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Remote Sensing
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Regional and Global Land Cover Mapping
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Regional and Global Land Cover Mapping

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 53230

Special Issue Editors

Dr. Stanisław Lewiński

E-Mail Website1 Website2 Website3
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
Dr. Garik Gutman

E-Mail Website
Guest Editor
NASA Headquarters, NASA Land-Cover/Land-Use Change Program, 300 E Street, SW Washington, DC 20546, USA
Interests: telecoupling of land use systems; land-atmosphere processes; land governance; land change trade-offs for ecosystem services and biodiversity; land management systems; urban-rural interactions; land use and conflict
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Since the beginning of space Remote Sensing, the basic application of satellite images is for mapping the surface of the Earth that is visible on them. Initially, in the 1970s, the images were visually interpreted. The development of digital techniques has made it possible to try and support this work with algorithms of varying degrees of automation. Initially, basic pixel-based algorithms of supervised and unsupervised classification were applied. The next stage of development was the introduction of object-oriented classification techniques. At the same time, the classification process was increasingly supported by information selected from the existing databases. Currently, machine learning techniques are commonly used, including very advanced deep learning algorithms. More and more often in satellite remote sensing, algorithms are entered that were originally developed for non-image data analysis purposes.

The first global land cover studies were made on the basis of low-resolution images, which resulted from the availability of data and the possibility of their effective processing. Currently, the Earth's surface is recorded with increasing spatial, spectral, and temporal resolution, and simultaneously, the technical capabilities allow for the processing of huge image files and data sets. Mapping large areas such as entire countries, continents, and even the whole world requires the use of dedicated classification approaches, which guarantee a very high degree of automation at each stage of the processing and analysis of satellite data.

This Special Issue invites manuscripts presenting classification approaches and services that are elaborated for global or regional land cover mapping, based on optical or SAR satellite data. The topics of interest include, but are not limited to:

  • Land Cover classification and monitoring
  • Global and regional mapping
  • Automation of data processing
  • Optical and SAR data processing

Dr. Stanisław Lewiński
Dr. Garik Gutman
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.

Published Papers (6 papers)

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Research

21 pages, 5685 KiB  
Article
Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network
by Jifa Chen, Guojun Zhai, Gang Chen, Bo Fang, Ping Zhou and Nan Yu
Remote Sens. 2021, 13(8), 1493; https://doi.org/10.3390/rs13081493 - 13 Apr 2021
Cited by 3 | Viewed by 2425
Abstract
Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the [...] Read more.
Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. Therefore, this paper proposes a category-space constrained adversarial method to execute category-level adaptive CLCM. Focusing on the underlying category information, we introduce a category-level adversarial framework to align semantic features. We summarize two diverse strategies to extract category-wise domain labels for source and target domains, where the latter is driven by self-supervised learning. Meanwhile, we generalize the lightweight adaptation module to multiple levels across a robust baseline, aiming to fine-tune the features at different spatial scales. Furthermore, the self-supervised learning approach is also leveraged as an improvement strategy to optimize the result within segmented training. We examine our method on two converse adaptation tasks and compare them with other state-of-the-art models. The overall visualization results and evaluation metrics demonstrate that the proposed method achieves excellent performance in the domain adaptation CLCM with high-resolution remotely sensed images. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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25 pages, 8485 KiB  
Article
Automated Production of a Land Cover/Use Map of Europe Based on Sentinel-2 Imagery
by Radek Malinowski, Stanisław Lewiński, Marcin Rybicki, Ewa Gromny, Małgorzata Jenerowicz, Michał Krupiński, Artur Nowakowski, Cezary Wojtkowski, Marcin Krupiński, Elke Krätzschmar and Peter Schauer
Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523 - 27 Oct 2020
Cited by 83 | Viewed by 10763
Abstract
Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of [...] Read more.
Up-to-date information about the Earth’s surface provided by land cover maps is essential for numerous environmental and land management applications. There is, therefore, a clear need for the continuous and reliable monitoring of land cover and land cover changes. The growing availability of high resolution, regularly collected remote sensing data can support the increasing number of applications that require high spatial resolution products that are frequently updated (e.g., annually). However, large-scale operational mapping requires a highly-automated data processing workflow, which is currently lacking. To address this issue, we developed a methodology for the automated classification of multi-temporal Sentinel-2 imagery. The method uses a random forest classifier and existing land cover/use databases as the source of training samples. In order to demonstrate its operability, the method was implemented on a large part of the European continent, with CORINE Land Cover and High-Resolution Layers as training datasets. A land cover/use map for the year 2017 was produced, composed of 13 classes. An accuracy assessment, based on nearly 52,000 samples, revealed high thematic overall accuracy (86.1%) on a continental scale, and average overall accuracy of 86.5% at country level. Only low-frequency classes obtained lower accuracies and we recommend that their mapping should be improved in the future. Additional modifications to the classification legend, notably the fusion of thematically and spectrally similar vegetation classes, increased overall accuracy to 89.0%, and resulted in ten, general classes. A crucial aspect of the presented approach is that it embraces all of the most important elements of Earth observation data processing, enabling accurate and detailed (10 m spatial resolution) mapping with no manual user involvement. The presented methodology demonstrates possibility for frequent and repetitive operational production of large-scale land cover maps. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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18 pages, 4044 KiB  
Article
Agricultural Expansion in Mato Grosso from 1986–2000: A Bayesian Time Series Approach to Tracking Past Land Cover Change
by Jacky Lee, Jeffrey A. Cardille and Michael T. Coe
Remote Sens. 2020, 12(4), 688; https://doi.org/10.3390/rs12040688 - 20 Feb 2020
Cited by 12 | Viewed by 3316
Abstract
Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create [...] Read more.
Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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22 pages, 9201 KiB  
Article
Mapping the Land Cover of Africa at 10 m Resolution from Multi-Source Remote Sensing Data with Google Earth Engine
by Qingyu Li, Chunping Qiu, Lei Ma, Michael Schmitt and Xiao Xiang Zhu
Remote Sens. 2020, 12(4), 602; https://doi.org/10.3390/rs12040602 - 11 Feb 2020
Cited by 70 | Viewed by 11810
Abstract
The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine [...] Read more.
The remote sensing based mapping of land cover at extensive scales, e.g., of whole continents, is still a challenging task because of the need for sophisticated pipelines that combine every step from data acquisition to land cover classification. Utilizing the Google Earth Engine (GEE), which provides a catalog of multi-source data and a cloud-based environment, this research generates a land cover map of the whole African continent at 10 m resolution. This land cover map could provide a large-scale base layer for a more detailed local climate zone mapping of urban areas, which lie in the focus of interest of many studies. In this regard, we provide a free download link for our land cover maps of African cities at the end of this paper. It is shown that our product has achieved an overall accuracy of 81% for five classes, which is superior to the existing 10 m land cover product FROM-GLC10 in detecting urban class in city areas and identifying the boundaries between trees and low plants in rural areas. The best data input configurations are carefully selected based on a comparison of results from different input sources, which include Sentinel-2, Landsat-8, Global Human Settlement Layer (GHSL), Night Time Light (NTL) Data, Shuttle Radar Topography Mission (SRTM), and MODIS Land Surface Temperature (LST). We provide a further investigation of the importance of individual features derived from a Random Forest (RF) classifier. In order to study the influence of sampling strategies on the land cover mapping performance, we have designed a transferability analysis experiment, which has not been adequately addressed in the current literature. In this experiment, we test whether trained models from several cities contain valuable information to classify a different city. It was found that samples of the urban class have better reusability than those of other natural land cover classes, i.e., trees, low plants, bare soil or sand, and water. After experimental evaluation of different land cover classes across different cities, we conclude that continental land cover mapping results can be considerably improved when training samples of natural land cover classes are collected and combined from areas covering each Köppen climate zone. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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24 pages, 7230 KiB  
Article
Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping
by Peter Potapov, Matthew C. Hansen, Indrani Kommareddy, Anil Kommareddy, Svetlana Turubanova, Amy Pickens, Bernard Adusei, Alexandra Tyukavina and Qing Ying
Remote Sens. 2020, 12(3), 426; https://doi.org/10.3390/rs12030426 - 29 Jan 2020
Cited by 141 | Viewed by 19056
Abstract
The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we [...] Read more.
The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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20 pages, 14262 KiB  
Article
Reclaimed Area Land Cover Mapping Using Sentinel-2 Imagery and LiDAR Point Clouds
by Marta Szostak, Marcin Pietrzykowski and Justyna Likus-Cieślik
Remote Sens. 2020, 12(2), 261; https://doi.org/10.3390/rs12020261 - 12 Jan 2020
Cited by 14 | Viewed by 4067
Abstract
This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the [...] Read more.
This paper investigates the possibility of using fusion Sentinel-2 imageries (2016, ESA) and light detection and ranging (LiDAR) point clouds for the automation of land cover mapping with a primary focus on detecting and monitoring afforested areas and deriving precise information about the spatial (2D and 3D) characteristics of vegetation for reclaimed areas. The study was carried out for reclaimed areas – two former sulfur mines located in Southeast Poland, namely, Jeziórko, where 216.5 ha of afforested area was reclaimed after borehole exploitation, and Machów, where 871.7 ha of dump area was reclaimed after open cast strip mining. The current land use and land cover (LULC) classes at the Machów and Jeziórko former sulfur mines are derived based on Sentinel-2 image processing, and confirmed the applied type of reclamation for both analysed areas. The following LULC classes showed a significant spatial range: broad-leaved forest, coniferous forest, and transitional woodland shrub. The progress of afforested areas, not only in terms of the occupied area, but also in terms of the growth of trees and shrubs, was confirmed. The results of the study showed differences in vegetation parameters, namely, height and canopy cover. Various stages of vegetation growth were also observed. This indicates an ongoing process of vegetation development, as an effect of the reclamation treatment for these areas. Full article
(This article belongs to the Special Issue Regional and Global Land Cover Mapping)
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