Waterborne diseases such as cholera, diarrhea, hepatitis A, typhoid, and polio are caused by contaminated drinking water and poor sanitation (World Health Organization). Inadequate management of urban, industrial, and agricultural wastewater worsens water quality in water bodies, introducing chemicals and exacerbating growth of pathogens in water. Every year, waterborne diseases are responsible for approximately one million deaths, the majority of which are children under the age of five. For improved availability of safe drinking water, it is necessary to monitor and manage watershed processes (e.g., rainfall, land use, runoff) and water quality in coastal regions, streams, and lakes for harmful pathogens and sediments. Globally available satellite observations have been found useful for monitoring temperature, sediments, and phytoplankton in water bodies as indicators of waterborne diseases. Also, flooding and pathways for waterborne diseases are studied using satellite observations. Satellite data are also used in disease (e.g., cholera) prediction models.

This two-part training will focus on describing and accessing remote sensing observations useful as water quality indicators of waterborne diseases, and will present case studies where remote sensing data are used to assess the likelihood of cholera outbreaks.

This training will provide an overview of spectral indices for land and aquatic applications as they are calculated with a QGIS platform.

Specifically, this training will begin with an introduction to the Normalized Difference Vegetation Index (NDVI), along with its calculation, uses, and applications in diverse thematic areas (agriculture, disasters, etc.). We will review additional land-based indices such as the Enhanced Vegetation Index (EVI), Soil-Adjusted Vegetation Index (SAVI), and Normalized Burn Ratio (NBR). Finally, we will concentrate on spectral indices primarily used in aquatic ecosystems, such as the Normalized Difference Chlorophyll Index (NDCI), Normalized Difference Aquatic Vegetation Index (NDAVI), Floating Algal Index (FAI), and Normalized Difference Turbidity Index (NDTI) for aquatic applications.

Not only will these sessions discuss the correct circumstances under which to use these indices, but participants will also take part in hands-on indices calculation over relevant study areas. Case studies using indices for land and aquatic applications will be highlighted.

Esta capacitación presentará un resumen general de los índices espectrales para aplicaciones terrestres y acuáticas y cómo calcularlos utilizando la plataforma QGIS.

La capacitación comenzará con una introducción al Índice de Vegetación de Diferencia Normalizada (Normalized Difference Vegetation Index o NDVI)), junto con su cálculo, usos y aplicaciones en diversas temáticas (agricultura, desastres, etc.). Repasaremos otros índices para aplicaciones terrestres, como el Índice de Vegetación Mejorado (Enhanced Vegetation Index o EVI), el Índice de Vegetación Ajustado al Suelo (Soil-Adjusted Vegetation Index o SAVI) y el Índice Normalizado de Área Quemada (Normalized Burn Ratio o NBR). Por último, nos enfocaremos en los índices espectrales utilizados principalmente en ecosistemas acuáticos, como el Índice de Clorofila de Diferencia Normalizada (Normalized Difference Chlorophyll Index o NDCI), el Índice de Vegetación Acuática de Diferencia Normalizada (Normalized Difference Aquatic Vegetation Index o NDAVI), el Índice de Algas Flotantes (Floating Algae Index o FAI) y el Índice de Turbidez de Diferencia Normalizada (Normalized Difference Turbidity Index o NDTI) para aplicaciones acuáticas.

Se discutirán las circunstancias correctas para utilizar estos índices y los participantes también participarán en cálculo de estos índices sobre áreas de estudio relevantes. Se destacarán estudios de caso que utilicen índices para aplicaciones terrestres y acuáticas.