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Land
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Advances in Land Use and Land Cover Mapping (Second Edition)
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Advances in Land Use and Land Cover Mapping (Second Edition)

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".

Deadline for manuscript submissions: 25 March 2025 | Viewed by 1023

Special Issue Editors

Dr. Kathryn Sheffield

E-Mail Website
Guest Editor
Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Bundoora 3083, Australia
Interests: land use and land cover mapping; validation; remote sensing; biosecurity; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Abuzar

E-Mail Website
Guest Editor
Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Bundoora 3083, Australia
Interests: remote sensing; land cover; crop water use; irrigation benchmarking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land use and land cover (LULC) information underpins our understanding of the earth, and our impact on it. While often referred to interchangeably, land use and cover are two distinct concepts. Land cover refers to the physical surface of the earth, while land use refers to the purpose to which the land is committed. Whilst distinct, the two components of land information are intrinsically linked, and can be mapped and analyzed both separately and together in order to highlight linkages between land use, cover and management, as well as land transition.

LULC data support policy making, strategic planning, and monitoring, with an increasing focus on climate change and sustainability. LULC change plays a critical role in the global cycle of greenhouse gases. Given the applied nature of LULC information, and the increasing need for research methodologies to be translated and applied in governmental policy and decision making, it is critical to evaluate the reliability of such data (and the means and technologies used to create it) in order to ensure that strong evidence-based decisions are made.

There have been many recent advances in the production of spatial LULC information, including data integration approaches, the application of machine learning and artificial intelligence, and advanced analytics. The enhanced availability and accessibility of spatial LULC data creates its own challenges of interpretation, presentation and communication of diverse datasets, with new approaches required in an ever-evolving technology landscape.

This Special Issue seeks to focus on innovative approaches to LULC mapping, including, but not limited to, the following:

  • Mapping and monitoring LULC at variety of spatial and temporal scales;
  • Spatial LULC data analytics, including change detection;
  • Validation of LULC information;
  • Spatial LULC data reporting and visualization/communication approaches;
  • Dataset development to support climate change and sustainability applications;
  • Evidence-based decision making based on critically evaluated LULC information.

Dr. Kathryn Sheffield
Dr. Mohammad Abuzar
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Land is an international peer-reviewed open access monthly 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 2600 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

  • land use mapping
  • land cover mapping
  • climate change
  • sustainability
  • validation
  • data communication

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Related Special Issue

Published Papers (1 paper)

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Research

23 pages, 14074 KiB  
Article
Comprehensive Representations of Subpixel Land Use and Cover Shares by Fusing Multiple Geospatial Datasets and Statistical Data with Machine-Learning Methods
by Yuxuan Chen, Rongping Li, Yuwei Tu, Xiaochen Lu and Guangsheng Chen
Land 2024, 13(11), 1814; https://doi.org/10.3390/land13111814 - 1 Nov 2024
Viewed by 847
Abstract
Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational [...] Read more.
Land use and cover change (LUCC) is a key factor influencing global environmental and socioeconomic systems. Many long-term geospatial LUCC datasets have been developed at various scales during the recent decades owing to the availability of long-term satellite data, statistical data and computational techniques. However, most existing LUCC products cannot accurately reflect the spatiotemporal change patterns of LUCC at the regional scale in China. Based on these geospatial LUCC products, normalized difference vegetation index (NDVI), socioeconomic data and statistical data, we developed multiple procedures to represent both the spatial and temporal changes of the major LUC types by applying machine-learning, regular decision-tree and hierarchical assignment methods using northeastern China (NEC) as a case study. In this approach, each individual LUC type was developed in sequence under different schemes and methods. The accuracy evaluation using sampling plots indicated that our approach can accurately reflect the actual spatiotemporal patterns of LUC shares in NEC, with an overall accuracy of 82%, Kappa coefficient of 0.77 and regression coefficient of 0.82. Further comparisons with existing LUCC datasets and statistical data also indicated the accuracy of our approach and datasets. Our approach unfolded the mixed-pixel issue of LUC types and integrated the strengths of existing LUCC products through multiple fusion processes. The analysis based on our developed dataset indicated that forest, cropland and built-up land area increased by 17.11 × 104 km2, 15.19 × 104 km2 and 2.85 × 104 km2, respectively, during 1980–2020, while grassland, wetland, shrubland and bare land decreased by 26.06 × 104 km2, 4.24 × 104 km2, 3.97 × 104 km2 and 0.92 × 104 km2, respectively, in NEC. Our developed approach accurately reconstructed the shares and spatiotemporal patterns of all LUC types during 1980–2020 in NEC. This approach can be further applied to the entirety of China, and worldwide, and our products can provide accurate data supports for studying LUCC consequences and making effective land use policies. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping (Second Edition))
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