Are NIR spectra useful for predicting site indices in sandy soils under Eucalyptus stands in Republic of Congo?

Bikindou, Fanny; Gomat, Hugues Yvan; Deleporte, Philippe; Bouillet, Jean‐Pierre; Moukini, Régis; Mbedi, Yves; Ngouaka, Eugène; Brunet, Didier; Sita, Seraphin; Diazenza, J.B.; Vouidibio, Joseph; Mareschal, Louis; Ranger, Jacques; Saint‐André, Laurent

doi:10.1016/j.foreco.2011.11.012

Cited by 21 publications

(12 citation statements)

References 58 publications

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“…Similarly, results were found by Stenberg et al claiming that a high sand content reduces the accuracy of SOC estimation [39]. In contrast to previous studies, Bikindou et al reported the ability of soil reflectance spectroscopy to accurately determine SOC in sandy soils with low SOM concentrations [47].…”

Section: Multivariate Calibrationsmentioning

confidence: 80%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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Rapid and cost-effective soil properties estimations are considered imperative for the monitoring and recording of agricultural soil condition for the implementation of site-specific management practices. Conventional laboratory measurements are costly and time-consuming, and, therefore, cannot be considered appropriate for large datasets. This article reviews laboratory and proximal sensing spectroscopy in the visible and near infrared (VNIR)–short wave infrared (SWIR) wavelength region for soil organic carbon and soil organic matter estimation as an alternative to analytical chemistry measurements. The aim of this work is to report the progress made in the last decade on data preprocessing, calibration approaches, and system configurations used for VNIR-SWIR spectroscopy of soil organic carbon and soil organic matter estimation. We present and compare the results of over fifty selective studies and discuss the factors that affect the accuracy of spectroscopic measurements for both laboratory and in situ applications.

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Section: Multivariate Calibrationsmentioning

confidence: 80%

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

et al. 2020

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“…It has been largely used in several scientific areas such as remote sensing (e.g. Bikindou et al 2012), wood technology (e.g. Alves et al 2012), chemometrics (e.g.…”

Section: Introductionmentioning

confidence: 99%

Predicting site index from climate and soil variables for cork oak (Quercus suber L.) stands in Portugal

et al. 2014

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Site productivity, assessed through site index, was modelled using partial least squares regression as a function of soil and climatic variables. Two alternative models were developed: a full model, considering all available explanatory variables, and a reduced model, considering only variables that can be obtained without digging a soil pit. The reduced model was used for mapping the site index distribution in Portugal, on the basis of existing digital cartography available for the whole country. The developed models indicate the importance of water availability and soil water holding capacity for site index value distribution. Site index was related to climate, namely evaporation and frost, and soil characteristics such as lithology, soil texture, soil depth, thickness of the A horizon and soil classification. The variability of the estimated values within the map (9.5-16.8 m with an average value of 13.4 m) reflects the impact of soil characteristics on the site productivity estimation. These variables should be taken into consideration during the establishment of new plantations of cork oak, and management of existing plantations. Results confirm the potential distribution of cork oak in coastal regions. They also suggest the existence of a considerable area, located both North and South of the Tagus river, where site indices values of medium (]13;15]) to high (]15;17]) productivity classes may be expected. The species is then expected to be able to have good productivity along the northern coastal areas of Portugal, where presently it is not a common species but where, according to historical records, it occurred until the middle of the sixteenth century. The present research focused on tree growth. Cork growth and cork quality distribution needs to be further researched through the establishment of long term experimental sites along the

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“…Marsden et al (2010) also found low correlations between the NDVI and the mean annual increment (MAI), where this correlation was progressively increasing throughout the plant size and establishment of the forest. Bikindou et al (2012) compared multiple regressions related to soil properties, and the NIR index was correlated with dominant height for productive capacity classification. According to these authors, the NIR provided a better correlation and a low mean square error for better predicting index variations in the studied area.…”

Section: Resultsmentioning

confidence: 99%

High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

et al. 2021

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In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

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Are NIR spectra useful for predicting site indices in sandy soils under Eucalyptus stands in Republic of Congo?

Cited by 21 publications

References 58 publications

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

From Laboratory to Proximal Sensing Spectroscopy for Soil Organic Carbon Estimation—A Review

Predicting site index from climate and soil variables for cork oak (Quercus suber L.) stands in Portugal

High-throughput phenotyping of two plant-size traits of Eucalyptus species using neural networks

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