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Land
Special Issues
Advances in Land Use and Land Cover Mapping
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Advances in Land Use and Land Cover Mapping

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

Deadline for manuscript submissions: 22 May 2024 | Viewed by 6645

Special Issue Editors

Dr. Kathryn Sheffield

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

E-Mail Website
Guest Editor
Agriculture Victoria Research, Department of Energy, Environment and Climate Action, Bundoora, VIC 3083, Australia
Interests: remote sensing; land cover; crop water use; irrigation benchmarking
Dr. Alison L. Cowood

E-Mail Website
Guest Editor
Department of Climate Change, Energy, The Environment and Water, Parkes, ACT 2600, Australia
Interests: landscape assessment; hydrogeology; hydrogeomorphology; anthropogenic and climate change pressures; analysis and application to government and policy iniatives; environmental-economic accounting

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
Dr. Alison L. Cowood
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

Published Papers (5 papers)

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Research

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22 pages, 14133 KiB  
Article
Spatial and Temporal Dynamics of Ecological Parameters in Various Land Use Types in China during the First 20 Years of the 21st Century
by Cong Zhang, Xiaojun Yao, Lina Xiu, Huian Jin and Juan Cao
Land 2024, 13(5), 572; https://doi.org/10.3390/land13050572 - 25 Apr 2024
Viewed by 363
Abstract
Ecological quality in China has experienced significant improvements due to the interplay of climate change and human activities. Nevertheless, previous studies exploring the trend of ecological parameters have always overlooked the effects of land use types. Therefore, in this study, we explored the [...] Read more.
Ecological quality in China has experienced significant improvements due to the interplay of climate change and human activities. Nevertheless, previous studies exploring the trend of ecological parameters have always overlooked the effects of land use types. Therefore, in this study, we explored the spatiotemporal variation in ecological parameters in various land use types and discussed the relationship between ecological parameters and climatic factors in China during the first 20 years of the 21st century. The results show that: (1) The area of grassland and unutilized land decreased, and the area of other land use types increased. (2) Distinct variations in the average, slope, and interval distribution of ecological parameters across various land use types were evident. Particularly significant increases in ecological parameters were observed in cultivated land and forest. (3) The influence of land use and land cover change on ecological parameters was evident. The conversion of cultivated land, forest, and grassland into water bodies, constructive land, and unutilized land resulted in a significant decrease in ecological parameters. (4) The distinct climatic conditions resulted in heightened monthly variations in the ecological parameters. Significant monthly fluctuations in ecological parameters were observed for cultivated land, forest, grassland, and constructed land, while water bodies and unutilized land did not exhibit such variations. (5) The correlation between ecological parameters and climatic factors varied considerably in various land use types in different regions. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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22 pages, 25598 KiB  
Article
A Multifaceted Approach to Developing an Australian National Map of Protected Cropping Structures
by Andrew Clark, Craig Shephard, Andrew Robson, Joel McKechnie, R. Blake Morrison and Abbie Rankin
Land 2023, 12(12), 2168; https://doi.org/10.3390/land12122168 - 14 Dec 2023
Viewed by 1456
Abstract
As the global population rises, there is an ever-increasing demand for food, in terms of volume, quality and sustainable production. Protected Cropping Structures (PCS) provide controlled farming environments that support the optimum use of crop inputs for plant growth, faster production cycles, multiple [...] Read more.
As the global population rises, there is an ever-increasing demand for food, in terms of volume, quality and sustainable production. Protected Cropping Structures (PCS) provide controlled farming environments that support the optimum use of crop inputs for plant growth, faster production cycles, multiple growing seasons per annum and increased yield, while offering greater control of pests, disease and adverse weather. Globally, there has been a rapid increase in the adoption of PCS. However, there remains a concerning knowledge gap in the availability of accurate and up-to-date spatial information that defines the extent (location and area) of PCS. This data is fundamental for providing metrics that inform decision making around forward selling, labour, processing and infrastructure requirements, traceability, biosecurity and natural disaster preparedness and response. This project addresses this need, by developing a national map of PCS for Australia using remotely sensed imagery and deep learning analytics, ancillary data, field validation and industry engagement. The resulting map presents the location and extent of all commercial glasshouses, polyhouses, polytunnels, shadehouses and permanent nets with an area of >0.2 ha. The outcomes of the project revealed deep learning techniques can accurately map PCS with models achieving F-Scores > 0.9 and accelerate the mapping where suitable imagery is available. Location-based tools supported by web mapping applications were critical for the validation of PCS locations and for building industry awareness and engagement. The final national PCS map is publicly available through an online dashboard which summarises the area of PCS structures at a range of scales including state/territory, local government area and individual structure. The outcomes of this project have set a global standard on how this level of mapping can be achieved through a collaborative, multifaceted approach. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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22 pages, 21832 KiB  
Article
Classification and Transition of Grassland in Qinghai, China, from 1986 to 2020 with Landsat Archives on Google Earth Engine
by Pengfei He, Yuli Shi, Haiyong Ding and Fangwen Yang
Land 2023, 12(9), 1686; https://doi.org/10.3390/land12091686 - 28 Aug 2023
Cited by 1 | Viewed by 952
Abstract
The lack of long-duration, high-frequency grassland classification products limits further understanding of the grasslands’ long-term succession. This study first explored the annual mapping of grassland with fourteen categories at 30 m in Qinghai, China, from 1986 to 2020 based on Google Earth Engine [...] Read more.
The lack of long-duration, high-frequency grassland classification products limits further understanding of the grasslands’ long-term succession. This study first explored the annual mapping of grassland with fourteen categories at 30 m in Qinghai, China, from 1986 to 2020 based on Google Earth Engine (GEE) and the Integrated Orderly Classification System (IOCSG). Specifically, we proposed an image composite strategy to obtain annual source images for classification, by quarterly compositing multi-sensor and multi-temporal Landsat surface reflectance images. Subsequently, the 35-year area time series of each category was analyzed in terms of trend, degree of change, and succession of each category. The results indicate that the different grasslands of the IOCSG can be effectively differentiated by utilizing the designed feature bands of remote sensing data. Additionally, the proposed annual image composition strategy can not only decrease the invalid pixels but also promote classification accuracy. The grasslands transition analysis from 1986 to 2020 implies the progressive urbanization, warming, and wetting trend in Qinghai. The generated 35-year annual grassland thematic data in Qinghai can serve as an elementary dataset for further regional ecological and climate change studies. The proposed methodology of large-scale grassland classification can also be referenced to other applications like land use/cover mapping and ecological resource monitoring. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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20 pages, 4120 KiB  
Article
Improving Dryland Urban Land Cover Classification Accuracy Using a Classical Convolution Neural Network
by Wenfei Luan, Ge Li, Bo Zhong, Jianwei Geng, Xin Li, Hui Li and Shi He
Land 2023, 12(8), 1616; https://doi.org/10.3390/land12081616 - 16 Aug 2023
Viewed by 898
Abstract
Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) [...] Read more.
Reliable information of land cover dynamics in dryland cities is crucial for understanding the anthropogenic impacts on fragile environments. However, reduced classification accuracy of dryland cities often occurs in global land cover data. Although many advanced classification techniques (i.e., convolutional neural networks (CNN)) have been intensively applied to classify urban land cover because of their excellent performance, specific classification models focusing on typical dryland cities are still scarce. This is mainly attributed to the similar features between urban and non-urban areas, as well as the insufficient training samples in this specific region. To fill this gap, this study trained a CNN model to improve the urban land classification accuracy for seven dryland cities based on rigorous training sample selection. The assessment showed that our proposed model performed with higher overall accuracy (92.63%) than several emerging land cover products, including Esri 2020 Land Cover (75.55%), GlobeLand30 (73.24%), GLC_FCS30-2020 (69.68%), ESA WorldCover2020 (64.38%), and FROM-GLC 2017v1 (61.13%). In addition, the classification accuracy of the dominant land types in the CNN-classified data exceeded the selected products. This encouraging finding demonstrates that our proposed architecture is a promising solution for improving dryland urban land classification accuracy and compensating the deficiency of large-scale land cover mapping. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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Review

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22 pages, 4206 KiB  
Review
A Comprehensive Review of Land Use and Land Cover Change Based on Knowledge Graph and Bibliometric Analyses
by Caixia Rong and Wenxue Fu
Land 2023, 12(8), 1573; https://doi.org/10.3390/land12081573 - 08 Aug 2023
Cited by 2 | Viewed by 2421
Abstract
Land use and land cover (LULC) changes are of vital significance in fields such as environmental impact assessment and natural disaster monitoring. This study, through an analysis of 1432 papers over the past decade employing quantitative, qualitative, bibliometric analysis, and knowledge graph techniques, [...] Read more.
Land use and land cover (LULC) changes are of vital significance in fields such as environmental impact assessment and natural disaster monitoring. This study, through an analysis of 1432 papers over the past decade employing quantitative, qualitative, bibliometric analysis, and knowledge graph techniques, aims to assess the evolution and current landscape of deep learning (DL) in LULC. The focus areas are: (1) trend analysis of the number and annual citations of published articles, (2) identification of leading institutions, countries/regions, and publication sources, (3) exploration of scientific collaborations among major institutions and countries/regions, and (4) examination of key research themes and their development trends. From 2013 to 2023 there was a substantial surge in the application of DL in LULC, with China standing out as the principal contributor. Notably, international cooperation, particularly between China and the USA, saw a significant increase. Furthermore, the study elucidates the challenges concerning sample data and models in the application of DL to LULC, providing insights that could guide future research directions to accelerate progress in this domain. Full article
(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)
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