Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang
Abstract
:1. Introduction
2. Materials and Methods
2.1. Overview of the Study Area
2.2. Data Sources
2.3. Data Preprocessing
2.4. Type Area Deviation Analysis
2.5. Compositional Similarity Analysis
2.6. Spatial Consistency Analysis
2.7. Accuracy Evaluation
2.7.1. Confusion Matrix Analysis
2.7.2. Land Cover Product Validation Sample Data Construction
3. Results
3.1. Type Area Deviation Analysis
3.2. Analysis of the Composition of Land Cover Types
3.3. Spatial Consistency of Multi-Source Products
3.3.1. Consistency of Spatial Distribution
3.3.2. Comparison with High-Resolution Imagery from Google Earth
3.4. Accuracy Assessment Results
4. Discussion
4.1. Impact of Geographic Features on Land Cover
4.2. Comparative of Research Findings across Different Regions
4.3. Inconsistency Factors
- (1)
- The disparity in sources of remote sensing images stands as a primary determinant of the inconsistency observed across land cover products. GlobeLand30 employs a combination of a single-phase Landsat TM/ETM+ image with HJ-1A/B and GF-1 [30] for its classification process. In contrast, CLCD integrates MODIS with Landsat imagery, and ESRI relies on the Sentinel-2 satellite image. Both FROM_GLC30 and GLC_FCS30 derive their classifications from the Landsat series of satellite images. Given the varied origins of these images, they inherently possess different spectral attributes, which in turn, profoundly influence the consistency of the classification outcomes. Furthermore, the spatial resolution cannot be overlooked in this context. While ESRI boasts a resolution of 10 m, GlobeLand30, FROM_GLC30, GLC_FCS30, and CLCD operate at a 30 m resolution. It is evident from studies that products with a higher resolution tend to have a purer pixel quality compared to their low-resolution counterparts. This translates to a depiction of more intricate spatial nuances and, typically, a more accurate classification.
- (2)
- Differences in classification methods and systems can result in inconsistencies in classification outcomes [60]. Specifically, in terms of classification approaches, three products, FROM_GLC30, GLC_FCS30, and CLCD, utilize the random forest classification algorithm, which is evaluated as the best in terms of computational efficiency and performance in global land cover mapping [61]. Notably, GLC_FCS30 is developed on the GEE (Google Earth Engine) platform using a locally adaptive random forest model combined with time-series Landsat images and a global prior training dataset from GSPECLib [51]. GlobeLand30 employs a pixel–object–knowledge (POK) classification approach to categorize each type one by one, and incorporates various information and experiences. The advantage of this method is that it can effectively suppress misclassification due to the phenomena of “same object, different spectrum” and “different object, same spectrum” [30]. ESRI employs deep learning models, notably convolutional neural networks (CNN), for land cover classification. Such models excel in handling complex data and discerning intricate patterns within them. When comparing classification systems, GlobeLand30, FROM_GLC30, CLCD, and ESRI categorize land cover into broad primary categories, whereas GLC_FCS30 offers a more detailed system. Although the five products have roughly analogous names for land cover types in their overarching classification systems, they differ in their specific thematic definitions of the same land cover type, as demonstrated in Table 3. Taking bare land as an example, GlobeLand30 defines it as areas with less than 10% vegetation cover, while GLC_FCS30 sets the threshold at 15%. Such a distinction might mean that what is classified as grassland in GlobeLand30 could be labeled as bare land in the GLC_FCS30. This naturally leads to discrepancies, especially between classifications of bare ground and grassland across the products. In essence, while the products have stark differences in the detailed definitions of identical land cover types, it is these very differences that introduce variances and uncertainties between the products that are hard to rectify. As a result, inaccuracies observed in broad land cover type assessments stem not only from the products themselves but also from the thematic nuances within each product’s classification system.
- (3)
- The quantity and precision of validation samples are crucial for the results of consistency analysis. Validation samples from the visual interpretation of Google Earth might be influenced by subjectivity and human interference. This can result in biases towards specific land types during sample selection, leading to an uneven distribution of the samples. Additionally, the number of actual measured samples is often limited, which could cause instability and inaccuracy in the evaluation outcomes. Therefore, obtaining scientifically robust and reasonable validation sample points is an essential research direction in future global land cover mapping. This might involve using higher accuracy ground observation data, field validations through drones or satellite imagery, and machine learning techniques, among others.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Liao, A.; Chen, L.; Chen, J.; He, C.; Cao, X.; Chen, J.; Peng, S.; Sun, F.; Gong, P. High-resolution remote sensing mapping of global land water. Sci. China Earth Sci. 2014, 57, 2305–2316. [Google Scholar] [CrossRef]
- Chen, Y.; Shao, H.; Li, Y. Consistency analysis and accuracy assessment of multi-source land cover products in the Yangtze River Delta. Trans. Chin. Soc. Agric. Eng. 2021, 37, 142–150. [Google Scholar] [CrossRef]
- Kayet, N.; Pathak, K.; Chakrabarty, A.; Sahoo, S. Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model. Earth Syst. Environ. 2016, 2, 127. [Google Scholar] [CrossRef]
- Sterling, S.M.; Ducharne, A.; Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Chang. 2013, 3, 385–390. [Google Scholar] [CrossRef]
- Salazar, A.; Baldi, G.; Hirota, M.; Syktus, J.; McAlpine, C.J.G.; Change, P. Land use and land cover change impacts on the regional climate of non-Amazonian South America: A review. Glob. Planet. Chang. 2015, 128, 103–119. [Google Scholar] [CrossRef]
- Keola, S.; Andersson, M.; Hall, O. Monitoring economic development from space: Using nighttime light and land cover data to measure economic growth. World Dev. 2015, 66, 322–334. [Google Scholar] [CrossRef]
- Cao, Q.; Yu, D.; Georgescu, M.; Han, Z.; Wu, J. Impacts of land use and land cover change on regional climate: A case study in the agro-pastoral transitional zone of China. Environ. Res. Lett. 2015, 10, 124025. [Google Scholar] [CrossRef]
- Giri, C.; Defourny, P.; Shrestha, S. Land cover characterization and mapping of continental Southeast Asia using multi-resolution satellite sensor data. Int. J. Remote Sens. 2003, 24, 4181–4196. [Google Scholar] [CrossRef]
- Wang, Z.; Yang, X.; Lu, C.; Yang, F. A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images. Int. J. Appl. Earth Obs. Geoinf. 2018, 69, 88–98. [Google Scholar] [CrossRef]
- LaFontaine, J.H.; Hay, L.E.; Viger, R.J.; Regan, R.S.; Markstrom, S.L. Effects of climate and land cover on hydrology in the Southeastern US: Potential impacts on watershed planning. J. Am. Water Resour. Assoc. 2015, 51, 1235–1261. [Google Scholar] [CrossRef]
- Ran, Y.; Li, X.; Lu, L. Evaluation of four remote sensing based land cover products over China. Int. J. Remote Sens. 2010, 31, 391–401. [Google Scholar] [CrossRef]
- Mora, B.; Tsendbazar, N.-E.; Herold, M.; Arino, O. Global land cover mapping: Current status and future trends. Land Use Land Cover Mapp. Eur. Pract. Trends 2014, 18, 11–30. [Google Scholar] [CrossRef]
- Weiss, M.; Jacob, F.; Duveiller, G. Remote sensing for agricultural applications: A meta-review. Remote Sens. Environ. 2020, 236, 111402. [Google Scholar] [CrossRef]
- Abdikan, S.; Sanli, F.B.; Ustuner, M.; Calò, F. Land cover mapping using sentinel-1 SAR data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 2016, 41, 757–761. [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. Remote Sens. 2015, 103, 1–6. [Google Scholar] [CrossRef]
- Loveland, T.R.; Reed, B.C.; Brown, J.F.; Ohlen, D.O.; Zhu, Z.; Yang, L.; Merchant, J.W. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. Int. J. Remote Sens. 2000, 21, 1303–1330. [Google Scholar] [CrossRef]
- Bartholome, E.; Belward, A.S. GLC2000: A new approach to global land cover mapping from Earth observation data. Int. J. Remote Sens. 2005, 26, 1959–1977. [Google Scholar] [CrossRef]
- Defourny, P.; Schouten, L.; Bartalev, S.; Bontemps, S.; Arino, O.J.N.L.W. Accuracy assessment of a 300 m global land cover map: The GlobCover experience. New Libr. World 2009, 112, 236–247. [Google Scholar] [CrossRef]
- Lamarche, C.; Santoro, M.; Bontemps, S.; d’Andrimont, R.; Radoux, J.; Giustarini, L.; Brockmann, C.; Wevers, J.; Defourny, P.; Arino, O. Compilation and validation of SAR and optical data products for a complete and global map of inland/ocean water tailored to the climate modeling community. Remote Sens. 2017, 9, 36. [Google Scholar] [CrossRef]
- Hansen, M.; DeFries, R.; Townshend, J.R.G.; Sohlberg, R. Umd Global Land Cover Classification, 1 Kilometer, 1981–1994, 1.0; Department of Geography, University of Maryland: College Park, MD, USA, 1998. [Google Scholar]
- 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]
- Li, Y.; Viña, A.; Yang, W.; Chen, X.; Zhang, J.; Ouyang, Z.; Liang, Z.; Liu, J. Effects of conservation policies on forest cover change in giant panda habitat regions, China. Land Use Policy 2013, 33, 42–53. [Google Scholar] [CrossRef] [PubMed]
- Dong, J.; Xiao, X.; Sheldon, S.; Biradar, C.; Duong, N.D.; Hazarika, M. A comparison of forest cover maps in Mainland Southeast Asia from multiple sources: PALSAR, MERIS, MODIS and FRA. Remote Sens. Environ. 2012, 127, 60–73. [Google Scholar] [CrossRef]
- 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]
- Verburg, P.H.; Neumann, K.; Nol, L. Challenges in using land use and land cover data for global change studies. Glob. Chang. Biol. 2011, 17, 974–989. [Google Scholar] [CrossRef]
- Congalton, R.G.; Gu, J.; Yadav, K.; Thenkabail, P.; Ozdogan, M. Global land cover mapping: A review and uncertainty analysis. Remote Sens. 2014, 6, 12070–12093. [Google Scholar] [CrossRef]
- Gómez, C.; White, J.C.; Wulder, M.A. Optical remotely sensed time series data for land cover classification: A review. ISPRS J. Photogramm. Remote Sens. 2016, 116, 55–72. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Liu, L.; Zhang, X.; Gao, Y.; Chen, X.; Shuai, X.; Mi, J. Finer-resolution mapping of global land cover: Recent developments, consistency analysis, and prospects. J. Remote Sens. 2021, 38, 5289697. [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 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 2015, 103, 7–27. [Google Scholar] [CrossRef]
- Zhang, X.; Liu, L.; Chen, X.; Gao, Y.; Xie, S.; Mi, J. GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery. Earth Syst. Sci. Data 2020, 13, 2753–2776. [Google Scholar] [CrossRef]
- Gong, P.; Wang, J.; Yu, L.; Zhao, Y.; Zhao, Y.; Liang, L.; Niu, Z.; Huang, X.; Fu, H.; Liu, S. 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]
- Zanaga, D.; Van De Kerchove, R.; Daems, D.; De Keersmaecker, W.; Brockmann, C.; Kirches, G.; Wevers, J.; Cartus, O.; Santoro, M.; Fritz, S. ESA WorldCover 10 m 2021 v200. Zenodo 2022. [Google Scholar]
- Karra, K.; Kontgis, C.; Statman-Weil, Z.; Mazzariello, J.C.; Mathis, M.; Brumby, S.P. Global land use/land cover with Sentinel 2 and deep learning. In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 11–16 July 2021; pp. 4704–4707. [Google Scholar]
- Chen, B.; Xu, B.; Zhu, Z.; Yuan, C.; Suen, H.P.; Guo, J.; Xu, N.; Li, W.; Zhao, Y.; Yang, J. Stable classification with limited sample: Transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017. Sci. Bull. 2019, 64, 3. [Google Scholar] [CrossRef]
- Wang, J.; Yang, X.; Wang, Z.; Cheng, H.; Kang, J.; Tang, H.; Li, Y.; Bian, Z.; Bai, Z. Consistency Analysis and Accuracy Assessment of Three Global Ten-Meter Land Cover Products in Rocky Desertification Region—A Case Study of Southwest China. ISPRS Int. J. Geo-Inf. 2022, 11, 202. [Google Scholar] [CrossRef]
- Pérez-Hoyos, A.; Rembold, F.; Kerdiles, H.; Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sens. 2017, 9, 1118. [Google Scholar] [CrossRef]
- Olofsson, P.; Stehman, S.V.; Woodcock, C.E.; Sulla-Menashe, D.; Sibley, A.M.; Newell, J.D.; Friedl, M.A.; Herold, M. A global land-cover validation data set, part I: Fundamental design principles. Int. J. Remote Sens. 2012, 33, 5768–5788. [Google Scholar] [CrossRef]
- Li, C.; Gong, P.; Wang, J.; Zhu, Z.; Biging, G.S.; Yuan, C.; Hu, T.; Zhang, H.; Wang, Q.; Li, X. The first all-season sample set for mapping global land cover with Landsat-8 data. Sci. Bull. 2017, 62, 508–515. [Google Scholar] [CrossRef]
- Tchuenté, A.T.K.; Roujean, J.-L.; De Jong, S.M. Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. Int. J. Appl. Earth Obs. 2011, 13, 207–219. [Google Scholar] [CrossRef]
- Bai, Y.; Feng, M.; Jiang, H.; Wang, J.; Zhu, Y.; Liu, Y. Assessing consistency of five global land cover data sets in China. Remote Sens. 2014, 6, 8739–8759. [Google Scholar] [CrossRef]
- Giri, C.; Zhu, Z.; Reed, B. A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sens. Environ. 2005, 94, 123–132. [Google Scholar] [CrossRef]
- Song, H.; Zhang, X. Exploratory analysis of category accuracy for multi-sources land cover products. Res. Soil Water Conserv. 2015, 22, 36–41. [Google Scholar]
- Song, H.; Zhang, X. Precision analysis and validation of multi-sources landcover products derived from remote sensing in China. Trans. Chin. Soc. Agric. Eng. 2012, 28, 207–214. [Google Scholar] [CrossRef]
- Wickham, J.; Stehman, S.V.; Gass, L.; Dewitz, J.A.; Sorenson, D.G.; Granneman, B.J.; Poss, R.V.; Baer, L.A. Thematic accuracy assessment of the 2011 national land cover database (NLCD). Remote Sens. Environ. 2017, 191, 328–341. [Google Scholar] [CrossRef] [PubMed]
- Xu, Y.; Yu, L.; Feng, D.; Peng, D.; Li, C.; Huang, X.; Lu, H.; Gong, P. Comparisons of three recent moderate resolution African land cover datasets: CGLS-LC100, ESA-S2-LC20, and FROM-GLC-Africa30. Int. J. Remote Sens. 2019, 40, 6185–6202. [Google Scholar] [CrossRef]
- Kang, J.; Wang, Z.; Sui, L.; Yang, X.; Ma, Y.; Wang, J. Consistency analysis of remote sensing land cover products in the tropical rainforest climate region: A case study of Indonesia. Remote Sens. 2020, 12, 1410. [Google Scholar] [CrossRef]
- Hu, Y.; Zhang, Q.; Dai, Z.; Huang, M.; Yan, H. Agreement analysis of multi-sensor satellite remote sensing derived land cover products in the Europe Continent. Geogr. Res. 2015, 34, 1839–1852. [Google Scholar]
- Hou, X.; Di, X.; Hou, W.; Wu, L.; Liu, J.; Wang, J.; Su, H.; Lu, X.; Ying, L.; Yu, X. Accuracy evaluation of land use mapping using remote sensing techniques in coastal zone of China. J. Geo-Inf. Sci. 2018, 20, 1478–1488. [Google Scholar] [CrossRef]
- Clark, M.L.; Aide, T.M.; Grau, H.R.; Riner, G. A scalable approach to mapping annual land cover at 250 m using MODIS time series data: A case study in the Dry Chaco ecoregion of South America. Remote Sens. Environ. 2010, 114, 2816–2832. [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. Geoinf. 2018, 71, 83–94. [Google Scholar] [CrossRef]
- Zhao, Y.; Gong, P.; Yu, L.; Hu, L.; Li, X.; Li, C.; Zhang, H.; Zheng, Y.; Wang, J.; Zhao, Y. Towards a common validation sample set for global land-cover mapping. Int. J. Remote Sens. 2014, 35, 4795–4814. [Google Scholar] [CrossRef]
- Fritz, S.; McCallum, I.; Schill, C.; Perger, C.; See, L.; Schepaschenko, D.; Van der Velde, M.; Kraxner, F.; Obersteiner, M. Geo-Wiki: An online platform for improving global land cover. Environ. Model. Softw. 2012, 31, 110–123. [Google Scholar] [CrossRef]
- Huang, Y.; Liao, S. Regional accuracy assessments of the first global land cover dataset at 30-meter resolution: A case study of Henan province. Geogr. Res. 2016, 35, 1433–1446. [Google Scholar]
- Zhang, Q.; Singh, V.P.; Li, J.; Jiang, F.; Bai, Y. Spatio-temporal variations of precipitation extremes in Xinjiang, China. J. Hydrol. 2012, 434, 7–18. [Google Scholar] [CrossRef]
- Luo, N.; Yu, R.; Mao, D.; Wen, B.; Liu, X. Spatiotemporal variations of wetlands in the northern Xinjiang with relationship to climate change. Wetl. Ecol. Manag. 2021, 29, 617–631. [Google Scholar] [CrossRef]
- Zheng, L.; Xia, Z.; Xu, J.; Chen, Y.; Yang, H.; Li, D. Exploring annual lake dynamics in Xinjiang (China): Spatiotemporal features and driving climate factors from 2000 to 2019. Clim. Chang. 2021, 166, 36. [Google Scholar] [CrossRef]
- Jiang, G.; Wang, J.; Li, K.; Xu, C.; Li, H.; Jin, Z.; Liu, J. Consistency Analysis and Accuracy Evaluation of Multi-Source Land Cover Data Products in the Eastern European Plain. Remote Sens. 2023, 15, 4254. [Google Scholar] [CrossRef]
- Wang, B.; Cheng, W. Geomorphic influences on land use/cover diversity and pattern. Catena 2023, 230, 107245. [Google Scholar] [CrossRef]
- Yang, Y.; Xiao, P.; Feng, X.; Li, H. Accuracy assessment of seven global land cover datasets over China. ISPRS J. Photogramm. Remote Sens. 2017, 125, 156–173. [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]
Name | Production Institution | Resolution (m) | Method | Time | Satellite | Overall Accuracy (%) |
---|---|---|---|---|---|---|
GlobeLand30 | National Geomatics Center of China | 30 | POK | 2020 | Landsat TM/ETM+, HJ-1/GF-1 | 85.72 |
GLC_FCS30 | Aerospace Information Research Institute, Chinese Academy of Sciences | 30 | Local random forest | 2020 | Landsat TM/ETM+/OLI | 82.5 |
FROM_GLC30 | Tsinghua University | 30 | Random forest | 2017 | Landsat TM/ETM+/OLI | 75.39 |
CLCD | Wuhan University | 30 | Random forest | 2020 | Landsat/Modis | 80 |
ESRI Land Cover | Environmental System Research Institute | 10 | Deep learning model | 2020 | Sentinel-2 | 85.96 |
Code | Type | GlobeLand30 | GLC_FCS30 | FROM_GLC30 | CLCD | ESRI |
---|---|---|---|---|---|---|
1 | Cropland | 10 | 10, 11, 20 | 10 | 1 | 5 |
2 | Forest | 20, 40 | 50–90, 120, 121, 122 | 20, 40 | 2, 3 | 2, 6 |
3 | Grassland | 30 | 130, 140, 153 | 30 | 4 | 3 |
4 | Bare land | 90 | 150, 200–202 | 90 | 7 | 8 |
5 | Wetland | 50 | 180 | 50 | 9 | 4 |
6 | Water | 60 | 210 | 60 | 5 | 1 |
7 | Snow/Ice | 100 | 220 | 100 | 6 | 9 |
8 | Impervious surface | 80 | 190 | 80 | 8 | 7 |
Code | GlobeLand30 | Code | FROM_GLC30 | Code | CLCD | Code | GLC_FCS30 | Code | ESRI |
---|---|---|---|---|---|---|---|---|---|
10 | Cropland | 10 | Cropland | 1 | Cropland | 10 | Rainfed cropland | 1 | Water |
20 | Forest | 20 | Forest | 2 | Forest | 11 | Herbaceous cover | 2 | Trees |
30 | Grassland | 30 | Grassland | 3 | Shrub | 12 | Tree or shrub cover (Orchard) | 3 | Grass |
40 | Shrubland | 40 | Shrubland | 4 | Grassland | 20 | Irrigated cropland | 4 | Flooded vegetation |
50 | Wetland | 50 | Wetland | 5 | Water | 61 | Open deciduous broadleaved forest (0.15 < fc < 0.4) | 5 | Crops |
60 | Water | 60 | Water | 6 | Snow/Ice | 62 | Closed deciduous broadleaved forest (fc > 0.4) | 6 | Scrub/shrub |
70 | Tundra | 70 | Tundra | 7 | Barren | 71 | Open evergreen needle-leaved forest (0.15 < fc < 0.4) | 7 | Built Area |
80 | Impervious surface | 80 | Impervious surface | 8 | Impervious | 72 | Closed evergreen needle-leaved forest (fc > 0.4) | 8 | Bare ground |
90 | Bare land | 90 | Bare land | 9 | Wetland | 81 | Open deciduous needle-leaved forest (0.15 < fc < 0.4) | 9 | Snow/Ice |
100 | Snow/Ice | 100 | Snow/Ice | 82 | Closed deciduous needle-leaved forest (fc > 0.4) | 10 | Clouds | ||
92 | Closed mixed leaf forest (broadleaved and needle-leaved) | ||||||||
120 | Shrubland | ||||||||
121 | Evergreen shrubland | ||||||||
122 | Deciduous shrubland | ||||||||
130 | Grassland | ||||||||
140 | Lichens and mosses | ||||||||
150 | Sparse vegetation (fc < 0.15) (255, 235, 175) | ||||||||
153 | Sparse herbaceous (fc < 0.15) | ||||||||
180 | Wetlands | ||||||||
190 | Impervious surfaces | ||||||||
200 | Bare areas | ||||||||
201 | Consolidated bare areas | ||||||||
210 | Water body | ||||||||
220 | Permanent ice and snow |
GlobeLand30 | FROM_GLC30 | CLCD | GLC_FCS30 | ESRI | |
---|---|---|---|---|---|
GlobeLand30 | 1.000 | 0.992 | 0.999 | 0.997 | 0.908 |
FROM_GLC30 | 0.992 | 1.000 | 0.990 | 0.995 | 0.848 |
CLCD | 0.999 | 0.990 | 1.000 | 0.996 | 0.915 |
GLC_FCS30 | 0.997 | 0.995 | 0.996 | 1.000 | 0.883 |
ESRI | 0.908 | 0.848 | 0.915 | 0.883 | 1.000 |
Google Samples | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | OA (%) | Kappa | ||
GlobeLand30 | PA (%) | 92.44 | 82.58 | 89.93 | 84.09 | 57.84 | 77.84 | 91.44 | 85.26 | 84.06 | 0.82 |
UA (%) | 89.62 | 91.21 | 65.67 | 88.10 | 90.03 | 78.21 | 98.22 | 89.74 | |||
FROM_GLC30 | PA (%) | 84.77 | 91.83 | 92.55 | 88.47 | 16.34 | 69.14 | 92.82 | 35.53 | 73.48 | 0.69 |
UA (%) | 88.47 | 86.79 | 55.42 | 62.86 | 69.81 | 74.20 | 99.12 | 86.70 | |||
CLCD | PA (%) | 87.33 | 89.89 | 88.32 | 82.31 | 6.62 | 65.28 | 88.40 | 31.58 | 70.05 | 0.65 |
UA (%) | 76.01 | 87.63 | 46.08 | 73.91 | 100 | 80.59 | 99.38 | 78.14 | |||
GLC_FCS30 | PA (%) | 87.44 | 94.84 | 57.37 | 88.15 | 14.35 | 64.69 | 91.71 | 60.90 | 71.18 | 0.66 |
UA (%) | 72.94 | 59.35 | 61.79 | 65.26 | 73.03 | 78.14 | 96.51 | 92.57 | |||
ESRI | PA (%) | 82.21 | 85.16 | 93.72 | 53.73 | 27.15 | 72.40 | 92.27 | 90.23 | 75.57 | 0.72 |
UA (%) | 94.52 | 96.12 | 47.10 | 69.83 | 93.89 | 80.26 | 99.70 | 89.39 |
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Liu, S.; Xu, Z.; Guo, Y.; Yu, T.; Xu, F.; Wang, Y. Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang. Land 2023, 12, 2178. https://doi.org/10.3390/land12122178
Liu S, Xu Z, Guo Y, Yu T, Xu F, Wang Y. Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang. Land. 2023; 12(12):2178. https://doi.org/10.3390/land12122178
Chicago/Turabian StyleLiu, Shen, Zhonglin Xu, Yuchuan Guo, Tingting Yu, Fujin Xu, and Yao Wang. 2023. "Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang" Land 12, no. 12: 2178. https://doi.org/10.3390/land12122178
APA StyleLiu, S., Xu, Z., Guo, Y., Yu, T., Xu, F., & Wang, Y. (2023). Consistency Analysis of Multi-Source Remote Sensing Land Cover Products in Arid Regions—A Case Study of Xinjiang. Land, 12(12), 2178. https://doi.org/10.3390/land12122178