Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach
Abstract
1. Introduction
2. Datasets and Preprocessing
2.1. Landsat Imagery and Datacube
2.1.1. Reprojection and Tiling
2.1.2. Cloud and Shadow Detection and Filling
2.2. Validation Dataset
3. Methods
3.1. The Spatial-Temporal Spectral Library
3.2. Normalization of the SPECLib Reflectance Spectra
3.3. Multi-Temporal Classification Method Based on SPECLib
3.3.1. Training the Base Classifier
3.3.2. Stacking of Base Classifiers
3.3.3. Rule-Based Verification
3.4. Accuracy Assessment
4. Results and Validation
5. Discussion
5.1. Influence of the Temporal Frequency
5.2. Consistency between MCD43A4 and Landsat SR for Land-Cover Mapping
5.3. Limitations of SPECLib for Fine-Resolution Land-Cover Mapping
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Azzari, G.; Lobell, D. Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring. Remote. Sens. Environ. 2017, 202, 64–74. [Google Scholar] [CrossRef]
- Zhao, Y.; Feng, D.; Yu, L.; Wang, X.; Chen, Y.; Bai, Y.; Hernández, H.J.; Galleguillos, M.; Estades, C.; Biging, G.S.; et al. Detailed dynamic land cover mapping of Chile: Accuracy improvement by integrating multi-temporal data. Remote. Sens. Environ. 2016, 183, 170–185. [Google Scholar]
- Wessels, K.J.; Bergh, F.V.D.; Roy, D.P.; Salmon, B.P.; Steenkamp, K.C.; MacAlister, B.; Swanepoel, D.; Jewitt, D. Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers. Remote. Sens. 2016, 8, 888. [Google Scholar] [CrossRef]
- Chen, J.; Chen, J.; Liao, A.; Cao, X.; Chen, L.; Chen, X.; He, C.; Han, G.; Peng, S.; Lu, M.; et al. Global land cover mapping at 30m resolution: A POK-based operational approach. ISPRS J. Photogramm. Sens. 2015, 103, 7–27. [Google Scholar] [CrossRef]
- Yu, L.; Wang, J.; Li, X.; Li, C.; Zhao, Y.; Gong, P. A multi-resolution global land cover dataset through multisource data aggregation. Sci. China Earth Sci. 2014, 57, 2317–2329. [Google Scholar] [CrossRef]
- Yang, Y.; Xiao, P.; Feng, X.; Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS J. Photogramm. Sens. 2017, 125, 156–173. [Google Scholar] [CrossRef]
- Tsendbazar, N.-E.; De Bruin, S.; Herold, M. Assessing global land cover reference datasets for different user communities. ISPRS J. Photogramm. Sens. 2015, 103, 93–114. [Google Scholar] [CrossRef]
- Tsendbazar, N.-E.; De Bruin, S.; Fritz, S.; Herold, M. Spatial Accuracy Assessment and Integration of Global Land Cover Datasets. Remote. Sens. 2015, 7, 15804–15821. [Google Scholar] [CrossRef]
- Ban, Y.; Gong, P.; Giri, C. Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS J. Photogramm. Sens. 2015, 103, 1–6. [Google Scholar] [CrossRef]
- Giri, C.; Pengra, B.; Long, J.; Loveland, T. Next generation of global land cover characterization, mapping, and monitoring. Int. J. Appl. Earth Obs. Geoinformation 2013, 25, 30–37. [Google Scholar]
- Gómez, C.; White, J.C.; Wulder, M.A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Sens. 2016, 116, 55–72. [Google Scholar] [CrossRef]
- Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S.; et al. Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. Int. J. Remote Sens. 2013, 34, 2607–2654. [Google Scholar] [CrossRef]
- Symeonakis, E.; Caccetta, P.; Koukoulas, S.; Furby, S.; Karathanasis, N. Multi-temporal land-cover classification and change analysis with conditional probability networks: the case of Lesvos Island (Greece). Int. J. Remote Sens. 2012, 33, 4075–4093. [Google Scholar] [CrossRef]
- Potapov, P.; Turubanova, S.; Hansen, M.C. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote. Sens. Environ. 2011, 115, 548–561. [Google Scholar] [CrossRef]
- Giménez, M.G.; De Jong, R.; Della Peruta, R.; Keller, A.; Schaepman, M.E. Determination of grassland use intensity based on multi-temporal remote sensing data and ecological indicators. Remote. Sens. Environ. 2017, 198, 126–139. [Google Scholar] [CrossRef]
- Franklin, S.E.; Ahmed, O.S.; Wulder, M.A.; White, J.C.; Hermosilla, T.; Coops, N.C. Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data. Can. J. Sens. 2015, 41, 293–314. [Google Scholar] [CrossRef]
- Senf, C.; Leitão, P.J.; Pflugmacher, D.; Van Der Linden, S.; Hostert, P. Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery. Remote. Sens. Environ. 2015, 156, 527–536. [Google Scholar] [CrossRef]
- Belgiu, M.; Csillik, O. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote. Sens. Environ. 2018, 204, 509–523. [Google Scholar] [CrossRef]
- Yamazaki, D.; Trigg, M.A.; Ikeshima, D. Development of a global ~90m water body map using multi-temporal Landsat images. Remote. Sens. Environ. 2015, 171, 337–351. [Google Scholar] [CrossRef]
- Dennison, P.E.; Roberts, D.A. The effects of vegetation phenology on endmember selection and species mapping in southern California chaparral. Remote. Sens. Environ. 2003, 87, 295–309. [Google Scholar] [CrossRef]
- Dudley, K.L.; Dennison, P.E.; Roth, K.L.; Roberts, D.A.; Coates, A.R. A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients. Remote. Sens. Environ. 2015, 167, 121–134. [Google Scholar] [CrossRef]
- Hansen, M.C.; Loveland, T.R. A review of large area monitoring of land cover change using Landsat data. Remote. Sens. Environ. 2012, 122, 66–74. [Google Scholar] [CrossRef]
- Yu, L.; Wang, J.; Clinton, N.; Xin, Q.; Zhong, L.; Chen, Y.; Gong, P. FROM-GC: 30 m global cropland extent derived through multisource data integration. Int. J. Digit. Earth 2013, 6, 521–533. [Google Scholar] [CrossRef]
- Li, C.; Gong, P.; Wang, J.; Zhu, Z.; Biging, G.S.; Yuan, C.; Hu, T.; Zhang, H.; Wang, Q.; Li, X.; et al. The first all-season sample set for mapping global land cover with Landsat-8 data. Sci. Bull. 2017, 62, 508–515. [Google Scholar] [CrossRef]
- Pax-Lenney, M.; E Woodcock, C.; A Macomber, S.; Gopal, S.; Song, C. Forest mapping with a generalized classifier and Landsat TM data. Remote. Sens. Environ. 2001, 77, 241–250. [Google Scholar] [CrossRef]
- E Woodcock, C.; A Macomber, S.; Pax-Lenney, M.; Cohen, W.B. Monitoring large areas for forest change using Landsat: Generalization across space, time and Landsat sensors. Remote. Sens. Environ. 2001, 78, 194–203. [Google Scholar] [CrossRef]
- Liu, L.; Xiao, Z.; Yong, H.; Wang, Y. Automatic land cover mapping for Landsat data based on the time-series spectral image database. In Proceedings of the Geoscience & Remote Sensing Symposium, 2017, Fort Worth, TX, USA, 23–28 July 2017. [Google Scholar]
- Dannenberg, M.P.; Hakkenberg, C.R.; Song, C. Consistent Classification of Landsat Time Series with an Improved Automatic Adaptive Signature Generalization Algorithm. Remote. Sens. 2016, 8, 691. [Google Scholar] [CrossRef]
- Hu, Y.; Liu, L. Landsat time-series land cover mapping with spectral signature extension method. Remote Sens. 2015, 19, 639–656. [Google Scholar]
- Zhang, H.K.; Roy, D.P. Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification. Remote. Sens. Environ. 2017, 197, 15–34. [Google Scholar] [CrossRef]
- Radoux, J.; Lamarche, C.; Van Bogaert, E.; Bontemps, S.; Brockmann, C.; Defourny, P. Automated Training Sample Extraction for Global Land Cover Mapping. Remote. Sens. 2014, 6, 3965–3987. [Google Scholar] [CrossRef]
- Olthof, I.; Butson, C.; Fraser, R. Signature extension through space for northern land cover classification: A comparison of radiometric correction methods. Remote Sens. Environ. 2005, 95, 290–302. [Google Scholar] [CrossRef]
- Zhang, X.; Liu, L.; Wang, Y.; Hu, Y.; Zhang, B. A SPECLib-based operational classification approach: A preliminary test on China land cover mapping at 30 m. Int. J. Appl. Earth Obs. Geoinformation 2018, 71, 83–94. [Google Scholar] [CrossRef]
- Chen, Z. Hasituya Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data. Remote. Sens. 2017, 9, 557. [Google Scholar]
- Egorov, A.; Hansen, M.; Roy, D.; Kommareddy, A.; Potapov, P. Image interpretation-guided supervised classification using nested segmentation. Remote. Sens. Environ. 2015, 165, 135–147. [Google Scholar] [CrossRef]
- Paola, J.; Schowengerdt, R. A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Trans. Geosci. Sens. 1995, 33, 981–996. [Google Scholar] [CrossRef]
- Breiman, L.; Friedman, J.H.; Olshen, R.A.; Stone, C.J. Classification and Regression Trees; Routledge: New York, NY, USA, 1984; Available online: https://doi.org/10.1201/9781315139470 (accessed on 4 May 2019).
- Vapnik, V.; Cortes, C. Support Vector Networks. Available online: https://doi.org/10.1007/BF00994018 (accessed on 4 May 2019).
- Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. Available online: https://doi.org/10.1023/A:1010933404324 (accessed on 4 May 2019). [CrossRef]
- Belgiu, M.; Drăguț, L. Random forest in remote sensing: A review of applications and future directions. ISPRS J. Photogramm. Sens. 2016, 114, 24–31. [Google Scholar] [CrossRef]
- Shao, Y.; Lunetta, R.S. Comparison of support vector machine, neural network, and CART algorithms for the land-cover classification using limited training data points. ISPRS J. Photogramm. Sens. 2012, 70, 78–87. [Google Scholar] [CrossRef]
- Pelletier, C.; Valero, S.; Inglada, J.; Champion, N.; Dedieu, G. Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Remote. Sens. Environ. 2016, 187, 156–168. [Google Scholar] [CrossRef]
- Du, P.; Samat, A.; Waske, B.; Liu, S.; Li, Z. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS J. Photogramm. Sens. 2015, 105, 38–53. [Google Scholar] [CrossRef]
- Teillet, P.; Guindon, B.; Goodenough, D. On the Slope-Aspect Correction of Multispectral Scanner Data. Can. J. Sens. 1982, 8, 84–106. [Google Scholar] [CrossRef]
- Tan, B.; Masek, J.G.; Wolfe, R.; Gao, F.; Huang, C.; Vermote, E.F.; Sexton, J.O.; Ederer, G. Improved forest change detection with terrain illumination corrected Landsat images. Remote. Sens. Environ. 2013, 136, 469–483. [Google Scholar] [CrossRef]
- Hu, Y.; Liu, L.; Liu, L.; Peng, D.; Jiao, Q.; Zhang, H. A Landsat-5 Atmospheric Correction Based on MODIS Atmosphere Products and 6S Model. IEEE J. Sel. Top. Appl. Earth Obs. Sens. 2014, 7, 1609–1615. [Google Scholar] [CrossRef]
- Roy, D.P.; Qin, Y.; Kovalskyy, V.; Vermote, E.F.; Ju, J.; Egorov, A.; Hansen, M.C.; Kommareddy, I.; Yan, L. Conterminous United States demonstration and characterization of MODIS-based Landsat ETM+ atmospheric correction. Remote Sens. Environ. 2014, 140, 433–449. [Google Scholar] [CrossRef]
- Vermote, E.; Justice, C.; Claverie, M.; Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote. Sens. Environ. 2016, 185, 46–56. [Google Scholar] [CrossRef]
- Kovalskyy, V.; Roy, D.P. The global availability of Landsat 5 TM and Landsat 7 ETM+ land surface observations and implications for global 30m Landsat data product generation. Remote Sens. Environ. 2013, 130, 280–293. [Google Scholar] [CrossRef]
- Li, J.; Roy, D.P. A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote. Sens. 2017, 9, 902. [Google Scholar]
- Lewis, A.; Oliver, S.; Lymburner, L.; Evans, B.; Wyborn, L.; Mueller, N.; Raevksi, G.; Hooke, J.; Woodcock, R.; Sixsmith, J.; et al. The Australian Geoscience Data Cube — Foundations and lessons learned. Remote. Sens. Environ. 2017, 202, 276–292. [Google Scholar] [CrossRef]
- Zhu, Z.; Wang, S.; Woodcock, C.E. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote. Sens. Environ. 2015, 159, 269–277. [Google Scholar] [CrossRef]
- Zhu, Z.; Woodcock, C.E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote. Sens. Environ. 2012, 118, 83–94. [Google Scholar] [CrossRef]
- Zhu, X.; Gao, F.; Liu, D.; Chen, J. A Modified Neighborhood Similar Pixel Interpolator Approach for Removing Thick Clouds in Landsat Images. IEEE Geosci. Sens. Lett. 2012, 9, 521–525. [Google Scholar] [CrossRef]
- Defourny, P.; Kirches, G.; Brockmann, C.; Boettcher, M.; Peters, M.; Bontemps, S.; Lamarche, C.; Schlerf, M.; Santoro, M. Land Cover CCI: Product User Guide Version 2. 2018. Available online: https://www.esa-landcover-cci.org/?q=webfm_send/84 (accessed on 4 May 2019).
- Friedl, M.A.; Sulla-Menashe, D.; Tan, B.; Schneider, A.; Ramankutty, N.; Sibley, A.; Huang, X. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote. Sens. Environ. 2010, 114, 168–182. [Google Scholar] [CrossRef]
- Feng, M.; Huang, C.; Channan, S.; Vermote, E.F.; Masek, J.G.; Townshend, J.R. Quality assessment of Landsat surface reflectance products using MODIS data. Comput. Geosci. 2012, 38, 9–22. [Google Scholar] [CrossRef]
- Bontemps, S.; Defourny, P.; Bogaert, E.V.; Arino, O.; Kalogirou, V.; Perez, J.R. GLOBCOVER 2009 Products Description and Validation Report. 2010. Available online: http://due.esrin.esa.int/files/GLOBCOVER2009_Validation_Report_2.2.pdf (accessed on 4 May 2019).
- Wang, Z.; Schaaf, C.B.; Strahler, A.H.; Chopping, M.J.; Román, M.O.; Shuai, Y.; Woodcock, C.E.; Hollinger, D.Y.; Fitzjarrald, D.R. Evaluation of MODIS albedo product (MCD43A) over grassland, agriculture and forest surface types during dormant and snow-covered periods. Remote. Sens. Environ. 2014, 140, 60–77. [Google Scholar] [CrossRef]
- Feng, M.; Sexton, J.O.; Huang, C.; Masek, J.G.; Vermote, E.F.; Gao, F.; Narasimhan, R.; Channan, S.; Wolfe, R.E.; Townshend, J.R. Global surface reflectance products from Landsat: Assessment using coincident MODIS observations. Remote. Sens. Environ. 2013, 134, 276–293. [Google Scholar] [CrossRef]
- Tucker, C.J. Red and photographic infrared linear combinations for monitoring vegetation. Remote. Sens. Environ. 1979, 8, 127–150. [Google Scholar] [CrossRef]
- Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Sens. 2006, 27, 3025–3033. [Google Scholar] [CrossRef]
- Zha, Y.; Gao, J.; Ni, S. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int. J. Sens. 2003, 24, 583–594. [Google Scholar] [CrossRef]
- Gomariz-Castillo, F.; Alonso-Sarría, F.; Cánovas-García, F. Improving Classification Accuracy of Multi-Temporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015. Remote. Sens. 2017, 9, 1058. [Google Scholar] [CrossRef]
- Rumpf, S.B.; Hülber, K.; Klonner, G.; Moser, D.; Schütz, M.; Wessely, J.; Willner, W.; Zimmermann, N.E.; Dullinger, S. Range dynamics of mountain plants decrease with elevation. Proc. Natl. Acad. Sci. USA 2018, 115, 1848–1853. Available online: https://www.pnas.org/content/115/8/1848 (accessed on 4 May 2019). [CrossRef]
- Yang, L.; Meng, X.; Zhang, X. SRTM DEM and its application advances. Int. J. Sens. 2011, 32, 3875–3896. [Google Scholar] [CrossRef]
- Jin, X.M.; Zhang, Y.K.; Schaepman, M.E.; Clevers, J.G.P.W.; Su, Z. Impact of Elevation and Aspect on the Spatial Distribution of Vegetation in the Qilian Mountain Area with Remote Sensing Data. Available online: https://bit.ly/2Lmlkkj (accessed on 4 May 2019).
- Gislason, P.O.; Benediktsson, J.A.; Sveinsson, J.R. Random Forests for land cover classification. Pattern Recognit. Lett. 2006, 27, 294–300. [Google Scholar] [CrossRef]
- Ghosh, A.; Fassnacht, F.E.; Joshi, P.; Koch, B. A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales. Int. J. Appl. Earth Obs. Geoinformation 2014, 26, 49–63. [Google Scholar] [CrossRef]
- Healey, S.P.; Cohen, W.B.; Yang, Z.; Brewer, C.K.; Brooks, E.B.; Gorelick, N.; Hernandez, A.J.; Huang, C.; Hughes, M.J.; Kennedy, R.E.; et al. Mapping forest change using stacked generalization: An ensemble approach. Remote. Sens. Environ. 2018, 204, 717–728. [Google Scholar] [CrossRef]
- Yang, X.; Lo, D.; Xia, X.; Sun, J. TLEL: A two-layer ensemble learning approach for just-in-time defect prediction. Inf. Softw. Technol. 2017, 87, 206–220. [Google Scholar] [CrossRef]
- Löw, F.; Conrad, C.; Michel, U. Decision fusion and non-parametric classifiers for land use mapping using multi-temporal RapidEye data. ISPRS J. Photogramm. Sens. 2015, 108, 191–204. [Google Scholar] [CrossRef]
- Yin, D.; Cao, X.; Chen, X.; Shao, Y.; Chen, J. Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery. Int. J. Sens. 2013, 34, 6529–6538. [Google Scholar] [CrossRef]
- Liu, C.; Frazier, P.; Kumar, L. Comparative assessment of the measures of thematic classification accuracy. Remote. Sens. Environ. 2007, 107, 606–616. [Google Scholar] [CrossRef]
- Olofsson, P.; Foody, G.M.; Herold, M.; Stehman, S.V.; Woodcock, C.E.; Wulder, M.A. Good practices for estimating area and assessing accuracy of land change. Remote. Sens. Environ. 2014, 148, 42–57. [Google Scholar] [CrossRef]
- Karakizi, C.; Karantzalos, K.; Vakalopoulou, M.; Antoniou, G. Detailed Land Cover Mapping from Multitemporal Landsat-8 Data of Different Cloud Cover. Remote. Sens. 2018, 10, 1214. [Google Scholar] [CrossRef]
- Yvan, S.; Iñaki, I.; Pedro, L. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23, 2507–2517. [Google Scholar]
- Roy, D.; Kovalskyy, V.; Zhang, H.; Vermote, E.; Yan, L.; Kumar, S.S.; Egorov, A. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote. Sens. Environ. 2016, 185, 57–70. [Google Scholar] [CrossRef]
- Irons, J.R.; Dwyer, J.L.; Barsi, J.A. The next Landsat satellite: The Landsat Data Continuity Mission. Remote. Sens. Environ. 2012, 122, 11–21. [Google Scholar] [CrossRef]
- Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P.; et al. Sentinel-2: ESA’s Optical High-Resolution Mission for GMES Operational Services. Remote Sens. Environ. 2012, 120, 25–36. [Google Scholar] [CrossRef]
- Tian, Y.; Chen, H.; Song, Q.; Zheng, K. A Novel Index for Impervious Surface Area Mapping: Development and Validation. Remote. Sens. 2018, 10, 1521. [Google Scholar] [CrossRef]
- Gao, F.; De Colstoun, E.B.; Ma, R.; Weng, Q.; Masek, J.G.; Chen, J.; Pan, Y.; Song, C. Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta, China. Int. J. Sens. 2012, 33, 7609–7628. [Google Scholar] [CrossRef]
Level-1 Vali-System | Level-2 Vali-System | Classification System | LC Id |
---|---|---|---|
Cropland | Herbaceous rainfed cropland | Herbaceous cover | 11 |
Tree rainfed cropland | Tree or shrub cover (Orchard) | 12 | |
Irrigated cropland | Irrigated cropland | 20 | |
Forest | Evergreen broadleaved forest | Evergreen broadleaved forest | 50 |
Deciduous broadleaved forest | Open deciduous broadleaved forest (0.15 < fc < 0.4) | 61 | |
Closed deciduous broadleaved forest (fc > 0.4) | 62 | ||
Evergreen needle-leaved forest | Open evergreen needle-leaved forest (0.15 < fc < 0.4) | 71 | |
Closed evergreen needle-leaved forest (fc > 40%) | 72 | ||
Deciduous needle-leaved forest | Open deciduous needle-leaved forest (0.15 < fc < 0.4) | 81 | |
Closed deciduous needle-leaved forest (fc > 0.4) | 82 | ||
Mixed leaf forest | Mixed leaf forest (broadleaved and needle-leaved) | 90 | |
Shrubland | Evergreen shrubland | Evergreen shrubland | 121 |
Deciduous shrubland | Deciduous shrubland | 122 | |
Grassland | Grassland | Grassland | 130 |
Wetlands | Lichens and mosses | Lichens and mosses | 140 |
Wetlands | Wetlands | 180 | |
Impervious | Impervious | Impervious | 190 |
Bare areas | Sparse vegetation | Sparse vegetation (tree, herbaceous cover) (fc < 15%) | 150 |
Consolidated bare areas | Consolidated bare areas | 201 | |
Unconsolidated bare areas | Unconsolidated bare areas | 202 | |
Water body | Water body | Water body | 210 |
Ice and snow | Permanent ice and snow | Permanent ice and snow | 220 |
HRC | TRC | ICL | EBF | DBF | ENF | DNF | MLF | ESH | DSH | GRL | LIM | SPV | WEL | Imp | CBA | UBA | Water | SNI | Total | P.A. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
HRC | 759 | 0 | 90 | 55 | 13 | 17 | 0 | 0 | 6 | 1 | 167 | 0 | 5 | 1 | 31 | 2 | 0 | 2 | 0 | 1149 | 0.661 |
TRC | 20 | 50 | 0 | 3 | 4 | 7 | 20 | 0 | 0 | 2 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 109 | 0.459 |
ICL | 127 | 8 | 479 | 33 | 1 | 6 | 0 | 0 | 4 | 1 | 23 | 0 | 3 | 1 | 43 | 4 | 0 | 4 | 0 | 737 | 0.650 |
EBF | 25 | 4 | 7 | 346 | 37 | 136 | 0 | 0 | 3 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 563 | 0.615 |
DBF | 20 | 4 | 7 | 57 | 434 | 21 | 26 | 32 | 13 | 0 | 18 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 634 | 0.685 |
ENF | 35 | 8 | 8 | 190 | 92 | 373 | 16 | 25 | 4 | 0 | 23 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 777 | 0.480 |
DNF | 1 | 0 | 0 | 1 | 38 | 3 | 187 | 15 | 2 | 0 | 11 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 260 | 0.719 |
MLF | 0 | 0 | 0 | 0 | 5 | 0 | 3 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 0.467 |
ESH | 4 | 2 | 1 | 17 | 1 | 16 | 0 | 0 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 95 | 0.568 |
DSH | 0 | 0 | 1 | 6 | 11 | 2 | 6 | 0 | 0 | 23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 0.469 |
GRL | 156 | 0 | 57 | 8 | 22 | 22 | 3 | 4 | 13 | 0 | 2372 | 0 | 114 | 6 | 21 | 194 | 1 | 6 | 17 | 3016 | 0.786 |
LIM | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 9 | 1 | 0 | 0 | 8 | 0 | 0 | 0 | 24 | 0.375 |
SVE | 4 | 0 | 4 | 1 | 0 | 0 | 0 | 0 | 2 | 0 | 98 | 0 | 125 | 3 | 9 | 28 | 0 | 0 | 1 | 275 | 0.455 |
WEL | 3 | 2 | 7 | 3 | 2 | 1 | 0 | 1 | 0 | 0 | 19 | 0 | 4 | 51 | 8 | 15 | 1 | 3 | 2 | 122 | 0.418 |
Imp | 46 | 1 | 27 | 3 | 8 | 6 | 1 | 0 | 1 | 0 | 35 | 0 | 7 | 4 | 152 | 4 | 0 | 5 | 0 | 300 | 0.507 |
CBA | 1 | 0 | 16 | 0 | 0 | 1 | 0 | 1 | 7 | 0 | 275 | 13 | 67 | 5 | 7 | 1932 | 6 | 2 | 7 | 2340 | 0.826 |
UBA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 1 | 0 | 0 | 11 | 38 | 0 | 0 | 56 | 0.679 |
Water | 9 | 0 | 9 | 1 | 5 | 1 | 1 | 1 | 3 | 0 | 0 | 0 | 1 | 4 | 3 | 0 | 0 | 427 | 5 | 470 | 0.909 |
SNI | 0 | 0 | 0 | 1 | 2 | 5 | 0 | 0 | 0 | 0 | 23 | 1 | 2 | 0 | 0 | 13 | 0 | 4 | 190 | 241 | 0.788 |
Total | 1210 | 79 | 713 | 725 | 675 | 617 | 263 | 86 | 112 | 32 | 3076 | 23 | 334 | 75 | 276 | 2212 | 46 | 454 | 224 | 11,232 | |
U.A. | 0.627 | 0.633 | 0.672 | 0.477 | 0.643 | 0.605 | 0.711 | 0.081 | 0.482 | 0.719 | 0.771 | 0.391 | 0.374 | 0.680 | 0.551 | 0.873 | 0.826 | 0.941 | 0.848 | ||
O.A. | 0.713 | ||||||||||||||||||||
Kappa | 0.664 |
CRL | FST | Shru | GRL | WEL | Imp | BareA | Water | SNI | Total | P.A. | |
---|---|---|---|---|---|---|---|---|---|---|---|
CRL | 1533 | 159 | 14 | 190 | 2 | 74 | 17 | 6 | 0 | 1995 | 0.768 |
FST | 119 | 2044 | 27 | 52 | 0 | 2 | 2 | 1 | 2 | 2249 | 0.909 |
Shru | 8 | 59 | 77 | 0 | 0 | 0 | 0 | 0 | 0 | 144 | 0.535 |
GRL | 213 | 59 | 13 | 2372 | 6 | 21 | 309 | 6 | 17 | 3016 | 0.786 |
WEL | 12 | 7 | 0 | 25 | 60 | 8 | 29 | 3 | 2 | 146 | 0.411 |
Imp | 74 | 18 | 1 | 35 | 4 | 152 | 11 | 5 | 0 | 300 | 0.507 |
BareA | 25 | 3 | 9 | 379 | 21 | 16 | 2208 | 2 | 8 | 2671 | 0.827 |
Water | 18 | 9 | 3 | 0 | 4 | 3 | 1 | 427 | 5 | 470 | 0.909 |
SNI | 0 | 8 | 0 | 23 | 1 | 0 | 15 | 4 | 190 | 241 | 0.788 |
Total | 2002 | 2366 | 144 | 3076 | 98 | 276 | 2592 | 454 | 224 | 11,232 | |
U.A. | 0.766 | 0.864 | 0.535 | 0.771 | 0.612 | 0.551 | 0.852 | 0.941 | 0.848 | ||
O.A. | 0.807 | ||||||||||
Kappa | 0.757 |
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, X.; Liu, L.; Chen, X.; Xie, S.; Gao, Y. Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach. Remote Sens. 2019, 11, 1056. https://doi.org/10.3390/rs11091056
Zhang X, Liu L, Chen X, Xie S, Gao Y. Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach. Remote Sensing. 2019; 11(9):1056. https://doi.org/10.3390/rs11091056
Chicago/Turabian StyleZhang, Xiao, Liangyun Liu, Xidong Chen, Shuai Xie, and Yuan Gao. 2019. "Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach" Remote Sensing 11, no. 9: 1056. https://doi.org/10.3390/rs11091056
APA StyleZhang, X., Liu, L., Chen, X., Xie, S., & Gao, Y. (2019). Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach. Remote Sensing, 11(9), 1056. https://doi.org/10.3390/rs11091056