Special Issue "Remote Sensing for Land Cover/Land Use Mapping at Local and Regional Scales"

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

Deadline for manuscript submissions: closed (30 November 2018).

Special Issue Editors

Dr. Giorgios Mallinis
E-Mail Website
Guest Editor
1. Laboratory of Photogrammetry and Remote Sensing, The Polytechnical School, Aristotle University of Thessaloniki, Greece 2. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Xanthi, Greece
Tel. +302552041107
Interests: remote sensing; land use/land cover (LULC) mapping; classification development and comparison; geographic object-based image analysis; natural disasters; UAS
Special Issues and Collections in MDPI journals
Dr. Charalampos Georgiadis
E-Mail Website
Guest Editor
School of Civil Engineering, Division of Geotechnical Engineering, The Aristotle University of Thessaloniki, Thessaloniki, Greece
Tel. +302310996171
Interests: remote sensing; land use/land cover (LULC) mapping; photogrammetry; unmanned aerial systems (UAS); LiDAR; GIS; 3D modeling; mobile mapping systems; image analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

More than ever, there is a need from policy and decision makers, national governments, non-governmental organizations, international initiatives, scientists and individual citizens for timely and accurate spatially-explicit information on Earth’s physical cover of Earth's surface and the socio-economic function of land at multiple scales. Information on land-cover and land-use patterns and monitoring of their changes, can support regional policies and local action for sustainable development in terms of efficiency in resources use, disaster risk reduction, and environmental protection.

Since the process and driving forces of change usually operate at multiple scales, it is important to capture and synthesize information on the land-cover and land-use patterns in regional, wall-to-wall maps, as well as at sub-regional, national, and local scales.

Nowadays, the availability of satellite data with improved characteristics at low or no cost, the technological evolution of Unmanned Aerial Systems in terms of autonomy and payload capacity, the introduction of robust algorithms, and the advent of the data processing and distribution technology, have motivated the progress of research and efforts from the remote-sensing community to cover the information needs on land use/land cover mapping at appropriate scales.

However, several challenges still exist, related, among others, to the development of stable processing approaches, local and regional products with higher spatial and temporal resolutions, as well as improved thematic content, multi-sensor and multi-platform fusion (i.e., UAS-satellite), web-processing services and big data analysis, consistency and harmonization of the information content among existing regional and local products, existing data gaps, and lack of standardized validation procedures.

This Special Issue calls for submissions presenting new conceptual frameworks and novel remote-sensing approaches addressing the abovementioned challenges, new or improved geospatial tools for extracting local and regional land use and land cover information from multiple remote sensing data sources, and sectoral as well as interdisciplinary research.

Review papers presenting the status and progress, as well as remaining challenges, are also welcomed.

Dr. Giorgos Mallinis
Dr. Charalampos Georgiadis
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 papers will be 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 2000 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

  • regional and local land use land cover
  • objects
  • change detection
  • LULC
  • multi-temporal
  • satellite
  • UAV
  • UAS
  • vegetation
  • forestry
  • agriculture
  • ecosystem
  • cities
  • urban
  • data fusion
  • classification

Published Papers (11 papers)

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Editorial

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Open AccessEditorial
Editorial of Special Issue “Remote Sensing for Land Cover/Land Use Mapping at Local and Regional Scales”
Remote Sens. 2019, 11(19), 2202; https://doi.org/10.3390/rs11192202 - 20 Sep 2019
Abstract
More than ever, there is a need from policy and decision makers, national governments, non-governmental organizations, international initiatives, scientists, and individual citizens for timely and accurate spatially-explicit information on Earth’s physical surface cover and the socio-economic function of land at multiple scales [...] [...] Read more.
More than ever, there is a need from policy and decision makers, national governments, non-governmental organizations, international initiatives, scientists, and individual citizens for timely and accurate spatially-explicit information on Earth’s physical surface cover and the socio-economic function of land at multiple scales [...] Full article

Research

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Open AccessArticle
A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks
Remote Sens. 2019, 11(3), 274; https://doi.org/10.3390/rs11030274 - 30 Jan 2019
Cited by 8
Abstract
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning [...] Read more.
Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics. Full article
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Open AccessArticle
Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data
Remote Sens. 2018, 10(12), 2019; https://doi.org/10.3390/rs10122019 - 12 Dec 2018
Cited by 6
Abstract
Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution [...] Read more.
Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to identify grassland species encroaching into Natura 2000 habitats and for supporting their monitoring. Full article
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Open AccessArticle
Time Tracking of Different Cropping Patterns Using Landsat Images under Different Agricultural Systems during 1990–2050 in Cold China
Remote Sens. 2018, 10(12), 2011; https://doi.org/10.3390/rs10122011 - 12 Dec 2018
Cited by 2
Abstract
Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China [...] Read more.
Rapid cropland reclamation is underway in Cold China in response to increases in food demand, while the lack analyses of time series cropping pattern mappings limits our understanding of the acute transformation process of cropland structure and associated environmental effects. The Cold China contains different agricultural systems (state and private farming), and such systems could lead to different cropping patterns. So far, such changes have not been revealed yet. Based on the Landsat images, this study tracked cropping information in five-year increments (1990–1995, 1995–2000, 2000–2005, 2005–2010, and 2010–2015) and predicted future patterns for the period of 2020–2050 under different agricultural systems using developed method for determining cropland patterns. The following results were obtained: The available time series of Landsat images in Cold China met the requirements for long-term cropping pattern studies, and the developed method exhibited high accuracy (over 91%) and obtained precise spatial information. A new satellite evidence was observed that cropping patterns significantly differed between the two farm types, with paddy field in state farming expanding at a faster rate (from 2.66 to 68.56%) than those in private farming (from 10.12 to 34.98%). More than 70% of paddy expansion was attributed to the transformation of upland crop in each period at the pixel level, which led to a greater loss of upland crop in state farming than private farming (9505.66 km2 vs. 2840.29 km2) during 1990–2015. Rapid cropland reclamation is projected to stagnate in 2020, while paddy expansion will continue until 2040 primarily in private farming in Cold China. This study provides new evidence for different land use change pattern mechanisms between different agricultural systems, and the results have significant implications for understanding and guiding agricultural system development. Full article
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Open AccessArticle
Comparison of Global and Continental Land Cover Products for Selected Study Areas in South Central and Eastern European Region
Remote Sens. 2018, 10(12), 1967; https://doi.org/10.3390/rs10121967 - 06 Dec 2018
Cited by 2
Abstract
Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across [...] Read more.
Land cover is one of the key terrestrial variables used for monitoring and as input for modelling in support of achieving the United Nations Strategical Development Goals. Global and Continental Land Cover Products (GCLCs) aim to provide the required harmonized information background across areas; thus, they are not being limited by national or other administrative nomenclature boundaries and their production approaches. Moreover, their increased spatial resolution, and consequently their local relevance, is of high importance for users at a local scale. During the last decade, several GCLCs were developed, including the Global Historical Land-Cover Change Land-Use Conversions (GLC), the Globeland-30 (GLOB), Corine-2012 (CLC) and GMES/ Copernicus Initial Operation High Resolution Layers (GIOS). Accuracy assessment is of high importance for product credibility towards incorporation into decision chains and implementation procedures, especially at local scales. The present study builds on the collaboration of scientists participating in the Global Observations of Forest Cover—Global Observations of Land Cover Dynamics (GOFC-GOLD), South Central and Eastern European Regional Information Network (SCERIN). The main objective is to quantitatively evaluate the accuracy of commonly used GCLCs at selected representative study areas in the SCERIN geographic area, which is characterized by extreme diversity of landscapes and environmental conditions, heavily affected by anthropogenic impacts with similar major socio-economic drivers. The employed validation strategy for evaluating and comparing the different products is detailed, representative results for the selected areas from nine SCERIN countries are presented, the specific regional differences are identified and their underlying causes are discussed. In general, the four GCLCs products achieved relatively high overall accuracy rates: 74–98% for GLC (mean: 93.8%), 79–92% for GLOB (mean: 90.6%), 74–91% for CLC (mean: 89%) and 72–98% for GIOS (mean: 91.6%), for all selected areas. In most cases, the CLC product has the lower scores, while the GLC has the highest, closely followed by GIOS and GLOB. The study revealed overall high credibility and validity of the GCLCs products at local scale, a result, which shows expected benefit even for local/regional applications. Identified class dependent specificities in different landscape types can guide the local users for their reasonable usage in local studies. Valuable information is generated for advancing the goals of the international GOFC-GOLD program and aligns well with the agenda of the NASA Land-Cover/Land-Use Change Program to improve the quality and consistency of space-derived higher-level products. Full article
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Open AccessArticle
Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid Hydrologic Modeling and Basin Planning
Remote Sens. 2018, 10(12), 1910; https://doi.org/10.3390/rs10121910 - 29 Nov 2018
Cited by 2
Abstract
This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI [...] Read more.
This paper discusses research methodology to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB) for basin planning, using both MODIS and Landsat satellite data. The 2010 MODIS MOD09 and MYD09 8-day reflectance data was processed into monthly NDVI maps with the Time Series Product Tool software package and then used to classify regionally common forest and agricultural LULC types. Dry season circa 2010 Landsat top of atmosphere reflectance mosaics were classified to map locally common LULC types. Unsupervised ISODATA clustering was used to derive most LULC classifications. MODIS and Landsat classifications were combined with GIS methods to derive final 250-m LULC maps for Sub-basins (SBs) 1–8 of the LMB. The SB 7 LULC map with 14 classes was assessed for accuracy. This assessment compared random locations for sampled types on the SB 7 LULC map to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data, and Mekong River Commission data (e.g., crop calendars). The SB 7 LULC map showed an overall agreement to reference data of ~81%. By grouping three deciduous forest classes into one, the overall agreement improved to ~87%. The project enabled updated regional LULC maps that included more detailed agriculture LULC types. LULC maps were supplied to project partners to improve use of Soil and Water Assessment Tool for modeling hydrology and water use, plus enhance LMB water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change as part of basin planning and assessment. Full article
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Open AccessArticle
Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model
Remote Sens. 2018, 10(11), 1713; https://doi.org/10.3390/rs10111713 - 30 Oct 2018
Cited by 4
Abstract
Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with [...] Read more.
Deep learning has become a standard processing procedure in land cover mapping for remote sensing images. Instead of relying on hand-crafted features, deep learning algorithms, such as Convolutional Neural Networks (CNN) can automatically generate effective feature representations, in order to recognize objects with complex image patterns. However, the rich spatial information still remains unexploited, since most of the deep learning algorithms only focus on small image patches that overlook the contextual information at larger scales. To utilize these contextual information and improve the classification performance for high-resolution imagery, we propose a graph-based model in order to capture the contextual information over semantic segments of the image. First, we explore semantic segments which build on the top of deep features and obtain the initial classification result. Then, we further improve the initial classification results with a higher-order co-occurrence model by extending the existing conditional random field (HCO-CRF) algorithm. Compared to the pixel- and object-based CNN methods, the proposed model achieved better performance in terms of classification accuracy. Full article
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Open AccessArticle
JAXA High-Resolution Land Use/Land Cover Map for Central Vietnam in 2007 and 2017
Remote Sens. 2018, 10(9), 1406; https://doi.org/10.3390/rs10091406 - 04 Sep 2018
Cited by 3
Abstract
Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived [...] Read more.
Robust remote monitoring of land cover changes is essential for a range of studies such as climate modeling, ecosystems, and environmental protection. However, since each satellite data has its own effective features, it is difficult to obtain high accuracy land cover products derived from a single satellite’s data, perhaps because of cloud cover, suboptimal acquisition schedules, and the restriction of data accessibility. In this study, we integrated Landsat 5, 7, and 8, Sentinel-2, Advanced Land Observing Satellite Advanced Visual, and Near Infrared Radiometer type 2 (ALOS/AVNIR-2), ALOS Phased Array L-band Synthetic Aperture Radar (PALSAR) Mosaic, ALOS-2/PALSAR-2 Mosaic, Shuttle Radar Topography Mission (SRTM), and ancillary data, using kernel density estimation to map and analyze land use/cover change (LUCC) over Central Vietnam from 2007 to 2017. The region was classified into nine categories, i.e., water, urban, rice paddy, upland crops, grassland, orchard, forest, mangrove, and bare land by an automatic model which was trained and tested by 98,000 reference data collected from field surveys and visual interpretations. Results were the 2007 and 2017 classified maps with the same spatial resolutions of 10 m and the overall accuracies of 90.5% and 90.6%, respectively. They indicated that Central Vietnam experienced an extensive change in land cover (33 ± 18% of the total area) during the study period. Gross gains in forests (2680 km2) and water bodies (570 km2) were primarily from conversion of orchards, paddy fields, and crops. Total losses in bare land (495 km2) and paddy (485 km2) were largely to due transformation to croplands and urban & other infrastructure lands. In addition, the results demonstrated that using global land cover products for specific applications is impaired because of uncertainties and inconsistencies. These findings are essential for the development of resource management strategy and environmental studies. Full article
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Open AccessArticle
Incorporating Density in Spatiotemporal Land Use/Cover Change Patterns: The Case of Attica, Greece
Remote Sens. 2018, 10(7), 1034; https://doi.org/10.3390/rs10071034 - 01 Jul 2018
Cited by 5
Abstract
This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic [...] Read more.
This paper looks at the periodic land use/cover (LUC) changes that occurred in Attica, Greece from 1991 to 2016. During this period, land transformations were mostly related to the artificial LUC categories; therefore, the aim was to map LUC with a high thematic resolution aimed at these specific categories, according to their density and continuity. The classification was implemented using the Random Forests (RF) machine learning algorithm and the presented methodological framework involved a high degree of automation. The results revealed that the majority of the expansion of the built-up areas took place at the expense of agricultural land. Moreover, mapping and quantifying the LUC changes revealed three uneven phases of development, which reflect the socioeconomic circumstances of each period. The discontinuous low-density urban fabric started to increase rapidly around 2003, reaching 7% (from 2.5% in 1991), and this trend continued, reaching 12% in 2016. The continuous as well as the discontinuous dense urban fabric, almost doubled throughout the study period. Agricultural areas were dramatically reduced to almost half of what they were in 1991, while forests, scrubs, and other natural areas remained relatively stable, decreasing only by 3% in 25 years. Full article
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Open AccessArticle
Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images
Remote Sens. 2018, 10(6), 901; https://doi.org/10.3390/rs10060901 - 08 Jun 2018
Cited by 8
Abstract
Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land [...] Read more.
Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy. Full article
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Open AccessArticle
Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes
Remote Sens. 2018, 10(3), 384; https://doi.org/10.3390/rs10030384 - 01 Mar 2018
Cited by 6
Abstract
Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy [...] Read more.
Over the last six decades, paddy fields on the Sanjiang Plain have experienced rapid expansion and aggregation. In our study, land use and land cover changes related to paddy fields were studied based on information acquired from topographic maps and remote-sensing images. Paddy field expansion and aggregation were investigated through landscape indices and trajectory codes. Furthermore, the possible causes of paddy field expansion and aggregation were explored. Results indicated that such fields have increased by approximately 42,704 ha·y−1 over the past six decades. Approximately 98% of paddy fields in 2015 were converted from other land use types. In general, the gravity center moved 254.51 km toward the northeast, at a rate of approximately 4.17 km·y−1. The cohesion index increased from 96.8208 in 1954 to 99.5656 in 2015, and the aggregation index grew from 91.3533 in 1954 to 93.4448 in 2015, indicating the apparent aggregation of paddy fields on the Sanjiang Plain. Trajectory analyses showed that the transformations from marsh as well as from grassland to dry farmland and then into paddy fields were predominant. Climate warming provided a favorable environment for rice planting. Meanwhile, population growth, technological progress, and government policies drove paddy field expansion and aggregation during the study period. Full article
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