The impact of gridding artifacts on the local spatial properties of MODIS data: Implications for validation, compositing, and band-to-band registration across resolutions

2006 ◽  
Vol 105 (2) ◽  
pp. 98-114 ◽  
Author(s):  
B. Tan ◽  
C.E. Woodcock ◽  
J. Hu ◽  
P. Zhang ◽  
M. Ozdogan ◽  
...  
Keyword(s):  
Modis Data ◽  
Spatial Properties ◽  
The Impact

Effects of Dubai coastline extreme urbanization in land and sea on local near-surface climate variables patterns

2021 ◽  
Author(s):  
Emily Elhacham ◽  
Pinhas Alpert
Keyword(s):  
Urban Heat Island ◽  
Heat Island ◽  
Rapid Urbanization ◽  
Near Surface ◽  
Modis Data ◽  
The World ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Coastal Urbanization ◽  
The Impact

<p>Over a billion people currently live in coastal areas, and coastal urbanization is rapidly growing worldwide. Here, we explore the impact of an extreme and rapid coastal urbanization on near-surface climatic variables, based on MODIS data, Landsat and some in-situ observations. We study Dubai, one of the fastest growing cities in the world over the last two decades. Dubai's urbanization centers along its coastline – in land, massive skyscrapers and infrastructure have been built, while in sea, just nearby, unique artificial islands have been constructed.</p><p>Studying the coastline during the years of intense urbanization (2001-2014), we show that the coastline exhibits surface urban heat island characteristics, where the urban center experiences higher temperatures, by as much as 2.0°C and more, compared to the adjacent less urbanized zones. During development, the coastal surface urban heat island has nearly doubled its size, expanding towards the newly developed areas. This newly developed zone also exhibited the largest temperature trend along the coast, exceeding 0.1°C/year on average.</p><p>Overall, we found that over land, temperature increases go along with albedo decreases, while in sea, surface temperature decreases and albedo increases were observed particularly over the artificial islands. These trends in land and sea temperatures affect the land-sea temperature gradient which influences the breeze intensity. The above findings, along with the increasing relative humidity shown, directly affect the local population and ecosystem and add additional burden to this area, which is already considered as one of the warmest in the world and a climate change 'hot spot'.</p><p> </p><p><strong>References:</strong></p><p>E. Elhacham and P. Alpert, "Impact of coastline-intensive anthropogenic activities on the atmosphere from moderate resolution imaging spectroradiometer (MODIS) data in Dubai (2001–2014)", <em>Earth’s Future</em>, 4, 2016. https://doi.org/10.1002/2015EF000325</p><p>E. Elhacham and P. Alpert, "Temperature patterns along an arid coastline experiencing extreme and rapid urbanization, case study: Dubai", submitted.</p>

scholarly journals The significance of spatial variability of rainfall on simulated runoff: an evaluation based on the Upper Lee catchment, UK

2016 ◽  
Vol 48 (4) ◽  
pp. 1118-1130 ◽  
Author(s):  
I. G. Pechlivanidis ◽  
N. McIntyre ◽  
H. S. Wheater
Keyword(s):  
Spatial Variability ◽  
Distributed Model ◽  
Model Parameters ◽  
Rainfall Time Series ◽  
Catchment Scale ◽  
Rainfall Events ◽  
Spatial Properties ◽  
Antecedent Conditions ◽  
Parameter Uncertainty Analysis ◽  
The Impact

The significance of spatial variability of rainfall on runoff is explored as a function of catchment scale and type, and antecedent conditions via the continuous time, semi-distributed probability distributed model (PDM) hydrological model applied to the Upper Lee catchment, UK. The impact of catchment scale and type is assessed using 11 nested catchments, and further assessed by artificially changing the catchment characteristics and translating these to model parameters (MPs) with uncertainty using model regionalisation. Dry and wet antecedent conditions are represented by ‘warming up’ the model under different rainfall time series. Synthetic rainfall events are introduced to directly relate the change in simulated runoff to the spatial variability of rainfall. Results show that runoff volume and peak are more sensitive to the spatial rainfall for more impermeable catchments; however, this sensitivity is significantly undermined under wet antecedent conditions. Although there is indication that the impact of spatial rainfall on runoff varies as a function of catchment scale, the variability of antecedent conditions between the synthetic catchments seems to mask this significance. Parameter uncertainty analysis highlights the importance of accurately representing the spatial variability of the catchment properties and their translation to MPs when investigating the effects of spatial properties of rainfall on runoff.

Investigating the impact of the temporal resolution of MODIS data on measured phenology in the prairie grasslands

2020 ◽  
Vol 57 (3) ◽  
pp. 395-410
Author(s):  
Tengfei Cui ◽  
Lawrence Martz ◽  
Liang Zhao ◽  
Xulin Guo
Keyword(s):  
Temporal Resolution ◽  
Modis Data ◽  
The Impact

scholarly journals A novel method for estimating shortwave direct radiative effect of above-cloud aerosols using CALIOP and MODIS data

2014 ◽  
Vol 7 (6) ◽  
pp. 1777-1789 ◽  
Author(s):  
Z. Zhang ◽  
K. Meyer ◽  
S. Platnick ◽  
L. Oreopoulos ◽  
D. Lee ◽  
...  
Keyword(s):  
Atlantic Ocean ◽  
Optical Depth ◽  
Radiative Forcing ◽  
Orthogonal Polarization ◽  
Radiative Effect ◽  
Cloud Optical Depth ◽  
Modis Data ◽  
Joint Histogram ◽  
The Impact ◽  
Southeastern Atlantic

Abstract. This paper describes an efficient and unique method for computing the shortwave direct radiative effect (DRE) of aerosol residing above low-level liquid-phase clouds using CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. It addresses the overlap of aerosol and cloud rigorously by utilizing the joint histogram of cloud optical depth and cloud top pressure while also accounting for subgrid-scale variations of aerosols. The method is computationally efficient because of its use of grid-level cloud and aerosol statistics, instead of pixel-level products, and a precomputed look-up table based on radiative transfer calculations. We verify that for smoke and polluted dust over the southeastern Atlantic Ocean the method yields a seasonal mean instantaneous (approximately 13:30 local time) shortwave DRE of above-cloud aerosol (ACA) that generally agrees with a more rigorous pixel-level computation within 4%. We also estimate the impact of potential CALIOP aerosol optical depth (AOD) retrieval bias of ACA on DRE. We find that the regional and seasonal mean instantaneous DRE of ACA over southeastern Atlantic Ocean would increase, from the original value of 6.4 W m−2 based on operational CALIOP AOD to 9.6 W m−2 if CALIOP AOD retrievals are biased low by a factor of 1.5 (Meyer et al., 2013) and further to 30.9 W m−2 if CALIOP AOD retrievals are biased low by a factor of 5 as suggested in Jethva et al. (2014). In contrast, the instantaneous ACA radiative forcing efficiency (RFE) remains relatively invariant in all cases at about 53 W m−2 AOD−1, suggesting a near-linear relation between the instantaneous RFE and AOD. We also compute the annual mean instantaneous shortwave DRE of light-absorbing aerosols (i.e., smoke and polluted dust) over global oceans based on 4 years of CALIOP and MODIS data. We find that given an above-cloud aerosol type the optical depth of the underlying clouds plays a larger role than above-cloud AOD in the variability of the annual mean shortwave DRE of above-cloud light-absorbing aerosol. While we demonstrate our method using CALIOP and MODIS data, it can also be extended to other satellite data sets.

scholarly journals MODIS Derived Sea Surface Salinity, Temperature, and Chlorophyll-a Data for Potential Fish Zone Mapping: West Red Sea Coastal Areas, Saudi Arabia

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2069 ◽  
Author(s):  
Saleh Daqamseh ◽  
A’kif Al-Fugara ◽  
Biswajeet Pradhan ◽  
Anas Al-Oraiqat ◽  
Maan Habib
Keyword(s):  
Linear Regression ◽  
Chlorophyll A ◽  
Red Sea ◽  
High Potential ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Surface Salinity ◽  
Chl A ◽  
Modis Data ◽  
The Impact

In this study, a multi-linear regression model for potential fishing zone (PFZ) mapping along the Saudi Arabian Red Sea coasts of Yanbu’ al Bahr and Jeddah was developed, using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data derived parameters, such as sea surface salinity (SSS), sea surface temperature (SST), and chlorophyll-a (Chl-a). MODIS data was also used to validate the model. The model expanded on previous models by taking seasonal variances in PFZs into account, examining the impact of the summer, winter, monsoon, and inter-monsoon season on the selected oceanographic parameters in order to gain a deeper understanding of fish aggregation patterns. MODIS images were used to effectively extract SSS, SST, and Chl-a data for PFZ mapping. MODIS data were then used to perform multiple linear regression analysis in order to generate SSS, SST, and Chl-a estimates, with the estimates validated against in-situ data obtained from field visits completed at the time of the satellite passes. The proposed model demonstrates high potential for use in the Red Sea region, with a high level of congruence found between mapped PFZ areas and fish catch data (R2 = 0.91). Based on the results of this research, it is suggested that the proposed PFZ model is used to support fisheries in determining high potential fishing zones, allowing large areas of the Red Sea to be utilized over a short period. The proposed PFZ model can contribute significantly to the understanding of seasonal fishing activity and support the efficient, effective, and responsible use of resources within the fishing industry.

scholarly journals Less rain, more water in ponds: a remote sensing study of the dynamics of surface waters from 1950 to present in pastoral Sahel (Gourma region, Mali)

2010 ◽  
Vol 14 (2) ◽  
pp. 309-324 ◽  
Author(s):  
J. Gardelle ◽  
P. Hiernaux ◽  
L. Kergoat ◽  
M. Grippa
Keyword(s):  
Remote Sensing ◽  
Surface Water ◽  
Regional Scale ◽  
Aerial Photographs ◽  
Modis Data ◽  
Maximal Area ◽  
Flooded Area ◽  
Shallow Soils ◽  
The Impact ◽  
Flooded Areas

Abstract. Changes in the flooded area of ponds in the Gourma region from 1950 to present are studied by remote sensing, in the general context of the current multi-decennial Sahel drought. The seasonal and interannual variations of the areas covered by surface water are assessed using multi-date and multi-sensor satellite images (SPOT, FORMOSAT, LANDSAT-MSS, –TM, and -ETM, CORONA, and MODIS) and aerial photographs (IGN). Water body classification is adapted to each type of spectral resolution, with or without a middle-infrared band, and each spatial resolution, using linear unmixing for mixed pixels of MODIS data. The high-frequency MODIS data document the seasonal cycle of flooded areas, with an abrupt rise early in wet season and a progressive decrease in the dry season. They also provide a base to study the inter-annual variability of the flooded areas, with sharp contrasts between dry years such as 2004 (low and early maximal area) and wetter years such as 2001 and 2002 (respectively high and late maximal area).The highest flooded area reached annually greatly depends on the volume, intensity and timing of rain events. However, the overall reduction by 20% of annual rains during the last 40 years is concomitant with an apparently paradoxical large increase in the area of surface water, starting from the 1970's and accelerating in the mid 1980's. Spectacular for the two study cases of Agoufou and Ebang Mallam, for which time series covering the 1954 to present period exist, this increase is also diagnosed at the regional scale from LANDSAT data spanning 1972–2007. It reaches 108% between September 1975 and 2002 for 91 ponds identified in central Gourma. Ponds with turbid waters and no aquatic vegetation are mostly responsible for this increase, more pronounced in the centre and north of the study zone. Possible causes of the differential changes in flooded areas are discussed in relation with the specifics in topography, soil texture and vegetation cover over the watersheds that feed each of the ponds. Changes in rain pattern and in ponds sedimentation are ruled out, and the impact of changes in land use, limited in the area, is found secondary, as opposed to what has often been advocated for in southern Sahel. Instead, major responsibility is attributed to increased runoff triggered by the lasting impact of the 1970–1980's droughts on the vegetation and on the runoff system over the shallow soils prevailing over a third of the landscape.

The Impact of Spatial Properties on Collaboration

2016 ◽  
Author(s):  
Alina Krischkowsky ◽  
Sandra Trösterer ◽  
Ulrike Bruckenberger ◽  
Bernhard Maurer ◽  
Katja Neureiter ◽  
...  
Keyword(s):  
Spatial Properties ◽  
The Impact

scholarly journals Statistical power: implications for planning MEG studies

2019 ◽  
Author(s):  
Maximilien Chaumon ◽  
Aina Puce ◽  
Nathalie George
Keyword(s):  
Sample Size ◽  
Statistical Power ◽  
Single Subject ◽  
List Type ◽  
Group Level ◽  
Spatial Properties ◽  
Human Connectome Project ◽  
Neurophysiological Studies ◽  
Subject Variability ◽  
The Impact

AbstractStatistical power is key for robust, replicable science. Here, we systematically explored how numbers of trials and subjects affect statistical power in MEG sensor-level data. More specifically, we simulated “experiments” using the MEG resting-state dataset of the Human Connectome Project (HCP). We divided the data in two conditions, injected a dipolar source at a known anatomical location in the “signal condition”, but not in the “noise condition”, and detected significant differences at sensor level with classical paired t-tests across subjects. Group-level detectability of these simulated effects varied drastically with anatomical origin. We thus examined in detail which spatial properties of the sources affected detectability, looking specifically at the distance from closest sensor and orientation of the source, and at the variability of these parameters across subjects. In line with previous single-subject studies, we found that the most detectable effects originate from source locations that are closest to the sensors and oriented tangentially with respect to the head surface. In addition, cross-subject variability in orientation also affected group-level detectability, boosting detection in regions where this variability was small and hindering detection in regions where it was large. Incidentally, we observed a considerable covariation of source position, orientation, and their cross-subject variability in individual brain anatomical space, making it difficult to assess the impact of each of these variables independently of one another. We thus also performed simulations where we controlled spatial properties independently of individual anatomy. These additional simulations confirmed the strong impact of distance and orientation and further showed that orientation variability across subjects affects detectability, whereas position variability does not.Importantly, our study indicates that strict unequivocal recommendations as to the ideal number of trials and subjects for any experiment cannot be realistically provided for neurophysiological studies. Rather, it highlights the importance of considering the spatial constraints underlying expected sources of activity while designing experiments.HighlightsAdequate sample size (number of subjects and trials) is key to robust neuroscienceWe simulated evoked MEG experiments and examined sensor-level detectabilityStatistical power varied by source distance, orientation & between-subject variabilityConsider source detectability at sensor-level when designing MEG studiesSample size for MEG studies? Consider source with lowest expected statistical power

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