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Land
Special Issues
Land Use/Cover Change and Its Impacts on Regional Sustainable Development
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Land Use/Cover Change and Its Impacts on Regional Sustainable Development

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Urban Contexts and Urban-Rural Interactions".

Deadline for manuscript submissions: closed (9 February 2024) | Viewed by 3136

Special Issue Editors

Dr. Shengbiao Wu

E-Mail Website
Guest Editor
Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Pokfulam, Hong Kong 999077, China
Interests: vegetation remote sensing; radiative transfer; vegetation phenology; urban greenspace exposure; urban heat exposure
Special Issues, Collections and Topics in MDPI journals
Dr. Xiao Zhang

E-Mail Website
Guest Editor
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Interests: global land cover mapping and dynamic monitoring; impervious surface mapping
Dr. Xidong Chen

E-Mail Website
Guest Editor
Division of Landscape Architecture, Faculty of Architecture, The University of Hong Kong, Hong Kong 999077, China
Interests: global land cover mapping; land cover change detection water dynamic mapping

Special Issue Information

Dear Colleagues,

With the global urbanization and economic development, monitoring and analyzing land use and land cover (LULC) change have become a focal point in many interdisciplinary research fields. Rapid urbanization, agricultural expansion and industrialization have contributed to significant alterations in land use and land cover patterns, often resulting in habitat fragmentation, deforestation and loss of biodiversity. These changes not only directly affect the ecological processes and functions of the affected areas, but also have far-reaching consequences on regional climate, water cycles and carbon sequestration. Moreover, the implications of LULC change on food security, human health and socio-economic well-being of communities cannot be underestimated. Therefore, accurately monitoring changes in LULC and comprehensively understanding their impacts on regional sustainable development is of paramount importance in formulating effective strategies for integrated land management. In this context, the integration of remote sensing, geographic information systems (GIS) and spatial modeling techniques has emerged as a powerful tool for monitoring, assessing and predicting LULC dynamics and their impacts on regional sustainable development. The application of these advanced technologies facilitates the generation of spatially explicit and temporally dynamic information on LULC patterns, allowing for a comprehensive analysis of the drivers, processes and consequences of LULC change.  

The goal of this Special Issue is to collect papers (original research articles and review papers) on the monitoring of changes in LULC and the assessment of their impacts related to sustainable development in the region, closely aligned with the scope of Land. The Special Issue specifically emphasizes the assessment of such impacts in relation to sustainable development in the region. Therefore, the papers published in this Special Issue are expected to contribute to the broader scope of research published in Land.

This Special Issue welcomes high-quality studies focusing on monitoring LULC changes and analyzing the impacts of their changes on regional sustainable development. Relevant themes include, but are not limited to:

  • Land cover and land use change monitoring;
  • Spatio-temporal data mining, data fusion, modeling and analysis of land cover change;
  • The relationship between land use/cover change and regional sustainable development;
  • The driving forces and mechanisms of land use/cover change;
  • Land cover changes and associated impacts on the environment.

We look forward to receiving your original research articles and reviews.

Dr. Shengbiao Wu
Dr. Xiao Zhang
Dr. Xidong Chen
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/ land cover
  • change detection
  • remote sensing
  • sustainable development
  • geographic information systems
  • driving forces
  • environment

Published Papers (2 papers)

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Research

26 pages, 14395 KiB  
Article
Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China
by Suiping Zeng, Xinyao Liu, Jian Tian and Jian Zeng
Land 2024, 13(4), 476; https://doi.org/10.3390/land13040476 - 7 Apr 2024
Cited by 1 | Viewed by 663
Abstract
The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of [...] Read more.
The spatial–temporal distribution and evolution characteristics of carbon stock under the influence of land use changes are crucial to the scientific management of environmental resources and the optimization of land spatial layout. Taking the Xiamen–Zhangzhou–Quanzhou urban agglomeration in the southeastern coastal region of China as an example, based on seven land use types from 1990 to 2020, including cultivated land, woodland, and construction land, we quantitatively investigate the spatial–temporal patterns of carbon stock development and the spatial correlation of carbon stock distribution. Additionally, two scenarios for the development of urban and ecological priorities in 2060 are established to investigate the effects of land use changes on carbon stock. The results indicate that (1) the research area has formed a land use spatial pattern centered around urban construction in the eastern bay area, with the western forest area and coastal forest belt serving as ecological barriers. Carbon stock is influenced by land use type, and the distribution of total carbon stock exhibits a spatial aggregation phenomenon characterized by “low in the southeast, high in the north, and medium in the center”. (2) Distance of trunk and secondary roads, elevation, slope, watershed borders, population size, and gross domestic product (GDP) factors are the main drivers of the growth of land use types. The primary causes of the reduction in carbon stock are the widespread conversion of cultivated land, woodland, and grassland into construction land, as well as water and unused land. (3) In 2060, there will be a decrease of 41,712,443.35 Mg in the urban priority development scenario compared to 2020, and a decrease of 29,577,580.48 Mg in the ecological priority development scenario. The estimated carbon stock under the two scenarios varies by 12,134,862.88 Mg. The average carbon storage of Zhangpu County, Quangang County, and Jimei County is expected to rise by one level under the ecological protection scenario, indicating that the vast forest area can become a potential area to maintain carbon stock. It is crucial to encourage the coordinated development of peri-urban agroforestry and ecological barriers, as well as to establish a harmonious spatial pattern of land use and carbon stock at the scale of urban agglomerations. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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13 pages, 2920 KiB  
Article
Development of Soil Fertility Index Using Machine Learning and Visible-Near-Infrared Spectroscopy
by Xiaolin Jia, Yi Fang, Bifeng Hu, Baobao Yu and Yin Zhou
Land 2023, 12(12), 2155; https://doi.org/10.3390/land12122155 - 12 Dec 2023
Cited by 1 | Viewed by 1823
Abstract
An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on [...] Read more.
An accurate assessment of soil fertility is crucial for monitoring environmental dynamics, improving agricultural productivity, and achieving sustainable land management and utilization. The inherent complexity and spatiotemporal heterogeneity of soils result in significant challenges in soil fertility assessment. Therefore, this study focused on developing a rapid, economical, and precise approach to evaluate soil fertility through the application of visible-near-infrared spectroscopy (VNIR). To achieve this, we utilized the Land Use and Cover Area Frame Survey (LUCAS) dataset and employed a variety of prediction models, including partial least squares regression, support vector machines (SVMs), random forest, and convolutional neural networks, to estimate various soil properties and overall soil fertility. The results showed that the SVM model had the highest prediction accuracy, particularly for clay content (coefficient of determination (R2) = 0.79, ratio of performance to interquartile range (RPIQ) = 3.04), pH (R2 = 0.84, RPIQ = 4.54), total nitrogen (N) (R2 = 0.80, RPIQ = 2.40), and cation exchange capacity (CEC) (R2 = 0.83, RPIQ = 3.16). A soil fertility index (SFI) was developed based on factor analysis, integrating nine essential soil properties: clay content, silt content, sand content, pH, carbonate content, N, soluble phosphorus, soluble potassium, and CEC. We compared direct and indirect prediction models for estimating SFI and found that both models showed high accuracy (mean value of R2 = 0.80, mean value of RPIQ = 2.21). Additionally, SFI was classified into five classes to provide insights for precision agriculture. The kappa coefficient was 0.63, which indicated that the SFI evaluation results between VNIR and chemical analysis were relatively consistent. This study provides a theoretical foundation of real-time soil fertility monitoring for the optimization of agricultural practices. Full article
(This article belongs to the Special Issue Land Use/Cover Change and Its Impacts on Regional Sustainable Development)
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