Application of red edge band in remote sensing extraction of surface water body: a case study based on GF-6 WFV data in arid area

This paper mainly researches the application method of red edge band in water body remote sensing extraction. Gaofen-6 (GF-6) WFV data were chosen for the high spatial resolution, more bands, and wide width. Two new methods were proposed: the single-band threshold method based on the red edge 2 band and the decision tree model method based on the combined operation of the green band, red band, near infrared band, red edge 1 band, and red edge 2 band. Four traditional methods were used for comparing the extraction accuracy. Two study areas with different characteristics were chosen to analyze the reliability of the proposed method, one mountainous and one urban region, both located in Minqin, Gansu, China, which is a typical arid area. The results showed that the two red edge bands of the GF-6 WFV data can be utilized to extract water body information. Kappa coefficients extracted from the single-band threshold method based on the red edge 2 band in water bodies in mountainous and urban areas reached 96.18% and 93.21%, respectively. The decision tree method has the best extraction effect. Kappa coefficients of this method in mountain and urban water bodies were 97.73% and 94.41%, respectively.

Crop Classification Based on Red Edge Features Analysis of GF-6 WFV Data

A red edge band is a sensitive spectral band of crops, which helps to improve the accuracy of crop classification. In view of the characteristics of GF-6 WFV data with multiple red edge bands, this paper took Hengshui City, Hebei Province, China, as the study area to carry out red edge feature analysis and crop classification, and analyzed the influence of different red edge features on crop classification. On the basis of GF-6 WFV red edge band spectral analysis, different red edge feature extraction and red edge indices feature importance evaluation, 12 classification schemes were designed based on GF-6 WFV of four bands (only including red, green, blue and near-infrared bands), stepwise discriminant analysis (SDA) and random forest (RF) method were used for feature selection and importance evaluation, and RF classification algorithm was used for crop classification. The results show the following: (1) The red edge 750 band of GF-6 WFV data contains more information content than the red edge 710 band. Compared with the red edge 750 band, the red edge 710 band is more conducive to improving the separability between different crops, which can improve the classification accuracy; (2) According to the classification results of different red edge indices, compared with the SDA method, the RF method is more accurate in the feature importance evaluation; (3) Red edge spectral features, red edge texture features and red edge indices can improve the accuracy of crop classification in different degrees, and the red edge features based on red edge 710 band can improve the accuracy of crop classification more effectively. This study improves the accuracy of remote sensing classification of crops, and can provide reference for the application of GF-6 WFV data and its red edge bands in agricultural remote sensing.

Landsat-8 vs. Sentinel-2: Landuse Landcover Change Analysis and Differences in Gudur Municipality

With the recent free availability of moderate to high spatial resolution data (10m-30m), land use analysis became more robust. The launch of Sentinel-2a by the European Space Agency, coupled with the availability of free Landsat data, availed more analysis capabilities for the science community with a wide variety of temporal, spatial, and spectral capabilities. This study compares the synergetic use of Landsat and Sentinel-2 in mapping Land Use Land cover themes in Gudur, explicitly utilizing the red edge band of Sentinel-2. A combination of both sentinel and Landsat data results in higher spatial resolution. Classification of the red edge band produces better resolution than the classification of Landsat Imagery.

Extraction of fractional vegetation cover in arid desert area based on Chinese GF-6 satellite

The red edge band is considered as one of the diagnosable characteristics of green plants, but the large-scale remote sensing retrieval of fractional vegetation coverage (FVC) based on the red edge band is still rare. To explore the application of the red edge band in the remote sensing estimation of FVC, this study proposed a new vegetation index (normalized difference red edge index, RENDVI) based on the two red edge bands of Chinese GaoFen-6 satellite (GF-6). The FVC estimated by using three vegetation indices (NDVI, RENDVI1, and RENDVI2) were evaluated based on the field survey FVC obtained in Minqin Basin of Gansu Province. The results showed that there was a good linear correlation between the FVC estimated by GF-6 WFV data and the FVC investigated in the field, and the most reasonable estimation of FVC was obtained based on RENDVI2 model (R 2 = 0.97611 and RMSE = 0.07075). Meanwhile, the impact of three confidence levels (1, 2, and 5%) on FVC was also analyzed in this study. FVC obtained from NDVI and RENDVI2 has the highest accuracy at 2% confidence, while FVC based on RENDVI1 achieved the best accuracy at 5% confidence. It could be concluded that it is feasible and reliable to estimate FVC based on red edge bands, and the GF-6 Wide Field View (WFV) data with high temporal and spatial resolution provide a new data source for remote sensing estimation of FVC.

Design selective band antenna using coupling sidewall and multi resonator for wireless communications

Design and simulation of antenna with performance operating at bandwidth (4.5-6.5) GHz, with center frequency 5.5 GHz. This antenna constriction from two cylindrical antenna shapes and four cylindrical resonators with include iris to matching impedance, to enhancement sharp edge band and four tuned between coupling sidewall to removed distortions. Improving the frequency selection of the bandwidth requirement antenna is by adjusting the cylinder length of the antenna The proposed antenna is operating ideal filter because the edge of the band rejection is matched the edge of the bandwidth requirement which makes it perform well including broad edges for the band rejection and sharp edges of the bandwidth requirement together with a little insertion between two types and good return loss of the bandwidth requirement. CST software used to investigate and simulated results.

Recognition of Banana Fusarium Wilt Based on UAV Remote Sensing

Fusarium wilt (Panama disease) of banana currently threatens banana production areas worldwide. Timely monitoring of Fusarium wilt disease is important for the disease treatment and adjustment of banana planting methods. The objective of this study was to establish a method for identifying the banana regions infested or not infested with Fusarium wilt disease using unmanned aerial vehicle (UAV)-based multispectral imagery. Two experiments were conducted in this study. In experiment 1, 120 sample plots were surveyed, of which 75% were used as modeling dataset for model fitting and the remaining were used as validation dataset 1 (VD1) for validation. In experiment 2, 35 sample plots were surveyed, which were used as validation dataset 2 (VD2) for model validation. An UAV equipped with a five band multispectral camera was used to capture the multispectral imagery. Eight vegetation indices (VIs) related to pigment absorption and plant growth changes were chosen for determining the biophysical and biochemical characteristics of the plants. The binary logistic regression (BLR) method was used to assess the spatial relationships between the VIs and the plants infested or not infested with Fusarium wilt. The results showed that the banana Fusarium wilt disease can be easily identified using the VIs including the green chlorophyll index (CIgreen), red-edge chlorophyll index (CIRE), normalized difference vegetation index (NDVI), and normalized difference red-edge index (NDRE). The fitting overall accuracies of the models were greater than 80%. Among the investigated VIs, the CIRE exhibited the best performance both for the VD1 (OA = 91.7%, Kappa = 0.83) and VD2 (OA = 80.0%, Kappa = 0.59). For the same type of VI, the VIs including a red-edge band obtained a better performance than that excluding a red-edge band. A simulation of imagery with different spatial resolutions (i.e., 0.5-m, 1-m, 2-m, 5-m, and 10-m resolutions) showed that good identification accuracy of Fusarium wilt was obtained when the resolution was higher than 2 m. As the resolution decreased, the identification accuracy of Fusarium wilt showed a decreasing trend. The findings indicate that UAV-based remote sensing with a red-edge band is suitable for identifying banana Fusarium wilt disease. The results of this study provide guidance for detecting the disease and crop planting adjustment.

Hydrothermal Minerals Mapping using based on Remotely Sensed Data from Sentinel 2 Sattelite: a Case Study in Vinh Phuc Province, Northern Vietnam

This paper presents the experiences obtained in the application of Principal Component Analysis (PCA) method to map hydrothermal minerals based on remotely sensed data. In this study, Sentinel-2B MultiSpectral Instrument (MSI) image is used to detect distribution of hydroxyl-bearing minerals in Vinh Phuc province, northern Vietnam. Four bands of Sentinel-2B image including blue band (band 2), Vegetation Red Edge band (band 8A) and SWIR bands (band 11 and 12) are used to calculate the Principal Components, then and then select the Principal Component, which containing provides information on the hydrothermal minerals information. The obtained results findings show that the methodology and data are effective in detecting and mapping hydrothermal mineralization.

Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.