This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
Leaf area index (leaf area index), also known as leaf area coefficient, refers to the multiple of total plant leaf area in land area per unit land area, which is a better dynamic index to reflect the size of crop population. Leaf area index (LAI) is an important structural parameter of forest ecosystem. It represents the density of leaves and canopy structure characteristics, and affects the physiological and biochemical processes such as photosynthesis, respiration and transpiration in the canopy. It is a key parameter to describe the material and energy exchange between soil, vegetation and atmosphere, and is also an important variable for estimating various ecological processes and functions. Based on MODIS leaf area index data from 2000 to 2016, the mcd15a3h product data of Pan third pole key node area were trimmed, and the 4-day leaf area index data of key node area from 2002 to 2016 were obtained. Data projection: sinusoidal projection The data area is 34 key nodes of Pan third pole (Abbas, Astana, Colombo, Gwadar, Mengba, Teheran, Vientiane, etc.).
Land use/cover maps are one of the most basic and fundamental datasets used in environmental issues and disaster risk prevention and control research. Based on the analysis of existing land use products, Sentinel datasets with a 10m resolution were used to classify in key node areas by object-oriented classification method based on eCognition platform software. At present, the Land use datasets (10-meter resolution ) of 8 key nodes include Kyaukpyu port, yangon,Djibouti, mandalay, hambantota port, Colombo port, rayong industrial zone, and Bangkok,were obtained. The accuracy of the data is analyzed based on a limited sample, the classification overall accuracy is about 90%. The classification system is: 10. Forest; 20. Cultivated Land; 21. Paddy filed; 22. Dry farmland; 30. Water; 31. River; 32. Lake (reservoir, pond); 33 .wetland marsh wetland; 40. Artificial surface; 43. Mining area; 50. Bareland bare soil, bare rock, desert, etc.; 60. Grassland; 70. Shrubland.
Evapotranspiration (ET) is the process which changes from liquid or solid to vapor returning to the atmosphere in hydrological cycles since precipitation arrives at the ground. It is usually the sum of evaporation of surface soil moisture and transpiration (T) in plants. It is the key parameter in the study of global change. At present, THE EVAPotranspiration data product of MODIS satellite is an important data source for monitoring the temporal and spatial changes of the surface, and surface evapotranspiration is an important part of water balance in the earth-gas interaction. Book which has high space-time resolution MODIS16 products as the foundation, global land evaporation in area along the whole area separated from 31 key nodes and Laos, Cambodia's railway, China and myanmar oil and gas pipeline and elegant high iron three key verification area ET cutting, estimation, get the key node area of 8 to 16 days ET products, time range is 2000-2016. Is mainly used in the areas related to all the way the surface of water and energy balance in the process of simulation and dynamic monitoring and management of regional water resources rationally, especially to the scientific allocation of water resources and realize the efficient utilization of water resources has important practical significance, to be able to have a purpose of the related research of area along the area to provide data support and reference.
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.
Wildfires can strongly affect the frozen soil environment by burning surface vegetation and soil organic matter. Vegetation affected by fire can take many years to return to mature pre-fire levels. In this data set, the effects of fires on vegetation regrowth in a frozen-ground tundra environment in the Anaktuvuk River Basin on the North Slope of Alaska were studied by quantifying changes in C-band and L-band SAR backscatter data over 15 years (2002-2017). After the fire, the C- and L-band backscattering coefficients increased by 5.5 and 4.4 dB, respectively, in the severe fire area compared to the unburned area. Five years after the fire, the difference in C-band backscattering between the fire zone and the unburned zone decreased, indicating that the post-fire vegetation level had recovered to the level of the unburned zone. This long recovery time is longer than the 3-year recovery estimated from visible wavelength-based NDVI observations. In addition, after 10 years of vegetation recovery, the backscattering of the L-band in the severe fire zone remains approximately 2 dB higher than that of the unburned zone. This continued difference may be caused by an increase in surface roughness. Our analysis shows that long-term SAR backscattering data sets can quantify vegetation recovery after fire in an Arctic tundra environment and can also be used to supplement visible-wavelength observations. The temporal coverage of the backscattering data is from 2002 to 2017, with a time resolution of one month, and the data cover the Anaktuvuk River area on the North Slope of Alaska. The spatial resolution is 30~100 m, the C- and L-band data are separated, and a GeoTIFF file is stored every month. For details on the data, see SAR Backscattering Data of the Anaktuvuk River Basin on the North Slope of Alaska - Data Description.
Surface albedo is a critical parameter in land surface energy balance. This dataset provides the monthly land surface albedo of UAV remote sensing for typical ground stations in the middle reaches of Heihe river basin during the vegetation growth stage in 2019. The algorithm for calculating albedo is an empirical method, which was developed based on a comprehensive forward simulation dataset based on 6S model and typical spectrums. This method can effectively transform the surface reflectance to the broadband surface albedo. The method was then applied to the surface reflectance acquired by UAV multi-spectral sensor and the broadband surface albedo with a 0.2-m spatial resolution was eventually obtained.
The data set is NDVI data of long time series acquired by NOAA's Advanced Very High Resolution Radiometer (AVHRR) sensor. The time range of the data set is from 1982 to 2015. In order to remove the noise in NDVI data, maximum synthesis and multi-sensor contrast correction are carried out. A NDVI image is synthesized every half month. The data set is widely used in the analysis of long-term vegetation change trend. The data set is cut out from the global data set, so as to carry out the research and analysis of the source areas of the three rivers separately. The data format of this data set is GeoTIFF with spatial resolution of 8 km and temporal resolution of 2 weeks, ranging from 1982 to 2015. Data transfer coefficient is 10000, NDVI = ND/10000.