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Shoreline information is fundamental for understanding coastal dynamics and for implementing environmental policy. The analysis of shoreline variability usually uses a group of shoreline indicators visibly discernible in coastal imagery, such as the seaward vegetation line, wet beach/dry beach line, and instantaneous water line. These indicators partition a beach into four zones: vegetated land, dry sand or debris, wet sand, and water. Unmanned aircraft system (UAS) remote sensing that can acquire imagery with sub-decimeter pixel size provides opportunities to map these four beach zones. This paper attempts to delineate four beach zones based on UAS hyperspatial RGB (Red, Green, and Blue) imagery, namely imagery of sub-decimeter pixel size, and feature textures. Besides the RGB images, this paper also uses USGS (the United States Geological Survey) Munsell HSV (Hue, Saturation, and Value) and CIELUV (the CIE 1976 (L*, u*, v*) color space) images transformed from an RGB image. The four beach zones are identified based on the Gray Level Co-Occurrence Matrix (GLCM) and Local Binary Pattern (LBP) textures. Experiments were conducted with South Padre Island photos acquired by a Nikon D80 camera mounted on the US-16 UAS during March 2014. The results show that USGS Munsell hue can separate land and water reliably. GLCM and LBP textures can slightly improve classification accuracies by both unsupervised and supervised classification techniques. The experiments also indicate that we could reach acceptable results on different photos while using training data from another photo for site-specific UAS remote sensing. The findings imply that parallel processing of classification is feasible. [ABSTRACT FROM AUTHOR]

Compensating for distortions in pushbroom satellite imagery has a bearing on subsequent earth observation applications. Traditional distortion correction methods usually depend on ground control points (GCPs) acquired from a high-accuracy geometric calibration field (GCF). Due to the high construction costs and site constraints of GCF, it is difficult to perform distortion detection regularly. To solve this problem, distortion detection methods without using GCPs have been proposed, but their application is restricted by rigorous conditions, such as demanding a large amount of calculation or good satellite agility which are not met by most remote sensing satellites. This paper proposes a novel method to correct interior distortions of satellite imagery independent of GCPs. First, a classic geometric calibration method for pushbroom satellite is built and at least three images with overlapping areas are collected, then the forward intersection residual between corresponding points in the images are used to calculate interior distortions. Experiments using the Gaofen-1 (GF-1) wide-field view-1 (WFV-1) sensor demonstrate that the proposed method can increase the level of orientation accuracy from several pixels to within one pixel, thereby almost eliminating interior distortions. Compared with the orientation accuracy by classic GCF method, there exists maximum difference of approximately 0.4 pixel, and the reasons for this discrepancy are analyzed. Generally, this method could be a supplementary method to conventional methods to detect and correct the interior distortion. [ABSTRACT FROM AUTHOR]

In many remote sensing projects on land cover mapping, the interest is often in a sub-set of classes presented in the study area. Conventional multi-class classification may lead to a considerable training effort and to the underestimation of the classes of interest. On the other hand, one-class classifiers require much less training, but may overestimate the real extension of the class of interest. This paper illustrates the combined use of cost-sensitive and semi-supervised learning to overcome these difficulties. This method utilises a manually-collected set of pixels of the class of interest and a random sample of pixels, keeping the training effort low. Each data point is then weighted according to its distance to its near positive data point to inform the learning algorithm. The proposed approach was compared with a conventional multi-class classifier, a one-class classifier, and a semi-supervised classifier in the discrimination of high-mangrove in Saloum estuary, Senegal, from Landsat imagery. The derived classification accuracies were high: 93.90% for the multi-class supervised classifier, 90.75% for the semi-supervised classifier, 88.75% for the one-class classifier, and 93.75% for the proposed method. The results show that accuracy achieved with the proposed method is statistically non-inferior to that achieved with standard binary classification, requiring however much less training effort. [ABSTRACT FROM AUTHOR]

Remote sensing plays an important role in flood mapping and is helping advance flood monitoring and management. Multi-scale flood mapping is necessary for dividing floods into several stages for comprehensive management. However, existing data systems are typically heterogeneous owing to the use of different access protocols and archiving metadata models. In this paper, we proposed a sharable and efficient metadata model (APEOPM) for constructing an Earth observation (EO) data system to retrieve remote sensing data for flood mapping. The proposed model contains two sub-models, an access protocol model and an enhanced encoding model. The access protocol model helps unify heterogeneous access protocols and can achieve intelligent access via a semantic enhancement method. The enhanced encoding model helps unify a heterogeneous archiving metadata model. Wuhan city, one of the most important cities in the Yangtze River Economic Belt in China, is selected as a study area for testing the retrieval of heterogeneous EO data and flood mapping. The past torrential rain period from 25 March 2015 to 10 April 2015 is chosen as the temporal range in this study. To aid in comprehensive management, mapping is conducted at different spatial and temporal scales. In addition, the efficiency of data retrieval is analyzed, and validation between the flood maps and actual precipitation was conducted. The results show that the flood map coincided with the actual precipitation. [ABSTRACT FROM AUTHOR]

Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) have been assessed. HyMap imaging spectrometer (430-2490 nm) data obtained during the SPARC-2003 campaign in Barrax, Spain, have been used to extract leaf area index (LAI) and leaf chlorophyll content (LCC) estimates. For both SR and ND formulations the most sensitive regions have been identified for two-band combinations of green (539-570 nm) with longwave SWIR (2421-2453 nm) for LAI (r²: 0.83) and far-red (692 nm) with NIR (1340 nm) or shortwave SWIR (1661-1686 nm) for LCC (r²: 0.93). Polynomial, logarithmic and linear fitting functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval. [ABSTRACT FROM AUTHOR]

This paper addresses the calibration of mobile mapping systems and the feasibility of using a total station as a sensor for indoor mobile mapping systems. For this purpose, the measuring system of HafenCity University in Hamburg is presented and discussed. In the second part of the calibration, the entire system will be described regarding the interaction of laser scanners and other parts of the system. Finally, the preliminary analysis of the use of a total station is presented in conjunction with the measurement system. The difficulty of time synchronization is also discussed. In multiple tests, a comparison was made versus a reference solution based on GNSS. Additionally, the suitability of the total station was also considered for indoor applications. [ABSTRACT FROM AUTHOR]

Remote sensing has become the most important data source for the digital elevation model (DEM) generation. DEM analyses can be applied in various fields and many of them require appropriate DEM visualization support. Analytical hill-shading is the most frequently used relief visualization technique. Although widely accepted, this method has two major drawbacks: identifying details in deep shades and inability to properly represent linear features lying parallel to the light beam. Several authors have tried to overcome these limitations by changing the position of the light source or by filtering. This paper proposes a new relief visualization technique based on diffuse, rather than direct, illumination. It utilizes the sky-view factor-a parameter corresponding to the portion of visible sky limited by relief. Sky-view factor can be used as a general relief visualization technique to show relief characteristics. In particular, we show that this visualization is a very useful tool in archaeology as it improves the recognition of small scale features from high resolution DEMs. [ABSTRACT FROM AUTHOR]

Biomass burning is a worldwide phenomenon, which emits large amounts of carbon into the atmosphere and strongly influences the environment. Burned area is an important parameter in modeling the impacts of biomass burning on the climate and ecosystem. The Medium Resolution Spectral Imager (MERSI) onboard FengYun-3C (FY-3C) has shown great potential for burned area mapping research, but there is still a lack of relevant studies and applications. This paper describes an automated burned area mapping algorithm that was developed using daily MERSI data. The algorithm employs time-series analysis and multi-temporal 1000-m resolution data to obtain seed pixels. To identify the burned pixels automatically, region growing and Support Vector Machine) methods have been used together with 250-m resolution data. The algorithm was tested by applying it in two experimental areas, and the accuracy of the results was evaluated by comparing them to reference burned area maps, which were interpreted manually using Landsat 8 OLI data and the MODIS MCD64A1 burned area product. The results demonstrated that the proposed algorithm was able to improve the burned area mapping accuracy at the two study sites. [ABSTRACT FROM AUTHOR]

The concept of planetary mapping constitutes different activities within different contexts. Much like the field of cartography, it is an amalgamation of science, techniques, and artistic disciplines. It has undergone considerable changes over the last decades to cope with increasing demands related to data management, analysis, and visualization. Planetary mapping employs abstraction, which involves simplifications and generalizations. It aims to produce accessible visualization of planetary surfaces to gain insights and knowledge. Here, we show that different manifestations of this concept are interdependent and we discuss how different mapping concepts relate to each other semantically. We reason that knowledge gain can only be achieved through thematic mapping. The reasoning for systematic mapping and exploration is an intellectual product of thematic mapping. In order to highlight these relationships, we (a) develop in-depth definitions for different types of planetary mapping, (b) discuss data and knowledge flow across different mapping concepts, and (c) highlight systemic limitations related to data that we acquire and attempt to abstract through models. We finally develop a semantic proto-model that focuses on the transformation of information and knowledge between mapping domains. We furthermore argue that due to compositionality, map products suffer not only from abstraction but also from limitations related to uncertainties during data processing. We conclude that a complete database is needed for mapping in order to establish contextualization and extract knowledge. That knowledge is needed for reasoning for planning and operational decision making. This work furthermore aims to motivate future community-based discussions on functional semantic models and ontologies for the future development of knowledge extraction from thematic maps. [ABSTRACT FROM AUTHOR]

In rockfall hazard management, the investigation and detection of potential rockfall source areas on rock cliffs by remote-sensing-based susceptibility analysis are of primary importance. However, when the rockfall analysis results are used as feedback to the fieldwork, the irregular slope surface morphology makes it difficult to objectively locate the risk zones of hazard maps on the real slopes, and the problem of straightforward on-site visualization of rockfall susceptibility remains a research gap. This paper presents some of the pioneering studies on the augmented reality (AR) mapping of geospatial information from cyberspace within 2D screens to the physical world for on-site visualization, which directly recognizes the rock mass and superimposes corresponding rock discontinuities and rockfall susceptibility onto the real slopes. A novel method of edge-based tracking of the rock mass target for mobile AR is proposed, where the model edges extracted from unmanned aerial vehicle (UAV) structure-from-motion (SfM) 3D reconstructions are aligned with the corresponding actual rock mass to estimate the camera pose accurately. Specifically, the visually prominent edges of dominant structural planes were first explored and discovered to be a robust visual feature of rock mass for AR tracking. The novel approaches of visual-geometric synthetic image (VGSI) and prominent structural plane (Pro-SP) were developed to extract structural planes with identified prominent edges as 3D template models which could provide a pose estimation reference. An experiment verified that the proposed Pro-SP template model could effectively improve the edge tracking performance and quality, and this approach was relatively robust to the changes of sunlight conditions. A case study was carried out on a typical roadcut cliff in the Mentougou District of Beijing, China. The results validate the scalability of the proposed mobile AR strategy, which is applicable and suitable for cliff-scale fieldwork. The results also demonstrate the feasibility, efficiency, and significance of the geoinformation AR mapping methodology for on-site zoning and locating of potential rockfalls, and providing relevant guidance for subsequent detailed site investigation. [ABSTRACT FROM AUTHOR]

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m. [ABSTRACT FROM AUTHOR]

Detecting buildings, segmenting building footprints, and extracting building edges from high-resolution remote sensing images are vital in applications such as urban planning, change detection, smart cities, and map-making and updating. The tasks of building detection, footprint segmentation, and edge extraction affect each other to a certain extent. However, most previous works have focused on one of these three tasks and have lacked a multitask learning framework that can simultaneously solve the tasks of building detection, footprint segmentation and edge extraction, making it difficult to obtain smooth and complete buildings. This study proposes a novel multiscale and multitask deep learning framework to consider the dependencies among building detection, footprint segmentation, and edge extraction while completing all three tasks. In addition, a multitask feature fusion module is introduced into the deep learning framework to increase the robustness of feature extraction. A multitask loss function is also introduced to balance the training losses among the various tasks to obtain the best training results. Finally, the proposed method is applied to open-source building datasets and large-scale high-resolution remote sensing images and compared with other advanced building extraction methods. To verify the effectiveness of multitask learning, the performance of multitask learning and single-task training is compared in ablation experiments. The experimental results show that the proposed method has certain advantages over other methods and that multitask learning can effectively improve single-task performance. [ABSTRACT FROM AUTHOR]

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification. [ABSTRACT FROM AUTHOR]

This study used a reef-up approach to map coral reef benthos, substrates and bathymetry, with high spatial resolution hyperspectral image data. It investigated a physics-based inversion method for mapping coral reef benthos and substrates using readily available software: Hydrolight and ENVI. Compact Airborne Spectrographic Imager (CASI) data were acquired over Heron Reef in July 2002. The spectral reflectance of coral reef benthos and substrate types were measured in-situ, and using the HydroLight 4.2 radiative transfer model a spectral reflectance library of subsurface reflectance was simulated using water column depths from 0.5-10.0 m at 0.5 m intervals. Using the Spectral Angle Mapper algorithm, sediment, benthic micro-algae, algal turf, crustose coralline algae, macro-algae, and live coral were mapped with an overall accuracy of 65% to a depth of around 8.0 m; in waters deeper than 8.0 m the match between the classified image and field validation data was poor. Qualitative validation of the maps showed accurate mapping of areas dominated by sediment, benthic micro-algae, algal turf, live coral, and macro-algae. A bathymetric map was produced for water column depths 0.5-10.0 m, at 0.5 m intervals, and showed high correspondence with in-situ sonar data (R2 value of 0.93). [ABSTRACT FROM AUTHOR]

An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES) technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC), Quick, Unbiased, Efficient Statistical Tree (QUEST), Random Forest (RF) and Support Vector Machine (SVM) were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats. [ABSTRACT FROM AUTHOR]

In light of rapid global urbanisation, monitoring and mapping of urban and population growth is of great importance. Population growth in Sana'a was investigated for this reason. The capital of the Republic of Yemen is a rapidly growing middle sized city where the population doubles almost every ten years. Satellite data from four different sensors were used to explore urban growth in Sana'a between 1989 and 2007, assisted by topographic maps and cadastral vector data. The analysis was conducted by delineating the built-up areas from the various optical satellite data, applying a fuzzy-rule-based composition of anisotropic textural measures and interactive thresholding. The resulting datasets were used to analyse urban growth and changes in built-up density per district, qualitatively as well as quantitatively, using a geographic information system. The built-up area increased by 87 % between 1989 and 2007. Built-up density has increased in all areas, but particularly in the northern and southern suburban districts, also reflecting the natural barrier of surrounding mountain ranges. Based on long-term population figures, geometric population growth was assumed. This hypothesis was used together with census data for 1994 and 2004 to estimate population figures for 1989 and 2007, resulting in overall growth of about 240%. By joining population figures to district boundaries, the spatial patterns of population distribution and growth were examined. Further, urban built-up growth and population changes over time were brought into relation in order to investigate changes in population density per built-up area. Population densities increased in all districts, with the greatest density change in the peripheral areas towards the North. The results reflect the pressure on the city's infrastructure and natural resources and could contribute to sustainable urban planning in the city of Sana'a. [ABSTRACT FROM AUTHOR]

The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment. [ABSTRACT FROM AUTHOR]

Updating the mapping of wind turbines farms—found in constant expansion—is important to predict energy production or to minimize the risk of these infrastructures during storms. This geoinformation is not usually provided by public mapping agencies, and the alternative sources are usually consortiums or individuals interested in mapping and study. However, they do not offer metadata or genealogy, and their quality is unknown. This article presents a methodology oriented to optimize the recognition and extraction of features (wind turbines) using hybrid architectures of semantic segmentation. The aim is to characterize the quality of these datasets and help to improve and update them automatically at a large-scale. To this end, we intend to evaluate the capacity of hybrid semantic segmentation networks trained to extract features representing wind turbines from high-resolution images and to characterize the positional accuracy and completeness of a dataset whose genealogy and quality are unknown. We built a training dataset composed of 5140 tiles of aerial images and their cartography to train six different neural network architectures. The networks were evaluated on five test areas (covering 520 km2 of the Spanish territory) to identify the best segmentation architecture (in our case, LinkNet as base architecture and EfficientNet-b3 as the backbone). This hybrid segmentation model allowed us to characterize the completeness—both by commission and by omission—of the available georeferenced wind turbine dataset, as well as its geometric quality. [ABSTRACT FROM AUTHOR]

Imagery from medium resolution satellites, such as Landsat, have long been used to map forest disturbances in the tropics. However, the Landsat spatial resolution (30 m) has often been considered too coarse for reliably mapping small-scale selective logging. Imagery from the recently launched Sentinel-2 sensor, with a resampled 10 m spatial resolution, may improve the detection of forest disturbances. This study compared the performance of Landsat 8 and Sentinel-2 data for the detection of selective logging in an area located in the Brazilian Amazon. Logging impacts in seven areas, which had governmental authorization for harvesting timber, were mapped by calculating the difference of a self-referenced normalized burn ratio (ΔrNBR) index over corresponding time periods (2016–2017) for imagery of both satellite sensors. A robust reference dataset was built using both high- and very-high-resolution imagery. It was used to define optimum ΔrNBR thresholds for forest disturbance maps, via a bootstrapping procedure, and for estimating accuracies and areas. A further assessment of our approach was also performed in three unlogged areas. Additionally, field data regarding logging infrastructure were collected in the seven study sites where logging occurred. Both satellites showed the same performance in terms of accuracy, with area-adjusted overall accuracies of 96.7% and 95.7% for Sentinel-2 and Landsat 8, respectively. However, Landsat 8 mapped 36.9% more area of selective logging compared to Sentinel-2 data. Logging infrastructure was better detected from Sentinel-2 (43.2%) than Landsat 8 (35.5%) data, confirming its potential for mapping small-scale logging. We assessed the impacted area by selective logging with a regular 300 m × 300 m grid over the pixel-based results, leading to 1143 ha and 1197 ha of disturbed forest on Sentinel-2 and Landsat 8 data, respectively. No substantial differences in terms of accuracy were found by adding three unlogged areas to the original seven study sites. [ABSTRACT FROM AUTHOR]

We tested the efficacy and skill of SnowCloud, a prototype web-based, cloud-computing framework for snow mapping and hydrologic modeling. SnowCloud is the overarching framework that functions within the Google Earth Engine cloud-computing environment. SnowCloudMetrics is a sub-component of SnowCloud that provides users with spatially and temporally composited snow cover information in an easy-to-use format. SnowCloudHydro is a simple spreadsheet-based model that uses Snow Cover Frequency (SCF) output from SnowCloudMetrics as a key model input. In this application, SnowCloudMetrics rapidly converts NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover product (MOD10A1) into a monthly snow cover frequency for a user-specified watershed area. SnowCloudHydro uses SCF and prior monthly streamflow to forecast streamflow for the subsequent month. We tested the skill of SnowCloudHydro in three snow-dominated headwaters that represent a range of precipitation/snowmelt runoff categories: the Río Elqui in Northern Chile; the John Day River, in the Northwestern United States; and the Río Aragón in Northern Spain. The skill of the SnowCloudHydro model directly corresponded to snowpack contributions to streamflow. Watersheds with proportionately more snowmelt than rain provided better results (R2 values: 0.88, 0.52, and 0.22, respectively). To test the user experience of SnowCloud, we provided the tools and tutorials in English and Spanish to water resource managers in Chile, Spain, and the United States. Participants assessed their user experience, which was generally very positive. While these initial results focus on SnowCloud, they outline methods for developing cloud-based tools that can function effectively across cultures and languages. Our approach also addresses the primary challenges of science-based computing; human resource limitations, infrastructure costs, and expensive proprietary software. These challenges are particularly problematic in countries where scientific and computational resources are underdeveloped. [ABSTRACT FROM AUTHOR]