Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model

Despite the popularity of random forests (RF) as a prediction algorithm, methods for constructing confidence intervals for population means using this technique are still only sparsely reported. For two regional study areas (Spain and Norway) RF was used to predict forest volume or aboveground biomass using remotely sensed auxiliary data obtained from multiple sensors. Additionally, the changes per unit area of these forest attributes were estimated using indirect and direct methods. Multiple inferential frameworks have attracted increased recent attention for estimating the variances required for confidence intervals. For this study, three different statistical frameworks, design-based expansion, model-assisted and model-based estimators, were used for estimating population parameters and their variances. Pairs and wild bootstrapping approaches at different levels were compared for estimating the variances of the model-based estimates of the population means, as well as for mapping the uncertainty of the change predictions. The RF models accurately represented the relationship between the response and remotely sensed predictor variables, resulting in increased precision for estimates of the population means relative to design-based expansion estimates. Standard errors based on pairs bootstrapping within or internal to RF were considerably larger than standard errors based on both pairs and wild external bootstrapping of the entire RF algorithm. Pairs and wild external bootstrapping produced similar standard errors, but wild bootstrapping better mimicked the original structure of the sample data and better preserved the ranges of the predictor variables.

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Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

Ecological Indicators ◽

10.1016/j.ecolind.2019.04.047 ◽

2019 ◽

Vol 103 ◽

pp. 630-641 ◽

Cited By ~ 9

Author(s):

George N. Zaimes ◽

Dimitrios Gounaridis ◽

Elias Symenonakis

Keyword(s):

Random Forests ◽

Vegetation Indices ◽

Remotely Sensed ◽

The Impact

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Mapping fuels in Yosemite National Park

Canadian Journal of Forest Research ◽

10.1139/cjfr-2012-0213 ◽

2013 ◽

Vol 43 (1) ◽

pp. 7-17 ◽

Cited By ~ 9

Author(s):

Seth H. Peterson ◽

Janet Franklin ◽

Dar A. Roberts ◽

Jan W. van Wagtendonk

Keyword(s):

Random Forests ◽

National Park ◽

Fire History ◽

Fire Regime ◽

Fire Suppression ◽

Remotely Sensed ◽

Yosemite National Park ◽

Natural Fire ◽

Fuel Loads ◽

Fuel Models

Decades of fire suppression have led to unnaturally large accumulations of fuel in some forest communities in the western United States, including those found in lower and midelevation forests in Yosemite National Park in California. We employed the Random Forests decision tree algorithm to predict fuel models as well as 1-h live and 1-, 10-, and 100-h dead fuel loads using a suite of climatic, topographic, remotely sensed, and burn history predictor variables. Climate variables and elevation consistently were most useful for predicting all types of fuels, but remotely sensed variables increased the kappa accuracy metric by 5%–12% age points in each case, demonstrating the utility of using disparate data sources in a topographically diverse region dominated by closed-canopy vegetation. Fire history information (time-since-fire) generally only increased kappa by 1% age point, and only for the largest fuel classes. The Random Forests models were applied to the spatial predictor layers to produce maps of fuel models and fuel loads, and these showed that fuel loads are highest in the low-elevation forests that have been most affected by fire suppression impacting the natural fire regime.

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Can the Random Forests Model Improve the Power to Predict the Intention of the Elderly in a Community to Participate in a Cognitive Health Promotion Program?

Iranian Journal of Public Health ◽

10.18502/ijph.v50i2.5346 ◽

2021 ◽

Author(s):

Haewon BYEON

Keyword(s):

Health Promotion ◽

Random Forests ◽

Family Income ◽

Panel Study ◽

The Elderly ◽

Health Promotion Program ◽

Outcome Variable ◽

Cognitive Health ◽

Promotion Program ◽

Random Forests Model

Background: We aimed to develop a model predicting the participation of the elderly in a cognitive health program using the random forest algorithm and presented baseline information for enhancing cognitive health. Methods: This study analyzed the raw data of Seoul Welfare Panel Study (SWPS) (20), which was surveyed by Seoul Welfare Foundation for the residents of Seoul from Jun 1st to Aug 31st, 2015. Subjects were 2,111 (879 men and 1232 women) persons aged 60 yr and older living in the community who were not diagnosed with dementia. The outcome variable was the intention to participate in a cognitive health promotion program. A prediction model was developed by the use of a Random forests and the results of the developed model were compared with those of a decision tree analysis based on classification and regression tree (CART). Results: The random forests model predicted education level, subjective health, subjective friendship, subjective family bond, mean monthly family income, age, smoking, living with a spouse or not, depression history, drinking, and regular exercise as the major variables. The analysis results of test data showed that the accuracy of the random forests was 72.3% and that of the CART model was 70.9%. Conclusion: It is necessary to develop a customized health promotion program considering the characteristics of subjects in order to implement a program effectively based on the developed model to predict participation in a cognitive health promotion program.

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