Landuse dataset in China (1980-2015)

The remote sensing monitoring database of China's land use status is a multi temporal land use status database covering the land area of China after years of accumulation under the support of national science and technology support plan, important direction project of knowledge innovation project of Chinese Academy of Sciences. The data set includes seven periods: the end of 1980s, 1990, 1995, 2000, 2005, 2010 and 2015. The data production is based on the Landsat TM / ETM Remote Sensing Images of each period as the main data source, which is generated by manual visual interpretation. Data are missing from some islands in the South China Sea. Spatial resolution: 30m, projection parameters: Albers_ Conic_ Equal_ Area central meridian 105, standard weft 1: 25, standard weft 2: 47. The remote sensing monitoring database of China's land use status is a relatively high precision land use monitoring data product in China, which has played an important role in the national land resources survey, hydrology and ecological research. The land use types include six first-class types of cultivated land, woodland, grassland, water area, residential land and unused land, and 25 second-class types.

0 2020-08-25

1:100,000 land use dataset of Sichuan province (1995)

This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.

0 2020-06-11

1:100,000 landuse dataset of Qinghai province (1995)

This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.

0 2020-06-10

1:100000 landuse dataset of Shaanxi province (1995)

This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.

0 2020-06-10

Siol map based Harmonized World Soil Database (v1.2)

Soil data is important both on a global scale and on a local scale, and due to the lack of reliable soil data, land degradation assessments, environmental impact studies, and sustainable land management interventions have received significant bottlenecks . Affected by the urgent need for soil information data around the world, especially in the context of the Climate Change Convention, the International Institute for Applied Systems Analysis (IIASA) and the Food and Agriculture Organization of the United Nations (FAO) and the Kyoto Protocol for Soil Carbon Measurement and FAO/International The Global Agroecological Assessment Study (GAEZ v3.0) jointly established the Harmonized World Soil Database version 1.2 (HWSD V1.2). Among them, the data source in China is the second national land in 1995. Investigate 1:1,000,000 soil data provided by Nanjing Soil. The resolution is 30 seconds (about 0.083 degrees, 1km). The soil classification system used is mainly FAO-90. The core soil system unit unique verification identifier: MU_GLOBAL-HWSD database soil mapping unit identifier, connected to the GIS layer. MU_SOURCE1 and MU_SOURCE2 source database drawing unit identifiers SEQ-soil unit sequence in the composition of the soil mapping unit; The soil classification system utilizes the FAO-7 classification system or the FAO-90 classification system (SU_SYM74 resp. SU_SYM90) or FAO-85 (SU_SYM85). The main fields of the soil property sheet include: ID (database ID) MU_GLOBAL (Soil Unit Identifier) ​​(Global) SU_SYMBOL soil drawing unit SU_SYM74 (FAO74 classification); SU_SYM85 (FAO85 classification); SU_SYM90 (name of soil in the FAO90 soil classification system); SU_CODE soil charting unit code SU_CODE74 soil unit name SU_CODE85 soil unit name SU_CODE90 soil unit name DRAINAGE (19.5); REF_DEPTH (soil reference depth); AWC_CLASS(19.5); AWC_CLASS (effective soil water content); PHASE1: Real (soil phase); PHASE2: String (soil phase); ROOTS: String (depth classification to the bottom of the soil); SWR: String (soil moisture content); ADD_PROP: Real (specific soil type in the soil unit related to agricultural use); T_TEXTURE (top soil texture); T_GRAVEL: Real (top gravel volume percentage); (unit: %vol.) T_SAND: Real (top sand content); (unit: % wt.) T_SILT: Real (surface layer sand content); (unit: % wt.) T_CLAY: Real (top clay content); (unit: % wt.) T_USDA_TEX: Real (top layer USDA soil texture classification); (unit: name) T_REF_BULK: Real (top soil bulk density); (unit: kg/dm3.) T_OC: Real (top organic carbon content); (unit: % weight) T_PH_H2O: Real (top pH) (unit: -log(H+)) T_CEC_CLAY: Real (cation exchange capacity of the top adhesive layer soil); (unit: cmol/kg) T_CEC_SOIL: Real (cation exchange capacity of top soil) (unit: cmol/kg) T_BS: Real (top level basic saturation); (unit: %) T_TEB: Real (top exchangeable base); (unit: cmol/kg) T_CACO3: Real (top carbonate or lime content) (unit: % weight) T_CASO4: Real (top sulfate content); (unit: % weight) T_ESP: Real (top exchangeable sodium salt); (unit: %) T_ECE: Real (top conductivity). (Unit: dS/m) S_GRAVEL: Real (bottom crushed stone volume percentage); (unit: %vol.) S_SAND: Real (bottom sand content); (unit: % wt.) S_SILT: Real (bottom sludge content); (unit: % wt.) S_CLAY: Real (bottom clay content); (unit: % wt.) S_USDA_TEX: Real (bottom USDA soil texture classification); (unit: name) S_REF_BULK: Real (bottom soil bulk density); (unit: kg/dm3.) S_OC: Real (underlying organic carbon content); (unit: % weight) S_PH_H2O: Real (bottom pH) (unit: -log(H+)) S_CEC_CLAY: Real (cation exchange capacity of the underlying adhesive layer soil); (unit: cmol/kg) S_CEC_SOIL: Real (cation exchange capacity of the bottom soil) (unit: cmol/kg) S_BS: Real (underlying basic saturation); (unit: %) S_TEB: Real (underlying exchangeable base); (unit: cmol/kg) S_CACO3: Real (bottom carbonate or lime content) (unit: % weight) S_CASO4: Real (bottom sulfate content); (unit: % weight) S_ESP: Real (underlying exchangeable sodium salt); (unit: %) S_ECE: Real (underlying conductivity). (Unit: dS/m) The database is divided into two layers, with the top layer (T) soil thickness (0-30 cm) and the bottom layer (S) soil thickness (30-100 cm). For other attribute values, please refer to the HWSD1.2_documentation documentation.pdf, The Harmonized World Soil Database (HWSD V1.2) Viewer-Chinese description and HWSD.mdb.

0 2020-06-03

Remote sensing monitoring dataset of land use status in six provinces in western China for many years (1970s, 1980s, 1995, 2000, 2005, 2010, 2015)

The remote sensing monitoring database of land use status in China is a multi-temporal land use status database covering the land area of China, which has been established after many years of accumulation under the support of the National Science and Technology Support Plan and the Key Direction Project of the Knowledge Innovation Project of the Chinese Academy of Sciences. It is the most accurate remote sensing monitoring data product of land use in China at present, which has played an important role in the national land resources survey, hydrology and ecological research.   This data set covers the six western provinces in China: Xinjiang, Tibet, Qinghai, Yunnan, Sichuan and Gansu. Based on Landsat TM/ETM remote sensing images in the late 1970s、1980s、1995、2000、2005、2010、2015, 1KM raster data are generated by using the professional software and manual visual interpretation on the basis of vector data.   The land use types include six primary land types which are cultivated land, forest land, grassland, water area, residential land and unused land, and 25 secondary types.

0 2020-05-29

1:100000 landuse dataset of Gansu province (1995)

This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". According to the 1:100,000 landuse data of gansu province, a hierarchical land cover classification system is adopted, which divides the whole country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.

0 2020-03-31

1:100000 landuse dataset of Hunan (1995)

This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.

0 2020-03-30

1:100,000 soil database in the upper reaches of the Yellow River (1995)

一.An overview The 1:100,000 soil database in the upper reaches of the Yellow River was tailored from the 1:100,000 soil database in China.The 1:100,000 soil database of China is based on the 1:100,000 soil map of the People's Republic of China compiled and published by the national soil census office in 1995.The database adopts the traditional "soil genetic classification" system, and the basic mapping unit is subcategories, which are divided into 12 classes of soil, 61 classes of soil and 227 classes of soil, covering all kinds of soil and its main attribute data in China. 二. Data processing instructions The 1:1 million soil database of China was established by the soil resources and digital management innovation research team led by shi xuezheng of nanjing soil research institute, Chinese academy of sciences, after four years.The database consists of two parts: soil spatial database and soil attribute database.The establishment of the database was funded by the knowledge innovation program of the Chinese academy of sciences and completed under the leadership of liu jiyuan and zhuang dafang. 三. data content description The soil spatial database, 1:1 million digitized soil maps of the country, is based on the 1:1 million soil maps of the People's Republic of China compiled and published by the national census offices in 1995.The digitized soil map faithfully reflects the appearance of the original soil map and inherited the mapping unit when the original soil map was compiled. Most of the basic mapping units are soil genera, which are divided into 12 classes, 61 classes and 235 subclasses. It is the only and most detailed digitized soil map in China. The soil attribute database, whose attribute data is quoted from the soil species record of China, is divided into six volumes, and nearly 2,540 soil species are collected.Soil property data can be divided into soil physical properties, soil chemical properties and soil nutrients.Soil physical properties soil particle composition and soil texture, soil chemical properties such as PH value, organic matter, soil nutrients include all N, all P, all K and effective P and effective K. 四. Data usage instructions Soil types and soil properties are an important content in the study of physical geography. With the help of 1:100,000 soil database in the upper reaches of the Yellow River, the type, quantity and spatial distribution of soil resources in the upper reaches of the Yellow River as well as the soil environment and characteristics can be understood and analyzed.This data set is of great significance for the early warning of large-scale soil erosion and the prediction of natural disasters in the upper reaches of the Yellow River.

0 2020-03-29

1:100000 land use data set of Guizhou Province (1995)

This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". In 1995, guizhou province adopted a hierarchical land cover classification system, which divided the country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.

0 2020-03-10