Moderate Spatial Resolution Research Articles

Ångström-resolution fluorescence microscopy

Fluorescence microscopy, with its molecular specificity, is one of the major characterization methods used in the life sciences to understand complex biological systems. Super-resolution approaches1–6 can achieve resolution in cells in the range of 15 to 20 nm, but interactions between individual biomolecules occur at length scales below 10 nm and characterization of intramolecular structure requires Ångström resolution. State-of-the-art super-resolution implementations7–14 have demonstrated spatial resolutions down to 5 nm and localization precisions of 1 nm under certain in vitro conditions. However, such resolutions do not directly translate to experiments in cells, and Ångström resolution has not been demonstrated to date. Here we introdue a DNA-barcoding method, resolution enhancement by sequential imaging (RESI), that improves the resolution of fluorescence microscopy down to the Ångström scale using off-the-shelf fluorescence microscopy hardware and reagents. By sequentially imaging sparse target subsets at moderate spatial resolutions of >15 nm, we demonstrate that single-protein resolution can be achieved for biomolecules in whole intact cells. Furthermore, we experimentally resolve the DNA backbone distance of single bases in DNA origami with Ångström resolution. We use our method in a proof-of-principle demonstration to map the molecular arrangement of the immunotherapy target CD20 in situ in untreated and drug-treated cells, which opens possibilities for assessing the molecular mechanisms of targeted immunotherapy. These observations demonstrate that, by enabling intramolecular imaging under ambient conditions in whole intact cells, RESI closes the gap between super-resolution microscopy and structural biology studies and thus delivers information key to understanding complex biological systems.

The Solar Upper Transition Region Imager (SUTRI) Onboard the SATech-01 Satellite

The Solar Upper Transition Region Imager (SUTRI) onboard the Space Advanced Technology demonstration satellite (SATech-01), which was launched to a Sun-synchronous orbit at a height of ∼500 km in 2022 July, aims to test the on-orbit performance of our newly developed Sc/Si multi-layer reflecting mirror and the 2k×2k EUV CMOS imaging camera and to take full-disk solar images at the Ne vii 46.5 nm spectral line with a filter width of ∼3 nm. SUTRI employs a Ritchey–Chrétien optical system with an aperture of 18 cm. The on-orbit observations show that SUTRI images have a field of view of ∼ 41.′6 × 41.′6 and a moderate spatial resolution of ∼8″ without an image stabilization system. The normal cadence of SUTRI images is 30 s and the solar observation time is about 16 hr each day because the earth eclipse time accounts for about 1/3 of SATech-01's orbit period. Approximately 15 GB data is acquired each day and made available online after processing. SUTRI images are valuable as the Ne vii 46.5 nm line is formed at a temperature regime of ∼0.5 MK in the solar atmosphere, which has rarely been sampled by existing solar imagers. SUTRI observations will establish connections between structures in the lower solar atmosphere and corona, and advance our understanding of various types of solar activity such as flares, filament eruptions, coronal jets and coronal mass ejections.

Flooding and inundation maps using interpolated CYGNSS reflectivity observations

Despite the myriad of remote sensing techniques currently used to map surface water, a gap remains in our ability to rapidly map inundation from flooding at the required temporal resolution to understand how floods evolve. However, the recent launch of constellations of Global Navigation Satellite System-Reflectometry (GNSS-R) satellites can provide data at a more frequent temporal repeat than a single satellite. These L-band instruments could provide moderate spatial resolution inundation maps at a better temporal resolution than other satellite radars. This paper describes a retrieval algorithm for flood inundation mapping using GNSS-R data from the Cyclone GNSS (CYGNSS) constellation. The algorithm employs a simple dielectric model to retrieve fractional inundation from an observation of reflectivity, requiring parameterizations of soil surface and water roughness as well as ancillary soil moisture data. Here, we give a brief overview of the model, describe our parameterization scheme, and present inundation maps using CYGNSS data. We describe four case studies (from the Amazon, Mozambique, Mali, and Australia) and compare the CYGNSS inundation maps to other surface water data (SWAMPS, PALSAR-2, Dartmouth Flood Observatory, MODIS, and the Global Surface Water Explorer). We identify sources of uncertainty in the CYGNSS inundation maps and discuss possible reasons for discrepancies between the inundation retrievals. We introduce the data portal, which houses the CYGNSS inundation maps, for use by the science community.

Forest status assessment in China with SDG indicators based on high-resolution satellite data

ABSTRACT To assess the status and change trend of forest in China, an indicator framework was developed using SDG sub-indicators. In this paper, we propose an improved methodology and a set of workflows for calculating SDG indicators. The main modification include the use of moderate and high spatial resolution satellite data, as well as state-of-the-art machine learning techniques for forest cover classification and estimation of forest above-ground biomass (AGB). This research employs GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest AGB across the whole territory of China. The study calculate two SDG sub-indicators: SDG15.1.1 for forest area and SDG15.2.1 for sustainable forest management. The evaluation results showed that the total forest area in China was approximately 219 million hectares at the end of 2021, accounting for about 23.51% of the land area. The average annual forest AGB from 2015 to 2021 was estimated to be 105.01Mg/ha, and the overall trend of forest AGB change in China was positive, albeit with some spatial differences.

Using the Sentinel-3B Satellite in Geospatial Analysis of Suspended Aerosols in the Kiev, Ukraine Region

The use of images from the Sentinel-3B SYN satellite (surface reflectance and aerosol parameters over land) is currently one of the most advanced technologies utilized to identify atmospheric aerosol concentrations on a global scale. The general aim of this study is to analyze the evolution of aerosols in the atmosphere of the Kiev region in northern Ukraine during 2019, 2020, 2021 and 2022. Due to this study’s timing, both prior to and during the current military incursion into Ukraine, this study also evaluates the consequences of the invasion of the Russian army on the territory of Ukraine, in relation to the quantitative levels of aerosols present in the atmosphere. Satellite image data were modelled in SNAP software (Sentinel Application Platform). Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The Sentinel-3B SYN satellite images were made available by the European Space Agency (ESA), with moderate spatial resolution (>300 m), calibrated and normalized to an average standard of 0.83 µg/mg, with a maximum error of 6.62% in the 30 sampled points. Satellite image data were modelled in SNAP software. Using the JASP software (version 0.14.1.0), clusters with variations of T550 (Aerosol Optical Thickness) were generated. The results show variations in the concentration of T550 in different periods, revealing that the military conflict between Russia and Ukraine directly influenced the dynamics of aerosol concentration, attributed to factors incompatible with environmental sustainability.

Characterizing Live Fuel Moisture Content from Active and Passive Sensors in a Mediterranean Environment

Live fuel moisture content (LFMC) influences many fire-related aspects, including flammability, ignition, and combustion. In addition, fire spread models are highly sensitive to LFMC values. Despite its importance, LFMC estimation is still elusive due to its dependence on plant species traits, local conditions, and weather patterns. Although LFMC mapping from active synthetic aperture radar has increased over the past years, their utility for LFMC estimation needs further analysis to include additional areas characterized by different vegetation species and fire regimes. This study extended the current knowledge using medium spatial resolution (20 m) time series acquired by active (Sentinel-1) and passive (Sentinel-2) sensors. Our results show that optical-based LFMC estimation may achieve acceptable accuracy (R2 = 0.55, MAE = 15.1%, RMSE = 19.7%) at moderate (20 m) spatial resolution. When ancillary information (e.g., vegetation cover) was added, LFMC estimation improved (R2 = 0.63, MAE = 13.4%). Contrary to other studies, incorporating Sentinel-1 radar data did not provide for improved LFMC estimates, while the use of SAR data alone resulted in increased estimation errors (R2 = 0.28, MAE = 19%, RMSE = 25%). For increased fire risk scenarios (LFMC < 120%), estimation errors improved (MAE = 9.1%, RMSE = 11.8%), suggesting that direct LFMC retrieval from satellite data may be achieved with high temporal and spatial detail.

MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM

Abstract. Forest dynamics are closely related to climate change, natural disasters, and ecological diversity. The accumulated Landsat archive provides an unprecedented opportunity for long-term forest dynamics monitoring globally. However, using Landsat time series to detect small-scale and low-intensity disturbance events is still challenging since the moderate spatial resolution of Landsat images and the mixed pixel problem. Towards improving the ability of vegetation index (VI) in characterizing sub-pixel forest dynamics, this paper introduced the spectral mixture analysis (SMA) to develop a novel Pure Forest Index (PFI). The Continuous Change Detection and Classification (CCDC) algorithm was used to detect forest disturbance based on the PFI time series. Cross-comparison shows that PFI is far superior to other conventional VI in indicating forest conditions since it can enhance the spectral signal of the forest and suppress noises from the background. Time series analysis further demonstrates the superiority of PFI in accurately characterizing forest dynamics. The high overall accuracy of 0.96 for the forest disturbance map generated by the proposed approach was achieved. This study highlights a novel VI for accurately tracking subtle forest changes in a heterogeneous landscape.

Using drone mapping to evaluate error of plot-based field surveys and its effects on moderate spatial resolution remote sensing retrieval of lichen cover

Effective plot-based field sampling involves a trade-off between implementation efficiency and sample error. Optimal field sampling therefore requires quantifying the sample error under various sampling designs. For remote sensing applications, it is also important to understand how field sample error and training sample size (the number of pixels) affect the retrieval of surface properties. In this research, drone imagery was used to simulate field plots and investigate plot sampling error for forage lichen cover in relation to plot size, number of plots, and sampling strategy. The effect of this error on remote sensing-based lichen cover retrieval was evaluated using varying training sampling sizes in two different study regions in northern Canada. Results showed that cover with high spatial variability increased the number of plots or plot size required to achieve a specified level of error. For lichen cover retrieval at moderate spatial resolution (10–30 m), field sampling (plot size and number of plots) did not have as significant of an effect as regional differences (spectral separability of cover types), sensor, and the number of pixels used for model training. This plot simulation approach using drone images can be applied to other surface properties and regions to provide field sampling guidance.

A downscaling model for derivation of 3-D flood products from VIIRS imagery and SRTM/DEM

As one of the costliest and most frequent natural disasters, flood is a major threat to human lives and property. Flood forecasting, simulation, and monitoring plays an important role in disaster relief and mitigation. With the support from the JPSS (Joint Polar Satellite System) Proving Ground and Risk Reduction (PGRR) Program, the VIIRS (Visible Infrared Imaging Radiometer Suite) global 375-m flood products in near real-time, daily composition, and 5-day composition have been developed and derived flood extent represented in water fractions from Suomi-NPP and NOAA-20. The products have shown good quality for the spatial distribution of floodwater. However, the moderate spatial resolution is a limiting factor when obtaining inundation detail. Moreover, no vertical information on the floodwater is included in the products. Based on water’s self-leveling nature, it is feasible to construct the vertical structure of floodwater using the VIIRS 375-m water fractions and high-resolution Digital Elevation Model (DEM) such as the 30-m Shuttle Radar Topography Mission (SRTM)/DEM through a downscaling process (Li et al., 2013). With impact factors including topography, land cover, tree cover, river network and watershed, the downscaling process has been integrated into a model called the Downscaling Model. By using the 30-m SRTM/DEM, the model can downscale the VIIRS 375-m floodwater fraction products into 30-m flood extent and water depth products. These products include flood information in both horizontal and vertical directions and thus are called 3-D flood products in this study. This paper presents a comprehensive introduction to the model with the results evaluated against high-resolution satellite images from Landsat-8 Operational Land Imager (OLI), Sentinel-2, and GeoEye, aerial photos, and river gauge observations. Evaluation results indicate reliable quality and promising performance of the model and the downscaled flood products. A routine system has been set up at the Cooperative Institute for Satellite Studies (CIMSS) at University of Wisconsin-Madison to generate experimental VIIRS downscaled flood extent and water depth products over the Continental USA (CONUS). The routinely generated VIIRS 30-m floodwater depth maps are available at: https://floods.ssec.wisc.edu/?products=VIIRS-3Dflood.

Customized whole brain-covering 3D GRASE in multi-delay pseudo-continuous arterial spin labeling for duplex distinct hemodynamic mapping contrasts of brain tissues and circulation pathways

Gradient and spin echo (GRASE) is widely employed in arterial spin labeling (ASL) as an efficient readout sequence. Hemodynamic parameter mappings of perfusion, such as cerebral blood flow (CBF) and arterial transit time (ATT), can be derived via multi-delay ASL acquisitions. Multi-delay ASL perfusion imaging inevitably suffers limited signal-to-noise ratio (SNR) since a motion-sensitized vessel suppressing module has to be employed to highlight perfusion signals. The present work reveals that in multi-delay ASL, manipulation of GRASE sequence on either planar imaging echo echo train for adjusted spatial resolutions or FSE echo train for modulated extent of T 2-blurring can significantly alter the mapping contrasts among tissues and among cerebral lobes under different pathways of blood circulation, and meanwhile regulates SNR. Four separate multi-delay ASL scans with different echo train designs in 3D whole brain covering GRASE were carried out for healthy subjects to evaluate the variations in regard to the parameter quantifications and SNR. Based on the quantification mappings, the GRASE acquisition with moderate spatial resolution (3.5 × 3.5 × 4 mm3) and segmented k z scheme was recognized for the first time to be recommended for more unambiguous CBF and ATT contrasts between GM and WM in conjunction with more enhanced ATT contrast between anterior and posterior cerebral circulations, with reasonably good SNR. The technical proposal is of great value for the cutting-edge research of a variety of neurological diseases of global concerns.

Assessing and modeling diurnal temperature buffering and evapotranspiration dynamics in forest restoration using ECOSTRESS thermal imaging

Restoration of temperate forests alters the diurnal dynamics of land surface temperature (LST) and evapotranspiration (ET) through increases in biomass, diversity, and complexity. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is the first space-based thermal imaging instrument that allows for a study of diurnal LST and ET dynamics at a moderate spatial resolution (70 × 70 m).We quantified, compared, and modeled the LST and ET dynamics of two groups of forest restoration sites (43 sites in total, between 2 and 75 ha) in Southern Ontario, Canada that were initially restored from mainly agriculture between 2007 and 2019. We included all useable LST (n = 29) and ET (n = 9) ECOSTRESS image products from a full growing season in 2020 (June 1st to September 30th). Forest restoration sites were compared by age and against agriculture (pre-restoration state), mature forest (post-restoration state), and suburban residential sites (competing land use).The ability of forest restoration sites to buffer temperatures was highest in the afternoon, around 14:00 local time (EDT). As predicted, restoration sites were significantly cooler (x̄ 4.4–7.4 °C) than residential and agricultural areas and significantly warmer (x̄1.6–2.9 °C) compared to mature reference forest sites at both groups of sites. Relative diurnal (24-h) LST variability of forest and restoration sites was also significantly lower (0.9–2.9 °C) compared to agriculture and residential sites (3.3–5.2 °C). Daytime LST decreased significantly by 0.1 °C (3.1%), per year since restoration for one of the groups of sites relative to nearby mature reference forest sites as per a linear mixed effects regression model. It would take these sites ~30 years to reach the same buffering as mature forests. In characterizing ET dynamics of a subset of sites, we found that more recently restored sites had a statistically significant higher overall ET than older ones and that daytime relative instantaneous ET decreased with years since restoration. The variation explained by the ET model was however low.Our study provides insights into how diurnal forest ecosystem energy conversion and storage dynamics changes over time after restoration. These diurnal thermal dynamics impact wildlife habitat as well as human wellbeing. The change in thermal buffering over time could be used to assess the pace and trajectory of restoration by managers. The thermal buffering provided by restored forest can also be quantified as an ecosystem service. As such, the study demonstrates the utility and also limitations of novel thermal remote sensing methods, using free and publicly available imagery from ECOSTRESS.

Uncertainty quantification in land surface temperature retrieved from Himawari-8/AHI data by operational algorithms

Himawari-8, a new-generation geostationary satellite, can retrieve sub-hourly land surface temperatures (LSTs) with moderate spatial resolution, providing a new scale for monitoring the thermal environment in Asia and Oceania. This study evaluated uncertainties of LSTs retrieved by three operational algorithms from Advanced Himawari Imager (AHI) data. We compared two nonlinear split-window algorithms (SOB and WAN algorithms) and one nonlinear three-band algorithm (YAM algorithm). First, the error characteristics of the retrieved LSTs caused by the input parameter errors were simulated under various land-atmospheric conditions using an atmospheric radiative transfer model. Thereafter, retrieved LSTs from actual AHI data were evaluated using in-situ observations from AsiaFlux and OzFlux networks and the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) LSTs. The simulated results showed that the YAM algorithm maintained the highest accuracy, whereas the WAN algorithm had the highest robustness to input errors. The YAM algorithm had the smallest total error including input errors over a wide range of retrieval conditions. Validation of the three algorithms via in-situ LSTs from 12 sites revealed nighttime mean RMSEs for all sites of ∼1.7 °C, and daytime mean RMSEs for semi-arid and humid sites of approximately 3.0 °C and 2.0 °C, respectively. These are comparable to the accuracies reported for LST products with higher spatial resolutions, such as the Moderate Resolution Imaging Spectroradiometer and Landsat. Within the Himawari-8 disk, the estimation error of the YAM algorithm was ∼1.0 °C lower than those of the SOB and WAN algorithms in regions with extremely high viewing angle, temperature, and humidity (e.g., northern China, Australia, and Southeast Asia). Furthermore, AHI LSTs showed closer agreement with ECOSTRESS compared to in-situ LSTs, suggesting the usefulness of ECOSTRESS for assessing the diurnal LSTs derived from geostationary satellites. The resulting LST products and the knowledge of their error characteristics have the potential to improve the collective understanding of terrestrial energy and water cycles based on improved accuracy and robustness.

Satellite remote sensing of coastal water quality in New Zealand

ABSTRACT Water quality in estuarine and coastal waters of Aotearoa New Zealand is of concern yet currently under-monitored, reflecting challenges of sampling at regular intervals across large areas. Satellite products are currently underutilised (globally) by management agencies but could be part of a holistic approach. NIWA-SCENZ (Seas, Coasts and Estuaries New Zealand) is a website providing access to MODIS-Aqua products at moderate spatial resolution (500 m) across relatively clear coastal waters (Case 1) to turbid coastal waters (Case 2). It also provides advanced analytical tools on an extracted area of interest for trend assessment (Shiny-SCENZ). For Case 1 areas product accuracy will be comparable to data from other websites using the same algorithms. For Case 2 and transition (blended) areas, although tuned to New Zealand conditions, accuracy of products requires validation. Data availability is mainly impacted by cloud cover, but also adjacency to land and bottom reflection. On average <5% within ∼1 km from coastlines, compared to ∼10%–30% outside 2 km. Extrapolation and blending from offshore into shorelines on month averaged maps extend estimates into many bays and estuaries. Active research efforts will deliver iterative improvements in data quality, new products, and analysis methods (e.g. variability and long-term trend maps).

Mapping of Teak (Tectona grandis) Growing Stock Volume based on Sentinel-2 Imagery Classification

Remote sensing utilization for plantation forests management in Java was not yet widely used, whereas the ability of remote sensing data for land cover monitoring and forest resource mapping has been developed, ranging from low resolution imagery for global areas to moderate and high resolution for small scale areas. Data availability and human resources often become obstacles in the application. The emergence of Sentinel satellite images becomes an alternative because the dataset is free access and has moderate spatial resolution and high temporal resolution. This study aims to map the growing stock volume in UGM Educational and Training Forest using Sentinel-2 imagery. Three kinds of classification method based on machine learning algorithms i.e. Random Forest, K-NN and SVM were compared for land cover classification. An NDVI algorithm was also used for mapping the spectral value distribution. Moreover, a stand age distribution which obtained from KHDTK UGM manager were also map. A stand classification map based on land cover types, NDVI value and stand age distribution was created as a basis of growing stock volume estimation. The analysis show that the growing stock volume can be estimated using these method with RMSE 177,8 m3 and MAPE 21,9 m3.

Volumetric change analysis of the Cauvery delta topography using radar remote sensing

Geomorphology refers to the study of the Earth’s physical land surface features. Digital Elevation Model (DEM) is a digital imagery where each pixel represents elevation value above or below mean sea level. The availability of DEM allows analysis of landscape morphology and related processes in terms of geomorphometry. DEM of Difference (DoD) techniques are used to estimate the elevation variations and volumetric changes over time. The volumetric change of terrain in the Cauvery delta (CD) for the past two decades is estimated using DoD algorithm by utilizing freely available moderate spatial resolution SRTM, PALSAR and Sentinel 1A DEMs which are derived by radar remote sensing technique. A detailed assessment of individual DEM’s errors and propagating errors into the DoD are estimated using the field elevation points measured by Real-Time Kinematic (RTK) Global Positioning System (GPS). The default errors in the SRTM, PALSAR and Sentinel 1A DEMs are observed with Root Mean Square Error (RMSE) of 3.33, 3.11 and 3.04 m respectively. The erosion and accretion rates determined are 25 and 30 million m3 / decade correspondingly. The total volume of sediment load due to erosion and accretion is 50 and 60 million m3 respectively during 2000–2020. Further the net area affected due to deposition (69 %) is higher than the total area affected due to erosion (30%). This study presents decadal sediment budget and rate of annual change due to erosion/accretion in the CD.

Preliminary Application of Space-Based Remote Sensing and Geospatial Technology for Investigation on the Geo-Environmental Consequences of Cyclone Aila 2009 in the NW Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Trials of transmission imaging using clinically used Ir-192 source for high-dose-rate brachytherapy

In high-dose-rate (HDR) brachytherapy, verification of an Ir-192 source's position during treatment is required. One of the methods for this used a high-energy pinhole gamma camera to image the position of the source, but the absolute position of the source cannot be measured. To confirm the absolute position, it will be useful to acquire the transmission image of a subject in addition to the gamma photon image at the same time without using an additional X-ray system. To measure the transmission images, we tried to use the high-energy gamma photons emitted from the Ir-192 source used for the therapy. We developed a high-energy gamma photon imaging system composed of 1-mm-thick Pr doped Gd2O2S (GOS), a surface mirror, and a cooled charge-coupled device (CCD) camera. The developed imaging system achieved transmission imaging of high-energy gamma photons by transporting the Ir-192 source in front of the imaging system. The spatial resolution of the imaging system was better than 2.4 mm FWHM with and without a 10-cm-thick acrylic block set between the imaging system and the source. Moderate spatial resolution and contrast images of phantoms were obtained with the system. For the dynamic imaging mode, continuous images of the phantoms were measured with 1-sec intervals. There was no observable difference in the transmission images by the movement of the Ir-192 source. Transmission imaging of subjects using an Ir-192 source for HDR brachytherapy could be achieved using our developed imaging system. The system offers a new method to measure the real-time transmission images of the subject during HDR brachytherapy.

Spatially explicit modeling of the probability of land abandonment in the Spanish Pyrenees

Many mountain regions in Europe have experienced massive migrations towards metropolitan areas, with far-reaching implications for societies and the environment, especially croplands and grasslands. In this work, we tailored a geospatial framework to envisage the probability of land abandonment in the Spanish Pyrenees at moderate spatial resolution. We predicted the likelihood of land abandonment combining machine learning algorithms, geospatial data and historical observations of land abandonment in the period 1980 to 2019. The model attained a high predictive performance (AUC = 0.85) at a moderate resolution (30 m), providing insights into the spatial behavior of the potential for both abandonment and its main driving forces. The highest rates of abandonment were found in rural settlements and towns in bottom valleys where tourism and recreational activities have proliferated over the years. Fast and comfortable connections between the main metropolitan areas (e.g. Barcelona and Zaragoza) and the mountain regions foster touristic activity and lead to the creation of new settlements. Ecotourism and mountain sports promote land abandonment as evidenced by the high probability predicted over the Central Pyrenees (from Pallars Jussà to Alto Gállego counties). Our results provide spatially explicit probability and uncertainty outputs, providing insights into site-specific abandonment potential and the influence of its drivers. These results can substantially assist land planners in decision-making, enabling assessments at local scale.

SOYBEAN YIELD FORECAST USING DUAL-POLARIMETRIC C-BAND SYNTHETIC APERTURE RADAR

Abstract. Crop yield forecast is important, but determining productivity is an on-going challenge for agricultural communities including policy makers. An increase in the number of satellites, improved temporal and spatial resolutions and more open data policies, are leading to wider interest by the agricultural sector in exploiting space-based data at moderate spatial resolutions (10–30 m) and varying wavelengths (optical and microwave) for crop yield monitoring. This study evaluated Sentinel-1 dual-polarimetric data to forecast soybean yields one month before the harvest at field scales over a site in central Argentina. Specifically, polarimetric features were extracted from the Sentinel-1 data using the M-Chi decomposition. An Artificial Neural Network (ANN) model was trained using a time series of the single-bounce and volume scattering parameters derived from M-Chi from stacks of data acquired during the growing season. To estimate soybean yield from the ANN model, an innovative iterative retrieval method was developed. This retrieval approach improved the accuracies of the soybean yield forecast delivering a final coefficient of determination (R2) of 0.81 with a root mean square error (RMSE) of 755.81 kg/ha and mean absolute error (MAE) of 581.65 kg/ha. These accuracies demonstrate a high potential of Synthetic Aperture Radar (SAR) data from Sentinel-1 for soybean yield forecast at field scales.

Framework for near real-time forest inventory using multi source remote sensing data

AbstractForestry inventory update is a critical component of sustainable forest management, requiring both the spatially explicit identification of forest cover change and integration of sampled or modelled components like growth and regeneration. Contemporary inventory data demands are shifting, with an increased focus on accurate attribute estimation via the integration of advanced remote sensing data such as airborne laser scanning (ALS). Key challenges remain, however, on how to maintain and update these next-generation inventories as they age. Of particular interest is the identification of remotely sensed data that can be applied cost effectively, as well as establishing frameworks to integrate these data to update information on forest condition, predict future growth and yield, and integrate information that can guide forest management or silvicultural decisions such as thinning and harvesting prescriptions. The purpose of this article is to develop a conceptual framework for forestry inventory update, which is also known as the establishment of a ‘living inventory’. The proposed framework contains the critical components of an inventory update including inventory and growth monitoring, change detection and error propagation. In the framework, we build on existing applications of ALS-derived enhanced inventories and integrate them with data from satellite constellations of free and open, analysis-ready moderate spatial resolution imagery. Based on a review of the current literature, our approach fits trajectories to chronosequences of pixel-level spectral index values to detect change. When stand-replacing change is detected, corresponding values of cell-level inventory attributes are reset and re-established based on an assigned growth curve. In the case of non–stand-replacing disturbances, cell estimates are modified based on predictive models developed between the degree of observed spectral change and relative changes in the inventory attributes. We propose that additional fine-scale data can be collected over the disturbed area, from sources such as CubeSats or remotely piloted airborne systems, and attributes updated based on these data sources. Cells not identified as undergoing change are assumed unchanged with cell-level growth curves used to increment inventory attributes. We conclude by discussing the impact of error propagation on the prediction of forest inventory attributes through the proposed near real-time framework, computing needs and integration of other available remote sensing data.