Bulk Density of Shrub Types and Tree Crowns to Use with Forest Inventories in the Iberian Peninsula

Nunes, Leónia; Pasalodos-Tato, María; Alberdi, Iciar; Sequeira, Ana Catarina; Vega, José A.; Silva, Vasco; Vieira, Pedro; Rego, Francisco

doi:10.3390/f13040555

Cited by 6 publications

(1 citation statement)

References 72 publications

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“…Estimated shrub biomass in formerly burned areas was estimated in many countries, e.g., in Portugal [18]; the authors used 10-m-Sentinel-2 NDVI to create a raster mask for identifying burnt areas, and using Minimum Distance Classifier and B2, B3, B4 and B8 bands (VIS-NIR), identified forested and shrubland areas with an overall accuracy (OA) oscillating around 80% (Cohen's K coefficient k = 0.75) [18]. Bulk density for shrubs has also been studied to estimate carbon sequestration, which could assist in predicting the potential fire risk for the Iberian Peninsula [19]. Vega et al [20], based on the field surveys and biomass measurements, raised the shrubs communities biomass modelling in Northern Spain and developed models for standing fuel loads by size and condition.…”

Section: Introductionmentioning

confidence: 99%

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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The Mediterranean ecosystem exhibits a particular geology and climate, which is characterized by mild, rainy winters and long, very hot summers with low precipitation; it has led to the emergence of resilient plant species. Such habitats contain a preponderance of shrubs, and collectively harbor 10% of the Earth's species, thus containing some of the most unique shrubby formations protecting against environmental natural degradation. Due to shrub species diversity, initial phases of forestland, heterogenous grasses, bare ground and stones, the monitoring of such areas is difficult. For this reason, the aim of this paper is to assess semi-automatic classifications of the shrubby formations based on multispectral Sentinel-2 and visible and near infrared (VINR) AISA-EAGLE II hyperspectral airborne images with a support of Canopy High Model (CHM) as a three-dimensional information and field-verified patterns, based on Match-T/DSM and aerial photos. Support Vector Machine (SVM) and Random Forest (RF) classifiers have been tested on a few scenarios featuring different combinations of spectral and Minimum Noise Fraction (MNF) transformed bands and vegetation indices. Referring to the results, the average overall accuracy for the SVM and AISA images (all tested data sets) was 78.23%, and for the RF: 79.85%. In the case of Sentinel-2, the SVM classifier obtained an average value of 83.63%, while RF: 85.32%; however, in the case of the shrubland, we would like to recommend the RF classifier, because the highest mean value of F1-score achieved was 91.86% (SVM offered few-percent-point worse results), and the required training time was quicker than SVM. Commonly available Sentinel-2 data offered higher accuracies for shrubland monitoring than did the airborne VNIR data.

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Section: Introductionmentioning

confidence: 99%

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

et al. 2022

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Developing Weibull-based diameter distributions for the major coniferous species in Heilongjiang Province, China

Jin

Pukkala

et al. 2023

J. For. Res.

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Diameter distribution models play an important role in forest inventories, growth prediction, and management. The Weibull probability density function is widely used in forestry. Although a number of methods have been proposed to predict or recover the Weibull distribution, their applicability and predictive performance for the major tree species of China remain to be determined. Trees in sample plots of three even-aged coniferous species (Larix olgensis, Pinus sylvestris and Pinus koraiensis) were measured both in un-thinned and thinned stands to develop parameter prediction models for the Weibull probability density function. Ordinary least squares (OLS) and maximum likelihood regression (MLER), as well as cumulative distribution function regression (CDFR) were used, and their performance compared. The results show that MLER and CDFR were better than OLS in predicting diameter distributions of tree plantations. CDFR produced the best results in terms of fitting statistics. Based on the error statistics calculated for different age groups, CDFR was considered the most suitable method for developing prediction models for Weibull parameters in coniferous plantations.

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Use of national forest inventory data to develop stand density driven models for understorey shrubs and overstorey fuel variables and associated temporal dynamics in commercial plantations

Castedo-Dorado,

Ruiz-González,

Hidalgo

et al. 2024

Forest Ecology and Management

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Bulk Density of Shrub Types and Tree Crowns to Use with Forest Inventories in the Iberian Peninsula

Cited by 6 publications

References 72 publications

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

Sentinel-2 and AISA Airborne Hyperspectral Images for Mediterranean Shrubland Mapping in Catalonia

Developing Weibull-based diameter distributions for the major coniferous species in Heilongjiang Province, China

Use of national forest inventory data to develop stand density driven models for understorey shrubs and overstorey fuel variables and associated temporal dynamics in commercial plantations

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