NDVI is a very important vegetation index for the research of vegetation growth and land cover classification. This dataset provides a monthly land surface albedo of UAV remote sensing with a spatial resolution of 0.2 m. It measured in the midstream of Heihe River Basin during the vegetation growth season over typical stations in 2019. The pix4D mapper software was used for image mosaic and NDVI calculation.
GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. The dataset includes the global vegetation index changes from 1981 to 2006, the format is ENVI standard format, the projection is ALBERS, and its time resolution is 15 days and its spatial resolution is 8km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data. 1. File format: The GIMMS-NDVI dataset contains all rar compressed files with a 15-day interval from July 1981 to 2006. After decompression, it includes an XML file, an .HDR header file, an .IMG file, and a .JPG image file. 2. File naming: The naming rules for compressed files in the NOAA / AVHRR-NDVI data set are: YYMMM15a (b) .n **-VIg_data_envi.rar, where YY-year, MMM-abbreviated English month letters, 15a-synthesized in the first half of the month, 15b-synthesized in the second half of the month, **-Satellite. After decompression, there are 4 files with the same file name, and the attributes are: XML document, header file (suffix: .HDF), remote sensing image file (suffix: .IMG), and JPEG image file. In this data set, the user uses the remote sensing image file with the suffix .IMG to analyze the vegetation index. Remote sensing image files with suffix of .IMG and .HDF used by users to analyze vegetation indices can be opened in ENVI and ERDAS software. 3. The data header file information is as follows: Coordinate System is: PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["standard_parallel_1", 25], PARAMETER ["standard_parallel_2", 47], PARAMETER ["latitude_of_center", 0], PARAMETER ["longitude_of_center", 105], PARAMETER ["false_easting", 0], PARAMETER ["false_northing", 0], UNIT ["Meter", 1]] Pixel Size = (8000.000000000000000, -8000.000000000000000) Corner Coordinates: Upper Left (-3922260.739, 6100362.950) (51d20'23.06 "E, 46d21'21.43" N) Lower Left (-3922260.739, 1540362.950) (71d16'1.22 "E, 8d41'42.21" N) Upper Right (3277739.261, 6100362.950) (151d 8'57.22 "E, 49d 9'35.37" N) Lower Right (3277739.261, 1540362.950) (133d30'58.46 "E, 10d37'13.35" N) Center (-322260.739, 3820362.950) (101d22'21.08 "E, 35d42'18.02" N) Band 1 Block = 900x1 Type = Int16, ColorInterp = Undefined Computed Min / Max = -16066.000,11231.000 4. Conversion relationship between DN value and NDVI NDVI = DN / 1000, divided by 10000 after 2003 The NDVI value should be between [-1,1]. Data outside this interval represent other features, such as water bodies.
1. Data source: MODIS/Terra Vegetation Indices 16-day L3 Global 250m SIN Grid V006 products (2000-2017) Download address https://search.earthdata.nasa.gov/ 2. Data name: (1) resize is automatically generated in the batch cropping process, which means that it has been extracted by mask and the data range after processing is xinjiang provice; (2) seven digits represent the time of data acquisition, the first four digits are years, and the last three digits are days of the year.For example, "2000049" means that the year of data acquisition is 2000 and the specific time is the 49th day of that year. (3) 250m represents the ground resolution, i.e. 250 meters; (4) 16_days represents the time resolution, that is, 16 days; (5) NDVI represents data type, namely normalized vegetation index; 3. Data time range: 2000049-2017353, data interval of 16 days; 4..Tif file and.hdr file . Tif file is the original NDVI data with the same name. HDR file is the mask data that supports normal use of. 5. To analyze the ecological effects of cryosphere
GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. This dataset is a long-term GIMMS vegetation index dataset of the Qinghai Lake Basin, which includes changes in the vegetation index from 1981 to 2006. The time resolution is 15 days and the spatial resolution is 8 km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data.
The VEGETATION sensor sponsored by the European Commission was launched by SPOT-4 in March 1998. Since April 1998, SPOTVGT data for global vegetation coverage observation has been received by Kiruna ground station in Sweden. The image quality monitoring center in Toulouse, France is responsible for image quality and provides relevant parameters (such as calibration coefficient). Finally, the Belgian flemish institute for technological research (Vito)VEGETATION processing Centre (CTIV) is responsible for preprocessing into global data of 1km per day. Pretreatment includes atmospheric correction, radiation correction, geometric correction, production of 10 days to maximize the synthesized NDVI data, setting the value of -1 to -0.1 to -0.1, and then converting to the DN value of 0-250 through the formula DN=(NDVI+0.1)/0.004. The data set is a subset extraction from China, including spectral reflectance of four bands synthesized every 10 days and 10 days' maximum NDVI. It is data from 1998 to 2007 with a spatial resolution of 1km and a temporal resolution of 10 days. File format: Hfr and img files. The file naming rule is: CHN _ NDV _ YYYMMDD, where YYYYMMDD is the date of the day represented by the file and is also the main identifier different from other files. The remote sensing image files with suffix. IMG and. HDF used by users to analyze vegetation index can be opened in ENVI and ERDAS software. Coordinate system and projection Plate_Carree (Lon/Lat) PROJ_CENTER_LON 0.000000 PROJ_CENTER_LAT 0.000000 PIXEL_SIZE_UNITS DEGREES/PIXEL PIXEL_SIZE_X 0.0089285714 PIXEL_SIZE_Y 0.0089285714 SEMI_AXIS_MAJ 6378137.000000 SEMI_AXIS_MIN 6356752.314000 UL_LON (DEG) 73.000000 UL_LAT (DEG) 54.000000 LR_LON (DEG) 135.500000 LR_LAT (DEG) 5.000000 Corner coordinates are: Corner Coordinates: Upper Left ( 69.9955357, 55.0044643) Lower Left ( 69.9955357, 14.9955358) Upper Right ( 137.0044641, 55.0044643) Lower Right ( 137.0044641, 14.9955358) Where Upper Left is the upper left corner, Lower Left is the lower left corner, Upper Right is the upper right corner, and Lower Right is the lower right corner.
Sponsored by the European commission VEGETATION sensors in March 1998 by SPOT - 4 was deployed, from April 1998 to receive SPOTVGT for global VEGETATION observation data, the data by the Swedish Kiruna ground station is responsible for receiving, the image quality monitoring center in Toulouse, France is responsible for the image quality and provide the related parameters (e.g., scaling),Eventually, Belgium's Flemish Institute for Technological Research (Vito) 's VEGETATION processing Centre (CTIV) was responsible for pre-processing the data into 1km of daily global data.Preprocessing includes atmospheric correction, radiometric correction, and geometric correction to produce the maximum synthesis of NDVI data in 10 days, and set the value from -1 to -0.1 to -0.1, and then convert to the DN value of 0-250 through the formula DN= (NDVI+0.1)/0.004. This data set is mainly for normalized vegetation index (NDVI) of the qaidam river basin in the long time series, including spectral reflectance of four bands synthesized every 10 days from 1998 to 2008 and maximum NDVI in 10 days. The spatial resolution is 1km and the temporal resolution is 10 days.File formats :.hfr and.img.The file naming rule is CHN_NDV_YYYYMMDD, where YYYYMMDD is the date of the day that the file represents and is the main identifier that distinguishes it from other files.Remote sensing image files with suffixes.img and.hdf, which are used by users to analyze vegetation index, can be opened in ENVI and ERDAS software
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
This data set contains 2018 global forest fire case data for the whole year and 2019, including the forest fire in California in November 2018, the forest fire in Attica, Greece in July 2018, and the forest fire in Shanxi Province in March 2019. Case data. Specific data include: fire intensity data of the monitoring range and data of vegetation index changes before and after the disaster. The data set is mainly used to describe the occurrence, development, impact and recovery of major global forest fire events in the first half of 2018-2019. The data mainly comes from NASA official website and EM-DAT database, it was processed by statistical and spatial analysis methods using EXCEL and ArcGIS tools. The data source is reliable, the processing method is scientific and rigorous, and it can be effectively applied to global (forest fire) disaster case analysis research.
This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 1986, 1990, 1995, 2000, 2005, 2010, 2015, and 2017. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 5, Landsat 8 and sentinel 2 channels from Red and NIR channels. The data are synthesized monthly through Google Earth Engine cloud platform, and the missing pixels are interpolated by calculating the index of the model. The quality of the data is good, and it can be used in environmental change monitoring and other fields.
This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2018. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels. The data are synthesized monthly through Google Earth Engine cloud platform, and the missing pixels are interpolated by calculating the index of the model. The quality of the data is good, and it can be used in environmental change monitoring and other fields.