High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm

By Onisimo Mutanga, Elhadi Adam, Moses Azong Cho. International Journal of Applied Earth Observation and Geoinformation, 2012

851 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/a946b485e5ddba101e2600cffe5ff0dc6efe85c2

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Citations in the corpus

Remote sensing for wetland classification: a comprehensive review (2018, background): “The reflectance value in this band is related to vegetation biochemical parameters (e.g. chlorophyll content), biophysical parameters (e.g. Leaf Area Index), and water deficit in vegetation biomass (Filella and Penuelas 1994; Clevers et al. 2002; Mutanga, Adam, and Cho 2012).”
A survey of remote sensing-based aboveground biomass estimation methods in forest ecosystems (2016, background): “Many studies have explored the use of nonparametric-based models in estimation of forest attributes with remote sensing data (Saatchi et al. 2009; Breidenbach et al. 2012; McRoberts et al. 2012; Mutanga et al. 2012; Jung et al. 2013; Mitchard et al. 2013).”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, background): “, biomass (Le Maire et al., 2011; Mutanga et al., 2012; Adam et al., 2014; Vaglio Laurin et al., 2014), canopy cover (Coulston et al.”
Predicting Eucalyptus spp. stand volume in Zululand, South Africa: an analysis using a stochastic gradient boosting regression ensemble with multi-source data sets (2015, background): “…can be explained by the fact that the inclusion of less important variables in analysis can result in the convergence and instability of models or influence random errors due to noise in information predictor variables that have no relation to the response variable (Mutanga, Adam, and Cho 2012).”
Estimation of Total Nitrogen Content in Forage Maize (Zea mays L.) Using Spectral Indices: Analysis by Random Forest (2020, background): “These percentages of data division are similar to those reported by other authors [12,29,53,54,57].”
Estimating crop chlorophyll content with hyperspectral vegetation indices and the hybrid inversion method (2016, result): “This result is consistent with previous studies (Adam et al. 2014; Antipov and Pokryshevskaya 2012; Mutanga, Adam, and Cho 2012), which reported that sensitive bands are not the same between LCC and CCC.”
Machine learning and soil sciences: a review aided by machine learning tools (2019, background): “Most of the implementations of these algorithms have sensible default hyper-parameters, but some studies report an important impact of them in their results (Mutanga et al., 2012; Lu et al., 2018).”