CORRELAÇÃO ENTRE UNIFORMIDADE E NDVI EM POVOAMENTOS DE Tectona grandis L. f.

Santos, Lucas Henderson de Oliveira; Ramirez, Gláucia Miranda; Roque, M. W.; Carvalho, Mariana Peres de Lima Chaves e; Díaz, Lila Mabel Gamarra Ruiz

doi:10.5380/biofix.v4i2.65085

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…However, other studies pointed out that better efficiencies in modeling forest aboveground biomass were achieved when a fusion of remote sensing data sources (e.g., hyperspectral, LiDAR and RADAR imagery) was used [41,42]. Moreover, the NDVI was used to monitor forest plantations' wood quality due to its positive correlation with a uniformity index that is stand height-based [14].…”

Section: Discussionmentioning

confidence: 99%

“…These indices use the most sensitive spectral bands that allow highlighting a particular target (e.g., land cover and/or its change and temporal trend). Nowadays, the most popular spectral indices in use are the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR), respectively, to monitor vegetation productivity [11][12][13][14][15][16] and to detect burned areas and their severity [2][3][4]9,[17][18][19]. These indices have also been incorporated in time series analysis to systematically detect burned areas and monitor long-term vegetation recovery [9,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Alegria

2022

Sustainability

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Wildfires are a major environmental issue that have an impact on land degradation. Remote sensing spectral indices provide valuable information for short-term mitigation and rehabilitation after wildfires. A study area in the Centre inland of Portugal occupied with Maritime pine and Eucalypts forests and affected by wildfires in 2003, 2017 and 2020 was used. The aims of the study were twofold: (1) to compute the Normalized Difference Vegetation Index (NDVI) and with forest inventory data derivate a Maritime pine production model, differentiate evergreen coniferous forests (e.g., Maritime pine), evergreen broadleaved forests (e.g., Eucalypts), and shrubland, and monitor vegetation and its post-fire recovery; and (2) to compute the Normalized Burn Ratio (NBR) difference between pre-fire and post-fire dates for burn severity levels assessment. The plots of a previous forest inventory were used to follow the NDVI values in 2007 and from 2020 to 2022. An aerial coverage in 2007 and the Sentinel-2 imagery in 2020–2022 were used. Linear models fitted maritime pine production with the transformed NDVI by age, showing a fitting efficiency of 60%. The stratification of cover types by stand development stage and fire occurrence was possible using the NDVI time curve, which also showed the impact of fire and of low precipitation. Cover types were ranked by decreasing NDVI values as follows: mature Eucalypts plantations, young Maritime pine regeneration, mature Maritime pine, young Eucalypts plantations, Strawberry tree shrubland, Eucalypts plantations post-fire, Maritime pine post-fire, tall shrubland, and short shrubland. Vegetation post-fire recovery was lower in higher burn severity level areas. Maritime pine areas have lost their natural regeneration capability due to the wildfires’ short cycles. Spectral indices were effective tools to differentiate cover types and assist in the evaluation of forest and shrubland conditions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Alegria

2022

Sustainability

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“…Indeed, the NDVI not only measures vegetation greenness in relation to the structural properties of plants (e.g., leaf area index and green biomass), but also to properties of vegetation productivity (e.g., absorbed photosynthetic active radiation and foliar nitrogen) [11]. For instance, the NDVI has been used with forest inventory data to model wood production and biomass [2,[12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Aboveground Biomass Mapping and Fire Potential Severity Assessment: A Case Study for Eucalypts and Shrubland Areas in the Central Inland Region of Portugal

Alegria

2023

Forests

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Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. The objectives of this study were as follows: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI, validate them with other data sources, and to compare fuel loads with fire severity levels. A study area in the central inland region of Portugal was considered. The wildfire on 4 August 2023 was considered for burn severity levels assessment using the Normalized Burn Index (NRB). The Sentinel-2 MSI imagery was used to compute the NDVI for the years of 2022 and 2023 and the NBR for the pre-fire and post-fire dates. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.77). The AGB maps provided a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust datasets for an independent validation of the model.

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Aboveground Biomass Mapping and Fire Potential Severity Assessment: A Case Study for Eucalypts and Shrubland Areas in the Centre Inland of Portugal

Alegria¹

2023

Preprint

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Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. This study aims were the following: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI and validate them with other data sources. A study area in the central inland Portugal was considered. The Sentinel-2 MSI imagery for the year of 2022 and 2023 were used to compute the NDVI. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.69). The AGB maps provide a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust data sets for models’ independent validation.

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CORRELAÇÃO ENTRE UNIFORMIDADE E NDVI EM POVOAMENTOS DE Tectona grandis L. f.

Cited by 3 publications

References 11 publications

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Vegetation Monitoring and Post-Fire Recovery: A Case Study in the Centre Inland of Portugal

Aboveground Biomass Mapping and Fire Potential Severity Assessment: A Case Study for Eucalypts and Shrubland Areas in the Central Inland Region of Portugal

Aboveground Biomass Mapping and Fire Potential Severity Assessment: A Case Study for Eucalypts and Shrubland Areas in the Centre Inland of Portugal

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