Advances in Remote Sensing

Volume 3, Issue 3 (September 2014)

ISSN Print: 2169-267X   ISSN Online: 2169-2688

Google-based Impact Factor: 1.5  Citations  

Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU® Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control

HTML  Download Download as PDF (Size: 4622KB)  PP. 141-196  
DOI: 10.4236/ars.2014.33012    4,749 Downloads   6,066 Views  Citations

ABSTRACT

In this research, we determined the feasibility of using a Personal Digital Assistant (PDA) as a mobile field data collection system by monitoring mapping and regressing digitized sub-meter resolution polygons of multiple, malaria, mosquito, Anopheline arabiensis s.s., aquatic, larval, habitat covariates. The system employed QuickBird raster imagery displayed on a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU®. The mobile mapping platform was employed to identify specific geographical locations of treated and untreated seasonal An. arabiensis s.s. aquatic larval habitats in Karima rice-village complex in the Mwea Rice Scheme, Kenya. As data pertaining to An. arabiensis s.s. larval habitats were entered, all treated and untreated rice paddies within a 2 km buffer of the agro-village, riceland-complex, epidemiological, study site were viewed and managed on the PDA.

Share and Cite:

Jacob, B. and Novak, R. (2014) Integrating a Trimble Recon X 400 MHz Intel PXA255 Xscale CPU® Mobile Field Data Collection System Using Differentially Corrected Global Positioning System Technology and a Real-Time Bidirectional Actionable Platform within an ArcGIS Cyberenvironment for Implementing Mosquito Control. Advances in Remote Sensing, 3, 141-196. doi: 10.4236/ars.2014.33012.

Cited by

[1] Health Security and Malaria: A Neural Network iOS Intelligent Platform to Create and Implement Seek and Destroy Integrated Larval Source Management (ILSM) …
Disruption, Ideation and Innovation …, 2022
[2] Location Intelligence Powered by Machine Learning Automation for Mapping Malaria Mosquito Habitats Employing an Unmanned Aerial Vehicle (UAV) for …
2021
[3] Geospatial Artificial Intelligence Infused into a Smartphone Drone Application for Implementing'Seek and Destroy'in Uganda
American Journal of …, 2021
[4] High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri …
Journal of Public …, 2020
[5] High-accuracy detection of malaria mosquito habitats using drone-based multispectral imagery and Artificial Intelligence (AI) algorithms in an agro-village peri-urban …
2020
[6] Geospatial and Negative Binomial Regression Analysis of Culex nigripalpus, Culex erraticus, Coquillettidia perturbans, and Aedes vexans Counts and …
2017
[7] Geospatial and Negative Binomial Regression Analysis of Culex nigripalpus, Culex erraticus, Coquillettidia perturbans, and Aedes vexans …
ProQuest Dissertations Publishing, 2017
[8] Geospatial and Negative Binomial Regression Analysis of Culex nigripalpus, Culex erraticus, Coquillettidia perturbans, and Aedes vexans Counts and Precipitation …
2017
[9] Recent literature in cartography and geographic information science
Cartography and Geographic Information Science, 2015

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.