Monitoring1 - AMESD SADC THEMA

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.