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
纸质出版日期： 2022-10-07
DOI： 10.11834/jrs.20210471
扫 描 看 全 文
浏览全部资源
扫码关注微信
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
Stacked ConvLSTM时间序列时空预测火烧迹地
Keywords
Stacked ConvLSTMtime seriesspatiotemporal predictionforest burned area
references
Bolton D K, Coops N C and Wulder M A. 2015. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne Lidar data. Remote Sensing of Environment, 163: 48-60 [DOI: 10.1016/j.rse.2015.03.004http://dx.doi.org/10.1016/j.rse.2015.03.004]
Cao Y C, Wu Z P, Zhou Y F, Wei S J and Feng X Q. 2020. Forest fire recognition method based on recurrent neural network. Forestry and Environmental Science, 36(5): 34-40
曹毅超, 吴泽鹏, 周宇飞, 魏书精, 封晓强. 2020. 基于循环神经网络的森林火灾识别研究. 林业与环境科学, 36(5): 34-40 [DOI: 10.3969/j.issn.1006-4427.2020.05.006http://dx.doi.org/10.3969/j.issn.1006-4427.2020.05.006]
Chance C M, Hermosilla T, Coops N C, Wulder M A, White J C. 2016. Effect of topographic correction on forest change detection using spectral trend analysis of Landsat pixel-based composites. International Journal of Applied Earth Observation and Geoinformation, 44:186–194[DOI:10.1016/j.jag.2015.09.003http://dx.doi.org/10.1016/j.jag.2015.09.003]
Cheng M, Xu Q, Lv J M, Liu W Y, Li Q and Wang J P. 2016. MS-LSTM: a multi-scale LSTM model for BGP anomaly detection//2016 IEEE 24th International Conference on Network Protocols (ICNP). Singapore: IEEE: 1-6 [DOI: 10.1109/ICNP.2016.7785326http://dx.doi.org/10.1109/ICNP.2016.7785326]
Chollet F,others.2015.Keras.GitHub.[https://github.com/fchollet/kerashttps://github.com/fchollet/keras]
Chuvieco E, Lizundia-Loiola J, Pettinari M L, Ramo R, Padilla M, Tansey K, Mouillot F, Laurent P, Storm T, Heil A and Plummer S. 2018. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, 10(4): 2015-2031 [DOI: 10.5194/essd-10-2015-2018http://dx.doi.org/10.5194/essd-10-2015-2018]
Chuvieco E, Yue C, Heil A, Mouillot F, Alonso-Canas I, Padilla M, Pereira J M, Oom D and Tansey K. 2016. A new global burned area product for climate assessment of fire impacts. Global Ecology and Biogeography, 25(5): 619-629 [DOI: 10.1111/geb.12440http://dx.doi.org/10.1111/geb.12440]
Gers F A and Schmidhuber J. 2000. Recurrent nets that time and count.//Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks//IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium. Como: IEEE: 189-194 [DOI: 10.1109/IJCNN.2000.861302http://dx.doi.org/10.1109/IJCNN.2000.861302]
Hilker T, Wulder M A, Coops N C, Linke J, McDermid G, Masek J G, Gao F and White J C. 2009. A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS. Remote Sensing of Environment, 113(8): 1613-1627 [DOI: 10.1016/j.rse.2009.03.007http://dx.doi.org/10.1016/j.rse.2009.03.007]
Hochreiter S and Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9(8): 1735-1780 [DOI: 10.1162/neco.1997.9.8.1735http://dx.doi.org/10.1162/neco.1997.9.8.1735]
Huang C Q, Goward S N, Masek J G, Thomas N, Zhu Z L and Vogelmann J E. 2010. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment, 114(1): 183-198 [DOI: 10.1016/j.rse.2009.08.017http://dx.doi.org/10.1016/j.rse.2009.08.017]
Huang C Q, Goward S N, Schleeweis K, Thomas N, Masek J G and Zhu Z L. 2009. Dynamics of national forests assessed using the Landsat record: case studies in eastern United States. Remote Sensing of Environment, 113(7): 1430-1442 [DOI: 10.1016/j.rse.2008.06.016http://dx.doi.org/10.1016/j.rse.2008.06.016]
Huete A, Didan K, Miura T, Rodriguez E P, Gao X and Ferreira L G. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1/2): 195-213 [DOI: 10.1016/S0034-4257(02)00096-2http://dx.doi.org/10.1016/S0034-4257(02)00096-2]
Huo L Z, Tang P, Zhang Z and Tuia D. 2015. Semisupervised classification of remote sensing images with hierarchical spatial similarity. IEEE Geoscience and Remote Sensing Letters, 12(1): 150-154 [DOI: 10.1109/LGRS.2014.2329713http://dx.doi.org/10.1109/LGRS.2014.2329713]
Jordan M I. 1997. Serial order: a parallel distributed processing approach. Advances in Psychology, 121: 471-495 [DOI: 10.1016/S0166-4115(97)80111-2http://dx.doi.org/10.1016/S0166-4115(97)80111-2]
Kennedy R E, Cohen W B and Schroeder T A. 2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3): 370-386 [DOI: 10.1016/j.rse.2007.03.010http://dx.doi.org/10.1016/j.rse.2007.03.010]
Kennedy R E, Yang Z Q and Cohen W B. 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr-temporal segmentation algorithms. Remote Sensing of Environment, 114(12): 2897-2910 [DOI: 10.1016/j.rse.2010.07.008http://dx.doi.org/10.1016/j.rse.2010.07.008]
Khan S H, He X M, Porikli F and Bennamoun M. 2017. Forest change detection in incomplete satellite images with deep neural networks. IEEE Transactions on Geoscience and Remote Sensing, 55(9): 5407-5423 [DOI: 10.1109/TGRS.2017.2707528http://dx.doi.org/10.1109/TGRS.2017.2707528]
Kim S, Hong S, Joh M and Song S K. 2017. DeepRain: ConvLSTM network for precipitation prediction using multichannel radar data. arXiv Preprint arXiv: 1711.02316[DOI: 10.48550/arXiv.1711.02316http://dx.doi.org/10.48550/arXiv.1711.02316]
Kong Y L, Huang Q Q, Wang C Y, Chen J B, Chen J S and He D X. 2018. Long short-term memory neural networks for online disturbance detection in satellite image time series. Remote Sensing, 10(3): 452 [DOI: 10.3390/rs1003 0452http://dx.doi.org/10.3390/rs10030452]
Längkvist M, Karlsson L and Loutfi A. 2014. A review of unsupervised feature learning and deep learning for time-series modeling. Pattern Recognition Letters, 42: 11-24 [DOI: 10.1016/j.patrec.2014.01.008http://dx.doi.org/10.1016/j.patrec.2014.01.008]
Liu C, Frazier P, Kumar L.2007. Comparative assessment of the measures of thematic classification accuracy. Remote Sensing of Environment, 107(4): 606–616[DOI:10.1016/j.rse.2006.10.010http://dx.doi.org/10.1016/j.rse.2006.10.010]
Markham B L, Helder D L.2012. Forty-year calibrated record of earth-reflected radiance from Landsat: a review. Remote Sensing of Environment, 122:30–40[DOI:10.1016/j.rse.2011.06.026http://dx.doi.org/10.1016/j.rse.2011.06.026]
Peng D L, Wu C Y, Li C J, Zhang X Y, Liu Z J, Ye H C, Luo S Z, Liu X J, Hu Y and Fang B. 2017. Spring green-up phenology products derived from MODIS NDVI and EVI: intercomparison, interpretation and validation using National Phenology Network and AmeriFlux observations. Ecological Indicators, 77: 323-336 [DOI: 10.1016/j.ecolind.2017.02.024http://dx.doi.org/10.1016/j.ecolind.2017.02.024]
Reddy D S and Prasad P R C. 2018. Prediction of vegetation dynamics using NDVI time series data and LSTM. Modeling Earth Systems and Environment, 4(1): 409-419 [DOI: 10.1007/s40808-018-0431-3http://dx.doi.org/10.1007/s40808-018-0431-3]
Ruiz J A M, Lázaro J R G, Cano I D Á and Leal P H. 2014. Burned area mapping in the North American boreal forest using Terra-MODIS LTDR (2001—2011): a comparison with the MCD45A1, MCD64A1 and BAGEOLAND-2 products. Remote Sensing, 6(1): 815-840 [DOI: 10.3390/rs6010815http://dx.doi.org/10.3390/rs6010815]
Shi X J, Chen Z R, Wang H, Yeung D Y, Wong W K and Woo W C. 2015. Convolutional LSTM network: a machine learning approach for precipitation nowcasting//Proceedings of the 28th International Conference on Neural Information Processing Systems. Montreal: MIT Press: 802-810[DOI:10.48550/arXiv.1506.04214http://dx.doi.org/10.48550/arXiv.1506.04214]
Sun H B, Wang Z L, Zhu Z W, Huang F F, Gan X H and Zhang W Q. 2018. Stoichiometry characteristics of dominant tree species and soil in Heritiera littoralis community in Baguang Wetland Park in Shenzhen. Forestry and Environmental Science, 34(6): 15-20
孙红斌, 王佐霖, 朱子维, 黄芳芳, 甘先华, 张卫强. 2018. 深圳市坝光湿地园银叶树群落优势树种与土壤生态化学计量特征分析. 林业与环境科学, 34(6): 15-20 [DOI: 10.3969/j.issn.1006-4427.2018.06.003http://dx.doi.org/10.3969/j.issn.1006-4427.2018.06.003]
Sun G F，Qin X L，Liu S C，et al. Potential analysis of typical vegetation index for identifying burned area[J].Remote Sensing for Land and Resources，2019，31( 1) : 204-211
孙桂芬, 覃先林, 刘树超, 等. 2019.典型植被指数识别火烧迹地潜力分析. 国土资源遥感, 31(1):204-211 [DOI:10.6046 /gtzyyg.2019.01.27http://dx.doi.org/10.6046/gtzyyg.2019.01.27]
Verbesselt J, Zeileis A and Herold M. 2012. Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123: 98-108 [DOI: 10.1016/j.rse.2012.02.022http://dx.doi.org/10.1016/j.rse.2012.02.022]
Wang Z S, Wei S J, Xie J H, Zhong Y X, Li X C, Tang H H, Wu Z P, Zhou Y F and Li Q. 2019. Effect of forest fire combustion environment on forest fire suppression. Forestry and Environmental Science, 35(2): 84-88
王振师, 魏书精, 谢继红, 钟映霞, 李小川, 唐洪辉, 吴泽鹏, 周宇飞, 李强. 2019. 林火燃烧环境对灭火效果的影响研究. 林业与环境科学, 35(2): 84-88 [DOI: 10.3969/j.issn.1006-4427.2019.02.014http://dx.doi.org/10.3969/j.issn.1006-4427.2019.02.014]
Wang Z X, Liu C and Huete A. 2003. From AVHRR-NDVI to MODIS-EVI: advances in vegetation index research. Acta Ecologica Sinica, 23(5): 979-987
王正兴, 刘闯, Huete A. 2003. 植被指数研究进展: 从AVHRR-NDVI到MODIS-EVI. 生态学报, 23(5): 979-987 [DOI: 10.3321/j.issn:1000-0933.2003.05.020http://dx.doi.org/10.3321/j.issn:1000-0933.2003.05.020]
Wei S J, Luo S S, Luo B Z, Li X C, Wang Z S, Wu Z P, Zhou Y F, Zhong Y X and Li Q. 2020. Occurrence regularity of forest fire under the background of climate change. Forestry and Environmental Science, 36(2): 133-143
魏书精, 罗斯生, 罗碧珍, 李小川, 王振师, 吴泽鹏, 周宇飞, 钟映霞, 李强. 2020. 气候变化背景下森林火灾发生规律研究. 林业与环境科学, 36(2): 133-143 [DOI: 10.3969/j.issn.1006-4427.2020.02.019http://dx.doi.org/10.3969/j.issn.1006-4427.2020.02.019]
Wu J W, Sun L Y, Ji R P, Feng R, Yu W Y and Zhang Y S. 2020. Research progress and prospect of remote sensing on extracting burned areas information. Journal of Catastrophology, 35(4): 151-156
武晋雯, 孙龙彧, 纪瑞鹏, 冯锐, 于文颖, 张玉书. 2020. 火烧迹地信息遥感提取研究进展与展望. 灾害学, 35(4): 151-156 [DOI: 10.3969/j.issn.1000-811X.2020.04.028http://dx.doi.org/10.3969/j.issn.1000-811X.2020.04.028]
Yang C. 2013. Monitoring Regional Forest Disturbance by Remote Sensing: A Case Study of Wuning County. Nanjing: Nanjing University of Information Science and Technology
杨辰. 2013. 区域森林植被扰动遥感监测研究——以武宁县为例. 南京: 南京信息工程大学
Yuan Y, Lin L, Huo L Z, Kong Y L, Zhou Z G, Wu B and Jia Y. 2020. Using an attention-based LSTM encoder-decoder network for near real-time disturbance detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13: 1819-1832 [DOI: 10.1109/JSTARS.2020.2988324http://dx.doi.org/10.1109/JSTARS.2020.2988324]
Zhang X Y, Friedl M A, Schaaf C B, Strahler A H, Hodges J C F, Gao F, Reed B C and Huete A. 2003. Monitoring vegetation phenology using MODIS. Remote Sensing of Environment, 84(3): 471-475 [DOI: 10.1016/S0034-4257(02)00135-9http://dx.doi.org/10.1016/S0034-4257(02)00135-9]
Zhang Z M, Tang D, He G J, Long T F and Wei M Y. 2020. Global 30-meter resolution burned area products over the globe based on Landsat 8 images. China Scientific Data, 5(4): 91-96
张兆明, 唐朝, 何国金, 龙腾飞, 魏明月. 2020. 全球30米分辨率火烧迹地产品. 中国科学数据(中英文网络版), 5(4): 91-96 [DOI:10.11922/sciencedb.976http://dx.doi.org/10.11922/sciencedb.976]
Zhao C J, Zhang P, Zhu J, Wu C R, Wang H M and Xu K L. 2019. Predicting tongue motion in unlabeled ultrasound videos using convolutional LSTM neural networks//2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)：5926-5930. IEEE, Piscataway, NJ.[DOI: 10.1109/ICASSP.2019.8683081http://dx.doi.org/10.1109/ICASSP.2019.8683081]
Zhong X G, Mohamed A S and Florian M W. 2012. Testing the accuracy of query optimizers//Proceedings of the Fifth International Workshop on Testing Database Systems (DBTest '12. Association for Computing Machinery, 11:1-6[DOI:10.1145/2304510.2304525http://dx.doi.org/10.1145/2304510.2304525]
Zhu Z. 2017. Change detection using Landsat time series: a review of frequencies, preprocessing, algorithms, and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 370-384 [DOI: 10.1016/j.isprsjprs.2017.06.013http://dx.doi.org/10.1016/j.isprsjprs.2017.06.013]
Zhu Z, Fu Y C, Woodcock C E, Olofsson P, Vogelmann J E, Holden C, Wang M, Dai S and Yu Y. 2016. Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: a case study from Guangzhou, China (2000—2014). Remote Sensing of Environment, 185: 243-257 [DOI: 10.1016/j.rse.2016.03.036http://dx.doi.org/10.1016/j.rse.2016.03.036]
Zhu Z and Woodcock C E. 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144: 152-171 [DOI: 10.1016/j.rse.2014.01.011http://dx.doi.org/10.1016/j.rse.2014.01.011]
文章被引用时，请邮件提醒。
提交
相关作者
暂无数据
相关机构
中国科学院南京地理与湖泊研究所 流域地理学重点实验室
河南理工大学 测绘与国土信息工程学院
首都师范大学 资源环境与旅游学院
水资源安全北京实验室
城市环境过程与数字模拟国家重点实验室培育基地
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
回到顶部