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Assessment of Land Use/Cover Change Using Geospatial Technology

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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: 15 November 2024 | Viewed by 8870

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Special Issue Editors

Prof. Dr. Weicheng Wu

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Guest Editor

Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
Interests: environmental remote sensing; land resource mapping; land degradation; multi-biome biomass; natural hazard risk zoning and machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Brice Anselme

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Guest Editor

Université Paris 1 Panthéon-Sorbonne, Universite de Paris I, Paris, France
Interests: land use/cover; geomatics; littoral environment; multi-agent modeling

Dr. Yalan Liu

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: land use/cover; geomatics; geo-disaster risk assessment; environment; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Qiulin Xiong

E-Mail Website
Guest Editor

School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, China
Interests: land use/cover; environmental monitoring; 3S technology

Special Issue Information

Dear Colleagues,

Land use/cover change (LUCC) has been a hot topic in global environmental research since the 1990s, when it was proposed as a core project of IGBP and IHDP. Geospatial technology, including remote sensing, GIS, GPS/Beidou, and processing systems, has been playing an important role in land use/cover (LUC) mapping, LUC change detection and dynamic monitoring and assessment. Especially with advancements in artificial intelligence, including machine learning and deep learning, geospatial technology is becoming an increasingly powerful tool, which can be used in different types and scales of LUC mapping, modeling, simulation and the prediction of future LUC patterns. Thus, it is necessary to provide a platform where scientists, who work in the fields of LUC, LUCC and LUCC modeling and prediction, can come together from across the world and communicate.

The goal of this Special Issue is to collect papers (original research articles and review papers) that provide give insights into LUC mapping, LUCC detection, modeling, simulation and prediction by geospatial technology, more concretely by remote sensing, GIS and artificial intelligence.

This SI will welcome manuscripts that link the following themes:

New approaches for land use/cover (LUC) mapping and land use/cover change (LUCC) detection;
LUCC modeling, simulation and future LUC prediction;
Land degradation monitoring and assessment;
LUC-related machine learning and big data mining technique;
Man–nature interaction analysis and modeling;
Analysis of environmental impacts on urbanization and urban planning.

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

Prof. Dr. Weicheng Wu
Dr. Brice Anselme
Dr. Yalan Liu
Dr. Qiulin Xiong
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/cover (LUC)
land use/cover change (LUCC)
remote sensing and GIS
modeling, simulation and prediction
artificial intelligence and big data mining
land degradation assessment
man–nature interaction

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Published Papers (6 papers)

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Research

22 pages, 7918 KiB

Open AccessArticle

Spatial and Temporal Change Characteristics and Climatic Drivers of Vegetation Productivity and Greenness during the 2001–2020 Growing Seasons on the Qinghai–Tibet Plateau

by Jinghan Liang, Armando Marino and Yongjie Ji

Land 2024, 13(8), 1230; https://doi.org/10.3390/land13081230 - 7 Aug 2024

Viewed by 628

Abstract

Exploring NDVI variation and what drives it on the Qinghai–Tibet Plateau can strategically inform environmental protection efforts in light of global climate change. For this analysis, we obtained MODIS NDVI data collected during the vegetative growing season, vegetation types for the region, and meteorological data for the same period from 2001 to 2020. We performed Theil–Sen trend analysis, Mann–Kendall significance testing, spatial autocorrelation analysis, and Hurst index calculation to review the spatiotemporal changes in NDVI characteristics on the plateau for various vegetation types. We used the correlation coefficients from these analyses to investigate how the NDVI responds to temperature and precipitation. We found the following: (1) Overall, the Qinghai–Tibet Plateau NDVI increased throughout the multi-year growing season, with a much larger area of improvement (65.68%) than of degradation (8.83%). (2) The four main vegetation types were all characterized by improvement, with meadows (72.13%) comprising the largest portion of the improved area and shrubs (18.17%) comprising the largest portion of the degraded area. (3) The spatial distribution of the NDVI had a strong positive correlation and clustering effect and was stable overall. The local clustering patterns were primarily low–low and high–high clustering. (4) The Hurst index had an average value of 0.46, indicating that the sustainability of vegetation is poor; that is, the trend of vegetation change in the growing season in a large part of the Qinghai–Tibet Plateau in the future is opposite to that in the past. (5) The plateau NDVI correlated positively with air temperature and precipitation. However, the correlations varied geographically: air temperature had a wide influence, whereas precipitation mainly influenced meadows and grassland in the northern arid zone. The overall temperature-driven effect was stronger than that of precipitation. This finding is consistent with the current research conclusion that global warming and humidification promote vegetation growth in high-altitude areas and further emphasizes the uniqueness of the Qinghai–Tibet Plateau as a climate-change-sensitive area. This study also offers a technical foundation for understanding how climate change impacts high-altitude ecosystems, as well as for formulating ecological protection strategies for the plateau. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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26 pages, 9045 KiB

Open AccessArticle

Land-Use/Cover Change and Driving Forces in the Pan-Pearl River Basin during the Period 1985–2020

by Wei Fan, Xiankun Yang, Shirong Cai, Haidong Ou, Tao Zhou and Dakang Wang

Land 2024, 13(6), 822; https://doi.org/10.3390/land13060822 - 7 Jun 2024

Viewed by 575

Abstract

Land use/cover change (LUCC) is a vital aspect representing global change and humans’ impact on Earth’s surface. This study utilized the ESRI Land Cover 2020 and China Land Cover Dataset (CLCD), along with historical imagery from Google Earth, to develop a method for the assessment of land use data quality. Based on the assessment, the CLCD was updated to generate an improved Re-CLCD for the Pan-Pearl River Basin (PPRB) from 1985 to 2020, and to analyze LUCC in the PPRB over the past 35 years. The results indicate the following: (1) Among the seven land uses, built-up land experienced the most dramatic change, followed by cropland, forestland, grassland, shrubland, waterbody, and bare land, with notable increases in built-up land and forestland, and rapid decreases in cropland, grassland, and shrubland. (2) The magnitude of land use changed very widely, with the highest change in the Pearl River Delta, followed by small coastal river basins in southern Guangdong and western Guangxi, the Dongjiang River Basin, the Hanjiang River Basin, the Xijiang River Basin, the Beijiang River Basin, and lastly, Hainan Island. (3) The largest increase happened in built-up land, with a total increase of 12,184 km², mainly due to the occupation of cropland and forestland, corresponding to the highest decrease in cropland, with a net loss of 10,435 km², which was primarily converted to forestland and built-up land. The study results are valuable in providing a scientific basis for policy overhaul regarding land resources and management to safeguard ecological balance and promote sustainable development in the Pan-Pearl River Basin. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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16 pages, 3119 KiB

Open AccessArticle

Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region

by Jinyao Lin and Qitong Chen

Land 2023, 12(11), 2073; https://doi.org/10.3390/land12112073 - 18 Nov 2023

Viewed by 1156

Abstract

Arid and semi-arid areas are facing severe land degradation and desertification due to water scarcity. To alleviate these environmental issues, the Chinese government has launched a “water conveyance” project for environmental protection along the Tarim River. While previous studies have mainly focused on environmental conditions, the influence of these policies on land use conditions remains less explored. Therefore, this study first simulated the land use and land cover (LULC) changes in a major city (Korla) around the Tarim River. We found that the water conveyance routes have exerted notable influences on surrounding LULC changes. Next, we primarily focused on the LULC changes among different reaches of the Tarim River. We found that water and forest areas in the lower reaches have increased at the expense of a slight decrease in such areas in the upper and middle reaches, which suggests that the water conveyance policy may also have unintended consequences. These findings could attract the attention of decision makers in many other arid and semi-arid areas, and they could provide practical policy implications for other similar inter-basin water conveyance projects. The benefits and risks of these man-made projects should be carefully balanced. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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27 pages, 9983 KiB

Open AccessArticle

Land Use/Cover Change Prediction Based on a New Hybrid Logistic-Multicriteria Evaluation-Cellular Automata-Markov Model Taking Hefei, China as an Example

by Yecheng He, Weicheng Wu, Xinyuan Xie, Xinxin Ke, Yifei Song, Cuimin Zhou, Wenjing Li, Yuan Li, Rong Jing, Peixia Song, Linqian Fu, Chunlian Mao, Meng Xie, Sicheng Li, Aohui Li, Xiaoping Song and Aiqing Chen

Land 2023, 12(10), 1899; https://doi.org/10.3390/land12101899 - 10 Oct 2023

Cited by 1 | Viewed by 1352

Abstract

Land use/cover change (LUCC) detection and modeling play an important role in global environmental change research, in particular, policy-making to mitigate climate change, support land spatial planning, and achieve sustainable development. For the time being, a couple of hybrid models, such as cellular automata–Markov (CM), logistic–cellular automata-Markov (LCM), multicriteria evaluation (MCE), and multicriteria evaluation–cellular automata–Markov (MCM), are available. However, their disadvantages lie in either dependence on expert knowledge, ignoring the constraining factors, or without consideration of driving factors. For this purpose, we proposed in this paper a new hybrid model, the logistic–multicriteria evaluation–cellular automata–Markov (LMCM) model, that uses the fully standardized logistic regression coefficients as impact weights of the driving factors to represent their importance on each land use type in order to avoid these defects but is able to better predict the future land use pattern with higher accuracy taking Hefei, China as a study area. Based on field investigation, Landsat images dated 2010, 2015, and 2020, together with digital elevation model (DEM) data, were harnessed for land use/cover (LUC) mapping using a supervised classification approach, which was achieved with high overall accuracy (AC) and reliability (AC > 95%). LUC changes in the periods 2010–2015 and 2015–2020 were hence detected using a post-classification differencing approach. Based on the LUC patterns of the study area in 2010 and 2015, the one of 2020 was simulated by the LMCM, CM, LCM, and MCM models under the same conditions and then compared with the classified LUC map of 2020. The results show that the LMCM model performs better than the other three models with a higher simulation accuracy, i.e., 1.72–5.4%, 2.14–6.63%, and 2.78–9.33% higher than the CM, LCM, and MCM models, respectively. For this reason, we used the LMCM model to simulate and predict the LUC pattern of the study area in 2025. It is expected that the results of the simulation may provide scientific support for spatial planning of territory in Hefei, and the LMCM model can be applied to other areas in China and the world for similar purposes. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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20 pages, 3034 KiB

Open AccessArticle

An Assessment of Landscape and Land Use/Cover Change and Its Implications for Sustainable Landscape Management in the Chittagong Hill Tracts, Bangladesh

by Masheli Chakma, Umer Hayat, Jinghui Meng and Mohammed A Hassan

Land 2023, 12(8), 1610; https://doi.org/10.3390/land12081610 - 15 Aug 2023

Cited by 4 | Viewed by 1987

Abstract

Human-caused environmental change has profoundly impacted resource management and land use patterns in Bangladesh’s Chittagong Hill Tracts. This study used multi-temporal Landsat images from 1998, 2008, and 2018 to analyze land use and land cover changes, particularly those associated with forest cover changes, in Bangladesh’s Chittagong Hill Tracts. Using object-based image classification, Landsat images from 1998, 2008, and 2018 were separated into four categories based on their dominant land use and land cover features: forest, grassland, water bodies, and bare land. Post-classification comparison was used to assess the degree and frequency of change, and this method was further developed to evaluate the balance, fluctuation, and adaptation of forests. In addition, the spatial structure of land cover and temporal trajectories related to changes in forest cover were studied. The CA–Markov chain model was also used to anticipate the 2048 LULC map. The image classification of the years 1998, 2008, and 2018 showed that the overall accuracy was 89.65%, 84.44%, and 86.26%; producer accuracy was 90.00%, 68.75%, and 72.22%; and the Kappa coefficient was 85.68, 82.84, and 76.36, respectively. The results showed that between 1998 and 2018, forest cover increased by 58.03%, transforming grassland to forest; grassland increased by 29.50%, converting bare land to grassland; and forest conversion to grassland was 13.34%. In addition, the result of the landscape metric revealed that during the whole study period, class level indicated a fragmentation of forest, bare land, grassland, and water in the CHT, and landscape level indicated by Shannon’s Diversity Index and Shannon’s Evenness Index showed a slight decrease in the land. Based on the CA–Markov model, forest area is predicted to expand to 9129 Km² in 2048; however, other land uses (bare land and grassland) continue to decrease. This substantial increase in forest cover results from effective forest management based on community forestry practices and the successful execution of Bangladesh’s national forest strategy. However, as Bangladesh’s population rises, so does the country’s need for lumber/timber. Bangladesh’s government should revise its forest policy to meet the local community’s needs without endangering the forest, and policymakers must take climate change seriously. Our strategy for evaluating the critical indicators of changes in forest cover and pathways of change will aid in connecting these patterns to the dynamics of change, such as deforestation and reforestation. It would therefore serve as a framework for developing effective conservation and management plans for the Chittagong Hill regions in Bangladesh. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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23 pages, 8544 KiB

Open AccessArticle

U-Net-STN: A Novel End-to-End Lake Boundary Prediction Model

by Lirong Yin, Lei Wang, Tingqiao Li, Siyu Lu, Zhengtong Yin, Xuan Liu, Xiaolu Li and Wenfeng Zheng

Land 2023, 12(8), 1602; https://doi.org/10.3390/land12081602 - 14 Aug 2023

Cited by 110 | Viewed by 2235

Abstract

Detecting changes in land cover is a critical task in remote sensing image interpretation, with particular significance placed on accurately determining the boundaries of lakes. Lake boundaries are closely tied to land resources, and any alterations can have substantial implications for the surrounding environment and ecosystem. This paper introduces an innovative end-to-end model that combines U-Net and spatial transformation network (STN) to predict changes in lake boundaries and investigate the evolution of the Lake Urmia boundary. The proposed approach involves pre-processing annual panoramic remote sensing images of Lake Urmia, obtained from 1996 to 2014 through Google Earth Pro Version 7.3 software, using image segmentation and grayscale filling techniques. The results of the experiments demonstrate the model’s ability to accurately forecast the evolution of lake boundaries in remote sensing images. Additionally, the model exhibits a high degree of adaptability, effectively learning and adjusting to changing patterns over time. The study also evaluates the influence of varying time series lengths on prediction accuracy and reveals that longer time series provide a larger number of samples, resulting in more precise predictions. The maximum achieved accuracy reaches 89.3%. The findings and methodologies presented in this study offer valuable insights into the utilization of deep learning techniques for investigating and managing lake boundary changes, thereby contributing to the effective management and conservation of this significant ecosystem. Full article

(This article belongs to the Special Issue Assessment of Land Use/Cover Change Using Geospatial Technology)

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