An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran

Hashemi, Seyed Armin; Chai, Mir Mozaffar Fallah; Bayat, Shohreh

doi:10.1007/s12517-012-0576-8

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Unlike the spectral reflectance, vegetation indices focused on the variability from vegetation characteristics by suppressing the spectral reflectance from non-vegetation features (Huete et al 2002 ; Vina et al 2011 ). Furthermore, our study confirmed their significant positive relationship with species diversity, including NDVI (Tibshirani 1996 ; Vina et al 2011 ), EVI (Cabacinha and de Castro 2009 ), NDWI (Vila-Vicosa et al 2020 ), and DVI (Hashemi et al 2013 ). It is not surprising that a combination of vegetation indices and spectral reflectance improves the ability to predict the PSD.…”

Section: Discussionsupporting

confidence: 78%

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Zhao

Yin

et al. 2021

Environ Sci Pollut Res

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Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.

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Section: Discussionsupporting

confidence: 78%

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Zhao

Yin

et al. 2021

Environ Sci Pollut Res

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“…De igual manera ocurrió en estudios realizados en maíz (Zea mays) donde se obtuvo valores de R² de 0,90 (Maresma et al, 2016). Por otro lado, el estudio realizado por Hashemi et al (2013) generó valores de R² de 0,45, siendo inferior a los valores obtenidos en este estudio. Los valores de NDVI generados pueden ser utilizados para estimar variaciones en el contenido de nitrógeno de árboles de aguacate, lo cual permitiría determinar el vigor de la planta, el cual, cuando disminuye podría deberse a una deficiencia nutricional o problemas fitosanitarios.…”

Section: Regresión Entre Valores Ndvi Y Resultados Del Análisis Foliarunclassified

Aplicación de índices vegetales (banda roja e infrarrojo cercano) en plantaciones de aguacate

Barahona

Viera

Cruz

et al. 2022

SIEMBRA

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El aguacate es una fruta tradicional en la dieta de los ecuatorianos y requiere un manejo adecuado que garantice su producción. La implementación de nuevas alternativas tecnológicas, como utilización de índices de espectroscopia, permite obtener datos que al correlacionarlos optimizarán el manejo del cultivo. Esta investigación validó el uso de índices vegetales basados en la banda roja e infrarrojo cercano con el contenido de nitrógeno foliar. Se utilizó los índices vegetales: índice normalizado diferencial de vegetación (NDVI) e índice de vegetación transformado (TVI) desde dos ortomosaicos obtenidos mediante imágenes que capturan banda roja e infrarrojo cercano en plantaciones de aguacate. Se realizaron regresiones y correlaciones entre los índices vegetales y los resultados de los análisis foliares de contenido de nitrógeno, generando valores de R² de 0,93 para NDVI y 0,95 para TVI. Los valores de los índices vegetales pueden utilizarse para estimar el vigor de la planta basado en el contenido de nitrógeno foliar.

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“…Vegetation indices are mathematical transformations that are designed for analyzing and evaluating plants in multispectral satellite observations. Fundamental of the function of each index is the difference between red and near-infrared bands, where, red light absorbed by pigments in the chlorophyll, causes low plants reflectance in this band and denser in the near-infrared band reflectance, (Hashemi et al,2013).…”

Section: Introductionmentioning

confidence: 99%

Remotely Identification and Differentiate of Some Family Fabaceae Species in Antoniadis Garden Using Spectral Reflectance Measurements

Mohamed

El-Shanhorey²,

Elsayed³

2022

Egyptian Academic Journal of Biological Sciences, H. Botany

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An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran

Cited by 9 publications

References 9 publications

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Aplicación de índices vegetales (banda roja e infrarrojo cercano) en plantaciones de aguacate

Remotely Identification and Differentiate of Some Family Fabaceae Species in Antoniadis Garden Using Spectral Reflectance Measurements

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