Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery

Gašparović, Mateo; Dobrinić, Dino

doi:10.3390/rs12121952

Cited by 57 publications

(32 citation statements)

References 105 publications

(109 reference statements)

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“…The Sentinel-1 SAR GRD dataset was collected using the interference wide-band (IW) mapping mode, with a spatial resolution of 10 m, a width of 250 km and an average incidence angle of 30-45 • . Each Sentinel-1 image stored by the GEE platform, which had been preprocessed using the European Space Agency's (ESA) Sentinel-1 Toolbox including orbit restitution, thermal noise removal, terrain correction and radiometric calibration [39][40][41].…”

Section: Sentinel-1 Sar Image and Preprocessingmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

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The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite images were segmented by simple noniterative clustering (SNIC) according to different sizes and finally, the training samples and processed images were input into a random forest classifier for crop classification. The results showed the following: 1) the overall accuracy of using the object-oriented classification method combined composite Sentinel-1 image represented a great improvement compared with the pixel-based classification method in areas with large average plots (increase by 10%), the applicable scope of the method depends on the plot size of the study area; 2) the shorter time interval of the composite Sentinel-1 image was, the higher the crop classification accuracy was; 3) the features with high importance of composite Sentinel-1 images with different time intervals were mainly distributed in July, August and September, which was mainly due to the large differences in crop growth in these months; and 4) the optimal segmentation size of crop classification was closely related to image resolution and plot size. Previous studies usually emphasize the advantages of object-oriented classification. Our research not only emphasizes the advantages of object-oriented classification but also analyzes the constraints of using object-oriented classification, which is very important for the follow-up research of crop classification using object-oriented and synthetic aperture radar (SAR).

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Section: Sentinel-1 Sar Image and Preprocessingmentioning

confidence: 99%

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Luo

Liu

et al. 2021

Remote Sensing

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“…The difference in the performance of classification algorithm have been demonstrated using different methods. (Gašparović and Dobrinić, 2020) used McNemar's χ 2 test to compare the performance of classifier algorithms, including XGB, SVM and Random Forest (RF). In this study we also compare the performance of the three classifiers, namely Naive Bayes, Decision Tree (DT) and Random Forest (RF) in a total of 84 classifications carried out across the multi-date images.…”

Section: Discussionmentioning

confidence: 99%

Object-Based Change Detection on Acacia Xanthophloea Species Degradation Along Lake Nakuru Riparian Reserve

Osio

Lefèvre

2021

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Automated mapping of heterogeneous riparian landscape is of high interest to assess our planet. Still, it remains a challenging task due to the occurrence of flooded vegetation. While both optical and radar images can be exploited, the latter has the advantage of being independent acquisition conditions. However, and despite their popularity, the threshold-based approaches commonly used present some drawbacks such as not taking into account the spatial context and providing mixed pixels within class boundaries. In this study, we propose a novel methodology to avoid such issues by using an object-based image analysis approach on polarimetric radar data. We use our workflow to map the degrading Acacia x. species along lake Nakuru Riparian reserve, and obtain highly-accurate results.

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“…(1) ALS data allowed for an automated and more accurate identification of AAL in terms of classification accuracy (>90%) and spatial resolution (<1.0 m) than did other RS platforms [53][54][55][56][57][58][59][60][61][62][63][64]. Potential improvements in process of AAL identification may be achieved using some qualitative variable of ALS data (e.g., intensity) or alternatively through multispectral ALS data [65][66][67][68].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have also demonstrated the potential of radar data as an alternative to optical images for the identification of AAL. Synthetic aperture radar systems provided all-weather mapping capability [64], but the overall accuracy of AAL identification varied from 63% to 93% [18,65] and thus did not exceed the limits of optical images. Moreover, the limited spatial resolution of both optical and radar images may not be sufficient to identify the fragmented occurrence and dynamics of land abandonment.…”

Section: Spatial Identification Of Abandoned Agricultural Landmentioning

confidence: 99%

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

2020

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Mapping aboveground woody biomass (AGB) on abandoned agricultural land (AAL) is required by relevant stakeholders to monitor the spatial dynamics of farmland afforestation, to assess the carbon sequestration, and to set the appropriate management of natural resources. The objective of this study was, therefore, to present and assess a workflow consisting of (1) the spatial identification of AAL based on a combination of airborne laser scanning (ALS) data, cadastral data, and Land Parcel Identification System data, and (2) the prediction of AGB on AAL using an area-based approach and a nonparametric random forest (RF) model based on a combination of field and ALS data. Part of the second objective was also to evaluate the applicability of (1) the author-developed algorithm for the calculation of ALS metrics and (2) a single comprehensive RF model for the whole area of interest. The study was conducted in the forest management unit Vígľaš (Slovakia, Central Europe) covering a total area of 12,472 ha. Specifically, five reference areas consisting of 11,194 reference points were used to assess the accuracy of the spatial identification of AAL, and seventy-five ground reference plots were used for the development of the ALS-based AGB model and for assessing the accuracy of the AGB map. The overall accuracy of the spatial identification of AAL was found to be 93.00% (Cohen’s kappa = 0.82). The difference between ALS-predicted and ground-observed AGB reached a relative root mean square error (RMSE) at 26.1%, 33.1%, and 21.3% for the whole sample size, plots dominated by shrub species, and plots dominated by tree species, respectively.

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Comparative Assessment of Machine Learning Methods for Urban Vegetation Mapping Using Multitemporal Sentinel-1 Imagery

Cited by 57 publications

References 105 publications

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine

Object-Based Change Detection on Acacia Xanthophloea Species Degradation Along Lake Nakuru Riparian Reserve

Mapping Aboveground Woody Biomass on Abandoned Agricultural Land Based on Airborne Laser Scanning Data

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