Allometric Equations for Estimation of Biomass and Carbon Stocks in Temperate Forests of North-Western Mexico

Vargas-Larreta, Benedicto; López-Sánchez, Carlos Antonio; Corral-Rivas, José Javier; López‐Martínez, Jorge Omar; Aguirre-Calderón, Cristóbal Gerardo; González, Juan Gabriel Álvarez

doi:10.20944/preprints201705.0178.v1

Cited by 12 publications

(1 citation statement)

References 53 publications

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“…Fuente:Cruz-Cobos et al, 2016;Vargas-Larreta et al, 2017. V = Vol umen (m 3 ); D = Di ámetro normal (cm); H = Al tura total (m); DB = Di ámetro a l a base (cm); DC = Diámetro de copa (m); NR = Número de ramas; VC = Relación diámetro a la base al cuadrado (cm) por la altura (m); 0, 1, 2, 3 = Parámetros a estimar.…”

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Ecuaciones de volumen para Prosopis articulata S. Watson y Lysiloma divaricata (Jacq.) J. F. Macbr. en el noroeste de México

Silva-García

Calderón

Alanís-Rodríguez

et al. 2018

RMCF

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Una herramienta de gran importancia en el manejo forestal son los modelos alométricos, con los cuales se puede estimar de forma confiable el volumen en árboles individuales y masas forestales, además de construirse tablas de volumen que faciliten la cuantificación de existencias maderables. En el presente trabajo se ajustaron datos de 160 árboles; para ello, se derribaron 80 individuos de cada especie que incluyeron todas las categorías diamétricas. Se midieron las variables: diámetro normal (D), diámetro de la base (DB) altura total (H) y diámetro de copa (DC), así como los diámetros y longitudes de cada una de las ramas mayores a 6 cm de diámetro en su base. Se probaron modelos de regresión lineal simple, múltiple y no-lineal con una y dos variables independientes, para estimar el volumen fustal total con corteza. La selección del mejor modelo se hizo con base en la RCME, R2 ajustado y el nivel de significancia de los parámetros. Para la predicción del volumen total en función del diámetro normal, el modelo alométrico de Berkhout presentó mejores ajustes, con R2 de 0.9053 y RCME de 0.0432 para Prosopis articulata; y R2 de 0.8178, RCME de 0.1048 para Lysiloma divaricata. A partir del diámetro normal y altura total, los mejores ajustes se obtuvieron con el modelo de Schumacher-Hall, con valores de 0.9139 en R2 y RCME de 0.0411 para Prosopis articulata; para Lysiloma divaricata el modelo de mejor ajuste fue el Spurr Potencial con R2 de 0.7936 y un valor de RCME de 0.1118.

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Ecuaciones de volumen para Prosopis articulata S. Watson y Lysiloma divaricata (Jacq.) J. F. Macbr. en el noroeste de México

Silva-García

Calderón

Alanís-Rodríguez

et al. 2018

RMCF

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Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

Huang,

Feng,

Gao

et al. 2024

Agronomy

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The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI’s over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a planting density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal planting density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha−1, slightly higher than the sampling yield. The root mean square error of the estimation was 6.25 Mg ha−1, which was 7.12% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale.

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Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

Altanzagas

Luo

Altansukh

et al. 2019

Forests

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Understanding the contribution of forest ecosystems to regulating greenhouse gas emissions and maintaining the atmospheric CO2 balance requires the accurate quantification of above-ground biomass (AGB) at the individual tree species level. The main objective of this study was to develop species-specific allometric equations for the total AGB and various biomass components, including stem, branch, and foliage biomass in Khangai region, northern Mongolia. We destructively sampled a total of 183 trees of five species (22–74 trees per species), including Siberian stone pine (Pinus sibirica Du Tour.), Asian white birch (Betula platyphylla Sukacz.), Mongolian poplar (Populus suaveolens Fisch.), Siberian spruce (Picea obovata Ldb.), and Siberian larch (Larix sibirica Ldb.), across this region. The results showed that for the five species, the average biomass proportion for the stems was 75%, followed by branches at 20% and foliage at 5%. The species-specific component and total AGB models for the Khangai region were developed using tree diameter at breast height (D) and D² and tree height (H) combined ( D 2 H ); and both D and H were used as independent variables. The best allometric model was lnŶ = lna + b × lnD + c × lnH for the various components and total AGB of B. platyphylla and L. sibirica, for the stems and total AGB of P. suaveolens, and for the stem and branch biomass of P. obovata. The equation lnŶ = lna + b × ln( D 2 × H ) was best for the various components and total AGB of P. sibirica, for the branch and foliage biomass of P. suaveolens, and for AGB of P. obovata. The equation lnŶ = lna + b × ln(D) was best only for the foliage biomass of P. obovata. Our results highlight that developing species-specific tree AGB models is very important for accurately estimating the biomass in the Khangai forest region of Mongolia. Our biomass models will be used at the tree level inventories with sample plots in the Khangai forest region.

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Allometric Equations for Estimation of Biomass and Carbon Stocks in Temperate Forests of North-Western Mexico

Cited by 12 publications

References 53 publications

Ecuaciones de volumen para Prosopis articulata S. Watson y Lysiloma divaricata (Jacq.) J. F. Macbr. en el noroeste de México

Ecuaciones de volumen para Prosopis articulata S. Watson y Lysiloma divaricata (Jacq.) J. F. Macbr. en el noroeste de México

Precise Estimation of Sugarcane Yield at Field Scale with Allometric Variables Retrieved from UAV Phantom 4 RTK Images

Allometric Equations for Estimating the Above-Ground Biomass of Five Forest Tree Species in Khangai, Mongolia

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