Land-cover harmonisation and semantic similarity: some methodological issues

Jansen, Louisa J. M.; Groom, Geoff; Carrai, Giancarlo

doi:10.1080/17474230802332076

Cited by 33 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another perceived advantage of the use of LCML formalized legends is the possibility to compare results obtained with different class definition, as presented by Reis et al [80]. However, although LCML formalized legends are easily understandable, they may not be directly comparable and a harmonization process would still be necessary [25].…”

Section: Discussionmentioning

confidence: 99%

“…As a result, many regional and global LULC maps have been produced and are available [13][14][15][16][17][18][19][20][21][22]. However, these maps have been created with different objectives, class definition and resolution, producing different maps useful for specific studies, rendering the harmonization and integration of the selected ones very challenging [23][24][25]. In this study, we will focus on problems associated with class definition, mainly within the Brazilian Amazon.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

Reis

Escada

Dutra

et al. 2018

Land

View full text Add to dashboard Cite

Land Use and Land Cover (LULC) classes defined by subjective criteria can diminish the significance of a study, hindering the reproducibility and the comparison of results with other studies. Having a standard legend for a given study area and objective could benefit a group of researchers focused on long-term or multidisciplinary studies in a given area, in the sense that they would be able to maintain class definition among different works, done by different teams. To allow for reproducibility, it is important that the classes in this legend are described using quantifiable elements of land cover, which can be measured on the ground, as is recommended by Land Cover Meta Language (LCML). The present study aims to propose LCML formalized hierarchical legends for LULC classes, focusing on the southwest of Pará state, within the Brazilian Amazon. In order to illustrate the potential of these legends, a secondary objective of the current study is to analyze classification results using legends derived from a particular Remote Sensing dataset and compare these results with the classification obtained using the LCML hierarchical legend proposed. To perform this analysis, firstly, we proposed a conceptual class model based on existing classification systems for the upland Brazilian Amazon Biome. From this model, 16 LULC classes were described in LCML, using quantifiable and easily recognizable physiognomic characteristics of land cover classes measured on the lower Tapajós river, in Pará state. These classes were grouped into legends with different levels of detail (number of classes), based on our model or on the image and clustering algorithms. All legends were used in supervised classification of a Landsat5/TM image. Results indicate that it is necessary to incorporate multi-temporal knowledge for class definition as well as the proposed thresholds (height and cover proportion of soil, litter, herbaceous vegetation, shrubs, and trees) in order to properly describe classes. However, the thresholds are useful to delimit classes that happen in a successive way. Classification results revealed that classes formed by the same elements of land cover with similar thresholds present high confusion. Additionally, classifications obtained using legends based on the class separability in a given Remote Sensing image tend to be more accurate but not always useful because they can hide or mix important classes. It was observed that the more generalized the legend (those with few details and number of classes), the more accurate the classifications results are for all types of legends.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

Reis

Escada

Dutra

et al. 2018

Land

View full text Add to dashboard Cite

show abstract

“…The source for methodological changes can be manifold like semantic differences due to different classifications (Fuller et al, 2003;Comber et al, 2004;Feng and Flewelling, 2004;Ahlqvist, 2008;Fritz and See, 2008;Jansen et al, 2008) and uncertainty in boundaries between classes at individual time steps (De Groeve and Lowell, 2001). A second source are issues associated with spectral properties of sensors, image acquisition conditions, spatial resolution and preprocessing of remote sensing material (Khorram, 1999;Foody, 2002;Fuller et al, 2003;Coppin et al, 2004;Lu et al, 2004;Neumann et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

A Dutch multi-date land use database: Identification of real and methodological changes

Hazeu

Bregt

Wit

et al. 2011

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

“…A prerequisite to compare land cover data from existing LC products is the harmonization of the different classification legends. Processing aspects and recommendations for LC harmonization are described in [38]. Although GLC2000 and GlobCover are based on different mixed unit definitions and LC legends, both consider 22 LC classes according to the United Nations (UN) Land Cover Classification System (LCCS) [8].…”

Section: Comparison With Existing Lc Productsmentioning

confidence: 99%

Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products

Vuolo

Atzberger

2012

Remote Sensing

View full text Add to dashboard Cite

Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cover maps. This work evaluates the classification performance of a least square support vector machine (LS-SVM) algorithm with respect to areas of agreement and disagreement between two existing land cover maps. The approach involves the use of time series of Moderate-resolution Imaging Spectroradiometer (MODIS) 250-m Normalized Difference Vegetation Index (NDVI) (16-day composites) and gridded climatic indicators. LS-SVM is trained on reference samples obtained through visual interpretation of Google Earth (GE) high resolution imagery. The core of the training process is based on repeated random splits of the training dataset to select a small set of suitable support vectors optimizing class separability. A large number of independent validation samples spread over three contrasting regions in Europe (Eastern Austria, Macedonia and Southern France) are used to calculate classification accuracies for the LS-SVM NDVI-derived LC map and for two (globally available) LC products: GLC2000 and GlobCover. The LS-SVM LC map reported an overall accuracy of 70%. Classification accuracies ranged from 71% where GlobCover and GLC2000 agreed to 68% for areas of disagreement. Results indicate that existing LC OPEN ACCESSRemote Sens. 2012, 4 3144 products are as accurate as the LS-SVM LC map in areas of agreement (with little margin for improvements), while classification accuracy is substantially better for the LS-SVM LC map in areas of disagreement. On average, the LS-SVM LC map was 14% and 18% more accurate compared to GlobCover and GLC2000, respectively.

show abstract

Land-cover harmonisation and semantic similarity: some methodological issues

Cited by 33 publications

References 29 publications

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies

A Dutch multi-date land use database: Identification of real and methodological changes

Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products

Contact Info

Product

Resources

About