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Monitoring long-term vegetation dynamics over the Yangtze River Basin, China, using multi-temporal remote sensing data

Cite this dataset

Fu, Jing et al. (2024). Monitoring long-term vegetation dynamics over the Yangtze River Basin, China, using multi-temporal remote sensing data [Dataset]. Dryad. https://doi.org/10.5061/dryad.w3r2280zh

Abstract

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 < 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.

README: 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 (https://ladsweb.modaps.eosdis.nasa.gov/). 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.

Methods

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.

Funding

Hunan Key Laboratory of Geospatial Big Data Mining and Application, Award: 2020-01

Hunan Provincial Education Department, Award: CX20221269

Hunan Meteorological Bureau, Award: XQKJ20B037