基于Stacked ConvLSTM的时间序列森林火烧迹地检测

李淑君; 郑柯; 唐娉; 霍连志; 袁媛

doi:10.11834/jrs.20210471

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专辑

基于Stacked ConvLSTM的时间序列森林火烧迹地检测
Forest burned area detection with time series data based on Stacked ConvLSTM
2022年26卷第10期页码：1976-1987
收稿：2020-10-26，

纸质出版：2022-10-07
DOI： 10.11834/jrs.20210471
稿件说明：

移动端阅览

李淑君，郑柯，唐娉，霍连志，袁媛.2022.基于Stacked ConvLSTM的时间序列森林火烧迹地检测.遥感学报，26（10）： 1976-1987 DOI： 10.11834/jrs.20210471.

Li S J，Zheng K，Tang P，Huo L Z and Yuan Y. 2022. Forest burned area detection with time series data based on Stacked ConvLSTM. National Remote Sensing Bulletin， 26（10）：1976-1987 DOI： 10.11834/jrs.20210471.

摘要

确定森林火烧迹地的准确时间点以及空间范围对于森林的受损评价、管理、碳核算以及森林恢复的管理有重要意义。由于森林火烧迹地在空间分布上具有一定的连续性，现有的森林火烧迹地提取方法大都采用先分类再后处理的两步处理策略来抑制虚警像素的影响。本文提出将时空检测方法Stacked ConvLSTM用于时间序列森林火烧迹地的检测，在保持结果具有较好空间连续性的基础上避免了具有主观性的后处理操作，实现端到端提取森林火烧迹地信息，提升了森林火烧迹地的提取精度。采用MODIS时间序列数据，基于2001年—2008年以及2001年—2016年的黑龙江沾河林业局伊南河林场和内蒙古自治区毕拉河林业局北大河林场两个区域的历史时间序列，分别对这两个区域2009年以及2017年发生的特大火灾区域进行火烧迹地检测，利用Stacked ConvLSTM、Stacked LSTM以及bfast算法在两个区域的MODIS时间序列中提取森林火烧迹地，并将火烧迹地检测结果与ESA发布的Fire_CCI 5.1火烧迹地产品进行对比分析。结果表明：首先，从目视效果来看，在研究区域Ⅰ，Stacked ConvLSTM检测的结果比Stacked LSTM和bfast算法错误检测点少，并且在空间分布也保持较高连续性；在研究区域Ⅱ，Stacked ConvLSTM检测到了较完整的火烧迹地区域。其次，在定量的精度评价指标上，在研究区域Ⅰ，Stacked ConvLSTM的精确度比Stacked LSTM和bfast算法分别高出0.120和0.405，并且召回率、准确度和F1-score也更高，Fire_CCI 5.1召回率虽更高，由于错检区域较大，其他精度指标远低于Stacked ConvLSTM；在研究区域Ⅱ，Stacked ConvLSTM精确度达0.924，召回率、准确度和F1-score相比Stacked LSTM和bfast算法以及Fire_CCI 5.1更高。

Abstract

As the largest land cover

forests play an important role in human living environment

biological habitat

and global carbon cycle. Forest health is directly related to global ecological security and sustainable development of human society. In recent years

urban construction

disasters

forest management and deforestation

and other factors have caused different degrees of disturbance to forests. It is important to determine the exact time point and spatial range of forest burned area for forest damage assessment

management

carbon accounting

and forest restoration management. Owing to the continuity of spatial distribution of forest burned areas

most of the existing methods of forest burned area extraction use the two-step treatment strategy of first classification and then post-processing to suppress the effect of false alarm pixels. In this paper

a spatiotemporal detection method

Stacked ConvLSTM

is proposed for the detection of forest fire tracks in time series. This method avoids subjective post-processing operations on the basis of maintaining better spatial continuity of the results

and achieves end-to-end extraction of forest burned area information

which improves the extraction accuracy of forest fire-burning land. This paper proposes to use Stacked ConvLSTM to detect forest disturbance in time and space. Combined with the characteristics of ConvLSTM in extracting temporal and spatial characteristics from long-term historical series

it can predict the change trend of vegetation in a period of time in the future

and accurately determine the time point and spatial range of forest disturbance. ConvLSTM is an LSTM variant proposed on the basis of LSTM. The full connection state from input layer to hidden layer and from hidden layer to hidden layer of LSTM is replaced by convolution connection

which can make full use of spatial information. Compared with single-pixel-based methods

ConvLSTM can extract the spatiotemporal structure information of time series images at the same time

which is better for spatiotemporal analysis. In this paper

Stacked ConvLSTM is used to detect the temporal and spatial distribution of forest burned areas

predict the change trend of vegetation in a period of time in the future

and determine the presence of forest burned areas by comparing with the newest time-series images. With MODIS long time series data

based on the historical time series of Yinanhe Forest Farm of Zhanhe Forestry Bureau in Heilongjiang Province and Beidahe Forest Farm of Bilahe Forestry Bureau in Inner Mongolia from 2001—2008 and 2001—2016

the extraction results of burned areas were compared with Stacked LSTM and bfast algorithm. The Stacked ConvLSTM

Stacked LSTM

and bfast algorithms were used to extract forest burned areas from MODIS time series in both regions

and to compare the detection results with the Fire_CCI 5.1 burned areas products released by ESA. Results show that

firstly

from the visual effect

in study area Ⅰ

the error detection of Stacked ConvLSTM is fewer than that of Stacked LSTM and bfast algorithm and maintains high continuity in spatial distribution. In study Area Ⅱ

Stacked ConvLSTM detected a more complete area of fire. Secondly

in study area Ⅰ

Stacked ConvLSTM was 0.120 and 0.405 more accurate than Stacked LSTM and bfast algorithms

respectively. Moreover

the recall rate

accuracy

and Fire_CCI 5.1 F1-score were higher. In study area Ⅰ

the accuracy of Stacked ConvLSTM is 0.924 had a higher recall rate

accuracy

and F1-score than Stacked LSTM

bfast algorithms

and Fire_CCI 5.1. The detection accuracy of ConvLSTM model in space is higher than that of the other two methods

and its continuity of detection results in space is better. The detection effect of ConvLSTM model is equivalent to that of Stacked LSTM in time

but both of them are closer to the real fire time point than bfast algorithm. Results show that Stacked ConvLSTM has advantages in obtaining the change trend of forest long-term historical series for spatiotemporal prediction

and improves the detection accuracy of forest fire to a certain extent.

关键词

Keywords

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