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Remote Sensing
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Ecological Change with Multi-Scale Spatial-Temporal Remote Sensing Data
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Ecological Change with Multi-Scale Spatial-Temporal Remote Sensing Data

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1465

Special Issue Editors

Prof. Dr. Linlin Cheng

E-Mail Website
Guest Editor
School of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: land use; land evaluation; land reclamation policy; remote sensing
Dr. Wei Tang

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: InSAR and GNSS; land subsidence monitoring; geophysical modeling and parameter inversion
Special Issues, Collections and Topics in MDPI journals
Dr. Deqin Fan

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
Interests: remote sensing data processin; vegetation and ecological remote sensing; sustainable development

Special Issue Information

Dear Colleagues,

Ecosystems are facing significant challenges due to intensifying global climate change and human activities, impacting their structure, functions, and services. This necessitates the multi-scale spatiotemporal monitoring of ecological dynamics, a core issue in environmental science. The United Nations 2030 Agenda for Sustainable Development emphasizes the need to improve ecosystem protection and restoration, and the accurate monitoring and scientific assessment of ecological changes have become essential for achieving sustainable development goals. Ecological changes occur across multiple spatiotemporal scales, ranging from local ecosystem dynamics to global ecological patterns, all of which require high-precision, multi-dimensional data support. Remote sensing technology provides large-scale, high-frequency observational data, and when combined with multi-scale analytical methods, it can effectively reveal the driving mechanisms and response patterns of ecological changes. Research on ecological changes using multi-scale spatiotemporal remote sensing data further addresses the limitations of traditional remote sensing in temporal continuity and spatial detail. This approach not only enhances our understanding of complex ecosystem processes but also offers a scientific foundation for ecological conservation and sustainable development.

This Special Issue of Remote Sensing aims to highlight cutting-edge applications of multi-scale remote sensing data in ecological change research. It seeks to cover multi-source data fusion, driving factor analysis, and ecological management decision support, providing scientific evidence to address global ecological challenges—fully aligning with the journal's core objectives. Topics may span from local ecosystem analyses to global-scale ecological process studies. We encourage the submission of papers focusing on multi-scale spatiotemporal remote sensing data integration, cross-scale analytical methodologies, and investigations into ecological change processes and their impacts. By publishing high-quality research, this Special Issue hopes to foster interdisciplinary innovation between remote sensing, ecology, and related fields.

Articles may address, but are not limited to, the following topics: land ecological governance and ecological resilience enhancement; the collaborative analysis of multi-source data; the dynamic monitoring of ecological processes; driving mechanisms and model simulation; ecosystem service evaluation; multi-scale ecological evolution; and artificial intelligence and digital applications.

Prof. Dr. Linlin Cheng
Dr. Wei Tang
Dr. Deqin Fan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ecological structure and function
  • remote sensing
  • ecological networks
  • multi-source data fusion
  • ecological dynamics

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Published Papers (2 papers)

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Research

22 pages, 4068 KB  
Article
A Novel Time-Series Algorithm for Detecting Shifting Cultivation Cycles and Fallow Periods
by Shidong Liu
Remote Sens. 2026, 18(9), 1318; https://doi.org/10.3390/rs18091318 - 25 Apr 2026
Viewed by 71
Abstract
Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow [...] Read more.
Shifting cultivation (SC) is a predominant land use across the tropics, feeding hundreds of millions of marginalized people, causing significant deforestation in tropical regions. A key question is how to realize rapid and large-scale identification of the spatial distribution, cycle numbers, and fallow periods of SC. Building the LandCycler algorithm that fully considers the inter-annual cycle of SC based on Landsat imagery from 1988 to 2020, we identify the distribution and fallow period of SC in Southeast Asia, including Assam in India and Yunnan Province in China. The results show that the LandCycler for the identification of SC is satisfactory (producer’s accuracy 82.12% and user’s accuracy 81.37%), and the accuracy in detecting the average cycle number, and calculating the average fallow period is 83.71%, and 96%, respectively. We found that the total area of SC is as high as 16.79 × 104 km2 in Southeast Asia, which uses almost 10% of the total forests. Meanwhile, the average cycle number and the average fallow period of SC are two times and 10 years, respectively. More than 98% of SC has repeated deforestation four times or less. The shorter the distance from settlements and the distance from roads, the larger the cycle number of SC. Although there was no significant correlation between elevation and slope and the cycle number of SC, SCs were mainly distributed at slopes of 18 ± 5° and elevations of 800 ± 300 m. These findings provide effective tools for sustainable agroforestry management as well as for global SC mapping. Full article
(This article belongs to the Special Issue Ecological Change with Multi-Scale Spatial-Temporal Remote Sensing Data)
25 pages, 3272 KB  
Article
Ecological Change in Minnesota’s Carbon Sequestration and Oxygen Release Service: A Multidimensional Assessment Using Multi-Temporal Remote Sensing Data
by Donghui Shi
Remote Sens. 2026, 18(3), 391; https://doi.org/10.3390/rs18030391 - 23 Jan 2026
Viewed by 535
Abstract
Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is [...] Read more.
Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article
(This article belongs to the Special Issue Ecological Change with Multi-Scale Spatial-Temporal Remote Sensing Data)
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