Please wait a minute...
img

Wechat

Remote Sensing Technology and Application  2022, Vol. 37 Issue (3): 721-730    DOI: 10.11873/j.issn.1004-0323.2022.3.0721
    
Spatial-temporal Variability of Amazon Tropical Rainforest Fire based on MODIS Data
Liyue Liu1,2(),Zelang Miao1,2(),Lixin Wu1,2
1.School of Geoscience and Info-Physics,Central South University,Changsha 410083,China
2.Lab of Geohazards Perception,Cognition and Prediction,Changsha 410083,China
Download:  HTML  PDF (4556KB) 
Export:  BibTeX | EndNote (RIS)      
Abstract  

Frequent forest fires have caused extensive vegetation destruction in the Amazon tropical rain forest. It’s of great importance to obtain the fire influence range and vegetation destruction in different years to understand the spatio-temporal evolution of fire in this area, study the interaction between fire and vegetation, and then explore the fire development mechanism, so as to provide a scientific basis for disaster forest and reduction. To this end, the MODIS vegetation index products and surface temperature products range from 2015 to 2019 were used in this paper to construct the MODIS Global Disturbance Model (MGDI), combined with fire point data (hereinafter collectively referred to as MOD14A1) and Vegetation Continuous Field (VCF)to extract combustion scope and intensity at 1 000 m resolution, and the spatial and temporal law of burned area within 5 years of the study area was analyzed. The results revealed that :(1) Burned area are mainly distributed in the central part of Brazil and the border between Brazil and Bolivia, accounting for about 67% of the total burning area;(2) The information of burned area and burned intensity comprehensively `indicated that the fire showing a “rise-drop-rise” trend;(3) The fire mainly occurred in shrub grassland(more than 50%) and broad-leaved forest(30%), and most of them took place during the dry season; under the global warming circumstance, the fire frequency increased a lot;(4) The expansion of human activities, unreasonable agricultural reclamation and deforestation lead to serious grassland degradation in the study area, and agricultural land and construction land are increasing year by year, which provides good conditions for the occurrence and conduction of fire to a certain extent.

Key words:  Amazon      Fire      MGDI      Burned area      Spatial and temporal variation     
Received:  21 November 2020      Published:  25 August 2022
ZTFLH:  TP79  
Corresponding Authors:  Zelang Miao     E-mail:  liulue@csu.edu.cn;Zelang.miao@csu.edu.cn
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Liyue Liu
Zelang Miao
Lixin Wu

Cite this article: 

Liyue Liu,Zelang Miao,Lixin Wu. Spatial-temporal Variability of Amazon Tropical Rainforest Fire based on MODIS Data. Remote Sensing Technology and Application, 2022, 37(3): 721-730.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2022.3.0721     OR     http://www.rsta.ac.cn/EN/Y2022/V37/I3/721

数据产品卫星系统空间分辨率/m时间分辨率起止时间
MOD13A1(EVI)MODIS-Terra50016-Day2004~2019
MOD11A2(LST)MODIS-Terra1 0008-Day2004~2019
MOD14A1MODIS-Terra1 0001-Day2015~2019
MOD44B(VCF)MODIS-Terra5001-Year2015~2019
FireCCI-BA(Pixel)Envisat-MERIS & MODIS Aqua & Terra25030-Day2015~2019
CCI-LandcoverEnvisat-MERIS & MODIS Aqua & Terra3001-Year2015~2018
Table 1  Description of the data product
Fig.1  Geolocation of Amazon Basin and its vegetation coverage status
Fig.2  MGDI burned area extraction result during 2015 to 2019
年份TPTNFPFNAccKappa
201569 07011 095 196202 302184 26096.65%0.25
201660 90011 113 070191 703178 03396.80%0.23
201784 97411 104 441187 011184 08596.79%0.30
201836 20111 281 372131 866120 41297.82%0.21
2019107 66811 092 463202 962184 71496.65%0.34
Table 2  Consistency test between MGDI and FireCCI-BA
Fig.3  Drastic burned area consistency test
年份TPTNFPFNAcc/%Kappa
20158 56475 1888 5911 02889.700.59
99633 09581562095.960.56
20161 56619 9592 65357486.960.30
1 81714 0991 60577986.970.30
201728562 93461551698.240.33
55632 4992311 04596.280.45
201835610 55646822694.010.48
26533 93316447498.170.45
20193 18025 5144 66897683.560.44
2 72329 1463 1031 17188.170.50
Table 3  Statistical table of drastic burned area consistency test
Fig.4  Distribution of burned area
Fig.5  Distribution of burned degree in different country during 2015 to 2019
年份燃烧强度玻利维亚巴西哥伦比亚秘鲁委内瑞拉年际占比/%
2015年内不同燃烧强度占比/%I7.8784.781.450.555.3527.06
II11.4080.801.360.945.4620.88
III9.9683.221.970.524.3324.99
2016I18.6471.211.314.204.6514.55
II20.8469.862.233.543.5415.53
III13.8876.201.493.804.6319.82
2017I20.9178.410.090.090.5319.10
II33.5464.880.240.241.1016.69
III27.5870.8500.221.3514.61
2018I21.1123.5830.762.4722.085.54
II25.1715.7338.812.1018.185.82
III21.2027.1724.462.7224.466.03
2019I59.1530.606.330.283.6433.75
II71.0617.947.330.203.4741.07
III63.9822.946.070.386.6434.56
Table 4  Different countries’ burned degree proportion during 2015 to 2019
Fig.6  Fire occurrence time in five years
Fig.7  Burned area distribution in high season and low season (2015~2019)
Fig.8  Interannual variation of different landcover in 5 years(Z-Score)
Fig.9  Landcover distribution in burned area
1 Jolly W M, Cochrane M A, Freeborn P H, et al. Climate-induced variations in global wildfire danger from 1979 to 2013[J].Nature Communications,2015,6(1):1-11. DOI: ).
doi: 10.1038/ncomms8537 (2015
2 Li Guanghui, Zhao Jun, Wang Zhi. Forest fire detection system based on wireless sensor network[J]. Journal of Transduction Technology, 2006,19(6): 2760-2764.
2 李光辉, 赵军, 王智. 基于无线传感器网络的森林火灾监测预警系统[J]. 传感技术学报, 2006,19(6): 2760-2764.
3 Zhang Jiawei, Zhang Hongli, Li Mingbao. TDLA-S-based early-stage forest fire detection system[J]. Forest Engineering,2013,29(2):139-142.
3 张佳薇, 张红丽, 李明宝. 基于TDLAS早期森林火灾检测系统[J]. 森林工程, 2013, 29(2): 139-142.
4 Lu Jiazheng, Wu Chuanping, Yang Li, et al. Research and application of forest fire monitor and early warning system for transmission line [J]. Power System Protection and Control, 2014(16): 89-95.
4 陆佳政, 吴传平, 杨莉, 等. 输电线路山火监测预警系统的研究及应用[J]. 电力系统保护与控制, 2014(16): 89-95.
5 Guo Zhixing, Wang Zongming, Song Kaishan, et al. Changes of vegetation coverage in Northeast China from 1982 to 2003 [J]. Acta Botanica Bor-eali-Occidentalia Sinica, 2008, 28(1): 155-163.
5 国志兴, 王宗明, 宋开山, 等. 1982~2003年东北地区植被覆盖变化特征分析[J]. 西北植物学报, 2008, 28(1): 155-163.
6 Healey S P, Cohen W B, Yang Z Q,et al. Comparison of tasseled cap-based Landsat data structures for use in forest disturbance detection[J]. Remote Sensing of Environment,2005,97(3):301-310. DOI: .
doi: 10.1016/j.rse.2005.05.009
7 Leblon B, Kasischke E, Alexander M, et al. Fire danger monitoring using ERS-1 SAR images in the case of Northern Boreal forests[J]. Natural Hazards, 2002, 27(3): 231-255. DOI: .
doi: 10.1023/A:1020375721520
8 Abbott K N, Leblon B, Staples G C, et al. Fire danger monitoring using Radarsat-1 over Northern Boreal forests[J]. International Journal of Remote Sensing, 2007, 28(5/6): 1317-1338. DOI: .
doi: 10.1080/01431160600904956
9 Lasaponara R, Tucci B. Identification of burned areas and severity using SAR Sentinel-1[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(6): 917-921. DOI: .
doi: 10.1109/LGRS.2018.2888641
10 Liu Kun, Ouyang Sida, Li Hongzhou, et al.Application of multi-sensor remote sensing data in forest fire emergency monitoring [J]. Satellite Application, 2020(7):53-57.
10 刘锟, 欧阳斯达, 李鸿洲, 等. 多源遥感数据在森林火灾应急监测中的应用[J]. 卫星应用, 2020(7): 53-37.
11 Rao Yueming, Wang Chuan, Huang Huaguo. Forest fire monitoring based on multisensor remote sensing techniques in Muli County, Sichuan Province [J]. National Remote Sensing Bulletin, 2020,24(5):559-570.
11 饶月明, 王川, 黄华国. 联合多源遥感数据监测四川木里县森林火灾[J]. 遥感学报, 2020, 24(5):559-570.
12 Meng R, Wu J, Zhao F, et al. Measuring short-term post-fire forest recovery across a burn severity gradient in a mixed pine-oak forest using multi-sensor remote sensing techniques[J]. Remote Sensing of Environment, 2018, 210: 282-296. DOI: .
doi: 10.1016/j.rse.2018.03.019
13 Wei J, Zhang Y, Wu H, et al. The automatic detection of fire scar in Alaska using multi-temporal PALSAR polarimetric SAR data[J]. Canadian Journal of Remote Sensing,2019: 1-15.
14 Lafarge F, Descombes X, Zerubia J. Forest fire detection based on Gaussian field analysis[C]∥2007 15th European Signal Processing Conference, 2007.
15 Li Z, Wang Y, Liang S. When convolutional neural networks meet remote sensing data for fire detection[C]∥Journal of Physics: Conference Series,2021,1914(1): 012002-012003. DOI: .
doi: 10.1088/1742-6596/1914/1/012002
16 Bao C, Huang G, Yang S. Application of fusion with SAR and Optical images in land use classification based on SVM[J]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012: 39-B1.
17 Maeda E E, Formaggio A R, Shimabukuro Y E, et al. Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks[J]. International Journal of Applied Earth Observation and Geoinformation, 2009, 11(4): 265-272.
18 Yuan C, Liu Z, Zhang Y. Aerial images-based forest fire detection for firefighting using optical remote sensing techniques and unmanned aerial vehicles[J]. Journal of Intelligent & Robotic Systems, 2017, 88(2): 635-654.
19 Wing M G, Burnett J D, Sessions J. Remote sensing and unmanned aerial system technology for monitoring and quantifying forest fire impacts[J]. International Journal of Remote Sensing Applications, 2014, 4(1): 18-35.
20 Xiong Yuan, Xu Weiheng, Huang Shaodong, et al. Fine extraction of forest burned area by using fusion visible light UAV image with Sentinel-2A image[J]. Journal of Southwest Forestry University(Natural Sciences Edition),2021,41(4):103-110.
20 熊源, 徐伟恒, 黄邵东, 等. 融合可见光无人机与哨兵2A影像的森林火灾迹地精细化提取[J]. 西南林业大学学报:自然科学版,2021,41(4):103-110.
21 Longo M. Amazon forest response to changes in rainfall regime: Results from an individual-based dynamic vegetation model[D].Cambridge Massachusetts,Havard University,2014.
22 Merten G H, Minella J. The expansion of Brazilian agriculture: Soil erosion scenarios[J]. International Soil and Water Conservation Research,2013,1(3):37-48. DOI: .
doi: 10.1016/S2095-6339(15)30029-0
23 Jimenez-munoz J C, Mattar C, Barichivich J, et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Nio 2015~2016[J]. Scientific Reports, 2016, 6: 33130. DOI: .
doi: 10.1038/srep33130
24 Alencar A A, Brando P M, Asner G P, et al. Landscape fragmentation, severe drought, and the new Amazon forest fire regime[J]. Ecological applications, 2015, 25(6): 1493-1505. DOI: .
doi: 10.1890/14-1528.1
25 Lizundia-loiola J, Oton G, Ramo R, et al. A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data[J]. Remote Sensing of Environment,2020,236:111493. DOI: .
doi: 10.1016/j.rse.2019.111493
26 Laurance W F, Vasconcelos H L, Lovejoy T E. Forest loss and fragmentation in the Amazon:Implications for wildlife conservation[J]. Oryx,2000,34(1):39-45. DOI: .
doi: 10.1046/j.1365-3008.2000.00094.x
27 Saatchi S S, Soares J V, Alves D S. Mapping deforestation and land use in amazon rainforest by using SIR-C imagery[J]. Remote Sensing of Environment,1997,59(2):191-202. DOI: .
doi: 10.1016/S0034-4257(96)00153-8
28 Roosevelt A C. The Amazon and the Anthropocene: 13 000 years of human influence in a tropical rainforest[J]. Anthropocene, 2013,4:69-87.DOI: ,05,001.
doi: 10.1016/j.ancene 2014
29 Mildrexler D J, Zhao M, Running H. A new satellite-based methodology for continental-scale disturbance detection[J]. Ecological Applications,2007,17(1):235-250. DOI: [0235:ANSMFC]2.0.CO;2.
doi: 10.1890/1051-0761(2007)017
30 Mildrexler D J, Zhao M, Running S W. Testing a MODIS global disturbance index across North America[J]. Remote Sensing of Environment, 2009, 113(10): 2103-2117. DOI: .
doi: 10.1016/j.rse.2009.05.016
31 Guo Xiaoyi. Detection forest disturbances in Nort-heastern China using remote sensing data [D].Changchun: Northeast Normal University,2015.
31 郭笑怡. 中国东北森林干扰遥感研究[D].长春:东北师范大学,2015.
32 Masocha M, Dube T, Mpofu N T,et al. Accuracy assessment of MODIS active fire products in Southern African savannah wo-odlands[J].African Journal of Ecology,2018,56(3):563-571.
33 Swenson J J, Carter C E, Domec J C, et al. Gold mining in the Peruvian Amazon: Global prices, deforestation, and mercury imports[J]. PLOS ONE,2011,6(4):e18875. DOI: .
doi: 10. 1371/journal.pone.0018875
34 Lizundia-loiola J, Pettinari M L, Chuvieco E. Temporal anomalies in burned area trends: Satellite estimations of the Amazonian 2019 fire crisis[J]. Remote Sensing, 2020, 12(1): 151. DOI: .
doi: 10.3390/rs12010151
35 Seidl A F, Silva J, Moraes A S. Cattle ranching and deforestation in the Brazilian Pantanal[J].Ecological Economics, 2001,36(3):413-425. DOI: .
doi: 10.1016/S0921-8009(00)00238-X
36 Romero-muoz A, Jansen M, Nuez A M, et al. Fires scorching Bolivia’s Chiquitano forest[J]. Science,2019,366(6469): 1082.1-1082.
37 Tollefson J. 2015 declared the hottest year on record[J]. Nature, 2016, 529(7587): 450-450.
38 Yuan Jian. The influence of climate change to forest fire and classification of forest fuel based on remote sensing in Chongqing[D]. Hangzhou: Zhejiang A&F University,2013.
38 袁建. 气候变化对重庆森林火灾的影响以及森林可燃物遥感分类[D]. 杭州:浙江农林大学,2013.
39 Filho J B, De Faria V G, Guedes Pinto L F, et al. Economic and social impacts of deforestation reduction in Brazil[R].International Association of Agricultural Economists,2018.
No Suggested Reading articles found!