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

Deadline for manuscript submissions: closed (22 May 2024) | Viewed by 16839

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Special Issue Editors

Dr. Kathryn Sheffield

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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

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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

Dr. Alison L. Cowood

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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

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Related Special Issue

Advances in Land Use and Land Cover Mapping (Second Edition) in Land (1 article)

Published Papers (10 papers)

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Research

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22 pages, 6154 KiB

Open AccessArticle

Per Capita Land Use through Time and Space: A New Database for (Pre)Historic Land-Use Reconstructions

by Chad Hill, Marco Madella, Nicki J. Whitehouse, Carolina Jiménez-Arteaga, Emily Hammer, Jennifer Bates, Lynn Welton, Stefano Biagetti, Johanna Hilpert and Kathleen D. Morrison

Land 2024, 13(8), 1144; https://doi.org/10.3390/land13081144 - 26 Jul 2024

Viewed by 1015

Abstract

Anthropogenic land cover change (ALCC) models, commonly used for climate modeling, tend to utilize relatively simplistic models of human interaction with the environment. They have historically relied on unsophisticated assumptions about the temporal and spatial variability of the area needed to support one person: per capita land use (PCLU). To help refine ALCC models, we used a range of data sources to build a new database that attempts to bring together PCLU data with significant time depth and a global perspective. This new database can provide new nuance for our understanding of the variability in land use among and between time periods and regions, data that will have wide applicability for continued research into past human land use and present land-use change, and can hopefully help improve existing ALCC models. An example is provided, showing the potential impact of new PCLU data on land-use mapping in the Middle East at 6000 BP. Full article

(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)

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17 pages, 1529 KiB

Open AccessArticle

Impacts of Integrated Watershed Management Interventions on Land Use/Land Cover of Yesir Watershed in Northwestern Ethiopia

by Abebaw Andarge Gedefaw, Mulutesfa Alemu Desta and Reinfried Mansberger

Land 2024, 13(7), 918; https://doi.org/10.3390/land13070918 - 24 Jun 2024

Viewed by 802

Abstract

Since 2002, numerous sustainable land management (SLM) interventions have been implemented in Ethiopia, such as agroforestry, area closure, forage development, gully rehabilitation, and conservation agriculture. In addition, watershed-based developments contributed comprehensively to a better use of existing natural resources. This study determined the impact of Integrated Watershed Management (IWM) on land use/land cover for the Yesir watershed in Northern Ethiopia. Supervised image classification algorithms were applied to a time series of Landsat 5 (2002) and Landsat 8 (2013 and 2022) images to produce land use/land cover maps. A Geographic Information System was applied to analyze and map changes in land use/land cover for settlements, agricultural land, grazing land, and land covered with other vegetation. In focus group discussions, the time series maps were analyzed and compared with the integrated watershed management practices to analyze their impacts. The results document that integrated watershed management practices have contributed to a significant change in land use/land cover in the study area over the past 20 years. The quantitative analysis of land use/land cover between the years 2002 and 2022 only revealed a downward trend in agricultural land. Considering the value of the Normalized Difference Vegetation Index (NDVI) as a biophysical feature for the increase of green mass, this indicator also documents an improvement in land use/land cover with regard to sustainable land management and consequently poverty alleviation. Full article

(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)

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23 pages, 5647 KiB

Open AccessArticle

The Spatial-Temporal Patterns and Driving Mechanisms of the Ecological Barrier Transition Zone in the Western Jilin, China

by Shibo Wen, Yongzhi Wang, Tianqi Tang, Congcong Su, Bowen Li, Muhammad Atif Bilal and Yibo Meng

Land 2024, 13(6), 856; https://doi.org/10.3390/land13060856 - 14 Jun 2024

Cited by 1 | Viewed by 842

Abstract

Land use change monitoring is a common theme in achieving sustainable development, while research on ecological barrier transition zones is relatively scarce. This study quantitatively analyzes the characteristics and patterns of land use change in Western Jilin, located in the transitional zone between the northeast forest belt and the northern sand prevention belt, from 1990 to 2020. Land dynamic change index and transition matrix are used to quantify the rates and intensities, and conversions between different land use types over time, respectively. Geodetector is adopted to analyze the impact of 12 factors on 12 types of land use change, such as using the factor detector to quantify the influence of temperature on the conversion from cropland to unused land. The results indicate that from 1990 to 2020, there have been noticeable changes in the area of various land use types in western Jilin. However, the conversion types are relatively limited, mainly involving interchanges between cropland, grassland, unused land, and water bodies. The cropland has increased by 20% overall, but 16% of that increase occurred from 1990–2000. The woodland area has steadily increased at a growth rate of 5–8% from 2000–2020, aligning with sustainable development strategies. Water bodies and grasslands are undergoing continuous recovery, and a positive growth trend is predicted to emerge by 2030. The built-up land is steadily expanding. The influencing factors vary for different types of land-use change. In a short time, policy factors play a significant role in land use, such as the implementation of the “River-lake Connection Project”, which has helped to reduce water-body fragmentation and enabled the stable recovery of water resources. However, in the long term, multiple topographic, climatic, and anthropogenic factors exhibit interactive effects in the land use change process in the area. Governments can take corresponding measures and management policies based on the influence of these factors to allocate and plan land use rationally. Full article

(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)

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20 pages, 14105 KiB

Open AccessArticle

Study on Tianjin Land-Cover Dynamic Changes, Driving Factor Analysis, and Forecasting

by Zhaoxu Zhang, Yuzhao Wei, Xutong Li, Dan Wan and Zhenwei Shi

Land 2024, 13(6), 726; https://doi.org/10.3390/land13060726 - 22 May 2024

Cited by 4 | Viewed by 957

Abstract

Land-use and land-cover changes constitute pivotal components in global environmental change research. Through an examination of spatiotemporal variations in land cover, we can deepen our understanding of land-cover change dynamics, shape appropriate policy frameworks, and implement targeted environmental conservation strategies. The judicious management of land is a critical determinant in fostering the sustainable growth of urban economies and enhancing quality of life for residents. This study harnessed remote sensing data to analyze land-cover patterns in Tianjin over five distinct time points: 2000, 2005, 2010, 2015, and 2020. It focused on evaluating the evolving dynamics, transition velocities, and transformation processes across various land categories within the region. Utilizing dynamic analysis and a transition matrix, the study traced shifts among different land-use classes. The center-of-gravity migration model was employed to elucidate land-cover pattern evolution. This research also integrated pertinent land-cover statistics to offer a holistic perspective on Tianjin’s land-cover transformations. Employing the CA–Markov model, we projected the prospective spatial layout of land cover for the area. Our findings revealed the following. (1) From 2000 to 2020, Tianjin experienced a significant reduction in cropland, forest, grassland, and water areas, alongside a substantial increase in impervious. (2) The impervious surface’s center of gravity, initially in Beichen District, shifted 4.20 km northwestward at an average rate of 0.84 km per year. (3) Principal component analysis indicated that the growth in the output value of the secondary and forestry industries is a key driver in expanding Tianjin’s impervious-surface area. (4) Predictions for 2025 suggest an increase in Tianjin’s impervious-surface area to 4659.78 km², with a concurrent reduction in cropland to 5656.18 km². The insights gleaned from this study provide a solid theoretical foundation and empirical evidence, aiding in the formulation of informed land-use strategies, the preservation of urban land resources, and guiding principles for sustainable urban development. Full article

(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)

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15 pages, 46193 KiB

Open AccessFeature PaperArticle

Feasibility of Using SWIR-Transformed Reflectance (STR) in Place of Surface Temperature (Ts) for the Mapping of Irrigated Landcover

by Mohammad Abuzar, Kathryn Sheffield and Andy McAllister

Land 2024, 13(5), 633; https://doi.org/10.3390/land13050633 - 8 May 2024

Viewed by 1305

Abstract

(1) Background: A simple approach to map irrigated landcover has been introduced by using measures derived from the optical spectral range as an alternative to the thermal range. It has been demonstrated that substituting surface temperature (Ts, ‘thermal approach’) with SWIR-transformed reflectance (STR, ‘optical approach’) to detect surface moisture is feasible with comparable results. (2) Methods: Using an iterative thresholding procedure to minimize within-class variance, the bilevel segmentation of variables derived from Landsat-8 representing surface moisture and vegetation cover was achieved for the 2020–2021 summer for a key irrigation district in Australia. (3) Results: The results of irrigated landcover by the optical approach were found to be comparable with those obtained by the thermal approach. The classification accuracy was assessed using water delivery records at the farm level. Although the overall accuracy was high in both cases, the optical approach (97.6%) performed slightly better than the thermal approach (93.9%). (4) Conclusions: The feasibility of using STR to map irrigated landcover has been confirmed by a high-level overall accuracy assessment. This has broader implications in terms of irrigated landcover assessment, as the use of satellite imagery in these applications may not necessarily be limited to microwave or thermal sensors. Full article

(This article belongs to the Special Issue Advances in Land Use and Land Cover Mapping)

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22 pages, 14133 KiB

Open AccessArticle

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 919

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 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

Open AccessFeature PaperEditor’s ChoiceArticle

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 1972

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 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

Open AccessArticle

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 2 | Viewed by 1448

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 (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

Open AccessArticle

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

Cited by 1 | Viewed by 1313

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)) 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

Open AccessReview

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 - 8 Aug 2023

Cited by 7 | Viewed by 4982

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, 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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