measurements data to be obtained, such as size of volume, biomass, canopy cover, heights, among others. It also allows to quantify some damage caused by forest fires by comparing LiDAR data point clouds at two different times (before and after fire). By correlating in situ measurements with statistical parameters from point clouds, we aim to evaluate postfire deviations in volume, biomass and canopy integrity. The aim of this study was to estimate the losses caused by the 2012 wildfire in the Public Utility Forest No.82 “Sierra de los Donceles” (Hellín, Albacete, Spain), which devastated more than 5000 ha. We used 65 plots from the 4th National Forest Inventory (NFI4) and the LiDAR data from 2009 to develop statistical models and train Artificial Neural Networks (ANN). Another independent set of 29 plots (of 14.1-m radius with sub-metric GPS) was employed to validate the regression and ANN models. The regression results showed better performance for estimating heights (R2 = 75.03%, RMSE = 28.90%), volume (R2 = 70.32%, RMSE = 37.61%) and basal area (R2 = 68.37%, RMSE = 37.18). The total biomass showed the best fit (R2 = 93.51%), but also the maximum error (RMSE = 40.08%). The worst fit result was the estimation of number of trees per hectare (R2 = 33.76%, RMSE = 30.97%). All the models were improved with neural network implementation: number of trees per hectare (R2 = 44.41%, RMSE = 26.74%), basal area (R2 = 68.81%, RMSE = 33.49%), dominant height (R2 = 75.55%, RMSE = 15.43%), volume (R2 = 61.03%, RMSE = 14.78%) and biomass (R2 = 99.21%, RMSE = 20.81%). With our top-performing models in the 2009- and 2016-point cloud data, we measured forest reductions in terms of trees per hectare, basal area, predominant height, volume and biomass. These models provided a ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| Relation: | Peña-Molina, E., Moya, D., Tomé, J. L., Marino, E., Fajardo-Cantos, Á., González-Romero, J., . & de las Heras, J. (2024). Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula. Remote Sensing Applications: Society and Environment, 33, 101114.; pa_21741496; https://doi.org/10.1016/j.rsase.2023.101114; https://hdl.handle.net/10578/37417 |
| DOI: | 10.1016/j.rsase.2023.101114 |
| الاتاحة: | https://hdl.handle.net/10578/37417 https://doi.org/10.1016/j.rsase.2023.101114 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.7A3BB029 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.rsase.2023.101114 |
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