Data Paper Zone II Versions EN1 Vol 2 (4) 2017
Grid data on land use and land cover of the Loess Plateau region (2015)
: 2017 - 04 - 21
: 2017 - 05 - 18
: 2017 - 10 - 26
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Abstract & Keywords
Abstract: Land use/cover data reflect the cultivation and coverage state of land surface. They are the bases for geographic and ecological research. To produce land use/cover data of the Loess Plateau, this study employs Landsat 8 OLI satellite images with a cloud coverage of less than 10%. The Loess Plateau region is divided into sub-regions by using Esri ArcGIS according to the availability of qualified satellite imageries. Object-oriented classification is used to extract land use/cover classes assisted by eCognition 8.7 software. After multi-scale segmentation, a collection of parameters are established as classification traits, including metrics on objects' adjacent relationship, spectrum, texture and shape. Based on these parameters, a rule set is constructed for conducting an automatic classification and a manual check and modification. Classification accuracy is assessed by using 1,028 field samples. Results show that the data set has an overall accuracy of 85.4% with a corresponding Kappa coefficient of 0.807. The classification results for each sub-region are exported as vector data in shapefile format. Then a vector-grid conversion is conducted through Arc/info AML procedure. Stitching operation on all obtained grid data is accomplished by using ArcGIS. The land use/cover grid data of the Loess Plateau, with a resolution of 250m, are generated as results of a subsequent clipping and resampling procedure.
Keywords: Loess Plateau; land use; land cover; Landsat 8; object-oriented classification; grid data
Dataset Profile
English titleGrid data on land use and land cover of the Loess Plateau region (2015)
Corresponding authorLiu Yu (
Data authorsZhu Yuan, Liu Yu, Zhao Liang
Geographical scopeThe dataset covers the whole Loess Plateau in China (33°43′N – 41°16′N, 100°54′E – 114°33′E).
Spatial resolution250 mData volume16.1 MB
Data formatGeotiffTime range2015
Data service system<>
Sources of fundingMajor Program of the National Natural Science Foundation of China – "Spatial Pattern of Ecological Carrying Capacity and Its Adjustment on the Loess Plateau" (Grant No. 41390464)
Dataset compositionThis dataset includes land use/cover data of the Loess Plateau in 2015 and corresponding shapefiles about the ground check points. The dataset contains the following files:
(1) "", which includes land use/cover data of the Loess Plateau in 2015, in ESRI grid format.
(2) "验证点[Verification Points].zip", made up of data on ground check points in shapefile format which are sampled for land cover/use classification validation. In the .dbf table, the field "class_name" records class codes for land use/cover types.
(3) "code_I.xlsx", which includes class codes and Chinese class names for the land use/cover data.
1.   Introduction
Land use/cover change (LUCC) is one of the direct causes of regional- to global-scale ecosystem changes. It has significant effects on water cycling, environment quality, biodiversity, net primary production of terrestrial ecosystems and the ecosystems' adaptability to climate changes. LUCC hence stands at the center of global change studies.1 Land use/cover (LUC) constitutes the fundamental data for researches on large-scale carbon cycle, water cycle, biodiversity assessment and ecosystem service assessment.2–4 High-quality LUC data are thus critical for generating credible scientific results in these fields. In 2014, a major program of the National Natural Science Foundation of China – "Spatial Pattern of Ecological Carrying Capacity and Its Adjustment on the Loess Plateau" (Grant No. 41390464) – was launched to explore the interaction between vegetation and hydrology at regional scale on the Loess Plateau, China. One of the fundamental tasks of this program is to produce LUC data covering the whole Loess Plateau. It is expected to provide land cover parameters for modelling water resource states and changes and for assessing the vegetation carrying capacity of regional hydrologic systems.
Previous studies by the Chinese Academy of Sciences have produced LUC datasets covering the whole terrestrial territory of China. These data are recorded at a temporal interval of 5 years from 1980s to 2010, and has a resolution of 30 m.5 Recently, an LUC dataset named "Globeland30" was released by the Geographical Information Center of China.6 To date, there have been six LUC datasets for open access globally, which are DISCover (USGS), Global MODIS LUC with a resolution of 1 km (Boston University), GLC2000 (European Commission Joint Research Center), GlobCover2 (European Commission Joint Research Center), and Globeland30-2010 (Geographical Information Center of China). However, most of these datasets are produced from the spectral information of satellite images at pixel level and follow a traditional supervised classification method, which have clear disadvantages in removing "pepper and salt" effects.7 Spatial relationship among objects on land surface and relevant geographical knowledge are often ignored. Besides, the classification is all too often inconsistent because of too much manual intervention, which leads to a rather low data accuracy.8 In our product, we depend on rule sets to conduct object-oriented classification based on the spectral characteristics of objects and their spatial arrangement. Then manual revision is adopted. By this approach, images are segmented into objects of relatively homogeneous spectral characteristics and textures for further classification. Both spectral information and the spatial relationship among objects are included for promising a higher accuracy.
2.   Data collection and processing
2.1   Geographic coverage of the data
The Loess Plateau is located in middle of the Yellow River basin, with a latitude and longitude range of 33°43′N – 41°16′N and 100°54′E – 114°33′E, respectively. It neighbors Mt. Qin to the south and Mt. Yinshan to the north. It reaches Taihang Mountain in the east and is bordered by Wuqiao Mountain and Riyue Mountain in the west. It covers 287 counties of Shannxi, Shanxi, Inner Mongolia, Ningxia, Qinghai, Gansu and Henan provinces. The Loess Plateau has a total acreage of 620,000 km2, accounting for 6.69% of China's terrestrial territory. Pushed by ecosystem remediation and restoration policies, as well as rapid economic development since the 1990s, the Loess Plateau stepped into an era featured by large-scale and rapid revegetation, which reshaped regional land use/cover significantly.9 As a result, the Loess Plateau became a hotspot for LUCC studies in China.
2.2   Sources and features of the imageries
This land use/cover dataset is derived from imageries obtained by Landsat 8 OLI sensor. It has a temporal range of 2014 – 2015, and a resolution of 30 m. In total, 117 imageries were collected during winter (Dec. 2014 – Feb. 2015), summer (Jun. 2015 – Sept. 2015), and autumn (Oct. 2015). The spatial coverage of the imageries is shown in Figure 1. All the imageries are collected by National Aeronautics and Space Administration (NASA) of the US and downloaded from the USGS website (

Figure 1   Spatial coverage of the imageries used in this study
2.3   Data processing
Object-oriented methodology is used for land use/cover classification, where eCognition8.7 is taken as the platform. We adopt an approach combining ruleset-based automatic classification and manual revision. Specific steps for the land use/cover data production are given below.
(1) Subregion division. Before land use/cover classification, ArcGIS is employed to divide the whole Loess Plateau region according to the availability of qualified imageries, wherein cloud coverage is a critical metric for qualified image selection.
(2) Multi-resolution image segmentation. Before classification, imageries are segmented into polygons (objects) with relatively homogeneous spectral and textural characteristics. A threshold of scale factor is obtained through test, which is then used in image segmentation. Results show that 70 is the most suitable scale factor in the classification of this data set.
(3) Classification traits establishment. Credible land use/cover classification relies on effective traits to distinguish and classify objects. In this study, indicators of spectral characteristics, texture, shape and adjacent relationship are selected to establish the ruleset. NDVI, mean value and standard variation of pixel DN within objects, GLCM homogeneity, length of shared border, and shape index of objects are included as classification metrics.
(4) Land use/cover classification. A preliminary list of land use/cover categories are extracted after multi-resolution image segmentation according to the spatial pattern, spectral and textural characteristics of objects using a rule set based on classification metrics. Then manual revision is conducted.
(5) Accuracy evaluation. Classification results of this study are validated using the confusion matrix and the Kappa coefficient. 1,028 sampling points, collected through ground survey in 2016, are used in data accuracy validation. Validation is performed by combining the high quality imageries of 2015 and real land use/cover categories. Patches with an area greater than 3 pixels × 3 pixels (8100 m2) and without changes in land use/cover types during 2015 – 2016 are selected. In the map, points are located in the center of patches.
(6) Esri grid data generation. Land use/cover data of each sub-region are exported from eCognition 8.7 in shapefile format. Then all the shapefiles are converted into Esri grid format with a spatial resolution of 30 m by using Arc/info.
(7) Clipping and mosaicking. Esri grid data of all the sub-regions are mosaicked and clipped using the "mosaic" and "clip" modules in the ArcGIS Data Management to produce a Geotiff file. Data are then resampled by the Majority method to produce a 250 m Geotiff dataset.
3.   Sample description
3.1   Description of the classification system
When establishing the data classification system, we referred to the National Ecosystem Survey and Assessment of China (2000 – 2010),10 and adopted the seven land use/cover types, i.e., forest land, shrub land, grassland, arable land, built-up area, waterbodies and other land (Table 1).
Table 1   Land use/cover classification system for the Loess Plateau
CodeLand use/coverDescription
1ForestLand covered by natural or artificial woodland with a canopy density greater than 30% and a height greater than 2 m
2ShrubLand covered by primitive or secondary shrubs or bushes with a coverage greater than 20% and a total vegetation coverage no less than 30%
3GrasslandLand whose vegetation is dominated by herb species, with a total vegetation coverage greater than 20%
4Arable landLand used for grain or other food cultivation, including irrigated and non-irrigated farmland
5Built-up areaLand used for residential habitats, commercial and manufacturing industries, traffic infrastructures, etc.
6WaterbodiesLand covered by natural waterbodies or man-made ponds and reservoirs
7OtherLand with a maximum seasonal vegetation coverage less than 20%, including barren areas, deserts and unused land
3.2   Data samples
Traditional land use/cover mapping through satellite imageries mainly depends on the spectral information of land objects. It doesn't take full use of other information of the imageries. For example, relationship among adjacent pixels or patches (land objects), shape of the patches, and geographical information of the objects are often ignored, which limit the accuracy of classification.8 This study follows the object-oriented classification approach. This approach makes use of not only the spectral information of the imageries but also the spatial relationship and arrangement of land objects. This methodology guarantees the reliability of the resulted land use/cover dataset (Figure 2).

Figure 2   Land use/cover map of the Loess Plateau in 2015
4.   Quality control and assessment
Ground sampling is conducted to collect samples for evaluating the accuracy of the land use/cover dataset. Randomness and representativeness are two principles for the sampling route design, which ensure a full coverage of the land use/cover types as well as enough samples for each type. The route goes through the core areas of the Loess Plateau. It starts at Taiyuan, passes through Xinzhou, Yulin, Erdos, Yan'an, Yinchuan, Qingyang, Tongchuan, Linfen and Jinzhong, and ends in Taiyuan. Besides, we collected land use/cover information of 78 vegetation survey plots.11 Totally, 1,028 samples were collected. These samples cover all land use/cover types across the Loess Plateau (Figure 3).

Figure 3   Distribution of ground samples for land use/cover accuracy evaluation
The selected samples consist of 277 forest sites, 70 shrub sites, 118 grassland sites, 379 arable land sites, 154 built-up sites, 20 waterbody samples and 10 barren and desert samples. The result of accuracy assessment is given by Table 2.
Table 2   Land use/cover classification accuracy of this dataset (unit: count)
Land use/coverForestlandShrub LandGrass landArable landBuilt-up areaWaterbodiesOther landSumProducer accuracy
Shrub Land15411131007157.7%
Grass land811921561013369.2%
Arable land113234770037093.4%
Built-up area54391351215984.9%
Other land00213181553.3%
User accuracy85.9%53.2%78.0%91.6%87.7%85.0%80.0%85.4%
(Total accuracy = 85.4%; Kappa coefficient = 0.807)
In Table 2, user accuracy of each type is defined as the ratio of samples correctly classified to total samples used (indicated by columns); producer accuracy indicates the proportion of objects of each type that match correct samples among total samples located in this type (indicated by rows). Total accuracy refers to the ratio of samples of all types correctly identified to the number of samples used. Kappa coefficient delineates the consistency of classification, wherein 1.0 indicates the highest consistency. As shown in Table 2, this land use/cover dataset has a good quality with a total accuracy of 85.4% and a corresponding Kappa coefficient of 0.807.
5.   Usage notes
This dataset is valuable for assessing ecosystem functions and services over the Loess Plateau, particularly for researches on carbon sequestration, landscape pattern evolution and its impacts on hydrology and water resource. Since this dataset focuses on the status of land surface cover with a classification system consistent with the National Ecosystem Survey and Assessment of China (2000 – 2010), it can be used as basic data for similar assessments on the Loess Plateau and its sub-regions. It should be noted that this dataset is suitable for studies involving a spatial scale of over 10000 km2 because of its 250-m spatial resolution.
This work is sponsored by the National Natural Science Foundation of China (Grant No. 41390464). We give our appreciation to Miss Dou Yuehan from Wageningen University for polishing this paper.
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Data citation
1. Zhu Y, Liu Y & Zhao L. Grid data on land use and land cover of the Loess Plateau region (2015). Science Data Bank. DOI: 10.11922/sciencedb.404
Article and author information
How to cite this article
Zhu Y, Liu Y & Zhao L. Grid data on land use and land cover of the Loess Plateau region (2015). China Scientific Data 2 (2017). DOI: 10.11922/csdata.170.2017.0137
Zhu Yuan
imagery preparation, preprocessing, and land use/cover extraction.
Master's student; research area: cartography and GIS. He is currently a visiting student in the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences.
Liu Yu
design of work plan, data quality control, etc.
PhD, Assistant Professor; research area: interaction between landscape patterns and land surface processes, ecosystem observation and assessment.
Zhao Liang
land use/cover classification.
Master's student; research area: cartography and GIS. He is currently a visiting student in the Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences.
National Natural Science Foundation of China – "Spatial Pattern of Ecological Carrying Capacity and Its Adjustment on the Loess Plateau" (Grant No. 41390464)
Publication records
Published: Oct. 26, 2017 ( VersionsEN1
Released: May 18, 2017 ( VersionsZH1
Published: Oct. 26, 2017 ( VersionsZH2