AN UNSUPERVISED METHOD BASED ON FIRE INDEX ENHANCEMENT AND GRNN FOR AUTOMATED BURNED AREA MAPPING FROM SINGLE-PERIOD REMOTE SENSING IMAGERY

Q Zhang,Y Xiao

doi:10.5194/isprs-annals-v-3-2021-37-2021

Abstract

Abstract. In the current situation of frequent forest fires, the study of forest burned area mapping is important. However, there is still room for improvement in the accuracy of existing forest burning area mapping methods. Therefore, in this paper, an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) is proposed for automated forest burned area mapping from single-date post-fire remote sensing imagery. The proposed method first uses adaptive spatial context information to enhance the generated fire index to improve its ability to indicate the burned areas. Then the uncertainty analysis is performed on the enhanced fire index to extract reliable burned samples and non-burned samples for subsequent classifier training. Finally, the improved GRNN model considering the spatial correlation of pixels is used as a classifier to binarize the enhanced fire index to generate the final burned area map. Based on two commonly used fire indexes, NBR (Normalized Burn Ratio) and BAI (Burned Area Index), this paper conducts burned area mapping experiments on a post-fire image of a forest area in Inner Mongolia, China to test the effectiveness of the proposed method, and two commonly used threshold methods (Otsu and Kmeans clustering) are also used to conduct burned area mapping based on threshold segmentation of fire index for comparison experiments. The experimental results prove the effectiveness and superiority of the proposed method. The proposed method is unsupervised and automated, so it has high application value and potential under the current situation of frequent forest fires.

Highlights

Forests are one of the most important natural resources on the earth, and they have irreplaceable value in regulating climate, maintaining ecological balance and many other aspects
In view of the above considerations, this paper proposes an unsupervised method based on fire index enhancement and GRNN (General Regression Neural Network) for automated forest burned area mapping from single-date post-fire remote sensing imagery
The fire index map generated directly from the original spectral band of the image cannot accurately reflect the probability that each pixel belongs to the burned areas. For this reason, considering the spatial continuity of the burned area in the image, this paper proposes an index enhancement method based on adaptive spatial context information to enhance the initial fire index map to improve its ability to indicate the burned areas

Summary

Introduction

Forests are one of the most important natural resources on the earth, and they have irreplaceable value in regulating climate, maintaining ecological balance and many other aspects. The mapping of forest burned areas helps to understand the severity of forest fires and their spatial and temporal changes, and helps forest recovery and management after fire events (Roy et al, 2019). The study of forest burned area mapping is very important and necessary. Pulvirenti et al (2020) constructed an automatic processing chain for near real-time mapping of burned forest areas using Sentinel-2 data based on the delta Normalized Burn Ratio (NBR) index and the Normalized Difference Vegetation Index. Engelbrecht et al (2017) proposed a Normalized Difference Alpha-Angle Index for burned area identification by using multi-polarisation C-band SAR. Liu et al (2020) proposed a new burned area change detection approach by using Landsat-8 OLI data based on the fire index and Otsu algorithm. More research on burned area mapping based on fire index can be found in (Chuvieco et al, 2002; Roteta et al, 2019; Woźniak et al, 2019)

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