Large-Scale and Long-Term Land Use and Land Cover Mapping

Topic Information

Dear Colleagues,

Large-scale land use and land cover change (LULCC) mapping is a cornerstone of global change research. Its applications are of profound importance, providing essential baseline data for investigating the climatic, ecological, and environmental impacts of land use change. However, despite significant advances in remote sensing technologies, several key challenges remain:

1. Long time scales: Due to limitations in remote sensing imagery, there is still considerable room for improving the accuracy of long-term land use dataset development—particularly for time series predating the 1970s. Urgent issues include land use area estimation, simulation and mapping of spatial patterns, and validation of dataset reliability and accuracy.

2. Large spatial scale mapping and dataset development: Challenges persist in fusing heterogeneous data sources, addressing scale transformation (e.g., from fine-resolution local data to coarse-resolution regional or global products), and effectively capturing fine-grained heterogeneity of land use systems across broad spatial extents.

3. Practical application challenges: These include bridging the gap between model outputs and real-world decision-making needs, and ensuring consistency of LULCC products across diverse geographic and socioecological contexts.

This Topic aims to advance the state of the art in large-scale LULCC mapping through the innovative use of remote sensing. It seeks contributions that enhance long-term dataset development, improve methods for multi-scale integration, and strengthen the link between LULCC products and their applications in climate, ecological, and policy research. By fostering cross-disciplinary approaches, this collection will bridge the gap between technological advances in remote sensing and their translation into actionable knowledge for Earth system understanding and sustainable land management.

We look forward to receiving your contributions.

Dr. Shicheng Li
Dr. Fan Yang
Dr. Mengmeng Wang
Dr. Prabuddh Kumar Mishra
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agronomy agronomy	4.1	7.6	2011	17.7 Days	CHF 2600	Submit
Land land	3.5	6.4	2012	16.4 Days	CHF 2600	Submit
Remote Sensing remotesensing	4.3	9.4	2009	22 Days	CHF 2700	Submit
Smart Cities smartcities	6.6	13.0	2018	25.1 Days	CHF 2000	Submit
Sustainability sustainability	4.1	8.9	2009	16.9 Days	CHF 2400	Submit

Preprints.org is a multidisciplinary platform offering a preprint service designed to facilitate the early sharing of your research. It supports and empowers your research journey from the very beginning.

MDPI Topics is collaborating with Preprints.org and has established a direct connection between MDPI journals and the platform. Authors are encouraged to take advantage of this opportunity by posting their preprints at Preprints.org prior to publication:

1. Share your research immediately: disseminate your ideas prior to publication and establish priority for your work.
2. Safeguard your intellectual contribution: Protect your ideas with a time-stamped preprint that serves as proof of your research timeline.
3. Boost visibility and impact: Increase the reach and influence of your research by making it accessible to a global audience.
4. Gain early feedback: Receive valuable input and insights from peers before submitting to a journal.
5. Ensure broad indexing: Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (3 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Land Remote Sensing Smart Cities Sustainability Agronomy

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

26 pages, 39421 KB  

Open AccessArticle

Optimizing Spatial Representativeness of LULC Samples over Complex Karst Terrain Using Remote Sensing Phenology and Landform-Constrained Joint Stratification

by Ya Li, Zhongfa Zhou, Denghong Huang, Huanhuan Lu, Ruiqi Fan, Qingqing Dai, Ying Luo, Changyan Huang and Yuexing Yu

Remote Sens. 2026, 18(12), 1915; https://doi.org/10.3390/rs18121915 - 10 Jun 2026

Viewed by 320

Abstract

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly [...] Read more.

Karst regions are characterized by fragmented topography and significant micro-relief mosaics, leading to prominent spectral aliasing of land features, which can result in insufficient spatial representativeness of remote sensing samples for Land Use and Land Cover (LULC). The accuracy of LULC data directly affects the scientific basis of decision-making for rocky desertification control and ecological conservation. This study selected the Beipanjiang River Basin in Guizhou Province, a typical karst region, as the study area. The study selected the SOS, LOS, OM, and EOS indices from the 2001–2020 MODIS MCD12Q2 phenological dataset, combined with topographic zoning data. This study developed a sample spatial optimization scheme for complex karst terrain by integrating Spearman’s correlation analysis, SKATER spatially constrained clustering, statistical tests, adaptive stratified sampling, and Random Forest classification. The scheme was designed to test a phenology–landform joint stratification strategy for spatial sample allocation. The results indicate that (1) the study area was divided into six phenological pattern subregions, with significant spatial differentiation observed among them; (2) the “phenology–landform joint stratification + dual-weighted sample allocation” method was associated with improved sample representativeness and greater internal homogeneity within sample strata under the current experimental setting; and (3) compared to simple random sampling, the remote sensing phenological pattern-driven spatial optimization scheme improved overall accuracy from 71.33% to 77.55% and increased the Kappa coefficient from 0.43 to 0.62. These results suggest that, under the current study-area, sample-size, and validation settings, the phenology–landform joint stratification and dual-weighted allocation scheme can improve the spatial organization of training samples and classification performance over complex karst terrain, although weakly vegetated or bare classes remain difficult to separate. Full article

(This article belongs to the Topic Large-Scale and Long-Term Land Use and Land Cover Mapping)

►▼ Show Figures

Figure 1

27 pages, 28603 KB  

Open AccessArticle

Semantic Reconstruction of Land Cover Classification in Karst Regions: A Natural-Attribute-Based NALCC Framework

by Denghong Huang, Zhongfa Zhou, Changyan Huang, Yi Li, Huanhuan Lu, Ya Li, Ying Luo and Yuexin Yu

Agronomy 2026, 16(11), 1026; https://doi.org/10.3390/agronomy16111026 - 22 May 2026

Viewed by 291

Abstract

Karst regions are commonly characterized by highly interwoven bare rock–bare soil–vegetation mosaics, strong coupling between surface and subsurface processes, and pronounced geomorphic fragmentation. Conventional land cover classification systems, which are primarily organized around land use patterns or generic ecological types, are often unable [...] Read more.

Karst regions are commonly characterized by highly interwoven bare rock–bare soil–vegetation mosaics, strong coupling between surface and subsurface processes, and pronounced geomorphic fragmentation. Conventional land cover classification systems, which are primarily organized around land use patterns or generic ecological types, are often unable to accurately represent these key surface components and their roles in ecological processes. From the perspective of reconstructing classification semantics, this study proposes a Natural-Attribute-Based Karst Land Cover Classification framework (NALCC). The framework takes bare rock, bare soil, vegetation, water bodies, and impervious surfaces as primary classes, and further develops a hierarchical system consisting of subclasses, attribute labels, hierarchical coding, multi-scale organization, and parameter mapping with ecosystem service models. Compared with conventional land cover classification systems, the innovation of this framework lies not in increasing the number of categories, but in reconstructing the semantic organization of classification units, so that land cover classification can move beyond surface-type description toward the expression of process-sensitive information. The classification objective of NALCC is not to develop a universal land cover classification system, but to establish a process-oriented classification framework for ecosystem service monitoring, rocky desertification diagnosis, and governance zoning in karst regions, which can directly represent key surface components and their ecological-process significance. However, its regional transferability and mapping performance still need to be further validated through case studies in representative areas. Full article

(This article belongs to the Topic Large-Scale and Long-Term Land Use and Land Cover Mapping)

►▼ Show Figures

Figure 1

22 pages, 925 KB  

Open AccessReview

Reconstruction and Drivers of Change in Historical Land Use Intensity in China: A Review and Prospect

by Fanxin Geng, Shicheng Li, Yu Qiu, Haiyan Huang and Meijiao Li

Land 2026, 15(5), 891; https://doi.org/10.3390/land15050891 - 21 May 2026

Viewed by 549

Abstract

Reconstructing historical land use intensity and analyzing its driving forces are crucial for understanding the impacts of human activities on the environment. This review systematically assesses the research on reconstructing historical land use intensity in China, focusing on four dimensions: land use type, [...] Read more.

Reconstructing historical land use intensity and analyzing its driving forces are crucial for understanding the impacts of human activities on the environment. This review systematically assesses the research on reconstructing historical land use intensity in China, focusing on four dimensions: land use type, harvest frequency, input intensity, and output intensity. The analysis reveals significant imbalances in the development of these dimensions, with reconstruction methods for land use types being the most mature, while quantitative methods for input intensity remain the weakest. The approaches are generally evolving from qualitative to quantitative analysis. Furthermore, studies on the driving forces behind intensity changes are predominantly qualitative, lacking integrated quantitative analyses of multiple factors. To overcome these limitations, the paper proposes that future research should integrate multi-source proxy indicators to construct a comprehensive, multi-dimensional assessment system. This would enable the spatiotemporal reconstruction of land use intensity and facilitate quantitative analysis of its driving forces using spatial data analysis and machine learning methods. Full article

(This article belongs to the Topic Large-Scale and Long-Term Land Use and Land Cover Mapping)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-3