Use Of Synthetic Aperture Radar Research Articles

Wildfire Threshold Detection and Progression Monitoring Using an Improved Radar Vegetation Index in California

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance.

Crop yield estimation at different growing stages using a synergy of SAR and optical remote sensing data

Crop yield forecasting is an essential component of crop production assessment, impacting people at the global scale down to the level of individual farms. Until now, yield forecasting has predominantly relied on optical data, particularly the maximum value of vegetation indexes. However, this approach only presents a short forecasting window, and it is essential to obtain yield estimates as early as possible in the growing season and then further improve forecasting even after the vegetation index has reached its peak. So far, optical satellite data at high-temporal resolution (1–3 days) has been actively used for real time crop yield monitoring, whereas fewer operational models make a use of synthetic aperture radar (SAR). In this study, we explore whether SAR data can capture distinct aspects of crop dynamics, providing new insights for yield estimation depending on the crop's phenological stage. We assess the efficiency of dual- (Sentinel-1) and quad-polarimetric (UAVSAR, RADARSAT-2) data to explain inter-field crop yield variability for corn, soybean, and rice over a test area in Arkansas, US (258 fields, 2019). We used optical imagery acquired by Planet/Dove-Classic, Sentinel-2, and Landsat 8, to establish a baseline performance of satellite-based indicators to explain yield variability and assess dual- and quad-polarimetric SAR data for crop yield assessment. In terms of polarimetric indexes, the results showed that in general the results for rice were mostly stable and better than the other crops (R2adj ∼ 0.4 on average). The best results were obtained for the Sentinel-1 VHasc with R2adj = 0.47 and RADARSAT-2 phase difference with R2adj = 0.45. The results for corn performed the least with an R2adj <0.35 for all the indexes. The results for soybeans were more variable and were highly correlated with certain indicators such as RADARSAT-2 HV, RADARSAT-2 Volume, and RADARSAT-2 Pauli HV with R2adj>0.4. We also investigated the day of year (DOY) with the maximum correlation between optical and SAR-derived features and the final yields for corn, soybean, and rice. The maximum correlation for optical features occurs over a short time between DOY 155 (June 4) and 185 (July 5) for corn and rice, and DOY 190 (July 9) and DOY 211 (July 30) for soybean, with these results being consistent across various optical-based sensors. On the contrary, the maximum correlation for SAR-derived features varied significantly and was between DOY 120 (April 30) to DOY 225 (August 13). A study of the time series parameters cross-correlation showed that the optical parameters were highly correlated, but the SAR parameters showed strong temporal decorrelation. We conducted a comparison between C-band and L-band to assess their sensitivity at each stage of growth. In this experiment, we determined that for low vegetation, the C band will be more useful at the beginning of the growth cycle, while the L band provides more information in later stages of growth. Using a random forest regression model combining SAR parameters with common difference vegetation index (DVI), we improved the error by 50% in comparison to the error using the (DVI) for corn, soybean, and rice.

Integrated Use of Synthetic Aperture Radar and Optical Data in Mapping Native Vegetation: A Study in a Transitional Brazilian Cerrado–Atlantic Forest Interface

Integrated Use of Synthetic Aperture Radar and Optical Data in Mapping Native Vegetation: A Study in a Transitional Brazilian Cerrado–Atlantic Forest Interface

INVESTIGATING LOSS FUNCTIONS FOR SEGMENTING AND DETECTING SHIPS ON SAR IMAGERY

Abstract. In accordance with the United Nations (UN) Sustainable Development Goal (SDG) 16: Peace, Justice, and Strong Institutions, this study explores ship monitoring through the use of Synthetic Aperture Radar (SAR) for its potential applications to economic and security purposes. One method to extract ships through SAR-derived imagery is to employ the use of convolutional neural networks (CNN). However, the extraction of small features continues to be a challenging task for CNNs. To improve the performance in such cases, one way is to employ the use of an appropriate loss function, which helps guide the CNN model during training. In this paper, Focal Combo (FC) loss, a recent loss function designed for extreme class imbalance, will be investigated to analyze its effects when applied to ship extraction. In doing so, this paper also presents a thorough comparison of existing loss functions in their capability to segment and detect ships on SAR imagery. Making use of the U-Net model, our results demonstrate that by using FC loss we can observe an increase in segmentation of about 9% in terms of f3-score and a decrease in missed detections by about 17 ships (after post-processing) when compared to cross-entropy loss. Unfortunately, it has also shown a significant drop in precision of about 35% resulting in an additional 270 ships being incorrectly detected in the background. In future work, varying CNN models shall be tested to see if the pattern persists and several trials shall be conducted to assess consistency.

Use of Synthetic Aperture Radar (SAR) imagery on oil spill detection: A case study on the MV X-Press Pearl container ship incident in 2021 in Sri Lanka

Oil spills on the ocean from ships have a strong impact on the environment as well as on human societies. The MV X-Press Pearl container ship incident in 2021 became the worst marine environmental disaster in Sri Lanka due to its powerful impact. Oil leakage from the ship to the ocean continued for months. Synthetic Aperture Radar (SAR) imagery has the advantage of penetrating cloud cover, as well as day and night operating capabilities. Therefore, SAR imagery can be used to detect as well as observe the temporal changes of oil spills. The study in this article aimed to detect the oil spill caused by the MV X-Press Pearl container ship on Sri Lanka’s west coast from May to August 2021 using Sentinel-1 SAR data and measure the temporal changes of the oil slick. Three SAR imageries with Vertical-Vertical (VV) and Vertical-Horizontal (VH) polarization from June, July, and August of 2021 have been processed by applying the orbit file, running the multi-looking algorithm, and making the ellipsoid correction. The possible oil spills have been identified from each set of imagery as dark linear shapes with lengths of 3.1 ±0.012 km with an average reflectance of 15.6 decibels for June and 3.6 ±0.012 km with an average reflectance of 16.5 decibels for the other two months, and the shapes correspond with the oil slick classifications from other literature. The results indicate that the VV polarization is the most suitable to detect oil spills, and that the oil spill from the X-Press Pearl continued from June to the end of August 2021.

Accuracy Assessment of different classifiers for Sustainable Development in Landuse and Landcover mapping using Sentinel SAR and Landsat-8 data

Sentinel satellites make use of Synthetic Aperture Radar (SAR) which produces images with backscattered signals at fine spatial resolution from 10 m to 50 m. This study is mainly focused on evaluating and assessing the accuracy of various supervised classifiers like Random Forest classifier, Minimum Distance to mean classifier, KDTree KNN classifier, and Maximum Likelihood classifier for landuse / landcover mapping in Maduranthakam Taluk, Kancheepuram district, Tamilnadu, India. These classifiers are widely used for classifying the Sentinel SAR images. The SAR images were processed using speckle and terrain correction and converted to backscattered energy. The training datasets for the landcover classes, such as vegetation, waterbodies, settlement, and barren land, were collected from Google Earth images in high-resolution mode. These collected training datasets were given as input for the various classifiers during the classification. The obtained classified output results of various classifiers were analyzed and compared using the overall classification accuracy. The overall accuracy achieved by the Random Forest classifier for the polarization VV and VH was 92.86%, whereas the classified accuracy of various classifiers such as KDTree KNN, Minimum distance to mean, and Maximum Likelihood are found to be 81.68%, 83.17%, and 85.64% respectively. The random forest classifier yields a higher classification accuracy value due to its greater stability in allocating the pixels to the right landuse class. In order to compare and validate the results with sentinel data, the random classifier is applied with optical Landsat-8 satellite data. The classification accuracy obtained for Landsat-8 data is 84.61%. It is clearly proved that the random forest classifier with sentinel data gives the best classification accuracy results due to its high spatial resolution and spectral sensitivity. Thus accurate landuse and landcover mapping promote sustainable development by supporting decision-making at local, regional, and national levels.

Lithuanian river ice detection and automated classification using machine-learning methods

In regions susceptible to river freezing and flooding, river ice detection is a priority. Localization of ice jams and ice drift zones could mean a faster and better response to possible flooding areas, and classification of river ice could help better predict freezing and thawing conditions that hinder the use of commercial and recreational river transport. As many freezing-prone rivers are located in regions with short winter days and common cloud cover, the use of optical sensors can be very limited, therefore, the use of Synthetic Aperture Radar (SAR) – a microwave imaging radar – is more applicable. In this article, Sentinel-1 SAR C-band imagery is used to create derivate texture rasters, which are analyzed, compared with known optical imagery and then considered for river ice detection and discrimination. These results are compared in terms of their effectiveness for river ice discrimination, and the most useful methods are selected. The chosen methods are then compared in an experimental machine-learning model capable of detecting and classifying ice and water. Various machine-learning approaches (both classical and deep-learning) are considered and compared, and the best models are selected. The purpose of this research is to analyze the capability of texture rasters, calculated from a gray-level co-occurrence matrix (GLCM), to discriminate river ice. Texture rasters have recently been applied for river ice classification by de Roda Husman et al. (de Roda Husman et al. 2021), but included only three metrics. This research aims to expand on this knowledge by comparing eight metrics instead of three, as well as including an experiment with a deep-learning model. The results demonstrate that in machine-learning experiments, only one texture measure out of eight (GLCM Mean calculation) is able to discriminate river ice better than discrimination from a standard SAR backscatter intensity image (the baseline).

Flash-Flood Potential Mapping in Agricultural Land Using Rule-Based Classification Approach on Multi-Temporal Synthetic-Aperture Radar (SAR) Data over Jhajjar and Rohtak Districts of Haryana State

Flash floods are known to be most dangerous flood. In recent decades there is change in the climate and urban development causing increase in the risk of flash flood for human population. Remote sensing and geographical information system playing a very important and feasible role in assessment of the flash flood over a decade. This paper describes the synergetic use of Synthetic-aperture radar (SAR) data to delineate and mapping of the flooded area at its peak. The methodology was tested on the flash flood event that occurs in Jhajjar and Rohtak districts of Haryana in July, 2022. Four Sentinel-1 Synthetic-aperture radar data were acquired and pre-processed, two images are acquired before the occurrence of the event and two SAR images were acquired after the event. For the identification and demarcation of the flooded area, the rule-based classification approach was used. The results of the study suggests the scope of using multi-temporal Sentinel-1 VV polarized C-band SAR data to develop an operational flash-flood area identification and framework.

Implementation of Synthetic Aperture Radar Active Decoy Hardware

As a result of the military's use of synthetic aperture radar (SAR), it has become necessary to use jamming techniques to protect sensitive sites of friendly forces from being intercepted by SAR. To combat SAR sensors, two jamming techniques are typically available: noise jamming and deceptive jamming. In this paper the design and hardware implementation of a proposed active decoy which works as deceptive jamming system, is achieved using field programmable gate array (FPGA). The performance of the developed system is evaluated by SystemVue. 2015.01, using “hardware in the loop” methodology.

Performance of Sentinel-1 and 2 imagery in detecting aquaculture waterbodies in Bangladesh

In this study, we evaluated the use of synthetic aperture radar (SAR) and multispectral data to detect aquaculture waterbodies in Southern Bangladesh to quantify fish production on a national scale. For this purpose, we developed an object-based framework comprised of three sequential stages: 1) water detection, 2) feature segmentation, and 3) feature classification. Techniques such as Edge-Otsu for binary thresholding, edge detection with convolution filters, and various supervised and unsupervised machine learning methods were used as part of a workflow. We found that ensemble products combining individual subproducts resulted in higher overall accuracy for water detection (overall detection rate around 60%) and waterbodies classification (overall accuracies up to 79%). Moreover, we showed that SAR data and shape indices played important roles in better-discriminating waterbodies. However, limitations in edge detection outcomes affected the identification of small and isolated aquaculture waterbodies, especially those integrated into rice fields, or in areas with trees.

Monitoring of Plastic Islands in River Environment Using Sentinel-1 SAR Data

Plastics in the river environment are of major concern due to their potential pathways into the ocean, their persistence in the environment, and their impacts on human and marine health. It has been documented that plastic concentrations in riparian environments are higher following major rain events, where plastic can be moved through surface runoff. Considering the hazard that plastic waste poses to the environment, monitoring techniques are needed to aid in locating, monitoring, and remediating plastic waste within these systems. Dams are known to trap sediments and pollutants, such as metals and Polychlorinated Biphenyls (PCBs). While there is an established background on the monitoring of dams using the synoptic coverage provided by satellite imaging to observe water quality and volume, the detection of marine debris in riparian systems remains challenging, especially in cloudy conditions. Herein, we exploit the use of Synthetic Aperture Radar (SAR) to understand its capabilities for monitoring marine debris. This research focuses on detecting plastic islands within the Drina River system in Bosnia and Herzegovina and Serbia. Here, the results show that the monitoring of these plastic accumulations is feasible using Sentinel-1 SAR data. A quantitative analysis of detection performance is presented using traditional and state-of-the-art change detectors. The analysis of these detectors indicates that detectors that can utilise the coherent data from Single Look Complex (SLC) acquisitions are perform better when compared with those that only utilise incoherent data from Ground Range-Detected (GRD) acquisitions, with true positive detection ratings of ~95% with 0.1% false alarm rates seen in the best-performing detector. We also found that that the cross-pol VH channel provides better detection than those based on single-pol VV polarisation.

Detecting Water Hyacinth Infestation in Kuttanad, India, Using Dual-Pol Sentinel-1 SAR Imagery

Water hyacinth (Pontederia crassipes, also known as Eichhornia crassipes) is a highly invasive aquatic macrophyte species, indigenous to Amazonia, Brazil and tropical South America. It was introduced to India in 1896 and has now become an environmental and social challenge throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering its large speed of propagation on the water surface under conducive conditions and the adverse impact the infesting weed has, constant monitoring is needed to aid civic bodies, governments and policy makers involved in remedial measures. The synoptic coverage provided by satellite imaging and other remote sensing practices make it convenient to find a solution using this type of data. While there is an established background for the practice of remote sensing in the detection of aquatic plants, the use of Synthetic Aperture Radar (SAR) has yet to be fully exploited in the detection of water hyacinth. This research focusses on detecting water hyacinth within Vembanad Lake, Kuttanad, India. Here, results show that the monitoring of water hyacinth has proven to be possible using Sentinel-1 SAR data. A quantitative analysis of detection performance is presented using traditional and state-of-the-art change detectors. Analysis of these more powerful detectors showed true positive detection ratings of ~95% with 0.1% false alarm, showing significantly greater positive detection ratings when compared to the more traditional detectors. We are therefore confident that water hyacinth can be monitored using SAR data provided the extent of the infestation is significantly larger than the resolution cell (bigger than a quarter of a hectare).

Geospatial technology in agroforestry: status, prospects, and constraints.

Agroforestry has an indispensable role in food and livelihood security in addition to its capacity to combat the detrimental effects of climate change. However, agroforestry has not been properly promoted and exploited due to lack of precise extent, geographical distribution, and carbon sequestration (CS) assessment. The recent advent of geospatial technologies, as well as free availability of spatial data and software, can provide new insights into agroforestry resources assessment, decision-making, and policy development despite agroforestry's small spatial extent, isolated nature, and higher structural and functional complexity of agroforestry. In this review, the existing application of geospatial technologies together with its constraints and limitations as well as the potential future application for agroforestry has been discussed. The review reveals that the application of optical remote sensing in agroforestry includes spatial extent mapping, production of tree species spectral signature, CS assessment, and suitability mapping. Simultaneously, the recent surge in the use of synthetic aperture radar in conjunction with algorithms based on vegetation photosynthesis and optical data enables a more accurate estimation of gross primary productivity at different scales. However, unmanned aerial vehicles equipped with sensors, such as multispectral, LiDAR, hyperspectral, and thermal, offer a considerably higher potential and accuracy than satellite-based datasets. In the future, the health monitoring of agroforestry systems can be a key concern that may be addressed by utilizing hyperspectral and thermal datasets to analyze plant biochemistry, chlorophyll fluorescence, and water stress. Additionally, current (GEDI, ECOSTRESS) and future space agency missions (BIOMASS, FLEX, NISAR, TRISHNA) have enormous potential to shed fresh light on agroforestry systems.

A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

In arid and semi-arid areas, timely and effective monitoring and mapping of salt-affected areas is essential to prevent land degradation and to achieve sustainable soil management. The main objective of this study is to make full use of synthetic aperture radar (SAR) polarization technology to improve soil salinity mapping in the Keriya Oasis, Xinjiang, China. In this study, 25 polarization features are extracted from ALOS PALSAR-2 images, of which four features are selected. In addition, three soil salinity inversion models, named the RSDI1, RSDI2, and RSDI3, are proposed. The analysis and comparison results of inversion accuracy show that the overall correlation values of the RSDI1, RSDI2, and RSDI3 models are 0.63, 0.61, and 0.62, respectively. This result indicates that the radar feature space models have the potential to extract information on soil salinization in the Keriya Oasis.

On the Combination of Dual-Polarization Sentinel-1 Ascending/Descending Orbiting Passes to Estimate Damage Due to the 2016 Central Italy Earthquake

This study investigates the extra benefit provided by the joint use of Synthetic Aperture Radar (SAR) polarimetric diversity and ascending/descending orbit passes to quantify postearthquake damage that occurred over the area of Amatrice, an Italian city significantly damaged by the 2016 Central Italy earthquake. First, the sensitivity of PolSAR features derived from SAR scenes collected under ascending/descending orbits to the damage is investigated. Then, the damage assessment is performed using a processing chain that consists of extracting dualpolarimetric SAR features to detect damage and, then, applying a Fuzzy clustering scheme, to partition the feature outputs into damage levels. This processing chain is first separately applied to Sentinel-1 SAR scenes collected under ascending/descending orbits; then the processing outputs are merged using two different approaches. To discuss the quality of the estimated damage maps, ground information collected by surveys performed by a trained team is used. Experimental results show that the joint use of ascending/descending orbit passes improves the estimation of damage levels (up to 78%) with respect to the estimation performed using the orbiting passes separately.

An assessment of flood event along Lower Niger using Sentinel-1 imagery.

Flood incidence, especially in global south countries, is one of the most challenging natural disasters in the light of changing climates, especially in Africa. This is because African countries have a large sub-section of vulnerable people who either live within flood-prone areas or depend on flood-prone areas for their means of livelihood such as we have in Nigeria. Recent flood disasters in Nigeria have been of major concern to people, communities, and institutions. Several studies have been conducted on flood events and their impacts in Nigeria. However, most of these studies are on public perception, flood modeling (rainfall-runoff), and the provision of binary maps with few studies engaging in the use of satellite observations, especially the use of Synthetic Aperture Radar, SAR, to enhance flood early warning designs, especially in Sub-Saharan Africa. This study is aimed at assessing the 2018 flood event in Lokoja, Kogi State, Nigeria, using the Sentinel-1 imagery. The study confirmed that a total of 69 buildings out of 611 buildings were affected by the flood disaster with about 24,902 people displaced by this singular flood event. The study shows that backscattering from microwave sensors provides very useful information for highlighting inundated areas that could prove useful in forecasting, monitoring, and precision-based flood early warning designs before, during, and after flood events.

Recent advancements in multi-temporal methods applied to new generation SAR systems and applications in South America

Detection and continuous monitoring of Earth's ground surface changes, triggered by natural phenomena or induced by human activities, is nowadays possible using Earth Observation (EO) technologies. Indeed, the exploitation of remotely sensed data collected by constellations of new-generation satellite platforms, complemented with in-situ measurements and ground-based observation systems, represents a well-established practice to get valuable information on Earth's crust and subsurface dynamics. The effects of extreme natural or man-induced events (e.g., earthquakes, volcanic eruptions, flooding phenomena, sea-level rise, big fires, etc.) have severe societal and economic impacts. In particular, the technologies based on the use of Synthetic Aperture Radar (SAR) images reached significant improvements in the last decade due to the growing availability of vast amounts of data collected by multiple-satellite sensors operating at different frequency bands and with complementary viewing angles, polarization and acquisition modes. Accordingly, to process a large amount of SAR data in a timely fashion, up-to-date high-performance computing (HPC) methods and tools are required. This paper addresses the state-of-the-art of SAR technologies for the analysis of long sequences of multiple sets of SAR images and provides a perspective on the forthcoming improvements of these technologies. In particular, the emphasis is placed on novel interferometric SAR and change detection methods, giving an overview of how those processing techniques have been used for investigating sites located in South and Central America. Moreover, an overview of the new generation of SAR sensors' observational capability, especially in the field of ground deformation analysis for mitigating the risk associated with natural and human-induced hazards, is provided. COSMO-SkyMed, ALOS, Sentinel-1, and SAOCOM data are exploited to show how natural and human-induced terrain displacement phenomena can be detected and investigated in different portions (X-, L- and C-band) of the microwave spectrum using SAR technologies.

Sentinel-1 interferometric coherence and backscattering analysis for crop monitoring

In this study, we investigate the synergic use of synthetic aperture radar (SAR) backscattering (i.e., sigma nought σ0) and InSAR coherence (γ) maps as a tool for crop growth monitoring. Experiments were carried out using Sentinel-1 TOPS SAR data and field observations in one of the State General Directorate of Agriculture Enterprise farms in Konya (Central Turkey). The phenological stages of maize, sunflower, and wheat have been analyzed and compared to coherence and backscatter time series of Sentinel-1 data on multiple tracks and polarizations. The results evidence a strong correlation between different phenological stages of the crops and the InSAR coherence. Specifically, the observed coherence values are the highest for the maize (γasc, desc = 0.47) and sunflower (γasc = 0.49, γdesc = 0.48) after plowing the fields and seeding the crops. The coherence decreases with the plants' growth and reaches the lowest values for maize, sunflower, and wheat (γ = 0.08, γ = 0.09 and γ = 0.07, respectively) when the ground is completely covered by plants. Then, a coherence increase is observed after the harvesting time (γ = 0.51, γ = 0.50 and γ = 0.42 for maize, sunflower, and wheat, respectively). In terms of multi-temporal SAR backscattering, we find significant changes of the σ0 values during the crops' growing stages due to the changes in their leaf geometry and physical structure. The highest σ0 values for the maize, sunflower, and wheat are obtained as −9.18 dB, −5.24 dB and −10.05 dB, respectively, for the ascending orbit, in mid growing stages. Results show the improved capacity of SAR-driven measurements for agriculture monitoring and precise farming activities when InSAR coherence and backscattering are synergistically used. Specifically, the coherence allows estimating the main growth stages of the different crop types. Moreover, SAR backscattering provides reliable information on the whole growth stages during the agricultural season, and it might be profitably exploited for crop assessment.

FLOOD HAZARD VULNERABILITY MAPPING FOR DARBHANGA DISTRICT OF BIHAR USING SAR DATA: EFFORT TOWARDS EFFECTIVE DISASTER MANAGEMENT

Bihar experiences floods every year due to heavy rainfall in monsoon season and melting of snow in upper reaches of Himalayas during the summers causing both infrastructural damage and loss of life. During the floods, the sky is overcast, due to which traditional techniques of flood mapping and monitoring like aerial surveys and optical sensor satellite imagery fail to provide a good quality data as they are unable to penetrate through clouds and mist. New and alternative technique for the mapping of floods is the use of Synthetic Aperture Radar (SAR) data, which is capable of penetrating through clouds since it uses microwaves to capture the data. For the present study, Darbhanga district of Bihar was taken as the study area. The district faces floods each year due to Kosi River which emerges from Nepal Himalayas and enters Bihar. Aim of the study was to create flood hazard vulnerability map using flood inundation extent maps for the years 2017 to 2020. These maps help in demarcating and identifying the areas which are highly prone to floods annually. Flood inundation extent maps for this study were generated for each year from 2017 to 2020 using Sentinel-1 data and ArcGIS software. These flood inundation extent maps were then superimposed, and a vulnerability map was created. This flood hazard vulnerability map is a valuable source of information while making disaster management plan for the DRR of the region. These maps can help to understand spatial extent of flooding, and can provide scientists and authorities with objective data sources for decision making. These maps can play an important role in pre-disaster and post-disaster strategy planning in an effort towards making the community aware, more resilient and well prepared to face the disaster.

A Deep Learning Method for Mapping Glacial Lakes from the Combined Use of Synthetic-Aperture Radar and Optical Satellite Images

Glacial lakes (GLs), a vital link between the hydrosphere and the cryosphere, participate in the local hydrological process, and their interannual dynamic evolution is an objective reflection and an indicator of regional climate change. The complex terrain and climatic conditions in mountainous areas where GLs are located make it difficult to employ conventional remote sensing observation means to obtain stable, accurate, and comprehensive observation data. In view of this situation, this study presents an algorithm with a high generalization ability established by optimizing and improving a deep learning (DL) semantic segmentation network model for extracting GL contours from combined synthetic-aperture radar (SAR) amplitude and multispectral imagery data. The aim is to use the high penetrability and all-weather advantages of SAR to reduce the effects of cloud cover as well as to integrate the multiscale and detail-oriented advantages of multispectral data to facilitate accurate, quantitative extraction of GL contours. The accuracy and reliability of the model and algorithm were examined by employing them to extract the contours of GLs in a large region of south-eastern Tibet from Landsat 8 optical remote sensing images and Sentinel-1A amplitude images. In this study, the contours of a total 8262 GLs in south-eastern Tibet were extracted. These GLs were distributed predominantly at altitudes of 4000–5500 m. Only 17.4% of these GLs were greater than 0.1 km2 in size, while a large number of small GLs made up the majority. Through analysis and validation, the proposed method was found highly capable of distinguishing rivers and lakes and able to effectively reduce the misidentification and extraction of rivers. With the DL model based on combined optical and SAR images, the intersection-over-union (IoU) score increased by 0.0212 (to 0.6207) on the validation set and by 0.038 (to 0.6397) on the prediction set. These validation data sufficiently demonstrate the efficacy of the model and algorithm. The technical means employed in this study as well as the results and data obtained can provide a reference for research and application expansion in related fields.