scholarly journals Predicting WNV Circulation in Italy Using Earth Observation Data and Extreme Gradient Boosting Model

2020 ◽  
Vol 12 (18) ◽  
pp. 3064
Author(s):  
Luca Candeloro ◽  
Carla Ippoliti ◽  
Federica Iapaolo ◽  
Federica Monaco ◽  
Daniela Morelli ◽  
...  
Keyword(s):  
At Risk ◽  
Environmental Conditions ◽  
Land Surface ◽  
Vegetation Index ◽  
Earth Observation ◽  
Gradient Boosting ◽  
Observation Data ◽  
West Nile ◽  
Extreme Gradient Boosting ◽  
Earth Observation Data

West Nile Disease (WND) is one of the most spread zoonosis in Italy and Europe caused by a vector-borne virus. Its transmission cycle is well understood, with birds acting as the primary hosts and mosquito vectors transmitting the virus to other birds, while humans and horses are occasional dead-end hosts. Identifying suitable environmental conditions across large areas containing multiple species of potential hosts and vectors can be difficult. The recent and massive availability of Earth Observation data and the continuous development of innovative Machine Learning methods can contribute to automatically identify patterns in big datasets and to make highly accurate identification of areas at risk. In this paper, we investigated the West Nile Virus (WNV) circulation in relation to Land Surface Temperature, Normalized Difference Vegetation Index and Surface Soil Moisture collected during the 160 days before the infection took place, with the aim of evaluating the predictive capacity of lagged remotely sensed variables in the identification of areas at risk for WNV circulation. WNV detection in mosquitoes, birds and horses in 2017, 2018 and 2019, has been collected from the National Information System for Animal Disease Notification. An Extreme Gradient Boosting model was trained with data from 2017 and 2018 and tested for the 2019 epidemic, predicting the spatio-temporal WNV circulation two weeks in advance with an overall accuracy of 0.84. This work lays the basis for a future early warning system that could alert public authorities when climatic and environmental conditions become favourable to the onset and spread of WNV.

scholarly journals Quantifying Tree Cover Loss in Urban Forests within Nairobi City Metropolitan Area from Earth Observation Data

2020 ◽  
Vol 3 (1) ◽  
pp. 78
Author(s):  
Francis Oloo ◽  
Godwin Murithi ◽  
Charlynne Jepkosgei
Keyword(s):  
Land Cover ◽  
Metropolitan Area ◽  
Land Surface ◽  
Urban Forest ◽  
Urban Forests ◽  
Earth Observation ◽  
Tree Cover ◽  
Observation Data ◽  
Vegetation Health ◽  
Earth Observation Data

Urban forests contribute significantly to the ecological integrity of urban areas and the quality of life of urban dwellers through air quality control, energy conservation, improving urban hydrology, and regulation of land surface temperatures (LST). However, urban forests are under threat due to human activities, natural calamities, and bioinvasion continually decimating forest cover. Few studies have used fine-scaled Earth observation data to understand the dynamics of tree cover loss in urban forests and the sustainability of such forests in the face of increasing urban population. The aim of this work was to quantify the spatial and temporal changes in urban forest characteristics and to assess the potential drivers of such changes. We used data on tree cover, normalized difference vegetation index (NDVI), and land cover change to quantify tree cover loss and changes in vegetation health in urban forests within the Nairobi metropolitan area in Kenya. We also used land cover data to visualize the potential link between tree cover loss and changes in land use characteristics. From approximately 6600 hectares (ha) of forest land, 720 ha have been lost between 2000 and 2019, representing about 11% loss in 20 years. In six of the urban forests, the trend of loss was positive, indicating a continuing disturbance of urban forests around Nairobi. Conversely, there was a negative trend in the annual mean NDVI values for each of the forests, indicating a potential deterioration of the vegetation health in the forests. A preliminary, visual inspection of high-resolution imagery in sample areas of tree cover loss showed that the main drivers of loss are the conversion of forest lands to residential areas and farmlands, implementation of big infrastructure projects that pass through the forests, and extraction of timber and other resources to support urban developments. The outcome of this study reveals the value of Earth observation data in monitoring urban forest resources.

scholarly journals Satellite Earth Observation Data in Epidemiological Modeling of Malaria, Dengue and West Nile Virus: A Scoping Review

2019 ◽  
Vol 11 (16) ◽  
pp. 1862 ◽  
Author(s):  
Elisavet Parselia ◽  
Charalampos Kontoes ◽  
Alexia Tsouni ◽  
Christos Hadjichristodoulou ◽  
Ioannis Kioutsioukis ◽  
...  
Keyword(s):  
West Nile Virus ◽  
Scoping Review ◽  
Earth Observation ◽  
Machine Learning Algorithms ◽  
Observation Data ◽  
Epidemiological Models ◽  
West Nile ◽  
Epidemiological Modeling ◽  
Transmission Cycle ◽  
Earth Observation Data

Earth Observation (EO) data can be leveraged to estimate environmental variables that influence the transmission cycle of the pathogens that lead to mosquito-borne diseases (MBDs). The aim of this scoping review is to examine the state-of-the-art and identify knowledge gaps on the latest methods that used satellite EO data in their epidemiological models focusing on malaria, dengue and West Nile Virus (WNV). In total, 43 scientific papers met the inclusion criteria and were considered in this review. Researchers have examined a wide variety of methodologies ranging from statistical to machine learning algorithms. A number of studies used models and EO data that seemed promising and claimed to be easily replicated in different geographic contexts, enabling the realization of systems on regional and national scales. The need has emerged to leverage furthermore new powerful modeling approaches, like artificial intelligence and ensemble modeling and explore new and enhanced EO sensors towards the analysis of big satellite data, in order to develop accurate epidemiological models and contribute to the reduction of the burden of MBDs.

scholarly journals Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

2021 ◽  
Author(s):  
Sophia Walther ◽  
Simon Besnard ◽  
Jacob A. Nelson ◽  
Tarek S. El-Madany ◽  
Mirco Migliavacca ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Eddy Covariance ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Earth Observation ◽  
Research Network ◽  
Observation System ◽  
Data Sets ◽  
Observation Data ◽  
Earth Observation Data

Abstract. The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at several hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, and Terrestrial Ecosystem Research Network (TERN) / OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the training and validation of ecosystem models. However, insufficient quality, frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions drawn from them. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40° off-nadir. We offer to the community pre-processed Earth observation data in a radius of 2 km around 338 flux sites based on the MCD43A4/A2, MxD11A1 MODIS products and Landsat collection~1 Tier1 and Tier2 products. The data sets we provide can widely facilitate the integration of activities in the fields of eddy-covariance, remote sensing and modelling.

scholarly journals Multi-Temporal Land Cover Change Mapping Using Google Earth Engine and Ensemble Learning Methods

2020 ◽  
Vol 10 (22) ◽  
pp. 8083 ◽  
Author(s):  
Nimisha Wagle ◽  
Tri Dev Acharya ◽  
Venkatesh Kolluru ◽  
He Huang ◽  
Dong Ha Lee
Keyword(s):  
Land Cover ◽  
Ensemble Learning ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Google Earth ◽  
Gradient Boosting ◽  
Observation Data ◽  
Learning Methods ◽  
Extreme Gradient Boosting ◽  
Google Earth Engine

The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs. Post classification change detection, validation and accuracy assessment were executed after the preparation of the LC maps. Three evaluation indices, namely overall accuracy (OA), Kappa coefficient, and F1 score from confusion matrix reports, were calculated for all the points used for validation purposes. We have obtained an OA of 0.8792 and 0.875 for RF and 0.8926 and 0.8603 for XGBoost at the 95% confidence level for 2010 and 2020 LC maps, which are better for mountainous terrain. The applied methodology could be significant in utilizing the big earth observation data and overcoming the traditional computational challenges using GEE. In addition, the quantification of changes over time would be helpful for decision-makers to understand current environmental dynamics in the study area.

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

Mapping slums and model population density using earth observation data and open source solutions

2019 ◽  
Author(s):  
Tais Grippa ◽  
Stefanos Georganos ◽  
Sabine Vanhuysse ◽  
Moritz Lennert ◽  
Nicholus Mboga ◽  
...  
Keyword(s):  
Population Density ◽  
Open Source ◽  
Earth Observation ◽  
Observation Data ◽  
Model Population ◽  
Earth Observation Data

scholarly journals Examining the Economic and Environmental Impacts of COVID-19 Using Earth Observation Data

2020 ◽  
Vol 13 (1) ◽  
pp. 5
Author(s):  
William Straka ◽  
Shobha Kondragunta ◽  
Zigang Wei ◽  
Hai Zhang ◽  
Steven D. Miller ◽  
...  
Keyword(s):  
Air Quality ◽  
Los Angeles ◽  
Environmental Impacts ◽  
Demographic Data ◽  
The United States ◽  
Earth Observation ◽  
Observation Data ◽  
Washington Dc ◽  
Mobility Data ◽  
Earth Observation Data

The COVID-19 pandemic has infected almost 73 million people and is responsible for over 1.63 million fatalities worldwide since early December 2019, when it was first reported in Wuhan, China. In the early stages of the pandemic, social distancing measures, such as lockdown restrictions, were applied in a non-uniform way across the world to reduce the spread of the virus. While such restrictions contributed to flattening the curve in places like Italy, Germany, and South Korea, it plunged the economy in the United States to a level of recession not seen since WWII, while also improving air quality due to the reduced mobility. Using daily Earth observation data (Day/Night Band (DNB) from the National Oceanic and Atmospheric Administration Suomi-NPP and NO2 measurements from the TROPOspheric Monitoring Instrument TROPOMI) along with monthly averaged cell phone derived mobility data, we examined the economic and environmental impacts of lockdowns in Los Angeles, California; Chicago, Illinois; Washington DC from February to April 2020—encompassing the most profound shutdown measures taken in the U.S. The preliminary analysis revealed that the reduction in mobility involved two major observable impacts: (i) improved air quality (a reduction in NO2 and PM2.5 concentration), but (ii) reduced economic activity (a decrease in energy consumption as measured by the radiance from the DNB data) that impacted on gross domestic product, poverty levels, and the unemployment rate. With the continuing rise of COVID-19 cases and declining economic conditions, such knowledge can be combined with unemployment and demographic data to develop policies and strategies for the safe reopening of the economy while preserving our environment and protecting vulnerable populations susceptible to COVID-19 infection.

scholarly journals 2nd Edition of Instrumenting Smart City Applications with Big Sensing and Earth Observatory Data: Tools, Methods and Techniques

2021 ◽  
Vol 13 (7) ◽  
pp. 1310
Author(s):  
Gabriele Bitelli ◽  
Emanuele Mandanici
Keyword(s):  
High Resolution ◽  
Exponential Growth ◽  
Smart City ◽  
Earth Observation ◽  
Urban Environments ◽  
Observation Data ◽  
High Resolution Imagery ◽  
Observatory Data ◽  
Methods And Techniques ◽  
Earth Observation Data

The exponential growth in the volume of Earth observation data and the increasing quality and availability of high-resolution imagery are increasingly making more applications possible in urban environments [...]

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