大兴安岭火烧迹地遥感提取研究
收稿日期: 2022-01-15
修回日期: 2022-02-23
网络出版日期: 2022-06-13
基金资助
内蒙古自然科学基金(2018MS03049);“十三五”国家重点研发计划“火烧及采伐迹地森林生态系统恢复和功能提升关键技术”(2017YFC0504003);黄山学院人才启动项目“树干直径微变化特征及其对环境因子的响应研究”(2020xkjq012)
Study on Remote Sensing Extractions of Burned Areas in Greater Khingan Mountains
Received date: 2022-01-15
Revised date: 2022-02-23
Online published: 2022-06-13
林火是森林生态系统的重要影响因子,遥感技术的发展为林火监测和损失估算提供了强有力的技术手段。以内蒙古根河市1987年、2003年和2015年火烧迹地为研究对象,基于Landsat遥感影像,对比分析NDVI,EVI,GEMI,BAI,NBR,dNBR,NDSWIR等7种常见遥感指数对火烧迹地的提取能力,从而筛选出适合大兴安岭火烧迹地提取的最佳遥感指数。通过对遥感指数和分离指数的对比分析,得出结论:1)经历林火干扰的植被,其光谱特征也相应改变,基于近红外波段的遥感指数可将火烧迹地与正常植被区分。2)dNBR为提取大兴安岭火烧迹地的最佳遥感指数,其提取精度在90%以上;其次为NBR,BAI,NDSWIR;NDVI和EVI最差。 3)基于dNBR提取的3个不同年份火烧迹地面积分别为3 145.23,197 726.67,48.06hm2。
郝帅 , 王星 , 张秋良 , 王冰 , 田原 . 大兴安岭火烧迹地遥感提取研究[J]. 林草资源研究, 2022 , 0(2) : 75 -81 . DOI: 10.13466/j.cnki.lyzygl.2022.02.011
Fire is an important influencing factor of forest ecosystem. The development of remote sensing technology provides a powerful technical means for forest fire monitoring and loss estimation. Based on Landsat images,the extraction abilities of seven common remote sensing indexes (NDVI,EVI,GEMI,BAI,NBR,dNBR,NDSWIR) for the burned area in 1987,2003 and 2015 of Greater Khingan Mountains were compared and analyzed,so as to select the best remote sensing index suitable for the extraction of burning areas in Greater Khingan Mountains. Through the comparative analysis of the remote sensing indexes and the separation index,the following results were obtained:(1) after the fire,the spectral characteristics of vegetation changed correspondingly. The remote sensing index based on near-infrared band can distinguish the burned area from the normal vegetation. (2) The dNBR was the best remote sensing index for extracting the burned area in Greater Khingan Mountains,followed by the NBR,BAI and NDSWIR,the NDVI and EVI were the worst. (3) The areas of three burned areas based on dNBR were 3 145.23,197 726.67 and 48.06 hm2,respectively.
| [1] | 安森鹏, 高振岭, 王磊. 林火对森林生态系统的影响[J]. 科技信息, 2013(6):90-91. |
| [2] | 杨伟, 张树文, 姜晓丽. 基于MODIS时序数据的黑龙江流域火烧迹地提取[J]. 生态学报, 2015, 35(17):5866-5873. |
| [3] | Mouillot F, Schultz M G, Yue C, et al. Ten years of global burned area products from spaceborne remote sensing—A review:Analysis of user needs and recommendations for future developments[J]. International Journal of Applied Earth Observations and Geoinformation, 2014, 26:64-79. |
| [4] | 钱峻屏, 李岩, 廖其芳, 等. 利用遥感植被指数分析中国东部样带农业生态系统的生产力格局[J]. 生态学杂志, 2001(6):46-49. |
| [5] | 孙桂芬, 覃先林, 刘树超, 等. 典型植被指数识别火烧迹地潜力分析[J]. 国土资源遥感, 2019, 31(1):204-211. |
| [6] | Key C H. Ecological and sampling constraints on defining landscape fire severity[J]. Fire Ecology, 2006, 2(2):34-59. |
| [7] | Roy D P, Jin Y, Lewis P E, et al. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data[J]. Remote Sensing of Environment, 2005, 97(2):137-162. |
| [8] | 孙桂芬. 森林火烧迹地识别及植被恢复卫星遥感监测方法[D]. 北京: 中国林业科学研究院, 2018. |
| [9] | 朱曦, 覃先林. 基于二阶段算法的HJ-CCD数据过火区制图[J]. 遥感技术与应用, 2013, 28(1):72-77. |
| [10] | 吴立叶, 沈润平, 李鑫慧, 等. 不同遥感指数提取林火迹地研究[J]. 遥感技术与应用, 2014, 29(4):567-574. |
| [11] | 祖笑锋, 覃先林, 尹凌宇, 等. 基于高分一号影像光谱指数识别火烧迹地的决策树方法[J]. 林业资源管理, 2015(4):73-78. |
| [12] | 康祥瑞. 基于Landsat TM影像的火烧迹地遥感提取及林火烈度的空间分析[D]. 哈尔滨: 东北林业大学, 2017. |
| [13] | 包月梅, 孙紫英, 赵鹏武, 等. 基于遥感数据的根河市火烧迹地植被覆盖度时空分析[J]. 东北林业大学学报, 2015, 43(11):62-69. |
| [14] | 罗火钱, 陈文惠. 火烧迹地森林恢复的植被指数时间序列特征谱研究[J]. 四川林勘设计, 2012(1):23-30. |
| [15] | 余哲修, 何超, 李昔纯, 等. 基于归一化燃烧率火烧迹地遥感提取识别规则研究[J]. 西北林学院学报, 2018, 33(1):216-224. |
| [16] | 王晓莉, 王文娟, 常禹, 等. 基于NBR指数分析大兴安岭呼中森林过火区的林火烈度[J]. 应用生态学报, 2013, 24(4):967-974. |
| [17] | Veraverbeke S, Harris S, Hook S. Evaluating spectral indices for burned area discrimination using MODIS/ASTER(MASTER)airborne simulator data[J]. Remote Sensing of Environment, 2011, 115(10):2702-2709. |
| [18] | Loboda T, O'Neal K J, Csiszar I. Regionally adaptable dNBR-based algorithm for burned area mapping from MODIS data[J]. Remote Sensing of Environment, 2007, 109(4):429-442. |
| [19] | Bastarrika A, Chuvieco E, Martín M P. Mapping burned areas from Landsat TM/ETM+data with a two-phase algorithm:Balancing omission and commission errors[J]. Remote Sensing of Environment, 2010, 115(4):1003-1012. |
| [20] | 钱安, 陈娅男, 王秋玲. 基于Landsat影像提取大兴安岭区域火烧迹地[J]. 山西建筑, 2017, 43(15):192-193. |
| [21] | Gitas I Z, Devereux B J. The role of topographic correction in mapping recently burned Mediterranean forest areas from LANDSAT TM images[J]. International Journal of Remote Sensing, 2006, 27(1):41-54. |
/
| 〈 |
|
〉 |
