Land use/cover classification of a complex agricultural landscape using single-dated very high spatial resolution satellite-sensed imagery

Okubo, Seisuke; Parikesit,; Muhamad, Dendi; Harashina, Koji; Takeuchi, Kazuhiko; Umezaki, Masahiro

doi:10.5589/m11-010

Cited by 16 publications

(4 citation statements)

References 33 publications

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“…Using only two land cover classes as a method of monitoring forest change, Woodcock et al, (2001) noted that spatial extension is possible, and in fact comparable to other methods, but only when used for nearby scenes. This observation supports the findings of other authors who noted that the success of land cover classification is hampered when an area is heterogeneous, as geographical complexity can have a negative effect on the spectral separability of classes and can reduce classification accuracy (Okubo et al, 2010). Spectral separability measures have been used to provide an indication of the potential accuracy of land cover classifications (Su et al, 2010) in heterogeneous areas.…”

Section: Introductionsupporting

confidence: 78%

Transferability of decision trees for land cover classification in a heterogeneous area

Verhulp¹,

Niekerk²

2017

SA J of Geomatics

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Section: Introductionsupporting

confidence: 78%

Transferability of decision trees for land cover classification in a heterogeneous area

Verhulp¹,

Niekerk²

2017

SA J of Geomatics

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“…In contrast, the VHR image bands or the combination of VHR and GLCM increase the OA by as much as 0.0536. Both Trias-Sanz et al [21] and Okubo et al [20] also found that the GLCM did not improve the GEOBIA LULC classification in very high resolution imagery (aerial and QuickBird). Since adding the GLCM to the S2 data has a bigger contribution than when added to the S2i and S2+ data, we can derive that the indices are enough to classify land use at the object level.…”

Section: Improvements Adding the Glcmmentioning

confidence: 95%

“…The combination of textural and spectral information in their GEOBIA approach improved the classification. However, the texture indices from GLCM should not be included in the segmentation process to generate objects because GLCM extracts pixel-wise metrics using a sliding window and when encountering heterogeneous classes, high contrast will be detected [20]. For example, a forest is a heavily textured patch due to the variation in vegetation and shadows.…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery

2023

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Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index.

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“…Detailed and precise information on land use and land cover (LULC) in rural areas is essential to establish sustainable rural development and ecosystem management [1,2]. After years of China's policies such as land exploitation, consolidation, rehabilitation, and the "new countryside" policy, rural settlements have changed enormously in terms of their amount, location, composition, and configuration [3,4].…”

Section: Introductionmentioning

confidence: 99%

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

Zheng

Weston

et al. 2017

Remote Sensing

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Multiple policy projects have changed land use and land cover (LULC) in China's rural regions over the past years, resulting in two types of rural settlements: new-fashioned and old-fashioned. Precise extraction of and discrimination between these two settlement types are vital for sustainable land use development. It is difficult to identify these two types via remote sensing images due to their similarities in spectrum, texture, and geometry. This study attempts to discriminate different types of rural settlements by using a spatial contextual information extraction method based on Gaofen 2 (GF-2) images, which integrate hierarchical multi-scale segmentation and landscape analysis. A preliminary LULC map was derived by using only traditional spectral and geometrical features from a finer scale. Subsequently, a vertical connection was built between superobjects and subobjects, and landscape metrics were computed. The vertical connection was used for assigning landscape contextual information to subobjects. Finally, a classification phase was conducted, in which only multi-scale contextual information was adopted, to discriminate between new-fashioned and old-fashioned rural settlements. Compared with previous studies on multi-scale contextual information, this paper employs landscape metrics to quantify contextual characteristics, rather than traditional spectral, textural, and topological relationship information, from superobjects. Our findings indicate that this approach effectively identified and discriminated two types of rural settlements, with accuracies over 80% for both producers and users. A comparison with a conventional top-down hierarchical classification scheme showed that this novel approach improved accuracy, precision, and recall. Our results confirm that multi-scale contextual information with landscape metrics provides valuable spatial information for classification, and indicates the practicability, applicability, and effectiveness of this synthesized approach in distinguishing different types of rural settlements.

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Land use/cover classification of a complex agricultural landscape using single-dated very high spatial resolution satellite-sensed imagery

Cited by 16 publications

References 33 publications

Transferability of decision trees for land cover classification in a heterogeneous area

Transferability of decision trees for land cover classification in a heterogeneous area

Land Use and Land Cover Mapping with VHR and Multi-Temporal Sentinel-2 Imagery

Rural Settlement Subdivision by Using Landscape Metrics as Spatial Contextual Information

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