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Land
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Integrating Remote Sensing and Geospatial Big Data for Land Use...
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Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring

A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".

Deadline for manuscript submissions: closed (17 November 2023) | Viewed by 14247

Special Issue Editors

Dr. Myroslava Lesiv

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Guest Editor
Novel Data Ecosystems for Sustainability Research Group (NoDES), Advancing Systems Analysis (ASA) Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361 Laxenburg, Austria
Interests: validation of land cover and lands use products, including change; collection and quality assessment of reference data on land cover/land use; crowdsourcing; land use/land cover mapping; spatial data integration; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Linda See

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: citizen science, crowdsourcing and volunteered geographic information (data collection, quality assessment, creating added value products with VGI, motivation and engagement, etc.); land cover/land use validation; creation of hybrid land cover products; serious gaming; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals
Dr. Dmitry Schepaschenko

E-Mail Website
Guest Editor
Agriculture, Forestry, and Ecosystem Services (AFE) Research Group, Biodiversity and Natural Resources (BNR) Program, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, 2361 Laxenburg, Austria
Interests: boreal forests; soil carbon; biomass; land use land cover mapping; biomass remote sensing; forest growth
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last decade, there has been an explosion of data, both from remote sensing and other sources of geospatial data (e.g., citizen science, low-cost sensors, mobile phones), which can benefit the mapping and monitoring of land cover and land use. The opening up of the Landsat archive, the spatial and temporal richness of data now available from Sentinel satellites, and the proliferation of small satellites photographing the Earth provide new opportunities for characterizing the land surface, particularly in relation to land use. By integrating remote sensing with other sources of big geospatial data and machine learning/data fusion, we can create new data sets on land use, e.g., land use management intensity (Dou et al., 2021), forest management (Lesiv et al., in review), and drivers of tropical deforestation (Laso Bayas et al., in review), all of which fill significant gaps in land use information.  

This Special Issue aims to bring together state-of-the-art research in this field. We invite papers on methods and applications that integrate remote sensing with geospatial big data in mapping and monitoring land use, including change detection.

Dr. Myroslava Lesiv
Dr. Linda See
Dr. Dmitry Schepaschenko
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.

Related Special Issue

Published Papers (9 papers)

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23 pages, 1181 KiB  
Article
Extracting Features from Satellite Imagery to Understand the Size and Scale of Housing Sub-Markets in Madrid
by Gladys Elizabeth Kenyon, Dani Arribas-Bel and Caitlin Robinson
Land 2024, 13(5), 575; https://doi.org/10.3390/land13050575 (registering DOI) - 26 Apr 2024
Viewed by 70
Abstract
The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to [...] Read more.
The following paper proposes a novel machine learning approach to the segmentation of urban housing markets. We extract features from globally available satellite imagery using an unsupervised machine learning model called MOSAIKS, and apply a k-means clustering algorithm to the extracted features to identify sub-markets at multiple intra-urban scales within a case study of Madrid (Spain). To systematically explore scale effects on the resulting clusters, the analysis is repeated with varying sizes of satellite image patches. We assess the resulting clusters across scales using several internal cluster-evaluation metrics. Additionally, we use data from online listings portal Idealista to measure the homogeneity of housing prices within the clusters, to understand how well sub-markets can be differentiated by the image features. This paper evaluates the strengths and weakness of the method to identify urban housing sub-markets, a task which is important for planners and policy makers and is often limited by a lack of data. We conclude that the approach seems useful to divide large urban housing markets according to different attributes and scales. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
20 pages, 8342 KiB  
Article
Exploring Switzerland’s Land Cover Change Dynamics Using a National Statistical Survey
by Isabel Nicholson Thomas and Gregory Giuliani
Land 2023, 12(7), 1386; https://doi.org/10.3390/land12071386 - 11 Jul 2023
Cited by 3 | Viewed by 1290
Abstract
Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, [...] Read more.
Timely and reliable Land Use and Cover change information is crucial to efficiently mitigate the negative impact of environmental changes. Switzerland has the ambitious objective of being a sustainable country while remaining an attractive business location with a high level of well-being. However, this aspiration is hampered by increasing pressures that are significantly impacting the environment and putting serious demands on land. In the present study, we used the national Land Cover (LC) dataset, named ArealStatistik, produced by the Federal Statistical Office, to explore the spatiotemporal patterns of Land Cover in Switzerland, providing a comprehensive assessment of land cover change at the national scale. Results indicate that, in general, Switzerland has undergone small, spatially dispersed, dynamic, and gradual change trends, with high rates of transition between low growing Brush Vegetation and forest LC classes in recent years. These pixel-level trends are more important in the lower altitude plateau and Jura regions, while greater changes in the spatial configuration of LC are observed in the alpine regions. However, findings also suggest that identifying drivers and understanding the rate of change are limited by the spatial resolution and temporal update frequency of the ArealStatistik. The ability to understand these drivers would benefit from a high-resolution annual LC dataset. Such a data product can be produced using the ArealStatistik together with dense satellite data time-series and Machine/Deep Learning techniques. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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26 pages, 55175 KiB  
Article
Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example
by Yimin Li, Xue Yang, Bowen Wu, Juanzhen Zhao and Xuanlun Deng
Land 2023, 12(6), 1210; https://doi.org/10.3390/land12061210 - 10 Jun 2023
Viewed by 949
Abstract
Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from [...] Read more.
Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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18 pages, 4903 KiB  
Article
Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping
by Kang Zheng, Huiyi Zhang, Haiying Wang, Fen Qin, Zhe Wang and Jinyi Zhao
Land 2022, 11(12), 2209; https://doi.org/10.3390/land11122209 - 05 Dec 2022
Viewed by 1488
Abstract
High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample “voting mechanism” based on multisource data and random forests to achieve [...] Read more.
High-quality urban land-use maps are essential for grasping the dynamics and scale of urban land use, predicting future environmental trends and changes, and allocating national land resources. This paper proposes a multisample “voting mechanism” based on multisource data and random forests to achieve fine mapping of urban land use. First, Zhengzhou City was selected as the study area. Based on full integration of multisource features, random forests were used to perform the preliminary classification of multiple samples. Finally, the preliminary classification results were filtered according to the “voting mechanism” to achieve high-precision urban land-use classification mapping. The results showed that the overall classification accuracy of Level I features increased by 5.66% and 14.32% and that the overall classification accuracy of Level II features increased by 9.02% and 12.46%, respectively, compared with the classification results of other strategies. Therefore, this method can significantly reduce the influence of mixed distribution of land types and improve the accuracy of urban land-use classification at a fine scale. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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19 pages, 4170 KiB  
Article
The Relative Timing of Population Growth and Land Use Change—A Case Study of North Taiwan from 1990 to 2015
by Hsiao-Chien Shih, Douglas A. Stow, John R. Weeks, Konstadinos G. Goulias and Leila M. V. Carvalho
Land 2022, 11(12), 2204; https://doi.org/10.3390/land11122204 - 05 Dec 2022
Viewed by 1864
Abstract
Urban expansion is a form of land cover and land use change (LCLUC) that occurs globally, and population growth can be a driver of and be driven by LCLUC. Determining the cause–effect relationship is challenging because the temporal resolution of population data is [...] Read more.
Urban expansion is a form of land cover and land use change (LCLUC) that occurs globally, and population growth can be a driver of and be driven by LCLUC. Determining the cause–effect relationship is challenging because the temporal resolution of population data is limited by decadal censuses for most countries. The purpose of this study is to explore the relationship and relative timing between population change and land use change based on a case study of northern Taiwan from 1990 to 2015. A unique dataset on population was acquired from annually-updated governmental-based population registers maintained at the district level, and land-use expansion data (Residential, Employment, and Transportation Corridor categories) were derived from dense time series of Landsat imagery. Linear regression was applied to understand the general relationship between population and land use and their changes. The strongest relationships were found between population and areal extent of Residential land use, and between population change and Residential areal change. Lagged correlation analysis was implemented for identifying the time lag between population growth and land use change. Most districts exhibited Residential and Employment expansion prior to population growth, especially for districts in the periphery of metropolitan areas. Conversely, the core of metropolitan areas exhibited population growth prior to Residential and Employment expansion. Residential and Employment expansion were deemed to be drivers of population change, so population change was modeled with ordinary least square and geographically weighted regression with Residential and Employment expansion in both synchronized and time lag manners. Estimated population growth was found to be the most accurate when geographic differences and time lags from urban land use expansion were both incorporated. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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15 pages, 722 KiB  
Communication
Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series
by Babak Ghassemi, Markus Immitzer, Clement Atzberger and Francesco Vuolo
Land 2022, 11(9), 1397; https://doi.org/10.3390/land11091397 - 25 Aug 2022
Cited by 3 | Viewed by 1631
Abstract
This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States [...] Read more.
This investigation evaluates the potential of combining Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) satellite data in producing a detailed Land Use and Land Cover (LULC) map with 19 crop type classes and 2 broader categories containing Woodland/Shrubland and Grassland over 28 Member States of Europe (EU-28). The Eurostat Land Use and Coverage Area Frame Survey (LUCAS) 2018 dataset is employed as ground truth for model training and validation. Monthly and yearly optical features from S2 spectral reflectance and spectral indices, alongside decadal (10-days) composites from an S1 microwave sensor, are extracted for the EU-28 territory for 2018 using Google Earth Engine (GEE). Five different feature sets using a mixture of indicators were created as input training data. A Random Forest (RF) machine learning algorithm was applied to classify these feature sets, and the generated classification models were compared using an identical validation dataset. Results show that S1 and S2 yearly features together are able to provide a full coverage map less dependent on cloud effects and having appropriate overall accuracy (OA). Based on this feature set, the 21 classes could be classified with an OA of 78.3% using the independent validation data set. The OA increases to 82.7% by grouping 21 classes into 8 broader categories. The comparison with similar studies using individual S1 and S2 data indicates that combining S1 and S2 time series can attain slightly better results while enhancing spatial coverage. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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20 pages, 2204 KiB  
Article
Adoption of Digital Aerial Photogrammetry in Forest Planning: A Case Study of Canavese Forestry Consortium, NW Italy with Technical and Economic Issues
by Enrico Borgogno-Mondino, Samuele De Petris, Filippo Sarvia, Evelyn Joan Momo, Fabio Sussio and Paolo Pari
Land 2022, 11(8), 1350; https://doi.org/10.3390/land11081350 - 19 Aug 2022
Viewed by 1641
Abstract
The forestry sector has been significantly affected by the recent advances in geomatics. Photogrammetric aerial acquisitions provide full coverage of forests, making possible a wall-to-wall mapping of the main forestry key variables, such as tree height and tree density, needed for forest planning [...] Read more.
The forestry sector has been significantly affected by the recent advances in geomatics. Photogrammetric aerial acquisitions provide full coverage of forests, making possible a wall-to-wall mapping of the main forestry key variables, such as tree height and tree density, needed for forest planning and management purposes. This work summarizes the role that digital aerial photogrammetry (DAP) is expected to have in the forest planning context and analyzes the main products from DAP that are significantly required. According to their technical features, some strategies are proposed to program acquisitions in order to consciously set operational parameters needed for obtaining accuracy of measures compliant with forest planning requirements. Admitting that, in most cases, forest planners externalize DAP acquisitions to flying companies, the authors propose simple strategies for making an ex-post evaluation of flight conditions through a conscious processing of the external orientation parameters of images resulting after the bundle adjustment. Theoretical aspects are exemplified with reference to a practical case study relying on an aerial acquisition that, in 2019, the Canavese Forestry Consortium (NW Italy) made to support the ongoing redaction of its new forest plans. Finally, some economic concerns are presented to demonstrate the capability of this technique to absorb the most of costs associated with ground surveys, especially when large areas have to be investigated. In particular, about 66% of cost savings were found when comparing DAP-based forest mapping to traditional forest inventory strategies. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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20 pages, 4620 KiB  
Article
Combining Tabular and Satellite-Based Datasets to Better Understand Cropland Change
by Kenneth Lee Copenhaver
Land 2022, 11(5), 714; https://doi.org/10.3390/land11050714 - 10 May 2022
Cited by 1 | Viewed by 1977
Abstract
In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. [...] Read more.
In recent years, regulatory agencies in the USA and Europe have begun to require documentation that land used to produce crops and biofuels has not been converted from carbon-capturing grasslands or forests. Precise measurement of these land cover changes, however, has proven difficult. Analysis to date has focused primarily on moderate resolution (30 m) satellite imagery, which has not provided the land cover granularity or accuracy needed. These studies have estimated large-scale land conversion to crops in the USA. This study analyzed the satellite datasets but included tabular datasets and aerial imagery of the USA to determine whether the combination of datasets, focusing on more detailed analysis in these locations, could more accurately identify potential locations of land use change. Analyses of satellite imagery data from 1985 to 2020 found that much of the land that 2008 to 2020 satellite datasets classified as natural-to-crop land change was idle cropland. The results indicate a dynamic landscape of marginal land moving in and out of cropland. Approximately as much land was allowed to go fallow (6145 hectares) as land going into crop (7901 hectares) from 1985 to 2020. The results from this study indicate regulatory agencies could more accurately measure the impacts of conversion of natural lands to crop if long-term historical land cover/land use was also analyzed. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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13 pages, 1537 KiB  
Technical Note
RePlant Alfa: Integrating Google Earth Engine and R Coding to Support the Identification of Priority Areas for Ecological Restoration
by Narkis S. Morales, Ignacio C. Fernández, Leonardo P. Durán and Waldo A. Pérez-Martínez
Land 2023, 12(2), 303; https://doi.org/10.3390/land12020303 - 21 Jan 2023
Cited by 2 | Viewed by 1241
Abstract
Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers [...] Read more.
Land degradation and climate change are among the main threats to the sustainability of ecosystems worldwide. As a result, the restoration of degraded landscapes is essential to maintaining the functionality of ecosystems, especially those with greater social, economic, and environmental vulnerability. Nevertheless, policymakers are frequently challenged by deciding where to prioritize restoration actions, which usually includes dealing with multiple and complex needs under an always limited budget. If these decisions are not taken based on proper data and processes, restoration implementation can easily fail. In order to help decision-makers take informed decisions on where to implement restoration activities, we have developed a semiautomatic geospatial platform to prioritize areas for restoration activities based on ecological, social, and economic variables. This platform takes advantage of the potential to integrate R coding, Google Earth Engine cloud computing, and GIS visualization services to generate an interactive geospatial decision-maker tool for restoration. Here, we present a prototype version called “RePlant alpha”, which was tested with data from the Central Zone of Chile. This exercise proved that integrating R and GEE was feasible, and that the analysis with at least six indicators for a specific region was also feasible to implement even from a personal computer. Therefore, the use of a virtual machine in the cloud with a large number of indicators over large areas is both possible and practical. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Geospatial Big Data for Land Use Mapping and Monitoring)
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