scholarly journals Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

2019 ◽  
Vol 11 (23) ◽  
pp. 6811 ◽  
Author(s):  
Kganyago ◽  
Shikwambana
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
Land Surface ◽  
Ghg Emissions ◽  
Ambient Air ◽  
Remotely Sensed ◽  
Sub Saharan Africa ◽  
Height Distribution ◽  
Remotely Sensed Data ◽  
Sub Saharan

Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land–atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007–2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December–January–February (DJF), June–July–August (JJA) and September–October–November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions.

scholarly journals Assessing the relationship between environmental factors and malaria vector breeding sites in Swaziland using multi-scale remotely sensed data

2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Sabelo Nick Dlamini ◽  
Jonas Franke ◽  
Penelope Vounatsou
Keyword(s):  
Environmental Factors ◽  
Spatial Resolution ◽  
Malaria Vector ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Breeding Sites ◽  
Multi Scale ◽  
The Relationship

Many entomological studies have analyzed remotely sensed data to assess the relationship between malaria vector distribution and the associated environmental factors. However, the high cost of remotely sensed products with high spatial resolution has often resulted in analyses being conducted at coarse scales using open-source, archived remotely sensed data. In the present study, spatial prediction of potential breeding sites based on multi-scale remotely sensed information in conjunction with entomological data with special reference to presence or absence of larvae was realized. Selected water bodies were tested for mosquito larvae using the larva scooping method, and the results were compared with data on land cover, rainfall, land surface temperature (LST) and altitude presented with high spatial resolution. To assess which environmental factors best predict larval presence or absence, Decision Tree methodology and logistic regression techniques were applied. Both approaches showed that some environmental predictors can reliably distinguish between the two alternatives (existence and non-existence of larvae). For example, the results suggest that larvae are mainly present in very small water pools related to human activities, such as subsistence farming that were also found to be the major determinant for vector breeding. Rainfall, LST and altitude, on the other hand, were less useful as a basis for mapping the distribution of breeding sites. In conclusion, we found that models linking presence of larvae with high-resolution land use have good predictive ability of identifying potential breeding sites.

scholarly journals Combining remotely sensed data using aggregation algorithms

1998 ◽  
Vol 2 (2/3) ◽  
pp. 149-158 ◽  
Author(s):  
W. J. Shuttleworth
Keyword(s):  
Surface Energy ◽  
Land Surface ◽  
Remotely Sensed ◽  
Surface Radiation ◽  
Control Surface ◽  
Future Research ◽  
Practical Implementation ◽  
Remotely Sensed Data ◽  
Theoretical Concepts ◽  
Average Value

Abstract. This paper describes a strategic approach for providing documentation of the surface energy exchange for heterogeneous land surfaces via the simultaneous, four-dimensional assimilation of several streams of remotely sensed data into a coupled land surface-atmosphere model. The basic concepts and underlying theory behind this proposed approach are presented with the intent that this will guide, facilitate, and stimulate future research focused on its practical implementation when appropriate data from the Earth Observing System (EOS) become available. The theoretical concepts that underlie the approach are derived from relationships between the values of parameters which control surface exchanges at pixel (or patch) scale and the area-average value of equivalent parameters applicable at larger, grid scale. A three-step implementation method is proposed which involves (a) estimating grid-average surface radiation fluxes from appropriate remotely sensed data; (b) absorbing these radiation flux estimates into a four-dimensional data assimilation model in which grid-average values of vegetation-related parameters are calculated from pertinent remotely sensed data using the equations that link pixel and grid scales; and (c) improving the resulting estimate of the surface energy balance-again using scale-linking equations by estimating the effect of soil-moisture availability, perhaps assuming that cloud-free pixels are an unbiased subsample of all the pixels in the grid square.

scholarly journals GIS and Remote Sensing Application in Water Management in Agriculture Field

2020 ◽  
Author(s):  
Sassi Mohamed Taher
Keyword(s):  
Water Management ◽  
Remote Sensing ◽  
Water Resources ◽  
Land Surface ◽  
Remotely Sensed ◽  
Irrigated Agriculture ◽  
Accurate Information ◽  
Remotely Sensed Data ◽  
Gis And Remote Sensing ◽  
Practical Applications

This document is meant to demonstrate the potential uses of remote sensing in managing water resources for irrigated agriculture and to create awareness among potential users. Researchers in various international programs have studied the potential use of remotely sensed data to obtain accurate information on land surface processes and conditions. These studies have demonstrated that quantitative assessment of the soil-vegetation-atmosphere transfer processes can lead to a better understanding of the relationships between crop growth and water management. Remote sensing and GIS was used to map the agriculture area and for detect the change. This was very useful for mapping availability and need of water resources but the problem was concentrating in data collection and analysis because this kind of information and expertise are not available in all country in the world mainly in the developing and under developed country or third world country. However, even though considerable progress has been made over the past 20 years in research applications, remotely sensed data remain underutilized by practicing water resource managers. This paper seeks to bridge the gap between researchers and practitioners first, by illustrating where research tools and techniques have practical applications and, second, by identifying real problems that remote sensing could solve. An important challenge in the field of water resources is to utilize the timely, objective and accurate information provided by remote sensing.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 µg/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

scholarly journals Precipitation as driver of carbon fluxes in 11 African ecosystems

2008 ◽  
Vol 5 (5) ◽  
pp. 4071-4105 ◽  
Author(s):  
L. Merbold ◽  
J. Ardö ◽  
A. Arneth ◽  
R. J. Scholes ◽  
Y. Nouvellon ◽  
...  
Keyword(s):  
Land Surface ◽  
Ecosystem Respiration ◽  
Carbon Assimilation ◽  
Vapour Pressure Deficit ◽  
Sub Saharan Africa ◽  
Annual Rainfall ◽  
C3 Plants ◽  
Water Vapour Pressure ◽  
Mean Annual Rainfall ◽  
Sub Saharan

Abstract. This study reports carbon and water fluxes between the land surface and atmosphere in eleven different ecosystems types in Sub-Saharan Africa, as measured using eddy covariance (EC) technology in the first two years of the CarboAfrica network operation. The ecosystems for which data were available ranged in mean annual rainfall from 320 mm (Sudan) to 1150 mm (The Republic of Congo) and include a spectrum of vegetation types (or land cover) (open savannas, woodlands, croplands and grasslands). Given the shortness of the record, the EC data were analysed across the network rather than longitudinally at sites, in order to understand the driving factors for ecosystem respiration and carbon assimilation, and to reveal the different water use strategies in these highly seasonal environments. Values for maximum net carbon assimilation rates (photosynthesis) ranged from 12 μmol CO2 m−2 s−1 in a dry, open Acacia savanna (C3-plants) up to 40 μmol CO2 m−2 s−1 for a tropical moist grassland. Maximum carbon assimilation rates were highly correlated with mean annual rainfall (R2=0.89). Maximum photosynthetic uptake rates were positively related to satellite-derived fAPAR. Ecosystem respiration was dependent on temperature at all sites, and was additionally dependent on soil water content at sites receiving less than 1000 mm of rain per year. All included ecosystems, except the Congolese grassland, showed a strong decrease in 30-min assimilation rates with increasing water vapour pressure deficit above 2.0 kPa.

Using Remotely Sensed Data To Monitor Land Surface Climatology Variations In A Semi-arid Grassland

2005 ◽  
Author(s):  
L.F. Johnson ◽  
N.A. Bryant ◽  
A.J. BrazeI ◽  
C.F. Hutchinson ◽  
R.C. Balling
Keyword(s):  
Land Surface ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Semi Arid

scholarly journals Uncertainty and Variation of Remotely Sensed Lake Ice Phenology across the Tibetan Plateau

2018 ◽  
Vol 10 (10) ◽  
pp. 1534 ◽  
Author(s):  
Linan Guo ◽  
Yanhong Wu ◽  
Hongxing Zheng ◽  
Bing Zhang ◽  
Junsheng Li ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Land Surface ◽  
Regional Climate ◽  
Local Environment ◽  
Remotely Sensed ◽  
The Tibetan Plateau ◽  
Remotely Sensed Data ◽  
Lake Ice ◽  
Reflectance Data ◽  
Ice Phenology

In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice.

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