|
|
|
|
|
| How do temporal and spectral features matter in crop classification in Heilongjiang Province, China? |
| HU Qiong1, WU Wen-bin1, SONG Qian1, 2, LU Miao1, CHEN Di1, YU Qiang-yi1, TANG Hua-jun1 |
1Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
2Remote Sensing Technology Center, Heilongjiang Academy of Agricultural Sciences, Harbin 150086, P.R.China |
|
|
|
|
Abstract How to fully use spectral and temporal information for efficient identification of crops becomes a crucial issue since each crop has its specific seasonal dynamics. A thorough understanding on the relative usefulness of spectral and temporal features is thus essential for better organization of crop classification information. This study, taking Heilongjiang Province as the study area, aims to use time-series moderate resolution imaging spectroradiometer (MODIS) surface reflectance product (MOD09A1) data to evaluate the importance of spectral and temporal features for crop classification. In doing so, a feature selection strategy based on separability index (SI) was first used to rank the most important spectro-temporal features for crop classification. Ten feature scenarios with different spectral and temporal variable combinations were then devised, which were used for crop classification using the support vector machine and their accuracies were finally assessed with the same crop samples. The results show that the normalized difference tillage index (NDTI), land surface water index (LSWI) and enhanced vegetation index (EVI) are the most informative spectral features and late August to early September is the most informative temporal window for identifying crops in Heilongjiang for the observed year 2011. Spectral diversity and time variety are both vital for crop classification, and their combined use can improve the accuracy by about 30% in comparison with single image. The feature selection technique based on SI analysis is superior for achieving high crop classification accuracy (producers’ accuracy of 94.03% and users’ accuracy of 93.77%) with a small number of features. Increasing temporal resolution is not necessarily important for improving the classification accuracies for crops, and a relatively high classification accuracy can be achieved as long as the images associated with key phenological phrases are retained.
|
|
Received: 19 December 2015
Accepted:
|
| Fund: This research was financially supported by the Non-Profit Research Grant of the National Administration of Surveying, Mapping and Geoinformation of China (201512028), and the National Natural Science Foundation of China (41271112). |
|
Corresponding Authors:
WU Wen-bin, Mobile: +86-13621050107, E-mail: wuwenbin@caas.cn; TANG Hua-jun, E-mail: tanghuajun@caas.cn
|
| About author: HU Qiong, Mobile: +86-13661273899, E-mail: huqiong02@caas.cn |
Cite this article:
HU Qiong, WU Wen-bin, SONG Qian, LU Miao, CHEN Di, YU Qiang-yi, TANG Hua-jun .
2017.
How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?. Journal of Integrative Agriculture, 16(02): 324-336.
|
| Biradar C M, Thenkabail P S, Noojipady P, Li Y, Dheeravath V, Turral H, Velpuri M, Gumma M K, Gangalakunta O R P, Cai X L, Xiao X, Schull M A, Alankara R D, Gunasinghe S, Mohideen S. 2009. A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing. International Journal of Applied Earth Observation and Geoinformation, 11, 114–129.Bolton D K, Friedl M A. 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173, 74–84.Bruzzone L, Roli F, Serpico S B. 1995. An extension of the Jeffreys-Matusita distance to multiclass cases for feature selection. IEEE Transactions on Geoscience and Remote Sensing, 33, 1318–1321.Carrão H, Gonçalves P, Caetano M. 2008. Contribution of multispectral and multitemporal information from MODIS images to land cover classification. Remote Sensing of Environment, 112, 986–997.Chang J, Hansen M C, Pittman K, Carroll M, DiMiceli C. 2007. Corn and soybean mapping in the United States using MODIS time-series data sets. Agronomy Journal, 99, 1654–1664.Colditz R R, López Saldaña G, Maeda P, Espinoza J A, Tovar C M, Hernández A V, Benítez C Z, Cruz López I, Ressl R. 2012. Generation and analysis of the 2005 land cover map for Mexico using 250 m MODIS data. Remote Sensing of Environment, 123, 541–552.Colditz R R, Schmidt M, Conrad C, Hansen M C, Dech S. 2011. Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions. Remote Sensing of Environment, 115, 3264–3275.Duro D C, Franklin S E, Dubé M G. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272.Foerster S, Kaden K, Foerster M, Itzerott S. 2012. Crop type mapping using spectral-temporal profiles and phenological information. Computers and Electronics in Agriculture, 89, 30–40.Hansen M C, Egorov A, Potapov P V, Stehman S V, Tyukavina A, Turubanova S A, Roy D P, Goetz S J, Loveland T R, Ju J, Kommareddy A, Kovalskyy V, Forsyth C, Bents T. 2014. Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD). Remote Sensing of Environment, 140, 466–484.Hu Q, Wu W, Xia T, Yu Q, Yang P, Li Z, Song Q. 2013. Exploring the use of google earth imagery and object-based methods in land use/cover mapping. Remote Sensing, 5, 6026–6042.Loew F, Michel U, Dech S, Conrad C. 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using Support Vector Machines. ISPRS Journal of Photogrammetry and Remote Sensing, 85, 102–119.Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247–259.Pal M. 2013. Hybrid genetic algorithm for feature selection with hyperspectral data. Remote Sensing Letters, 4, 619–628.Pan Y, Li L, Zhang J, Liang S, Zhu X, Sulla-Menashe D. 2012. Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index. Remote Sensing of Environment, 119, 232–242.Peña-Barragán J M, Ngugi M K, Plant R E, Six J. 2011. Object-based crop identification using multiple vegetation indices, textural features and crop phenology. Remote Sensing of Environment, 115, 1301–1316.Piiroinen R, Heiskanen J, Mõttus M, Pellikka P. 2015. Classification of crops across heterogeneous agricultural landscape in Kenya using AisaEAGLE imaging spectroscopy data. International Journal of Applied Earth Observation and Geoinformation, 39, 1–8.Shi S Q, Cao Q W, Yao Y M, Tang H J, Yang P, Wu W B, Xu H Z, Liu J, Li Z G. 2014. Influence of climate and socio-economic factors on the spatio-temporal variability of soil organic matter: A case study of central Heilongjiang Province, China. Journal of Integrative Agriculture, 13, 1486–1500.Somers B, Asner G P. 2012. Hyperspectral time series analysis of native and invasive species in Hawaiian rainforests. Remote Sensing, 4, 2510–2529.Somers B, Asner G P. 2013. Multi-temporal hyperspectral mixture analysis and feature selection for invasive species mapping in rainforests. Remote Sensing of Environment, 136, 14–27.Somers B, Asner G P. 2014. Tree species mapping in tropical forests using multi-temporal imaging spectroscopy: Wavelength adaptive spectral mixture analysis. International Journal of Applied Earth Observation and Geoinformation, 31, 57–66.Sun J, Wu W, Tang H, Liu J. 2015. Spatiotemporal patterns of non-genetically modified crops in the era of expansion of genetically modified food. Scientific Reports, 5, doi: 10.1038/srep14180Wang J, Xiao X, Qin Y, Dong J, Zhang G, Kou W, Jin C, Zhou Y, Zhang Y. 2015. Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Scientific Reports, 5, doi: 10.1038/srep10088Wardlow B D, Egbert S L. 2008. Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the US Central Great Plains. Remote Sensing of Environment, 112, 1096–1116.Wardlow B D, Egbert S L, Kastens J H. 2007. Analysis of time-series MODIS 250 m vegetation index data for crop classification in the US Central Great Plains. Remote Sensing of Environment, 108, 290–310.Wu B, Meng J, Li Q, Yan N, Du X, Zhang M. 2013. Remote sensing-based global crop monitoring: Experiences with China’s CropWatch system. International Journal of Digital Earth, 7, 113–137.Wu W B, Yu Q Y, Peter V H, You L Z, Yang P, Tang H J. 2014. How could agricultural land systems contribute to raise food production under global change? Journal of Integrative Agriculture, 13, 1432–1442.Xiao X, Boles S, Frolking S, Li C, Babu J Y, Salas W, Moore III B. 2006. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sensing of Environment, 100, 95–113.Xiao X, Boles S, Liu J, Zhuang D, Frolking S, Li C, Salas W, Moore III B. 2005. Mapping paddy rice agriculture in southern China using multi-temporal MODIS images. Remote Sensing of Environment, 95, 480–492.Yang C, Everitt J H, Murden D. 2011. Evaluating high resolution SPOT 5 satellite imagery for crop identification. Computers and Electronics in Agriculture, 75, 347–354.Zhang G, Xiao X, Dong J, Kou W, Jin C, Qin Y, Zhou Y, Wang J, Menarguez M A, Biradar C. 2015. Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data. ISPRS Journal of Photogrammetry and Remote Sensing, 106, 157–171.Zhang J, Feng L, Yao F. 2014. Improved maize cultivated area estimation over a large scale combining MODIS-EVI time series data and crop phenological information. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 102–113.Zhong L, Gong P, Biging G S. 2014. Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery. Remote Sensing of Environment, 140, 1–13.Zhong L, Hawkins T, Biging G S, Gong P. 2011. A phenology-based approach to map crop types in the San Joaquin Valley, California. International Journal of Remote Sensing, 32, 7777–7804.Zhou F, Zhang A, Townley-Smith L. 2013. A data mining approach for evaluation of optimal time-series of MODIS data for land cover mapping at a regional level. ISPRS Journal of Photogrammetry and Remote Sensing, 84, 114–129. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
