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Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation

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

Deadline for manuscript submissions: closed (15 January 2023) | Viewed by 20496

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Special Issue Editors

Dr. Lucie Kupková

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Guest Editor

Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University, Albertov 6, 12800 Prague, Czech Republic
Interests: remote sensing; land-use/land-cover change; landscape; laboratory and image spectroscopy; hyperspectral image data from UAV; remote sensing for nature conservation

Dr. Markéta Potůčková

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Guest Editor

Department of Applied Geoinformatics and Cartography, Charles University, Prague, Czech Republic
Interests: remote sensing; photogrammetry; LiDAR; image and point cloud analysis; laboratory and image spectroscopy

Special Issue Information

Dear Colleagues,

Historical and recent remote sensing technologies have been producing many types of data acquired by various sensors with different spatial coverage, ranging from the local to the global level. Depending on the applied technology, the obtained data vary significantly in time, spectral and spatial resolutions, and a wide range of methods for their preprocessing and analysis exist. Time series of remote sensing data can be used for various environmental analyses, and during the last decades, huge developments have also been recorded in the field of remote sensing for long-term and multitemporal land-use/land-cover change (LULCC) detection and evaluation. Thus, the aim of this Special Issue is to document the development of research activities in this field, with focus on (but not limited to) the following issues:

Recently developed general concepts and innovative methodological approaches for long-term and multitemporal LULCC evaluation using remote sensing;
Advances in the field of remote sensing data sources for long-term and multitemporal/multiseasonal LULC change detection and evaluation; comparison and/or a combination of data sources (data fusion, data harmonization, use of multiple data sources); integration of remote sensing with other types of data;
Case studies focused on long-term and multitemporal LULC change detection/evaluation using different remote sensing technologies (aerial, spaceborne, UAV, LiDAR, radar) on various scales (local, regional, continental, global), with various spectral resolutions (panchromatic, multispectral, hyperspectral) and time resolutions (long-term, multitemporal/multiseasonal);
Definition, detection, and reliability of the change in LULCC evaluation/research by means of remote sensing;
Accuracy of LULC change detection/evaluation based on remote sensing data; potential of dense time series (multitemporal/multiseasonal) of remote sensing data to increase the accuracy of change detection analysis;
Remote sensing datasets and products for LULC change evaluation: possibilities for their use, quality assessment, and intercomparison, problematic of used nomenclatures;
Applications of remote sensing in the field of LULC change evaluation for operational use and practice.

Dr. Lucie Kupková
Dr. Markéta Potůčková
Guest Editors

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Keywords

long-term land-use/land-cover change
multitemporal land use/land cover change
multiseasonal land use/land cover change
definition of change
change detection
reliability and accuracy of change detection
time series of remote sensing data
data fusion and data harmonization
time, spectral, and spatial resolution

Published Papers (7 papers)

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Research

20 pages, 7156 KiB

Open AccessArticle

Analysis of Spatial and Temporal Variability of Global Wetlands during the Last 20 Years Using GlobeLand30 Data

by Mengjuan Li, Peng Ti, Xiuli Zhu, Tao Xiong, Yuting Mei and Zhilin Li

Remote Sens. 2022, 14(21), 5553; https://doi.org/10.3390/rs14215553 - 03 Nov 2022

Cited by 2 | Viewed by 1586

Abstract

Knowing the distributions and changes in global wetlands and their conversion to other land cover types could facilitate our understanding of wetland development, causes of variations, and decision-making for restoration and protection. This study aimed to comprehensively analyze the changes in wetland distributions at global, continental, typical regional, and national scales and the conversions between wetlands and other land cover types in the last 20 years. This study used GlobeLand30 (GL30) data with a 30 m resolution for the years 2000, 2010, and 2020. The main findings of this study are as follows: (1) the area of wetlands continued to increase globally from 2000 to 2020, with a total increase of approximately 4%. Wetland changes from 2010 to 2020 were more significant than those from 2000 to 2010. The regions with significant wetland changes were mainly in the north middle- and high-latitude, and the equatorial middle- and low-latitude, and Oceania and North America were the continents with the highest increase and decrease, respectively; (2) the major conversion of wetlands was mainly natural land cover types, including forest, grassland, water, and tundra, and there were minor conversions due to human activities, including the conversion of wetlands to cropland (~4600 km²) and artificial land (~3400 km²); (3) from 2000 to 2020, the increase in global wetlands was uneven, while the decrease was nearly even at a national scale. Australia had the highest increase due to the conversions from grass, bare land, and water, and Canada had the highest decrease due to the conversion into tundra and forest. The analysis results could more comprehensively characterize the distributions and changes of global wetlands, which may provide basic information and knowledge for related research work and policymaking. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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18 pages, 2874 KiB

Open AccessArticle

Evaluation of Global Historical Cropland Datasets with Regional Historical Evidence and Remotely Sensed Satellite Data from the Xinjiang Area of China

by Meijiao Li, Fanneng He, Caishan Zhao and Fan Yang

Remote Sens. 2022, 14(17), 4226; https://doi.org/10.3390/rs14174226 - 27 Aug 2022

Viewed by 1277

Abstract

Global land use/cover change (LUCC) datasets are essential for quantitatively assessing the impacts of LUCC on global change, but many uncertainties in existing global datasets seriously hamper climate modeling. Evaluating the reliability of existing global LUCC datasets is a precondition for improved data quality. In this study, based on the regional historical document-based reconstructions, satellite-based data, and historical reclamation evidence for the Xinjiang area of China, the accuracy and rationality of cropland data for this area in the HYDE 3.2 and SAGE datasets were evaluated by utilizing comparative analysis regarding three aspects, namely the change tendency of the cropland area, the area of cropland, and the differences in spatial pattern. This study concluded that the amount of cropland in the Xinjiang area in the global and regional datasets shows both disparate trends and large differences in absolute values. Spatially, historical reclamation evidence indicated that agricultural cultivation in the Xinjiang area underwent expansion from south to north and from east to west over the past 300 years; however, the global datasets revealed that the cropland spatial patterns in the Xinjiang area in the historical period are similar to those in the current period. These differences are attributable to the uncertainties of the basic assumptions, per capita cropland area estimates, and reconstruction methods in the global datasets. The findings of the study highlight the necessity of regional studies on historical LUCC in the Xinjiang area. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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26 pages, 33857 KiB

Open AccessArticle

Identifying Land-Use Related Potential Disaster Risk Drivers in the Ayeyarwady Delta (Myanmar) during the Last 50 Years (1974–2021) Using a Hybrid Ensemble Learning Model

by Anissa Vogel, Katharina Seeger, Dominik Brill, Helmut Brückner, Khin Khin Soe, Nay Win Oo, Nilar Aung, Zin Nwe Myint and Frauke Kraas

Remote Sens. 2022, 14(15), 3568; https://doi.org/10.3390/rs14153568 - 25 Jul 2022

Cited by 2 | Viewed by 2048

Abstract

Land-use and land-cover change (LULCC) dynamics significantly impact deltas, which are among the world’s most valuable but also vulnerable habitats. Non-risk-oriented LULCCs can act as disaster risk drivers by increasing levels of exposure and vulnerability or by reducing capacity. Making thematically detailed long-term LULCC data available is crucial to improving understanding of those dynamics interlinked at different spatiotemporal scales. For the Ayeyarwady Delta, one of the least studied mega-deltas, such comprehensive information is still lacking. This study used 50 Landsat and Sentinel-1A images spanning five decades from 1974 to 2021 in five-year intervals. A hybrid ensemble model consisting of six machine-learning classifiers was employed to generate land-cover maps from the images, achieving accuracies of about 90%. The major identified potential risk-relevant LULCC dynamics include urban growth towards low-lying areas, mangrove deforestation, and the expansion of irrigated agricultural areas and cultivated aquatic surfaces. The novel area-wide LULCC products achieved through the analyses provide a basis to support future risk-sensitive development decisions and can be used for regionally adapted disaster risk management plans and models. Developed with freely available data and open-source software, they hold great potential to increase research activity in the Ayeyarwady Delta and will be shared upon request. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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22 pages, 9213 KiB

Open AccessArticle

Mapping Arable Land and Permanent Agriculture Extent and Change in Southern Greece Using the European Union LUCAS Survey and a 35-Year Landsat Time Series Analysis

by Aaron M. Sparks, Imen Bouhamed, Luigi Boschetti, Ioannis Z. Gitas and Chariton Kalaitzidis

Remote Sens. 2022, 14(14), 3369; https://doi.org/10.3390/rs14143369 - 13 Jul 2022

Cited by 3 | Viewed by 2673

Abstract

Agricultural land extent and change information is needed to assess food security, the effectiveness of land use policy, and both environmental and societal impacts. This information is especially valuable in biodiversity hotspots such as the Mediterranean region, where agricultural land expansion can result in detrimental effects such as soil erosion and the loss of native species. There has also been a growing concern that changing agricultural extent in fire-prone regions of the Mediterranean may increase fire risk due to accumulation of fuel in abandoned areas. In this study, we assessed the extent and change of agricultural land in Southern Greece from 1986 to 2020 using a combined European Land Use/Cover Area frame Survey (LUCAS) and Landsat time series approach. The LUCAS data and Landsat spectral-temporal metrics were used to train a random forest classifier, which was used to classify arable land and permanent agriculture (e.g., olive orchards, vineyards) at annual time steps. A post-processing step was taken to reduce spurious landcover class transitions using transition likelihoods and annual class membership likelihoods. A validation dataset consisting of 2666 samples, identified via a stratified random sampling approach and high-resolution imagery and time series analysis, were used to evaluate stable and change strata accuracies. Overall accuracies were greater than 70% and strata-specific accuracies were highly variable between stable and change strata. The results show that southern Greece has experienced a recent gain in arable land (~12,000 ha from ~2009–2020) and a much larger gain in permanent agriculture (>115,000 ha from ~1993–2020). Arable land loss mainly occurred from 1987 to ~2002 when extent decreased by 15,000 ha, of which 66% was abandoned. The semi-automated approach described in this paper provides a promising approach for monitoring agricultural land change and enabling assessments of agriculture policy effectiveness and environmental impacts. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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18 pages, 2258 KiB

Open AccessArticle

Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

by Yifeng Hou, Yaning Chen, Zhi Li, Yupeng Li, Fan Sun, Shuai Zhang, Chuan Wang and Meiqing Feng

Remote Sens. 2022, 14(12), 2797; https://doi.org/10.3390/rs14122797 - 10 Jun 2022

Cited by 15 | Viewed by 2342

Abstract

The Tarim River Basin is the largest inland river basin in China. It is located in an extremely arid region, where agriculture and animal husbandry are the main development industries. The recent rapid rise in population and land demand has intensified the competition for urban land use, making the water body ecosystem increasingly fragile. In light of these issues, it is important to comprehensively grasp regional land structure changes, improve the degree of land use, and reasonably allocate water resources to achieve the sustainable development of both the social economy and the ecological environment. This study uses the CA-Markov model, the PLUS model and the gray prediction model to simulate and validate land use/cover change (LUCC) in the Tarim River Basin, based on remote sensing data. The aim of this research is to discern the dynamic LUCC patterns and predict the evolution of future spatial and temporal patterns of land use. The study results show that grassland and barren land are currently the main land types in the Tarim River Basin. Furthermore, the significant expansion of cropland area and reduction in barren land area are the main characteristics of the changes during the study period (1992–2020), when about 1.60% of grassland and 1.36% of barren land converted to cropland. Over the next 10 years, we anticipate that land-use types in the basin will be dominated by changes in grassland and barren land, with an increasing trend in land area other than for cropland and barren land. Grassland will add 31,241.96 km², mainly in the Dina River and the lower parts of the Weigan-Kuqu, Kashgar, Kriya, and Qarqan rivers, while barren land will decline 2.77%, with significant decreases in the middle and lower reaches of the Tarim River Basin. The findings of this study will provide a solid scientific basis for future land resource planning. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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26 pages, 6161 KiB

Open AccessArticle

Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

by Shaker Ul Din and Hugo Wai Leung Mak

Remote Sens. 2021, 13(16), 3337; https://doi.org/10.3390/rs13163337 - 23 Aug 2021

Cited by 46 | Viewed by 6062

Abstract

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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23 pages, 17980 KiB

Open AccessArticle

Understanding Urban Expansion on the Tibetan Plateau over the Past Half Century Based on Remote Sensing: The Case of Xining City, China

by Xinhao Pan, Yihang Wang, Zhifeng Liu, Chunyang He, Haimeng Liu and Zhirong Chen

Remote Sens. 2021, 13(1), 46; https://doi.org/10.3390/rs13010046 - 24 Dec 2020

Cited by 15 | Viewed by 3010

Abstract

The Tibetan Plateau (TP) is an important area that affects global sustainable development. Quantifying spatiotemporal patterns of urbanization is crucial for maintaining the sustainability on the TP. This study took Xining City, the largest city on the TP, as an example to understand the urban expansion in this region in the past 50 years. We combined the high-resolution spy satellite data and China’s long-term urban land dataset (CULD) to quantify the urban expansion of Xining City. The object-oriented random forest classification was performed to extract urban land from spy satellite data in 1969, and the inter-annual correction was used to combine urban land information from 1969 to 2017. We found that the proposed approach can accurately quantify the urban expansion of Xining City over the past half century with an overall accuracy of 91% and a kappa coefficient of 0.86. Such high accuracy benefits from the fine resolution of spy satellite data and the consistency of CULD. We also found that Xining City experienced accelerated and fragmented urban sprawl to higher altitude areas, as a result of socioeconomic development and topographical limitations. The acceleration of urban expansion was more obvious, and the urban landscape fragmentation was more serious at high altitude areas. Such urban expansion encroached on cropland and grassland, and caused increased risks of landslides and other geological disasters. Therefore, Xining City urgently needs to promote the development of compact cities to control urban sprawl at higher altitude areas and provide a reference for improving urban sustainability across the TP. In this study, we analyzed the urban expansion of Xining city from 1969 to 2017, and provided a reliable way to understand the long-term spatiotemporal urbanization based on remote sensing, which has the potential for wide applications. In addition, the extracted urban information can help to improve the urban sustainability of Xining City and the entire TP. Full article

(This article belongs to the Special Issue Remote Sensing for Long-Term and Multitemporal Land Use/Land Cover Changes Evaluation)

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