Method for national fuel types classification based on multi-source data

Xiaotong LI; Qian LIU; Xianlin QIN; Shuchao LIU; Chongyang WANG

doi:10.11834/jrs.20219208

Forestry and Agriculture | Views : 0 下载量: 260 CSCD: 0 更多指标

PDF
Export
Share
Collection
Album

Method for national fuel types classification based on multi-source data
Vol. 26, Issue 3, Pages: 480-492(2022)
Published： 07 March 2022 ，
DOI： 10.11834/jrs.20219208

扫描看全文

李晓彤，刘倩，覃先林，刘树超，王崇阳.2022.基于多源数据的全国可燃物类型划分方法.遥感学报，26（3）： 480-492

Li X T，Liu Q，Qin X L，Liu S C and Wang C Y. 2022. Method for national fuel types classification based on multi-source data. National Remote Sensing Bulletin， 26（3）：480-492
李晓彤，刘倩，覃先林，刘树超，王崇阳.2022.基于多源数据的全国可燃物类型划分方法.遥感学报，26（3）： 480-492 DOI： 10.11834/jrs.20219208.

Li X T，Liu Q，Qin X L，Liu S C and Wang C Y. 2022. Method for national fuel types classification based on multi-source data. National Remote Sensing Bulletin， 26（3）：480-492 DOI： 10.11834/jrs.20219208.

摘要

为满足全国可燃物类型制图的需要，在国内外前人的研究工作基础上，建立了全国尺度的可燃物分类体系，并基于MODIS数据产品和全国植被区划数据，结合地理信息空间分析技术，对全国森林、灌木和草本等3类可燃物进行精细划分和成图。利用实地调查数据和其他数据产品，采用直接验证和交叉验证相结合方式对可燃物类型分类结果进行精度评价。评价结果表明，一级可燃物类型总精度为90.89%，Kappa系数为0.81；二级可燃物类型总精度84.14%，Kappa系数为0.74；三级可燃物类型总精度为68.16%，Kappa系数0.6。利用多源数据和地理信息空间分析技术相结合，有效地实现了在全国尺度上的森林、灌木和草本等3类可燃物类型的精细划分，为森林草原火灾的预防管理提供技术支持。

Abstract

The rate and intensity of forest fire spread can be predicted

and forest fire prevention measures can be formulated according to the classifying results of fuel types. Accurately exploring the types and spatial distribution of fuel is crucial for predicting the occurrence of forest fires

predicting forest fire behavior

commanding fire-fighting

and biological fire prevention. At present

most researches on fuel types classification based on remote sensing in China are carried out in local areas

but research based on the national scale will become one of the trends in this field. To meet the needs of China’s national-scale fuel types mapping

a fuel type classification system was developed combined with the characteristics of vegetation distribution and phenology in Chinabased on the previous research. The fuels in China

including forest

shrub

and grass

were classified and mapped based on MODIS products and the Chinese national vegetation regionalization map using geographical spatial analysis technology. A method for national fuel types classification in forests

shrubs and grasses based on remote sensing and geospatial analysis was explored. Non-tree cover

average vegetation canopy height and area occupied by each fuel types were calculated

using the product datasets of MODIS VCF（Vegetation Continuous Fields）and forest canopy height. The classification results were validated using field survey data and other data products. The results show that the total accuracies of the classification result at levels 1

and 3 are 90.89%

84.14%

and 68.16%

respectively; the Kappa coefficients of the classification result at levels 1

and 3 are 0.81

0.74

and 0.6

respectively. The national-scale fuel types

including forest

shrub

and grass

were classified and mapped by using the multi-source data and geographical spatial analysis technology. The study will provide technical support for the prevention and management of forest and grassland fire in China.

关键词

遥感可燃物类型遥感分类空间分析技术MCD12Q1产品MOD44B产品

Keywords

remote sensingfuel typeremote sensing classificationspatial analysis technologyMCD12Q1MOD44B

references

Arroyo L A, Healey S P, Cohen W B, Cocero D and Manzanera J A. 2006. Using object-oriented classification and high-resolution imagery to map fuel types in a Mediterranean region. Journal of Geophysical Research, 111(G4): G04S04 [DOI: 10.1029/2005JG000120http://dx.doi.org/10.1029/2005JG000120]

Cao M, Zhang Y J, Zheng S Q and Chen L W. 2012. Accuracy analysis of MODIS land cover data product: a case study of yellow river source region. Remote Sensing Information, 27(4): 22-27

曹明, 张友静, 郑淑倩, 陈立文. 2012. MODIS土地覆盖数据产品精度分析——以黄河源区为例. 遥感信息, 27(4): 22-27 [DOI: 10.3969/j.issn.1000-3177.2012.04.004http://dx.doi.org/10.3969/j.issn.1000-3177.2012.04.004]

Chen J Y and Niu S K. 2018. Classification and change analysis of forest fuels by multi-temporal high resolution remote sensing images. Journal of Beijing Forestry University, 40(12): 38-48

陈冀岱, 牛树奎. 2018. 多时相高分辨率遥感影像的森林可燃物分类和变化分析. 北京林业大学学报, 40(12): 38-48 [DOI: 10.13332/j.1000-1522.20180269http://dx.doi.org/10.13332/j.1000-1522.20180269]

Chen Y Z, Chen C C, Wang X Q and Tang L Y. 2008. Forest fuel types classification and change detection by remote sensing in Zhangping City. Geo-Information Science, 10(6): 792-797

陈芸芝, 陈崇成, 汪小钦, 唐丽玉. 2008. 漳平市森林可燃物类型变化遥感动态监测. 地球信息科学, 10(6): 792-797 [DOI: 10.3969/j.issn.1560-8999.2008.06.019http://dx.doi.org/10.3969/j.issn.1560-8999.2008.06.019]

Du J H, Gong Y T and Jiang L W. 2019. Study on the characteristics of forest fires in China and their relationship with major climatic factors. Forest Resources Management, (2): 7-14

杜建华, 宫殷婷, 蒋丽伟. 2019. 中国森林火灾发生特征及其与主要气候因子的关系研究.林业资源管理, (2): 7-14 [DOI: 10.13466/j.cnki.lyzygl.2019.02.002]

Friedl M A, Sulla-Menashe D, Tan B, Schneider A, Ramankutty N, Sibley A and Huang X M. 2010. MODIS collection 5 global land cover: algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1): 168-182 [DOI: 10.1016/j.rse.2009.08.016http://dx.doi.org/10.1016/j.rse.2009.08.016]

Gao C D, Tian X R, Shu L F and Wang M Y. 2005. Types of forest fuel and their combustibility in Tie-shan-ping Forestry Centre of Chongqing City. Forest Fire Prevention, (2): 29-30

高成德, 田晓瑞, 舒立福, 王明玉. 2005. 重庆铁山坪森林可燃物类型划分及其燃烧性. 森林防火, (2): 29-30 [DOI: 10.3969/j.issn.1002-2511.2005.02.012]

Guo H J, Yu T, Ma C, Zhang X H and Yan C L. 2014. The division for the forest combustible type based on the remote sensing technology. Forestry Science and Technology Information, 46(2): 1-3

郭鸿郡, 俞童, 马超, 张薛昊, 闫长路. 2014. 基于遥感技术的森林可燃物类型划分. 林业科技情报, 46(2): 1-3 [DOI: 10.3969/j.issn.1009-3303.2014.02.001http://dx.doi.org/10.3969/j.issn.1009-3303.2014.02.001]

Hansen M C, DeFries R S, Townshend J R G, Carroll M, Dimiceli C and Sohlberg R A. 2004. The MODIS 500 meter global vegetation continuous field products//Smits P and Bruzzone L, eds. Analysis of Multi-Temporal Remote Sensing Images. New Jersey: World Scientific: 295-301 [DOI: 10.1142/9789812702630_0033http://dx.doi.org/10.1142/9789812702630_0033]

He H S, Chang Y, Hu Y M and Liu Z H. 2010. Contemporary studies and future perspectives of forest fuel and fuel management. Chinese Journal of Plant Ecology, 34(6): 741-752

贺红士, 常禹, 胡远满, 刘志华. 2010. 森林可燃物及其管理的研究进展与展望. 植物生态学报, 34(6): 741-752 [DOI: 10.3773/j.issn.1005-264x.2010.06.013http://dx.doi.org/10.3773/j.issn.1005-264x.2010.06.013]

Hu H Q, Zhang Z and Wu X W.2007. Type classification of forest fuel in Tahe forestry bureau based on remote sensing. Journal of Northeast Forestry University, 35(7): 20-21, 26

胡海清, 张喆, 吴学伟. 2007. 基于遥感的塔河林业局森林可燃物类型划分. 东北林业大学学报, 35(7): 20-21, 26 [DOI: 10.3969/j.issn.1000-5382.2007.07.008http://dx.doi.org/10.3969/j.issn.1000-5382.2007.07.008]

Huesca M, Riaño D and Ustin S L. 2019. Spectral mapping methods applied to LiDAR data: application to fuel type mapping. International Journal of Applied Earth Observation and Geoinformation, 74: 159-168 [DOI: 10.1016/j.jag.2018.08.020http://dx.doi.org/10.1016/j.jag.2018.08.020]

Jia K, Liang S L, Liu S H, Li Y W, Xiao Z Q, Yao Y J, Jiang B, Zhao X, Wang X X, Xu S and Cui J. 2015. Global land surface fractional vegetation cover estimation using general regression neural networks from MODIS surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 53(9): 4787-4796 [DOI: 10.1109/TGRS.2015.2409563http://dx.doi.org/10.1109/TGRS.2015.2409563]

Lasaponara R and Lanorte A. 2007. Remotely sensed characterization of forest fuel types by using satellite ASTER data. International Journal of Applied Earth Observation and Geoinformation, 9(3): 225-234 [DOI: 10.1016/j.jag.2006.08.001http://dx.doi.org/10.1016/j.jag.2006.08.001]

Marino E, Ranz P, Tomé J L, Noriega M Á, Esteban J and Madrigal J. 2016. Generation of high-resolution fuel model maps from discrete airborne laser scanner and landsat-8 OLI: a low-cost and highly updated methodology for large areas. Remote Sensing of Environment, 187: 267-280 [DOI: 10.1016/j.rse.2016.10.020http://dx.doi.org/10.1016/j.rse.2016.10.020]

Mou N X, Liu W B, Wang H Y and Dai H L. 2012. ArcGIS 10 Tutorial: from Beginner to Master. Beijing: Surveying and Mapping Press: 311-367

牟乃夏, 刘文宝, 王海银, 戴洪磊. 2012. ArcGIS 10地理信息系统教程——从初学到精通. 北京: 测绘出版社: 311-367

Pettinari M L and Chuvieco E. 2014. Global assessment of fire risk: using a global fuel map and climatological data to estimate fire behavior with FCCS//Domingos Xavier Viegas. Advances in Forest Fire Research. [s.l.]: Coimbra University Press: 1165-1174 [DOI: 10.14195/978-989-26-0884-6_127http://dx.doi.org/10.14195/978-989-26-0884-6_127]

Simard M, Pinto N, Fisher J B and Baccini A. 2011. Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research, 116(G4): G04021 [DOI: 10.1029/2011JG001708http://dx.doi.org/10.1029/2011JG001708]

Stefanidou A, Dragozi E, Stavrakoudis D and Gitas I Z. 2018. Fuel type mapping using object-based image analysis of DMC and Landsat-8 OLI imagery. Geocarto International, 33(10): 1064-1083 [DOI: 10.1080/10106049.2017.1333532http://dx.doi.org/10.1080/10106049.2017.1333532]

Sun S Z. 1998. Concerning the vegetation Chinese regionalization map as a part of in the natural geographical atlas of the Peoples republic of China. Acta Phytoecologica Sinica, 22(6): 523-537

孙世洲. 1998. 关于中国国家自然地图集中的中国植被区划图. 植物生态学报, 22(6): 523-537

Tian X R, Dai X A, Wang M Y, Shu L F and Gao C D. 2006. Study on the fuel types classification of forests in Beijing. Scientia Silvae Sinicae, 42(11): 76-80

田晓瑞, 戴兴安, 王明玉, 舒立福, 高成德. 2006. 北京市森林可燃物分类研究. 林业科学, 42(11): 76-80 [DOI: 10.3321/j.issn:1001-7488.2006.11.014http://dx.doi.org/10.3321/j.issn:1001-7488.2006.11.014]

Wang J A and Zuo W. 2010. Geographic Atlas of China. Beijing: China Cartographic Publishing House: 51

王静爱, 左伟. 2010. 中国地理图集. 北京: 中国地图出版社: 51

Wei Y X and Wang L W. 2010. Progress in research on land cover products of MODIS. Spectroscopy and Spectral Analysis, 30(7): 1848-1852

卫亚星, 王莉雯. 2010. MODIS土地覆盖产品研究进展. 光谱学与光谱分析, 30(7): 1848-1852) [DOI: 10.3964/j.issn.1000-0593(201007-1848-05http://dx.doi.org/10.3964/j.issn.1000-0593(2010)07-1848-05]

Xia W T, Wang Y, Feng Q S and Liang T G. 2010. Accuracy assessment of MODIS land cover product of Gannan prefecture. Pratacultural Science, 27(9): 11-18

夏文韬, 王莺, 冯琦胜, 梁天刚. 2010. 甘南地区MODIS土地覆盖产品精度评价. 草业科学, 27(9): 11-18 [DOI: 10.3969/j.issn.1001-0629.2010.09.003http://dx.doi.org/10.3969/j.issn.1001-0629.2010.09.003]

Yuan C M and Wen D Y. 2001. Current status and prospect of the study on classisification and modeling of forest fuel. World Forestry Research, 14(2): 29-35

袁春明, 文定元. 2001. 森林可燃物分类与模型研究的现状与展望. 世界林业研究, 14(2): 29-35

Zhao Y H and Gao L. 2008. Study on dividing forest Fuel type area of Tahe forest bureau. Forestry Science and Technology Information, 40(1): 16-17

赵颖慧, 高利. 2008. 塔河林业局森林可燃物类型区划分的研究. 林业科技情报, 40(1): 16-17 [DOI: 10.3969/j.issn.1009-3303.2008.01.009http://dx.doi.org/10.3969/j.issn.1009-3303.2008.01.009]

Zhao Y S. 2018. Principles and Methods of Remote Sensing Application Analysis. 2nd ed. Beijing: Science Press

赵英时. 2018. 遥感应用分析原理与方法. 2版. 北京: 科学出版社

Alert me when the article has been cited

提交

Transparent earth-observing： Exploring the new generation of earth observation system

Transferring deep convolutional neural network models for generalization mapping of autumn crops

Grid A*：A fast path planning algorithm for air-ground cooperative response of emergency rescue in the field

Construction method of a Spartina alterniflora index based on hyperspectral satellite images