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Remote Sensing for Land System Mapping and Monitoring
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Remote Sensing for Land System Mapping and Monitoring

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 15506

Special Issue Editors

Prof. Dr. Danfeng Sun

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: land system; desertification; heavy metals; land degradation; soil erosion; land-use management; environmental analysis; spatial analysis
Dr. Qiangqiang Sun

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100193, China
Interests: land degradation; land use change; vegetation mapping; environmental impact assessment; satellite image analysis
Prof. Dr. Wei Wei

E-Mail Website
Guest Editor
College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China
Interests: land use change; ecological system mapping and monitoring; remote sensing application
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land systems include the terrestrial result of human land use interactions with the natural environments (such as soil and surface cover). Understanding the status, response, and impact of different land systems (cropland, forest, rangeland, etc.) from perspectives on soil–cover interaction helps to mitigate and adapt to the changing environmental and socio-ecological context. Hence, a mapping, quantitative assessment, and long-term monitoring of the spatiotemporal structure and function of soil, land cover, and their interactions is a high-priority and urgently needed research hotspot for land system representation. Remote sensing techniques have advanced rapidly in the past decades, and such advances may provide us with knowledge about such demand for the characterization of land systems.

This issue is dedicated to newly developed approaches and corresponding applications to improve the observation of land systems from perspectives on soil–cover interactions and is related to the journal's scope of remote sensing applications, multi-spectral and hyperspectral remote sensing, change detection, image processing and pattern recognition, and so on.

Topics covering anything from the remotely sensed assessment and monitoring of various land systems using multisource data integration (e.g., multispectral, hyperspectral, and thermal), including various digital soil mapping, land cover classification, land/soil degradation and restoration, time series analysis, and other related subjects are welcome. Articles may address, but are not limited, to the following topics that focus on both remoted sensed algorithms and applications:

  • Land system mapping;
  • Digital soil mapping;
  • Long-term histories of land system dynamics and impacts;
  • Land system function assessment and management;
  • Urban–rural teleconnections quantification;
  • Modeling and forecasting land system change.

Prof. Dr. Danfeng Sun
Dr. Qiangqiang Sun
Prof. Dr. Wei Wei
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 cover
  • soil mapping
  • structural and functional land system
  • remote sensing mapping
  • time series analysis
  • assessment
  • simulated prediction
  • land use management

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Published Papers (5 papers)

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24 pages, 16481 KiB  
Article
An Integrated Approach for Analyzing the Morphological Evolution of the Lower Reaches of the Minjiang River Based on Long-Term Remote Sensing Data
by Nie Zhou, Sheng Sheng, Li-Ying He, Bing-Ru Tian, Hua Chen and Chong-Yu Xu
Remote Sens. 2023, 15(12), 3093; https://doi.org/10.3390/rs15123093 - 13 Jun 2023
Cited by 2 | Viewed by 2441
Abstract
Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes [...] Read more.
Understanding the evolution of river morphology is crucial for comprehending changes in water resources and implementing development projects along rivers. This study proposes an integrated approach utilizing remote sensing image data combined with deep learning and visual interpretation algorithms to analyze continuous-type changes in river morphology. This research focuses on the lower reaches of the Minjiang River in China and comprehensively analyzes the river’s morphological evolution from 1986 to 2021. The results show that the proposed method of river water identification in this study demonstrates high accuracy and effectiveness, with an F1 score and Kappa coefficient greater than 0.96 and 0.91, respectively. The morphology of the river channel remains stable in the upstream and estuarine sections of the study region while undergoing substantial alterations in the middle section. Additionally, this study also identifies several factors that significantly impact the evolution of river morphology, including reservoir construction, river sediment mining, river training measures, geological conditions, and large flood events. The findings of this study can provide some insights into the management and conservation of water resources. Full article
(This article belongs to the Special Issue Remote Sensing for Land System Mapping and Monitoring)
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18 pages, 9625 KiB  
Article
Spatiotemporal Evolution and Risk Analysis of Land Use in the Coastal Zone of the Yangtze River Delta Region of China
by Lingzhi Yin, Yafei Wang, Chenglai Sun and Yuxuan Ye
Remote Sens. 2023, 15(9), 2261; https://doi.org/10.3390/rs15092261 - 25 Apr 2023
Cited by 4 | Viewed by 1978
Abstract
The extensive accumulation of big data, along with the development of a high-performance platform, bridge the gap between the previous inability to provide long-term time series and broad-scale coastal zone monitoring and risk warnings with remote sensing techniques. Based on 20 years of [...] Read more.
The extensive accumulation of big data, along with the development of a high-performance platform, bridge the gap between the previous inability to provide long-term time series and broad-scale coastal zone monitoring and risk warnings with remote sensing techniques. Based on 20 years of Landsat images from the Google Earth Engine platform, the time series land cover in the coastal zone of the Yangtze River Delta in China was classified. Then, a spatiotemporal clustering method based on grid segmentation was proposed to analyze the spatiotemporal evolution details of artificial surface expansion and the risks of cropland loss and ecological degradation caused by this. The results showed that significant changes have taken place in the quantitative structure and spatial morphology of coastal land use in the past 20 years. The artificial surface maintained a growth trend, increasing by 229%, while cropland decreased by 19%. Natural land showed a fluctuation pattern of “up→down→up”. The spatiotemporal details of land use obtained through 1km grid segmentation and clustering analysis were more significant. The artificial surface mainly underwent a progressive spatial expansion along the central urban area and important transportation axes (types III and IV), with the most dramatic changes occurring from 2010 to 2013. Type III cropland loss was the most significant, falling from 75.02% in 2000 to 38.23% in 2020. At the same time, the change in type III water body corresponds to the newly increased area of reclamation, which has decreased by 17% in the past 20 years, indicating that the degradation of coastal natural wetlands was significant. This paper provided a comprehensive diagnosis of coastal land use change, which could help policy makers and implementers to propose more targeted and differentiated coastal development and protection policies. Full article
(This article belongs to the Special Issue Remote Sensing for Land System Mapping and Monitoring)
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16 pages, 5523 KiB  
Article
Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China
by Ting Wang, Wei Zhou, Jieyun Xiao, Haoran Li, Li Yao, Lijuan Xie and Keming Wang
Remote Sens. 2023, 15(8), 2118; https://doi.org/10.3390/rs15082118 - 17 Apr 2023
Cited by 15 | Viewed by 4217
Abstract
Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of [...] Read more.
Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of optical remote sensing data and environmental factors to predict SOC content, few studies have explored their applicability in karst areas. Therefore, it remains unclear how SOC content can be accurately simulated in these areas. In this study, 160 soil samples, 8 environmental covariates and 14 optical remote sensing variables were used to build SOC content prediction models. Three machine learning models, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were applied for each of three land use classes, including the entire study area, as well as farmland and forest areas. The variables with the greatest influence were the optical remote sensing bands, derived indices, as well as precipitation and temperature for forest areas, and optical remote sensing band11 and Pop-density for farmland. The results from this study suggest that RF and XGBoost are superior to SVM in prediction accuracy. Additionally, the simulation accuracy of the RF model for the forest areas (R2 = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R2 = 0.28, RMSE = 4.03, MAE = 3.27) was the greatest. The prediction model based on different land use types could obtain a higher simulation accuracy than that based on the whole study area. These findings provide new insights for the estimation of SOC content with high precision in karst areas. Full article
(This article belongs to the Special Issue Remote Sensing for Land System Mapping and Monitoring)
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24 pages, 20114 KiB  
Article
Characteristics and Driving Mechanism of Regional Ecosystem Assets Change in the Process of Rapid Urbanization—A Case Study of the Beijing–Tianjin–Hebei Urban Agglomeration
by Yanni Cao, Lingqiao Kong and Zhiyun Ouyang
Remote Sens. 2022, 14(22), 5747; https://doi.org/10.3390/rs14225747 - 14 Nov 2022
Cited by 7 | Viewed by 2425
Abstract
Land urbanization has reduced the amount of area for natural ecosystem assets. However, with the development of the social economy, will the quality of natural ecosystem assets be improved? If one comprehensively considers the changes in the area and quality of natural ecosystem [...] Read more.
Land urbanization has reduced the amount of area for natural ecosystem assets. However, with the development of the social economy, will the quality of natural ecosystem assets be improved? If one comprehensively considers the changes in the area and quality of natural ecosystem assets, is the dominant impact of urbanization on natural ecosystem assets positive or negative? In this study, detailed research is conducted on the area, pattern, quality, and overall situation of the ecosystem assets in the Beijing–Tianjin–Hebei urban agglomeration during the rapid urbanization process. The impact of urbanization on the overall situation of ecosystem assets is also analyzed. The research methods used to generate statistics, accounting, and analysis of the ecosystem assets include ArcGIS, satellite remote sensing images, R language programming, and other data analysis tools. The research results show that: (1) The ecosystem area was dominated by degradation, and the landscape pattern became increasingly fragmented, with the exception of farmland and wetland areas. (2) However, the quality of the natural ecosystem assets was significantly improved, and the overall situation of the natural ecosystem assets was optimized. (3) In addition to the population urbanization rate, the growth in the population density, land urbanization rate, and GDP per unit area had a significant negative impact on the overall situation of natural ecosystem assets. This reminds people that the improvement in asset quality can compensate for the reduction in area to some extent, and, in addition to the population urbanization rate, the levels of population density, land urbanization, and economic density should be appropriately controlled. Full article
(This article belongs to the Special Issue Remote Sensing for Land System Mapping and Monitoring)
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Other

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16 pages, 8648 KiB  
Technical Note
Characterizing the Development of Photovoltaic Power Stations and Their Impacts on Vegetation Conditions from Landsat Time Series during 1990–2022
by Su Ma, Junhui Liu, Ping Zhang, Xingyue Tu, Jianan Zhou, Yang Liu and Yingjuan Zheng
Remote Sens. 2023, 15(12), 3101; https://doi.org/10.3390/rs15123101 - 14 Jun 2023
Cited by 8 | Viewed by 3022
Abstract
To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. [...] Read more.
To achieve carbon peaking and carbon neutrality in China, photovoltaic (PV) power generation has become increasingly important for promoting a low-carbon transition. The central and western desert areas of China have been identified as major areas for the construction of large PV bases. Remote sensing technology has been used to map the spatial distribution and development status of PV power stations quickly and accurately in ecologically fragile areas, as well as assess the ecological and environmental impact of their construction. However, current remote sensing monitoring of PV power stations focuses mainly on mapping and time series analysis to measure their development process and assess the environmental conditions on a large scale over a long period of time. Therefore, we constructed a random forest model based on image spectral and texture features and mapped 2022 PV power stations in the junction area of Hobq Desert, Ulan Buh Desert, Tengger Desert, and Mu Us Sands in China. Following that, we identified the construction time of the PV power stations by identifying the turning points of the normalized construction land index (NDBI) time series from 1990–2022 using the LandTrendr method. To assess the ecological impact of PV power stations, we used the NDVI to measure the change in vegetation condition before and after the construction of PV power stations and constructed NDVI changes for PV power stations constructed in different years. The results showed that this mapping method achieved an overall classification accuracy of 96.65% and a Kappa coefficient of 0.92. The root mean square error (RMSE) for construction year identification was less than 0.5, and the number of new PV power stations increased significantly after 2010, reaching a total area of 14.52 km2 by 2016, which is consistent with the trend driven by national and regional development plans. Furthermore, the study found that the vegetation cover level could be restored to the average level before construction within 5–6 years and continued to increase after that. These findings may help government policymakers and practitioners make decisions on PV power station planning and ecological environment protection, thus contributing promptly to the achievement of China’s dual carbon goals. Full article
(This article belongs to the Special Issue Remote Sensing for Land System Mapping and Monitoring)
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