Big Data Analytics for Climate and Human Impacts on Terrestrial Ecosystems

Topic Information

Dear Colleagues,

Terrestrial ecosystems worldwide face pressure from both climate change and human activities, demanding precise monitoring and mechanistic understanding through advanced remote sensing technologies. This Topic focuses on cutting-edge applications of remote sensing in unraveling ecosystem responses to climate warming, extreme events, and land-use changes while quantifying the ecological effects of anthropogenic activities (e.g., urbanization, agricultural intensification, and deforestation). By integrating multi-source remote sensing and high-throughput sequencing data (including satellites, UAVs, and in situ sensors) with ecological modeling, we aim to establish dynamic assessment frameworks spanning local to global scales, providing scientific support for sustainable ecosystem management and climate adaptation strategies.

With breakthroughs in next-generation remote sensing technologies (e.g., high-spatiotemporal-resolution sensors and AI-driven data analytics), this Topic encourages interdisciplinary research that bridges climate science, ecology, geospatial science, and socioeconomics. We welcome innovative methodologies, case studies, and theoretical advances across diverse ecosystems (including forests, grasslands, wetlands, and croplands), emphasizing synergy between technological innovation, mechanism discovery, and policy implementation to transform remote sensing from an observational tool into a decision-support system.

Four Key Themes and Subtopics

1. Ecosystem Dynamics via Multi-Scale Data Fusion:
   
   • Cross-sensor calibration for vegetation carbon–water flux inversion;
   • Biodiversity loss tracking using airborne LiDAR and eDNA metabarcoding;
   • AI-driven early warning systems for extreme climate events;
   • Data-model assimilation in ecosystem service prediction.
2. Climate Stressors: From Genomics to Geospatial Patterns:
   
   • Thermal stress impacts on plant phenomics via UAV-omics integration;
   • Permafrost methane flux modeling with ground-penetrating radar and microbial genomics;
   • Phenological mismatch analysis using satellite time-series and transcriptomics;
   • Ecological risk mapping in arctic–alpine transition zones.
3. Human Footprint Assessment through Data Convergence:
   
   • Urban sprawl impacts on microclimate and soil microbiomes;
   • Agricultural intensification effects on soil health, namely hyperspectral and metagenomic biomarkers;
   • Blockchain-enabled illegal logging detection with SAR and acoustic sensors;
   • Efficacy of ecological restoration via multi-temporal NDVI and functional trait analysis.
4. Next-Gen Methodologies for Ecosystem Informatics:
   
   • Graph neural networks for cross-domain data harmonization (remote sensing × omics);
   • Quantum computing applications in ecosystem scenario modeling;
   • Crowdsourced data validation platforms with blockchain traceability;
   • SDG-aligned decision systems integrating planetary boundary thresholds.

Prof. Dr. Qing Zhang
Dr. Qingfu Liu
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Atmosphere atmosphere	2.3	4.9	2010	16.9 Days	CHF 2400	Submit
Drones drones	4.8	7.4	2017	20.1 Days	CHF 2600	Submit
Forests forests	2.5	4.6	2010	17.1 Days	CHF 2600	Submit
Grasses grasses	-	-	2022	22.3 Days	CHF 1000	Submit
Land land	3.2	5.9	2012	16 Days	CHF 2600	Submit
Sustainability sustainability	3.3	7.7	2009	19.3 Days	CHF 2400	Submit

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Published Papers (1 paper)

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All Atmosphere Drones Forests Grasses Land Sustainability

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19 pages, 60167 KiB  

Open AccessArticle

Mapping Ecosystem Carbon Storage in the Nanling Mountains of Guangdong Province Using Machine Learning Based on Multi-Source Remote Sensing

by Wei Wang, Liangbo Tang, Ying Zhang, Junxing Cai, Xiaoyuan Chen and Xiaoyun Mao

Atmosphere 2025, 16(8), 954; https://doi.org/10.3390/atmos16080954 - 10 Aug 2025

Viewed by 459

Abstract

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This [...] Read more.

Accurate assessment of terrestrial ecosystem carbon storage is essential for understanding the global carbon cycle and informing climate change mitigation strategies. However, traditional estimation models face significant challenges in complex mountainous regions due to difficulties in data acquisition and high ecosystem heterogeneity. This study focuses on the Nanling Mountains in Guangdong Province, China, utilizing the Google Earth Engine (GEE) platform to integrate multi-source remote sensing data (Sentinel-1/2, ALOS, GEDI, MODIS), topographic/climatic variables, and field-collected samples. We employed machine learning models to achieve high-precision prediction and high-resolution mapping of ecosystem carbon storage while also analyzing spatial differentiation patterns. The results indicate that the Random Forest algorithm outperformed Gradient Boosting Decision Tree and Classification and Regression Tree (CART) algorithms by suppressing overfitting through dual randomization. The integration of multi-source data significantly enhanced model performance, achieving a coefficient of determination (R²) of 0.87 for aboveground biomass (AGB) and 0.65 for soil organic carbon (SOC). Integrating precipitation, temperature, and topographic variables improved SOC prediction accuracy by 96.77% compared to using optical data alone. The total carbon storage reached 404 million tons, with forest ecosystems contributing 96.7% of the total and soil carbon pools accounting for 60%. High carbon density zones (>160 Mg C/ha) were mainly concentrated in mid-elevation gentle slopes (300–700 m). The proposed integrated “optical-radar-topography-climate” framework offers a scalable and transferable solution for monitoring carbon storage in complex terrains and provides robust scientific support for carbon sequestration planning in subtropical mountain ecosystems. Full article

(This article belongs to the Topic Big Data Analytics for Climate and Human Impacts on Terrestrial Ecosystems)

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