In recent years, the Yellow River Delta has been affected by invasive species Spartina alterniflora ( S. alterniflora ), resulting in a fragile ecological environment. It is of great significance to monitor the ground object types in the Yellow River Delta wetlands. The classification accuracy based on Synthetic Aperture Radar (SAR) backscattering coefficient is limited by the small difference between some ground objects. To solve this problem, a decision tree classification method for extracting the ground object types in wetland combined time series SAR backscattering and coherence characteristics was proposed. The Yellow River Delta was taken as the study area and the 112 Sentinel-1A GRD data with VV/VH dual-polarization and 64 Sentinel-1A SLC data with VH polarization were used. The decision tree method was established, based on the annual mean VH and VV backscattering characteristics, the new constructed radar backscattering indices, and the annual mean VH coherence characteristics were suitable for extracting the wetlands in the Yellow River Delta. Then the classification results in the Yellow River Delta wetlands from 2018 to 2021 were obtained using the new method proposed in this paper. The results show that the overall accuracy and Kappa coefficient of the proposed method w5ere 89.504% and 0.860, which were 9.992% and 0.127 higher than multi-temporal classification by Support Vector Machine classifier. Compared with the decision tree without coherence, the overall accuracy and Kappa coefficient were improved by 8.854% and 0.108. The spatial distributions of wetland types in the Yellow River Delta from 2018 to 2021 were obtained using the constructed decision tree. The spatio-temporal evolution analysis was conducted. The results showed that the area of S. alterniflora decreased significantly in 2020 but it increased to the area of 2018 in 2021. In addition, S. alterniflora seriously affected the living space of Phragmites australis ( P. australis ) and in 4 years, 10.485 km 2 living space of P. australis was occupied by S. alterniflora . The proposed method can provide a theoretical basis for higher accuracy SAR wetland classification and the monitoring results can provide an effective reference for local wetland protection.
Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.
Scientific and accurate estimation of rice yield is of great significance to food security protection and agricultural economic development. Due to the weak penetration of high frequency microwave band, most of the backscattering comes from the rice canopy, and the backscattering coefficient is highly correlated with panicle weight, which provides a basis for inversion of wet biomass of rice ear. To solve the problem of rice yield estimation at the field scale, based on the traditional water cloud model, a modified water-cloud model based on panicle layer and the radar data with Ku band was constructed to estimate rice yield at panicle stage. The wet weight of rice ear scattering model and grain number per rice ear scattering model were constructed at field scale for rice yield estimation. In this paper, the functional area of grain production in Xiashe Village, Xin'an Town, Deqing County, Zhejiang Province, China was taken as the study area. For the first time, the MiniSAR radar system carried by DJI M600 UAV was used in September 2019 to obtain the SAR data with Ku band under polarization HH of the study area as the data source. Then the rice yield was estimated by using the newly constructed modified water-cloud model based on panicle layer. The field investigation was carried out simultaneously for verification. The study results show: the accuracies of the inversion results of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel B were 95.03% and 94.15%; and the accuracies of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel C+D+E were over 91.8%. In addition, different growth stages had effects on yield estimation accuracy. For rice at fully mature, the yield estimation accuracies of wet weight of ear and grain number per ear were basically similar, both exceeding 94%. For rice at grouting stage, the yield estimation accuracy of wet weight of ear was 92.7%, better than that of grain number per ear. It was proved that it can effectively estimate rice yield using the modified water-cloud model based on panicle layer constructed in this paper at panicle stage at field scale.
Winter wheat is one of the major food crops in China. It is of great importance to timely and accurately monitor winter wheat cultivation for the formulation of agricultural policies. Therefore, to accurately and effectively calculate the planting information of winter wheat, we proposed a method for extracting winter wheat from short-time series synthetic aperture radar (SAR) data. It combines three new SAR indices, SAR backscatter features, and coherence features for extracting the winter wheat based on Sentinel-1 images from October 2021 to June 2022. First, the SAR backscatter coefficients were counted, and the newly constructed SAR indices and coherence features were introduced to increase the distinction between the winter wheat and other ground objects. Subsequently, the artificial neural network (ANN), support vector machine (SVM), and random forest (RF) classifiers were used to identify the main ground objects. The spatial distribution of winter wheat was obtained, and the accuracy was verified. Finally, the planting area of different growth stages for winter wheat was compared, and the relative errors between the extraction area of winter wheat and official statistics data also were calculated. The results showed that: (1) The accuracy of the RF classifier is better than SVM and ANN in extracting winter wheat, the overall accuracy is 95.653%, the Kappa coefficient is 0.933, and producer accuracy and user accuracy are 97.68% and 98.19%, respectively. (2) A more accurate thematic map of winter wheat can be obtained by combining the SAR backscatter features, new SAR indices, and coherence features. (3) By comparing the extraction results of different growth stages, the accuracy of the tassel stage is the highest. The short-time series SAR data of the tassel stage are constructed to replace the time series SAR data of the complete growth stage. It provides basic data for carrying out acreage and yield estimation of winter wheat before maturity.
It is of great significance to qccurately monitor the invasive vegetation in coastal wetlands for protection of ecological balance and stability. Aiming at the problems of low accuracy and high cost of extracting ground objects in wide area by the traditional method, this paper constructs a lightweight adaptive weighted semantic segmentation network based on PSPNet (named Light-AdaPSPNet) for automaticly extracting the spatial distribution of the Spartina alterniflora (S.alterniflora). Firstly, the backbone network ResNet50 of PSPNet is replaced by the MobileNet v2 with introducing the SENet and traversing the inverted residual module (SE-DeepMobileNet v2), which strengthens the feature extraction capability of the backbone network while reducing the number of parameters. Then the self-attention is added to the pyramid pooling module to realize the feature adaptive weighted fusion under different pooling scales. Finally, taking the pseudocolor image synthesized based on Sentinel-1 backscattering and coherence features after feature screening as data source, and the invasive vegetation S.alterniflora of the Yellow River Delta in China is extracted. The results show that the IOU, PA, and Precision of the newly constructed Light-AdaPSPNet reach 90.62%, 95.11%, and 95.04%. Compared with PSPNet, IOU, PA and Precision improved by 5.07%, 1.08% and 4.57%, while GFLOPs and the number of parameters decreased by 45.56G and 41.11M, respectively. The extraction accuracy is also better than UNet and Deeplab V3+. According to the monitoring results in the Yellow River Delta wetland, the area of S.alterniflora decreased slightly from 2018 to 2022, but the distribution location changed greatly. The spread of S.alterniflora was obvious in the Yellow River estuary and the south island. However, according to the results of coastline extraction, S.alterniflora also plays a positive role in sand fixation and stabilizing coastline. The new method and monitoring results in this paper can provide an effective reference for the accurate monitoring and management of invasive vegetation.
Vegetation is the functional subject in the wetland ecosystem and plays an irreplaceable role in biodiversity conservation. It is of great significance to monitor wetland vegetation for scientific assessment of the impact of vegetation on ecological environment and biodiversity. In this paper, a method for extracting wetland vegetation based on short time series Normalized Difference Vegetation Index (NDVI) data set was constructed. First, time series NDVI data were constructed using Sentinel-2 images. Then, the Support Vector Machine (SVM) classifier was used to classify the wetland vegetation types. The distributions of the main wetland vegetation in the study area in 2018 and 2020 were got. Finally, the land cover transfer matrix was calculated to analyze the spatial pattern and change of wetland vegetation emphatically from 2018 to 2020. Based on 46 Sentinel-2 images acquired in 2018 and 2020, the spatial pattern and change of vegetation in the Yellow River Delta wetlands were extracted and analyzed in this paper. The results show that: (1) The method for extracting wetland vegetation in estuary delta based on PIE-Engine platform and short time series NDVI data constructed in this paper can effectively extract the wetland vegetation information. The overall accuracy of the classification results reached 90.47% in 2018 and 80.30% in 2020. The Kappa coefficient of the classification results are 0.874 in 2018 and 0.739 in 2020 respectively. Compared with the results from the random forest classification method and the maximum likelihood classification method, the accuracy is improved by 6.40% and 13.04%, and the Kappa coefficient is improved by 0.055 and 0.069. (2) There were significant changes in vegetation coverage in the Yellow River Delta wetlands from 2018 to 2020. The Spartina alterniflora increased by 3.74km 2 . The Suaeda salsa degraded seriously, and the total area decreased by 20.38km 2 . In addition, the increase of Spartina alterniflora effectively guaranteed the stability of the coastline in the study area. This study can provide a theoretical basis for wetlands vegetation classificaton, and the classificaton results can provide scientific reference for protecting the ecological environment of wetlands and maintaining ecological stability.
Wetlands in estuary deltas functionally protect biodiversity, store water, and regulate ecological balance. However, wetland monitoring accuracy is low when using only synthetic aperture radar (SAR) images or optical images. This study proposes a novel method for extracting ground objects in a wetland using principal component analysis (PCA) and random forest (RF) classification, which combines the features of fully polarimetric SAR images and optical images. Firstly, polarization decomposition features and texture features were extracted based on polarimetric SAR data, and spectral features were extracted based on optical data. Secondly, the optical image was registered to SAR image. Then PCA was performed on the nine polarimetric features of the SAR images and the four spectral features of the optical images to obtain the first two principal components of each. After combining these components, a RF classification algorithm was used to extract the objects. The objects in the Yellow River Delta wetland were successfully extracted using our proposed method with Gaofen-3 fully polarimetric SAR data and Sentinel-2A optical data acquired in November 2018. The overall accuracy of the proposed method was 86.18%, and the Kappa coefficient was 0.84. This was an improvement of 18.96% and 0.22, respectively, over the GF-3 polarimetric features classification, and 11.02% and 0.13, respectively, over the Sentinel-2A spectral features classification. Compared with the results of the support vector machine, maximum likelihood, and minimum distance classification algorithms, the overall accuracy of the RF classification based on joint features was 2.03, 5.69, and 23.36% higher, respectively, and the Kappa coefficient was 0.03, 0.07, and 0.27 higher, respectively. Therefore, this novel method can increase the accuracy of the extraction of objects in a wetland, providing a reliable technical means for wetland monitoring.
The spectral similarity of regular and irregular ground objects and the problem of image noise and abnormal pixels in a wide range of ground object extraction have been difficult for accurate wetland classification. This paper proposes a two-order hierarchical classification method (HSNIC), and three new indices are constructed to extract salt marsh vegetation more accurately. Finally, the classification results of Yellow River Delta wetlands from 1990 to 2023 were obtained, and the invasion mechanism of Spartina alterniflora was further analyzed. The results show that: (1) The classification accuracy of the Yellow River Delta by HSNIC reached 88.48%. Compared with RF and object-oriented RF classification, the accuracy is improved. (2) Introducing the three new indices significantly enhanced the classification accuracy. (3) The invasion of S.alterniflora influenced the survival environment of local salt marsh vegetation. These research findings provide a basis for decision-making regarding wetland restoration and conservation in the Yellow River Delta.
This Flood disasters, with their high frequency and major hazards, seriously endanger the safety of human life and property. Therefore, flood monitoring is of great importance. Synthetic aperture radar (SAR) is not affected by clouds and rain, and obtain effective data for flood monitoring. In July 2021, Weihui City encountered heavy rainfall, causing severe flooding. This paper selects the Sentinel-1A SAR data of Weihui City before the flood (July 15), during the flood (July 27) and after the flood (August 8), and uses the object-oriented threshold method to extract the water body information, and conduct the flood inundation area monitoring and analysis. The results demonstrate that the use of Sentinel-1A data based on the object-oriented threshold method can achieve rapid monitoring of flood areas. Before the flood occurred in the main urban area of Weihui City, the water body coverage area is 4.18 km2 , and the water body coverage area is 45.72 km2 during the flood disaster. After the flood receded, the coverage area of the water body is reduced to 15.98 km2 . This indicates that the method proposed in this paper can effectively extract the coverage of water body and provide a reliable technical means for flood monitoring.
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