DOI: 10.57760/sciencedb.j00001.00856
title: Monthly NDVI spatial and temporal fusion dataset at 250 m resolution on the Tibetan Plateau, 1981-2020
title: 1981–2020年青藏高原250 m分辨率逐月NDVI时空融合数据集

description: The Tibetan Plateau is a unique natural geographic unit with the highest average altitude in the world, known as the world's "Third Pole", which is extremely sensitive to global climate change and has a fragile ecological environment, and is an important ecological security barrier in China and even in Asia. Vegetation cover is an important indicator of climate change and ecological environment, and its spatial and temporal distribution patterns and trends are important indicators for assessing the regional ecological environment. In this study, based on the GIMMS NDVI3g and MOD13Q1 NDVI datasets, the monthly maximum values were synthesized by calling the Arcpy service using Python, and then the Savitzky-Golay filtering and denoising, regression analysis and 250 m resolution NDVI data were performed on the month-by-month NDVI data during the year using the GDAL and sklearn packages of the python language. The Savitzky-Golay filter and sklearn package were used to remove noise from the month-by-month NDVI data, regress the overlapping years of the two data sets, analyze the data, and extend the NDVI dataset at 250 m resolution, and finally integrate the month-by-month NDVI time series dataset at 250 m resolution for the Tibetan Plateau for the period 1981–2020. In order to ensure the accuracy and reliability of the data, this dataset is quality-controlled by various means such as quality control of the data source, consistency analysis, SG filtering, month-by-month and image-by-image fitting, and the confidence test for the data products, which ensures the good accuracy and quality of the data.This dataset can reflect the spatial and temporal changes of NDVI on the Tibetan Plateau from 1981 to 2020, and can be used to improve the spatial and temporal resolution of long time series data for the study of vegetation dynamics and spatial pattern of the Tibetan Plateau, as well as for ecological and environmental monitoring.
description: The Tibetan Plateau is a unique natural geographic unit with the highest average altitude in the world, known as the world's "Third Pole", which is extremely sensitive to global climate change and has a fragile ecological environment, and is an important ecological security barrier in China and even in Asia. Vegetation cover is an important indicator of climate change and ecological environment, and its spatial and temporal distribution patterns and trends are important indicators for assessing the regional ecological environment. In this study, based on the GIMMS NDVI3g and MOD13Q1 NDVI datasets, the monthly maximum values were synthesized by calling the Arcpy service using Python, and then the Savitzky-Golay filtering and denoising, regression analysis and 250 m resolution NDVI data were performed on the month-by-month NDVI data during the year using the GDAL and sklearn packages of the python language. The Savitzky-Golay filter and sklearn package were used to remove noise from the month-by-month NDVI data, regress the overlapping years of the two data sets, analyze the data, and extend the NDVI dataset at 250 m resolution, and finally integrate the month-by-month NDVI time series dataset at 250 m resolution for the Tibetan Plateau for the period 1981–2020. In order to ensure the accuracy and reliability of the data, this dataset is quality-controlled by various means such as quality control of the data source, consistency analysis, SG filtering, month-by-month and image-by-image fitting, and the confidence test for the data products, which ensures the good accuracy and quality of the data.This dataset can reflect the spatial and temporal changes of NDVI on the Tibetan Plateau from 1981 to 2020, and can be used to improve the spatial and temporal resolution of long time series data for the study of vegetation dynamics and spatial pattern of the Tibetan Plateau, as well as for ecological and environmental monitoring.