DOI: 10.5061/dryad.w3r2280zh
title: Monitoring long-term vegetation dynamics over the Yangtze River Basin, China, using multi-temporal remote sensing data

description: Vegetation plays a crucial role in nature, with intricate interactions
 between it and the geographical environment. The Yangtze River Basin (YRB)
 refers to the third-largest river basin globally and an essential
 ecological security barrier in China. Monitoring vegetation dynamics in
 the basin is of profound significance for addressing climate change, soil
 erosion, and biodiversity loss in the basin’s ecosystems. Here, we
 investigate the spatiotemporal variations of vegetation at both the basin
 and land-cover scales in the YRB from 2000 to 2020. We elucidate the
 determinants driving the changes and explore future NDVI trends. The
 results indicate that NDVI in the YRB increased at a rate of 0.0032 yr−1
 (P &lt; 0.01) over the past 21 years, and it is anticipated to
 maintain an upward trend in the future. Regions in the upper and middle
 reaches of the YRB demonstrated higher NDVI, whereas regions in the
 headwater area and the lower reaches showed lower NDVI. Significant
 vegetation improvement was primarily concentrated in the central part of
 the basin, while noticeable vegetation degradation was observed in the
 eastern region. Temperature and wind speed were identified as the primary
 controlling factors affecting vegetation greenness. Global-scale climate
 oscillations played a significant role in driving periodic variations in
 NDVI, with La Niña events tending to increase NDVI, while El Niño events
 hindered its rise. Land cover types were influenced by long-term
 interactions between natural factors and human activities, although
 short-term vegetation variations might be more affected by the latter. Our
 findings provide valuable insights into the mechanisms behind vegetation
 variability driven by multiple variables, and the strong vegetation carbon
 sink capacity advances the conservation and development of ecosystems.
description: MODIS NDVI time series data from February 2000 to December 2020
 were selected, and the MODIS Reprojection Tool was used for image mosaic,
 format and projection transformation. For missing data in January and
 February 2000, mean values from the corresponding period over multiple
 years were utilized for interpolation. Referring to the quality control
 file, a maximum-value composites procedure was employed to generate
 monthly NDVI dataset to eliminate the influence of invalid values or
 outliers. Furthermore, the mean method was utilized to generate annual
 NDVI datasets. Using ArcGIS software, the NDVI time series dataset of the
 study area from 2000 to 2020 was clipped and true value conversion was
 performed. Meteorological data was adopted from the
 annual climate dataset of 704 Chinese national meteorological stations in
 the YRB between 2000 and 2020. The dataset includes average  temperature,
 average relative humidity, average wind speed, annual total precipitation,
 and annual total sunshine duration. These data were sourced from the Hunan
 Meteorological Bureau and have been undergone quality control procedures.
 The meteorological stations in the YRB are uniformly distributed
 spatially, although the upper reaches (including the headwater region) has
 relatively sparse station coverage. Linear interpolation was conducted to
 fill in missing data for some years to ensure data integrity. IDW
 interpolation implemented in ArcGIS software was performed to obtain
 raster images consistent in projection and resolution with NDVI for the
 above-mentioned climate variables.
description: # GENERAL INFORMATION 1. Title of Dataset: Monitoring long-term vegetation
 dynamics over the Yangtze River Basin, China, using multi-temporal remote
 sensing data 2. Date of data collection: 2000-2020 3. Geographic location
 of data collection: Hengyang Normal University, Hunan Province, China #
 DESCRIPTION OF THE DATA AND FILE STRUCTURE The dataset includes NDVI and
 climate raster data. 1. Month_NDVI_yyyy.zip: This archive contains monthly
 NDVI (Normalized Difference Vegetation Index) raster datasets with a
 spatial resolution of 250 meters. The "yyyy" in the filename
 represents a specific year. For instance, Month_NDVI_2000.zip comprises
 monthly NDVI raster data for the entire year of 2000, from January to
 December. Within this archive, 200001.tif denotes the NDVI raster data for
 January 2000, 200002.tif corresponds to February 2000, and so forth. The
 naming convention is similar for other years. 2. Year_NDVI.zip: This
 compressed file encompasses annual NDVI raster datasets, also with a
 spatial resolution of 250 meters. It encompasses annual value raster data
 for NDVI spanning from 2000 to 2020. 3. Climate_Data.zip: This archive
 houses annual meteorological raster datasets, again with a spatial
 resolution of 250 meters. It encompasses five subsets: Temperature,
 Relative Humidity, Wind Speed, Precipitation, and Sunshine Duration. Using
 the Temperature dataset as an exemplar, it comprises annual value raster
 data for Temperature from 2000 to 2020. Tem2000.tif signifies the
 Temperature raster data for the year 2000, Tem2001.tif represents the data
 for 2001, and so on. The naming patterns for the other four meteorological
 datasets follow a similar structure. # SHARING/ACCESS INFORMATION 1.
 Licenses/restrictions placed on the data: CC0 1.0 Universal (CC0 1.0)
 Public Domain Dedication 2. Links to other publicly accessible locations
 of the data: None 3. Links/relationships to ancillary data sets: None 4.
 Was data derived from another source? Yes. A. The NDVI raster data was
 sourced from the National Aeronautics and Space Administration (NASA)
 website, particularly the Land, Atmosphere Near-real-time Capability for
 EOS (LANCE) system (). This
 dataset boasts a spatial resolution of 250 meters. B. Climate raster data
 was sourced from the annual climate dataset compiled by Chinese national
 meteorological stations in the Yangtze River Basin (YRB) between 2000 and
 2020. This comprehensive dataset encompasses average temperature, average
 relative humidity, average wind speed, annual total precipitation, and
 annual total sunshine duration. It should be noted that the original
 climate dataset from meteorological stations was provided by the Hunan
 Meteorological Bureau, and the climate raster dataset that our research
 institute has uploaded is a derivative product created through the
 interpolation of this original station data.