Object-based classification of vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images

Ruiz, Luis Fernando Chimelo; Guasselli, Laurindo Antônio; Simioni, João Paulo Delapasse; Belloli, Tássia Fraga; Fernandes, Pâmela Caroline Barros

doi:10.1016/j.srs.2021.100017

Cited by 23 publications

(18 citation statements)

References 47 publications

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“…Finally, the spatial context features were applied to wetlands classification by object-oriented analysis, whose results were closer to the real landcover [22]. The superpixels analysis facilitates the utilization of spatial and contextual features, and has great potential in SAR image classification [23][24][25][26].…”

Section: Introductionmentioning

confidence: 90%

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Yang

Liu

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Wetland is of significant ecological value, which is very important and challenging for large-scale mapping. Sentinel-1 can continuously record wetland changes with its all-day, allweather working capability. How to fully utilize multidimensional information, such as time and statistical texture of Synthetic Aperture Radar (SAR) image, to classify wetlands more accurately has become a research focus. Thus, this paper constructed Time Series Similarity parameters to describe the temporal change information of targets in wetland and introduced G 0 statistical texture parameters to describe texture characteristics of SAR images. Combining multi-features and superpixels, we proposed a classification method based on Random Forest (RF) to map Dongting Lake wetland. The overall classification accuracy was 97.57%, and the Kappa coefficient was 0.97. The classification accuracy of reed beach, grass marsh etc. was above 95%. The results showed the proposed SAR Time Series Similarity features could effectively utilize dynamical information among classes and was helpful to identify mudflat, grass marsh and reed beach with high dynamics in wetland. The introduced statistical texture features expressed the heterogeneity of targets, and enhanced the recognition and extraction ability of forest beach, mudflat and reed beach. Compared with Support Vector Machine (SVM), Decision Tree (DT) and RF classification methods, RF with superpixels optimized not only got high precision but also could effectively reduce the pepper-salt error in classification, because of the consideration of superpixels context information.

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Section: Introductionmentioning

confidence: 90%

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Yang

Liu

Chen

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In addition, of the features that conformed with the separability requirements, the top five features in order of importance were RE1, NDVI, Red, SWIR2, and Aerosols, and the growth stage associated with these five features was between September 17 and September 28, i.e., within this 10-day growth stage, the differences in the spectral features of the three crops were the most significant and the easiest to extract [65]. In addition, it can be thus determined that in addition to using the conventional wavebands (visible light and near infrared (NIR)) [66], the addition of 703.9 nm (S2A)/703.8 nm (S2B) from RE1 and 2202.4 nm (S2A)/2185.7 nm (S2B) from SWIR2 had important significance for crop classification [60]. The wavelength of the red edge is between those of Red and NIR, and the waveband range is approximately 670-780 nm, which is an area in which the spectral albedo of green vegetation rises rapidly within a certain waveband range; it is a sensitive spectral waveband for vegetation that is closely related to the pigment status and physical and chemical properties of crops and other vegetation.…”

Section: Evaluation Of the Features Selectedmentioning

confidence: 99%

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

Zhang²,

Luo

et al. 2022

Remote Sensing

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In recent years, the scale of rural land transfer has gradually expanded, and the phenomenon of non-grain-oriented cultivated land has emerged. Obtaining crop planting information is of the utmost importance to guaranteeing national food security; however, the acquisition of the spatial distribution of crops in large-scale areas often has the disadvantages of excessive calculation and low accuracy. Therefore, the IO-Growth method, which takes the growth stage every 10 days as the index and combines the spectral features of crops to refine the effective interval of conventional wavebands for object-oriented classification, was proposed. The results were as follows: (1) the IO-Growth method obtained classification results with an overall accuracy and F1 score of 0.92, and both values increased by 6.98% compared to the method applied without growth stages; (2) the IO-Growth method reduced 288 features to only 5 features, namely Sentinel-2: Red Edge1, normalized difference vegetation index, Red, short-wave infrared2, and Aerosols, on the 261st to 270th days, which greatly improved the utilization rate of the wavebands; (3) the rise of geographic data processing platforms makes it simple to complete computations with massive data in a short time. The results showed that the IO-Growth method is suitable for large-scale vegetation mapping.

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“…Three data types are used in this research to accurately identify different vegetation types in the study area, namely, Sentinel-2 images, Digital Elevation Model (DEM) data, and field data. Sentinel-2 provides images with satisfactory spatial resolution, revisit cycle, and abundant spectral bands among all free-accessed satellite data [26,27]. It has been widely used in many fields, such as vegetation type identification, forest resource monitoring, food safety assurance, and environmental monitoring.…”

Section: Data Source and Preprocessingmentioning

confidence: 99%

Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Liu

et al. 2021

Forests

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Obtaining accurate forest coverage of tree species is an important basis for the rational use and protection of existing forest resources. However, most current studies have mainly focused on broad tree classification, such as coniferous vs. broadleaf tree species, and a refined tree classification with tree species information is urgently needed. Although airborne LiDAR data or unmanned aerial vehicle (UAV) images can be used to acquire tree information even at the single tree level, this method will encounter great difficulties when applied to a large area. Therefore, this study takes the eastern regions of the Qilian Mountains as an example to explore the possibility of tree species classification with satellite-derived images. We used Sentinel-2 images to classify the study area’s major vegetation types, particularly four tree species, i.e., Sabina przewalskii (S.P.), Picea crassifolia (P.C.), Betula spp. (Betula), and Populus spp. (Populus). In addition to the spectral features, we also considered terrain and texture features in this classification. The results show that adding texture features can significantly increase the separation between tree species. The final classification result of all categories achieved an accuracy of 86.49% and a Kappa coefficient of 0.83. For trees, the classification accuracy was 90.31%, and their producer’s accuracy (PA) and user’s (UA) were all higher than 84.97%. We found that altitude, slope, and aspect all affected the spatial distribution of these four tree species in our study area. This study confirms the potential of Sentinel-2 images for the fine classification of tree species. Moreover, this can help monitor ecosystem biological diversity and provide references for inventory estimation.

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Object-based classification of vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images

Cited by 23 publications

References 47 publications

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Mapping Dongting Lake Wetland Utilizing Time Series Similarity, Statistical Texture, and Superpixels With Sentinel-1 SAR Data

Crop Mapping in the Sanjiang Plain Using an Improved Object-Oriented Method Based on Google Earth Engine and Combined Growth Period Attributes

Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

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