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Advances of Remote Sensing in Land Cover and Land Use Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 31281

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Prof. Dr. Sébastien Gadal

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CNRS ESPACE UMR 7300, Aix-Marseille University, Technopôle de l’Environnement Arbois Méditerranée, Avenue Louis Philibert, Bâtiment Laennec Hall C, BP 80, CEDEX 04, 13545 Aix-en-Provence, France
Interests: remote sensing; land use/land cover modelling; geo-simulation; artificial intelligence; urban dynamics; GIS

Prof. Dr. Gintautas Mozgeris

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Guest Editor

Agriculture Academy, Faculty of Forest Sciences and Ecology, Department of Forest Sciences, Vytautas Magnus University, Studentų Str. 11, LT-53361 Akademija, Kaunas Region, Lithuania
Interests: geomatics; forest management; environmental policy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Remote Sensing is focused on the latest advances in the mapping of LULC. This Issue intends to provide an overview of the progress made in the methodologies of mapping LULC applications, approaches, and methods. Due to climate variability, territorial dynamics, and societal changes, efficient planning strategies cannot be developed without considering processes related to land cover and land use changes. This requires solutions that support the observation, mapping, monitoring, analysis, and modelling of land-related activities. Remote sensing applications for exploring land covers and land uses have experienced notable progress with the development of current approaches, especially advances in artificial intelligence, the proliferation of remote sensing sensors, and the availability of space and airborne data with ancillary databases and massive geodata processing.

The aim of this Special Issue related to the mapping of land covers and land uses by remote sensing is to review the latest methods and to increase the methodological soundness: that is, the consistency, comparability, accuracy, and transparency of assessing, monitoring, and predicting land uses, land covers and their changes using spatial analysis, artificial intelligence, and remote sensing techniques. New advances in the spatial modelling of territorial patterns and their dynamics and evolutions by remote sensing introduce new conceptual questions related to the actual environmental and territorial processes: the mapping of multi-scalars’ dynamics as well as different land use and land cover temporalities and changes.

Articles should focus on new methodologies for mapping land use patterns, land covers, their evolution, and dynamics based on successful operational examples from geography, environmental sciences, climate change mitigation, spatial planning and other studies with land use and land cover information being an important factor. Studies must be based on methodological innovations.

Prof. Dr. Sébastien Gadal
Prof. Dr. Gintautas Mozgeris
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. Remote Sensing is an international peer-reviewed open access semimonthly 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 2700 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

LULC
multi-scalar LULC change modelling
multi-temporal LULC change modelling
artificial intelligence
big data
cartography
geosimulation

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Published Papers (9 papers)

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Research

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17 pages, 2422 KiB

Open AccessArticle

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

by Congcong Li, George Xian and Suming Jin

Remote Sens. 2024, 16(19), 3717; https://doi.org/10.3390/rs16193717 - 6 Oct 2024

Viewed by 935

Abstract

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data. Full article

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

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25 pages, 5632 KiB

Open AccessArticle

Predicting the Impacts of Land Use/Cover and Climate Changes on Water and Sediment Flows in the Megech Watershed, Upper Blue Nile Basin

by Mulugeta Admas, Assefa M. Melesse and Getachew Tegegne

Remote Sens. 2024, 16(13), 2385; https://doi.org/10.3390/rs16132385 - 28 Jun 2024

Cited by 1 | Viewed by 1106

Abstract

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes’ impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios. Full article

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

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23 pages, 18551 KiB

Open AccessArticle

Evaluating Machine-Learning Algorithms for Mapping LULC of the uMngeni Catchment Area, KwaZulu-Natal

by Orlando Bhungeni, Ashadevi Ramjatan and Michael Gebreslasie

Remote Sens. 2024, 16(12), 2219; https://doi.org/10.3390/rs16122219 - 19 Jun 2024

Cited by 1 | Viewed by 1287

Abstract

Analysis of land use/land cover (LULC) in catchment areas is the first action toward safeguarding freshwater resources. LULC information in the watershed has gained popularity in the natural science field as it helps water resource managers and environmental health specialists develop natural resource conservation strategies based on available quantitative information. Thus, remote sensing is the cornerstone in addressing environmental-related issues at the catchment level. In this study, the performance of four machine learning algorithms (MLAs), namely Random Forests (RFs), Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), and Naïve Bayes (NB), were investigated to classify the catchment into nine relevant classes of the undulating watershed landscape using Landsat 8 Operational Land Imager (L8-OLI) imagery. The assessment of the MLAs was based on a visual inspection of the analyst and commonly used assessment metrics, such as user’s accuracy (UA), producers’ accuracy (PA), overall accuracy (OA), and the kappa coefficient. The MLAs produced good results, where RF (OA = 97.02%, Kappa = 0.96), SVM (OA = 89.74%, Kappa = 0.88), ANN (OA = 87%, Kappa = 0.86), and NB (OA = 68.64%, Kappa = 0.58). The results show the outstanding performance of the RF model over SVM and ANN with a significant margin. While NB yielded satisfactory results, its sensitivity to limited training samples could primarily influence these results. In contrast, the robust performance of RF could be due to an ability to classify high-dimensional data with limited training data. Full article

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

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26 pages, 16090 KiB

Open AccessArticle

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

by Salman A. H. Selmy, Dmitry E. Kucher, Gintautas Mozgeris, Ali R. A. Moursy, Raimundo Jimenez-Ballesta, Olga D. Kucher, Mohamed E. Fadl and Abdel-rahman A. Mustafa

Remote Sens. 2023, 15(23), 5522; https://doi.org/10.3390/rs15235522 - 27 Nov 2023

Cited by 13 | Viewed by 5788

Abstract

Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management. Full article

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

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28 pages, 15631 KiB

Open AccessArticle

Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology

by Mengmeng Sun, Adu Gong, Xiang Zhao, Naijing Liu, Longping Si and Siqing Zhao

Remote Sens. 2023, 15(13), 3353; https://doi.org/10.3390/rs15133353 - 30 Jun 2023

Cited by 6 | Viewed by 2303

Abstract

The normalized difference vegetation index (NDVI) is one of the most common metrics used to describe vegetation dynamics. Unfortunately, low-quality pixels resulting from contamination (by features including clouds, snow, aerosols, and mixed factors) have impeded NDVI products’ widespread application. Researchers have thought of several ways to improve NDVI quality when contamination occurs. However, most of these algorithms are based on the noise-negative deviation principle, which aligns low-value NDVI products to an upper line but ignores cases where absolute surface values are low. Consequently, to fill in these research gaps, in this article, we use the random forest model to produce a set of high-quality NDVI products to represent actual surface characteristics more accurately and naturally. Climate and geographical products are used as model inputs to describe environmental factors. They represent the random forest (RF) model that establishes relationships between MODIS NDVI products and meteorological products in high-quality areas. In addition, auxiliary data and empirical knowledge are employed to meet filling requirements. Notably, the random forest (RF) algorithm exhibits a mean absolute error (MAE) of 0.024 and a root mean squared error (RMSE) of 0.034, in addition to a coefficient of determination (R²) value of 0.974. Furthermore, the MAE and RMSE of the RF-based method decreased by 0.014 and 0.019, respectively, when compared to those of the STSG (spatial–temporal Savitzky–Golay) plan and by 0.013 and 0.015, respectively, when compared to the LSTM (long short-term memory) method. R² increased by 0.039 and 0.027, respectively, compared to the STSG and LSTM methods. We introduced a novel series of NDVI products that demonstrated consistent spatial and temporal connectivity. The novel product exhibits enhanced adaptability to intricate environmental conditions and promises the potential for utilization in investigating vegetation dynamics within the Chinese region. Full article

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

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22 pages, 18725 KiB

Open AccessArticle

A High-Performance Automated Large-Area Land Cover Mapping Framework

by Jiarui Zhang, Zhiyi Fu, Yilin Zhu, Bin Wang, Keran Sun and Feng Zhang

Remote Sens. 2023, 15(12), 3143; https://doi.org/10.3390/rs15123143 - 16 Jun 2023

Cited by 1 | Viewed by 1706

Abstract

Land cover mapping plays a pivotal role in global resource monitoring, sustainable development research, and effective management. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, thereby bringing challenges to creating multi-timesteps large-area products for monitoring dynamic land cover. Therefore, improving the efficiency of each stage in land cover mapping and automating the mapping process is currently an urgent issue to be addressed. This study proposes a high-performance automated large-area land cover mapping framework (HALF). By leveraging Docker and workflow technologies, the HALF effectively tackles model heterogeneity in complex land cover mapping processes, thereby simplifying model deployment and achieving a high degree of decoupling between production models. It optimizes key processes by incorporating high-performance computing techniques. To validate these methods, this study utilized Landsat imagery data and extracted samples using GLC_FCS and FROM_GLC, all of which were acquired at a spatial resolution of 30 m. Several 10° × 10° regions were chosen globally to illustrate the viability of generating large-area land cover using the HALF. In the sample collection phase, the HALF introduced an automated method for generating samples, which overlayed multiple prior products to generate a substantial number of samples, thus saving valuable manpower resources. Additionally, the HALF utilized high-performance computing technology to enhance the efficiency of the sample–image matching phase, thereby achieving a speed that was ten times faster than traditional matching methods. In the mapping stage, the HALF employed adaptive classification models to train the data in each region separately. Moreover, to address the challenge of handling a large number of classification results in a large area, the HALF utilized a parallel mosaicking method for classification results based on the concept of grid division, and the average processing time for a single image was approximately 6.5 s. Full article

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

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25 pages, 9158 KiB

Open AccessArticle

Spatiotemporal Analysis of Rainfall and Droughts in a Semiarid Basin of Brazil: Land Use and Land Cover Dynamics

by Lizandra de Barros de Sousa, Abelardo Antônio de Assunção Montenegro, Marcos Vinícius da Silva, Thayná Alice Brito Almeida, Ailton Alves de Carvalho, Thieres George Freire da Silva and João Luis Mendes Pedroso de Lima

Remote Sens. 2023, 15(10), 2550; https://doi.org/10.3390/rs15102550 - 12 May 2023

Cited by 8 | Viewed by 2495

Abstract

Precipitation estimation is a challenging task, especially in regions where its spatial distribution is irregular and highly variable. This study evaluated the spatial distribution of annual rainfall in a semiarid Brazilian basin under different regimes and its impact on land use and land cover dynamics. Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) records and observed data from 40 weather stations over a time series of 55 years were used, in addition to the Standardized Precipitation Index. Spatiotemporal analysis was carried out based on geostatistics. Remote sensing images were also interpreted for different rainfall regimes using the Normalized Difference Vegetation Index and Modified Normalized Difference Water Index. The Gaussian semivariogram model best represented the rainfall spatial structure, showing strong spatial dependence. Results indicated that rainfall amount in the basin significantly increases with elevation, as expected. There is high variation in the dynamics of water storage that can threaten water security in the region. Our findings point out that the application of geostatistics for mapping both the annual precipitation and the Standardized Precipitation Index provides a powerful framework to support hydrological analysis, as well as land use and land cover management in semiarid regions. Full article

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

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24 pages, 9358 KiB

Open AccessArticle

Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia

by Paulos Lukas, Assefa M. Melesse and Tadesse Tujuba Kenea

Remote Sens. 2023, 15(4), 1148; https://doi.org/10.3390/rs15041148 - 20 Feb 2023

Cited by 33 | Viewed by 11225

Abstract

Land use/land cover change evaluation and prediction using spatiotemporal data are crucial for environmental monitoring and better planning and management of land use. The main objective of this study is to evaluate land use/land cover changes for the time period of 1991–2022 and predict future changes using the CA-ANN model in the Upper Omo–Gibe River basin. Landsat-5 TM for 1991, 1997, and 2004, Landsat-7 ETM+ for 2010, and Landsat-8 (OLI) for 2016 and 2022 were downloaded from the USGS Earth Explorer Data Center. A random forest machine learning algorithm was employed for LULC classification. The LULC classification result was evaluated using an accuracy assessment technique to assure the correctness of the classification method employing the kappa coefficient. Kappa coefficient values of the classification indicate that there was strong agreement between the classified and reference data. Using the MOLUSCE plugin of QGIS and the CA-ANN model, future LULC changes were predicted. Artificial neural network (ANN) and cellular automata (CA) machine learning methods were made available for LULC change modeling and prediction via the QGIS MOLUSCE plugin. Transition potential modeling was computed, and future LULC changes were predicted using the CA-ANN model. An overall accuracy of 86.53% and an overall kappa value of 0.82 were obtained by comparing the actual data of 2022 with the simulated LULC data from the same year. The study findings revealed that between 2022 and 2037, agricultural land (63.09%) and shrubland (5.74%) showed significant increases, and forest (−48.10%) and grassland (−0.31%) decreased. From 2037 to 2052, the built-up area (2.99%) showed a significant increase, and forest and agricultural land (−2.55%) showed a significant decrease. From 2052 to 2067, the projected LULC simulation result showed that agricultural land (3.15%) and built-up area (0.32%) increased, and forest (−1.59%) and shrubland (−0.56%) showed significant decreases. According to the study’s findings, the main drivers of LULC changes are the expansion of built-up areas and agricultural land, which calls for a thorough investigation using additional data and models to give planners and policymakers clear information on LULC changes and their environmental effects. Full article

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

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Review

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28 pages, 5447 KiB

Open AccessReview

A Systematic Literature Review and Bibliometric Analysis of Semantic Segmentation Models in Land Cover Mapping

by Segun Ajibola and Pedro Cabral

Remote Sens. 2024, 16(12), 2222; https://doi.org/10.3390/rs16122222 - 19 Jun 2024

Cited by 3 | Viewed by 2396

Abstract

Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, and ISPRS Journal Of Photogrammetry And Remote Sensing. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by the USA (11.76%), France (5.88%), Spain (4%), and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most used data sources. Benchmark datasets such as ISPRS Vaihingen and Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder–decoder and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions. Full article

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

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