scholarly journals Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

2021 ◽  
Author(s):  
Adam Collingwood ◽  
Paul Treitz ◽  
Francois Charbonneau ◽  
David M. Atkinson
Keyword(s):  
Synthetic Aperture Radar ◽  
High Arctic ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Optical Data ◽  
Neural Network Modeling ◽  
Artificial Neural ◽  
Sar Data ◽  
The Relationship ◽  
Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

scholarly journals Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

2021 ◽  
Author(s):  
Adam Collingwood ◽  
Paul Treitz ◽  
Francois Charbonneau ◽  
David M. Atkinson
Keyword(s):  
Synthetic Aperture Radar ◽  
High Arctic ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Optical Data ◽  
Neural Network Modeling ◽  
Artificial Neural ◽  
Sar Data ◽  
The Relationship ◽  
Aperture Radar

Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

scholarly journals Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data

2021 ◽  
Vol 15 (4) ◽  
pp. 1907-1929
Author(s):  
Georg Pointner ◽  
Annett Bartsch ◽  
Yury A. Dvornikov ◽  
Alexei V. Kouraev
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Lake Ice ◽  
Gas Emissions ◽  
L Band ◽  
Optical Imagery ◽  
Sar Data ◽  
Sar Imagery ◽  
Aperture Radar

Abstract. Regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of lake ice of Lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake’s sediments. However, to assess this connection, only analyses of data from boreholes in the vicinity of Lake Neyto and visual comparisons to medium-resolution optical imagery have been provided due to a lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with very high resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel spacing) and holes in lake ice from the VHR data (0.5 m pixel spacing) and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes (71 %) in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier, and similarities to SAR imagery acquired more than a month before are evident, supporting the hypothesis that anomalies may be related to gas emissions. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by lake ice subsidence and consequent flooding through the holes over the ice top leading to wetting and/or slushing of the snow around the holes, which might also explain outcomes of polarimetric analyses of auxiliary L-band Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data. C-band SAR data are considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

scholarly journals Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 116 ◽  
Author(s):  
Manuela Hirschmugl ◽  
Carina Sobe ◽  
Janik Deutscher ◽  
Mathias Schardt
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Synthetic Aperture Radar ◽  
Data Availability ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Series Data ◽  
Forest Mapping ◽  
Sar Data ◽  
Aperture Radar

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination.

THE USE OF SYNTHETIC APERTURE RADAR (SAR) DATA IN THE ANALYSIS OF INUNDATED AREAS DURING THE SPRING FLOOD

2018 ◽  
Vol 12 (7-8) ◽  
pp. 54-60 ◽  
Author(s):  
V. A. ZELENTSOV ◽  
S. A. POTRYASAEV ◽  
I. YU. PIMANOV ◽  
M. R. PONOMARENKO
Keyword(s):  
Synthetic Aperture Radar ◽  
Real Time ◽  
Spatial Resolution ◽  
Cloud Cover ◽  
Flood Forecasting ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Sar Data ◽  
Space Data ◽  
Aperture Radar

The paper discusses the opportunities of remote sensing data application as one of the main sources of information for monitoring river floods. Effective operation of flood forecasting systems requires reliable real-time data on inundation areas for timely calibration and verification of the used hydrodynamic models. The opportunity to obtain data from optical sensors might be limited because of dense cloud cover. Synthetic aperture radar (SAR) techniques are increasingly used today due to ability to operate independently of the surface illumination and the state of cloud cover receiving high spatial resolution data in near real-time mode. An important feature of SAR from space today is the increase in the number of freely distributed space data, in particular — images from Sentinel satellites developed by the European Space Agency. For instance, for the territory of Russia Sentinel-1 performs SAR imaging with 2–3 days coverage frequency. Within the framework of the project carried out by the authors, the research area is the city of Velikiy Ustuyg (Russia) located at the confluence of rivers Suhona and Ug. To identify flooded areas the RADARSAT-2 and Sentinel-1 images classification based on thresholding was carried out in open-source software. The visualization of the results was performed on the basis of information analytical system “Prostor”. The results of SAR data processing were compared with contours obtained on the basis of the calculation of the NDWI index from optical data from the Sentinel-2 and Resurs-P satellites. According to the spatial resolution of the data and the selected processing technology, it is possible to achieve high accuracy of flood mapping in open areas with low urbanization. The result confirms that SAR data can be successfully applied for operational flood forecasting.

scholarly journals Mapping potential signs of gas emissions in ice of lake Neyto, Yamal, Russia using synthetic aperture radar and multispectral remote sensing data

2020 ◽  
Author(s):  
Georg Pointner ◽  
Annett Bartsch ◽  
Yury A. Dvornikov ◽  
Alexei V. Kouraev
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
The Arctic ◽  
Alos Palsar ◽  
Lake Ice ◽  
Gas Emissions ◽  
Optical Imagery ◽  
Sar Data ◽  
Sar Imagery ◽  
Aperture Radar

Abstract. Regions of anomalously low backscatter in C-band Synthetic Aperture Radar (SAR) imagery of lake ice of lake Neyto in northwestern Siberia have been suggested to be caused by emissions of gas (methane from hydrocarbon reservoirs) through the lake's sediments before. However, to assess this connection, only analyses of data from boreholes in the vicinity of lake Neyto and visual comparisons to medium-resolution optical imagery have been provided so far due to lack of in situ observations of the lake ice itself. These observations are impeded due to accessibility and safety issues. Geospatial analyses and innovative combinations of satellite data sources are therefore proposed to advance our understanding of this phenomenon. In this study, we assess the nature of the backscatter anomalies in Sentinel-1 C-band SAR images in combination with Very High Resolution (VHR) WorldView-2 optical imagery. We present methods to automatically map backscatter anomaly regions from the C-band SAR data (40 m pixel-spacing) and holes in lake ice from the VHR data (0.5 m pixel-spacing), and examine their spatial relationships. The reliability of the SAR method is evaluated through comparison between different acquisition modes. The results show that the majority of mapped holes in the VHR data are clearly related to anomalies in SAR imagery acquired a few days earlier and also more than a month before, supporting the hypothesis of gas emissions as the cause of the backscatter anomalies. Further, a significant expansion of backscatter anomaly regions in spring is documented and quantified in all analysed years 2015 to 2019. Our study suggests that the backscatter anomalies might be caused by expanding cavities in the lake ice, formed by strong emissions of gas, which could also explain outcomes of polarimetric analyses of auxiliary L-band ALOS PALSAR-2 data. C-band SAR data is considered to be valuable for the identification of lakes showing similar phenomena across larger areas in the Arctic in future studies.

scholarly journals Quantification of Mangrove Extent using a Combination of Optical and Radar Images in Google Earth Engine Platform: The case of Anlo Beach Wetland Complex, Shama District, Western Region, Ghana

2021 ◽  
Author(s):  
Daniel Aja ◽  
Michael Miyittah ◽  
Donatus Bapentire Angnuureng
Keyword(s):  
Synthetic Aperture Radar ◽  
Google Earth ◽  
Kappa Coefficient ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Radar Images ◽  
Google Earth Engine ◽  
Sar Data ◽  
Aperture Radar

Abstract Mangrove Forest classification in tropical coastal zones based on only passive remote sensing methods is hampered by Mangrove complexities, topographic considerations and cloud cover effects among other things. This paper reports on a novel approach that combines Optical Satellite images and Synthetic Aperture Radar alongside their derived parameters to overcome the challenges of distinguishing Mangrove stand in cloud prone regions. Google Earth Engine (GEE) cloud-based geospatial processing platform was used to extract several scenes of Landsat Surface Reflectance Tier1 and synthetic aperture radar (C-band and L-band). The imageries were enhanced by creating a function that masks out clouds from the optical satellite image and by using speckle filter to remove noise from the radar data. The random forest algorithm proved to be a robust and accurate machine learning approach for mangrove classification and assessment. Our result show that about 16% of the mangrove extent was lost in the last decade. The accuracy was assessed based on three classification scenarios: classification of optical data only, classification of SAR data only, and combination of both optical and SAR data. The overall accuracies were 99.1% (Kappa Coefficient =0.797), 84.6% (Kappa Coefficient = 0.687) and 98.9% (Kappa Coefficient = 0.828) respectively. This case study demonstrates how mangrove mapping can help focus conservation practices locally in climate change setting, coupled with sea level rise and related threats to coastal ecosystems.

scholarly journals Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1189
Author(s):  
Yanxi Li ◽  
Xingwen Quan ◽  
Zhanmang Liao ◽  
Binbin He
Keyword(s):  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Optical Data ◽  
Fuel Load ◽  
Load Estimation ◽  
Fuel Loads ◽  
Different Types ◽  
L Band ◽  
Sar Data ◽  
Aperture Radar

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.

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