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Remote Sensing-Guided Land-Use Optimization for Carbon Neutrality

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

Deadline for manuscript submissions: 1 September 2026 | Viewed by 1417

Editors


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Guest Editor
Aix-Marseille Université, CNRS UMR 7300 ESPACE, Technopôle de l’Environnement Arbois Méditerranée, Avenue Louis Philibert, Bâtiment Laennec Hall C BP 80, 13545 Aix-en-Provence Cedex 04, France
Interests: urban transition; sustainable planning; GIS; remote sensing; spatial analysis
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Guest Editor
Agriculture Academy, Faculty of Forest Sciences and Ecology, Department of Forest Sciences, Vytautas Magnus University, Studentų Str. 11, LT-53361 Akademija, Lithuania
Interests: geomatics; forest management; environmental policy
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Special Issue Information

Dear Colleagues,

Understanding land use patterns and their temporal dynamics is critical for effective climate change mitigation. Sustainable land management can substantially contribute to mitigation efforts by enhancing terrestrial carbon sinks and reducing greenhouse gas emissions. The success of global warming mitigation strategies depends not only on coordinated actions at both global and local scales but also on the availability of accurate, timely, and spatially explicit information about the systems being managed. In this regard, remote sensing serves as a vital source of geospatial data, enabling more informed decision-making in support of a sustainable and climate-adapted future.

Digital space remote sensing for environmental monitoring emerged in the 1970s as a powerful instrument for land use mapping, enabling researchers and planners to efficiently acquire spatial data and monitor changes over time. Its application in climate-aligned land use mapping requires a synergistic approach that integrates high-resolution and multi-temporal spatial data, an understanding of land–climate–society interactions, advanced computational methods, and insights into land use change dynamics. Although advances in remote sensing technology have greatly improved our capacity to monitor land use and associated ecological variables, there remain critical gaps in the comprehensive understanding and integration needed for effective climate impact management.

In this Special Issue, manuscripts are expected to explore the potential of diverse data sources and sensor characteristics, as well as assess the performance of modern classification algorithms and change detection techniques. Contributions that examine how remote sensing can be linked with climate scenarios to model land use development or risk under future conditions are also encouraged.

Research reporting on the role of remote sensing in monitoring, modelling, reporting, and verification (MRV) of changes in aboveground biomass and soil carbon proxies, particularly in the context of climate-related land use policies such as REDD, LULUCF, UAST accounting, is especially welcome. Studies addressing the use of remote sensing to identify opportunities for reforestation, afforestation, wetland restoration, regenerative agriculture, sustainable urban planning, or urban adaptation for climate and energy are also of high interest.

Furthermore, submissions that describe methodologies for implementing new European Union legal requirements for monitoring and reporting in the LULUCF and UAST sectors—particularly using geographically explicit land use conversion data—are strongly encouraged.

Prof. Dr. Sébastien Gadal
Prof. Dr. Gintautas Mozgeris
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-anonymized 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

  • remote sensing
  • carbon neutrality
  • climate change adaptation
  • land use
  • land–climate interactions
  • LULUCF
  • UAST
  • land planning adaptation
  • energy transition
  • above ground biomass
  • soil carbon

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Published Papers (2 papers)

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Research

25 pages, 42987 KB  
Article
Dynamic Three-Dimensional Zoning of Ecosystem Service Interactions Under Future Land-Use Scenarios: A Songnen Plain Case Study
by Sisi Yu, Zhanzhong Tang, Li Yang, Jiacheng Huang, Aihui Jiang, Shangshu Cai and Kun Jin
Remote Sens. 2026, 18(12), 2014; https://doi.org/10.3390/rs18122014 - 17 Jun 2026
Viewed by 325
Abstract
Dynamic trade-offs and synergies among ecosystem services (ESs) are highly sensitive to land-use change, spatial scale, and future uncertainty. However, most ES-based zoning studies rely on static assessments that overlook temporal dynamics and scenario robustness. To address this limitation, we propose a novel [...] Read more.
Dynamic trade-offs and synergies among ecosystem services (ESs) are highly sensitive to land-use change, spatial scale, and future uncertainty. However, most ES-based zoning studies rely on static assessments that overlook temporal dynamics and scenario robustness. To address this limitation, we propose a novel intensity–trend–stability framework that integrates historical interaction strength, projected future trajectories, and cross-scenario consistency to assess and spatially zone ES interactions. The framework was applied to the Songnen Plain, China, using multi-scale analysis and four contrasting land-use scenarios for 2030. An XGBoost–SHAP model was further employed to identify key drivers and nonlinear effects underlying ES interaction dynamics. Results show that (1) land-use transitions exhibit strong scenario dependency under different development pathways. (2) Water yield consistently exhibits trade-offs with other ESs, whereas soil retention, carbon sequestration, and habitat quality maintain stable synergies, with interaction intensity generally weakening at coarser scales. (3) The proposed framework effectively identifies stable conflict zones, synergistic hotspots, and transitional areas, with HHH zones of water-related interactions accounting for 30.72–37.43% of the study area, while LLH zones of other ES pairs each occupy more than 39%. (4) Climatic and topographic factors primarily regulate water-related interactions, whereas vegetation conditions and landscape configuration dominate synergistic ES relationships, with pronounced nonlinear threshold effects. The proposed framework improves the detection of dynamic ES interaction patterns and supports scenario-based ecological zoning and sustainable land-use management. Full article
(This article belongs to the Special Issue Remote Sensing-Guided Land-Use Optimization for Carbon Neutrality)
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31 pages, 9295 KB  
Article
Hidden Forest in Non-Forest Land: A Remote Sensing-Based Mapping Case in Lithuania
by Monika Papartė, Donatas Jonikavičius and Gintautas Mozgeris
Remote Sens. 2026, 18(10), 1665; https://doi.org/10.3390/rs18101665 - 21 May 2026
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Abstract
Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas [...] Read more.
Woody vegetation growing outside officially designated forest land represents a significant but poorly quantified resource in many countries, where institutional and methodological limitations hinder its systematic accounting. This study develops and applies a multi-stage remote sensing-based framework to identify and characterize forest-eligible areas (FEAs) in Lithuania by integrating airborne LiDAR, Sentinel-2 time series, historical orthophotos, and national geospatial datasets. The workflow combines (i) LiDAR-derived canopy height model generation and object-based segmentation, (ii) rule-based aggregation of vegetation segments according to legal forest criteria, (iii) multi-index Sentinel-2 change detection to exclude recent disturbances, and (iv) deep learning-based classification of historical orthophotos to assess stand age. Three detection approaches were evaluated—LiDAR-based, land parcel identification system (LPIS)-based, and their combination. A total of 111,754.4 ha of FEAs were identified outside official forest land, of which 76,204.6 ha meet the minimum age criterion for classification as forest land under national legislation. The designation of these areas as forest land would increase national forest cover from 33.9% to 35.0%. The LiDAR-based approach achieved the highest overall accuracy after dataset refinement (91.5%), while the combined approach yielded the highest precision (97.1%). Accuracy improved notably when reference points affected by definitional conflicts and temporal inconsistencies were excluded, indicating that apparent detection errors were largely attributable to reference data limitations rather than algorithmic failure. The proposed framework offers a scalable solution for wall-to-wall identification and monitoring of unregistered forest resources, with direct applications for national forest inventories and LULUCF reporting. Full article
(This article belongs to the Special Issue Remote Sensing-Guided Land-Use Optimization for Carbon Neutrality)
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