A methodology to generate a synergetic land-cover map by fusion of different land-cover products

Pérez-Hoyos, Ana; Garcı́a-Haro, Francisco Javier; San-Miguel-Ayanz, Jesús

doi:10.1016/j.jag.2012.04.011

Cited by 62 publications

(58 citation statements)

References 30 publications

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“…They were identified from a hybrid land-cover map by the synergistic combination of four classifications: CORINE, GLC2000, MODIS and GlobCover [49]. To ensure a good homogeneity and reduce the impact of misregistration errors, we imposed the agreement between the pixel label and modal land-cover label within a 3 × 3 pixel window (with a frequency of modal value higher than 6).…”

Section: Simulation Of Fapar Time Seriesmentioning

confidence: 99%

Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

et al. 2014

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Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR) is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted) local regression filter (LOESS) and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG), smoothing spline (SSP), Interpolation for Data Reconstruction (IDR) and adaptive Savitzky-Golay (ASG). This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC) MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20%) reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud OPEN ACCESSRemote Sens. 2014, 6 8239 and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal dynamics in situ where rapid seasonal changes are produced.

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Section: Simulation Of Fapar Time Seriesmentioning

confidence: 99%

Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

et al. 2014

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“…Current satellite missions (SPOT, MODIS and MERIS) have become an increasingly vital component of the global observing system used for the monitoring of land cover and its dynamics [31]. In this paper, two commonly used coarse-resolution satellite land cover products are evaluated over the area where the CASI transects were obtained.…”

Section: Coarse-resolution Land Covermentioning

confidence: 99%

“…As a consequence, the assessment of the accuracy of land cover maps derived from satellite data has been an important preoccupation of the remote sensing community, and a variety of approaches, including direct [21][22][23][24][25][26][27][28][29] and indirect [30][31][32][33][34] methods, have been developed. Among them, the sub-pixel fractional error matrix has been an effective and accurate evaluation approach for coarse-resolution land cover products, since the sub-fractional error matrix was introduced as a more appropriate way for defining accuracy based on areal sampling units.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China

Wang

Liu

2014

Remote Sensing

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Abstract:The accuracy of different coarse-resolution land cover products is an important consideration for product users at the regional or global scale, and different evaluation methods inevitably result in discrepancies in accuracy for the same land cover product. The remote sensing community has responded to this increased interest by improving methodologies for more accurately evaluating the correctness of land cover information. In this study, a pixel-based hierarchical classification strategy followed by an object-based classification method was applied to compact airborne spectrographic imager (CASI) hyperspectral data in order to produce highly accurate, high spatial resolution classification reference data. Some aspects of the fuzzy/conventional evaluation of MODIS land cover (MODISLC) (500 m) and GlobCover (300 m) data based on sub-pixel class fractions derived from high spatial resolution reference data at different thematic resolutions are also discussed. Relationships between homogeneity and fuzzy accuracy for two land cover products were obtained at different thematic resolutions. Additionally, the influences on the relationship resulting from the thematic resolution were also studied, and these are reported in this paper. Attempts were made to establish fuzzy/conventional evaluation rules for fuzzy classes, and the different performances of the fuzzy and conventional evaluations for hard/fuzzy labels were compared. The adjusted GlobCover accuracy after theoretical removal of the effect caused by spatial resolution was calculated based on the relationship OPEN ACCESSRemote Sens. 2014, 6 2865 between homogeneity and accuracy; the result was a higher accuracy than for MODISLC at the same thematic resolution. In addition, the different performance characteristics of the relationships between homogeneity and adjusted GlobCover accuracy/MODISLC accuracy at different thematic resolutions were compared and analyzed over the area where the CASI transects were obtained.

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“…Several researchers tried to produce a hybrid global land cover map by fusion of existing land cover products [7,23,24]. See and Fritz [24] firstly produced a hybrid land cover map by fusion of the GLC2000 and MODIS products.…”

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confidence: 99%

“…Fritz et al [25] then generated a synergy cropland map in sub-Saharan Africa from five global land cover products, which requires subjective ranking by experts and does not consider legends conversion. Perez-Hoyos et al [7] developed a general framework of building a hybrid land-cover map for Europe using four land-cover data-sets. This approach can be applied to any set of existing products; however it requires enough training data, which limits its application to the global scale.…”

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confidence: 99%

A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products

Zhang

Chen

et al. 2014

Remote Sensing

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Global land cover is an important parameter of the land surface and has been derived by various researchers based on remote sensing images. Each land cover product has its own disadvantages and limitations. Data fusion technology is becoming a notable method to fully integrate existing land cover information. In this paper, we developed a method to generate a synergetic global land cover map (synGLC) based on Bayes theorem. A state probability vector was defined to precisely and quantitatively describe the land cover classification of every pixel and reduce the errors caused by legends harmonization and spatial resampling. Simple axiomatic approaches were used to generate the prior land cover map, in which pixels with high consistency were regarded to be correct and then used as benchmark to obtain posterior land cover map. Validation results show that our hybrid land cover map (synGLC, the dataset is available on request) has the best overall performance compared with the existing global land cover products. Closed shrub-lands and permanent wetlands have the highest uncertainty in our fused land cover map. 5590This novel method can be extensively applied to fusion of land cover maps with different legends, spatial resolutions or geographic ranges.

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A methodology to generate a synergetic land-cover map by fusion of different land-cover products

Cited by 62 publications

References 30 publications

Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

Assessment of Coarse-Resolution Land Cover Products Using CASI Hyperspectral Data in an Arid Zone in Northwestern China

A Bayesian Based Method to Generate a Synergetic Land-Cover Map from Existing Land-Cover Products

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