Previous: Exercises Next: Visual interpretation of NDVI (Normalised Difference Vegetation Index) Table of contents Index Glossary Images Frames U09-NRM-127: The role of Distributed Data Access Technologies in NRM - for ITC-IDV version 2.7 > Thematic Expert Models > Food security > Remotely Sensed Indices > NDVI > Exercises

7.0.0.0.0.0 Indexed approaches to monitoring of Crops, Rangeland and Food Security at National Level

1. Monitoring of Crops, Rangeland and Food Security at National Level

 

 

Introduction

Objectives

Objectives of the exercise:

  • Obtaining skills in using satellite images for monitoring of vegetation development
  • Capability to use the ITC-IDV software
  • Learning to interprete satellite products in the context of food security

 

Background
Purpose

A food security early warning system aims to provide reliable and timely information to national level decision makers in order to enable decision making on possible short term interventions and to adjust long term planning.

 

Means

Remote sensing in combination with a GIS database can be a useful tool for monitoring aspects of food production and environmental changes. Especially satellites with a high temporal resolution, such as NOAA and METEOSAT are most valuable for a "Food Security Early Warning system". This system can be used to relate the monitoring products to other available information, such as biophysical and socio-economic information to assess the impact of changes in production.

 

Tool

Traditionally, this demo and associated materials used a closed-source software package called WinDisp. The package was specifically designed for displaying, processing and analysis of large time series of meteorological satellite images. Here we will use IDV instead, as the Vegetation Index images from the NOAA satellite as well as rainfall estimate images can better be accessed remotely through a so-called client-server architecture that uses remote data access technology to ensure complete and up-to-date archives are used at all times and storage footprints are kept to a minimum. For the whole of Africa, some parts of South America, and Asia these NOAA images can be freely obtained from the Internet. Similarly, Rainfall Estimate images for the whole African continent are available. Remote data sources will be introduced in the next section og this exercsie. Other related datasets, such as maps and tables can be displayed and analyzed in the IDV as explained in GIS and Miscellaneous Displays.

Output

Amongst others, below some of the products are listed that can be prepared through IDV's diagnostic functions Formulas and Functions:

  • Early crop yield estimation, appr. 2 months before harvest
  • Identification of areas with other than normal vegetation development
  • Identification of drought risk areas (water use efficiency)
  • Information on increasing or decreasing desertification
  • Assessment of growing season quality (length, rainfall distribution)
  • Timely identification of favourable conditions for diseases
  • Identification of vulnerable areas (coeff. of variance)
 

You can now start with section W 2, entitled Visual interpretation of NDVI (Normalised Difference Vegetation Index)

 

7.0.0.0.0.0.0  Visual interpretation of NDVI (Normalised Difference Vegetation Index)
7.0.0.0.0.0.1  Analysis of vegetation index and rainfall estimate images

 


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U09-NRM-127: The role of Distributed Data Access Technologies in NRM - for ITC-IDV version 2.7 > Thematic Expert Models > Food security > Remotely Sensed Indices > NDVI > Exercises