Land | Special Issue : Applying Earth Observation Data for Urban Land-Use Change Mapping

Research

18 pages, 3758 KiB

Open AccessArticle

Analysis of the Spatiotemporal Differentiation and Influencing Factors of Land Use Efficiency in the Beijing–Tianjin–Hebei Urban Agglomeration

by Haixin Huang and Jiageng Yang

Land 2024, 13(9), 1508; https://doi.org/10.3390/land13091508 - 17 Sep 2024

Viewed by 613

Abstract

Optimizing urban land use is of significant practical importance for promoting economic development, enhancing the standard of living for individuals residing in metropolitan areas, enhancing urban infrastructure and public services, driving urban transformation and upgrading, and attaining synchronized progress of the economy, society, [...] Read more.

Optimizing urban land use is of significant practical importance for promoting economic development, enhancing the standard of living for individuals residing in metropolitan areas, enhancing urban infrastructure and public services, driving urban transformation and upgrading, and attaining synchronized progress of the economy, society, and environment. This paper uses the super-efficiency SBM model to measure the urban land use efficiency (ULUE) of 13 cities in the Beijing–Tianjin–Hebei (BTH) urban agglomeration from 2005 to 2020 and explores the spatiotemporal evolution characteristics and influencing factors of ULUE in this urban agglomeration using analysis of spatial data and application of geographic detector methods. The results show that (1) from 2005 to 2020, the ULUE of the BTH urban agglomeration had an initial rise followed by a decline; however, the overall efficiency score is above 1, suggesting an overall effective state; (2) a distribution pattern with Beijing as its core was established, exhibiting greater ULUE in the northern region and poorer efficiency in the southern region, with significant correlation characteristics in efficiency values between adjacent cities; and (3) capital input, labor input, social welfare, and ecological environment are all influencing factors that promote the improvement in ULUE in the BTH region, and the interaction of any two factors explains the ULUE in this region better than a single factor. The empirical research results can provide useful references for improving the input–output ratio of land units and further spatial planning and policy formulation in the BTH region. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

19 pages, 5970 KiB

Open AccessArticle

Optimization of Land Use Structure Based on the Coupling of GMOP and PLUS Models: A Case Study of Lvliang City, China

by Zhen Wang, Anya Zhong and Quanzhi Li

Land 2024, 13(8), 1335; https://doi.org/10.3390/land13081335 - 22 Aug 2024

Viewed by 629

Abstract

Reasonable land use planning and management efficiently allocates land resources, promotes socio-economic development, protects the ecological environment, and fosters sustainable development. It is a crucial foundation for achieving harmonious coexistence between humans and nature. Optimizing land use is key to land use planning [...] Read more.

Reasonable land use planning and management efficiently allocates land resources, promotes socio-economic development, protects the ecological environment, and fosters sustainable development. It is a crucial foundation for achieving harmonious coexistence between humans and nature. Optimizing land use is key to land use planning and management. Four scenarios are established: an economic development scenario (EDS), an ecological protection scenario (EPS), a natural development scenario (NDS), and a coordinated development scenario (CDS). This study simulates land use patterns under these scenarios through the coupling of the GMOP and PLUS models. It analyzes the land use efficiency transformation index, landscape ecological index, comprehensive land use benefits, and ecosystem service value (ESV) for each pattern. The optimal land use pattern is determined by balancing these factors. The results indicated that under the CDS, the areas of wasteland, grassland, forest land, water bodies, construction land, and unused land in Lvliang City were 6724.29 km², 6664.74 km², 6581.84 km², 126.94 km², 1017.33 km², and 0.42 km², respectively. This represented the optimal land use plan for Lvliang City. The plan minimized human interference with the landscape pattern, achieved the highest land use efficiency transformation index, and reached a reasonable balance between land use benefits and ESV. The research findings provide valuable insights and decision support for regional land use planning, territorial space planning, and related policy formulation. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

20 pages, 5469 KiB

Open AccessArticle

Harmonized Pan-European Time Series for Monitoring Soil Sealing

by Christophe Sannier, Eva Ivits, Gergely Maucha, Joachim Maes and Lewis Dijkstra

Land 2024, 13(7), 1087; https://doi.org/10.3390/land13071087 - 19 Jul 2024

Cited by 1 | Viewed by 739

Abstract

The European Copernicus Land Monitoring Service (CLMS) has been producing datasets on imperviousness every 3 years since 2006. However, for 2018, the input for the production of the imperviousness dataset was switched from mixed inputs to the Sentinel constellation. While this led to [...] Read more.

The European Copernicus Land Monitoring Service (CLMS) has been producing datasets on imperviousness every 3 years since 2006. However, for 2018, the input for the production of the imperviousness dataset was switched from mixed inputs to the Sentinel constellation. While this led to an improvement in the spatial detail from 20 m to 10 m, this also resulted in a break in the time series as the 2018 update was not comparable to the previous reference years. In addition, the European CLMS has been producing a new dataset from 2018 onward entitled CLC+ Backbone, which also includes a sealed area thematic class. When comparing both datasets with sampled reference data, it appears that the imperviousness dataset substantially underestimates sealed areas at the European level. However, the CLC+ dataset is only available from 2018 and currently does not include any change layer. To address these issues, a harmonized continental soil sealing combined dataset for Europe was produced for the entire observation period. This new dataset has been validated to be the best current dataset for monitoring soil sealing as a direct input for European policies with an estimated total sealed area of 175,664 km² over Europe and an increase in sealed areas of 1297 km² or 0.7% between 2015 and 2018, which is comparable to previous time periods. Finally, recommendations for future updates and the validation of imperviousness degree geospatial products are given. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

30 pages, 15012 KiB

Open AccessArticle

A Spatio-Temporal Examination of Land Use and Land Cover Changes in Smart Cities of the Delhi–Mumbai Industrial Corridor

by Arun Kanchan, Vilas Nitivattananon, Nitin K. Tripathi, Ekbordin Winijkul and Ranadheer Reddy Mandadi

Land 2024, 13(7), 957; https://doi.org/10.3390/land13070957 - 29 Jun 2024

Cited by 1 | Viewed by 1339

Abstract

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, [...] Read more.

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department’s sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

20 pages, 5113 KiB

Open AccessArticle

Feature-Differencing-Based Self-Supervised Pre-Training for Land-Use/Land-Cover Change Detection in High-Resolution Remote Sensing Images

by Wenqing Feng, Fangli Guan, Chenhao Sun and Wei Xu

Land 2024, 13(7), 927; https://doi.org/10.3390/land13070927 - 26 Jun 2024

Viewed by 1003

Abstract

Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep learning solutions, particularly convolutional neural networks (CNNs), represent the [...] Read more.

Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep learning solutions, particularly convolutional neural networks (CNNs), represent the state of the art (SOTA) for CD. However, CNN-based models require substantial amounts of annotated data, which can be both expensive and time-consuming. Conversely, acquiring a large volume of unannotated images is relatively easy. Recently, self-supervised contrastive learning has emerged as a promising method for learning from unannotated images, thereby reducing the need for annotation. However, most existing methods employ random values or ImageNet pre-trained models to initialize their encoders and lack prior knowledge tailored to the demands of CD tasks, thus constraining the performance of CD models. To address these challenges, we introduce a novel feature-differencing-based framework called Barlow Twins for self-supervised pre-training and fine-tuning in CD (BTCD). The proposed approach employs absolute feature differences to directly learn unique representations associated with regions that have changed from unlabeled bi-temporal remote sensing images in a self-supervised manner. Moreover, we introduce invariant prediction loss and change consistency regularization loss to enhance image alignment between bi-temporal images in both the decision and feature space during network training, thereby mitigating the impact of variation in radiation conditions, noise, and imaging viewpoints. We select the improved UNet++ model for fine-tuning self-supervised pre-training models and conduct experiments using two publicly available LULC CD datasets. The experimental results demonstrate that our proposed approach outperforms existing SOTA methods in terms of competitive quantitative and qualitative performance metrics. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

24 pages, 8293 KiB

Open AccessArticle

Spatial and Temporal Variation Characteristics of Ecological Environment Quality in China from 2002 to 2019 and Influencing Factors

by Junjie Li, Xiangbin Peng, Ruomei Tang, Jing Geng, Zipeng Zhang, Dong Xu and Tingting Bai

Land 2024, 13(1), 110; https://doi.org/10.3390/land13010110 - 19 Jan 2024

Cited by 4 | Viewed by 1446

Abstract

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the [...] Read more.

Since the beginning of the new century, there has been a notable enhancement in China’s ecological environment quality (EEQ), a development occurring in tandem with climate change and the extensive ecological restoration projects (ERPs) undertaken in the country. However, comprehensive insights into the spatial and temporal characteristics of China’s EEQ, and its responses to both climate change and human activities over the past two decades, have remained largely elusive. In this study, we harnessed a combination of multi-source remote-sensing data and reanalysis data. We employed Theil–Sen median trend analysis, multivariate regression residual analysis, and the Hurst index to examine the impacts and changing patterns of climatic factors and human activities on China’s EEQ during the past two decades. Furthermore, we endeavored to forecast the future trajectory of EEQ. Our findings underscore a significant improvement in EEQ across most regions of China between 2002 and 2019, with the most pronounced enhancements observed in the Loess Plateau, Northeast China, and South China. This transformation can be attributed to the combined influence of climatic factors and human activities, which jointly accounted for alterations in EEQ across 78.25% of China’s geographical expanse. Human activities (HA) contributed 3.93% to these changes, while climatic factors (CC) contributed 17.79%. Additionally, our projections indicate that EEQ is poised to continue improving in 56.70% of China’s territory in the foreseeable future. However, the Loess Plateau, Tarim Basin, and Inner Mongolia Plateau are anticipated to experience a declining trend. Consequently, within the context of global climate change, the judicious management of human activities emerges as a critical imperative for maintaining EEQ in China. This study, bridging existing gaps in the literature, furnishes a scientific foundation for comprehending the evolving dynamics of EEQ in China and informs the optimization of management policies in this domain. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

21 pages, 3841 KiB

Open AccessArticle

Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

by Tommaso Orusa, Annalisa Viani and Enrico Borgogno-Mondino

Land 2024, 13(1), 80; https://doi.org/10.3390/land13010080 - 10 Jan 2024

Cited by 7 | Viewed by 1711

Abstract

Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service [...] Read more.

Nowadays, European program Copernicus’ Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LC_b). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LC_b, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

16 pages, 12292 KiB

Open AccessArticle

Investigating the Spatial Heterogeneity and Influencing Factors of Urban Multi-Dimensional Network Using Multi-Source Big Data in Hangzhou Metropolitan Circle, Eastern China

by Jing Zhang, Lei Li, Congmou Zhu, Qi Hao, Xinming Chen, Zhoulu Yu, Muye Gan and Wuyan Li

Land 2023, 12(9), 1808; https://doi.org/10.3390/land12091808 - 19 Sep 2023

Viewed by 1150

Abstract

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, [...] Read more.

Exploring the spatial heterogeneity of urban multi-dimensional networks and influencing factors are of great significance for the integrated development of metropolitan circle. This study took Hangzhou metropolitan circle as an example, using multi-source geospatial big data to obtain urban population, transportation, goods, capital, and information flow information among sub-cities. Then, spatial visualization analysis, social network analysis, and geographical detector were applied to analyze the differences in spatial structure of multiple urban networks and influencing factors in Hangzhou metropolitan circle, respectively. The results showed that (1) the network connections of population, traffic, goods, and capital flows transcended geographical proximity except that of information flow, and population and traffic flow networks were found to be more flattened in Hangzhou metropolitan circle than in other urban networks; (2) the comprehensive urban network of Hangzhou metropolitan circle was imbalanced across sub-cities, presenting hierarchical and unipolar characteristics; and (3) the influence of traffic distance on the network spatial structure of Hangzhou metropolitan was stronger than the geographical distance, and the interactions between traffic distance and socioeconomic factors would further enhance the regional differentiation of the network spatial structure. This study could provide scientific reference for constructing a coordinated and integrated development pattern in a metropolitan circle. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

12 pages, 20916 KiB

Open AccessArticle

Spatial Study of Enzymatic Activities from Bacterial Isolates in a Mediterranean Urban Park

by Sergi Maicas and Jaume Segura-Garcia

Land 2023, 12(3), 655; https://doi.org/10.3390/land12030655 - 10 Mar 2023

Viewed by 1347

Abstract

Urban parks are a rich source of microbial diversity, as they are heavily used by city residents. In this study, we sampled a Mediterranean park and were able to isolate bacteria that have the ability to inhibit the growth of control microorganisms. Out [...] Read more.

Urban parks are a rich source of microbial diversity, as they are heavily used by city residents. In this study, we sampled a Mediterranean park and were able to isolate bacteria that have the ability to inhibit the growth of control microorganisms. Out of the 560 bacteria we tested, many displayed antibacterial activity, particularly against Salmonella sp. and K. pneumoniae. These results suggest that the microorganisms in the park are in close proximity to the human population. Additionally, the isolated bacteria demonstrated diverse enzymatic activities, possibly as a response to the environmental substances available to them, which could aid in the degradation of different compounds of interest. The study of the spatial distribution of soil parameters and the inhibition against relative-safe pathogens in an urban park in València (Spain) demonstrated a higher proportion of isolates in certain areas. These spatial data maps can help researchers understand the behaviors of bacterial populations on a regional level, which can assist in the creation of novel antimicrobial agents and promote advancements in public health. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

24 pages, 2289 KiB

Open AccessArticle

Modelling Impact of Urban Expansion on Ecosystem Services: A Scenario-Based Approach in a Mixed Natural/Urbanised Landscape

by Fatemeh Mohammadyari, Ardavan Zarandian, Mir Mehrdad Mirsanjari, Jurate Suziedelyte Visockiene and Egle Tumeliene

Land 2023, 12(2), 291; https://doi.org/10.3390/land12020291 - 19 Jan 2023

Cited by 8 | Viewed by 2298

Abstract

The present study aims at predicting future land use/land cover (LULC) and quantifying and mapping the ecosystem services (ESs) of water yield, outdoor recreation opportunity and food production in current (here, 2017) and future landscapes in Northern Iran, using the InVEST, Recreation Opportunity [...] Read more.

The present study aims at predicting future land use/land cover (LULC) and quantifying and mapping the ecosystem services (ESs) of water yield, outdoor recreation opportunity and food production in current (here, 2017) and future landscapes in Northern Iran, using the InVEST, Recreation Opportunity Spectrum (ROS) and yield models. To that end, two LULC scenarios known as business as usual (BAU) and protection-based (PB) plan were applied for 2028, using the Markov Artificial Neural Network and Multi-objective land allocation (MOLA) models. The results show that rapid urbanisation, caused by the expansion of human settlements and industrial areas, has led to a decline in the ESs in the region. Compared to the ESs in 2017, the service of water yield increases as urban expansion increases, whereas food production and recreation services decrease as urban expansion increases, under the BAU scenario. On the other hand, in the PB scenario, relatively better conditions can be observed for all three ESs. Considering that the ecological structures of this region have been severely affected by rapid urban expansion, the results of this research will be useful for maintaining the existing ESs and can greatly affect planning and decision-making regarding future development towards urban sustainability. Full article

(This article belongs to the Special Issue Applying Earth Observation Data for Urban Land-Use Change Mapping)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Applying Earth Observation Data for Urban Land-Use Change Mapping

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (10 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI