Land Use Classification Study based on Landsat 8 OLI Time Series Imagery and Support Vector Machine

Kun Wang

doi:10.54097/29yv7p45

Authors

Kun Wang

DOI:

https://doi.org/10.54097/29yv7p45

Keywords:

Landsat 8 OLI; Time Series; Remote Sensing; Land Use Type Classification.

Abstract

Accurate classification of land use types is of great significance for urban planning and effective utilization of land resources, and although remote sensing data have been successfully used in land use type classification studies, previous studies were mostly based on single-period remote sensing imagery, ignoring the phenological characteristics on vegetation time series. Therefore, in this study, based on Landsat 8 OLI remote sensing images for the whole year of 2017, land use classification research was carried out on different time series (single-period, growing season, non-growing season, and annual data) imagery data through the support vector machine algorithm, respectively, with a view to exploring the influence of time series on land use classification, and thus improving the classification accuracy.

Downloads

Download data is not yet available.

References

Porter J D. Forestry in Europe: reports from the consuls of the United State[M]. Washington DC: United States Government Printing Office,1887:5-23.

Calhoun J M. Riparian management practices of the department of naturalresources [R]. AEDEKE K. Stream side management: riparian wildlife and forestryinteractions. Washington DC: Institute of Forest Resources, University of Washington,1988:207-211.

Hewitt, M.J. Synoptic inventory of riparian ecosystems: the utility of Landsat Thematic Mapper data[J]. Forest Eco. Manage. 1990,33/34,605-620.

Sunil Narumalani, Yingchun Zhou, John R. Jensen. Application of remote sensing and geographic information systems to the delineation and analysis of riparian bufferzones [J]. Aquatic Botany,1997,58:393-409.

Chen T,Bao L, Zhu L B,et al. The diversity of birds in typical urbanlake-wetlands and its response to the landscape heterogeneity in the buffer zone basedon GIS and field investigation in Daqing, China[J]. European Journal of RemoteSensing.2020,54(2):1-9.

Shou-Jing Yin,Chuan-Qing Wu,Chen Wang,et al.Remote sensing assessment ofecological health of the riparian buffer along Huaihe River[J].China Environmental Science, 2016, 36(1): 299-306.

Myint S W,Gober P, Brazel A,et al.Per-pixel vs.object-based classification ofurban land cover extraction using high spatial resolution imagery[J].Remote Sensingof Environment, 2011, 115 (5):1145-1161.

Achanta R, Shaji, Smith K, et al. SLIC Super pixels Compared to State of the Art Super pixel Methods[J]. IEEE Transactions on Pattern Analysis&MachineIntelligence,2012,34(11):2274-2282.

Gong, Maoguo, Zhang, et al. Super pixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(6):2658-2673.

Comaneci D, Meer P. Mean shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002,24(5):603–619.