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Remote Sensing
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Land Use Monitoring Based on Remote Sensing and Artificial Intelligence
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Land Use Monitoring Based on Remote Sensing and Artificial Intelligence

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 3630

Special Issue Editors

Dr. Qiangyi Yu

E-Mail Website
Guest Editor
Institute of Agriculture Resource and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: agricultural land use; land use intensity; land use and land cover chang
Special Issues, Collections and Topics in MDPI journals
Dr. Sathishkumar Samiappan

E-Mail Website
Guest Editor
Geosystems Research Institute, Mississippi State University, 2 Research Boulevard, Starkville, MS 39759, USA
Interests: machine learning; pattern recognition; signal processing; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Miao Zhang

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: crop monitoring with remote sensing; big earth data for cropland monitoring; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Wei Su

E-Mail Website
Guest Editor
College of Land Science and Technology, China Agricultural University, Beijing 100081, China
Interests: LiDAR application in vegetation; vegetation parameter retrieval; vegetation monitoring; hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land use has always been a key element for the sustainable development of human society. Understanding the state of land use plays a crucial role in urban and rural planning, environmental protection, resource management, and addressing climate change. However, the existing land use monitoring activities largely ignore the multi-faceted characteristics of land use, and the traditional monitoring methods are often limited by factors such as time, space, and cost, making it difficult to meet the precise monitoring needs of large-scale and complex geographical environments.

With the rapid development of sensing technology and artificial intelligence (AI), we now have more powerful tools to address the challenges of land use monitoring. Remote sensing technologies, such as satellite and aerial sensors, can provide high-resolution geographic information data, covering extensive geographic areas. A crowdsourcing approach—supported by smartphones and tablets—transmits on-site land use information to end users if the location is far away from them. Meanwhile, AI methods, including deep learning and machine learning, have the capability to handle large-scale or unstructured data, perform automated classification, and make predictions. This opens up new opportunities for land use monitoring.

By combining remote sensing, crowdsourcing, and AI, we can efficiently obtain land cover information, monitor changes in land use, identify urbanization trends, quantitatively assess the impact of human activities on the environment, and predict future land use trends. This interdisciplinary approach provides more comprehensive and accurate data for land use monitoring, benefiting government decision makers, urban planners, environmental scientists, agricultural experts, and professionals from various fields in gaining a better understanding of dynamic land changes.

This Special Issue will focus on exploring the synergy between advanced sensing technology and AI in the field of land use monitoring and how they collectively drive further advancements in land use monitoring. We look forward to receiving contributions from researchers and practitioners, discussing the latest developments in this interdisciplinary field, and sharing insights on how to better apply these emerging technologies.

Dr. Qiangyi Yu
Dr. Sathishkumar Samiappan
Dr. Miao Zhang
Prof. Dr. Wei Su
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
  • remote sensing
  • crowdsourcing
  • artificial intelligence

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Published Papers (3 papers)

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Research

25 pages, 5983 KiB  
Article
Quality Evaluation of Multi-Source Cropland Data in Alpine Agricultural Areas of the Qinghai-Tibet Plateau
by Shenghui Lv, Xingsheng Xia, Qiong Chen and Yaozhong Pan
Remote Sens. 2024, 16(19), 3611; https://doi.org/10.3390/rs16193611 - 27 Sep 2024
Viewed by 409
Abstract
Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land [...] Read more.
Accurate cropland distribution data are essential for efficiently planning production layouts, optimizing farmland use, and improving crop planting efficiency and yield. Although reliable cropland data are crucial for supporting modern regional agricultural monitoring and management, cropland data extracted directly from existing global land use/cover products present uncertainties in local regions. This study evaluated the area consistency, spatial pattern overlap, and positional accuracy of cropland distribution data from six high-resolution land use/cover products from approximately 2020 in the alpine agricultural regions of the Hehuang Valley and middle basin of the Yarlung Zangbo River (YZR) and its tributaries (Lhasa and Nianchu Rivers) area on the Qinghai-Tibet Plateau. The results indicated that (1) in terms of area consistency analysis, European Space Agency (ESA) WorldCover cropland distribution data exhibited the best performance among the 10 m resolution products, while GlobeLand30 cropland distribution data performed the best among the 30 m resolution products, despite a significant overestimation of the cropland area. (2) In terms of spatial pattern overlap analysis, AI Earth 10-Meter Land Cover Classification Dataset (AIEC) cropland distribution data performed the best among the 10 m resolution products, followed closely by ESA WorldCover, while the China Land Cover Dataset (CLCD) performed the best for the Hehuang Valley and GlobeLand30 performed the best for the YZR area among the 30 m resolution products. (3) In terms of positional accuracy analysis, the ESA WorldCover cropland distribution data performed the best among the 10 m resolution products, while GlobeLand30 data performed the best among the 30 m resolution products. Considering the area consistency, spatial pattern overlap, and positional accuracy, GlobeLand30 and ESA WorldCover cropland distribution data performed best at 30 m and 10 m resolutions, respectively. These findings provide a valuable reference for selecting cropland products and can promote refined cropland mapping of the Hehuang Valley and YZR area. Full article
(This article belongs to the Special Issue Land Use Monitoring Based on Remote Sensing and Artificial Intelligence)
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19 pages, 5250 KiB  
Article
A Remote Sensing Approach to Estimating Cropland Sustainability in the Lateritic Red Soil Region of China
by Dingding Duan, Xiao Sun, Chenrui Wang, Yan Zha, Qiangyi Yu and Peng Yang
Remote Sens. 2024, 16(6), 1069; https://doi.org/10.3390/rs16061069 - 18 Mar 2024
Cited by 2 | Viewed by 1180
Abstract
Spatiotemporal assessment and a comprehensive understanding of cropland sustainability are prerequisites for ensuring food security and promoting sustainable development. However, a remote sensing-based approach framework that is suitable for large-scale and high-precision assessment and can reflect the overall sustainability of cropland has not [...] Read more.
Spatiotemporal assessment and a comprehensive understanding of cropland sustainability are prerequisites for ensuring food security and promoting sustainable development. However, a remote sensing-based approach framework that is suitable for large-scale and high-precision assessment and can reflect the overall sustainability of cropland has not yet been developed. This study considered a typical lateritic red soil region of Guangdong Province, China, as an example. Cropland sustainability was examined from three aspects: natural capacity, management level, and food productivity. Ten typical indicators, including soil organic matter, pH, irrigation guarantee capability, multiple cropping index, and food productivity, among others, were constructed using remote sensing technology and selected to represent these three aspects. Based on the indicator system, we assessed the spatiotemporal patterns of cropland sustainability from 2010 to 2020. The results showed that the natural capacity, management level, and food productivity of cropland had improved over the 10 years. The cropland sustainability score increased from 67.95 to 69.08 over this period. The sustainability scores for 68.64% of cropland were increased and were largely distributed in the eastern and western region of the study area. The croplands with declining sustainability scores were mostly distributed in the central region. The prefecture-level regions differed in cropland sustainability, with Zhongshan, Zhuhai, and Qingyuan cities exhibiting the highest values, and Zhanjiang the lowest. Exploring the underlying mechanisms of cropland sustainability and proposing improvement measures can guide decision-making, cropland protection, and efficient utilization, especially in similar lateritic red soil regions of the world. Full article
(This article belongs to the Special Issue Land Use Monitoring Based on Remote Sensing and Artificial Intelligence)
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23 pages, 7321 KiB  
Article
Interannual Monitoring of Cropland in South China from 1991 to 2020 Based on the Combination of Deep Learning and the LandTrendr Algorithm
by Yue Qu, Boyu Zhang, Han Xu, Zhi Qiao and Luo Liu
Remote Sens. 2024, 16(6), 949; https://doi.org/10.3390/rs16060949 - 8 Mar 2024
Cited by 1 | Viewed by 1249
Abstract
Timely and accurate acquisition of spatial distribution and changes in cropland is of significant importance for food security and ecological preservation. Most studies that monitor long-term changes in cropland tend to overlook the rationality in the process of cropland evolution, and there are [...] Read more.
Timely and accurate acquisition of spatial distribution and changes in cropland is of significant importance for food security and ecological preservation. Most studies that monitor long-term changes in cropland tend to overlook the rationality in the process of cropland evolution, and there are conflicts between the interannual cropland data, so they cannot be used to analyze land use change. This study focuses on the rationality of annual identification results for cropland, considering the long-term evolution and short-term variations influenced by natural environmental changes and human activities. An approach for annual monitoring of cropland based on long time series and deep learning is also proposed. We acquired imagery related to cropland’s vegetation lush period (VLP) and vegetation differential period (VDP) from Landsat images on the Google Earth Engine (GEE) platform and used the ResUNet-a structural model for training. Finally, a long-time-series cropland correction algorithm based on LandTrendr is introduced, and interannual cropland maps of Guangdong Province from 1991 to 2020 were generated. Evaluating the cropland monitoring results in Guangdong Province every five years, we found an overall accuracy of 0.91–0.93 and a kappa coefficient of 0.80–0.83. Our results demonstrate good consistency with agricultural statistical data. Over the past 30 years, the total cropland area in Guangdong Province has undergone three phases: a decrease, significant decrease, and stabilization. Significant regional variations have also been observed. Our approach can be applied to long-time-series interannual cropland monitoring in the southern regions of China, providing valuable data support for the further implementation of cropland protection. Full article
(This article belongs to the Special Issue Land Use Monitoring Based on Remote Sensing and Artificial Intelligence)
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