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Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing

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

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 25032

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Dr. Wojciech Drzewiecki

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Guest Editor

Department of Photogrammetry, Remote Sensing of Environment and Spatial Engineering, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology, Krakow, Poland
Interests: image processing; image classification; image analysis; machine learning; predictive models; land-use and land cover change monitoring; geoinformation; spatial analysis
Special Issues, Collections and Topics in MDPI journals

Dr. Stanisław Lewiński

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Guest Editor

Head of Earth Observation Department, Space Research Centre of Polish Academy of Sciences (CBK PAN), Bartycka 18A, 00-716 Warszawa, Poland
Interests: image processing and GIS; land cover and land use classification using object-oriented and pixel-based approaches; change detection
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To support sustainable development policies, decision-makers need accurate and reliable information on land cover and land use patterns. Monitoring of their changes is needed to evaluate the results of decisions taken. Land cover (or land use) is a crucial factor in different applications, e.g., in precision agriculture problems, watershed management, or climate change mitigation. However, land cover and land use (LCLU) systems may be defined differently for different scale levels, from the scale of the field or farm, through watershed, county or country, to the continent or even globe.

Remote sensing offers a wide range of technologies and tools to satisfy the needs of land cover and land use mapping at an appropriate scale. Because of its repeat-pass possibilities, it is also an ideal source of data for land cover and land use systems monitoring and change detection. Depending on the needs, one can select the platform from UAV to satellites as well as choose from different types of sensors (optical, radar, thermal, multispectral, or hyperspectral). Multi-platform and multi-sensor approaches are also possible. The range of tools for data processing and analysis is also wide: from classical hard classification algorithms at pixel or object level, through soft classification approaches to subpixel or superpixel analysis. Dedicated methods to analyse multitemporal data or time-series of remote sensing images are available as well. The role of machine learning and deep learning algorithms in land cover and land use mapping and monitoring is also growing increasingly.

This Special Issue calls for submissions dedicated to LCLU mapping and monitoring LCLU, mainly on a local and country scale. Papers focused on applications, where information about LCLU change detection plays a crucial role are also welcome.

Dr. Wojciech Drzewiecki
Dr. Stanisław Lewiński
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

Land-use and land cover systems
LULC monitoring
UAV
Aerial remote sensing
Satellite remote sensing
Change detection

Published Papers (10 papers)

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Research

25 pages, 13670 KiB

Open AccessArticle

Evaluation of High-Resolution Land Cover Geographical Data for the WRF Model Simulations

by Jolanta Siewert and Krzysztof Kroszczynski

Remote Sens. 2023, 15(9), 2389; https://doi.org/10.3390/rs15092389 - 02 May 2023

Viewed by 2144

Abstract

Increased computing power has made it possible to run simulations of the Weather Research and Forecasting (WRF) numerical model in high spatial resolution. However, running high-resolution simulations requires a higher-detail mapping of landforms, land use, and land cover. Often, higher-resolution data have limited coverage or availability. This paper presents the feasibility of using CORINE Land Cover (CLC) land use and land cover data and alternative high-resolution global coverage land use/land cover (LULC) data from Copernicus Global Land Service Land Cover Map (CGLS-LC100) V2.0 in high-resolution WRF simulations (100 × 100 m). Global LULC data with a resolution of 100 m are particularly relevant for areas not covered by CLC. This paper presents the method developed by the authors for reclassifying land cover data from CGLS-LC100 to MODIS land use classes with defined parameters in the WRF model and describes the procedure for their implementation into the model. The obtained simulation results of the basic meteorological parameters from the WRF simulation using CLC, CGLS-LC100 and default geographical data from MODIS were compared to observations from 13 meteorological stations in the Warsaw area. The research has indicated noticeable changes in the forecasts of temperature, relative humidity wind speed, and direction after using higher-resolution LULC data. The verification results show a significant difference in weather predictions in terms of CLC and CGLS-LC100 LULC data implementation. Due to the fact that better results were obtained for CLC simulations than for CGLS-LC100, it is suggested that CLC data are first used for simulations in numerical weather prediction models and to use CGLS-LC100 data when the area is outside of CLC coverage. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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31 pages, 85006 KiB

Open AccessArticle

Spatial Multi-Criteria Analysis of Water-Covered Areas: District City of Katowice—Case Study

by Natalia Janczewska, Magdalena Matysik, Damian Absalon and Łukasz Pieron

Remote Sens. 2023, 15(9), 2356; https://doi.org/10.3390/rs15092356 - 29 Apr 2023

Cited by 3 | Viewed by 1184

Abstract

The following databases contains information on land use with water in Poland: Corine Land Cover (CLC), the Urban Atlas (UA); Database of Topographic Objects (BDOT) the digital Map of Poland’s Hydrographic Division (MPHP); and the Register of Lands and Buildings (EGiB). All these data are referenced in scientific analyses and the Polish water management system, so the results of their processing should be the same (or at least similar); if not, output materials will be inconsistent and unreliable. In the Katowice sample, we checked the quality of this data using multi-criteria analyses, which is based on a grid of equal-area hexagons. Additionally, we applied the Normalized Difference Water Index to check real-time water presence. We detected discrepancies between all the data. The CLC does not reference any flowing water in Katowice. Most data overlapped between MPHP and BDOT, and both databases were similar to UA. However, a lot of uncertainty was also observed in the EGiB, which is considered to be the most accurate of the databases surveyed. In conclusion, we argue that water land cover data should be used with caution, and depending on the scales of analysis, that most actual data could be remote sensed data. We also include a diagram which can be useful in the data selection process. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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20 pages, 7682 KiB

Open AccessArticle

Monitoring Land Use/Land Cover and Landscape Pattern Changes at a Local Scale: A Case Study of Pyongyang, North Korea

by Yong Piao, Yi Xiao, Fengdi Ma, Sangjin Park, Dongkun Lee, Yongwon Mo, Seunggyu Jeong, Injae Hwang and Yujin Kim

Remote Sens. 2023, 15(6), 1592; https://doi.org/10.3390/rs15061592 - 15 Mar 2023

Cited by 2 | Viewed by 2298

Abstract

One method of understanding landscape pattern changes is through an understanding of land use/land cover (LULC) changes, which are closely related to landscape pattern changes. Previous studies have monitored LULC changes across North Korea but did not consider landscape changes at a local scale. Using multiple LULC products to construct sample points, the LULC was classified using a random-forest algorithm and Landsat satellite dataset. The overall accuracy of the classification was 97.66 ± 1.36%, and the Kappa coefficient was 0.95 ± 0.03. Based on the classification results, landscape indices were used to quantify and monitor landscape pattern changes. The results showed that, from 2000 to 2020, there was an increasing trend in built-up and forest areas in Pyongyang, while cropland showed a decreasing trend, and landscape fragmentation increased. However, urban expansion was not the main factor affecting fragmentation. The main factors were forest recovery and cropland reduction, leading to an increase in landscape fragmentation in Pyongyang. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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27 pages, 9100 KiB

Open AccessArticle

A Google Earth Engine-Based Framework to Identify Patterns and Drivers of Mariculture Dynamics in an Intensive Aquaculture Bay in China

by Peng Wang, Jian Wang, Xiaoxiang Liu and Jinliang Huang

Remote Sens. 2023, 15(3), 763; https://doi.org/10.3390/rs15030763 - 28 Jan 2023

Cited by 2 | Viewed by 1804

Abstract

Although mariculture contributes significantly to regional/local economic development, it also promotes environmental degradation. Therefore, it is essential to understand mariculture dynamics before taking adaptive measures to deal with it. In the present study, a framework that integrates the Google Earth Engine (GEE) based methods and GeoDetector software was developed to identify patterns and drivers of mariculture dynamics. This framework was then applied to Zhao’an Bay, which is an intensive aquaculture bay in Coastal China, based on Landsat 8 OLI (2013–2022) and Sentinel-2 (December 2015–May 2022) data. The results show that the GEE-based method produces acceptable classification accuracy. The overall accuracy values for the interpretation are >85%, where the kappa coefficients are >0.9 for all years, excluding 2015 (0.83). Mariculture increased in the study area from 2013 to 2022, and this is characterised by distinct spatiotemporal variations. Cage mariculture is primarily concentrated around islands, whereas raft mariculture is dominant in bay areas, and pond and mudflat mariculture types are mostly in nearshore areas. The growth of mariculture in Zhao’an Bay is attributed to a combination of geographic and human factors. The initial area associated with mariculture in a grid significantly impacted the expansion of the raft, cage, and mudflat mariculture. The distance to an island, spatial proximity to similar types of mariculture and types of mariculture are the main drivers of change in mariculture. Human activities greatly contribute to the dynamics of mudflat mariculture; regulation regarding the clearing of waterways directly impacts the dynamics of mariculture. The present study demonstrates that the proposed framework facilitates the effective monitoring of the mariculture dynamics and identification of driving factors. These findings can be exploited for the local planning and management of mariculture in similar coastal bays. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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25 pages, 9703 KiB

Open AccessArticle

Spatiotemporal Land Use/Land Cover Mapping and Prediction Based on Hybrid Modeling Approach: A Case Study of Kano Metropolis, Nigeria (2020–2050)

by Auwalu Faisal Koko, Zexu Han, Yue Wu, Ghali Abdullahi Abubakar and Muhammed Bello

Remote Sens. 2022, 14(23), 6083; https://doi.org/10.3390/rs14236083 - 30 Nov 2022

Cited by 12 | Viewed by 2974

Abstract

The change dynamics of land use/land cover (LULC) is a vital factor that significantly modifies the natural environment. Therefore, mapping and predicting spatiotemporal LULC transformation is crucial in effectively managing the built environment toward achieving Sustainable Development Goal 11, which seeks to make cities all-inclusive, sustainable, and reliable. The study aims to examine the change dynamics of LULC in Kano Metropolis, Nigeria from 1991 to 2020 and predict the city’s future land uses over the next 15 and 30 years, i.e., 2035 and 2050. The maximum likelihood algorithm (MLA) of the supervised classification method was utilized to classify the study area’s land uses using Landsat satellite data and various geographic information system (GIS) techniques. A hybrid simulation model comprising cellular automata and Markov chain (CA-Markov) was then employed in validating and modeling the change dynamics of future LULC. The model integrated the spatial continuity of the CA model with the Markov chain’s ability to address the limitations of individual models in simulating long-term land use prediction. The study revealed substantial changes in the historical LULC pattern of Kano metropolis from 1991 to 2020. It indicated a considerable decline in the city’s barren land from approximately 413.47 km² in 1991 to 240.89 km² in 2020. Built-up areas showed the most extensive development over the past 29 years, from about 66.16 km² in 1991 to 218.72 km² in 2020. This trend of rapid urban growth is expected to continue over the next three decades, with prediction results indicating the city’s built-up areas expanding to approximately 307.90 km² in 2035 and 364.88 km² in 2050. The result also suggests that barren lands are anticipated to decline further with the continuous sustenance of various agricultural activities, while vegetation and water bodies will slightly increase between 2020 and 2050. The findings of this study will help decision-makers and city administrators formulate sustainable land use policies for a more inclusive, safe, and resilient city. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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19 pages, 6417 KiB

Open AccessArticle

A Transferable Learning Classification Model and Carbon Sequestration Estimation of Crops in Farmland Ecosystem

by Lijun Wang, Yang Bai, Jiayao Wang, Fen Qin, Chun Liu, Zheng Zhou and Xiaohao Jiao

Remote Sens. 2022, 14(20), 5216; https://doi.org/10.3390/rs14205216 - 18 Oct 2022

Cited by 2 | Viewed by 3578

Abstract

Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F₁ values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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13 pages, 38076 KiB

Open AccessArticle

The Substantial Increase of Forest Cover in Central Poland Following Extensive Land Abandonment: Szydłowiec County Case Study

by Mahsa Shahbandeh, Dominik Kaim and Jacek Kozak

Remote Sens. 2022, 14(16), 3852; https://doi.org/10.3390/rs14163852 - 09 Aug 2022

Cited by 1 | Viewed by 2253

Abstract

Nowadays, Poland is one of the European countries most affected by agricultural land abandonment (ALA). Though considered to be a negative phenomenon, ALA opens up several options for planning future land uses critical for biodiversity conservation or future carbon sequestration. So far, many studies of ALA have been done in the mountainous areas in Poland, but less is known about the magnitude of ALA in other regions. In this paper we use the declassified CORONA satellite imagery (1969) to backdate the information on land cover and land abandonment from topographic maps from 1970s for the region located in central Poland and currently affected by widespread ALA. The information from archival materials is compared with current High-Resolution Layers and airborne laser scanning products, indicating that a forest cover increase of 23% was observed. The output of vegetation height analysis confirmed significant land use transformation from non-forest and ALA into forest area. Additionally, analysis of forest pattern change revealed that although forest core areas have increased since 1970, its share in total forest cover decreased due to newly established small forest patches. Our research shows the importance of archival remote sensing materials and indicates their role in understanding ALA-related forest cover change in Poland over the last 50 years. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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18 pages, 4951 KiB

Open AccessArticle

Linking Land Cover Change with Landscape Pattern Dynamics Induced by Damming in a Small Watershed

by Zheyu Xie, Jihui Liu, Jinliang Huang, Zilong Chen and Xixi Lu

Remote Sens. 2022, 14(15), 3580; https://doi.org/10.3390/rs14153580 - 26 Jul 2022

Cited by 3 | Viewed by 1639

Abstract

Cascade damming can shape land surfaces; however, little is known about the specific impacts of dam construction on watershed land cover changes. Therefore, we developed a framework in which remote sensing, transition patterns, and landscape metrics were coupled to measure the impact of dam construction on watershed land cover changes and landscape patterns in the Longmen–Su (L–S) Creek, a small headwater watershed in Southeast China. During the transition and post-impact periods of dam construction, the land cover in the L–S Creek watershed underwent dynamic changes within the affected area. Changes in land cover were dominated by a surge in water and buildup and a decrease in woodland and cropland areas; bareland also increased steadily during construction. Woodlands and croplands were mainly flooded into water areas, although some were converted to bareland and built-up areas owing to the combined impact of dam construction and urbanization. By linking land cover changes with landscape patterns, we found that land use changes in water were significantly associated with landscape fragmentation and heterogeneity in the impacted zone. Our research demonstrates how damming can change land cover locally and may provide a basis for sustainable land management within the context of the extensive development of cascade hydropower dams. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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23 pages, 3549 KiB

Open AccessArticle

Climate-Based Regionalization and Inclusion of Spectral Indices for Enhancing Transboundary Land-Use/Cover Classification Using Deep Learning and Machine Learning

by Blessing Kavhu, Zama Eric Mashimbye and Linda Luvuno

Remote Sens. 2021, 13(24), 5054; https://doi.org/10.3390/rs13245054 - 13 Dec 2021

Cited by 14 | Viewed by 2656

Abstract

Accurate land use and cover data are essential for effective land-use planning, hydrological modeling, and policy development. Since the Okavango Delta is a transboundary Ramsar site, managing natural resources within the Okavango Basin is undoubtedly a complex issue. It is often difficult to accurately map land use and cover using remote sensing in heterogeneous landscapes. This study investigates the combined value of climate-based regionalization and integration of spectral bands with spectral indices to enhance the accuracy of multi-temporal land use/cover classification using deep learning and machine learning approaches. Two experiments were set up, the first entailing the integration of spectral bands with spectral indices and the second involving the combined integration of spectral indices and climate-based regionalization based on Koppen–Geiger climate zones. Landsat 5 TM and Landsat 8 OLI images, machine learning classifiers (random forest and extreme gradient boosting), and deep learning (neural network and deep neural network) classifiers were used in this study. Supervised classification using a total of 5140 samples was conducted for the years 1996, 2004, 2013, and 2020. Average overall accuracy and Kappa coefficients were used to validate the results. The study found that the integration of spectral bands with indices improves the accuracy of land use/cover classification using machine learning and deep learning. Post-feature selection combinations yield higher accuracies in comparison to combinations of bands and indices. A combined integration of spectral indices with bands and climate-based regionalization did not significantly improve the accuracy of land use/cover classification consistently for all the classifiers (p < 0.05). However, post-feature selection combinations and climate-based regionalization significantly improved the accuracy for all classifiers investigated in this study. Findings of this study will improve the reliability of land use/cover monitoring in complex heterogeneous TDBs. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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23 pages, 3686 KiB

Open AccessArticle

Urban Growth Derived from Landsat Time Series Using Harmonic Analysis: A Case Study in South England with High Levels of Cloud Cover

by Matthew Nigel Lawton, Belén Martí-Cardona and Alex Hagen-Zanker

Remote Sens. 2021, 13(16), 3339; https://doi.org/10.3390/rs13163339 - 23 Aug 2021

Cited by 4 | Viewed by 2498

Abstract

Accurate detection of spatial patterns of urban growth is crucial to the analysis of urban growth processes. A common practice is to use post-classification change analysis, overlaying multiple independently derived land cover layers. This approach is problematic as propagation of classification errors can lead to overestimation of change by an order of magnitude. This paper contributes to the growing literature on change classification using pixel-based time series analysis. In particular, we have developed a method that identifies change in the urban fabric at the pixel level based on breaks in the seasonal and year-on-year trend of the normalised difference vegetation index (NDVI). The method is applied to a case study area in the south of England that is characterised by high levels of cloud cover. The study uses the Landsat data archive over the period 1984–2018. The performance of the method was assessed using 500 ground truth points. These points were randomly selected and manually assessed for change using high-resolution earth observation imagery. The method identifies pixels where a land cover change occurred with a user’s accuracy of change 45.3 ± 4.45% and outperforms a post-classification analysis of an otherwise more advanced land cover product, which achieved a user’s accuracy of 17.8 ± 3.42%. This method performs better where changes exhibit large differences in NDVI dynamics amongst land cover types, such as the transition from agricultural to suburban, and less so where small differences of NDVI are observed, such as changes in land cover within pixels that are densely built up already. The method proved relatively robust for outliers and missing data, for example, in the case of high levels of cloud cover, but does rely on a period of data availability before and after the change event. Future developments to improve the method are to incorporate spectral information other than NDVI and to consider multiple change events per pixel over the analysed period. Full article

(This article belongs to the Special Issue Local Scale Land Use and Land Cover Systems Monitoring Using Remote Sensing)

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