scholarly journals Integration of NDVI Imagery and Crop Coverage Registration System for Apiary Schedule

2020 ◽  
Vol 64 (1) ◽  
pp. 105-121
Author(s):  
Fatih Sari ◽  
İrfan Kandemir ◽  
Durmuş A. Ceylan
Keyword(s):  
Spatial Resolution ◽  
Field Studies ◽  
High Temporal Resolution ◽  
Agricultural Lands ◽  
Registration System ◽  
Natural Plant ◽  
Modis Imagery ◽  
Nectar Source ◽  
Crop Types ◽  
Agricultural Areas

AbstractBeekeepers need to establish migratory apiaries to benefit from pollen and nectar source plants as in order to increase honey yield. Thus, following the flowering seasons of honey source plants has vital importance when deciding the route of migration. In this study, MODIS imagery was used to generate weekly NDVI data between 1st April to 31st August 2018, when beekeeping activities start and end in the study area. Although MODIS images have high temporal resolution, low spatial resolution (250 meters) makes them insufficient when deciding the crop types and plants. While detecting plants in natural plant areas requires high spatial resolution NDVI, Crop Coverage Registration System (CCRS) parcel-based crop coverage records can enrich the NDVI data without increasing spatial resolution in agricultural lands. Thus, the CCRS data were integrated with NDVI images for migratory beekeeping in agricultural areas as an innovation. To generate both high temporal and spatial resolution, NDVI and CCRS data were integrated together with a beekeeping suitability map to generate the apiary schedule. The results were verified with 176 existing apiary locations and production dates retrieved from field studies which revealed the existence of three seasons in the study area as early and late apiaries (in natural plant areas) and apiaries in agricultural lands. Accuracy analysis showed that 82% of the apiaries intersected with suitable locations and that apiaries in agricultural areas were detected five days earlier than in field studies and obtained more accurately than natural plant apiaries.

scholarly journals Time series of Inland Surface Water Dataset in China (ISWDC) for 2000–2016 derived from MODIS archives

2018 ◽  
Author(s):  
Shanlong Lu ◽  
Jin Ma ◽  
Xiaoqi Ma ◽  
Hailong Tang ◽  
Hongli Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Spatial Resolution ◽  
Reference Data ◽  
Input Parameter ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Data Set ◽  
Modis Imagery ◽  
Global Surface

Abstract. The moderate spatial resolution and high temporal resolution of the MODIS imagery make it an ideal resource for the time series surface water monitoring and mapping. We used MODIS MOD09Q1 surface reflectance archive images to create Inland Surface Water Dataset in China (ISWDC), which maps the water body larger than 0.0625 km2 in the terrestrial land of China for the period 2000–2016, in 8-day temporal and 250 m spatial resolution. We assessed the accuracy of the ISWDC by comparing with the national land cover derived surface water data and the Global Surface Water (GSW) data. The results show that the ISWDC is closely correlated with the national reference data with the determinant coefficients (R2) greater than 0.99 in 2000, 2005, and 2010, while the ISWDC has similar spatial patterns in different regions with the GSW data set in 2015 too. The ISWDC data set can be used for studies on the inter-annual and seasonal variation of the surface water systems. It can also be used as reference data for other surface water data set verification and as input parameter for regional and global hydro-climatic models. The ISWDC data are available at http://doi.org/10.5281/zenodo.1463694.

scholarly journals Time series of the Inland Surface Water Dataset in China (ISWDC) for 2000–2016 derived from MODIS archives

2019 ◽  
Vol 11 (3) ◽  
pp. 1099-1108 ◽  
Author(s):  
Shanlong Lu ◽  
Jin Ma ◽  
Xiaoqi Ma ◽  
Hailong Tang ◽  
Hongli Zhao ◽  
...  
Keyword(s):  
Time Series ◽  
Surface Water ◽  
Spatial Resolution ◽  
Reference Data ◽  
Input Parameter ◽  
Coefficient Of Determination ◽  
High Temporal Resolution ◽  
Change Dynamics ◽  
Modis Imagery ◽  
Global Surface

Abstract. The moderate spatial resolution and high temporal resolution of MODIS imagery make it an ideal resource for time series surface water monitoring and mapping. We used MODIS MOD09Q1 surface reflectance archive images to create an Inland Surface Water Dataset in China (ISWDC), which maps water bodies larger than 0.0625 km2 within the land mass of China for the period 2000–2016, with 8 d temporal and 250 m spatial resolution. We assessed the accuracy of the ISWDC by comparing it with the national land cover derived surface water data and global surface water (GSW) data. The results show that the ISWDC is closely correlated with the national reference data with coefficient of determination (R2) greater than 0.99 in 2000, 2005, and 2010, while the ISWDC possesses very good consistency, very similar change dynamics, and similar spatial patterns in different regions with the GSW dataset. The ISWDC dataset can be used for studies on the inter-annual and seasonal variation of the surface water systems. It can also be used as reference data for verification of the other surface water dataset and as an input parameter for regional and global hydro-climatic models. The ISWDC data are available at: https://doi.org/10.5281/zenodo.2616035.

scholarly journals Wintertime aerosol chemical composition, volatility, and spatial variability in the greater London area

2016 ◽  
Vol 16 (2) ◽  
pp. 1139-1160 ◽  
Author(s):  
L. Xu ◽  
L. R. Williams ◽  
D. E. Young ◽  
J. D. Allan ◽  
H. Coe ◽  
...  
Keyword(s):  
Biomass Burning ◽  
Solid Fuel ◽  
Field Studies ◽  
Rural Site ◽  
Urban Site ◽  
High Temporal Resolution ◽  
Volatile Organics ◽  
Aerosol Mass ◽  
Rural And Urban ◽  
Wide Range

Abstract. The composition of PM1 (particulate matter with diameter less than 1 µm) in the greater London area was characterized during the Clean Air for London (ClearfLo) project in winter 2012. Two high-resolution time-of-flight aerosol mass spectrometers (HR-ToF-AMS) were deployed at a rural site (Detling, Kent) and an urban site (North Kensington, London). The simultaneous and high-temporal resolution measurements at the two sites provide a unique opportunity to investigate the spatial distribution of PM1. We find that the organic aerosol (OA) concentration is comparable between the rural and urban sites, but the contribution from different sources is distinctly different between the two sites. The concentration of solid fuel OA at the urban site is about twice as high as at the rural site, due to elevated domestic heating in the urban area. While the concentrations of oxygenated OA (OOA) are well-correlated between the two sites, the OOA concentration at the rural site is almost twice that of the urban site. At the rural site, more than 70 % of the carbon in OOA is estimated to be non-fossil, which suggests that OOA is likely related to aged biomass burning considering the small amount of biogenic SOA in winter. Thus, it is possible that the biomass burning OA contributes a larger fraction of ambient OA in wintertime than what previous field studies have suggested. A suite of instruments was deployed downstream of a thermal denuder (TD) to investigate the volatility of PM1 species at the rural Detling site. After heating at 250 °C in the TD, 40 % of the residual mass is OA, indicating the presence of non-volatile organics in the aerosol. Although the OA associated with refractory black carbon (rBC; measured by a soot-particle aerosol mass spectrometer) only accounts for < 10 % of the total OA (measured by a HR-ToF-AMS) at 250 °C, the two measurements are well-correlated, suggesting that the non-volatile organics have similar sources or have undergone similar chemical processing as rBC in the atmosphere. Although the atomic O : C ratio of OOA is substantially larger than that of solid fuel OA and hydrocarbon-like OA, these three factors have similar volatility, which is inferred from the change in mass concentration after heating at 120 °C. Finally, we discuss the relationship between the mass fraction remaining (MFR) of OA after heating in the TD and atomic O : C of OA and find that particles with a wide range of O : C could have similar MFR after heating. This analysis emphasizes the importance of understanding the distribution of volatility and O : C in bulk OA.

scholarly journals Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

2020 ◽  
Author(s):  
Paolo Colosio ◽  
Marco Tedesco ◽  
Xavier Fettweis ◽  
Roberto Ranzi
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Spatial Resolution ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Melting ◽  
Passive Microwave ◽  
High Temporal Resolution ◽  
Ice Dynamics ◽  
Enhanced Resolution

Abstract. Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

scholarly journals Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

2013 ◽  
Vol 15 (3) ◽  
pp. 381-386 ◽  
Keyword(s):  
Spatial Resolution ◽  
Brain Function ◽  
Methodological Approach ◽  
Brain Networks ◽  
Indirect Measurement ◽  
Synaptic Activity ◽  
High Temporal Resolution ◽  
Neural Mass ◽  
The Brain

Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and provides high temporal resolution. FMRI is an indirect measurement of neural activity and based on hemodynamic changes, and offers high spatial resolution. Both methods are very sensitive to changes of synaptic activity, suggesting that with simultaneous EEG and fMRI the same neural events can be characterized with both high temporal and spatial resolution. Since neural oscillations that can be assessed with EEG are a key mechanism for multi-site communication in the brain, EEG-fMRI can offer new insights into the connectivity mechanisms of brain networks.

Large-scale Arctic sea ice motion from Sentinel-1 and the RADARSAT Constellation Mission

2021 ◽  
Author(s):  
Stephen Howell ◽  
Mike Brady ◽  
Alexander Komarov
Keyword(s):  
Sea Ice ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
High Temporal Resolution ◽  
Sar Images ◽  
Sea Ice Extent ◽  
Arctic Sea

&lt;p&gt;As the Arctic&amp;#8217;s sea ice extent continues to decline, remote sensing observations are becoming even more vital for the monitoring and understanding of this process.&amp;#160; Recently, the sea ice community has entered a new era of synthetic aperture radar (SAR) satellites operating at C-band with the launch of Sentinel-1A in 2014, Sentinel-1B in 2016 and the RADARSAT Constellation Mission (RCM) in 2019. These missions represent a collection of 5 spaceborne SAR sensors that together can routinely cover Arctic sea ice with a high spatial resolution (20-90 m) but also with a high temporal resolution (1-7 days) typically associated with passive microwave sensors. Here, we used ~28,000 SAR image pairs from Sentinel-1AB together with ~15,000 SAR images pairs from RCM to generate high spatiotemporal large-scale sea ice motion products across the pan-Arctic domain for 2020. The combined Sentinel-1AB and RCM sea ice motion product provides almost complete 7-day coverage over the entire pan-Arctic domain that also includes the pole-hole. Compared to the National Snow and Ice Data Center (NSIDC) Polar Pathfinder and Ocean and Sea Ice-Satellite Application Facility (OSI-SAF) sea ice motion products, ice speed was found to be faster with the Senintel-1AB and RCM product which is attributed to the higher spatial resolution of SAR imagery. More sea ice motion vectors were detected from the Sentinel-1AB and RCM product in during the summer months and within the narrow channels and inlets compared to the NSIDC Polar Pathfinder and OSI-SAF sea ice motion products. Overall, our results demonstrate that sea ice geophysical variables across the pan-Arctic domain can now be retrieved from multi-sensor SAR images at both high spatial and temporal resolution.&lt;/p&gt;

scholarly journals Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland

2020 ◽  
Vol 12 (6) ◽  
pp. 959 ◽  
Author(s):  
Mohammad Pashaei ◽  
Hamid Kamangir ◽  
Michael J. Starek ◽  
Philippe Tissot
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Land Cover ◽  
Spatial Resolution ◽  
Unmanned Aircraft ◽  
High Temporal Resolution ◽  
Semantic Image Segmentation ◽  
Image Labeling ◽  
Training Samples ◽  
Learning Architectures

Deep learning has already been proved as a powerful state-of-the-art technique for many image understanding tasks in computer vision and other applications including remote sensing (RS) image analysis. Unmanned aircraft systems (UASs) offer a viable and economical alternative to a conventional sensor and platform for acquiring high spatial and high temporal resolution data with high operational flexibility. Coastal wetlands are among some of the most challenging and complex ecosystems for land cover prediction and mapping tasks because land cover targets often show high intra-class and low inter-class variances. In recent years, several deep convolutional neural network (CNN) architectures have been proposed for pixel-wise image labeling, commonly called semantic image segmentation. In this paper, some of the more recent deep CNN architectures proposed for semantic image segmentation are reviewed, and each model’s training efficiency and classification performance are evaluated by training it on a limited labeled image set. Training samples are provided using the hyper-spatial resolution UAS imagery over a wetland area and the required ground truth images are prepared by manual image labeling. Experimental results demonstrate that deep CNNs have a great potential for accurate land cover prediction task using UAS hyper-spatial resolution images. Some simple deep learning architectures perform comparable or even better than complex and very deep architectures with remarkably fewer training epochs. This performance is especially valuable when limited training samples are available, which is a common case in most RS applications.

scholarly journals Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

2019 ◽  
Vol 11 (11) ◽  
pp. 1266 ◽  
Author(s):  
Mingzheng Zhang ◽  
Dehai Zhu ◽  
Wei Su ◽  
Jianxi Huang ◽  
Xiaodong Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Kalman Filter ◽  
Spatial Resolution ◽  
Temporal Resolution ◽  
Meteorological Data ◽  
High Temporal Resolution ◽  
Growth Monitoring ◽  
Landsat 8 ◽  
Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

scholarly journals Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data

2019 ◽  
Vol 23 (6) ◽  
pp. 2647-2663 ◽  
Author(s):  
Yingchun Huang ◽  
András Bárdossy ◽  
Ke Zhang
Keyword(s):  
Spatial Resolution ◽  
Temporal Resolution ◽  
Model Performance ◽  
Daily Rainfall ◽  
High Temporal Resolution ◽  
Dense Network ◽  
Spatially Distributed ◽  
Important Input ◽  
Temporal And Spatial ◽  
Improved Model

Abstract. Rainfall is the most important input for rainfall–runoff models. It is usually measured at specific sites on a daily or sub-daily timescale and requires interpolation for further application. This study aims to evaluate whether a higher temporal and spatial resolution of rainfall can lead to improved model performance. Four different gridded hourly and daily rainfall datasets with a spatial resolution of 1 km × 1 km for the state of Baden-Württemberg in Germany were constructed using a combination of data from a dense network of daily rainfall stations and a less dense network of sub-daily stations. Lumped and spatially distributed HBV models were used to investigate the sensitivity of model performance to the spatial resolution of rainfall. The four different rainfall datasets were used to drive both lumped and distributed HBV models to simulate daily discharges in four catchments. The main findings include that (1) a higher temporal resolution of rainfall improves the model performance if the station density is high; (2) a combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement in the tested models; and (3) for the present research, the increase in spatial resolution improves the performance of the model insubstantially or only marginally in most of the study catchments.

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