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Validation and Inter-Comparison of Land Cover and Land Use Data

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Special Issue Information
Published Papers

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

Deadline for manuscript submissions: closed (30 March 2016) | Viewed by 108808

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Special Issue Editors

Prof. Dr. Martin Herold

E-Mail Website
Guest Editor

Department of Environmental Sciences, Wageningen University, Wageningen, The Netherlands
Interests: large area land and forest monitoring; monitoring and reporting for UNFCCC and Sustainable Development Goals
Special Issues, Collections and Topics in MDPI journals

Dr. Linda See

E-Mail Website
Guest Editor

International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: citizen science, crowdsourcing and volunteered geographic information (data collection, quality assessment, creating added value products with VGI, motivation and engagement, etc.); land cover/land use validation; creation of hybrid land cover products; serious gaming; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The generation of land cover and land use data remains an evolving field in providing essential information for many policy and scientific applications, such as climate modeling, biodiversity, ecosystem assessment, food security, and environmental modeling. Data are being generated using different approaches and novel technologies (i.e., remote sensing, crowdsourcing, sensor webs, surveying, etc.) with increasing levels of detail and accuracy in space, time, and thematic attributes. While datasets are derived from various initiatives and with different strategies for using input data, methodologies, and standards, they often lack comparability and (accuracy) assessments of their strengths and weaknesses for certain users and applications. Approaches for validation and inter-comparison remain vital in a field that is dynamic and is responding to both the opportunities from novel technologies and increasing demands from a growing user community. The ambitions in science and practice are to address (for example) more detailed spatial scales (i.e., global land monitoring from Landsat-type data), more consistent land change using a longer-time series (i.e., via big-data approaches), integration of different data sources (i.e., remote sensing and crowdsourcing), and increasingly a focus on land use (rather than land cover only). These areas of interest require robust approaches and studies for accuracy-driven comparisons and assessments of data and methods and the incorporation of user needs to synthesize the full potential of new technologies for applications. In that context, we would like to invite you to submit articles about your recent research with respect to the following topics:

(1)   Methodologies for validation and inter-comparison: theory and practice
(2)   Accuracy-driven inter-comparison of land cover and land use datasets (including regional and global case studies)
(3)   Integration of land cover and land use data from different observation sources including crowdsourced data
(4)   Comparison of large-area estimates for land change
(5)   Generation and analysis of reference/validation datasets
(6)   Monitoring land cover and land use (change): novel concepts and methods
(7)   Approaches and experiences dealing with conflating information
(8)   User needs and implications for land monitoring, intercomparison and accuracy assessments
(9)   Review articles covering one or more of these topics are also welcome.

Prof. Dr. Martin Herold
Dr. Linda See
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 (9 papers)

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Editorial

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363 KiB

Open AccessEditorial

Towards an Integrated Global Land Cover Monitoring and Mapping System

by Martin Herold, Linda See, Nandin-Erdene Tsendbazar and Steffen Fritz

Remote Sens. 2016, 8(12), 1036; https://doi.org/10.3390/rs8121036 - 20 Dec 2016

Cited by 20 | Viewed by 8476

Abstract

Global land cover mapping has evolved in a number of ways over the past two decades including increased activity in the areas of map validation and inter-comparison, which is the main focus of this Special Issue in Remote Sensing. Here we describe the major trends in global land cover mapping that have occurred, followed by recent advances as exemplified by the papers in the Special Issue. Finally, we consider what the future holds for global land cover mapping. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Research

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6195 KiB

Open AccessArticle

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community

by Céline Lamarche, Maurizio Santoro, Sophie Bontemps, Raphaël D’Andrimont, Julien Radoux, Laura Giustarini, Carsten Brockmann, Jan Wevers, Pierre Defourny and Olivier Arino

Remote Sens. 2017, 9(1), 36; https://doi.org/10.3390/rs9010036 - 11 Jan 2017

Cited by 72 | Viewed by 11867

Abstract

Accurate maps of surface water extent are of paramount importance for water management, satellite data processing and climate modeling. Several maps of water bodies based on remote sensing data have been released during the last decade. Nonetheless, none has a truly (90 [...] Read more.

^{\circ}

N/90

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S) global coverage while being thoroughly validated. This paper describes a global, spatially-complete (void-free) and accurate mask of inland/ocean water for the 2000–2012 period, built in the framework of the European Space Agency (ESA) Climate Change Initiative (CCI). This map results from the synergistic combination of multiple individual SAR and optical water body and auxiliary datasets. A key aspect of this work is the original and rigorous stratified random sampling designed for the quality assessment of binary classifications where one class is marginally distributed. Input and consolidated products were assessed qualitatively and quantitatively against a reference validation database of 2110 samples spread throughout the globe. Using all samples, overall accuracy was always very high among all products, between

98 %

and

100 %

. The CCI global map of open water bodies provided the best water class representation (F-score of

89 %

) compared to its constitutive inputs. When focusing on the challenging areas for water bodies’ mapping, such as shorelines, lakes and river banks, all products yielded substantially lower accuracy figures with overall accuracies ranging between

74 %

and

89 %

. The inland water area of the CCI global map of open water bodies was estimated to be 3.17 million km

^{2}

± 0.24 million km

^{2}

. The dataset is freely available through the ESA CCI Land Cover viewer. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Graphical abstract

4337 KiB

Open AccessArticle

Calibration and Validation of Landsat Tree Cover in the Taiga−Tundra Ecotone

by Paul Mannix Montesano, Christopher S. R. Neigh, Joseph Sexton, Min Feng, Saurabh Channan, Kenneth J. Ranson and John R. Townshend

Remote Sens. 2016, 8(7), 551; https://doi.org/10.3390/rs8070551 - 29 Jun 2016

Cited by 29 | Viewed by 8002

Abstract

Monitoring current forest characteristics in the taiga−tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover >80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Graphical abstract

1898 KiB

Open AccessArticle

Four National Maps of Broad Forest Type Provide Inconsistent Answers to the Question of What Burns in Canada

by Guillermo Castilla, Sebastien Rodrigue, Rob S. Skakun and Ron J. Hall

Remote Sens. 2016, 8(7), 539; https://doi.org/10.3390/rs8070539 - 24 Jun 2016

Cited by 4 | Viewed by 6372

Abstract

Wildfires are burning increasingly extensive areas of forest in Canada, reducing their capacity as carbon sinks. Here we compare the answers that four independent land cover datasets, produced from different satellite images (SPOT, Landsat, and MODIS), provide for the question of what burned in Canada in recent years. We harmonized the different datasets into a common, simpler legend consisting of three classes of forest (needle-leaf, broadleaf, and mixed) plus non-forest, and resampled them to a common pixel size (250 m). Then we used annual maps of burned area to count, for each map and year from 2011 to 2014, the number of burned pixels of each class, and we tabulated them by terrestrial ecozone and Canada-wide. While all four maps agree that needle-leaf forest is the most frequently burned class in Canada, there is great disparity in the results from each map regarding the proportion of burned area that each class represents. Proportions reported by one map can be more than double those reported by another map, and more than four times at the ecozone level. We discuss the various factors that can explain the observed discrepancies and conclude that none of the maps provides a sufficiently accurate answer for applications such as carbon accounting. There is a need for better information in areas lacking forest inventory, especially in the vast unmanaged forest of Canada. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Graphical abstract

1420 KiB

Open AccessArticle

Bayesian Analysis of Uncertainty in the GlobCover 2009 Land Cover Product at Climate Model Grid Scale

by Tristan Quaife and Edward Cripps

Remote Sens. 2016, 8(4), 314; https://doi.org/10.3390/rs8040314 - 08 Apr 2016

Cited by 9 | Viewed by 6102

Abstract

Land cover data derived from satellites are commonly used to prescribe inputs to models of the land surface. Since such data inevitably contains errors, quantifying how uncertainties in the data affect a model’s output is important. To do so, a spatial distribution of possible land cover values is required to propagate through the model’s simulation. However, at large scales, such as those required for climate models, such spatial modelling can be difficult. Also, computer models often require land cover proportions at sites larger than the original map scale as inputs, and it is the uncertainty in these proportions that this article discusses. This paper describes a Monte Carlo sampling scheme that generates realisations of land cover proportions from the posterior distribution as implied by a Bayesian analysis that combines spatial information in the land cover map and its associated confusion matrix. The technique is computationally simple and has been applied previously to the Land Cover Map 2000 for the region of England and Wales. This article demonstrates the ability of the technique to scale up to large (global) satellite derived land cover maps and reports its application to the GlobCover 2009 data product. The results show that, in general, the GlobCover data possesses only small biases, with the largest belonging to non–vegetated surfaces. In vegetated surfaces, the most prominent area of uncertainty is Southern Africa, which represents a complex heterogeneous landscape. It is also clear from this study that greater resources need to be devoted to the construction of comprehensive confusion matrices. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Graphical abstract

2950 KiB

Open AccessArticle

Comparison of Data Fusion Methods Using Crowdsourced Data in Creating a Hybrid Forest Cover Map

by Myroslava Lesiv, Elena Moltchanova, Dmitry Schepaschenko, Linda See, Anatoly Shvidenko, Alexis Comber and Steffen Fritz

Remote Sens. 2016, 8(3), 261; https://doi.org/10.3390/rs8030261 - 22 Mar 2016

Cited by 37 | Viewed by 7809

Abstract

Data fusion represents a powerful way of integrating individual sources of information to produce a better output than could be achieved by any of the individual sources on their own. This paper focuses on the data fusion of different land cover products derived from remote sensing. In the past, many different methods have been applied, without regard to their relative merit. In this study, we compared some of the most commonly-used methods to develop a hybrid forest cover map by combining available land cover/forest products and crowdsourced data on forest cover obtained through the Geo-Wiki project. The methods include: nearest neighbour, naive Bayes, logistic regression and geographically-weighted logistic regression (GWR), as well as classification and regression trees (CART). We ran the comparison experiments using two data types: presence/absence of forest in a grid cell; percentage of forest cover in a grid cell. In general, there was little difference between the methods. However, GWR was found to perform better than the other tested methods in areas with high disagreement between the inputs. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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2895 KiB

Open AccessArticle

Methods to Quantify Regional Differences in Land Cover Change

by Alexis Comber, Heiko Balzter, Beth Cole, Peter Fisher, Sarah C.M. Johnson and Booker Ogutu

Remote Sens. 2016, 8(3), 176; https://doi.org/10.3390/rs8030176 - 25 Feb 2016

Cited by 21 | Viewed by 7945

Abstract

This paper describes and illustrates methods for quantifying regional differences in land use/land cover changes. A series of approaches are used to analyse differences in land cover change from data held in change matrices. These are contingency tables and are commonly used in remote sensing to describe the spatial coincidence of land cover recorded over two time periods. Comparative analyses of regional change are developed using odds ratios to analyse data in two regions. These approaches are extended using generalised linear models to analyse data for three or more regions. A generalised Poisson regression model is used to generate a comparative index of change based on differences in change likelihoods. Mosaic plots are used to provide a visual representation of statistically surprising land use losses and gains. The methods are explored using a hypothetical but tractable dataset and then applied to a national case study of coastal land use changes over 50 years conducted for the National Trust. The suitability of the different approaches to different types of problem and the potential for their application to land cover accuracy measures are briefly discussed. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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2010 KiB

Open AccessArticle

Spatial Accuracy Assessment and Integration of Global Land Cover Datasets

by Nandin-Erdene Tsendbazar, Sytze De Bruin, Steffen Fritz and Martin Herold

Remote Sens. 2015, 7(12), 15804-15821; https://doi.org/10.3390/rs71215804 - 26 Nov 2015

Cited by 65 | Viewed by 9829

Abstract

Along with the creation of new maps, current efforts for improving global land cover (GLC) maps focus on integrating maps by accounting for their relative merits, e.g., agreement amongst maps or map accuracy. Such integration efforts may benefit from the use of multiple GLC reference datasets. Using available reference datasets, this study assesses spatial accuracy of recent GLC maps and compares methods for creating an improved land cover (LC) map. Spatial correspondence with reference dataset was modeled for Globcover-2009, Land Cover-CCI-2010, MODIS-2010 and Globeland30 maps for Africa. Using different scenarios concerning the used input data, five integration methods for an improved LC map were tested and cross-validated. Comparison of the spatial correspondences showed that the preferences for GLC maps varied spatially. Integration methods using both the GLC maps and reference data at their locations resulted in 4.5%–13% higher correspondence with the reference LC than any of the input GLC maps. An integrated LC map and LC class probability maps were computed using regression kriging, which produced the highest correspondence (76%). Our results demonstrate the added value of using reference datasets and geostatistics for improving GLC maps. This approach is useful as more GLC reference datasets are becoming publicly available and their reuse is being encouraged. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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Review

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2647 KiB

Open AccessReview

A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring

by Neha Joshi, Matthias Baumann, Andrea Ehammer, Rasmus Fensholt, Kenneth Grogan, Patrick Hostert, Martin Rudbeck Jepsen, Tobias Kuemmerle, Patrick Meyfroidt, Edward T. A. Mitchard, Johannes Reiche, Casey M. Ryan and Björn Waske

Remote Sens. 2016, 8(1), 70; https://doi.org/10.3390/rs8010070 - 16 Jan 2016

Cited by 480 | Viewed by 40420

Abstract

The wealth of complementary data available from remote sensing missions can hugely aid efforts towards accurately determining land use and quantifying subtle changes in land use management or intensity. This study reviewed 112 studies on fusing optical and radar data, which offer unique spectral and structural information, for land cover and use assessments. Contrary to our expectations, only 50 studies specifically addressed land use, and five assessed land use changes, while the majority addressed land cover. The advantages of fusion for land use analysis were assessed in 32 studies, and a large majority (28 studies) concluded that fusion improved results compared to using single data sources. Study sites were small, frequently 300–3000 km 2 or individual plots, with a lack of comparison of results and accuracies across sites. Although a variety of fusion techniques were used, pre-classification fusion followed by pixel-level inputs in traditional classification algorithms (e.g., Gaussian maximum likelihood classification) was common, but often without a concrete rationale on the applicability of the method to the land use theme being studied. Progress in this field of research requires the development of robust techniques of fusion to map the intricacies of land uses and changes therein and systematic procedures to assess the benefits of fusion over larger spatial scales. Full article

(This article belongs to the Special Issue Validation and Inter-Comparison of Land Cover and Land Use Data)

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