1. Event, Date
Application of remote sensing to
monitor agricultural performance
Farai. M Marumbwa & Masego. R Nkepu BDMS
2. Event, Date
1) Relevance of Application:
• Agriculture plays an important role in most countries of the
SADC region where most economies dependent on crop
production.
• It is therefore of great significance to obtain the crop condition
information at early stages in the crop growing season.
• Sometimes it is even more important than acquiring the exact
production after harvest time, especially when large scale
food supply shortage or surplus happens.
• Accurate monitoring can actually avert a disastrous situation
and help in strategic planning to meet the demands.
3. Event, Date
1) Relevance of Application:
• Ground based crop monitoring - expensive, prone to large
errors, and cannot provide real-time monitoring of crop
condition.
• Satellite systems provide temporally and spatially
continuous data cover most of the globe, which makes it
possible to monitor the crop continuously
• AMESD 2007- initiative makes use of Earth observation
technologies and data to set-up operational environmental
and climate monitoring applications.
• AMESD _SADC Agricultural Service monitor the state of
the crops and rangeland.
4. Event, Date
2) Objectives
• The main objectives is to develop a method that allows
agricultural managers to do an up-to-date assessment of
the current growing conditions using Remote sensing
data
5. Event, Date
3) Data used
Local / regional (in-situ) data
• Input 1- Crop Masks for staple crops (or zones of
interest)
• Input 2 - Administrative Boundaries
Data from GEONETCast – DevCoCast
• Input 3 - Dekadal S-10 NDVI raster data
• Input4 – Long term NDVI raster data
6. Event, Date
4) Methodology and data pre-processing
The main steps are illustrated below:
1) Extract the crop specific NDVI by crossing the NDVI images
with the sadc crop map mask.
2) Extract the decadal crop specific NDVI data for each district
from the current season the and the long term averages
3) Cross the crop specific map with Administrative
boundaries and extract the average (current season and
the long term averages) for each district.
4) Plot the data into line graph from the two tables (Current
and long term average) in a graph
11. Event, Date
5) Results: Discussion
• The fewsnest NDVI has provides historical database of
27 (1981-2008) years which makes it the best option
when calculating Longterm averages
• SPOT VGT which date back from 1998 good resolution
• The main weakness of the Fews Ndvi is that the spatial
resolution is very coarse 8km.
• It is recommended that for small scale study Spot VGT
NDVI should be used.
• Sharp drop in NDVI ?????
