Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

Gudmann, András; Csikós, Nándor; Szilassi, Péter; Mucsi, László

doi:10.3390/rs12213580

Cited by 32 publications

(19 citation statements)

References 46 publications

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“…This is because in Binh Duong province in particular as well as in Vietnam in general, these areas often consist of a mixture of low-albedo construction areas and green spaces, which have a spatial pattern and structure similar to new high-end residential areas in medium-resolution images. The integration of landscape metrics into classification stages, which is similar to studies by Zheng et al [57] and Gudmann et al [58], will probably help in this case. This approach should be considered in future research.…”

Section: Discussionsupporting

confidence: 53%

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

Bui

Mucsi

2021

Remote Sensing

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It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

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Section: Discussionsupporting

confidence: 53%

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

Bui

Mucsi

2021

Remote Sensing

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“…This non-parametric land use classification method offers a more superior method for working with missing data. It substitutes missing values with a variable appearing the most in a particular node [56]. However, in our case, the most important advantage of using the random forest classifier has been the improvement in the mapping accuracy.…”

Section: Pros and Cons Of Using The Random Forest In Land Use Classificationmentioning

confidence: 97%

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

et al. 2021

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Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.

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“…However, other studies used the CLC directly as reference data. Gudmann et al [19] performed a LULC classification using Sentinel-2 and Landsat-8 images combined with OBIA (object-based image analysis) and the landscape metric approach [60] and found good correspondence. Their study relied on the accuracy assessment with the CLC2018; thus, this research had the most similarity to our work.…”

Section: Clc Classes and The Mixture Of Spectral Featuresmentioning

confidence: 99%

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

Varga

Kovács²,

Bekő

et al. 2021

Remote Sensing

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We analyzed the Corine Land Cover 2018 (CLC2018) dataset to reveal the correspondence between land cover categories of the CLC and the spectral information of Landsat-8, Sentinel-2 and PlanetScope images. Level 1 categories of the CLC2018 were analyzed in a 25 km × 25 km study area in Hungary. Spectral data were summarized by land cover polygons, and the dataset was evaluated with statistical tests. We then performed Linear Discriminant Analysis (LDA) and Random Forest classifications to reveal if CLC L1 level categories were confirmed by spectral values. Wetlands and water bodies were the most likely to be confused with other categories. The least mixture was observed when we applied the median to quantify the pixel variance of CLC polygons. RF outperformed the LDA’s accuracy, and PlanetScope’s data were the most accurate. Analysis of class level accuracies showed that agricultural areas and wetlands had the most issues with misclassification. We proved the representativeness of the results with a repeated randomized test, and only PlanetScope seemed to be ungeneralizable. Results showed that CLC polygons, as basic units of land cover, can ensure 71.1–78.5% OAs for the three satellite sensors; higher geometric resolution resulted in better accuracy. These results justified CLC polygons, in spite of visual interpretation, can hold relevant information about land cover considering the surface reflectance values of satellites. However, using CLC as ground truth data for land cover classifications can be questionable, at least in the L1 nomenclature.

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Improvement in Satellite Image-Based Land Cover Classification with Landscape Metrics

Cited by 32 publications

References 46 publications

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

Validation of Visually Interpreted Corine Land Cover Classes with Spectral Values of Satellite Images and Machine Learning

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