EXTENDING ACCURACY ASSESSMENT PROCEDURES OF GLOBAL COVERAGE LAND COVER MAPS THROUGH SPATIAL ASSOCIATION ANALYSIS

Abstract. High-resolution land cover maps are in high demand for many environmental applications. Yet, the information they provide is uncertain unless the accuracy of these maps is known. Therefore, accuracy assessment should be an integral part of land cover map production as a way of ensuring reliable products. The traditional accuracy metrics like Overall Accuracy and Producer’s and User’s accuracies &ndash; based on the confusion matrix &ndash; are useful to understand global accuracy of the map, but they do not provide insight into the possible nature or source of the errors. The idea behind this work is to complement traditional accuracy metrics with the analysis of error spatial patterns. The aim is to discover errors underlying features which can be later employed to improve the traditional accuracy assessment. The designed procedure is applied to the accuracy assessment of the GlobeLand30 global land cover map for the Lombardy Region (Northern Italy) by means of comparison with the DUSAF regional land cover map. Traditional accuracy assessment quantified the classification accuracies of the map. Indeed, critical errors were pointed out and further analyses on their spatial patterns were performed by means of the Moran’s I indicator. Additionally, visual exploration of the spatial patterns was performed. This allowed describing possible sources of errors. Both software and analysis strategies were described in detail to facilitate future improvement and replication of the procedure. The results of the exploratory experiments are critically discussed in relation to the benefits that they potentially introduce into the traditional accuracy assessment procedure.

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INTER-COMPARISON OF THE GLOBAL LAND COVER MAPS IN AFRICA SUPPLEMENTED BY SPATIAL ASSOCIATION OF ERRORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w14-11-2019 ◽

2019 ◽

Vol XLII-4/W14 ◽

pp. 11-16

Author(s):

G. Bratic ◽

D. Oxoli ◽

M. A. Brovelli

Keyword(s):

Land Cover ◽

Large Scale ◽

Confusion Matrix ◽

Spatial Association ◽

Probability Of Error ◽

Earth Observations ◽

Area Of Interest ◽

Global Land Cover ◽

Land Cover Maps ◽

Single Pixel

Abstract. Recent advances in Earth Observations supported development of high-resolution land cover (LC) maps on a large-scale. This is an important step forward, especially for developing countries, which experienced problems in the past due to absence of reliable LC information. Nevertheless, increasing number of LC products is imposing additional validation workload to confirm their quality. In this paper inter-comparison of two recent LC products (GlobeLand30 and S2 prototype LC 20m map of Africa) for country of Rwanda in Africa was done. It is a way to facilitate validation by identifying the areas with higher probability of error. Specific approach of comparison of single pixel of one map with multiple pixels of another map provided confusion matrix and sub-pixel agreement table. In this work, accuracy indexes based on the confusion matrix were computed as a measure of similarity between the two maps. Furthermore, Moran’s I index was computed for estimation of spatial association of the pixels in disagreement. Also, total disagreement, as well as disagreement of particularly confused classes was visualised to analyse their spatial distribution. The results are showing that similarity of the two maps is about 66%. Disagreements are spatially associated and the most evident in the eastern and north-western part of the area of interest. This coincides also with the distribution of the two most confused classes Wetland and Shrubland. The results delineate areas of inconsistency between the two maps, and therefore areas where careful accuracy analysis are needed.

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Global Land Cover Assessment Using Spatial Uniformity Validation Dataset

Remote Sensing ◽

10.3390/rs13152950 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2950

Author(s):

Yoshie Ishii ◽

Koki Iwao ◽

Tsuguki Kinoshita

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Validation Dataset ◽

Validation Data ◽

Global Land Cover ◽

Spatial Uniformity ◽

Global Land ◽

Land Cover Map ◽

Map Validation ◽

Land Cover Maps

The Degree Confluence Project (DCP) is a volunteer-based validation dataset that comprises useful information for global land cover map validation. However, there is a problem with using DCP points as validation data for the accuracy assessment of land cover maps. While resolutions of typical global land cover maps are several hundred meters to several kilometers, DCP points can only guarantee an area of several tens of meters that can be confirmed by ground photographs. So, the objective of this study is to create a land cover map validation dataset with added spatial uniformity information using satellite images and DCP points. For this, we devised a new method to semiautomatically guarantee the spatial uniformity of DCP validation data points at any resolution. This method can judge the validation data with guaranteed uniformity with a user’s accuracy of 0.954. Furthermore, we conducted the accuracy assessment for the existing global land cover maps by the DCP validation data with guaranteed spatial uniformity and found that the trends differed by class and region.

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Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

Remote Sensing ◽

10.3390/rs10081212 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1212 ◽

Cited By ~ 4

Author(s):

Xiaohong Yang ◽

Zhong Xie ◽

Feng Ling ◽

Xiaodong Li ◽

Yihang Zhang ◽

...

Keyword(s):

Land Cover ◽

Spatial Pattern ◽

Spatial Resolution ◽

Super Resolution ◽

Land Cover Mapping ◽

Fuzzy C Means Clustering ◽

Land Cover Map ◽

Spatio Temporal ◽

Fine Resolution ◽

Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

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First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia

Data ◽

10.3390/data5040117 ◽

2020 ◽

Vol 5 (4) ◽

pp. 117

Author(s):

Céline Bassine ◽

Julien Radoux ◽

Benjamin Beaumont ◽

Taïs Grippa ◽

Moritz Lennert ◽

...

Keyword(s):

Land Cover ◽

Land Use Planning ◽

Climate Modeling ◽

Decision Rules ◽

Ancillary Data ◽

Map Labeling ◽

Small Water ◽

Very High Spatial Resolution ◽

Land Cover Map ◽

Land Cover Maps

Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.

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Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-w3-503-2015 ◽

2015 ◽

Vol XL-7/W3 ◽

pp. 503-510 ◽

Cited By ~ 4

Author(s):

M. Schultz ◽

N. E. Tsendbazazr ◽

M. Herold ◽

M. Jung ◽

P. Mayaux ◽

...

Keyword(s):

Land Cover ◽

Climate Models ◽

Accuracy Assessment ◽

Careful Consideration ◽

Global Climate Models ◽

Reference Dataset ◽

Heterogeneous Landscape ◽

Global Land Cover ◽

Global Land ◽

Land Cover Maps

Many investigators use global land cover (GLC) maps for different purposes, such as an input for global climate models. The current GLC maps used for such purposes are based on different remote sensing data, methodologies and legends. Consequently, comparison of GLC maps is difficult and information about their relative utility is limited. The objective of this study is to analyse and compare the thematic accuracies of GLC maps (i.e., IGBP-DISCover, UMD, MODIS, GLC2000 and SYNMAP) at 1 km resolutions by (a) re-analysing the GLC2000 reference dataset, (b) applying a generalized GLC legend and (c) comparing their thematic accuracies at different homogeneity levels. The accuracy assessment was based on the GLC2000 reference dataset with 1253 samples that were visually interpreted. The legends of the GLC maps and the reference datasets were harmonized into 11 general land cover classes. There results show that the map accuracy estimates vary up to 10-16% depending on the homogeneity of the reference point (HRP) for all the GLC maps. An increase of the HRP resulted in higher overall accuracies but reduced accuracy confidence for the GLC maps due to less number of accountable samples. The overall accuracy of the SYNMAP was the highest at any HRP level followed by the GLC2000. The overall accuracies of the maps also varied by up to 10% depending on the definition of agreement between the reference and map categories in heterogeneous landscape. A careful consideration of heterogeneous landscape is therefore recommended for future accuracy assessments of land cover maps.

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Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

Remote Sensing ◽

10.3390/rs12213479 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3479

Author(s):

Yuan Gao ◽

Liangyun Liu ◽

Xiao Zhang ◽

Xidong Chen ◽

Jun Mi ◽

...

Keyword(s):

European Union ◽

Land Cover ◽

Hydrological Cycle ◽

Accuracy Assessment ◽

Confusion Matrix ◽

The European Union ◽

Mountain Areas ◽

Land Cover Types ◽

Spatial Consistency ◽

The Eu

Land-cover plays an important role in the Earth’s energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

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Assessing the impacts of human uncertainty in the accuracy assessment of land-cover maps using linguistic scales and fuzzy intervals

International Journal of Remote Sensing ◽

10.1080/01431161.2015.1043034 ◽

2015 ◽

Vol 36 (10) ◽

pp. 2524-2547 ◽

Cited By ~ 1

Author(s):

Pedro Sarmento ◽

Cidália C. Fonte ◽

Joel Dinis ◽

Stephen V. Stehman ◽

Mário Caetano

Keyword(s):

Land Cover ◽

Accuracy Assessment ◽

Land Cover Maps

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Conventional and fuzzy accuracy assessment of the New York Gap Analysis Project land cover map

Remote Sensing of Environment ◽

10.1016/s0034-4257(02)00020-2 ◽

2002 ◽

Vol 81 (2-3) ◽

pp. 443-455 ◽

Cited By ~ 40

Author(s):

M Laba ◽

S.K Gregory ◽

J Braden ◽

D Ogurcak ◽

E Hill ◽

...

Keyword(s):

New York ◽

Land Cover ◽

Gap Analysis ◽

Accuracy Assessment ◽

Land Cover Map

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Land Use/Land Cover Changes Monitoring and Analysis of Dubai Emirate, UAE Using Multi-Temporal Remote Sensing Data

10.29007/jvz3 ◽

2018 ◽

Cited By ~ 2

Author(s):

Mohamed Mostafa Mohamed ◽

Samy Elmahdy

Keyword(s):

Land Use ◽

Land Cover ◽

Accuracy Assessment ◽

Low Cost ◽

Confusion Matrix ◽

Land Development ◽

Remote Sensing Data ◽

Land Use Land Cover ◽

Spectral Angle Mapper ◽

Fast Pace

Dubai is a rapidly urbanizing emirate with land development succeeding at a fast pace. The present study aims to develop a low-cost classifier based on the spectral angle mapper (SAM) and image difference (ID) algorithms. The proposed approach was developed in order to improve Land use/ Land cover (LULC) classification maps for the purpose of monitoring and analysing LULC change during the period from 2000 to 2015 for the Emirate of Dubai. The approach starts by collecting 320 training samples from high resolution images such as QuickBird with a spatial resolution of 60 cm followed by applying a 3×3 spatial convulsion filter, majority/ minority analysis, sieving classes and clump map of the produced LULC maps. After that, the accuracy of the maps were assigned using confusion matrix. The accuracy assessment demonstrated that the targeted 2000, 2005,2010 and 2015 LULC maps have 88.125%, 89.069%, 90.122% and 96.096% accuracy, respectively. The results exhibited that the built-up areas increased by 233.72 km2 (5.81%) from 2000 to 2005 and keeps to increase even up and till the present time. The results also showed that the changes in the periods 2000-2005 and 2010-2015 confirmed that net vegetation area loses were more obvious from 2005 to 2005 than from 2010 to 2015, reducing from 47.618 km2 to 40,820 km2, respectively. This study is of great help to urban planners and decision makers.

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