scholarly journals OPTIMIZATION OF A RANDOM FOREST CLASSIFIER FOR BURNED AREA DETECTION IN CHILE USING SENTINEL-2 DATA

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 337-342
Author(s):  
E. Roteta ◽  
P. Oliva
Keyword(s):  
Random Forest ◽  
Visual Assessment ◽  
Random Forest Classifier ◽  
Burned Area ◽  
National Forest ◽  
Central Chile ◽  
Burned Areas ◽  
Area Product ◽  
Sentinel 2

Abstract. Due to the high variability of biomes throughout the country, the classification of burned areas is a challenge. We calibrated a random forest classifier to account for all this variability and ensure an accurate classification of burned areas. The classifier was optimized in three steps, generating a version of the burned area product in each step. According to the visual assessment, the final version of the BA product is more accurate than the perimeters created by the Chilean National Forest Corporation, which overestimate large burned areas because it does not consider the inner unburned areas and, it omits some small burned areas. The total burned surface from January to March 2017 was 5,000 km2 in Chile, 20 % of it belonging to a single burned area in the Maule Region, and with 91 % of the total burned surface distributed in 6 adjacent regions of Central Chile.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

scholarly journals A Deep Learning Approach for Burned Area Segmentation with Sentinel-2 Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2422
Author(s):  
Lisa Knopp ◽  
Marc Wieland ◽  
Michaela Rättich ◽  
Sandro Martinis
Keyword(s):  
Deep Learning ◽  
Network Performance ◽  
Near Infrared ◽  
Remote Sensing Data ◽  
Economic Consequences ◽  
Validation Dataset ◽  
Burned Area ◽  
Burned Areas ◽  
Spectral Bands ◽  
Sentinel 2

Wildfires have major ecological, social and economic consequences. Information about the extent of burned areas is essential to assess these consequences and can be derived from remote sensing data. Over the last years, several methods have been developed to segment burned areas with satellite imagery. However, these methods mostly require extensive preprocessing, while deep learning techniques—which have successfully been applied to other segmentation tasks—have yet to be fully explored. In this work, we combine sensor-specific and methodological developments from the past few years and suggest an automatic processing chain, based on deep learning, for burned area segmentation using mono-temporal Sentinel-2 imagery. In particular, we created a new training and validation dataset, which is used to train a convolutional neural network based on a U-Net architecture. We performed several tests on the input data and reached optimal network performance using the spectral bands of the visual, near infrared and shortwave infrared domains. The final segmentation model achieved an overall accuracy of 0.98 and a kappa coefficient of 0.94.

scholarly journals Synergistic Use of Radar Sentinel-1 and Optical Sentinel-2 Imagery for Crop Mapping: A Case Study for Belgium

2018 ◽  
Vol 10 (10) ◽  
pp. 1642 ◽  
Author(s):  
Kristof Van Tricht ◽  
Anne Gobin ◽  
Sven Gilliams ◽  
Isabelle Piccard
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Satellite Remote Sensing ◽  
Random Forest Classifier ◽  
Optical Observations ◽  
Optical Data ◽  
Crop Monitoring ◽  
Crop Types ◽  
Optical Imagery ◽  
Sentinel 2

A timely inventory of agricultural areas and crop types is an essential requirement for ensuring global food security and allowing early crop monitoring practices. Satellite remote sensing has proven to be an increasingly more reliable tool to identify crop types. With the Copernicus program and its Sentinel satellites, a growing source of satellite remote sensing data is publicly available at no charge. Here, we used joint Sentinel-1 radar and Sentinel-2 optical imagery to create a crop map for Belgium. To ensure homogenous radar and optical inputs across the country, Sentinel-1 12-day backscatter mosaics were created after incidence angle normalization, and Sentinel-2 normalized difference vegetation index (NDVI) images were smoothed to yield 10-daily cloud-free mosaics. An optimized random forest classifier predicted the eight crop types with a maximum accuracy of 82% and a kappa coefficient of 0.77. We found that a combination of radar and optical imagery always outperformed a classification based on single-sensor inputs, and that classification performance increased throughout the season until July, when differences between crop types were largest. Furthermore, we showed that the concept of classification confidence derived from the random forest classifier provided insight into the reliability of the predicted class for each pixel, clearly showing that parcel borders have a lower classification confidence. We concluded that the synergistic use of radar and optical data for crop classification led to richer information increasing classification accuracies compared to optical-only classification. Further work should focus on object-level classification and crop monitoring to exploit the rich potential of combined radar and optical observations.

scholarly journals Classification of EEG data for human mental state analysis using Random Forest Classifier

2018 ◽  
Vol 132 ◽  
pp. 1523-1532 ◽  
Author(s):  
Damodar Reddy Edla ◽  
Kunal Mangalorekar ◽  
Gauri Dhavalikar ◽  
Shubham Dodia
Keyword(s):  
Random Forest ◽  
Mental State ◽  
Random Forest Classifier ◽  
Eeg Data ◽  
State Analysis

scholarly journals Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3176
Author(s):  
Beata Hejmanowska ◽  
Piotr Kramarczyk ◽  
Ewa Głowienka ◽  
Sławomir Mikrut
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Training Set ◽  
Validation Data ◽  
Comparative Accuracy ◽  
The Difference ◽  
Sentinel 2

The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data.

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