Assessing Forest Type and Tree Species Classification Using Sentinel-1 C-Band SAR Data in Southern Sweden

Udali, Alberto; Lingua, Emanuele; Persson, Henrik J.

doi:10.3390/rs13163237

Cited by 26 publications

(15 citation statements)

References 47 publications

(92 reference statements)

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“…Sentinel-2 is equipped with a new multispectral imager with high spatial resolution, and contains three red-edge bands, which are used to detect the distribution and parameter inversion of forest resources. The Sentinel-1 C-band SAR acquires images under all light and weather conditions and has served with forest vegetation research [21]. The ALOS-2 L-band SAR has a better penetration of branches and leaves than the C-band and has greater potential for forest-resource monitoring [22,23].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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Forests are crucial in carbon sequestration and oxygen release. An accurate assessment of forest carbon storage is meaningful for Chinese cities to achieve carbon peak and carbon neutrality. For an accurate estimation of regional-scale forest aboveground carbon density, this study applied a Sentinel-2 multispectral instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) L-band, and Sentinel-1 C-band synthetic aperture radar (SAR) to estimate and map the forest carbon density. Considering the forest field-inventory data of eastern China from 2018 as an experimental sample, we explored the potential of the deep-learning algorithms convolutional neural network (CNN) and Keras. The results showed that vegetation indices from Sentinel-2, backscatter and texture characters from ALOS-2, and coherence from Sentinel-1 were principal contributors to the forest carbon-density estimation. Furthermore, the CNN model was found to perform better than traditional models. Results of forest carbon-density estimation validated the improvements effectively by combining the optical and radar data. Compared with traditional regression methods, deep learning has a higher potential for accurately estimating forest carbon density using multisource remote-sensing data.

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Section: Introductionmentioning

confidence: 99%

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

et al. 2022

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“…The UA refers to the proportion of correctly classified pixels within the image and represents the commission error. The kappa coefficient is a statistical measure used to test interrater reliability [56].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

2023

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Impervious surface cover reflects the urban environment and urban expansion. Lhasa City is a historical city and one of the most populous on the Qinghai–Tibetan Plateau, and has been experiencing rapid urbanization in recent years. Analyzing the impervious surface distribution can reveal urban development characteristics and provide data for sustainable urban planning to protect the heritage. This study explored the spatial and temporal changes and expansion patterns of impervious surfaces in different zones of Lhasa City. Impervious surface maps (2014 and 2021) were extracted from Gaofen-1 images with a high spatial resolution (2 m) using an object-based image analysis method. Next, a gravity center, standard deviational ellipses and landscape indices were used to characterize impervious surface expansions in different zones. The result indicated that the impervious surface in Lhasa expanded from 51.149 km2 in 2014 to 63.299 km2 in 2021. The growth rates of impervious surfaces inside the Environmental Coordination zone were lower than in the zones outside. From 2014 to 2021, the impervious surface of Lhasa expanded in the southeast direction. Infilling and consolidation were the primary impervious surface development patterns. The expansion of the impervious surface was related to topography, population, and economic and policy factors.

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“…Derived from a dual-polarization C-band SAR instrument, this homogenous subset of the S1 data includes the S1 ground range detected (GRD) scenes, processed using the Sentinel-1 Toolbox to generate calibrated, orthocorrected backscatter coefficient (σ • ) in decibels (dB) [19]. Alberto et al [42] used C-band SAR backscatter to evaluate the accuracy of forest type and tree species classification and improved the tree species classification results by combining the VV and VH bands of Sentinel-1 data. Therefore, this paper introduces the dual-polarization band data of Sentinel-1 data to further improve the results of tree species classification.…”

Section: Experimental Data 221 Sentinel-1 Data and Sentinel-2 Datamentioning

confidence: 99%

Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy

et al. 2022

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With the rapid development of remote sensing technology, researchers have attempted to improve the accuracy of tree species classifications from both data sources and methods. Although previous studies on tree species recognition have utilized the spectral and textural features of remote sensing images, they are unable to effectively extract tree species due to the problems of “same object with different spectrum” and “foreign object with the same spectrum”. Therefore, this study introduces vegetation functional datasets to further improve tree species classification. Using vegetation functional datasets, Sentinel-2 (S2) spectral datasets, and environmental datasets, combined with a Random Forest (RF) model, the classification of six types of land cover in Leye, Guangxi was completed and the planting distribution of Illicium verum in Leye County was extracted. Our results showed that the combination of vegetation functional datasets, S2 spectral datasets, and environmental datasets provided the highest overall accuracy (OA) (0.8671), Kappa coefficient (0.8382), and F1-Score (0.79). We believe that the vegetation functional datasets can enhance the accuracy of Illicium verum classification and provide new directions for tree species identification research. If vegetation functional datasets from more tree species are obtained in the future, we can extend them to the level of multiple tree species, and this approach may help to extract more information about forest species from remote sensing data in future studies.

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Assessing Forest Type and Tree Species Classification Using Sentinel-1 C-Band SAR Data in Southern Sweden

Cited by 26 publications

References 47 publications

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Estimation of Aboveground Carbon Density of Forests Using Deep Learning and Multisource Remote Sensing

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

Remote Sensing Extraction Method of Illicium verum Based on Functional Characteristics of Vegetation Canopy

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