2014
DOI: 10.3832/ifor0968-006
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Mapping Leaf Area Index in subtropical upland ecosystems using RapidEye imagery and the randomForest algorithm

Abstract: Canopy leaf area, frequently quantified by the Leaf Area Index (LAI), serves as the dominant control over primary production, energy exchange, transpiration, and other physiological attributes related to ecosystem processes. Maps depicting the spatial distribution of LAI across the landscape are of particularly high value for a better understanding of ecosystem dynamics and processes, especially over large and remote areas. Moreover, LAI maps have the potential to be used by process models describing energy an… Show more

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Cited by 30 publications
(29 citation statements)
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“…The results show that variable selection did improve predictions of both TCC and AGB. This finding is in line with previous related remote sensing research [84,86,106], and suggests that the effect of variable selection should be evaluated when RF is used for predicting tree cover attributes from remote sensing data. A plausible explanation to the better performance of the reduced models is that the mechanisms of RF partly fail to block the influence of noisy predictor variables [106].…”
Section: Random Forest Regression and Variable Selectionsupporting
confidence: 79%
See 1 more Smart Citation
“…The results show that variable selection did improve predictions of both TCC and AGB. This finding is in line with previous related remote sensing research [84,86,106], and suggests that the effect of variable selection should be evaluated when RF is used for predicting tree cover attributes from remote sensing data. A plausible explanation to the better performance of the reduced models is that the mechanisms of RF partly fail to block the influence of noisy predictor variables [106].…”
Section: Random Forest Regression and Variable Selectionsupporting
confidence: 79%
“…The resulting VIM provides means to assess the contribution of each predictor variable to the modeling performance. This VIM is also useful for variable selection, which may improve model performance [84,85] and facilitate the interpretability of the model by reducing its complexity [86]. We applied a backward variable elimination method to identify the most accurate and efficient models [85,87].…”
Section: Predictor Variable Selectionmentioning
confidence: 99%
“…The empirical prediction of tree LAI in such complex and dynamic forest ecosystems using remotely sensed data may require efficient and robust machine learning regression algorithms like RF, support vector machines (SVM), artificial neural networks (ANN) and partial least squares (PLS). RF is a robust non-linear algorithm for predicting forest LAI [45]. However, one drawback of RF regression algorithm when many input predictors are utilized is that it selects predictors that could be correlated to each other [46,47].…”
Section: Introductionmentioning
confidence: 99%
“…As a constellation of five, the RapidEye satellite platform can provide imagery over relatively large areas (swath of 77 km) at a spatial resolution of 5 m and a temporal resolution of one day, increasing the rate of success to acquire cloud-free imagery data. RapidEye's traditional broadband and red-edge indices were evaluated for grassland biomass and nitrogen [17], forest LAI [18], crop canopy chlorophyll content [19], wheat ground cover and LAI [20] and for forecasting yield at regional scale [21,22], but this satellite platform has not been used to forecast within-field variation for grain yield in corn (Zea mays L.).…”
mentioning
confidence: 99%