Shrub Biomass Estimates in Former Burnt Areas Using Sentinel 2 Images Processing and Classification

Abstract

Shrubs growing in former burnt areas play two diametrically opposed roles. On the one hand, they protect the soil against erosion, promote rainwater infiltration, carbon sequestration and support animal life. On the other hand, after the shrubs’ density reaches a particular size for the canopy to touch and the shrubs’ biomass accumulates more than 10 Mg ha−1, they create the necessary conditions for severe wild fires to occur and spread. The creation of a methodology suitable to identify former burnt areas and to track shrubs’ regrowth within these areas in a regular and a multi temporal basis would be beneficial. The combined use of geographical information systems (GIS) and remote sensing (RS) supported by dedicated land survey and field work for data collection has been identified as a suitable method to manage these tasks. The free access to Sentinel images constitutes a valuable tool for updating the GIS project and for the monitoring of regular shrubs’ accumulated biomass. Sentinel 2 VIS-NIR images are suitable to classify rural areas (overall accuracy = 79.6% and Cohen’s K = 0.754) and to create normalized difference vegetation index (NDVI) images to be used in association to allometric equations for the shrubs’ biomass estimation (R2 = 0.8984, p-value < 0.05 and RMSE = 4.46 Mg ha−1). Five to six years after a forest fire occurrence, almost all the former burnt area is covered by shrubs. Up to 10 years after a fire, the accumulated shrubs’ biomass surpasses 14 Mg ha−1. The results described in this paper demonstrate that Northwest Portugal presents larger shrubland areas and greater shrub biomass accumulation (average 18.3 Mg ha−1) than the Northeast (average 7.7 Mg ha−1) of the country.

Use of Bayesian, Lasso Binary Quantile Regression to Identify Suitable Habitat for Tiger Prey Species in Thap Lan National Park, Eastern Thailand

“…The models for forage crops included the additive and interactive effects of canopy cover. In forested habitats, the relationship between canopy cover and ground vegetation biomass was positive for shrubs but negative for grass, consistent with Aranha et al (2020), who reported that canopy closure is positively correlated with shrub biomass because photosynthetic capacity is more strongly related to canopy cover (Kaur 2007;Moreno-de las Heraset al, 2015). Canopy cover was a strong (negative) predictor of grass biomass; increasing canopy cover significantly reduced grass (Randle, 2018).…”

Uncovering Vegetation Changes in the Urban–Rural Interface through Semi-Automatic Methods

“…The model developed by [26] registered a general accuracy higher than 90%, but because it is a model based on pre-established limits according to the spectral characteristics of the area analysed, this accuracy can change according to the study area. Concerning the models for detecting changes applied in Portugal, one can find the works of [42] to estimate the increase in vegetation biomass after fires in the north of the country, where the overall accuracy of 79.6% was recorded and of [31], in the search for vegetation cuts around the road network which yielded an accuracy of 79% for an annual temporal cut-off and 98% for a monthly temporal cut-off.…”

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