Vegetation–Atmosphere Interactions in a Changing Climate

A special issue of Atmosphere (ISSN 2073-4433).

Deadline for manuscript submissions: 31 December 2025 | Viewed by 185

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Guest Editor
College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Interests: soil and water conservation; forestry ecological engineering; structured forest management; forest hydrology; soil ecology; forest ecosystem and climate change
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Guest Editor
College of Forestry, Shanxi Agricultural University, Jinzhong, China
Interests: soil and water conservation; wind erosion; PM concentration; forest ecology; forest management

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Guest Editor
School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China
Interests: LiDAR; point cloud; geophysical image processing; image classification; GIS; RS; forest
National Forestry and Grassland Administration Key Laboratory of Forest Resources Conservation and Ecological Safety on the Upper Reaches of the Yangtze River, College of Forestry, Sichuan Agricultural University, Chengdu 611130, China
Interests: remote sensing on snow; ecological remote sensing; remote sensing in forestry; forest system and climate change

Special Issue Information

Dear Colleagues,

The study of the climate has been evolving for decades. While scholarship has always focused on temperature, ecosystems, etc., when it comes to climate change, there has been a recent shift in the priority of this issue relative to other issues, with a new focus on vegetation–atmosphere interactions in a changing climate.

In light of this shift in focus, the open access journal Atmosphere will host a Special Issue on water transport in arid regions, forest ecohydrology, regional climate change, the restoration of vegetation degradation, remote sensing on snow, forest systems and climate change, and more. This Special Issue is also an appropriate venue for papers dealing with human thermal comfort and productivity, as recent research shows that desertification can contribute greatly to climate change. Ultimately, this Special Issue aims to present the latest comparable evidence on the impacts of desertification.

Raw results from subjective surveys, models, and review papers related to climate and forest hydrology in decertified regions are welcome. Authors are encouraged to include sections that address future issues, opportunities, and/or concerns related to their topic in 5-, 10-, and 20-year horizons.

Dr. Guirong Hou
Dr. Xiaomin Chang
Dr. Guangpeng Fan
Dr. Lin Xiao
Guest Editors

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Keywords

  • remote sensing
  • water and soil conservation
  • climate change
  • forest ecology
  • forest hydrology
  • structured forest management
  • wind erosion
  • particular matter concentration
  • forest LiDAR
  • landscape ecology

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Published Papers (1 paper)

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Research

22 pages, 3221 KiB  
Article
Exploring NDVI Responses to Regional Climate Change by Leveraging Interpretable Machine Learning: A Case Study of Chengdu City in Southwest China
by Ying Xiang, Guirong Hou, Junjie Li, Yidan Zhang, Jie Lu, Zhexiu Yu, Fabao Niu and Hanqing Yang
Atmosphere 2025, 16(8), 974; https://doi.org/10.3390/atmos16080974 (registering DOI) - 17 Aug 2025
Abstract
Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its [...] Read more.
Regional extreme climate change remains a major environmental issue of global concern. However, in the context of the joint effects of urban expansion and the urban ecological environment, the responses of the normalized difference vegetation index (NDVI) to regional climate change and its driving mechanism remain unclear. This study takes Chengdu as an example, selects the air temperature (Ta), precipitation (P), wind speed (WS), and soil water content (SWC) within the period from 2001 to 2023 as influencing factors, and uses Theil-Sen median trend analysis and interpretable machine learning models (random forest (RF), BP neural network, support vector machine (SVM), and extreme gradient boosting (XG-Boost) models). The average absolute value of Shapley additive explanations (SHAPs) is adopted as an indicator to explore the key mechanism driving regional climate change in Chengdu in terms of NDVI changes. The analysis results reveal that the NDVI exhibited an extremely significant increasing trend during the study period (p = 8.6 × 10−6 < 0.001), and that precipitation showed a significant increasing trend (p = 1.2 × 10−4 < 0.001); however, the air temperature, wind speed, and soil-relative volumetric water content all showed insignificant increasing trends. A simulation of interpretable machine learning models revealed that the random forest (RF) model performed exceptionally well in terms of simulating the dynamics of the urban NDVI (R2 = 0.746), indicating that the RF model has an excellent ability to capture the complex ecological interactions of a city without prior assumptions. The dependence relationship between the simulation results and the main driving factors indicates that the Ta and P are the main factors affecting the NDVI changes. In contrast, the SWC and WS had relatively small influences on the NDVI changes. The prediction analysis results reveal that a monthly average temperature of 25 °C and a monthly average precipitation of approximately 130 mm are conducive to the stability of the NDVI in the study area. This study provides a reference for exploring the responses of NDVI changes to regional climate change in the context of urban expansion and urban ecological construction. Full article
(This article belongs to the Special Issue Vegetation–Atmosphere Interactions in a Changing Climate)
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