Please wait a minute...
 
自然资源遥感  2022, Vol. 34 Issue (1): 115-126    DOI: 10.6046/zrzyyg.2021064
     技术方法 本期目录 | 过刊浏览 | 高级检索 |
基于集成学习和多时相遥感影像的枸杞种植区分类
史飞飞1,2,3,4,5(), 高小红1,3,4,6(), 肖建设2,5, 李宏达1,3,4, 李润祥1,3,4, 张昊1,3,4
1.青海师范大学地理科学学院,西宁 810008
2.青海省气象科学研究所,西宁 810008
3.青海省自然地理与环境过程重点实验室,西宁 810008
4.青藏高原地表过程与生态保育教育部重点实验室,西宁 810008
5.青海省防灾减灾重点实验室,西宁 810008
6.高原科学与可持续发展研究院, 西宁 810008
Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images
SHI Feifei1,2,3,4,5(), GAO Xiaohong1,3,4,6(), XIAO Jianshe2,5, LI Hongda1,3,4, LI Runxiang1,3,4, ZHANG Hao1,3,4
1. School of Geographical Sciences, Qinghai Normal University, Xining 810008, China
2. Institute of Qinghai Meteorological Science Research, Xining 810008, China
3. Key Laboratory of Physical Geography and Environmental Process of Qinghai Province, Xining 810008, China
4. Ministry of Education Key Laboratory of Tibetan Plateau Land Surface Processes and Ecological Conservation, Xining 810008, China
5. Key Laboratory of Disaster Prevention and Mitigation of Qinghai Province, Xining 810008, China
6. Academy of Plateau Science and Sustainability, Xining 810008, China
全文: PDF(10347 KB)   HTML  
输出: BibTeX | EndNote (RIS)      
摘要 

利用遥感技术对柴达木盆地枸杞种植区进行精准提取对当地政府开展市场管理与调控具有重要意义。以典型枸杞种植区诺木洪农场为例,选取Landsat8 OLI和GF-1 WFV影像构建作物生长期内时序NDVI/EVI数据,并采用4种新颖的集成学习分类器(LightGBM,GBDT,XGBoost,RF)和2种应用广泛的机器学习分类器(SVM,MLPC)对枸杞种植区进行分类。研究结果表明: ①LightGBM(90.4%),GBDT(90.4%),XGBoost(89.31%)和RF(86.96%)分类器能获得较高的分类精度,并以LightGBM+EVI的总体分类精度最高,达到了91.67%,Kappa系数为0.90; ②EVI指数在枸杞生长中后期表现更为灵敏,并在同一分类器下使用EVI时序数据能获得更好的枸杞作物制图效果; ③利用GBDT,XGBoost和RF分类器的特征重要性评分方法进行枸杞种植区分类时相特征优选,能够在获取高分类精度的同时进一步降低数据冗余。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
史飞飞
高小红
肖建设
李宏达
李润祥
张昊
关键词 作物分类枸杞NDVI/EVI时序集成学习    
Abstract

It is significant for the market management and regulation of local government to accurately extract wolfberry planting areas in the Qaidam Basin using remote sensing technology. Taking the Nuomuhong Farm, a typical wolfberry planting area, as an example, this study selected Landsat8 OLI and GF-1 WFV images to construct the time-series NDVI/EVI data of the crop growth period. Then, this study employed four novel ensemble learning classifiers (i.e., LightGBM, GBDT, XGBoost, and RF) and two widely used machine learning classifiers (SVM and MLPC) to classify wolfberry planting areas. The results show that: ① Relatively high classification accuracy were obtained using LightGBM (90.4%), GBDT (90.4%), XGBoost (89.31%), and RF (86.96%). Most especially, LightGBM-EVI yielded the highest overall classification accuracy (91.67%), with a Kappa coefficient of 0.90; ② Enhanced vegetation index (EVI) is more sensitive in the middle-late stage of the wolfberry growth period. For the same classifier, better mapping effects of wolfberry planting areas can be obtained when time-series EVI data were used; ③ Data redundancy can be further reduced while obtaining high classification accuracy by determining the optimal temporal features of NDVI/EVI classification using the feature importance scores of the GBDT, XGBoost, and RF classifiers.

Key wordscrop classification    wolfberry    NDVI/EVI time series    ensemble learning
收稿日期: 2021-03-10      出版日期: 2022-03-14
ZTFLH:  TP79S5  
基金资助:青海省自然科学基金项目“基于GEE云平台与Landsat卫星长时间序列数据的湟水流域30多年土地利用/土地覆被时空变化研究”资助编号(2021-ZJ-913)
通讯作者: 高小红
作者简介: 史飞飞(1991-),男,博士研究生,工程师,主要研究方向为遥感应用与地理数据空间分析。Email: shifeifei1203@126.com
引用本文:   
史飞飞, 高小红, 肖建设, 李宏达, 李润祥, 张昊. 基于集成学习和多时相遥感影像的枸杞种植区分类[J]. 自然资源遥感, 2022, 34(1): 115-126.
SHI Feifei, GAO Xiaohong, XIAO Jianshe, LI Hongda, LI Runxiang, ZHANG Hao. Classification of wolfberry planting areas based on ensemble learning and multi-temporal remote sensing images. Remote Sensing for Natural Resources, 2022, 34(1): 115-126.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/zrzyyg.2021064      或      https://www.gtzyyg.com/CN/Y2022/V34/I1/115
Fig.1  枸杞种植区分布
影像获
取时间
数据类型 传感
空间分
辨率/m
影像质量 数据用途
2017-04-25 Landsat8 OLI 30 良(5%云量) 构建时序多源遥感数据
2017-05-11 Landsat8 OLI 30 优(无云)
2017-05-27 Landsat8 OLI 30 良(8%云量)
2017-06-28 Landsat8 OLI 30 优(无云)
2017-07-14 Landsat8 OLI 30 优(无云)
2017-08-02 GF-1 WFV 16 优(无云)
2017-08-14 GF-1 WFV 16 优(无云)
2017-09-27 GF-1 WFV 16 优(无云)
2017-11-03 Landsat8 OLI 30 优(无云)
Tab.1  影像数据列表
Fig.2  研究区样点分布
Fig.3  枸杞作物物候期
Fig.4  NDVI和EVI曲线
Fig.5  NDVI及EVI指数增加与减少幅度
Fig.6  基于NDVI时序数据的分类结果
Fig.7  基于EVI时序数据的分类结果
Fig.8  制图精度和用户精度的热图
Fig.9  总体分类精度和Kappa系数的雷达图
Fig.10  时相特征选取前后模型的总体分类精度和Kappa系数对比
Fig.11  时序NDVI/EVI的时相选择次数统计
Fig.12  预处理前后GF-1和Landsat8数据的NDVI及EVI指数点密度对比
[1] 徐常青, 刘赛, 徐荣, 等. 我国枸杞主产区生产现状调研及建议[J]. 中国中药杂志, 2014, 39(11):1979-1984.
Xu C Q, Liu S, Xu R, et al. Investigation of production status in major wolfberry producing areas of China and some suggestions[J]. China Journal of Chinese Materia Medica, 2014, 39(11):1979-1984.
[2] 朱时佳, 谢韶璟, 余秦胤, 等. 边远地区农特产品供需失配问题研究——以青海枸杞为例[J]. 现代商贸工业, 2020, 41(2):25-27.
Zhu S J, Xie S J, Yu Q Y, et al. Research on the problem of supply and demand mismatch of special agricultural products in remote areas:Taking Qinghai wolfberry as an example[J]. Modern Business Trade Industry, 2020, 41(2):25-27.
[3] 董金玮, 吴文斌, 黄健熙, 等. 农业土地利用遥感信息提取的研究进展与展望[J]. 地球信息科学学报, 2020, 22(4):772-783.
doi: 10.12082/dqxxkx.2020.200192
Dong J W, Wu W B, Huang J X, et al. State of the art and perspective of agricultural land use remote sensing information extraction[J]. Journal of Geo-Information Science, 2020, 22(4):772-783.
[4] Yi Z W, Jia L, Chen Q T. Crop classification using multi-temporal Sentinel-2 data in the Shiyang River basin of China[J]. Remote Sensing, 2020, 12(24):4052-4073.
doi: 10.3390/rs12244052
[5] Zhong L H, Gong P, Biging G S. Efficient corn and soybean mapping with temporal extendability:A multi-year experiment using Landsat imagery[J]. Remote Sensing of Environment, 2014, 140:1-13.
doi: 10.1016/j.rse.2013.08.023
[6] Conese C, Maselli F. Use of multitemporal information to improve classification performance of TM scenes in complex terrain[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1991, 46(4):187-197.
doi: 10.1016/0924-2716(91)90052-W
[7] Pena M A, Brenning A. Assessing fruit-tree crop classification from Landsat8 time series for the Maipo Valley,Chile[J]. Remote Sensing of Environment, 2015, 171:234-244.
doi: 10.1016/j.rse.2015.10.029
[8] Lobell D B, Asner G P. Cropland distributions from temporal unmixing of MODIS data[J]. Remote Sensing of Environment, 2004, 93:412-422.
doi: 10.1016/j.rse.2004.08.002
[9] Richard M, Sankey T T, Congalton R G, et al. MODIS phenology-derived,multi-year distribution of conterminous US crop types[J]. Remote Sensing of Environment, 2017, 198:490-503.
doi: 10.1016/j.rse.2017.06.033
[10] 许青云, 杨贵军, 龙慧灵, 等. 基于MODIS NDVI多年时序数据的农作物种植识别[J]. 农业工程学报, 2014, 30(11):134-144.
Xu Q Y, Yang G J, Long H L, et al. Crop information identification based on MODIS NDVI time-series data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(11):134-144.
[11] 平跃鹏, 臧淑英. 基于MODIS 时间序列及物候特征的农作物分类[J]. 自然资源学报, 2016, 31(3):503-514.
Ping Y P, Zang S Y. Crop identification based on MODIS NDVI time-series data and phenological characteristics[J]. Journal of Natural Resources, 2016, 31(3):503-514.
[12] 郭昱杉, 刘庆生, 刘高焕, 等. 基于MODIS 时序NDVI主要农作物种植信息提取研究[J]. 自然资源学报, 2017, 32(10):1808-1818.
Guo Y S, Liu Q S, Liu G H, et al. Extraction of main crops in Yellow River delta based on MODIS NDVI time series[J]. Journal of Natural Resources, 2017, 32(10):1808-1818.
[13] 汪小钦, 邱鹏勋, 李娅丽, 等. 基于时序Landsat遥感数据的新疆开孔河流域农作物类型识别[J]. 农业工程学报, 2019, 35(16):180-188.
Wang X Q, Qiu P X, Li Y L, et al. Crops identification in Kaikong River basin of Xinjiang based on time series Landsat remote sensing images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(16):180-188.
[14] 白燕英, 高聚林, 张宝林. 基于Landsat8影像时间序列NDVI的作物种植结构提取[J]. 干旱区地理, 2019, 42(4):893-901.
Bai Y Y, Gao J L, Zhang B L. Extraction of crop planting structure based on time-series NDVI of Landsat8 images[J]. Arid Land Geography, 2019, 42(4):893-901.
[15] Pareeth S, Karimi P, Shafiei M, et al. Mapping agricultural landuse patterns from time series of Landsat8 using random forest based hierarchial approach[J]. Remote Sensing, 2019, 11(5):601-615.
doi: 10.3390/rs11050601
[16] 魏鹏飞, 徐新刚, 杨贵军, 等. 基于多时相影像植被指数变化特征的作物遥感分类[J]. 中国农业科技导报, 2019, 21(2):54-61.
Wei P F, Xu X G, Yang G J, et al. Remote sensing classification of crops based on the change characteristics of multi-phase vegetation index[J]. Journal of Agricultural Science and Technology, 2019, 21(2):54-61.
[17] 刘雅清, 王磊, 赵希妮, 等. 基于GF-1/WFV时间序列的绿洲作物类型提取[J]. 干旱区研究, 2019, 36(3):781-789.
Liu Y Q, Wang L, Zhao X N, et al. Extraction of crops in oasis based on GF-1/WFV time series[J]. Arid Zone Research, 2019, 36(3):781-789.
[18] 杜保佳, 张晶, 王宗明, 等. 应用Sentinel-2A NDVI时间序列和面向对象决策树方法的农作物分类[J]. 地球信息科学学报, 2019, 21(5):740-751.
doi: 10.12082/dqxxkx.2019.180412
Du B J, Zhang J, Wang Z M, et al. Crop mapping based on Sentinel-2A NDVI time series using object-oriented classification and decision tree model[J]. Journal of Geo-Information Science, 2019, 21(5):740-751.
[19] Ali N, Nicolas B, Mario M, et al. A novel approach for mapping wheat areas using high resolution Sentinel-2 images[J]. Sensors, 2018, 18(7):2089-2110.
doi: 10.3390/s18072089
[20] Vrieling A, Meroni M, Darvishzadeh R, et al. Vegetation phenology from Sentinel-2 and field cameras for a Dutch Barrier island[J]. Remote Sensing of Environment, 2018, 215:517-529.
doi: 10.1016/j.rse.2018.03.014
[21] 谷祥辉, 张英, 桑会勇, 等. 基于哨兵2时间序列组合植被指数的作物分类研究[J]. 遥感技术与应用, 2020, 35(3):702-711.
Gu X H, Zhang Y, Sang H Y, et al. Research on crop classification method based on Sentinel-2 time series combined vegetation index[J]. Remote Sensing Technology and Application, 2020, 35(3):702-711.
[22] 李亚飞, 董红斌. 基于卷积神经网络的遥感图像分类研究[J]. 智能系统学报, 2018, 13(4):550-556.
Li Y F, Dong H B. Classification of remote sensing image based on convolutional neural network[J]. CAAI Transactions on Intelligent Systems, 2018, 13(4):550-556.
[23] Masoud M, Bahram S, Mohammad R, et al. Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery[J]. Remote Sensing, 2018, 10(7):1119-1140.
doi: 10.3390/rs10071119
[24] 徐光志, 徐涵秋. Sentinel-2A MSI和Landsat8 OLI两种传感器多光谱信息的交互对比[J]. 遥感技术与应用, 2021, 36(1):165-175.
Xu G Z, Xu H Q. Cross-comparison of Sentinel-2A MSI and Landsat8 OLI multispectral information[J]. Remote Sensing Technology and Application, 2021, 36(1):165-175.
[25] Mancino G, Ferrara A, Padula A, et al. Cross-comparison between Landsat8(OLI)and Landsat7(ETM+)derived vegetation indices in a mediterranean environment[J]. Remote Sensing, 2020, 12(2):291-311.
doi: 10.3390/rs12020291
[26] 史飞飞, 雷春苗, 肖建设, 等. 基于多源遥感数据的复杂地形区农作物分类[J]. 地理与地理信息科学, 2018, 34(5):49-55,2.
Shi F F, Lei C M, Xiao J S, et al. Classification of crops in complicated topography area based on multisource remote sensing data[J]. Geography and Geo-Information Science, 2018, 34(5):49-55,2.
[27] Zhang H, Li Y, Jiang Y, et al. Hyperspectral classification based on Lightweight 3-D-CNN with transfer learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8):5813-5828.
doi: 10.1109/TGRS.36
[28] Markus I, Clement A, Tatjana K. Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data[J]. Remote Sensing, 2012, 4(9):2661-2693.
doi: 10.3390/rs4092661
[29] 蔡耀通, 刘书彤, 林辉, 等. 基于多源遥感数据的CNN水稻提取研究[J]. 自然资源遥感, 2020, 32(4):97-104.doi: 10.6046/gtzyyg.2020.04.14.
doi: 10.6046/gtzyyg.2020.04.14
Cai Y T, Liu S T, Lin H, et al. Extraction of paddy rice based on convolutional neural network using multi -source remote sensing data[J]. Remote Sensing for Land and Resources, 2020, 32(4):97-104.doi: 10.6046 /gtzyyg.2020.04.14.
doi: 10.6046 /gtzyyg.2020.04.14
[30] 崔亚莉, 刘峰, 郝奇琛, 等. 诺木洪冲洪积扇地下水氢氧同位素特征及更新能力研究[J]. 水文地质工程地质, 2015, 42(6):1-7.
Cui Y L, Liu F, Hao Q C, et al. Characteristics of hydrogen and oxygen isotopes and renewability of groundwater in the Nuomuhong alluvial fan[J]. Hydrogeology and Engineering Geology, 2015, 42(6):1-7.
[31] Foga S, Scaramuzza P L, Guo S, et al. Cloud detection algorithm comparison and validation for operational Landsat data products[J]. Remote Sensing of Environment, 2017, 194:379-390.
doi: 10.1016/j.rse.2017.03.026
[32] Villa P, Bresciani M, Braga F, et al. Comparative assessment of broadband vegetation indices over aquatic vegetation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(7):3117-3127.
doi: 10.1109/JSTARS.4609443
[33] 白燕英, 高聚林, 张宝林. 基于NDVI与EVI的作物长势监测研究[J]. 农业机械学报, 2019, 50(9):153-161.
Bai Y Y, Gao J L, Zhang B L. Monitoring of crops growth based on NDVI and EVI[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(9):153-161.
[34] 解毅, 张永清, 荀兰, 等. 基于多源遥感数据融合和LSTM算法的作物分类研究[J]. 农业工程学报, 2019, 35(15):129-137.
Xie Y, Zhang Y Q, Xun L, et al. Crop classification based on multi-source remote sensing data fusion and LSTM algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(15):129-137.
[35] Huang G M, Wu L F, Ma X, et al. Evaluation of CatBoost method for prediction of reference evapotranspiration in humid regions[J]. Journal of Hydrology, 2019, 574:1029-1041.
doi: 10.1016/j.jhydrol.2019.04.085
[36] Su H, Yang X, Lu W F, et al. Estimating subsurface thermohaline structure of the global ocean using surface remote sensing observations[J]. Remote Sensing, 2019, 11(13):1598-1620.
doi: 10.3390/rs11131598
[37] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016:171-196.
Zhou Z H. Machine learning[M]. Beijing: Tsinghua University Press, 2016:171-196.
[38] 李宏达, 高小红, 汤敏. 基于CNN 的不同空间分辨率影像土地覆被分类研究[J]. 遥感技术与应用, 2020, 35(4):749-758.
Li H D, Gao X H, Tang M. Land cover classification for different spatial resolution images from CNN[J]. Remote Sensing Technology and Application, 2020, 35(4):749-758.
[39] Pleoianu A I, Stupariu M S, Sandric I, et al. Individual tree-crown detection and species classification in very high-resolution remote sensing imagery using a deep learning ensemble model[J]. Remote Sensing, 2020, 12(15):2426-2448.
doi: 10.3390/rs12152426
[40] 宋军伟, 张友静, 李鑫川, 等. 基于GF-1与Landsat8影像的土地覆盖分类比较[J]. 地理科学进展, 2016, 35(2):255-263.
doi: 10.18306/dlkxjz.2016.02.012
Song J W, Zhang Y J, Li X C, et al. Comparison between GF-1 and Landsat8 images in land cover classification[J]. Progress in Geography, 2016, 35(2):255-263.
[41] Vasilakos C, Kavroudakis D, Georganta A. Machine learning classification ensemble of multitemporal Sentinel-2 images:The case of a mixed mediterranean ecosystem[J]. Remote Sensing, 2020, 12(12):2005-2030.
doi: 10.3390/rs12122005
No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
京ICP备05055290号-2
版权所有 © 2015 《自然资源遥感》编辑部
地址:北京学院路31号中国国土资源航空物探遥感中心 邮编:100083
电话:010-62060291/62060292 E-mail:zrzyyg@163.com
本系统由北京玛格泰克科技发展有限公司设计开发