Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

Fu, Bolin; Liang, Yiyin; Lao, Zhinan; Sun, Xidong; Li, Sunzhe; He, Hongchang; Sun, Weiwei; Fan, Donglin

doi:10.1016/j.jag.2023.103446

Cited by 15 publications

(8 citation statements)

References 49 publications

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“…Machine learning algorithms have been widely applied with a high level of confidence in tasks such as classification and regression. Fu Bolin et al (2023) [ 29 ] have achieved the classification of mangrove species by combing multidimensional optical and SAR images with machine learning. The OA increased by 12.85%.…”

Section: Introductionmentioning

confidence: 99%

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI

Zhang,

Wang,

et al. 2024

PLoS ONE

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Winter wheat is one of the most important crops in the world. It is great significance to obtain the planting area of winter wheat timely and accurately for formulating agricultural policies. Due to the limited resolution of single SAR data and the susceptibility of single optical data to weather conditions, it is difficult to accurately obtain the planting area of winter wheat using only SAR or optical data. To solve the problem of low accuracy of winter wheat extraction only using optical or SAR images, a decision tree classification method combining time series SAR backscattering feature and NDVI (Normalized Difference Vegetation Index) was constructed in this paper. By synergy using of SAR and optical data can compensate for their respective shortcomings. First, winter wheat was distinguished from other vegetation by NDVI at the maturity stage, and then it was extracted by SAR backscattering feature. This approach facilitates the semi-automated extraction of winter wheat. Taking Yucheng City of Shandong Province as study area, 9 Sentinel-1 images and one Sentinel-2 image were taken as the data sources, and the spatial distribution of winter wheat in 2022 was obtained. The results indicate that the overall accuracy (OA) and kappa coefficient (Kappa) of the proposed method are 96.10% and 0.94, respectively. Compared with the supervised classification of multi-temporal composite pseudocolor image and single Sentinel-2 image using Support Vector Machine (SVM) classifier, the OA are improved by 10.69% and 5.66%, respectively. Compared with using only SAR feature for decision tree classification, the producer accuracy (PA) and user accuracy (UA) for extracting the winter wheat are improved by 3.08% and 8.25%, respectively. The method proposed in this paper is rapid and accurate, and provide a new technical method for extracting winter wheat.

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Section: Introductionmentioning

confidence: 99%

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI

Zhang,

Wang,

et al. 2024

PLoS ONE

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“…Although this method has high accuracy, it is limited by a small detection range, low turnover, and data representativeness and requires considerable resources [3][4][5]. With the maturation of satellite remote sensing technology, multisource remote sensing can generate large-scale soil water content estimates with high spatial and temporal resolution, facilitating the acquisition of dynamic real-time information and compensating for the shortcomings of traditional monitoring methods [6].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Inversion of Soil Water Content in Alpine Meadow Area Based on Multitemporal Polarimetric SAR and Optical Remote Sensing Data

Kong,

Zuo,

2024

Journal of Spectroscopy

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Soil water content is a critical environmental parameter in research and practice, though various technological and contextual constraints limit its estimation in arid areas with vegetation cover. This study combined the multitemporal remote sensing data of Sentinel-1 and Landsat 8 to conduct an inversion study on surface soil water content under low vegetation cover in Nagqu, central Tibetan Plateau. Four vegetation indices (NDVI, ARVI, EVI, and RVI) were extracted from optical remote sensing data. A water cloud model was used to eliminate the influence of the vegetation layer on the backscattering coefficient associated with vegetation cover, and a predictive model suitable for the Nagqu area was constructed. The water cloud model effectively incorporated a vegetation index instead of vegetation water content. We found that VV polarization was more suitable for soil water content inversion than VH polarization. Among the four vegetation indices, the soil water content inversion model constructed with RVI under VV polarization had the best fit (R2 = 0.8212; RMSE = 6.30). The second-best fit was observed for vegetation water content-NDVI (R2 = 0.8201). The soil water content inversion models all had an R2 > 0.6, regardless of the vegetation index used, though the RVI had the best fitting effect, indicating that this vegetation index is highly applicable in the water cloud model, as a substitute for vegetation water content, and is expected to perform well in similar study sites.

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“…Remote sensing plays a unique role in monitoring inaccessible coastal ecosystems [11]. With the development of satellite remote sensing and intelligent classification algorithms, significant progress has been made in the extraction and monitoring of mangrove wetland areas [15][16][17][18]. In recent years, high-resolution GF and ZY series satellites have been widely used for mangrove monitoring tasks [14,19].…”

Section: Introductionmentioning

confidence: 99%

Using Multisource High-Resolution Remote Sensing Data (2 m) with a Habitat–Tide–Semantic Segmentation Approach for Mangrove Mapping

Sun,

Jiang,

Ling

et al. 2023

Remote Sensing

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Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development.

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Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series

Cited by 15 publications

References 49 publications

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI

Extracting the winter wheat using the decision tree based on time series dual-polarization SAR feature and NDVI

Collaborative Inversion of Soil Water Content in Alpine Meadow Area Based on Multitemporal Polarimetric SAR and Optical Remote Sensing Data

Using Multisource High-Resolution Remote Sensing Data (2 m) with a Habitat–Tide–Semantic Segmentation Approach for Mangrove Mapping

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