Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique
Data files
Dec 12, 2023 version files 1.58 MB
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AVHRR_NDVI_Inner_Mongolia_(1982-2020).rar
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HDT_data.xlsx
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README.md
Abstract
Exploring the effect of climate change and human activities on vegetation is a key requisite for the reconstruction of regional ecological environments. Therefore, based on long-term vegetation GIMMS NDVI data, climate data, and statistical data, the present study applied the Hasse diagram technique and combined the multivariate regression residual analysis to quantitatively analyze the impact of human activities and climate change on vegetation in Inner Mongolia from detail human activities with some innovations. The results showed that (1) NDVI showed an overall increasing trend over the last 39 years, with an abrupt change in 2000; moreover, vegetation growth was better before the abrupt change (PⅠ: 1982–2000) than after it (PⅡ: 2001–2020), with significant downward trends in Xilin Gol and Hulunbuir. (2) Human activities can promote as well as inhibit vegetation, and the promotion effect was larger during 1982–2000 than during 2001–2020, whereas the inhibition effect was larger during 2001–2020. In addition, during PI, vegetation in Inner Mongolia generally experienced promotion by human activities and climate change, while during PII, climate-driven promotion had the strongest effect, followed by human-driven inhibition mainly distributed in Xilin Gol. (3) The result of the Hasse diagram analysis showed that the dominant pathways of human activities affecting most of the cities were economic factors and urbanization during PⅠ and economization during PII.
README: Reference Information
Provenance for this README
- File name: README_Dataset_v0.1.0.txt
- Authors: Mu Shuangyan
- Other contributors: Tong Siqin, Batunacun, Ah Rong, Li Mei, Bao Gang, Huang Xiaojun, Bao Yuhai
- Date created: 2023-11-18
- Date modified: 2023-11-30
Dataset Version and Release History
Current Version:
- Date: 2023-11-30
- Persistent identifier: DOI: 10.5061/dryad.tdz08kq5q
- Summary of changes: n/a
Embargo Provenance: n/a
- Scope of embargo: n/a
- Embargo period: n/a
Dataset Attribution and Usage
Dataset Title: Data for the article "Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique"
Persistent Identifier: https://doi.org/10.5061/dryad.tdz08kq5q
Dataset Contributors:
- Creators: Mu Shuangyan, Tong Siqin, Batunacun, Ah Rong, Li Mei, Bao Gang, Huang Xiaojun, Bao Yuhai
Date of Issue: 2023-11-30
Dataset citation:
Mu Shuangyan; Tong Siqin; Batunacun; Ah Rong; Li Mei, Bao Gang, Huang Xiaojun, Bao Yuhai (2023), Identifying the drivers of vegetation changes in Inner Mongolia based on residual analysis and Hasse diagram technique, Dryad, Dataset, https://doi.org/10.5061/dryad.tdz08kq5q
Contact Information
- Name: Tong Siqin
- Affiliations: College of Geographical Sciences, Inner Mongolia Normal University, China;
- ORCID ID: https://orcid.org/0000-0003-0522-2211
- Email: tsq118446@163.com
- Alternate Email: tongsq223@imnu.edu.cn
- Address: e-mail preferred
Additional Dataset Metadata
Dates and Sources
Dates of data collection: collected between 1982 to 2020
NDVI data:AVHRR (Advanced Very High Resolution Radiometer) NDVI3g datasets were download from https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5.
Statistical data:downloaded from http://tj.nmg.gov.cn/datashow/
Methodological Information
- Methods of data collection/generation: see manuscript for details
Data and File Overview
Summary Metrics
- File count: 2
- Total file size: 1.6 MB
- File formats: .rar and .xlsx
Table of Contents
- AVHRR_NDVI_Inner_Mongolia (1982-2020).rar
- HDT_data.xlsx
File/Folder Details
Details for: AVHRR_NDVI_Inner_Mongolia (1982-2020).rar
Description:a compressed file for annual NDVI images of growing season in Inner Mongolia during 1982-2020.
Format(s): .rar
Size(s): 1.56 MB
Details for: HDT_data.xlsx
Description:include different periods and factors statistical value with standardization in Inner Mongolia during 1982-2020.
Format(s): .xlsx
Size(s): 12.46 KB
Dimensions: 32 rows x 9 columns
Variables:
- Year: study period : PI from 1982-2000, PII from 2001-2020
- City: the random order of twelve cities in Inner Mongolia
- Coal: the coal production in different cities
- Construction land: land is used for various construction projects
- Population:the total number of individuals within a certain time point and region
- Livestock: include the number of large livestock and sheep
- Total production: the total of all objects and services produced in a certain period of time in different cities
- Primary production: the output value created by various agricultural original products
- Secondary production:the output value created by various professional workers and various industries or products
- Tertiary production: the output value created by various services or objects
END OF README
Methods
(1) To monitor the vegetation, the AVHRR_version 5 (Advanced Very High Resolution Radiometer) NDVI3g datasets were used because of their high quality, with 0.05° and 1-day spatial and temporal resolution, respectively (download from https://developers.google.com/earth-engine/datasets/catalog/NOAA_CDR_AVHRR_NDVI_V5). To reduce the effects of atmospheric and aerosol scattering, we used the maximum value composite (MVC) method to develop a monthly NDVI dataset. The dataset covers the period from 1982 to 2020. In the text, the growing season of vegetation in Inner Mongolia is defined as April–October.
(2) To further explore the anthropogenic factors inhibiting vegetation growth and achieve a detailed stripping of human activities, we adopted the statistical data and the Hasse diagram technique to be combined. The directed Hasse diagram technique (HDT) is an extension of the ISM explanatory structure model, realization based on the theory of partial order, and has been widely used in several fields, such as chemical risk assessment and environmental science, as well as factor ranking of land degradation, to rank the impacts of each factor. The Hasse diagram technique can be used to reflect the correlations of all elements in an ensemble and has shown good performance in the analysis of drivers of land degradation.