扫 描 看 全 文
LI Peng, JIANG Ningsang, FENG Zhiming, et al. Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos. [J]. National Remote Sensing Bulletin 26(11):2329-2343(2022)
刀耕火种农业是热带广泛存在且备受争议的传统土地利用类型，在多山且森林覆盖率高的老挝尤甚。人口增长、林业政策与气候变化等正在加剧刀耕火种农业自身演变及其向商品化种植园（如橡胶林）转变。因农林时空转变动态性、相较于现代农业的边缘性及其斑块破碎且分布随机等，热带刀耕火种农业遥感监测历来受到挑战，有关其从业人口、确切分布及其时空动态等问题仍悬而未决，且资料匮乏。为探究机器学习算法在刀耕火种监测中的应用潜力，利用2016年旱季（4月）两景Landsat OLI 影像，基于支持向量机算法，并通过消除建设用地噪声以提高新开辟刀耕火种提取精度（总体精度95%，Kappa系数0.81），据此揭示了老挝丰沙里省新开辟刀耕火种县域差异、与居民点‒道路距离以及地形特征。结果表明：（1）当年新开辟刀耕火种约为987.93 km,2,（全省占比6.10%），刀耕火种仍是该省重要土地利用类型，斑块南多北少、西多东少且呈破碎化分布。（2）各县新开辟刀耕火种面积介于100—210 km,2,，桑潘县最大（占该省面积的1/5），本讷县最小（1/10）。（3）近九成新开辟刀耕火种集中分布在距居民点5 km范围内，且距不同等级道路（次要公路,>,山路,>,主要公路）表现出距离衰减规律，次要公路两侧5 km内尤甚。（4）新开辟刀耕火种常见于低山（500—1000 m）斜坡（15°—25°）地带并以东南坡为主，县域变化差异小。本研究可为探索机器学习算法在热带刀耕火种演变遥感监测提供借鉴。
Swidden agriculture is a widespread but controversial traditional land-use type in the tropics, especially in mountainous Laos with high percentage of forest cover. Driven by population growth, forestry policies, and climate change, swidden agriculture has been experiencing rapid evolution itself and drastic transformations into commercial plantations, such as rubber plantation. However, the remote sensing monitoring of tropical swidden agriculture has always been challenged, primarily because of the spatial and temporal dynamics in agricultural and forest cover, marginal feature compared with modern agriculture, and fragmentation and random distribution of swidden patches, hence with many unsettled issues and very limited information on its involved population, exact distribution and spatio-temporal dynamics. To explore the application potentials of machine learning algorithms in monitoring swidden agriculture, with two Landsat Operational Land Imager (OLI) images acquired in April, or the peak of the 2016 dry season, a support machine algorithm (SVM) was modified by masking out the information of construction land to improve the classification accuracy, or an overall accuracy of 95% and a Kappa coefficient of 0.81, followed by the examination of spatial (e.g., district-level) differences of freshly-opened swidden in Phongsaly Province, Laos, and their characteristics to local settlements and varied-level roads as well as topographical features. The results showed that: (1) Swidden agriculture remains an important land use type in Phongsaly because the newly-opened swidden was about 987.93 km,2, (6.10% of the province) in 2016. More swidden patches were detected in the south and west parts of the province, with a fragmented distribution. (2) The area of newly-opened swiddens at district level ranged between 100—210 km,2, with Samphanh District ranking the first (1/5) and Boonneua District the last (1/10). (3) Approximately 90% of newly-opened swiddens were concentrated within five km to residential points, particularly within four km. Similarly, these swiddens exhibited a distance decay pattern along the minor roads, tracks and major roads, in particular within a distance of five km of minor roads. (4) The newly-opened swiddens were mainly distributed in low mountain area (500—1000 m) with slope gradients of 15°—25° and aspects of southeast, showing slight variations among the districts of Phongsaly Province. This study provides reference for exploring machine learning algorithms in remote sensing monitoring of swidden agriculture in transition in the tropics.
刀耕火种支持向量机Landsat可达性分析地形特征老挝
swidden agricultureSupport Vector Machine (SVM)Landsataccessibility analysistopographic featuresLaos
Béliveau A, Lucotte M, Davidson R, Do Canto Lopes L O and Paquet S. 2009. Early Hg mobility in cultivated tropical soils one year after slash-and-burn of the primary forest, in the Brazilian Amazon. Science of the Total Environment, 407(15): 4480-4489 [DOI: 10.1016/j.scitotenv.2009.04.012http://dx.doi.org/10.1016/j.scitotenv.2009.04.012]
Bourgoin C, Betbeder J, Le Roux R, Gond V, Oszwald J, Arvor D, Baudry J, Boussard H, Le Clech S, Mazzei L, Dessard H, Läderach P, Reymondin L, and Blanc L, 2021. Looking beyond forest cover: an analysis of landscape-scale predictors of forest degradation in the Brazilian Amazon. Environmental Research Letters, 16(11): 114045 [DOI:10.1088/1748-9326/ac31ebhttp://dx.doi.org/10.1088/1748-9326/ac31eb]
Brady N C. 1996. Alternatives to slash-and-burn: a global imperative. Agriculture, Ecosystems and Environment, 58(1): 3-11 [DOI: 10.1016/0167-8809(96)00650-0http://dx.doi.org/10.1016/0167-8809(96)00650-0]
Butler J H. 1980. Economic geography: spatial and environmental aspects of economic activity. New York: John Wiley and Sons.: 402
Colson D, Petropoulos G P and Ferentinos K P. 2018. Exploring the potential of Sentinels-1 and 2 of the Copernicus Mission in support of rapid and cost-effective wildfire assessment. International Journal of Applied Earth Observation and Geoinformation, 73: 262-276 [DOI: 10.1016/j.jag.2018.06.011http://dx.doi.org/10.1016/j.jag.2018.06.011]
Cramb R A, Colfer C J P, Dressler W, Laungaramsri P, Le Q T, Mulyoutami E, Peluso N L and Wadley R L. 2009. Swidden transformations and rural livelihoods in Southeast Asia. Human Ecology, 37(3): 323-346 [DOI: 10.1007/s10745-009-9241-6http://dx.doi.org/10.1007/s10745-009-9241-6]
Curtis P G, Slay C M, Harris N L, Tyukavina A and Hansen M C. 2018. Classifying drivers of global forest loss. Science, 361(6407): 1108-1111 [DOI: 10.1126/science.aau3445http://dx.doi.org/10.1126/science.aau3445]
Deng S B, Chen Q J, She H J and Xu E H. 2014. ENVI remote sensing image processing method. Beijing: Higher Education Press, 474
邓书斌, 陈秋锦, 社会建, 徐恩惠. 2014. ENVI遥感图像处理方法. 北京：高等教育出版社, 474
Dressler W, Wilson D, Clendenning J, Cramb R, Mahanty S, Lasco R, Keenan R, To P and Gevana D. 2015. Examining how long fallow swidden systems impact upon livelihood and ecosystem services outcomes compared with alternative land-uses in the uplands of Southeast Asia. Journal of Development Effectiveness, 7(2): 210-229 [DOI: 10.1080/19439342.2014.991799http://dx.doi.org/10.1080/19439342.2014.991799]
Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256
杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]
Feng Z M, Li W J, Li P and Xiao C W. 2020. Relief degree of land surface and its geographical meanings in the Qinghai-Tibet Plateau, China. Acta Geographica Sinica, 75(7): 1359-1372
封志明, 李文君, 李鹏, 肖池伟. 2020. 青藏高原地形起伏度及其地理意义. 地理学报, 75(7): 1359-1372 [DOI: 10.11821/dlxb202007003http://dx.doi.org/10.11821/dlxb202007003]
Fox J, Castella J C and Ziegler A D. 2014. Swidden, rubber and carbon: can REDD+ work for people and the environment in Montane Mainland Southeast Asia? Global Environmental Change, 29: 318-326 [DOI: 10.1016/j.gloenvcha.2013.05.011http://dx.doi.org/10.1016/j.gloenvcha.2013.05.011]
Gao Y, Skutsch M, Paneque-Gálvez J and Ghilardi A. 2020. Remote sensing of forest degradation: a review. Environmental Research Letters, 15(10): 103001 [DOI: 10.1088/1748-9326/abaad7http://dx.doi.org/10.1088/1748-9326/abaad7]
Hansen M C, Potapov P V, Moore R, Hancher M, Turubanova S A, Tyukavina A, Thau D, Stehman S V, Goetz S J, Loveland T R, Kommareddy A, Egorov A, Chini L, Justice C O and Townshend J R G. 2013. High-resolution global maps of 21st-Century forest cover change. Science, 342(6160): 850-853 [DOI: 10.1126/science.1244693http://dx.doi.org/10.1126/science.1244693]
Heinimann A, Mertz O, Frolking S, Christensen A E, Hurni K, Sedano F, Chini L P, Sahajpal R, Hansen M and Hurtt G. 2017. A global view of shifting cultivation: recent, current, and future extent. PLoS One, 12(9): e0184479 [DOI: 10.1371/journal.pone.0184479http://dx.doi.org/10.1371/journal.pone.0184479]
Hui W H. 2006. TM image classification based on Support Vector Machine. Journal of Earth Sciences and Environment 28(2): 93-95
惠文华. 2006. 基于支持向量机的遥感图像分类方法. 地球科学与环境学报, 28(2): 93-95
Hurni K, Hett C, Heinimann A, Messerli P and Wiesmann U. 2013. Dynamics of shifting cultivation landscapes in Northern Lao PDR between 2000 and 2009 based on an analysis of MODIS time series and Landsat images. Human Ecology, 41(1): 21-36 [DOI: 10.1007/s10745-012-9551-yhttp://dx.doi.org/10.1007/s10745-012-9551-y]
Hurtt G C, Chini L P, Frolking S, Betts R A, Feddema J, Fischer G, Fisk J P, Hibbard K, Houghton R A, Janetos A, Jones C D, Kindermann G, Kinoshita T, Goldewijk K K, Riahi K, Shevliakova E, Smith S, Stehfest E, Thomson A, Thornton P, van Vuuren D P and Wang Y P. 2011. Harmonization of land-use scenarios for the period 1500—2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 109(1-2): 117-161 [DOI: 10.1007/s10584-011-0153-2http://dx.doi.org/10.1007/s10584-011-0153-2]
Ingalls M L, Diepart J C, Truong N, Hayward D, Neil T, Phomphakdy C, Bernhard R, Fogarizzu S, Epprecht M, Nanhthavong V, Vo D H, Nguyen D, Nguyen P A, Saphangthong T, Inthavong C, Hett C and Tagliarino N. 2018. State of land in the Mekong Region. Bern, Switzerland: Bern Open Publishing, 105
Inoue Y, Kiyono Y, Asai H, Ochiai Y, Qi J G, Olioso A, Shiraiwa T, Horie T, Saito K and Dounagsavanh L. 2010. Assessing land-use and carbon stock in slash-and-burn ecosystems in tropical mountain of Laos based on time-series satellite images. International Journal of Applied Earth Observation and Geoinformation, 12(4): 287-297 [DOI: 10.1016/j.jag.2010.04.004http://dx.doi.org/10.1016/j.jag.2010.04.004]
Jakovac C C, Dutrieux L P, Siti L, Peña-Claros M and Bongers F. 2017. Spatial and temporal dynamics of shifting cultivation in the middle-Amazonas River: expansion and intensification. PLoS One, 12(7): e0181092 [DOI: 10.1371/journal.pone.0181092http://dx.doi.org/10.1371/journal.pone.0181092]
Kesikoglu M H, Atasever U H, Dadaser-Celik F and Ozkan C. 2019. Performance of ANN, SVM and MLH techniques for land use/cover change detection at Sultan Marshes wetland, Turkey. Water Science and Technology, 80(3): 466-477 [DOI: 10.2166/wst.2019.290http://dx.doi.org/10.2166/wst.2019.290]
Lao Statistics Bureau. 2014. Laos Statistical Yearbook (2013). Vientiane Capital, June 2014: 128 [Webpage: http/www.lsb.gov.lawww.lsb.gov.la]
Lao Statistics Bureau. 2018. Laos Statistical Yearbook (2017). Vientiane Capital, June 2018: 166 [Webpage: http/www.lsb.gov.lawww.lsb.gov.la]
Leisz S J and Rasmussen M S. 2012. Mapping fallow lands in Vietnam’s north-central mountains using yearly Landsat imagery and a land-cover succession model. International Journal of Remote Sensing, 33(20): 6281-6303 [DOI: 10.1080/01431161.2012.681712http://dx.doi.org/10.1080/01431161.2012.681712]
Li N, Zhu X F, Pan Y Z and Zhan P. 2018. Optimized SVM based on artificial bee colony algorithm for remote sensing image classification. Journal of Remote Sensing, 22(4):559-569
李楠, 朱秀芳, 潘耀忠, 詹培. 2018. 人工蜂群算法优化的SVM遥感影像分类. 遥感学报, 22(4): 559-569 [DOI: 10.11834/jrs.20187176http://dx.doi.org/10.11834/jrs.20187176]
Li P and Feng Z M. 2016. Extent and area of swidden in montane mainland southeast Asia: estimation by Multi-Step thresholds with landsat-8 OLI data. Remote Sensing, 8(1): 44 [DOI: 10.3390/rs8010044http://dx.doi.org/10.3390/rs8010044]
Li P, Feng Z M, Jiang L G, Liao C H and Zhang J H. 2014. A review of swidden agriculture in Southeast Asia. Remote Sensing, 6(2): 1654-1683 [DOI: 10.3390/rs6021654http://dx.doi.org/10.3390/rs6021654]
Li P, Feng Z M and Xiao C W. 2018b. Acquisition probability differences in cloud coverage of the available Landsat observations over mainland Southeast Asia from 1986 to 2015. International Journal of Digital Earth, 11(5): 437-450 [DOI: 10.1080/17538947.2017.1327619http://dx.doi.org/10.1080/17538947.2017.1327619]
Li P, Feng Z M, Xiao C W, Boudmyxay K and Liu Y. 2018a. Detecting and mapping annual newly-burned plots (NBP) of swiddening using historical Landsat data in Montane Mainland Southeast Asia (MMSEA) during 1988—2016. Journal of Geographical Sciences, 28(9): 1307-1328 [DOI: 10.1007/s11442-018-1527-4http://dx.doi.org/10.1007/s11442-018-1527-4]
Li P, Xiao C, and Feng Z, 2022. Swidden agriculture in transition and its roles in tropical forest loss and industrial plantation expansion. Land Degradation & Development, 33(2): 388-392 [DOI:10.1002/ldr.4152http://dx.doi.org/10.1002/ldr.4152]
Liao C H, Feng Z M, Li P and Liu X N. 2014. The spatio-temporal variation of shifting cultivation in the border region of Myanmar, Laos and Thailand. Geographical Research, 33(8): 1529-1541
廖谌婳, 封志明, 李鹏, 刘晓娜. 2014. 缅老泰交界地区刀耕火种农业的时空变化格局. 地理研究, 33(8): 1529-1541 [DOI: 10.11821/dlyj201408012http://dx.doi.org/10.11821/dlyj201408012]
Liao C H, Feng Z M, Li P and Zhang J H. 2015. Monitoring the spatio-temporal dynamics of swidden agriculture and fallow vegetation recovery using Landsat imagery in Northern Laos. Acta Geographica Sinica, 70(4): 591-603
廖谌婳, 封志明, 李鹏, 张景华. 2015. 老挝北部刀耕火种农业变化及植被恢复效应. 地理学报, 70(4): 591-603 [DOI: 10.11821/dlxb201504007http://dx.doi.org/10.11821/dlxb201504007]
Mallinis G and Koutsias N. 2012. Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data. International Journal of Remote Sensing, 33(14): 4408-4433 [DOI: 10.1080/01431161.2011.648284http://dx.doi.org/10.1080/01431161.2011.648284]
Mertz O. 2009. Trends in shifting cultivation and the REDD mechanism. Current Opinion in Environmental Sustainability, 1(2): 156-160 [DOI: 10.1016/j.cosust.2009.10.002http://dx.doi.org/10.1016/j.cosust.2009.10.002]
Mertz O, Leisz S J, Heinimann A, Rerkasem K, Thiha, Dressler W, Pham V C, Vu K C, Schmidt-Vogt D, Colfer C J P, Epprecht M, Padoch C and Potter L. 2009. Who counts? Demography of swidden cultivators in Southeast Asia. Human Ecology, 37(3): 281-289 [DOI: 10.1007/s10745-009-9249-yhttp://dx.doi.org/10.1007/s10745-009-9249-y]
Naismith W W. 1892. Excursions: Cruach Ardran, Stobinian, and Ben More. The Scottish Mountaineering Club Journal，2(3): 136.
Padoch C, Coffey K, Mertz O, Leisz S J, Fox J and Wadley R L. 2007. The demise of swidden in Southeast Asia? Local realities and regional ambiguities. Geografisk Tidsskrift-Danish Journal of Geography, 107(1): 29-41 [DOI: 10.1080/00167223.2007.10801373http://dx.doi.org/10.1080/00167223.2007.10801373]
Pasha S V, Behera M D, Mahawar S K, Barik S K and Joshi S R. 2020. Assessment of shifting cultivation fallows in Northeastern India using Landsat imageries. Tropical Ecology, 61(1): 65-75 [DOI: 10.1007/s42965-020-00062-0http://dx.doi.org/10.1007/s42965-020-00062-0]
Pu R L and Gong P. 2004. Determination of burnt scars using logistic regression and neural network techniques from a single post-fire Landsat 7 ETM+ image. Photogrammetric Engineering and Remote Sensing, 70(7): 841-850 [DOI: 10.14358/PERS.70.7.841http://dx.doi.org/10.14358/PERS.70.7.841]
Roder W, Phengchanh S and Maniphone S. 1997. Dynamics of soil and vegetation during crop and fallow period in slash-and-burn fields of Northern Laos. Geoderma, 76(1-2): 131-144 [DOI: 10.1016/S0016-7061(96)00100-0http://dx.doi.org/10.1016/S0016-7061(96)00100-0]
Rossi J P, Celini L, Mora P, Mathieu J, Lapied E, Nahmani J, Ponge J F and Lavelle P. 2010. Decreasing fallow duration in tropical slash-and-burn agriculture alters soil macroinvertebrate diversity: a case study in southern French Guiana. Agriculture, Ecosystems and Environment, 135(1-2): 148-154 [DOI: 10.1016/j.agee.2009.08.012http://dx.doi.org/10.1016/j.agee.2009.08.012]
Ryan C M, Pritchard R, McNicol I, Owen M, Fisher J A and Lehmann C. 2016. Ecosystem services from southern African woodlands and their future under global change. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1703): 20150312 [DOI: 10.1098/rstb.2015.0312http://dx.doi.org/10.1098/rstb.2015.0312]
Schmidt-Vogt D, Leisz S J, Mertz O, Heinimann A, Thiha T, Messerli P, Epprecht M, Cu P V, Chi V K, Hardiono M and Dao T M. 2009. An assessment of trends in the extent of swidden in Southeast Asia. Human Ecology, 37(3): 269-280 [DOI: 10.1007/s10745-009-9239-0http://dx.doi.org/10.1007/s10745-009-9239-0]
Silva J M N, Carreiras J M B, Rosa I and Pereira J M C. 2011. Greenhouse gas emissions from shifting cultivation in the tropics, including uncertainty and sensitivity analysis. Journal of Geophysical Research, 116(D20): D20304 [DOI: 10.1029/2011JD016056http://dx.doi.org/10.1029/2011JD016056]
Tang X Z, Machimura T, Li J F, Liu W and Hong H Y. 2020. A novel optimized repeatedly random undersampling for selecting negative samples: a case study in an SVM-based forest fire susceptibility assessment. Journal of Environmental Management, 271: 111014 [DOI: 10.1016/j.jenvman.2020.111014http://dx.doi.org/10.1016/j.jenvman.2020.111014]
Tinker P B, Ingram J S I and Struwe S. 1996. Effects of slash-and-burn agriculture and deforestation on climate change. Agriculture, Ecosystems and Environment, 58(1): 13-22 [DOI: 10.1016/0167-8809(95)00651-6http://dx.doi.org/10.1016/0167-8809(95)00651-6]
Van Vliet N, Mertz O, Heinimann A, Langanke T, Pascual U, Schmook B, Adams C, Schmidt-Vogt D, Messerli P, Leisz S, Castella J C, Jørgensen L, Birch-Thomsen T, Hett C, Bech-Bruun T, Ickowitz A, Vu K C, Yasuyuki K, Fox J, Padoch C, Dressler W and Ziegler A D. 2012. Trends, drivers and impacts of changes in swidden cultivation in tropical forest-agriculture frontiers: a global assessment. Global Environmental Change, 22(2): 418-429 [DOI: 10.1016/j.gloenvcha.2011.10.009http://dx.doi.org/10.1016/j.gloenvcha.2011.10.009]
Wada Y, Rajan K S and Shibasaki R. 2007. Modelling the spatial distribution of shifting cultivation in Luangprabang, Lao PDR. Environment and Planning B: Planning and Design, 34(2): 261-278 [DOI: 10.1068/b31180http://dx.doi.org/10.1068/b31180]
Wu H A, Jiang J J, Zhang H L, Zhang L and Zhou J. 2006. Application of ratio resident-area index to retrieve urban residential areas based on Landsat TM data. Journal of Nanjing Normal University (Natural Science), 29(3): 118-121
吴宏安, 蒋建军, 张海龙, 张丽, 周杰. 2006. 比值居民地指数在城镇信息提取中的应用. 南京师大学报(自然科学版), 29(3): 118-121 [DOI: 10.3969/j.issn.1001-4616.2006.03.026http://dx.doi.org/10.3969/j.issn.1001-4616.2006.03.026]
Wu H A and Zhang Y H. 2011. Study on InSAR model of ALOS/PALSAR data. Remote Sensing Information, (2): 106-110
吴宏安, 张永红. 2011. ALOS/PALSAR InSAR数学模型研究. 遥感信息, (2): 106-110 [DOI: 10.3969/j.issn.1000-3177.2011.02.020http://dx.doi.org/10.3969/j.issn.1000-3177.2011.02.020]
Xie D W and Shi S L. 2014. Prediction for burned area of forest fires based on SVM model. Applied Mechanics and Materials, 513-517: 4084-4089 [DOI: 10.4028/www.scientific.net/AMM.513-517.4084http://dx.doi.org/10.4028/www.scientific.net/AMM.513-517.4084]
Xiong D and Yan L. 2019. Early smoke detection of forest fires based on SVM image segmentation. Journal of Forest Science, 65(4): 150-159 [DOI: 10.17221/82/2018-JFShttp://dx.doi.org/10.17221/82/2018-JFS]
Xu Q，Zhang J S，Zhang F，Ge S， Yang Z and Duan Y M. 2022. Applicability of weak samples to deep learning crop classification. National Remote Sensing Bulletin， 26(7)：1395-1409
许晴，张锦水，张凤，盖爽，杨志，段雅鸣. 2022. 深度学习农作物分类的弱样本适用性.遥感学报, 26(7): 1395-1409 [DOI: 10.11834/jrs.20221127http://dx.doi.org/10.11834/jrs.20221127]
Zha Y, Ni S X and Yang S. 2003. An effective approach to automatically extract urban land-use from TM imagery. Journal of Remote Sensing, 7(1): 37-40
查勇, 倪绍祥, 杨山. 2003. 一种利用TM图像自动提取城镇用地信息的有效方法. 遥感学报, 7(1): 37-40 [DOI: 10.11834/jrs.20030107http://dx.doi.org/10.11834/jrs.20030107]
Zu X F, Qin X L, Yin L Y, Chen X Z and Zhong X Q. 2015. Decision tree method for burned area identification based on the spectral index of GF-1 WFV image. Forest Resources Management, 4: 73-78, 83
祖笑锋, 覃先林, 尹凌宇, 陈小中, 钟祥清. 2015. 基于高分一号影像光谱指数识别火烧迹地的决策树方法. 林业资源管理, (4): 73-78, 83 [DOI: 10.13466/j.cnki.lyzygl.2015.04.013http://dx.doi.org/10.13466/j.cnki.lyzygl.2015.04.013]
相关文章
相关作者
相关机构
京公网安备11010802024621