Monitoring1 - AMESD SADC THEMA

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R. Tsheko, M. Tapela, N.J. Batisani
Botswana College of Agriculture
Crop and Rangeland Monitoring
Agriculture Service
Agenda SADC THEMA Services
Course Content
1.
2.
Introduction
Vegetation Indicies
 NDVI
 NDWI
 DMP
 VPI
1) Introduction
 Monitoring
biomass through time can provide important
information about the likelihood of good or poor harvest;

Moreover, it also gives information about the stability of
the natural ecosystem and whether significant changes
are taking place.
 If
the goal is to monitor biomass of vegetation, it is to
collect satellite data at the height of the growing season
when the biomass produced is at maximum.
1) Introduction
 The
primary remote sensing derived products used for
monitoring agriculture are namely the rainfall and
vegetation products.
 Rainfall
products are derived from local observations and
satellite remote sensing products while vegetation
products are mainly NDVI products derived from
geostationary meteorological and other polar orbiting
satellites.

Moreover, it also gives information about the stability of
the natural ecosystem and whether significant changes
are taking place.
2) Vegetation Indices
NDVI:

Much of the effort put into extracting vegetation biophysical variables
using remote sensing has gone into the development of vegetation
indices.

These indices are indicators of relative abundance and activity of
green vegetation.

Vegetation index must have the following characteristics;

maximize sensitivity to plant biological parameters, sensitive on
a wide range of vegetation conditions and the index should be
easily validated and calibrated;

index must be insensitive (normalized) to external effects such
as sun angle, viewing angle, and the atmospheric condition to
facilitate spatial and temporal comparisons;
2) Vegetation Indices
NDVI:
 the
index must be invariant to internal effects
such as canopy background variations,
topography (slope and aspect), background soil
variations; and
 index
must be coupled to some specific
measurable biophysical parameter such as
biomass (this allows for validation and quality
control).
2) Vegetation Indices
NDVI:
 There
are more than twenty (20) vegetation
indices developed so far though most of them are
functionally equivalent (redundant) in information
content.
2) Vegetation Indices
Vegetation index
Year
developed
Simple Ratio
1968
Normalized Difference Vegetation Index (NDVI)
1974, 1975
Kauth -Thomas Transformation
1976,
1985
Infrared Index (II)
1983
Perpendicular Vegetation Index (PVI)
1977
Greeness Above Bare Soil (GRABS)
1979
Moisture Stress Index (MSI)
1986
Leaf Relative Water Content Index (LWCI)
1987
MidIR Index (MIRI)
1988
Soil Adjusted Vegetation Index (SAVI)
1988,
1995
Atmospherically Resistant Vegetation Index (ARVI)
1992, 1994
Soil and Atmospherically Resistant Vegetation Index (SARVI)
1994
Enhanced Vegetation Index (EVI)
1999
Normalized Difference Water Index (NDWI)
1996
Dry Matter Productivity (DMP)
1972
Vegetation Production Indicator (VPI)
1998
Phenology (PHENO)
1974
1979,
1994,
2) Vegetation Indices
NDVI :
 NDVI
imagery is calculated from the red and near infra-red
reflectances observed by the AVHRR (Advanced Very High
Resolution Radiometer) sensor on NOAA meteorological
satellites and SPOTVGT (1.1km res).
2) Vegetation Indices
2) Vegetation Indices
NDVI :
 The
NDVI image provides an indication of the vigor and
density of vegetation at the surface.
 Images
of NDVI are sometimes referred to as "greenness
maps" since they represent the vegetative vigor of plants.

The time series of NDVI data (from 1982-present- NOAA)
allows analysis of changes in vegetation vigor and density
in response to bio-physical conditions (including plant
type, weather and soil).
2) Vegetation Indices
Low or no-cost data streams from satellite sensors utilized for land surface studies
Sensor
Satellite
Overpass/
Orbit
Frequency
Data Record
(years)
Spatial
Resolution(s)
Processed
Time Step
AVHRR
NOAA
series
Daily
1989-present
1 km
1-week, 2-week
AVHRR
NOAA
series
Daily
1982-present
8 km
Twice monthly
Vegetation
SPOT
1-2 days
1999-present
1.15 km
10-day
MODIS
Terra
1-2 days
2000-present
250 m, 500 m, 1
km
8-day, 16-day
 NIR  VIS 
NDVI  

 NIR  VIS 
2) Vegetation Indices
RGB=NIR,RED,GREEN

Vegetation reflectance in the IR
could discriminate it from non
vegetative ‘green’
2) Vegetation Indices
NDVI :
 Processed
by NASA and the U.S. Geological Survey, the
data are represented as pixels (cells), with each pixel
representing an area of 8.0 x 8.0 km.
 NDVI
values range between -1 and +1, with dense
vegetation having higher values (e.g., 0.4 - 0.7), and lightly
vegetated regions having lower values (e.g., 0.1 - 0.2).
 The
primary use of these images is to compare the current
state of vegetation with previous time periods, for
example the same time in an average year or a reference
year (a particularly good or bad year) to detect anomalous
conditions.
2) Vegetation Indices
NDVI :
 In
a cropped area, NDVI values steadily increase as the
season progresses (from young green healthy crops), this
reaches a maximum value before falling with senescence
of the plant then harvest.
 The
time series analysis of NDVI data provides a method
of estimating net primary production over varying
biomass types, of monitoring phenological patterns, and
of assessing the length of the crop growing season.
 NDVI
values can further be used to calculate other
secondary products such as Dry Matter Productivity
(DMP) and Vegetation Production Index (VPI).
2) Vegetation Indices
NDVI:

NDVI as a function of
vegetation growth
2) Vegetation Indices
NDVI :
 NDVI
values can also be calculated per administrative unit
and/or per land cover or crop cover type.
 This
further enhances the product for the purpose of
monitoring in specific areas and/or for specific vegetation
classes or crop types.
 This
Regional Un-mixed Mean (RUM) statistics could then
be compared with statistics commonly available from
respective agricultural regions.
 The
status map (a product which is produced in addition
to NDVI) is used to filter out contaminated pixels, this
contamination is mainly due to clouds, shadow and
radiometric quality of the bands used to calculate NDVI.
2) Vegetation Indices
SADC GLC2000 Map
Time Series NDVI analysis – Agricultural pixels
2) Vegetation Indices
NDVI time series for Pandamatenga Region in Botswana
1
0.9
0.8
min
max
mean
2STD
ndvi-2010
0.7
NDVI values
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
Dekad
25
30
35
40
Time Series NDVI analysis – Agricultural pixels
2) Vegetation indices
CNDV : Corine NDVI
 NDVI
images are converted to represent an agricultural
production regional; this is achieved by computing
regional NDVI means and weighting the values according
to each pixels’s area occupied by the land cover type of
interest (a specific crop such as maize).
 The
CNDVI method has been developed to extract NDVI
profiles from satellite imagery. This technique aggregates
NDVI information by administrative regions and also
focuses on agricultural (production) areas only. The
calculated CNDVI is a more appropriate indicator of crop
yield (agro-statistical).
3) Vegetation indices
CNDV :

Average NDVI based
on the polygon for
seasons 2001 to 2006
Nr ,i
CNDVI r ,c,t 
W
r ,c,t ,i .NDVI i
i
Wr , c , t , i 
c , t , i . f c , i
N r ,i

i
c, t , i . f c, i
2) Vegetation indices
CNDV :
c,t ,i
is an auxiliary variable used to indicate
whether the pixel I has to included or rejected in
the computation of the regional NDVI mean
W is a relative weight for each class of interest
c is the land use class (non-irrigated arable land,
pastures, permanently irrigated land etc)
r is the region
t is the threshold surface fraction in % (0, 5, 10,
20, 30, 40, 50 and 60)

Wr , c , t , i 
Nr ,i
CNDVI r ,c,t 
W
r ,c,t ,i .NDVI i
i
c , t , i . f c , i
N r ,i

i
c, t , i . f c, i
3) Vegetation Indices

Qualitative
analysis of
CNDVI profiles
for Maize in
Kenya
2) Vegetation Indices
NDWI – Normalised Difference Water Index
 Dimensionless index with values ranging from -1 to 1.
 It can be used to indicate the presence of water in the plant tissue
and within the top layer of the soil (incase the soil is bare).
 It is calculated as the differences in the Near Infrared (NIR) and Short
Wave Infrared (SWIR).
 The SWIR reflectance shows changes in both the vegetation water
content and the spongy mesophyll structure in vegetation canopies,
while the NIR reflectance is affected by leaf internal structure and
leaf dry matter content but not by water content.
 The combination of the NIR with the SWIR removes variations
induced by leaf internal structure and leaf dry matter content,
improving the accuracy in retrieving the vegetation water content.
2) Vegetation Indicies
 NIR  SWIR 
NDWI  

 NIR  SWIR 
2) Vegetation Indices
NDWI – Normalised Difference Water Index
 NIR  SWIR 
NDWI  

 NIR  SWIR 
2) Vegetation Indices
 NDWI
is a good indicator for vegetation liquid water
 It is less sensitive to atmospheric scattering effects than NDVI.
 Time series analysis of NDWI such as differentiating multi-temporal data
sets can show evidence of water availability, water stress or drought
2) Vegetation Indices
DMP
 Dry
Matter Product is an indicator of the increase or decrease of dry matter
biomass (growth rate) expressed in kg dry matter per hectare per day.
 DMP
is calculated from the short wave radiation which is composed of
photosyntheticaly active radiation.
 Other
parameters involved are the fraction absorption of this radiation by the
green vegetation and the efficiency of this radiation conversion into biomass.
 In
the VGT Africa products, DMP is calculated from the daily temperature,
daily radiation and the VGTS10 NDVI.
 Most
crop yield forecasting models use DMP as one of the input. The DMP
values vary from 0 to 327.76 kg dry matter/ha/day.
2) Vegetation Indices
DMP
2) Vegetation Indices

Difference of consecutive images gives us information on progression of dry matter
productivity and possible vegetation growth rates during the season.

Differences in DMP images from different seasons allow the detection of anomalies in
vegetation growth.

The accumulation of DMP over time provides an estimation of the final dry matter
production. Regional Un-mixed Mean (RUM) statistics for DMP can be calculated per
administrative unit or crop type. These statistics could then be compared to DMP
statistics derived in-situ by other means. The DMP can also be used as one of the
inputs of crop yield prediction models
2) Vegetation Indices
VPI

The Vegetation Productivity Indicator is used to assess the overall vegetation
condition and is a categorical type of difference vegetation index, referenced
against the NDVI percentiles of the historical year.
2) Vegetation Indices
2) Vegetation Indices
VPI

The VPI can either be calculated based on DMP or NDVI values (cumulative
histogram of long term historical data)

This product contains values ranging from 0 - 255, these include flags such as
missing data, clouds, snow, sea and background pixels.

The Vegetation Productivity Indicator is used to assess the overall vegetation
condition.

The VPI is classified in 5 main groups

The VPI method is a statistical distribution of the NDVI for each 10-day period of
the year by applying techniques commonly used in hydrology for the prediction of
extreme events.

Combination decadal VPI images produces an integrated image showing the
number of occurrences where a below (or above) average normal value is
registered

VPI can then be used to identify (flag) areas at risk for low agricultural production
or drought
2) Vegetation Indices
2) Vegetation Indices
2) Vegetation Indices
The END
Thank You.
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