High Temporal Resolution Imagery Research Articles

Observation of super-Alfvénic slippage of reconnecting magnetic field lines on the Sun

AbstractSlipping motions of magnetic field lines are a distinct signature of three-dimensional magnetic reconnection, a fundamental process driving solar and stellar flares. While being a key prediction of numerical experiments, the rapid super-Alfvénic field line slippage driven by the ‘slip-running’ reconnection has remained elusive in previous observations. New frontiers into exploring transient flare phenomena were introduced by recently designed high cadence observing programs of the Interface Region Imaging Spectrograph (IRIS). By exploiting high temporal resolution imagery (~2 s) of IRIS, here we reveal slipping motions of flare kernels at speeds reaching thousands of kilometres per second. The fast kernel motions are direct evidence of slip-running reconnection in quasi-separatrix layers, regions where magnetic field strongly changes its connectivity. Our results provide observational proof of theoretical predictions unaddressed for nearly two decades and extend the range of magnetic field configurations where reconnection-related phenomena can occur.

Optimal window period for mapping Parthenium weed in South Africa, using high temporal resolution imagery and the ExtraTrees classifier

Parthenium weed (Parthenium hysterophorus) is one of the most noxious herbaceous weeds in the world with adverse impacts on among others animal and human health, crop production, the environment, local as well as national economies. To optimize Parthenium mitigation, it is necessary to accurately monitor its spread using earth observation data. However, one of the challenges of mapping Parthenium weed is that its spectral response is similar to that of surrounding herbaceous plant species, resulting in low classification accuracies. Due to variability in its phenological characteristics and associated species, determining differences within the growing season may optimize the discrimination and subsequent mapping of the Parthenium weed. However, determination of the window(s) with the most prominent variability has been overlooked in past studies. Furthermore, no specific algorithm has been determined to be efficient in finding such window(s). ExtraTrees (EXT), an underused classifier in earth observation studies, possess interesting properties for satellite image processing such as high speed and performance. In this regard, this study attempted to (1) determine the optimal window period for discriminating Parthenium weed from coexisting plant species and (2) to compare the performance of EXT and the random forest (RF) algorithms. Results showed that the beginning of February was the optimal period for mapping Parthenium weed, with overall accuracy of 88.1%. EXT outperformed RF for most of the dates. This study lays the foundation for optimizing earth observation data derived models for characterizing invasive species, leveraging on the high temporal resolution of the new generation sensors.

Application of Dynamic Threshold in a Lake Ice Detection Algorithm

The traditional method involved in the classification of surface types such as water, ice, and snow rely on thresholds values of spectral properties that are fixed. However, the use of daily fixed thresholds leaves a substantial number of either unclassified and/or misclassified ice and water pixels. In this study, a new dynamic threshold technique is proposed to identify and map lake ice cover in the imagery of GOES-I to P series satellites. In addition, dynamic threshold can be used as an alternative solution to Bidirectional Reflectance Distribution Function (BRDF) models. The technique has been applied using GOES-13 imager data over Lake Michigan, one of five of the Great Lakes. Nine scenes based on an hourly acquisition of a single day are used to visually sample water and ice pixels. A threshold for the visible (0.62 µm) and the reflective component of the mid-infrared (3.9 µm) is determined for each scene by the intersection of the probability distributions of the water and ice samples. The thresholds are used as decision thresholds in a binary test algorithm applied on a per-pixel basis. Both fixed threshold (single scene) and dynamic thresholds (multiple scenes) have been compared. Dynamic threshold shows a significant gain in classified pixels over fixed threshold. A preliminary quantitative assessment is introduced to evaluate the algorithm’s performance using sensitivity and specificity testing. The classification results show a sensitivity of 98% when delineating thick ice and water and 87% when delineating thick/thin ice and water. Implementing a dynamic threshold, can be used in constructing ice maps in applications that benefit from high temporal resolution imagery.

Collect Earth: Land Use and Land Cover Assessment through Augmented Visual Interpretation

Collect Earth is a free and open source software for land monitoring developed by the Food and Agriculture Organization of the United Nations (FAO). Built on Google desktop and cloud computing technologies, Collect Earth facilitates access to multiple freely available archives of satellite imagery, including archives with very high spatial resolution imagery (Google Earth, Bing Maps) and those with very high temporal resolution imagery (e.g., Google Earth Engine, Google Earth Engine Code Editor). Collectively, these archives offer free access to an unparalleled amount of information on current and past land dynamics for any location in the world. Collect Earth draws upon these archives and the synergies of imagery of multiple resolutions to enable an innovative method for land monitoring that we present here: augmented visual interpretation. In this study, we provide a full overview of Collect Earth’s structure and functionality, and we present the methodology used to undertake land monitoring through augmented visual interpretation. To illustrate the application of the tool and its customization potential, an example of land monitoring in Papua New Guinea (PNG) is presented. The PNG example demonstrates that Collect Earth is a comprehensive and user-friendly tool for land monitoring and that it has the potential to be used to assess land use, land use change, natural disasters, sustainable management of scarce resources and ecosystem functioning. By enabling non-remote sensing experts to assess more than 100 sites per day, we believe that Collect Earth can be used to rapidly and sustainably build capacity for land monitoring and to substantively improve our collective understanding of the world’s land use and land cover.

Evaluating Crop Area Mapping from MODIS Time-Series as an Assessment Tool for Zimbabwe's "Fast Track Land Reform Programme".

Moderate Resolution Imaging Spectroradiometer (MODIS) data forms the basis for numerous land use and land cover (LULC) mapping and analysis frameworks at regional scale. Compared to other satellite sensors, the spatial, temporal and spectral specifications of MODIS are considered as highly suitable for LULC classifications which support many different aspects of social, environmental and developmental research. The LULC mapping of this study was carried out in the context of the development of an evaluation approach for Zimbabwe’s land reform program. Within the discourse about the success of this program, a lack of spatially explicit methods to produce objective data, such as on the extent of agricultural area, is apparent. We therefore assessed the suitability of moderate spatial and high temporal resolution imagery and phenological parameters to retrieve regional figures about the extent of cropland area in former freehold tenure in a series of 13 years from 2001–2013. Time-series data was processed with TIMESAT and was stratified according to agro-ecological potential zoning of Zimbabwe. Random Forest (RF) classifications were used to produce annual binary crop/non crop maps which were evaluated with high spatial resolution data from other satellite sensors. We assessed the cropland products in former freehold tenure in terms of classification accuracy, inter-annual comparability and heterogeneity. Although general LULC patterns were depicted in classification results and an overall accuracy of over 80% was achieved, user accuracies for rainfed agriculture were limited to below 65%. We conclude that phenological analysis has to be treated with caution when rainfed agriculture and grassland in semi-humid tropical regions have to be separated based on MODIS spectral data and phenological parameters. Because classification results significantly underestimate redistributed commercial farmland in Zimbabwe, we argue that the method cannot be used to produce spatial information on land-use which could be linked to tenure change. Hence capabilities of moderate resolution data are limited to assess Zimbabwe’s land reform. To make use of the unquestionable potential of MODIS time-series analysis, we propose an analysis of plant productivity which allows to link annual growth and production of vegetation to ownership after Zimbabwe’s land reform.

First results from the generation and assimilation of 10-minute atmospheric motion vectors in the Australian region

Ten-minute interval infrared and visible satellite images, available from MTSAR-1R (Himawari-6) for a limited period over and around Australia, have been used to generate atmospheric motion vectors. These vectors—which were available every 10 minutes—have been quality controlled, error characterised and assimilated into the Australian Bureau of Meteorology's next generation operational regional forecast model as part of the new operational database. Results from this study indicate that this high temporal resolution imagery has the ability to produce high spatial and temporal density atmospheric motion vectors, and the quality of these vectors is such that they have the potential to improve numerical analysis and prognosis. In late 2013 and early 2014 the Japanese Meteorological Agency (JMA) provided several months of MTSat-1R rapid scan imagery in support of the 2014 High Altitude Ice Crystal (HAIC) – High Ice Water Content (HIWC) field campaign in the Darwin area. This 10-minute imagery was also provided in support of activities in preparation for Himawari-8. The imagery was renavigated, calibrated and used to generate atmospheric motion vectors (AMVs) over much of the Australian region including the HAIC-HIWC test area. Each vector was error characterised and assigned an expected error. In preparation for the operational assimilation of 10-minute data from the full disk footprint of Himawari-8, these high temporal and spatial resolution data generated over the Australian region test area were combined with the operational database—including high spatial but lower temporal density local Bureau and also JMA AMV data from MTSat-2—to provide forecasts using the next generation operational regional forecast model ACCESS APS2. This model will be used during the introduction of operational Himawari-8 data. First results from these tests indicate the 10-minute data have the potential to produce high density, high quality AMVs which will improve model initialisation and forecasts. This includes cases associated with extreme weather. The results have also provided indications of appropriate timing, data selection and application methods for the effective use of these high temporal resolution data. The quality and density of the 10-minute MTSat-1R data have also been compared to that available from imagery using a new processing paradigm which is to be used in future for the operational Himawari-8 AMV generation.

First results from the generation and assimilation of 10-minute atmospheric motion vectors in the Australian region

Ten-minute interval infrared and visible satellite images, available from MTSAR-1R (Himawari-6) for a limited period over and around Australia, have been used to generate atmospheric motion vectors. These vectors—which were available every 10 minutes—have been quality controlled, error characterised and assimilated into the Australian Bureau of Meteorology's next generation operational regional forecast model as part of the new operational database. Results from this study indicate that this high temporal resolution imagery has the ability to produce high spatial and temporal density atmospheric motion vectors, and the quality of these vectors is such that they have the potential to improve numerical analysis and prognosis.

Surfzone Monitoring Using Rotary Wing Unmanned Aerial Vehicles

AbstractThis study investigates the potential of rotary wing unmanned aerial vehicles (UAVs) to monitor the surfzone. This paper shows that these UAVs are extremely flexible surveying platforms that can gather near-continuous moderate spatial resolution and high temporal resolution imagery from a fixed position high above a study site. The rotary wing UAVs used in this study can fly for ~12 min with a mean loiter radius of 1–3.5 m and a mean loiter error of 0.75–4.5 m. These numbers depend on the environmental conditions, flying style, battery type, and vehicle type. The images obtained from the UAVs, and in combination with surveyed ground control points (GCPs), can be georectified to a pixel resolution between 0.01 and 1 m, and a reprojection error—that is, the difference between the surveyed GPS location of a GCP and the location of the GCP obtained from the georectified image—of O(1 m). The flexibility of rotary wing UAVs provides moderate spatial resolution and high temporal resolution imagery, which are highly suitable to quickly obtain surfzone and beach characteristics in response to storms or for day-to-day beach safety information, as well as scientific pursuits of surfzone kinematics on different spatial and temporal scales, and dispersion and advection estimates of pollutants.

Mapping seasonal rice cropland extent and area in the high cropping intensity environment of Bangladesh using MODIS 500 m data for the year 2010

Rice is the most consumed staple food in the world and a key crop for food security. Much of the world’s rice is produced and consumed in Asia where cropping intensity is often greater than 100% (more than one crop per year), yet this intensity is not sufficiently represented in many land use products. Agricultural practices and investments vary by season due to the different challenges faced, such as drought, salinity, or flooding, and the different requirements such as varietal choice, water source, inputs, and crop establishment methods. Thus, spatial and temporal information on the seasonal extent of rice is an important input to decision making related to increased agricultural productivity and the sustainable use of limited natural resources. The goal of this study was to demonstrate that hyper temporal moderate-resolution imaging spectroradiometer (MODIS) data can be used to map the spatial distribution of the seasonal rice crop extent and area. The study was conducted in Bangladesh where rice can be cropped once, twice, or three times a year.MODIS normalized difference vegetation index (NDVI) maximum value composite (MVC) data at 500m resolution along with seasonal field-plot information from year 2010 were used to map rice crop extent and area for three seasons, boro (December/January–April), aus (April/May–June/July), and aman (July/August–November/December), in Bangladesh. A subset of the field-plot information was used to assess the pixel-level accuracy of the MODIS-derived rice area. Seasonal district-level rice area statistics were used to assess the accuracy of the rice area estimates. When compared to field-plot data, the maps of rice versus non-rice exceeded 90% accuracy in all three seasons and the accuracy of the five rice classes varied from 78% to 90% across the three seasons. On average, the MODIS-derived rice area estimates were 6% higher than the sub-national statistics during boro, 7% higher during aus, and 3% higher during the aman season. The MODIS-derived sub-national areas explained (R2 values) 96%, 93%, and 96% of the variability at the district level for boro, aus, and aman seasons, respectively.The results demonstrated that the methods we applied for analysing and interpreting moderate spatial and high temporal resolution imagery can accurately capture the seasonal variability in rice crop extent and area. We discuss the robustness of the approach and highlight issues that must be addressed before similar methods are used across other areas of Asia where a mix of rainfed, irrigated, or supplemental irrigation permits single, double, and triple cropping in a single calendar year.

Enhancing temporal resolution of satellite imagery for public health studies: A case study of West Nile Virus outbreak in Los Angeles in 2007

Although remotely sensed data have been used in public health studies, these studies are often limited by the critical choice that has to be made in data selection: either using data with high spatial but low temporal resolution, or data with high temporal but low spatial resolution. This choice creates significant limitations for time-dependent epidemiological studies, since it is often essential to have datasets with high spatial and temporal resolution. Effectively synthesizing high temporal resolution imagery with high spatial resolution imagery can potentially ease this limitation. To this end, we conducted an experiment by creating a series of simulated ASTER datasets by fusing ASTER and MODIS data with the STARFM image fusion model. These simulated datasets are then used to derive the following urban environmental variables: normalized difference vegetation index, normalized difference water index, and land surface temperature. These variables are used to quantitatively examine the effects of urban environmental characteristics on West Nile Virus dissemination in Los Angeles County, California, where the epidemic was most prevalent in the United States in 2007. Mosquito surveillance data were collected from the weekly summary reports published in the California West Nile Virus website. A spatial–temporal analysis of WNV dissemination was conducted by synthesizing remote sensing variables and mosquito surveillance records. We focused on assessment of WNV risk areas in July through September due to data sufficiency of tested-positive mosquito pools. Moderate- and high-risk areas of WNV infections in mosquitoes were identified for five selected time windows, i.e., epidemiological weeks 30–31, 32–33, 34–35, 36–37, and 38–39. The results show that elevation and urban built-up conditions were negatively associated with the WNV propagation, while LST positively correlated with viral transmission. NDVI was not significantly associated with WNV transmission during the studied time intervals. San Fernando Valley was found to be the most vulnerable to mosquito infections of WNV within the City of Los Angeles. With the complementation of high-spatial resolution ASTER and high-temporal resolution MODIS images, the fused image datasets allow for estimating environmental parameters at desired epidemiological weeks. This paper provides important insights into how high temporal resolution remote sensing imagery might be used to study time-dependant events in public health, especially in the operational surveillance and control of vector-borne or other epidemic diseases.