Classification and Evolutionary Analysis of Yellow River Delta Wetlands Using Decision Tree Based on Time Series SAR Backscattering Coefficient and Coherence

Abstract

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 km2 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.

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

“…Using band math tool to perform mean processing on time series images. The purpose of this step is to further remove noise and reveal the mean backscattering features of different ground objects [ 38 ].…”

Vegetation changes in Yellow River Delta wetlands from 2018 to 2020 using PIE-Engine and short time series Sentinel-2 images

“…Confusion matrix verification (Townsend, 1971) has been proved to be effective in verifying classification accuracy. In this paper, combined with GF-2 remote sensing images with high resolution, Google Earth images and the classification results of other scholars (Wang et al, 2022;Li Z. J. et al, 2022;Zhang B. et al, 2019), 127 verification samples were used to evaluate the accuracy of classification results. The locations of verification samples are shown in Figure 8.…”

“…With the further study, researchers found that Spartina alterniflora and other vegetation have significant differences in growth cycle. This is manifested in the differences of different spectral features (Zheng et al, 2017) and texture features (Guo et al, 2020) in optical images, and in the characteristics of backscattering coefficient (Hu et al, 2021) and coherence (Li Z. J. et al, 2022) in SAR images. At present, medium and low resolution remote sensing images are effective materials for monitoring Spartina alterniflora and vegetation classification in a large area.…”

“…The cloud computing method provides researchers with powerful computing capacity and cloud storage space. It can overcome the problems of low efficiency such as local download, storage and pre-processing, and is widely used in monitoring the land cover and change in a large scale (Liang et al, 2020;Akhoondzadeh, 2022;Li Z. J. et al, 2022;Ning et al, 2022). PIE-Engine (Cheng et al, 2022) is an online remote sensing cloud computing open platform integrating real-time distributed computing, interactive analysis and data visualization.…”

Field-scale rice yield estimation based on UAV-based MiniSAR data with Ku band and modified water-cloud model of panicle layer at panicle stage

“…This has limited the large-scale application and promotion of related yield estimation methods. Synthetic Aperture Radar (SAR) is not influenced by cloud, fog, rain, snow and other weather, and it can obtain image data with the advantages of day/night data acquisition, all-weather imaging capability, and strong penetrability ( Li et al., 2022 ; Wang et al., 2022 ; Yu et al., 2022 ). Satellite remote sensing can observe the earth from space over a large area.…”