Random forest in remote sensing: A review of applications and future directions
A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …
trees, using a randomly selected subset of training samples and variables. This classifier …
Review of studies on tree species classification from remotely sensed data
Spatially explicit information on tree species composition of managed and natural forests,
plantations and urban vegetation provides valuable information for nature conservationists …
plantations and urban vegetation provides valuable information for nature conservationists …
Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
E Raczko, B Zagajewski - European Journal of Remote Sensing, 2017 - Taylor & Francis
Knowledge of tree species composition in a forest is an important topic in forest
management. Accurate tree species maps allow for much more detailed and in-depth …
management. Accurate tree species maps allow for much more detailed and in-depth …
Optimal input features for tree species classification in Central Europe based on multi-temporal Sentinel-2 data
M Immitzer, M Neuwirth, S Böck, H Brenner, F Vuolo… - Remote Sensing, 2019 - mdpi.com
Detailed knowledge about tree species composition is of great importance for forest
management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites …
management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites …
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents
Abstract Models of radiative transfer (RT) are important tools for remote sensing of
vegetation, allowing for forward simulations of remotely sensed data as well as inverse …
vegetation, allowing for forward simulations of remotely sensed data as well as inverse …
Hyperspectral classification of plants: A review of waveband selection generalisability
Hyperspectral sensing, measuring reflectance over visible to shortwave infrared
wavelengths, has enabled the classification and mapping of vegetation at a range of …
wavelengths, has enabled the classification and mapping of vegetation at a range of …
Will remote sensing shape the next generation of species distribution models?
Two prominent limitations of species distribution models (SDM s) are spatial biases in
existing occurrence data and a lack of spatially explicit predictor variables to fully capture …
existing occurrence data and a lack of spatially explicit predictor variables to fully capture …
Importance of sample size, data type and prediction method for remote sensing-based estimations of aboveground forest biomass
Estimates of forest biomass are needed for various technical and scientific applications,
ranging from carbon and bioenergy policies to sustainable forest management. As local …
ranging from carbon and bioenergy policies to sustainable forest management. As local …
The new hyperspectral satellite PRISMA: Imagery for forest types discrimination
Different forest types based on different tree species composition may have similar spectral
signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery …
signatures if observed with traditional multispectral satellite sensors. Hyperspectral imagery …
Comparison of support vector machine and random forest algorithms for invasive and expansive species classification using airborne hyperspectral data
A Sabat-Tomala, E Raczko, B Zagajewski - Remote Sensing, 2020 - mdpi.com
Invasive and expansive plant species are considered a threat to natural biodiversity
because of their high adaptability and low habitat requirements. Species investigated in this …
because of their high adaptability and low habitat requirements. Species investigated in this …