Citation: | Guo Zhengqi, Zhang Xiaoli, Wang Yueting. Ability evaluation of coniferous forest aboveground biomass inversion using Sentinel-2A multiple characteristic variables[J]. Journal of Beijing Forestry University, 2020, 42(11): 27-38. DOI: 10.12171/j.1000-1522.20200097 |
[1] |
Hu X, Zhang L, Ye L, et al. Locating spatial variation in the association between road network and forest biomass carbon accumulation[J]. Ecological Indicators, 2017, 73: 214−223. doi: 10.1016/j.ecolind.2016.09.042.
|
[2] |
Seedre M, Janda P, Trotsiuk V, et al. Biomass carbon accumulation patterns throughout stand development in primary uneven-aged forest driven by mixed-severity natural disturbances[J/OL]. Forest Ecology and Management, 2020, 455 (2019−11−18) [2020−02−01]. https://doi.org/10.1016/j.foreco.2019.117676.
|
[3] |
Drake J F, Swisdak M, Cattell C, et al. Formation of electron holes and particle energization during magnetic reconnection[J]. Science, 2003, 299(5608): 873−877. doi: 10.1126/science.1080333.
|
[4] |
刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015, 19(1):62−74. doi: 10.11834/jrs.20154108.
Liu Q, Yang L, Liu Q H, et al. Summary of remote sensing inversion methods for forest aboveground biomass[J]. Journal of Remote Sensing, 2015, 19(1): 62−74. doi: 10.11834/jrs.20154108.
|
[5] |
冯宗炜, 陈楚莹, 张家武, 等. 湖南会同地区马尾松林生物量的测定[J]. 林业科学, 1982(2):127−134.
Feng Z W, Chen C Y, Zhang J W, et al. Biomass determination of Pinus massoniana forest in Huitong Area, Hunan[J]. Forestry Science, 1982(2): 127−134.
|
[6] |
李德仁, 王长委, 胡月明, 等. 遥感技术估算森林生物量的研究进展[J]. 武汉大学学报(信息科学版), 2012, 37(6):631−635.
Li D R, Wang C W, Hu Y M, et al. Research advances in forest biomass estimation using remote sensing technology[J]. Journal of Wuhan University (Information Science Edition), 2012, 37(6): 631−635.
|
[7] |
Dong J, Kaufmann R K, Myneni R B, et al. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks[J]. Remote Sensing of Environment, 2003, 84(3): 393−410. doi: 10.1016/S0034-4257(02)00130-X.
|
[8] |
Kumar S, Khati U G, Chandola S, et al. Polarimetric SAR interferometry based modeling for tree height and aboveground biomass retrieval in a tropical deciduous forest[J]. Advances in Space Research, 2017, 60(3): 571−586. doi: 10.1016/j.asr.2017.04.018.
|
[9] |
Ho T M, Le T T, Rocca F, et al. SAR tomography for the retrieval of forest biomass and height: cross-validation at two tropical forest sites in French Guiana[J]. Remote Sensing of Environment, 2016, 175: 138−147. doi: 10.1016/j.rse.2015.12.037.
|
[10] |
王月婷. 基于多源遥感数据的森林蓄积量估算[D]. 北京: 北京林业大学, 2015.
Wang Y T. Forest volume estimation based on multi-source remote sensing data[D]. Beijing: Beijing Forestry University, 2015.
|
[11] |
Castillo M, Rivard B, Sánchez-Azofeifa A, et al. LIDAR remote sensing for secondary tropical dry forest identification[J]. Remote Sensing of Environment, 2012, 121: 132−143. doi: 10.1016/j.rse.2012.01.012.
|
[12] |
Nelson R, Oderwald R, Gregoire T G. Separating the ground and airborne laser sampling phases to estimate tropical forest basal area, volume, and biomass[J]. Remote Sensing of Environment, 1997, 60(3): 311−326. doi: 10.1016/S0034-4257(96)00213-1.
|
[13] |
田昕, 陈尔学, 李增元, 等. 基于多极化星载SAR数据的水稻/旱田识别——以江苏省海安县为例[J]. 遥感技术与应用, 2012, 27(3):406−412. doi: 10.11873/j.issn.1004-0323.2012.3.406.
Tian X, Chen E X, Li Z Y, et al. Rice/dry field recognition based on multipolar spaceborne SAR data: a case study of Hai'an county, Jiangsu Province[J]. Remote Sensing Technology and Application, 2012, 27(3): 406−412. doi: 10.11873/j.issn.1004-0323.2012.3.406.
|
[14] |
Israelsson H, Askne J, Sylvander R. Potential of SAR for forest bole volume estimation[J]. International Journal of Remote Sensing, 1994, 15(14): 2809−2826. doi: 10.1080/01431169408954286.
|
[15] |
Sandberg G, Ulander L M H, Fransson J E S, et al. L- and P-band backscatter intensity for biomass retrieval in hemiboreal forest[J]. Remote Sensing of Environment, 2011, 115(11): 2874−2886. doi: 10.1016/j.rse.2010.03.018.
|
[16] |
冯琦, 陈尔学, 李增元, 等. 基于机载P-波段全极化SAR数据的复杂地形森林地上生物量估测方法[J]. 林业科学, 2016, 52(3):10−22.
Feng Q, Chen E X, Li Z Y, et al. Estimation of aboveground biomass in complex terrain based on airborne P-band full-polarization SAR data[J]. Forestry Science, 2016, 52(3): 10−22.
|
[17] |
谭炳香. 浅谈星载SAR的森林应用[J]. 遥感信息, 1995(3):40−41.
Tan B X. The forest application of spaceborne SAR[J]. Remote Sensing Information, 1995(3): 40−41.
|
[18] |
肖虹雁, 岳彩荣. 合成孔径雷达技术在林业中的应用综述[J]. 林业调查规划, 2014, 39(2):132−137. doi: 10.3969/j.issn.1671-3168.2014.02.029.
Xiao H Y, Yue C R. Overview of the application of synthetic aperture radar technology in forestry[J]. Forestry Survey Planning, 2014, 39(2): 132−137. doi: 10.3969/j.issn.1671-3168.2014.02.029.
|
[19] |
吴一戎, 朱敏慧. 合成孔径雷达技术的发展现状与趋势[J]. 遥感技术与应用, 2000(2):121−123. doi: 10.3969/j.issn.1004-0323.2000.02.012.
Wu Y R, Zhu M H. Development status and trend of synthetic aperture radar technology[J]. Remote Sensing Technology and Application, 2000(2): 121−123. doi: 10.3969/j.issn.1004-0323.2000.02.012.
|
[20] |
Næsset E, Gobakken T, Solberg S, et al. Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: a case study from a boreal forest area[J]. Remote Sensing of Environment, 2011, 115(12): 3599−3614. doi: 10.1016/j.rse.2011.08.021.
|
[21] |
Zhao K, Popescu S, Nelson R. Lidar remote sensing of forest biomass: a scale-invariant estimation approach using airborne lasers[J]. Remote Sensing of Environment, 2009, 113(1): 182−196. doi: 10.1016/j.rse.2008.09.009
|
[22] |
邹永林. 归并排序的概念与算法设计[J]. 现代计算机(专业版), 2015(20):48−51.
Zou Y L. Concept and algorithm design of merge sort[J]. Modern Computer (Professional Edition), 2015(20): 48−51.
|
[23] |
Muukkonen P, Heiskanen J. Estimating biomass for boreal forests using ASTER satellite data combined with standwise forest inventory data[J]. Remote Sensing of Environment, 2005, 99(4): 434−447. doi: 10.1016/j.rse.2005.09.011.
|
[24] |
田颖, 陈卓奇, 惠凤鸣, 等. 欧空局哨兵卫星Sentinel-2A/B数据特征及应用前景分析[J]. 北京师范大学学报(自然科学版), 2019, 55(1):57−65.
Tian Y, Chen Z Q, Hui F M, et al. Data characteristics and application prospect analysis of ESA sentinel satellite Sentinel-2A/B[J]. Journal of Beijing Normal University (Natural Science Edition), 2019, 55(1): 57−65.
|
[25] |
安海波, 李斐, 赵萌莉, 等. 基于优化光谱指数的牧草生物量估算[J]. 光谱学与光谱分析, 2015, 35(11):3155−3160.
An H B, Li F, Zhao M L, et al. Forage biomass estimation based on optimized spectral index[J]. Spectroscopy and Spectral Analysis, 2015, 35(11): 3155−3160.
|
[26] |
郑阳, 吴炳方, 张淼. Sentinel-2数据的冬小麦地上干生物量估算及评价[J]. 遥感学报, 2017, 21(2):318−328. doi: 10.11834/jrs.20176269.
Zheng Y, Wu B F, Zhang M. Aboveground dry biomass estimation and evaluation of winter wheat based on Sentinel-2 data[J]. Journal of Remote Sensing, 2017, 21(2): 318−328. doi: 10.11834/jrs.20176269.
|
[27] |
曹霖, 彭道黎, 王雪军, 等. 应用Sentinel-2A卫星光谱与纹理信息的森林蓄积量估算[J]. 东北林业大学学报, 2018, 46(9):54−58. doi: 10.3969/j.issn.1000-5382.2018.09.012.
Cao L, Peng D L, Wang X J, et al. Forest volume estimation using Sentinel-2A satellite spectrum and texture information[J]. Journal of Northeast Forestry University, 2018, 46(9): 54−58. doi: 10.3969/j.issn.1000-5382.2018.09.012.
|
[28] |
Pandit S, Tsuyuki S, Dube T. Estimating above-ground biomass in sub-tropical buffer zone community forests, nepal, using sentinel 2 data[J]. Remote Sensing, 2018, 10(4): 601. doi: 10.3390/rs10040601.
|
[29] |
陈瑜云. 基于Sentinel-2影像数据的毛竹林生物量估测[D]. 杭州: 浙江农林大学, 2019.
Chen Y Y. Biomass estimation of Phyllostachys pubescens forest based on Sentinel-2 image data[D]. Hangzhou: Zhejiang Agriculture and Forestry University, 2019.
|
[30] |
Immitzer M, Vuolo F, Atzberger C. First experience with Sentinel-2 data for crop and tree species classifications in Central Europe[J]. Remote Sensing, 2016, 8(3): 166. doi: 10.3390/rs8030166.
|
[31] |
毕恺艺, 牛铮, 黄妮, 等. 基于Sentinel-2A时序数据和面向对象决策树方法的植被识别[J]. 地理与地理信息科学, 2017, 33(5):16−20. doi: 10.3969/j.issn.1672-0504.2017.05.003.
Bi K Y, Niu Z, Huang N, et al. Vegetation recognition based on Sentinel-2A time series data and object-oriented decision tree method[J]. Geography and Geographic Information Science, 2017, 33(5): 16−20. doi: 10.3969/j.issn.1672-0504.2017.05.003.
|
[32] |
杨斌, 李丹, 王磊, 等. 基于Sentinel-2A岷江上游地表生物量反演与植被特征分析[J]. 科技导报, 2017, 35(21):74−80.
Yang B, Li D, Wang L, et al. Analysis of surface biomass inversion and vegetation characteristics based on Sentinel-2A upper Minjiang River[J]. Science & Technology Review, 2017, 35(21): 74−80.
|
[33] |
李海奎, 雷渊才. 中国森林植被生物量和碳储量评估[M]. 北京: 中国林业出版社, 2010.
Li H K, Lei Y C. Assessment of biomass and carbon storage of forest vegetation in China[M]. Beijing: China Forestry Publishing House, 2010.
|
[34] |
Wang M, Sun R, Xiao Z. Estimation of forest canopy height and aboveground biomass from spaceborne LiDAR and Landsat Imageries in Maryland[J]. Remote Sensing, 2018, 10(2): 344. doi: 10.3390/rs10020344.
|
[35] |
Chiang S H, Valdez M, Chen C F. Forest tree species distribution mapping using Landsat satellite imagery and topographic variables with the maximum entropy method in Mongolia[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2016, XLI-B8: 593−596. doi: 10.5194/isprsarchives-XLI-B8-593-2016.
|
[36] |
Dorren L K A, Maier B, Seijmonsbergen A C. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification[J]. Forest Ecology and Management, 2003, 183(1): 31−46.
|
[37] |
Liu Y, Gong W, Hu X, et al. Forest type identification with Random Forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data[J]. Remote Sensing, 2018, 10(6): 946. doi: 10.3390/rs10060946.
|