Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

Forkuor, Gerald; Dimobe, Kangbéni; Sermé, Idriss; Tondoh, Jérôme E.

doi:10.1080/15481603.2017.1370169

Cited by 234 publications

(139 citation statements)

References 69 publications

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“…RF generates a multitude of CARTs (typically 500–1000 trees) through bootstrapping‐based randomization approaches for the selection of both training samples for a tree and predictors at each node of the tree (Im et al, 2016; Richardson et al, 2017). This approach alleviates any existing problems in the CART such as overfitting and sensitivity to training samples (Forkuor et al, 2018; Yoo et al, 2018). R software with the ‘randomForest’ package was used to develop and apply the statistical models using default model parameter settings (Ho et al, 2014; Yoo et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

110

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Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi-model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next-day maximum and minimum air temperatures (T maxtþ1 and T mintþ1 ) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in-situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R 2 of 0.69, a bias of −0.85°C and an RMSE of 2.08°C for T maxtþ1 forecast, whereas the proposed models resulted in the improvement with R 2 from 0.75 to 0.78, bias from −0.16 to −0.07°C and RMSE from 1.55 to 1.66°C by hindcast validation. For forecasting T mintþ1 , the LDAPS model had an R 2 of 0.77, a bias of 0.51°C and an RMSE of 1.43°C by hindcast, while the bias correction models showed R 2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03°C, and RMSEs from 0.98 to 1.02°C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave-one-station-out cross-validation.

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Section: Methodsmentioning

confidence: 99%

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Cho

Yoo

et al. 2020

Earth and Space Science

110

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“…RF is widely used in various remote sensing applications for both classification and regression [35][36][37][38][39]. RF is based on Classification and Regression Tree (CART) methodology [40], which is a rule-based decision tree.…”

Section: Random Forestmentioning

confidence: 99%

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

Jang

Kang

et al. 2019

Remote Sensing

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Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy~99.16%, probability of detection (POD) 93.08%, probability of false detection (POFD)~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD~93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD~50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires.

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“…When Group B was used as the test case, for example, Group A was used as the base training dataset while Groups C and D were used as the tuning dataset. RF was adopted as the machine learning approach in this study because it has shown robust performance and the explainable ability for the results of many classification and regression studies in remote sensing [27][28][29][30][31][32][33][34][35]. RF has been used in the previous studies of CI detection with other machine learning approaches [6,9,15].…”

Section: Machine Learning-based CI Modellingmentioning

confidence: 99%

A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data

Han

Lee

et al. 2019

Remote Sensing

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This paper proposes a complete framework of a machine learning-based model that detects convective initiation (CI) from geostationary meteorological satellite data. The suggested framework consists of three main processes: (1) An automated sampling tool; (2) machine learning-based CI detection modelling; (3) repeated model tuning through validation. In this study, the automated sampling tool was able to track the CI objects iteratively, even without ancillary data such as an atmospheric motion vector (AMV). The collected samples were used to train the machine learning model for CI detection. Random forest (RF) was used to classify the CI and non-CI. To enhance the advantages of the machine learning approach, we adopted model tuning to iteratively update the training dataset from each validation result by adding hits and misses to the CI samples, and false alarms and correct negatives to the non-CI samples. Using 12 interest fields from the Himawari-8 Advanced Himawari Imager (AHI) over the Korean Peninsula, this simple and intuitive tuning process increased the overall probability of detection (POD) from 0.79 to 0.82 and decreased the overall false alarm rate (FAR) from 0.46 to 0.37 with around 40 min of the lead-time. Amongst the 12 interest fields, T b (11.2) µm was identified as the most significant predictor in the RF model, followed by T b (8.6-11.2) µm, and T b (6.2-7.3) µm. The effect of model tuning on the CI detection performance was also analyzed using spatiotemporal validation maps. By automatically collecting and updating the machine learning training dataset, the suggested framework is expected to help the maintenance of the CI detection model from an operational perspective.

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Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso

Cited by 234 publications

References 69 publications

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Comparative Assessment of Various Machine Learning‐Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas

Detection and Monitoring of Forest Fires Using Himawari-8 Geostationary Satellite Data in South Korea

A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data

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