scholarly journals Mapping Height and Aboveground Biomass of Mangrove Forests on Hainan Island Using UAV-LiDAR Sampling

2019 ◽  
Vol 11 (18) ◽  
pp. 2156 ◽  
Author(s):  
Dezhi Wang ◽  
Bo Wan ◽  
Penghua Qiu ◽  
Zejun Zuo ◽  
Run Wang ◽  
...  
Keyword(s):  
Low Cost ◽  
Hainan Island ◽  
Average Height ◽  
Mangrove Forests ◽  
Infrared Band ◽  
Red Edge ◽  
Extensive Field ◽  
Aerial Vehicle ◽  
Shortwave Infrared ◽  
Sentinel 2

Hainan Island is the second-largest island in China and has the most species-diverse mangrove forests in the country. To date, the height and aboveground ground biomass (AGB) of the mangrove forests on Hainan Island are unknown, partly as a result of the challenges faced during extensive field sampling in mangrove habitats (intertidal mudflats inundated by periodic seawater). Therefore, this study used a low-cost UAV-LiDAR (light detection and ranging sensor mounted on an unmanned aerial vehicle) system as a sampling tool and Sentinel-2 imagery as auxiliary data to estimate and map the mangrove height and AGB on Hainan Island. Hainan Island has 3697.02 hectares of mangrove forests with an average patch area of approximately 1 ha. The results show that the mangroves on whole Hainan Island have an average height of 6.99 m, a total AGB of 474,199.31 Mg and an AGB density of 128.27 Mg ha−1. The AGB hot spots are located in Qinglan Harbor and the south of Dongzhai Harbor. The proposed height model LiDAR-S2 performed well with an R2 of 0.67 and an RMSE (root mean square error) of 1.90 m; the proposed AGB model G~LiDAR~S2 performed better (an R2 of 0.62 and an RMSE of 50.36 Mg ha−1) than the traditional AGB model G~S2 that directly related ground plots and Sentinel-2 data. The results also indicate that the LiDAR metrics describing the canopy’s thickness and its top and bottom characteristics are the most important variables for mangrove AGB estimation. For the Sentinel-2 indices, the red-edge and shortwave infrared features, especially the red-edge 1 and shortwave infrared Band 11 features, play the most important roles in estimating mangrove AGB and height. In conclusion, this paper presents the first mangrove height and AGB maps of Hainan Island and demonstrates the feasibility of using UAV-LiDAR as a sampling tool for mangrove forests.

scholarly journals Sharpening the Sentinel-2 10 and 20 m Bands to Planetscope-0 3 m Resolution

2020 ◽  
Vol 12 (15) ◽  
pp. 2406 ◽  
Author(s):  
Zhongbin Li ◽  
Hankui K. Zhang ◽  
David P. Roy ◽  
Lin Yan ◽  
Haiyan Huang
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Near Infrared ◽  
Detailed Examination ◽  
Modulation Transfer ◽  
Computationally Efficient ◽  
Red Edge ◽  
Shortwave Infrared ◽  
Sentinel 2 ◽  
Surface Changes

Combination of near daily 3 m red, green, blue, and near infrared (NIR) Planetscope reflectance with lower temporal resolution 10 m and 20 m red, green, blue, NIR, red-edge, and shortwave infrared (SWIR) Sentinel-2 reflectance provides potential for improved global monitoring. Sharpening the Sentinel-2 reflectance with the Planetscope reflectance may enable near-daily 3 m monitoring in the visible, red-edge, NIR, and SWIR. However, there are two major issues, namely the different and spectrally nonoverlapping bands between the two sensors and surface changes that may occur in the period between the different sensor acquisitions. They are examined in this study that considers Sentinel-2 and Planetscope imagery acquired one day apart over three sites where land surface changes due to biomass burning occurred. Two well-established sharpening methods, high pass modulation (HPM) and Model 3 (M3), were used as they are multiresolution analysis methods that preserve the spectral properties of the low spatial resolution Sentinel-2 imagery (that are better radiometrically calibrated than Planetscope) and are relatively computationally efficient so that they can be applied at large scale. The Sentinel-2 point spread function (PSF) needed for the sharpening was derived analytically from published modulation transfer function (MTF) values. Synthetic Planetscope red-edge and SWIR bands were derived by linear regression of the Planetscope visible and NIR bands with the Sentinel-2 red-edge and SWIR bands. The HPM and M3 sharpening results were evaluated visually and quantitatively using the Q2n metric that quantifies spectral and spatial distortion. The HPM and M3 sharpening methods provided visually coherent and spatially detailed visible and NIR wavelength sharpened results with low distortion (Q2n values > 0.91). The sharpened red-edge and SWIR results were also coherent but had greater distortion (Q2n values > 0.76). Detailed examination at locations where surface changes between the Sentinel-2 and the Planetscope acquisitions occurred revealed that the HPM method, unlike the M3 method, could reliably sharpen the bands affected by the change. This is because HPM sharpening uses a per-pixel reflectance ratio in the spatial detail modulation which is relatively stable to reflectance changes. The paper concludes with a discussion of the implications of this research and the recommendation that the HPM sharpening be used considering its better performance when there are surface changes.

scholarly journals Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China

2020 ◽  
Vol 12 (24) ◽  
pp. 4052
Author(s):  
Zhiwei Yi ◽  
Li Jia ◽  
Qiting Chen
Keyword(s):  
River Basin ◽  
Spatial Resolution ◽  
Temporal Information ◽  
Shiyang River Basin ◽  
Spectral Bands ◽  
Red Edge ◽  
Multi Temporal ◽  
Crop Classification ◽  
Shortwave Infrared ◽  
Sentinel 2

Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

scholarly journals Optimal Bands Combination Selection for Extracting Garlic Planting Area with Multi-Temporal Sentinel-2 Imagery

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5556
Author(s):  
Shuang Wu ◽  
Han Lu ◽  
Hongliang Guan ◽  
Yong Chen ◽  
Danyu Qiao ◽  
...  
Keyword(s):  
Extraction Methods ◽  
Growth Cycle ◽  
Importance Score ◽  
Remotely Sensed Data ◽  
Infrared Band ◽  
Temporal Features ◽  
Multi Temporal ◽  
Data Source ◽  
Shortwave Infrared ◽  
Sentinel 2

Garlic is one of the main economic crops in China. Accurate and timely extraction of the garlic planting area is critical for adjusting the agricultural planting structure and implementing rural policy actions. Crop extraction methods based on remote sensing usually use spectral–temporal features. Still, for garlic extraction, most methods simply combine all multi-temporal images. There has been a lack of research on each band’s function in each multi-temporal image and optimal bands combination. To systematically explore the potential of the multi-temporal method for garlic extraction, we obtained a series of Sentinel-2 images in the whole garlic growth cycle. The importance of each band in all these images was ranked by the random forest (RF) method. According to the importance score of each band, eight different multi-temporal combination schemes were designed. The RF classifier was employed to extract garlic planting area, and the accuracy of the eight schemes was compared. The results show that (1) the Scheme VI (the top 39 bands in importance score) achieved the best accuracy of 98.65%, which is 6% higher than the optimal mono-temporal (February, wintering period) result, and (2) the red-edge band and the shortwave-infrared band played an essential role in accurate garlic extraction. This study gives inspiration in selecting the remotely sensed data source, the band, and phenology for accurately extracting garlic planting area, which could be transferred to other sites with larger areas and similar agriculture structures.

scholarly journals A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery

2019 ◽  
Vol 11 (17) ◽  
pp. 2043 ◽  
Author(s):  
Jia ◽  
Wang ◽  
Wang ◽  
Mao ◽  
Zhang
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Near Infrared ◽  
Aquatic Vegetation ◽  
Vegetation Indices ◽  
Short Wave ◽  
Mangrove Forests ◽  
Red Edge ◽  
Jensen Shannon Divergence ◽  
Sentinel 2

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.

scholarly journals Evaluating Sentinel-2 Red-Edge Bands for Wetland Classification

Proceedings ◽  
2019 ◽  
Vol 18 (1) ◽  
pp. 12
Author(s):  
KAPLAN ◽  
AVDAN
Keyword(s):  
Remote Sensing Data ◽  
Radar Data ◽  
Support Vector ◽  
Wetland Classification ◽  
Future Studies ◽  
Learning Classifier ◽  
Red Edge ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Sentinel 2

Due to the high spatial heterogeneity and temporal variability, wetlands are one of the most difficult ecosystems to observe using remote sensing data. With the additional Sentinel-2 vegetation red-edge bands, an improvement of the vegetated classes classification is expected. In order to investigate the influence of the Sentinel-2 red-edge bands, in this paper we evaluate two classification scenarios over wetland classes. The first scenario excludes the red-edge bands, while in the second scenario all red-edge bands are included in the classification dataset where two different wetland classes—intensive vegetated wetland classes such as swamps and partially decayed vegetated wetland areas such as bogs—are classified using a support vector machine (SVM) learning classifier. The classes are defined using high-resolution images from an Unmanned Aerial Vehicle (UAV) obtained on the same date with the passing of the Sentinel-2 satellite over the study area. As expected, the results show a significant improvement of the intensive vegetated wetlands, with more than 30% in both user and producer accuracy, while no significant changes are noted in the partially decayed vegetated wetlands. For future studies, we recommend evaluating the influence of the Sentinel radar data over wetland areas.

scholarly journals Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Forests ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 291 ◽  
Author(s):  
Kalkidan Mulatu ◽  
Mathieu Decuyper ◽  
Benjamin Brede ◽  
Lammert Kooistra ◽  
Johannes Reiche ◽  
...  
Keyword(s):  
Forest Structure ◽  
Structural Parameters ◽  
Cloud Forest ◽  
Poor Performance ◽  
Structure Parameters ◽  
Moisture Index ◽  
Infrared Band ◽  
Terrestrial Lidar ◽  
Red Edge ◽  
Sentinel 2

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.

scholarly journals A NEW INDEX FOR IDENTIFYING WATER BODY FROM SENTINEL-2 SATELLITE REMOTE SENSING IMAGERY

2020 ◽  
Vol V-3-2020 ◽  
pp. 33-38
Author(s):  
W. Jiang ◽  
Y. Ni ◽  
Z. Pang ◽  
G. He ◽  
J. Fu ◽  
...  
Keyword(s):  
Water Body ◽  
Salt Lake ◽  
Spectral Characteristics ◽  
River Estuary ◽  
Yangtze River Estuary ◽  
Infrared Band ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Shortwave Infrared ◽  
Sentinel 2

Abstract. Water body plays an irreplaceable role in the global ecosystem and climate system. Sentinel-2 is a new satellite data with higher spatial and spectral resolution. Through analysing spectral characteristics of Sentinel-2 satellite imagery, the brightness of water body in vegetation red edge band and shortwave infrared band showe sharply different than that of the not water body. Therefore, a new type of water index SWI (Sentinel-2 Water Index) was proposed by combing those two bands. Four representative water types, which included Taihu Lake, the Yangtze River Estuary, the ChaKa Salt Lake and the Chain Lake, were selected as experimental areas. Normalized difference water index (NDWI) and Sentinel-2 Water Index (SWI) with Otsu method were employed to extract water body. The results showed that overall accuracy and Kappa coefficient of SWI were higher than that of NDWI and SWI was efficient index to rapidly and accurately extract water for Sentinel-2 data. Therefore, SWI had application potential for larger scale water mapping in the future.

scholarly journals A Novel Electrically Small Ground-Penetrating Radar Patch Antenna with a Parasitic Ring for Respiration Detection

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1930
Author(s):  
Di Shi ◽  
Taimur Aftab ◽  
Gunnar Gidion ◽  
Fatma Sayed ◽  
Leonhard M. Reindl
Keyword(s):  
Ground Penetrating Radar ◽  
Patch Antenna ◽  
Low Cost ◽  
Substrate Material ◽  
Geometric Parameters ◽  
Gas Explosions ◽  
Aerial Vehicle ◽  
Electromagnetic Characteristics ◽  
Ground Penetrating ◽  
Quick Search

An electrically small patch antenna with a low-cost high-permittivity ceramic substrate material for use in a ground-penetrating radar is proposed in this work. The antenna is based on a commercial ceramic 915 MHz patch antenna with a size of 25 × 25 × 4 mm3 and a weight of 12.9 g. The influences of the main geometric parameters on the antenna’s electromagnetic characteristics were comprehensively studied. Three bandwidth improvement techniques were sequentially applied to optimize the antenna: tuning the key geometric parameters, adding cuts on the edges, and adding parasitic radiators. The designed antenna operates at around 1.3 GHz and has more than 40 MHz continuous −3 dB bandwidth. In comparison to the original antenna, the −3 and −6 dB fractional bandwidth is improved by 1.8 times and 4 times, respectively. Two antennas of the proposed design together with a customized radar were installed on an unmanned aerial vehicle (UAV) for a quick search for survivors after earthquakes or gas explosions without exposing the rescue staff to the uncertain dangers of moving on the debris.

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