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To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use 'iVS dataset' and a data annotation tool entitled 'ezLabel'. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well. [ABSTRACT FROM AUTHOR]

Abstract: This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use. [ABSTRACT FROM AUTHOR]

Bodies of water, particularly swimming pools, are land covers of high interest. Their maintenance involves energy costs that authorities must take into consideration. In addition, swimming pools are important water sources for firefighting. However, they also provide a habitat for mosquitoes to breed, potentially posing a serious health threat of mosquito-borne disease. This paper presents a novel semi-automatic method of detecting swimming pools in urban environments from aerial images and LIDAR data. A new index for detecting swimming pools is presented (Normalized Difference Swimming Pools Index) that is combined with three other decision indices using the Dempster-Shafer theory to determine the locations of swimming pools. The proposed method was tested in an urban area of the city of Alcalá de Henares in Madrid, Spain. The method detected all existing swimming pools in the studied area with an overall accuracy of 99.86%, similar to the results obtained by support vector machines (SVM) supervised classification. [ABSTRACT FROM AUTHOR]

Because traditional decision tree (DT) induction methods cannot efficiently take advantage of geospatial knowledge in the classification of remotely sensed imagery, several researchers have presented a co-location decision tree (CL-DT) method that combines the co-location technique with the traditional DT method. However, the CL-DT method only considers the Euclidean distance of neighborhood events, which cannot truly reflect the co-location relationship between instances for which there is a nonlinear distribution in a high-dimensional space. For this reason, this paper develops the theory and method for a maximum variance unfolding (MVU)-based CL-DT method (known as MVU-based CL-DT), which includes unfolding input data, unfolded distance calculations, MVU-based co-location rule generation, and MVU-based CL-DT generation. The proposed method has been validated by classifying remotely sensed imagery and is compared with four other types of methods, i.e., CL-DT, classification and regression tree (CART), random forests (RFs), and stacked auto-encoders (SAE), whose classification results are taken as “true values.” The experimental results demonstrate that: (1) the relative classification accuracies of the proposed method in three test areas are higher than CL-DT and CART, and are at the same level compared to RFs; and (2) the total number of nodes, the number of leaf nodes, and the number of levels are significantly decreased by the proposed method. The time taken for the data processing, decision tree generation, drawing of the tree, and generation of the rules are also shortened by the proposed method compared to CL-DT, CART, and RFs. [ABSTRACT FROM AUTHOR]

Recent guidance on environmental modeling and global land-cover validation stresses the need for a probability-based design. Additionally, spatial balance has also been recommended as it ensures more efficient sampling, which is particularly relevant for understanding land use change. In this paper I describe a global sample design and database called the Global Grid (GG) that has both of these statistical characteristics, as well as being flexible, multi-scale, and globally comprehensive. The GG is intended to facilitate collaborative science and monitoring of land changes among local, regional, and national groups of scientists and citizens, and it is provided in a variety of open source formats to promote collaborative and citizen science. Since the GG sample grid is provided at multiple scales and is globally comprehensive, it provides a universal, readily-available sample. It also supports uneven probability sample designs through filtering sample locations by user-defined strata. The GG is not appropriate for use at locations above ±85° because the shape and topological distortion of quadrants becomes extreme near the poles. Additionally, the file sizes of the GG datasets are very large at fine scale (resolution ~600 m × 600 m) and require a 64-bit integer representation. [ABSTRACT FROM AUTHOR]

To overcome the limitations of standard datasets with data at a wide-variety of scales and captured in the various conditions necessary to train neural networks to yield efficient results in ADAS applications, this paper presents a self-built open-to-free-use ‘iVS dataset’ and a data annotation tool entitled ‘ezLabel’. The iVS dataset is comprised of various objects at different scales as seen in and around real driving environments. The data in the iVS dataset are collected by employing a camcorder in vehicles driving under different conditions, e.g., light, weather and traffic, and driving scenarios ranging from city traffic during peak and normal hours to freeway traffics during busy and normal conditions. Thus, the collected data are wide-ranging and captured all possible objects at various scales appearing in real-time driving situations. The data collected in order to build the dataset has to be annotated before use in training the CNNs and so this paper presents an open-to-free-use data annotation tool, ezLabel, for data annotation purposes as well.

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation. [ABSTRACT FROM AUTHOR]

Marine remote sensing (MRS) data provide an important tool for advancing global change research. However, the existing product service practices are insufficient for meeting the needs of a full-experience online application. This paper introduces a framework named SatANA, which is unified by a data tiling method with a spatial-aware feature, for integrated and intelligent improvements in visualization, storage and computing. The SatANA framework is supported by a hybrid database storage ideal for the cloud storage of massive MRS data. The raw data are displayed and roamed on a virtual globe through the Internet as tiles, enhancing their spatial awareness, that can be intelligently used for visualization result tuning, data storage preloading and distributed computing optimized indexing. To verify its feasibility and effectiveness, we applied this framework to a platform called SatCO2, which is devoted to providing convenient access to and the efficient utilization of MRS data. [ABSTRACT FROM AUTHOR]

HaiYang-2A (HY-2A, where 'Haiyang' means 'Ocean' in Chinese) has provided reliable sea surface height observations for gravity with uniform ocean data coverage on a global scale for more than 8 years, particularly with denser across track sampling during the geodetic mission since March 2016. This paper aims at modeling and evaluating the regional marine gravity field at 1′×1′ resolution by incorporating HY-2A altimeter waveform data from 7 complete 168-day cycles in the geodetic mission phase. Initial evaluation indicates that, firstly, the measurements in the geodetic mission stay at a consistent accuracy level with observations at the start-of-life stage according to statistics of discrepancies at crossover points cycle by cycle. Secondly, range precision improvement can be achieved using a two-pass weighted least-squares retracker. Thirdly, a downsampling procedure combined with a low-pass filter is designed for HY-2A 20 Hz data to obtain 5 Hz measurements with enhanced precision. We calculate the 1′×1′ marine gravity field model over the South China Sea area by using the EGM2008 model as a reference field with the remove/restore method. The verifications with published models and shipborne gravimetric data show that HY-2A GM data is capable of improving marine gravity field modeling. Results show slightly higher accuracy than other models with similar input datasets but not including HY-2A. The accuracy is also compared with the latest DTU17 and SIO V27.1 model. [ABSTRACT FROM AUTHOR]

The scattering transform, which applies multiple convolutions using known filters targeting different scales of time or frequency, has a strong similarity to the structure of convolution neural networks (CNNs), without requiring training to learn the convolution filters, and has been used for hyperspectral image classification in recent research. This paper investigates the application of the scattering transform framework to hyperspectral unmixing (STFHU). While state-of-the-art research on unmixing hyperspectral data utilizing scattering transforms is limited, the proposed end-to-end method applies pixel-based scattering transforms and preliminary three-dimensional (3D) scattering transforms to hyperspectral images in the remote sensing scenario to extract feature vectors, which are then trained by employing the regression model based on the k-nearest neighbor (k-NN) to estimate the abundance of maps of endmembers. Experiments compare performances of the proposed algorithm with a series of existing methods in quantitative terms based on both synthetic data and real-world hyperspectral datasets. Results indicate that the proposed approach is more robust to additive noise, which is suppressed by utilizing the rich information in both high-frequency and low-frequency components represented by the scattering transform. Furthermore, the proposed method achieves higher accuracy for unmixing using the same amount of training data with all comparative approaches, while achieving equivalent performance to the best performing CNN method but using much less training data. [ABSTRACT FROM AUTHOR]

Data describing aircraft position and attitude are essential to computing return positions from ranging data collected during airborne laser scanning (ALS) campaigns. However, these data are often excluded from the products delivered to the client and their recovery after the contract is complete can require negotiations with the data provider, may involve additional costs, or even be infeasible. This paper presents a rigorous, fully automated, novel method for recovering aircraft positions using only the point cloud. The study used ALS data from five acquisitions in the US Pacific Northwest region states of Oregon and Washington and validated derived aircraft positions using the smoothed best estimate of trajectory (SBET) provided for the acquisitions. The computational requirements of the method are reduced and precision is improved by relying on subsets of multiple-return pulses, common in forested areas, with widely separated first and last returns positioned at opposite sides of the aircraft to calculate their intersection, or closest point of approach. To provide a continuous trajectory, a cubic spline is fit to the intersection points. While it varies by acquisition and parameter settings, the error in the computed aircraft position seldom exceeded a few meters. This level of error is acceptable for most applications. To facilitate use and encourage modifications to the algorithm, the authors provide a code that can be applied to data from most ALS acquisitions. [ABSTRACT FROM AUTHOR]

The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost. [ABSTRACT FROM AUTHOR]

Laurel wilt (Lw) is a very destructive disease and poses a serious threat to the commercial production of avocado in Florida, USA. External symptoms of Lw are similar to those that are caused by other diseases and disorders. A rapid technique to distinguish Lw infected avocado from healthy trees and trees with other abiotic stressors is presented in this paper. A novel method was developed to analyze data from hyperspectral data using finite difference approximation (FDA) and bivariate correlation (BC) to discriminate Lw, Nitrogen (N), and Iron (Fe) deficiencies from healthy avocado plants. Several combinatorial methods were used in preprocessing the data, such as standard normal transformation of data, smoothing of the data, and polynomial fit. The FDA technique was derived using a Taylor Polynomial finite difference approximation. This FDA accentuates inflection points in the spectrum. These, in turn, reveal variance in the data that can be used to identify spectral signature associated with healthy and diseased states. By statistical correlation using the bivariate correlation coefficient of these enhanced spectral patterns, an algorithm (FDA-BC) for distinguishing Lw avocado leaves from all other categories of healthy or mineral deficient avocado leaves is achieved with an overall accuracy of 100%. [ABSTRACT FROM AUTHOR]

Over the past decade deep learning has driven progress in 2D image understanding. Despite these advancements, techniques for automatic 3D sensed data understanding, such as point clouds, is comparatively immature. However, with a range of important applications from indoor robotics navigation to national scale remote sensing there is a high demand for algorithms that can learn to automatically understand and classify 3D sensed data. In this paper we review the current state-of-the-art deep learning architectures for processing unstructured Euclidean data. We begin by addressing the background concepts and traditional methodologies. We review the current main approaches, including RGB-D, multi-view, volumetric and fully end-to-end architecture designs. Datasets for each category are documented and explained. Finally, we give a detailed discussion about the future of deep learning for 3D sensed data, using literature to justify the areas where future research would be most valuable. [ABSTRACT FROM AUTHOR]

Airborne LiDAR has been traditionally used for power line cruising. Nevertheless, data acquisition with airborne LiDAR is constrained by the complex environments in urban areas as well as the multiple parallel line structures on the same power line tower, which means it is not directly applicable to the extraction of urban power lines. Vehicle-borne LiDAR system has its advantages upon airborne LiDAR and this paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines. First, power line points are extracted using a voxel-based hierarchical method in which geometric features of each voxel are calculated. Then, a bottom-up method for filtering the power lines belonging to each power line is proposed. The initial clustering and clustering recovery procedures are conducted iteratively to identify each power line. The final experiment demonstrates the high precision of this technique. [ABSTRACT FROM AUTHOR]

An addendum is presented to the article "Using Satellite Data for the Characterization of Local Animal Reservoir Populations of Hantaan Virus on the Weihe Plain, China" which was published in the perodical.

An addendum is presented to the article "Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends" which was published in the perodical.

The use of small Unmanned Aircraft Systems (sUAS) as platforms for data capture has rapidly increased in recent years. However, while there has been significant investment in improving the aircraft, sensors, operations, and legislation infrastructure for such, little attention has been paid to supporting the management of the complex data capture pipeline sUAS involve. This paper reports on a four-year, community-based investigation into the tools, data practices, and challenges that currently exist for particularly researchers using sUAS as data capture platforms. The key results of this effort are: (1) sUAS captured data—as a set that is rapidly growing to include data in a wide range of Physical and Environmental Sciences, Engineering Disciplines, and many civil and commercial use cases—is characterized as both sharing many traits with traditional remote sensing data and also as exhibiting—as common across the spectrum of disciplines and use cases—novel characteristics that require novel data support infrastructure; and (2), given this characterization of sUAS data and its potential value in the identified wide variety of use case, we outline eight challenges that need to be addressed in order for the full value of sUAS captured data to be realized. We conclude that there would be significant value gained and costs saved across both commercial and academic sectors if the global sUAS user and data management communities were to address these challenges in the immediate to near future, so as to extract the maximal value of sUAS captured data for the lowest long-term effort and monetary cost.

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at mid-growing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions. [ABSTRACT FROM AUTHOR]

The U.S. Geological Survey (USGS) has begun the development of operational, 30-m resolution annual thematic land cover data to meet the needs of a variety of land cover data users. The Continuous Change Detection and Classification (CCDC) algorithm is being evaluated as the likely methodology following early trials. Data for training and testing of CCDC thematic maps have been provided by the USGS Land Cover Trends (LC Trends) project, which offers sample-based, manually classified thematic land cover data at 2755 probabilistically located sample blocks across the conterminous United States. These samples represent a high quality, well distributed source of data to train the Random Forest classifier invoked by CCDC. We evaluated the suitability of LC Trends data to train the classifier by assessing the agreement of annual land cover maps output from CCDC with output from the LC Trends project within 14 Landsat path/row locations across the conterminous United States. We used a small subset of circa 2000 data from the LC Trends project to train the classifier, reserving the remaining Trends data from 2000, and incorporating LC Trends data from 1992, to evaluate measures of agreement across time, space, and thematic classes, and to characterize disagreement. Overall agreement ranged from 75% to 98% across the path/rows, and results were largely consistent across time. Land cover types that were well represented in the training data tended to have higher rates of agreement between LC Trends and CCDC outputs. Characteristics of disagreement are being used to improve the use of LC Trends data as a continued source of training information for operational production of annual land cover maps. [ABSTRACT FROM AUTHOR]

Road traffic has severe effects on animals, especially when road-kills are involved. In many countries, official road-kill data are provided by hunters or police; there are also road-kill observations reported by citizen scientists. The aim of the current study was to test whether road-kill reports by hunters stem from similar landscapes than those reported by citizen scientists. We analysed the surrounding landscapes of 712 road-kill reportings of European hares in the province of Lower Austria. Our data showed that road-killed hares reported both by hunters and citizens are predominantly surrounded by arable land. No difference of hedges and solitary trees could be found between the two datasets. However, significant differences in landcover classes and surrounding road networks indicate that hunters’ and citizen scientists’ data are different. Hunters reported hares from landscapes with significantly higher percentages of arable land, and greater lengths of secondary roads. In contrast, citizens reported hares from landscapes with significantly higher percentages of urban or industrial areas and greater lengths of motorways, primary roads, and residential roads. From this we argue that hunters tend to report data mainly from their hunting areas, whereas citizens report data during their daily routine on the way to/from work. We conclude that a citizen science approach is an important source for road-kill data when used in addition to official data with the aim of obtaining an overview of road-kill events on a landscape scale. [ABSTRACT FROM AUTHOR]

The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are "changed", "unchanged", or "unknown", and quantifying the changes. The designation "unknown" is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between "changed", "unchanged", and "unknown". Rules are defined to solve problems caused by the lack of data. Further, points labelled as "changed" are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%-90% of real changes are found, of which approximately 50% are considered relevant. [ABSTRACT FROM AUTHOR]

Frame hyperspectral sensors, in contrast to push-broom or line-scanning ones, produce hyperspectral datasets with, in general, better geometry but with unregistered spectral bands. Being acquired at different instances and due to platform motion and movements (UAVs, aircrafts, etc.), every spectral band is displaced and acquired with a different geometry. The automatic and accurate registration of hyperspectral datasets from frame sensors remains a challenge. Powerful local feature descriptors when computed over the spectrum fail to extract enough correspondences and successfully complete the registration procedure. To this end, we propose a generic and automated framework which decomposes the problem and enables the efficient computation of a sufficient amount of accurate correspondences over the given spectrum, without using any ancillary data (e.g., from GPS/IMU). First, the spectral bands are divided in spectral groups according to their wavelength. The spectral borders of each group are not strict and their formulation allows certain overlaps. The spectral variance and proximity determine the applicability of every spectral band to act as a reference during the registration procedure. The proposed decomposition allows the descriptor and the robust estimation process to deliver numerous inliers. The search space of possible solutions has been effectively narrowed by sorting and selecting the optimal spectral bands which under an unsupervised manner can quickly recover hypercube's geometry. The developed approach has been qualitatively and quantitatively evaluated with six different datasets obtained by frame sensors onboard aerial platforms and UAVs. Experimental results appear promising. [ABSTRACT FROM AUTHOR]

Web tools for statistical investigation with an interactive and friendly interface enable users without programming knowledge to conduct their analyses. We develop an Interactive Correlation Environment (ICE) in an open access platform to perform spectral collinearity analysis for biogeochemical activity retrieval. We evaluate its performance on different browsers and applied it to retrieve chlorophyll-a (chl-a) concentration in a tropical reservoir. The use of ICE to retrieve water chl-a concentration got a Root Mean Square Error (RMSE) lower than 7% for seasonal datasets, enhancing ICE's ability to adapt it within season. An RMSE of 17% was found for the mixed dataset with a large range of chl-a concentrations. We conclude that the use of ICE is recommended, due to its quick response, easily manipulation, high accuracy, and empirical adaptation to seasonal variability. Its use is enhanced by the development of hyperspectral sensors, which allow the identification of several biogeochemical components, such as chl-a, phycocyanin (PC), soil salinity, soil types, leaf nitrogen, and leaf chl-a concentration. [ABSTRACT FROM AUTHOR]

The state-of-art satellite observations of atmospheric aerosols over the last two decades from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been extensively utilized in climate change and air quality research and applications. The operational algorithms now produce Level 2 aerosol data at varying spatial resolutions (1, 3, and 10 km) and Level 3 data at 1 degree. The local and global applications have benefited from the coarse resolution gridded data sets (i.e., Level 3, 1 degree), as it is easier to use since data volume is low, and several online and offline tools are readily available to access and analyze the data with minimal computing resources. At the same time, researchers who require data at much finer spatial scales have to go through a challenging process of obtaining, processing, and analyzing larger volumes of data sets that require high-end computing resources and coding skills. Therefore, we created a high spatial resolution (high-resolution gridded (HRG), 0.1 × 0.1 degree) daily and monthly aerosol optical depth (AOD) product by combining two MODIS operational algorithms, namely Deep Blue (DB) and Dark Target (DT). The new HRG AODs meet the accuracy requirements of Level 2 AOD data and provide either the same or more spatial coverage on daily and monthly scales. The data sets are provided in daily and monthly files through open an Ftp server with python scripts to read and map the data. The reduced data volume with an easy to use format and tools to access the data will encourage more users to utilize the data for research and applications. [ABSTRACT FROM AUTHOR]

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing. [ABSTRACT FROM AUTHOR]

Access to temporally dense time series such as data from the Landsat and Sentinel-2 missions has lead to an increase in methods which aim to monitor land cover change on a per-acquisition rather than a yearly basis. Evaluating the accuracy and limitations of these methods can be difficult because validation data are limited and often rely on human interpretation. Simulated time series offer an objective method for evaluating and comparing between change detection algorithms. A set of simulated time series was used to evaluate four change detection methods: (1) Breaks for Additive and Seasonal Trend (BFAST); (2) BFAST Monitor; (3) Continuous Change Detection and Classification (CCDC); and (4) Exponentially Weighted Moving Average Change Detection (EWMACD). In total, 151,200 simulations were generated to represent a range of abrupt, gradual, and seasonal changes. EWMACD was found to give the best performance overall, correctly identifying the true date of change in 76.6% of cases. CCDC performed worst (51.8%). BFAST performed well overall but correctly identified less than 10% of seasonal changes (changes in amplitude, length of season, or number of seasons). All methods showed some decrease in performance with increased noise and missing data, apart from BFAST Monitor which improved when data were removed. The following recommendations are made as a starting point for future studies: EWMACD should be used for detection of lower magnitude changes and changes in seasonality; CCDC should be used for robust detection of complete land cover class changes; EWMACD and BFAST are suitable for noisy datasets, depending on the application; and CCDC should be used where there are high quantities of missing data. The simulated datasets have been made freely available online as a foundation for future work. [ABSTRACT FROM AUTHOR]

Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our case a rainfall field, into another one, by applying a spatial transformation. A benefit of this approach is that it can take both the position and the intensity of a rain event into account. Its potential is investigated with two case studies. In the first case, the rain events are synthetic, represented by elliptic shapes, while the second case uses real data from a rainfall event occurring during the monsoon season in southern Ghana. In the second case, the satellite-based estimate IMERG-Late (Integrated Multi-Satellite Retrievals for GPM) is adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. The results show that the position errors can be corrected, while preserving the higher spatial variability of the satellite-based estimate. [ABSTRACT FROM AUTHOR]

Mid-season nitrogen (N) application in rice crops can maximize yield and profitability. This requires accurate and efficient methods of determining rice N uptake in order to prescribe optimal N amounts for topdressing. This study aims to determine the accuracy of using remotely sensed multispectral data from satellites to predict N uptake of rice at the panicle initiation (PI) growth stage, with a view to providing optimum variable-rate N topdressing prescriptions without needing physical sampling. Field experiments over 4 years, 4–6 N rates, 4 varieties and 2 sites were conducted, with at least 3 replicates of each plot. One WorldView satellite image for each year was acquired, close to the date of PI. Numerous single- and multi-variable models were investigated. Among single-variable models, the square of the NDRE vegetation index was shown to be a good predictor of N uptake (R 2 = 0.75, RMSE = 22.8 kg/ha for data pooled from all years and experiments). For multi-variable models, Lasso regularization was used to ensure an interpretable and compact model was chosen and to avoid over fitting. Combinations of remotely sensed reflectances and spectral indexes as well as variety, climate and management data as input variables for model training achieved R 2 < 0.9 and RMSE < 15 kg/ha for the pooled data set. The ability of remotely sensed data to predict N uptake in new seasons where no physical sample data has yet been obtained was tested. A methodology to extract models that generalize well to new seasons was developed, avoiding model overfitting. Lasso regularization selected four or less input variables, and yielded R 2 of better than 0.67 and RMSE better than 27.4 kg/ha over four test seasons that weren't used to train the models. [ABSTRACT FROM AUTHOR]

The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line. [ABSTRACT FROM AUTHOR]

Exotic conifers can provide significant ecosystem services, but in some environments, they have become invasive and threaten indigenous ecosystems. In New Zealand, this phenomenon is of considerable concern as the area occupied by invasive exotic trees is large and increasing rapidly. Remote sensing methods offer a potential means of identifying and monitoring land infested by these trees, enabling managers to efficiently allocate resources for their control. In this study, we sought to develop methods for remote detection of exotic invasive trees, namely Pinus sylvestris and P. ponderosa. Critically, the study aimed to detect these species prior to the onset of maturity and coning as this is important for preventing further spread. In the study environment in New Zealand's South Island, these species reach maturity and begin bearing cones at a young age. As such, detection of these smaller individuals requires specialist methods and very high-resolution remote sensing data. We examined the efficacy of classifiers developed using two machine learning algorithms with multispectral and laser scanning data collected from two platforms—manned aircraft and unmanned aerial vehicles (UAV). The study focused on a localized conifer invasion originating from a multi-species pine shelter belt in a grassland environment. This environment provided a useful means of defining the detection thresholds of the methods and technologies employed. An extensive field dataset including over 17,000 trees (height range = 1 cm to 476 cm) was used as an independent validation dataset for the detection methods developed. We found that data from both platforms and using both logistic regression and random forests for classification provided highly accurate (kappa < 0.996 ) detection of invasive conifers. Our analysis showed that the data from both UAV and manned aircraft was useful for detecting trees down to 1 m in height and therefore shorter than 99.3% of the coning individuals in the study dataset. We also explored the relative contribution of both multispectral and airborne laser scanning (ALS) data in the detection of invasive trees through fitting classification models with different combinations of predictors and found that the most useful models included data from both sensors. However, the combination of ALS and multispectral data did not significantly improve classification accuracy. We believe that this was due to the simplistic vegetation and terrain structure in the study site that resulted in uncomplicated separability of invasive conifers from other vegetation. This study provides valuable new knowledge of the efficacy of detecting invasive conifers prior to the onset of coning using high-resolution data from UAV and manned aircraft. This will be an important tool in managing the spread of these important invasive plants. [ABSTRACT FROM AUTHOR]

Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The marsh edge from elevation data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the "marsh scarp." It is detected as the maximum slope along a cross-shore transect between mean high water and mean tide level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the marsh edge by image processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The two methods were compared to each other using high resolution unmanned aircraft systems (UAS) data, and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods, and less than 0.43 meters from the digitized shoreline. The MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability. [ABSTRACT FROM AUTHOR]