Remote Sensing Data Sets Research Articles

Application of a multiple model integration framework for mapping evapotranspiration with high spatial–temporal resolution in the Haihe River Basin, China

Evapotranspiration (ET) is a key component of the water and carbon cycles. Because it cannot be observed directly on large regional scales at present, many satellite-based ET datasets have been widely used for different purposes. However, their use has been limited at regional and field scales because of their coarse spatial and temporal resolution. In this study, the Bayesian model averaging (BMA) method was used to simulate daily ET values with 500 m spatial resolution in the Haihe River Basin (HRB) from 2000 to 2019. Validation performed with the in-situ observations showed that the BMA ET values had higher accuracy (R2 = 0.69, RMSE = 1.15 mm/day) than those found with individual models. The BMA ET values also had higher accuracy and more credibility based on a water balance equation validation in the HRB. We used interpolated meteorological datasets, reanalysis net radiation products, and remote-sensing datasets to drive the BMA ET model. The mean annual BMA ET in the HRB from 2000 to 2019 was about 601.8 mm/year. The monthly change characteristics of this BMA ET product reflected water consumption characteristics and irrigation regularity of the winter wheat and summer maize rotation system. The BMA ET in the HRB showed a significant increasing trend from 2000 to 2019 of 3.39 mm/y2. The BMA ET values showed a strong positive trend at 80 % of HRB areas. The increasing trend of the BMA ET values was mainly attributed to an increase in the leaf area index. The high spatiotemporal resolution product showed variations in ET trends more accurately than the coarse resolution products. This high spatiotemporal product has great potential for applications in studies of regional microclimate change, interactions between human activities and climate change, drought disaster monitoring, agricultural policy making, and water security.

Landsat-8 and Sentinel-2 Based Prediction of Forest Plantation C Stock Using Spatially Varying Coefficient Bayesian Hierarchical Models

This study sought to establish the performance of Spatially Varying Coefficient (SVC) Bayesian Hierarchical models using Landsat-8, and Sentinel-2 derived auxiliary information in predicting plantation forest carbon (C) stock in the eastern highlands of Zimbabwe. The development and implementation of Zimbabwe’s land reform program undertaken in the year 2000 and the subsequent redistribution and resizing of large-scale land holdings are hypothesized to have created heterogeneity in aboveground forest biomass in plantation ecosystems. The Bayesian hierarchical framework, accommodating residual spatial dependence and non-stationarity of model predictors, was evaluated. Firstly, SVC models utilizing Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI), derived from Landsat-8 and Sentinel-2 data and 191 sampled C stock observations were constructed. The SVC models built for each of the two multispectral remote sensing data sets were assessed based on the goodness of fit criterion as well as the predictive performance using a 10-fold cross-validation technique. The introduction of spatial random effects in the form of Landsat-8 and Sentinel-2 derived covariates to the model intercept improved the model fit and predictive performance where residual spatial dependence was dominant. For the Landsat-8 C stock predictive model, the RMSPE for the non-spatial, Spatially Varying Intercept (SVI) and SVC models were 8 MgCha−1, 7.77 MgCha−1, and 6.42 MgCha−1 whilst it was 7.85 MgCha−1, 7.69 MgCha−1 and 6.23 MgCha−1 for the Sentinel-2 C stock predictive models, respectively. Overall, the Sentinel-2-based SVC model was preferred for predicting C stock in plantation forest ecosystems as its model provided marginally tighter credible intervals, [1.17–1.60] MgCha−1 when compared to the Landsat-8 based SVC model with 95% credible intervals of [1.13–1.62] Mg Cha−1. The built SVC models provided an understanding of the performance of the multispectral remote sensing derived predictors for modeling C stock and thus provided an essential foundation required for updating the current carbon forest plantation databases.

An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches

The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO2 sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository.

Estimation of National Forest Aboveground Biomass from Multi-Source Remotely Sensed Dataset with Machine Learning Algorithms in China

Forests are the largest terrestrial ecosystem carbon pool and provide the most important nature-based climate mitigation pathway. Compared with belowground biomass (BGB) and soil carbon, aboveground biomass (AGB) is more sensitive to human disturbance and climate change. Therefore, accurate forest AGB mapping will help us better assess the mitigation potential of forests against climate change. Here, we developed six models to estimate national forest AGB using six machine learning algorithms based on 52,415 spaceborne Light Detection and Ranging (LiDAR) footprints and 22 environmental features for China in 2007. The results showed that the ensemble model generated by the stacking algorithm performed best with a determination coefficient (R2) of 0.76 and a root mean square error (RMSE) of 22.40 Mg/ha. The verifications at pixel level (R2 = 0.78, RMSE = 16.08 Mg/ha) and provincial level (R2 = 0.53, RMSE = 14.05 Mg/ha) indicated the accuracy of the estimated forest AGB map is satisfactory. The forest AGB density of China was estimated to be 53.16 ± 1.63 Mg/ha, with a total of 11.00 ± 0.34 Pg. Net primary productivity (NPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), average annual rainfall, and annual temperature anomaly are the five most important environmental factors for forest AGB estimation. The forest AGB map we produced is expected to reduce the uncertainty of forest carbon source and sink estimations.

MSSDet: Multi-Scale Ship-Detection Framework in Optical Remote-Sensing Images and New Benchmark

Ships comprise the only and most important ocean transportation mode. Thus, ship detection is one of the most critical technologies in ship monitoring, which plays an essential role in maintaining marine safety. Optical remote-sensing images contain rich color and texture information, which is beneficial to ship detection. However, few optical remote-sensing datasets are open publicly due to the issue of sensitive data and copyrights, and only the HRSC2016 dataset is built for the ship-detection task. Moreover, almost all general object detectors suffer from the failure of multi-scale ship detection because of the diversity of spatial resolution and ship size. In this paper, we re-annotate the HRSC2016 dataset and supplement 610 optical remote-sensing images to build a new open source ship-detection benchmark dataset with rich multi-scale ship objects named the HRSC2016-MS dataset. In addition, we further explore the potential of a recursive mechanism in the field of object detection and propose a novel multi-scale ship-detection framework (MSSDet) in optical remote-sensing images. The success of detecting multi-scale objects depends on the hierarchical pyramid structure in the object-detection framework. However, the inherent semantic and spatial gaps among hierarchical pyramid levels seriously affect detection performance. To alleviate this problem, we propose a joint recursive feature pyramid (JRFP), which can generate semantically strong and spatially refined multi-scale features. Extensive experiments were conducted on the HRSC2016-MS, HRSC2016, and DIOR datasets. Detailed ablation studies directly demonstrated the effectiveness of the proposed JRFP architecture and also showed that the proposed method has excellent generalizability. Comparisons with state-of-the-art methods showed that the proposed method achieves competitive performance, i.e., 77.3%, 95.8%, and 73.3% mean average precision accuracy on the HRSC2016-MS, HRSC2016, and DIOR datasets, respectively.

Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity—A Review

Forests are essential for global environmental well-being because of their rich provision of ecosystem services and regulating factors. Global forests are under increasing pressure from climate change, resource extraction, and anthropologically-driven disturbances. The results are dramatic losses of habitats accompanied with the reduction of species diversity. There is the urgent need for forest biodiversity monitoring comprising analysis on α, β, and γ scale to identify hotspots of biodiversity. Remote sensing enables large-scale monitoring at multiple spatial and temporal resolutions. Concepts of remotely sensed spectral diversity have been identified as promising methodologies for the consistent and multi-temporal analysis of forest biodiversity. This review provides a first time focus on the three spectral diversity concepts “vegetation indices”, “spectral information content”, and “spectral species” for forest biodiversity monitoring based on airborne and spaceborne remote sensing. In addition, the reviewed articles are analyzed regarding the spatiotemporal distribution, remote sensing sensors, temporal scales and thematic foci. We identify multispectral sensors as primary data source which underlines the focus on optical diversity as a proxy for forest biodiversity. Moreover, there is a general conceptual focus on the analysis of spectral information content. In recent years, the spectral species concept has raised attention and has been applied to Sentinel-2 and MODIS data for the analysis from local spectral species to global spectral communities. Novel remote sensing processing capacities and the provision of complementary remote sensing data sets offer great potentials for large-scale biodiversity monitoring in the future.

Ecological Policies Dominated the Ecological Restoration over the Core Regions of Kubuqi Desert in Recent Decades

Climate change and human activities significantly affected environmental changes in drylands. However, the relative roles remain unclear regarding these factors’ effects on environment changes in drylands. Herein, we analyzed vegetation change trends using remote-sensing datasets to determine the interactions of vegetation, climate, and anthropogenic activities in an arid region of China, Kubuqi Desert. Our study showed that 67.64% of the pixels of fractional vegetation coverage (FVC) increased in 2020 in comparison with those of 1986. The FVC exhibited a significant greening trend (0.0011/yr, p < 0.05) in 1986–2020 as a whole. This greening trend revealed two distinct periods separated by a turning point in 2001. There was no clear trend of FVC before 2001, and then there was a dramatically greening trend since 2001 in most regions of the study area. The increasing rate (0.0036/yr) in the later period was three times higher than the entire period. The accelerated increasing trend was due to the variable compound effects of climate and human activities. The correlation between FVC and precipitation was mainly positive, which outweighs the significantly negative correlation between vegetation and temperature. However, both climatic factors cannot well explain the trends of vegetation dynamics, implying a possible role for human activities. Generally, climate change and anthropogenic activities contributed 42.15% and 57.85% to the overall vegetation variations in 1986–2020. Specifically, the relative role of the two factors was vastly different in two distinct periods. Climate change led the dominant roles (58.68%) in the vegetation variations in 1986–2001, while anthropogenic activities dominated (86.79%) in driving vegetation recovery in the period after 2001. Due to the massive ecological conservation programs such as the Grain for Green Project launched in 2001, substantial deserts have been transformed into grasslands and forests. This analysis highlights the ecological policies largely responsible for vegetation restoration and provides references for ecological protection and sustainable development in eco-fragile ecosystems.

Blind Deblurring of Remote-Sensing Single Images Based on Feature Alignment.

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.

Spatio-temporal distribution of pollutant trace gases (CO, CH4, O3 and NO2) in India: an observational study

ABSTRACT India is one of the largest contributors to anthropogenic emissions during the recent decade associated with its rapid economic growth in India. Trace gases are important components in the climate change process and due to that climate change, there will be a change in their atmospheric concentrations as the climate is sensitive to Earth’s; therefore, proper assessment of trace gases is necessary for ongoing sudden changes in climate. In this study, we used remote-sensing datasets from the Atmospheric Infrared Sounder (AIRS) and Ozone Monitoring Instrument (OMI) to analyze the spatio-temporal variations of four trace gases, like methane (CH4), ozone (O3), carbon monoxide (CO), and nitrogen dioxide (NO2) over India region during 2006–2015 and taken four seasons (i.e., winter, spring, summer, and winter) to interpret the seasonal variation. The project focuses on the temporal pattern of pollutant trace gases i.e., monthly, seasonal, and annual mean variations of trace gases, trend analysis of trace gases, and a comparison of the seasonal behavior of the trace gases by trend analysis was assessed. Higher concentrations of CO show east-to-west, CH4 show north-to-south, and O3 south-to-north gradient, indicating the variations in trace gases due to the impact of emissions and local meteorology. On the other hand, due to immense population density, huge traffic emissions, tremendous, polluted air, and overgrown industrial activities, total NO2 concentrations shoot up over Delhi, Lucknow, and Kolkata. Now as a result of seasonal variation in the long-range transport of air parcels and biomass burning activities, all trace gases shown significant seasonal variations in the spring season and substantially reduced in the summer season. However, in the winter season, O3 concentration evaluates minimum due to less amount of heat on cold days which leads to the reduction of O3 formation. Due to trace gases, all are significant to get regional climate variability. In this study by taking 2006 as a base year and investigate the behaviors of gases for 2007–2015 years to exhibit the increment and decrements in four seasons of all trace gases by taking the most populated 11 different cities of India.

Multithematic Geoscience Data Integration for Identification of Mineral Potential Areas in Parts of Central Indian Precambrian Craton

Abstract The paper highlights the results of multi-thematic geoscience data integration involving geological, geophysical, geochemical and remote sensing data sets, for an area of 4000 Sq. Km in parts of Precambrian Bastar craton, Chhattisgarh. The present team has been able to identify two discrete mineral potential areas for base metal sulphides, Sn, W and REE in sheared and altered felsic volcanics in Kotra-Bhansula area and for gold in propylitically altered meta basalts in Bhursatola-Khampura area. The dataset utilized during collation and integration includes (i) high resolution aeromagnetic data (ii) 1:50000 geological data covering litho-structural and mineralisation attributes (iii) 1:50 k geochemical data for 61 elements (iv) multispectral ASTER satellite data and its derivative alteration maps. Thematic data and derivative maps were studied and interpreted for evaluation of concerned parameters for estimating their role in mineralisation. The data integration has been implemented in three steps beginning with GIS based spatial modelling using Fuzzy logic method resulting into production of mineral favourability map, followed by detailed field evaluation and characterization of the identified mineral favourability areas. Eventual identification of potential areas was based on synthesis of modelling output, field outcome and laboratory results of analyses carried out on critical samples collected from mineral favourability areas. The identified two mineral potential areas are recommended for further detailed exploration for gold, base metal, Sn, W and REE through surface and sub-surface probing.

Dynamics and attributions of ecosystem water yields in China from 2001 to 2020

• China’s ecosystem water yield (WYe) increased at a rare of 3.0 mm/yr during 2001 to 2020. • WYe in 61.5%/38.5% of China increased/decreased. • Changes in WYe in 43.6% of China’s regions are dominated by climate change. • Vegetation contributions to WYe changes were highly spatially heterogeneous. • Dominant factors of WYe changes in nine river basins in China were identified. China is plagued by water security and its associated environmental issues under climate change. Clarifying the spatial and temporal changing patterns of ecosystem water yield (WYe) and its driving factors is essential for optimizing climate-resilient development strategies and achieving sustainable development goals. However, the understanding of the spatial and temporal dynamics of WYe changes in China based on continuous time series and their dominant factors has been limited. This creates a challenge to accurately assess water resource dynamics and environmental sustainability under climate change. Therefore, this study explored WYe dynamic trends in China at the grid-scale from 2001 to 2020 based on water balance and remote-sensing datasets. The contribution and dominance of vegetation (leaf area index and net primary productivity), climate (temperature, wind speed, relative humidity, and net radiation), and human activities, were examined. Results showed that WYe changes exhibited an overall increasing trend (3.0 mm/yr, p = 0.1) in China, with 61.5 % and 38.5 % of the regions showing increasing and decreasing trends, respectively. Climate change (dominant area share, 43.6 %) was the primary driver of WYe dynamics in China, followed by vegetation change (23.8 %) and human activities (21.5 %). The dominant driving factors contributing to WYe dynamics in nine river basins were temperature, net primary productivity, net radiation, and relative humidity. The results emphasize that the high spatial heterogeneity of climate-driven WYe changes may exacerbate regional inequalities in water resources. Future vegetation-driven climate intervention programs and ecological restoration should be implemented more cautiously, and regional adaptive programs must be considered to avoid worsening water scarcity further.

Deep Unsupervised Weighted Hashing for Remote Sensing Image Retrieval

Deep unsupervised hashing methods are gaining attention in the field of remote sensing (RS) image retrieval due to the rapid growth in the volume of unlabeled RS data. Most previous unsupervised hashing research used only natural image-based pre-trained models to generate label matrices; however, this method cannot capture the semantic information of RS images well and limits the accuracy of retrieval. To solve this problem, the authors propose a deep unsupervised weighted hashing (DUWH) model that uses a similarity matrix updating strategy based on a weighted similarity structure to achieve the mutual optimization of the similarity matrix and hash network. The authors devise a novel combinatorial loss function to improve the hash performance that can be used to obtain higher quality hash codes by assigning different weights to the sample pairs with different difficulties. Experiments were conducted on two RS datasets to verify the excellent performance of the proposed method.

ANN-Based DEM Fusion and DEM Improvement Frameworks in Regions of Assam and Meghalaya using Remote Sensing Datasets

The Digital Elevation Models (DEMs) are a key and primary input to a large number of modelling processes such as disaster risk monitoring, flood modelling, hydrology, geology, geomorphology, climatology, and environmental study applications. The DEM serves as an important source of topographic information representing the continuous surface of the earth in 3 dimensions with x, y and z coordinates of any point in a grided raster form or a vector TIN form. This study is based on developing a new method using the universal approximation capability of neural networks for the fusion and improvement of L-band and X-band SAR (Synthetic Aperture Radar) DEMs in the complex terrain of Assam and Meghalaya states of the Indian geographic region using the DEM fusion technique. The high-spatial-resolution ALOS PALSAR RTC HR 12.5 m DEM products are used in a fusion framework designed with neural network models. The network adaptively learns the terrain information to produce fused DEM products. The neural network models generate the relationship between the input elevation information from ALOS PALSAR DEMs and precise reference elevation from ICESat-2 spaceborne altimetry as target data. The training and testing data samples are prepared and filtered by checking the correct range of elevation from the toposheets of this region. Different models are used to separately train for the relatively plain valley portion of the Assam region and mountainous portions of the highland Shillong plateau. The obtained fused DEMs from the developed neural net structure are assessed for their quality and accuracy by estimating the RMSE parameter. The fused DEM attains an RMSE value of 7 meters for the complete region which is a significant improvement over the input DEM RMSE of approximately 11 meters. The plain area points and mountainous region points are assessed separately to analyze the predictions from neural nets in the two types of terrains observed in this study site. Moreover, TanDEM-X 90 m DEM is improved using the neural network modeling in the geometric distortions affected areas, which shows an improvement of around 33% overall at the study site. The assessment of plain and mountainous region points for near-ground points shows an improvement of 47% and 55% respectively. The fusion framework designed using the neural network models is an effective and efficient method for obtaining fused DEMs as well as for the improvement of the existing DEMs for complex terrain.

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.

(Retracted) Support vector machine parallelized remote sensing image classification algorithm based on big data

With the development of big data technology, machine learning classification methods have been widely used in the classification and recognition of remote sensing images. For remote sensing big data, how to quickly and efficiently use machine learning classification algorithms to classify remote sensing images is an urgent problem. It is a general term for the theory, method, technology, and activities of obtaining valuable information based on massive remote sensing data sets, synthesizing auxiliary data from other sources, and using big data thinking and means. The purpose of this paper is to study the support vector machine (SVM) parallelized remote sensing image classification algorithm based on big data. We propose a parallel nesting of GPU in MPI multiprocesses based on the big data framework, which can more effectively improve the calculation processing speed and build a high-performance SVM parallel computing framework based on the big data framework. The optimization problem of SVM considers both empirical risk and structural risk minimization and requires maximum edge distance when constructing hyperplane decision boundaries, so there is ample space between the interval boundaries to accommodate the test samples. Based on this framework, we improve the machine learning SVM algorithm and realize the high-performance parallel computing of SVM classification algorithm on this platform. It is an efficient hybrid parallel mode to nest GPU in MPI multiprocess in parallel. When the number of nodes is 2, 4, and 6, the speedup of the SVM classification algorithm is 1.52, 2.24, and 2.55.

Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation

Although the application of deep learning in remote sensing (RS) has achieved fruitful results, systematic research on exploring the model performance and guiding the design of new convolutional neural network (CNN) architectures is still lacking. This subject is of great concern to researchers or practitioners in this field because existing CNN structures may not be adequate to deal with complex RS scenarios. In this study, an empirical formula of CNN model performance is delivered based on a literature review. Extensive experiments are conducted on six public RS data sets to investigate the influences of three architectural factors, namely, network depth, width, and cardinality. Two types of CNN architectures, i.e., VGG and ResNet, are adopted as baselines. We monitor and visualize the data distributions and gradients of the utilized CNNs to prevent the gradient vanishing or exploding problem. Grad-CAM is adopted to open the black box of CNNs and to illustrate the effects of adjusting architectural factors. Our experiments indicate that (1) increasing the network depth is beneficial to the semantic feature learning capacity of a CNN model, but excessive depth also leads to a decline of overall accuracy; (2) a partly widening strategy is effective because it can improve the model performance while maintaining the network complexity; and (3) network cardinality has huge potential in achieving a balance between model efficiency and accuracy. Suggestions for improving the CNN model performance and developing new structures are summarized in this paper.

BLUR KERNEL’S EFFECT ON PERFORMANCE OF SINGLE-FRAME SUPER-RESOLUTION ALGORITHMS FOR SPATIALLY ENHANCING HYPERION AND PRISMA DATA

Abstract. Single-frame super-resolution (SFSR) achieves the goal of generating a high-resolution image from a single low-resolution input in a three-step process, namely, noise removal, up-sampling and deblurring. Scale factor and blur kernel are essential parameters of the up-sampling and deblurring steps. Few studies document the impact of these parameters on the performance of SFSR algorithms for improving the spatial resolution of real-world remotely-sensed datasets. Here, the effect of changing blur kernel has been studied on the behaviour of two classic SFSR algorithms: iterative back projection (IBP) and gaussian process regression (GPR), which are applied to two spaceborne hyperspectral datasets for scale factors 2, 3 and 4. Eight full-reference image quality metrics and algorithm processing time are deployed for this purpose. A literature-based re-interpretation of Wald’s reduced resolution protocol has also been used in this work for choosing the reference image. Intensive intra-algorithm comparisons of various simulation scenarios reveal each algorithm’s best performing Gaussian blur kernel parameters. Inter-algorithm comparison shows the better performing algorithm out of the two, thereby paving the way for further research in SFSR of remotely-sensed images.

Multi-criteria ground water potentiality mapping utilizing remote sensing and geophysical data: A case study within Sinai Peninsula, Egypt

Groundwater demand has dramatically increased due to the swift demographic explosion, especially in arid areas, where groundwater is considered the main source for all purposes. Thus, this research integrates climatological, lithological, structural, topographical, hydro-morphological, and geoelectrical data to explore groundwater potential zones in the central part of Sinai Peninsula, Egypt using the GIS-based Analytic Hierarchy Process (AHP). Seven thematic layers of soil moisture, rainfall, normalized difference vegetation index, drainage density, lineament density, slope, and land use/land cover were built from different remote sensing data sets. The eighth layer represents a high-resolution lithological map of the study area, constructed utilizing the power of the support vector machine over Sentinel 2 data and accurately assessed with a previously published geological map. Ground Water Potentiality Map (GWPM) was constructed and highlighted four main areas as promising zones. Comprehensive geoelectrical analysis was executed through seventeen deep vertical electrical soundings (VESs) using Schlumberger configuration, isoresistivity mapping, and geoelectric cross-sections along five different profiles. 3D view of the studied area's subsurface geological and structural pattern with groundwater flow direction specification, and adequate aquifer characterization, revealed four main geoelectrical units. Geoelectrical data results reasonably coincided with remote sensing data findings in highlighting three freshwater potential zones. Furthermore, the study strongly recommends integrating low-cost remote sensing datasets in narrowing the zone to be intensively investigated using further costly geophysical approaches or drilling test boreholes.

Innovative use of change detection in large numbers of satellite scenes, with geological applications

The large number of remote-sensing datasets available necessitates the development of efficient methods when assessing change between such data. A series of techniques, optimizing the analysis of change detection, specifically on large remote-sensing dataset collections, is demonstrated. Iterative (online) statistical measures for mean and standard deviation give the ability to gain a measure of change over potentially hundreds of datasets without excessive computing power being needed. From this, the coefficient of variation can be used to provide further insight. Using such measures, seasonal change can be detected on outcrop (as opposed to vegetation), illustrating that change detection can be used to further extend a spectral signature for rocks. Twelve Sentinel-2 scenes over a 3 year period were used in this study. Thematic collection: This article is part of the Remote sensing for site investigations on Earth and other planets collection available at: https://www.lyellcollection.org/cc/remote-sensing-for-site-investigations-on-earth-and-other-planets

Anomalous surface elevation, velocity, and area changes of Split Lake Glacier, western Prince of Wales Icefield, Canadian High Arctic

Here we use a variety of remote sensing data sets to characterize the evolving extent, surface features, dynamics, and surface elevations of Split Lake Glacier, a small outlet of the Prince of Wales Icefield, Nunavut. The glacier started advancing between 1959 and 1975, with a continued increase in terminus area up to the present day, coincident with significant upper elevation thinning and lower elevation thickening that cannot be accounted for by surface mass balance. The highest velocities reach >600 m year−1, with the region of fastest ice motion focused around an icefall that occurs in a bedrock constriction. Distinctive ogives are present in a 1975 air photo of the glacier for the first time, which suggests that rapid motion started by 1970. These patterns are anomalous when compared with the geometry, velocity, and area changes of all other nearby areas of western Prince of Wales Icefield and suggest that Split Lake Glacier may be a slowly surging glacier. The surge duration of 50+ years is longer than any other previously described surge within the Canadian Arctic Archipelago. These results give further information concerning the wide variety of dynamic and geometrical changes of glaciers across this region.