Applicability of different vegetation indices for pasture biomass estimation in the north-central region of Mongolia

Bayaraa, Batbileg; Hirano, Atsushi; Purevtseren, Myagmartseren; Vandansambuu, Battsengel; Damdin, Byambasuren; Natsagdorj, Enkhjargal

doi:10.1080/10106049.2021.1974956

Cited by 10 publications

(4 citation statements)

References 50 publications

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“…It uses blue light reflectance measurements to correct for the atmospheric scattering effects that also influence the reflectance of red light [70]. The use of this vegetation index in biomass modeling in different landscapes and various forest stands is proven by other research [71][72][73].…”

Section: Discussionmentioning

confidence: 99%

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Fadil,

Sebari,

Ajerame

et al. 2024

Land

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Spatialization of biomass and carbon stocks is essential for a good understanding of the forest stand and its characteristics, especially in degraded Mediterranean cork oak forests. Furthermore, the analysis of biomass and carbon stock changes and dynamics is essential for understanding the carbon cycle, in particular carbon emissions and stocks, in order to make projections, especially in the context of climate change. In this research, we use a multidimensional framework integrating forest survey data, LiDAR UAV data, and extracted vegetation indices from Landsat imagery (NDVI, ARVI, CIG, etc.) to model and spatialize cork oak biomass and carbon stocks on a large scale. For this purpose, we explore the use of univariate and multivariate regression modeling and examine several types of regression, namely, multiple linear regression, stepwise linear regression, random forest regression, simple linear regression, logarithmic regression, and quadratic and cubic regression. The results show that for multivariate regression, stepwise regression gives good results, with R2 equal to 80% and 65% and RMSE equal to 2.59 and 1.52 Mg/ha for biomass and carbon stock, respectively. Random forest regression, chosen as the ML algorithm, gives acceptable results, explaining 80% and 60% of the variation in biomass and carbon stock, respectively, and an RMSE of 2.74 and 1.72 Mg/ha for biomass and carbon stock, respectively. For the univariate regression, the simple linear regression is chosen because it gives satisfactory results, close to those of the quadratic and cubic regressions, but with a simpler equation. The vegetation index chosen is ARVI, which shows good performance indices, close to those of the NDVI and CIG. The assessment of biomass and carbon stock changes in the study area over 35 years (1985–2020) showed a slight increase of less than 10 Mg/ha and a decrease in biomass and carbon stock over a large area.

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Section: Discussionmentioning

confidence: 99%

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Fadil,

Sebari,

Ajerame

et al. 2024

Land

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“…Many authors used VI to estimate the biomass. In pasture, seven VI, used to estimate the biomass, and the NDVI showed a good relationship described by the accuracy and precision of the models [13], the soybean biomass estimate showed a strong correlation with the VI in Brazil [14], and in corn with the use of VI it was possible to estimate the biomass 77 days after sowing of the irrigated plants [15].…”

Section: Introductionmentioning

confidence: 93%

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

et al. 2023

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Remote sensing technology applied to agricultural crops has emerged as an efficient tool to speed up the data acquisition process in decision-making. In this study, we aimed to evaluate the performance of the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Red Edge (NDRE) in estimating biomass accumulation in common bean crops. The research was conducted at the Federal University of Lavras, where the ANFC 9 cultivar was used in an area of approximately seven hectares, in a second crop, in 2022. A total of 31 georeferenced points spaced at 50 m were chosen to evaluate height, width and green biomass, with collections on days 15, 27, 36, 58, 62 and 76 of the crop cycle. The images used in the study were obtained from the PlanetScope CubeSat satellite, with a spatial resolution of 3 m. The data obtained were subjected to a Pearson correlation (R) test and multiple linear regression analysis. The green biomass variable was significantly correlated with plant height and width. The NDVI performed better than the NDRE, with higher values observed at 62 Days After Sowing (DAS). The model that integrates the parameters of height, width and NDVI was the one that presented the best estimate for green biomass in the common bean crop. The M1 model showed the best performance to estimate green biomass during the initial stage of the crop, at 15, 27 and 36 DAS (R2 = 0.93). These results suggest that remote sensing technology can be effectively applied to assess biomass accumulation in common bean crops and provide accurate data for decision-makers.

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“…37 Table 1 Vegetation indices with equations and band definitions for AVIRIS-NG data. 35,36 Vegetation index Equation Band definition for AVIRIS-NG…”

Section: Performance Matrixmentioning

confidence: 99%

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

Verma

Sharma

Lele

2023

J. Appl. Rem. Sens.

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Forest biomass is an important biophysical parameter, which delivers vital and valuable information about forest health, growth, productivity, carbon cycle monitoring, forest degradation, and its ecosystem. It is an important inconstant for ecological modeling, carbon stock assessment, and climate change. Forest biomass estimation has been progressively investigated, in which the accuracy of results is good enough to estimate accuracy of biomass; therefore, more accurate estimation of biomass is important for refining the precision and its applicability of these techniques. Hyperspectral remote sensing provides more accurate information about vegetation, so with the combination of advanced hyperspectral datasets it may be a better technique to enhance the results and accuracy of spatial biomass. Airborne hyperspectral data of airborne visible infrared imaging spectrometer-next generation data were demonstrated to estimate above ground biomass (AGB) of a tropical dry deciduous forest. Atmospherically resistant vegetation index, simple ratio index (SRI), normalized difference vegetation index, and enhanced vegetation index (EVI) were used to estimate AGB, in which EVI performs better than other vegetation indices with 0.55 R square value. Plant senescence reflectance index was used to estimate the dry and senescence condition of the forest and its correlations were performed with ground biomass and other vegetation indices.

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Applicability of different vegetation indices for pasture biomass estimation in the north-central region of Mongolia

Cited by 10 publications

References 50 publications

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

An Integrating Framework for Biomass and Carbon Stock Spatialization and Dynamics Assessment Using Unmanned Aerial Vehicle LiDAR (LiDAR UAV) Data, Landsat Imagery, and Forest Survey Data in the Mediterranean Cork Oak Forest of Maamora

Performance of Vegetation Indices to Estimate Green Biomass Accumulation in Common Bean

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

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