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Remote Sensing
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Operational Land Cover/Land Use Mapping
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Operational Land Cover/Land Use Mapping

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

Deadline for manuscript submissions: closed (31 August 2020) | Viewed by 41893

Special Issue Editors

Dr. Clément Mallet

E-Mail Website
Guest Editor
Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mandé, France
Interests: remote sensing; 3D point cloud processing; classification; machine learning; land cover mapping
Dr. Nesrine Chehata

E-Mail Website
Guest Editor
ENSEGID - Bordeaux INP, EA Géoressources & Environnement, France
Interests: machine learning; data fusion; image processing; land cover mapping; urban; agriculture
Dr. Mathieu Fauvel

E-Mail Website
Guest Editor
CESBIO, Université de Toulouse, CNES, CNRS, IRD, UPS, INRA Toulouse, France
Interests: signal processing; image processing; pattern recognition; remote sensing; kernel methods
Dr. Lionel Gueguen

E-Mail
Guest Editor
UBER, United States
Interests: computer vision; large scale computing; machine learning
Dr. Arnaud Le Bris

E-Mail Website
Guest Editor
Institut National de L’Information Géographique et Forestière, Saint-Mande, France
Interests: land use land cover mapping; remote sensing; classification; machine learning; deep learning; very high resolution imagery; historical images
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jun Chen

E-Mail Website
Guest Editor
National Geomatics Center of China, 28 Lianhuachi West Road, Beijing 100830, China
Interests: geospatial data modeling and updating; spatial relation; global land cover mapping; SDGs monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover/land use (LC/LU) description is the core information layer for many interdisciplinary scientific and environmental studies. Accurate and up-to-date maps over large areas are mandatory baselines. A significant number of public policies, from global to local scales, are driven by such knowledge, such as climate change mitigation, reduction of risks and threats, sustainable development, and urban planning. After decades of methodological developments, remote sensing through automatic Earth-Observation (EO) image analysis has been widely recognised as the most feasible approach to derive LC/LU information, in particular in operational contexts, especially because, in recent years, the advent of EO satellite missions with short revisit times and increased spatial resolution, such as Landsat and Copernicus programs, has led to an unprecedented amount of images of a heterogeneous physical nature. Today, LU/LC mapping is cast as a supervised classification problem based on one or several of these data sources.

Operational LC/LU mapping is opposed to experimental mapping. It focuses on process upscaling and reliable product delivery within a predefined time schedule. In terms of research, it consists of several key issues:

  • Automatic and semi-automatic data processing: for fusion, classification, and post-processing tasks. Manual intervention for training set design or hyperparameter tuning should also be prescribed, but crowdsourcing-based solutions are, for instance, encouraged.
  • Versatility and reproducibility of processing chains, involving easy transfer to other areas without manual intervention. Such methods should not be site-specific, but rather both locally relevant and globally consistent. Transparency should be reinforced. Methods can leverage efficiently human inputs.
  • Upscaling: LC/LU maps should be accurate at large scales, and processing chains (training and prediction steps) should be tailored to handle very large areas.
  • Continuous monitoring and change detection: these techniques allow one to switch ontologies over time, which is a relevant part of monitoring and change detection work, as LU/LC class definitions change over time.
  • Optimal exploitation of existing data sources: with the new era of free and open-access data (proliferation of images, vector data, existing LC/LU maps) and software, a trade-off has to be found between multiple sources and scalability in an operational framework.
  • Rigorous accuracy assessment protocols: these have been barely investigated and implemented so far. This is highly intertwined with land change assessment.

This Special Issue aims at collecting new developments and methodologies, best practices, and applications related to operational land cover/land use mapping. We welcome submissions that provide the community with the most recent advancements on all aspects mentioned above.

Dr. Clément Mallet
Dr. Nesrine Chehata
Dr. Mathieu Fauvel
Dr. Lionel Gueguen
Dr. Arnaud Le Bris
Dr. Chen Jun
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/land use mapping
  • Regional/national mapping
  • Multi-modal
  • Multi-source fusion
  • Automation
  • Scalability
  • Transferability
  • Reproducibility
  • Accuracy assessment

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

18 pages, 3178 KiB  
Article
Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks
by Zixia Tang, Mengmeng Li and Xiaoqin Wang
Remote Sens. 2020, 12(18), 2935; https://doi.org/10.3390/rs12182935 - 10 Sep 2020
Cited by 21 | Viewed by 4847
Abstract
Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural [...] Read more.
Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality of deep features, and the selected features were then used for classifying tea objects via a random forest. The proposed method was first applied to Google Earth images and then transferred to GF-2 satellite images. We compared the proposed classification with existing methods: Object-based classification using random forest, Mask R-CNN, and object-based CNN without fine-tuning. The results show the proposed method achieved a higher classification accuracy than other methods and produced smaller over- and under-classification geometric errors than Mask R-CNN in terms of shape integrity and boundary consistency. The proposed approach, trained using Google Earth images, achieved comparable results when transferring to the classification of tea objects from GF-2 images. We conclude that the proposed method is effective for mapping tea plantations using very high-resolution remote sensing images even with limited training samples and has huge potential for mapping tea plantations in large areas. Full article
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
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18 pages, 2182 KiB  
Article
Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery
by Olivia S.B. Spagnuolo, Julie C. Jarvey, Michael J. Battaglia, Zachary M. Laubach, Mary Ellen Miller, Kay E. Holekamp and Laura L. Bourgeau-Chavez
Remote Sens. 2020, 12(7), 1086; https://doi.org/10.3390/rs12071086 - 28 Mar 2020
Cited by 12 | Viewed by 5157
Abstract
Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations [...] Read more.
Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-date radar (PALSAR-2 and Sentinel-1) and optical (Sentinel-2) imagery with field data and visual interpretation of aerial imagery to classify land cover in the Masai Mara National Reserve, Kenya using machine learning (Random Forests). This study area comprises a diverse array of land cover types and changes over time due to seasonal changes in precipitation, seasonal movements of large herds of resident and migratory ungulates, fires, and livestock grazing. We classified twelve land cover types with user’s and producer’s accuracies ranging from 66%–100% and an overall accuracy of 86%. These methods were able to distinguish among short, medium, and tall grass cover at user’s accuracies of 83%, 82%, and 85%, respectively. By yielding a highly accurate, fine-resolution map that distinguishes among grasses of different heights, this work not only outlines a viable method for future grassland mapping efforts but also will help inform local management decisions and research in the Masai Mara National Reserve. Full article
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
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14 pages, 5058 KiB  
Communication
Copernicus Global Land Cover Layers—Collection 2
by Marcel Buchhorn, Myroslava Lesiv, Nandin-Erdene Tsendbazar, Martin Herold, Luc Bertels and Bruno Smets
Remote Sens. 2020, 12(6), 1044; https://doi.org/10.3390/rs12061044 - 24 Mar 2020
Cited by 442 | Viewed by 26673
Abstract
In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover [...] Read more.
In May 2019, Collection 2 of the Copernicus Global Land Cover layers was released. Next to a global discrete land cover map at 100 m resolution, a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel. This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme. Overall, 20 layers are provided which allow customization of land cover maps to specific user needs or applications (e.g., forest monitoring, crop monitoring, biodiversity and conservation, climate modeling, etc.). However, Collection 2 was not just a global up-scaling, but also includes major improvements in the map quality, reaching around 80% or more overall accuracy. The processing system went into operational status allowing annual updates on a global scale with an additional implemented training and validation data collection system. In this paper, we provide an overview of the major changes in the production of the land cover maps, that have led to this increased accuracy, including aligning with the Sentinel 2 satellite system in the grid and coordinate system, improving the metric extraction, adding better auxiliary data, improving the biome delineations, as well as enhancing the expert rules. An independent validation exercise confirmed the improved classification results. In addition to the methodological improvements, this paper also provides an overview of where the different resources can be found, including access channels to the product layer as well as the detailed peer-review product documentation. Full article
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
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28 pages, 67723 KiB  
Article
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30
by Cong Lin, Peijun Du, Alim Samat, Erzhu Li, Xin Wang and Junshi Xia
Remote Sens. 2019, 11(12), 1397; https://doi.org/10.3390/rs11121397 - 12 Jun 2019
Cited by 16 | Viewed by 4245
Abstract
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely [...] Read more.
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy. Full article
(This article belongs to the Special Issue Operational Land Cover/Land Use Mapping)
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