Published August 3, 2024 | Version v1
Dataset Open

Temporally enhanced RSEI and Nighttime Lights Reveal Long-Term Ecological Changes and Effective Protection in China's Inaugural National Parks

Authors/Creators

  • 1. ROR icon Aerospace Information Research Institute

Contributors

Contact person:

Data manager:

  • 1. ROR icon Aerospace Information Research Institute

Description

China's inaugural national parks play a crucial role in preserving biodiversity and maintaining ecosystem services. These protected areas are characterized by diverse landscapes and sensitive ecological environments. Over recent decades, the interplay between intensified human activities and global climate change has posed significant challenges to the ecological quality of these regions. Accurate and scientific assessment of ecological quality is essential for informed management and policy-making.

This dataset is based on multiple MODIS datasets, incorporating NDVI, LST, WET, and NDBSI as indicators. Using principal component analysis (PCA), we produced the Improved Remote Sensing Ecological Index (RSEI) for these parks from 2000 to 2022 at a 500m spatial resolution.

The RSEI was calculated using four component indices: greenness, heat, dryness, and wetness. Data for dryness and wetness were derived from the 8-day composite 500m resolution surface reflectance product MOD09A1. Heat was calculated using the 8-day composite 1km resolution land surface temperature product MOD11A2, which was resampled to 500m resolution. Greenness was derived from the 16-day composite 500m resolution vegetation index product MOD13A1.

The improved RSEI calculation method enhances the temporal stability and comparability of the data, making it more suitable for long-term ecological monitoring.

The improved RSEI effectively integrates dynamic changes of multiple variables and offers better temporal comparability for long-term ecological monitoring. Our results indicate that the ecological environment quality within the inaugural national parks significantly improved over the study period, with more noticeable improvements following the implementation of pilot conservation programs.

This dataset provides foundational information for understanding the long-term ecological trends in China's national parks. It serves as a crucial resource for researchers, policymakers, and conservationists dedicated to the sustainable management and development of these vital ecological regions.

The dataset contains five RAR compressed files, each corresponding to one of the national parks. These files include the Remote Sensing Ecological Index (RSEI) data from 2000 to 2022 for each respective park:

  • NTLNP-RSEI.rar: Contains the RSEI data for the Northeast Tiger and Leopard National Park (NTLNP) from 2000 to 2022.
  • HTRNP-RSEI.rar: Contains the RSEI data for the Hainan Tropical Rainforest National Park (HTRNP) from 2000 to 2022.
  • WNP-RSEI.rar: Contains the RSEI data for the Wuyishan National Park (WNP) from 2000 to 2022.
  • SNP-RSEI.rar: Contains the RSEI data for the Sanjiangyuan National Park (SNP) from 2000 to 2022.
  • GPNP-RSEI.rar: Contains the RSEI data for the Giant Panda National Park (GPNP) from 2000 to 2022.

Each of these compressed files includes the improved RSEI calculations for the respective national park, providing a comprehensive view of the ecological quality changes over the 22-year period.

The details of the data are as follows:

  • Data Format: GeoTiff
  • Pixel Values: Represent RSEI, ranging from 0 to 1, with no units.
  • Compatibility: The data can be directly opened and processed using remote sensing and GIS software such as ENVI and ArcGIS.
  • Data Quality: Due to the application of water and snow masks to remove the influence of water bodies and snow/ice on the WET component, there are some missing data areas.

These datasets offer valuable insights into the ecological quality changes within each national park over the specified period, making them essential for researchers, policymakers, and conservationists involved in the sustainable management and development of these protected areas.

For using the data and code provided in this dataset, please cite the following paper:

Wen, C., Long, T., He, G., Jiao, W., & Jiang, W. (2025). Temporally enhanced RSEI and nighttime lights reveal long-term ecological changes and effective protection in China’s inaugural national parks. Ecological Indicators, 170, 112981. https://doi.org/10.1016/j.ecolind.2024.112981

The calculation of the RSEI is completed using Google Earth Engine. The link to the calculation code is:

https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021

Files

Files (70.8 MB)

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Additional details

Identifiers

URL
https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021

Related works

Is supplemented by
Other: https://code.earthengine.google.com/089d74f423e91a0da9490f5098c55021 (URL)

Dates

Created
2024-08-03

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