Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest

Stratoulias, Dimitris; Nuthammachot, Narissara; Suepa, Tanita; Phoungthong, Khamphe

doi:10.3390/ijgi11030199

Cited by 7 publications

(7 citation statements)

References 93 publications

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“…The aboveground biomass model included species equations for temperate forests, making it easier to estimate fuel load [17,45,46]. The use of species equations combined with field data helps to reduce prediction bias [62]. Although the models allowed the prediction of the fuel load within the temperate forest ecosystem in Tamaulipas, it is advisable to carry out measurements to contrast the results obtained in this research.…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Aradillas-González

Vargas-Tristán

Azuara-Domínguez

et al. 2022

Forests

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The prediction of fuel load areas and species associated with these events reduces the response time to fight forest fires. The objective of this study was to estimate the annual fuel load from 2009–2013, predict the annual fuel load in the rest of the ecosystem, identify species that contribute most to this load and compare the percentage of area by risk category in the temperate forests of Tamaulipas. Fuel load was estimated with inventory data using three models. Fuel load was predicted with elevation, total annual precipitation, mean annual temperature, and enhanced vegetation index from satellite scenes using partial least squares regression. The highest concentration of fuel load was associated with the oak, oak-pine, pine forest and mountain mesophyll forest ecosystems. The contribution of genera to fuel load was different. Quercus contributed the most variation among clusters, and the contribution among Quercus species was similar. The results highlight the importance of focusing fuel management programs on this type of ecosystem, emphasizing actions in particular Quercus, and the results can also serve as a basis for future research, such as carbon sequestration and forest management programs.

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Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Aradillas-González

Vargas-Tristán

Azuara-Domínguez

et al. 2022

Forests

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“…All green plants strongly absorb visible electromagnetic radiation and reflect near-infrared radiation, and the spectral vegetation index emphasizes this difference by mathematically combining different spectral regions, which can represent the amount and condition of vegetation within a pixel [66]. Existing studies have used exhaustive methods to build vegetation indices to avoid the problems of existing vegetation indices ignoring important information that may be contained in other spectral bands and that different study areas and data producing different responses to different spectral bands [38]. It has been demonstrated that the weighting of the NIR band in the vegetation index affects the saturation point and that indices with higher weighting coefficients in the NIR band favor an increase in the sensitivity of the vegetation index [67].…”

Section: Establishment Of the Vegetation Index (Vi)mentioning

confidence: 99%

“…Dimitris Stratoulias used the spectral ratio of band 8 and band 2 of Sentinel-2 to obtain an R 2 of 0.70, while the best performing popular spectral index, EVI, achieved an R 2 of 0.65. The study found that a full wave combination survey of small-scale studies, which can make use of current satellite systems to obtain amplified information and avoid assumptions about environmental, structural, and ecosystem characteristics for the selection of the vegetation index, can provide stronger correlations and more accurate results than the blind application of popular spectral indices [38].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A

Wang

2023

Remote Sensing

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In order to evaluate forest quality and carbon stocks and improve our understanding of ecosystems and carbon cycling processes, the accurate measurement of aboveground biomass (AGB) and other forest characteristics is crucial. This paper considers the response differences between the bands obtained from Landsat 8 and Sentinel-2A sensors, respectively, and combines the exhaustive combination of spectral indices with normalization and ratio techniques to establish suitable weights for the bands in the vegetation index using relative sensitivity and noise equivalent (NE) to improve the saturation effect between the vegetation index and forest parameters (canopy closure (CC), forest stand density (S), basal area (BA), and AGB) and extend the linear relationship between them. This paper also considers the effects of window size, direction, and principal component analysis on texture features, adds weight to textures and combines textures using linear correlation and NE, establishes texture indices to improve the limitations of information contained in individual texture features, analyzes the potential of texture features to evaluate each forest parameter under different conditions, and better captures the variation of forest parameters. In this paper, we only analyze the planted coniferous forest in Saihanba to avoid the differences in electromagnetic wave effects that are difficult to judge and analyze because of the differences in leaf size and leaf orientation between coniferous and broad-leaf forests. In contrast, the vegetation indices and texture indices obtained from Sentinel-2A could better estimate each vegetation parameter, and the linear estimation of each vegetation parameter using the new texture index reached an R2 above 0.65. The results of this study indicate that Sentinel-2A and Landsat 8 are promising remote sensing datasets for estimating vegetation parameters at the regional scale, and Sentinel-2A data can be employed as the primary source of earth observation data for assessing forest resources in the Saihanba area.

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“…Jue Xiao et al and Nichol et al used the texture features to further improve the accuracy of AGB inversion from remote sensing images [18,19]. However, passive optical remote sensing images are unable to obtain vertical structural information of trees and are limited by the influence of cloud cover at the time of observation [20]. These limitations would result in the generally low accuracy of AGB estimation, especially in the dense vegetation areas with high biomass for the image spectra have a strong saturation effect and tend to lose sensitivity [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

Zhao,

Hu,

Han

et al. 2024

Remote Sensing

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Accurate estimating of above-ground biomass (AGB) of vegetation in urbanized areas is essential for urban ecosystem services. NASA’s Global Ecosystem Dynamics Investigation (GEDI) mission can obtain precise terrestrial vegetation structure, which is very useful for AGB estimation in large forested areas. However, the spatial heterogeneity and sparse distribution of vegetation in urban areas lead to great uncertainty in AGB estimation. This study proposes a method for estimating vegetation heights by fusing GEDI laser observations with features extracted from optical images. GEDI is utilized to extract the accurate vegetation canopy height, and the optical images are used to compensate for the spatial incoherence of GEDI. The correlation between the discrete vegetation heights of GEDI observations and image features is constructed using Random Forest (RF) to obtain the vegetation canopy heights in all vegetated areas, thus estimating the AGB. The results in Xuzhou of China using GEDI observations and image features from Sentinel-2 and Landsat-8 satellites indicate that: (1) The method of combining GEDI laser observation data with optical images is effective in estimating AGB, and its estimation accuracy ( = 0.58) is higher than that of using only optical images ( = 0.45). (2) The total AGB in the shorter vegetation region is higher than the other two in the broadleaf forest and the coniferous forest, but the AGB per unit area is the lowest in the shorter vegetation area at 33.60 Mg/ha, and it is the highest in the coniferous forest at 46.60 Mg/ha. And the highest average AGB occurs in October–December at 59.55 Mg/ha in Xuzhou. (3) The near-infrared band has a greater influence on inverted AGB, followed by textural features. Although more precise information about vegetation should be considered, this paper provides a new method for the AGB estimation and also a way for the evaluation and utilization of urban vegetation space.

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Assessing the Spectral Information of Sentinel-1 and Sentinel-2 Satellites for Above-Ground Biomass Retrieval of a Tropical Forest

Cited by 7 publications

References 93 publications

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Estimation of Forest Parameters in Boreal Artificial Coniferous Forests Using Landsat 8 and Sentinel-2A

Urban Above-Ground Biomass Estimation Using GEDI Laser Data and Optical Remote Sensing Images

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