Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data

Verly, Otávio Miranda; Leite, Rodrigo Vieira; Tavares-Junior, Ivaldo da Silva; Rocha, Samuel José Silva Soares da; Leite, Hélio Garcia; Gleriani, José Marinaldo; Rufino, Maria Paula Miranda Xavier; Silva, Valéria de Fatima; Torres, Carlos Moreira Miquelino Eleto; Plata-Rueda, Angélica; Castro, Bárbara Monteiro de Castro e; Zanúncio, José Cola; Javocine, Laércio Antônio Gonçalves

doi:10.1016/j.ecolind.2023.109870

“… Reyes-Cárdenas et al (2017) correlated field data with spectral data and obtained similar values. Aguirre-Salado et al (2012) adjusted a linear model to estimate aerial biomass based on the NDVI and continuous vegetation fields (CVFs) (adjusted R 2 = 0.77 CME = 26.00 t ha −1 ), showing high validation correlation coefficients ( r = 0.87**); Verly et al (2023) estimated the C of the Atlantic Forest with Sentinel 2 images, showing a high correlation coefficient between the estimated and observed carbon averages ( r = 0.84).…”

Section: Discussionmentioning

confidence: 99%

Carbon mapping in pine-oak stands under timber management in southern Mexico

Ambrosio-Lazo,

Rodríguez-Ortiz,

Rincón-Ramírez

et al. 2023

PeerJ

0

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The destructive and empirical methods commonly used to estimate carbon pools in forests managed timber are time-consuming, expensive and unfeasible at a large scale; satellite images allow evaluations at different scales, reducing time and costs. The objective of this study was to evaluate the tree biomass (TB) and carbon content (CC) through satellite images derived from Sentinel 2 in underutilized stands in southern Mexico. In 2022, 12 circular sites of 400 m2 with four silvicultural treatments (STs) were established in a targeted manner: 1st thinning (T1), free thinning (FT), regeneration cut (RC) and unmanaged area (UA). A tree inventory was carried out, and samples were obtained to determine their TB based on specific gravity and CC through the Walkey and Black method. The satellite image of the study area was downloaded from Sentinel 2 to fit a simple linear model as a function of the Normalized Difference Vegetation Index (10 m pixel−1) showing significance (p ≤ 0.01) and a adjusted R2 = 0.92. Subsequently, the TB and CC (t ha−1) were estimated for each ST and managed area. The total managed area (3,201 ha−1) had 126 t TB ha−1 and 57 t C ha−1. Of the areas with STs, the area with FT showed the highest accumulation of TB (140 t ha−1) and C (63 t ha−1) without showing differences (p > 0.05) with respect to those of the UA, which presented 129 t TB ha−1 and 58 t C ha−1. The satellite images from Sentinel 2 provide reliable estimates of the amounts of TB and CC in the managed stands. Therefore, it can be concluded that an adequate application of STs maintains a balance in the accumulation of tree C.

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“…The sample plot inventory method is an intuitive measurement of FCS commonly employed in academics, and it includes the average biomass method, the average conversion factor method, and the conversion factor continuous function method. The stock expansion method for estimating plant carbon storage based on establishing the relationship between biomass and stock is widely used 28 . The stock expansion method continues the traditional biomass conversion factor method.…”

Section: Calculation Of Fcsmentioning

confidence: 99%

Towards carbon neutrality: a study on the spatial spillover effects of FCS in the Yellow River Basin, China

Ma,

Ke,

Li

2023

Preprint

0

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Developing forest carbon sinks (FCS) is significant for China to achieve carbon neutrality. The Yellow River Basin is a principal area for China’s energy consumption, and the forest resource distribution of this vast basin is spatially dependent, determining that the development of FCS cannot be separated geographically. Based on the spatial panel data of 69 prefecture-level cities in the Yellow River basin in 1988-2018, firstly, we depict a picture of the spatial-temporal trajectories for FCS variations using geospatial analysis. Secondly, fully considering the economic and geographical attributes of forest resources, we develop a spatial econometrics model to investigate the inter-regional correlations and spatial spillover effects of FCS from the spatial dimension and comprehensively explore the direct and indirect impact of different explanatory variables on FCS. Thirdly, pertinent policy suggestions are put out to serve as a guide for increasing FCS.

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“…The mainstream FCS estimation methods mainly include sample plot inventory, microscale meteorology, box, model simulation, remote sensing, etc. (Grelle et al 2023;Verly et al 2023).…”

Section: The Measurement and Influencing Factors Of Fcsmentioning

confidence: 99%

“…The sample plot inventory method is an intuitive measurement of FCS commonly employed in academics, and it includes the average biomass method, the average conversion factor method, and the conversion factor continuous function method. The stock expansion method for estimating plant carbon storage based on establishing the relationship between biomass and stock is widely used (Verly et al 2023). The stock expansion method continues the traditional biomass conversion factor method.…”

Section: Calculation Of Fcsmentioning

confidence: 99%

An exploration of spatial-temporal characteristics and spatial spillover effects of forest carbon sinks in the Yellow River Basin, China

Ma

¹

,

Ke

²

,

Li

³

2023

Preprint

0

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Developing forest carbon sinks (FCS) is significant for China to achieve carbon neutrality. The Yellow River Basin is a principal area for China’s energy consumption, and the forest resource distribution of this vast basin is spatially dependent, determining that the development of FCS cannot be separated geographically. Based on the spatial panel data of 69 prefecture-level cities in the Yellow River basin from 1988 to 2018, we used ESDA to reveal the spatial–temporal characteristics of FCS, and we established a spatial econometric model to investigate the transregional spillover effects of FCS. The results showed that: ① the overall FCS showed a gradually increasing trend, with a general distribution characteristic of “upstream > midstream > downstream” from 1988 to 2018. ②FCS presented an agglomeration distribution pattern with significant spatial spillover effects, and the degree of uneven spatial agglomeration varied across the years. ③Urbanization rate, forestry fixed assets investment, labor input, and afforestation management level directly promote FCS growth, whereas forest harvesting, precipitation, and temperature decrease FCS. ④Urbanization rate, forest harvesting, forestry fixed assets investment, labor input, and afforestation management level have positive spillover effects on FCS, while precipitation and temperature have adverse spillover effects on FCS. Hence, pertinent policy suggestions are put out to serve as a guide for increasing FCS.

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Atlantic forest woody carbon stock estimation for different successional stages using Sentinel-2 data

Cited by 8 publications

References 70 publications

Carbon mapping in pine-oak stands under timber management in southern Mexico

Carbon mapping in pine-oak stands under timber management in southern Mexico

Towards carbon neutrality: a study on the spatial spillover effects of FCS in the Yellow River Basin, China

An exploration of spatial-temporal characteristics and spatial spillover effects of forest carbon sinks in the Yellow River Basin, China

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