Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data

Fu, Bolin; Wang, Yeqiao; Campbell, Andrew R.; Li, Ying; Zhang, Bai; Yin, Shuiqing; Xing, Zeyu; Jin, Xiaomin

doi:10.1016/j.ecolind.2016.09.029

Cited by 165 publications

(77 citation statements)

References 41 publications

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“…Therefore, we used VV polarization of the constructed SAR image to fuse with the WV-3 image. The image fusion was conducted by wavelet principal component analysis (W-PCA), which can provide the highest accuracy in land cover classification and vegetation mapping based on the use of optical and SAR data for information extraction [18,19]. The steps of the W-PCA fusion are: (i) apply PCA to the multispectral WV-3 image data and obtain the first principal component (PC1); (ii) match the histograms of the Sentinel-1 and PC1 image data; (ii) utilize a wavelet decomposition to merge Sentinel-1 images into the PC1 image; (iv) apply the inverse PCA transform so that the embedded Sentinel-1 information carried by the PC1 image can be integrated to obtain the fused image.…”

Section: Image Fusionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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The surface mining activities in grassland and rangeland zones directly affect the livestock production, forage quality, and regional grassland resources. Mine rehabilitation is necessary for accelerating the recovery of the grassland ecosystem. In this work, we investigate the integration of data obtained via a synthetic aperture radar (Sentinel-1 SAR) with data obtained by optical remote sensing (Worldview-3, WV-3) in order to monitor the conditions of a vegetation area rehabilitated after coal mining in North China. The above-ground biomass (AGB) is used as an indicator of the rehabilitated vegetation conditions and the success of mine rehabilitation. The wavelet principal component analysis is used for the fusion of the WV-3 and Sentinel-1 SAR images. Furthermore, a multiple linear regression model is applied based on the relationship between the remote sensing features and the AGB field measurements. Our results show that WV-3 enhanced vegetation indices (EVI), mean texture from band8 (near infrared band2, NIR2), the SAR vertical and horizon (VH) polarization, and band 8 (NIR2) from the fused image have higher correlation coefficient value with the field-measured AGB. The proposed AGB estimation model combining WV-3 and Sentinel 1A SAR imagery yields higher model accuracy (R 2 = 0.79 and RMSE = 22.82 g/m 2 ) compared to that obtained with any of the two datasets only. Besides improving AGB estimation, the proposed model can also reduce the uncertainty range by 7 g m −2 on average. These results demonstrate the potential of new multispectral high-resolution datasets, such as Sentinel-1 SAR and Worldview-3, in providing timely and accurate AGB estimation for mine rehabilitation planning and management.

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Section: Image Fusionmentioning

confidence: 99%

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Bao

et al. 2019

Remote Sensing

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“…We used McNemar's test for paired-sample nominal scale data (Agresti 2002) to assess whether statistically significant differences exist between the classifications. This test is suitable to assess the performance of multiple classifications that use the same test and training samples (Foody 2004) and has been applied widely in thematic map comparison (Duro et al 2012;Fu et al 2017).…”

Section: Classificationmentioning

confidence: 99%

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Crowson

Warren‐Thomas

Hill

et al. 2018

Remote Sens Ecol Conserv

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The loss of huge areas of peat swamp forest in Southeast Asia and the resulting negative environmental effects, both local and global, have led to an increasing interest in peat restoration in the region. Satellite remote sensing offers the potential to provide up‐to‐date information on peat swamp forest loss across large areas, and support spatial explicit conservation and restoration planning. Fusion of optical and radar remote sensing data may be particularly valuable in this context, as most peat swamp forests are in areas with high cloud cover, which limits the use of optical data. Radar data can ‘see through’ cloud, but experience so far has shown that it doesn't discriminate well between certain types of land cover. Various approaches to fusion exist, but there is little information on how they compare. To assess this untapped potential, we compare three different classification methods with Sentinel‐1 and Sentinel‐2 images to map the remnant distribution of peat swamp forest in the area surrounding Sungai Buluh Protection Forest, Sumatra, Indonesia. Results show that data fusion increases overall accuracy in one of the three methods, compared to the use of optical data only. When data fusion was used with the pixel‐based classification using the original pixel values, overall accuracy increased by a small, but statistically significant amount. Data fusion was not beneficial in the case of object‐based classification or pixel‐based classification using principal components. This indicates optical data are still the main source of information for land cover mapping in the region. Based on our findings, we provide methodological recommendations to help those involved in peatland restoration capitalize on the potential of big data.

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“… Component substitution techniques such as principal component analysis (PCA) (Fu et al., ; Yonghong, ) and intensity‐hue‐saturation (IHS) transformation (Chen, Hepner, & Forster, ; Leung, Liu, & Zhang, ) are among the most widely used pixel‐fusion techniques (Pohl & Yen, ). During PCA fusion, the original pixel values extracted from the radar and multispectral images are used to define new axes along which data variability is maximised; the new, fused pixel values are essentially linear combinations of their position along these new axes (Amarsaikhan et al., ).…”

Section: Overview Of Multispectral‐radar Srs Data Fusion Techniques mentioning

confidence: 99%

“…Some insights are provided byFu et al (2017), who found that fusing ALOS PALSAR/RADARSAT-2 and multispectral imagery from GF-1 increased mapping accuracy of wetland vegetation types beyond that achieved using these data on their own. Similarly, when multispectral and radar imagery (Landsat TM5 and ALOS PALSAR respectively) were integrated in a classification tree modelling approach, misclassification of different types of wetland vegetation and the extent of standing F I G U R E 3 Spatial resolution and launch date of freely available SRS data with global coverage from active, long-term missions.…”

mentioning

confidence: 99%

Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science

Bühne

Pettorelli

2017

Methods Ecol Evol

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The availability and accessibility of multispectral and radar satellite remote sensing (SRS) imagery are at an unprecedented high. These data have both become standard source of information for investigating species ecology and ecosystems structure, composition and function at large scales. Since they capture complementary aspects of the Earth's surface, synergies between these two types of imagery have the potential to greatly expand research and monitoring opportunities. However, despite the benefits of combining multispectral and radar SRS data, data fusion techniques, including image fusion, are not commonly used in biodiversity monitoring, ecology and conservation. To help close this application gap, we provide for the first time an overview of the most common SRS data fusion techniques, discussing their benefits and drawbacks, and pull together case studies illustrating the added value for biodiversity research and monitoring. Integrating and fusing multispectral and radar images can significantly improve our ability to assess the distribution as well as the horizontal and vertical structure of ecosystems. Additionally, SRS data fusion has the potential to increase opportunities for mapping species distribution and community composition, as well as for monitoring threats to biodiversity. Uptake of these techniques will benefit from more effective collaboration between remote sensing and biodiversity experts, making the reporting of methodologies more transparent, expanding SRS image processing capacity and promoting widespread open access to satellite imagery. In the context of a global biodiversity crisis, being able to track subtle changes in the biosphere across adequate spatial and temporal extents and resolutions is crucial. By making key parameter estimates derived from SRS data more accurate, SRS data fusion promises to become a powerful tool to help address current monitoring needs, and could support the development of essential biodiversity variables.

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Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data

Cited by 165 publications

References 41 publications

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

Biomass Estimation for Semiarid Vegetation and Mine Rehabilitation Using Worldview-3 and Sentinel-1 SAR Imagery

A comparison of satellite remote sensing data fusion methods to map peat swamp forest loss in Sumatra, Indonesia

Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science

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