Land degradation & change detection of biophysical products using multi temporal SPOT NDVI image data : a case in Blue Nile river basin, Ethiopia Taye.

Land degradation & change detection of biophysical products using multi temporal

SPOT NDVI image data:a case in Blue Nile river basin, Ethiopia

Taye Gidyelew

International Livestock Research Institute (ILRI)

Introduction

Land degradation is a process of progressive deterioration of biological and physical resources of the land

It is a cause of low agricultural productivity for Sub–Saharan Africa in general and Ethiopia in Particular.

Biophysical products such as NDVI,NPP and biomass indirectly determines land degradation

Intro…

Remote sensing is the best method for obtaining such data

Recently large volume of high quality global remotely sensed data available

– SPOT– MODIS– MERIS– AVRIS/NOAA

Objective of the study

To identify the trend of NDVI to monitor land degradation through analysis of multi temporal and multi spectral SPOT-Vegetation imageries and

To determine the amount of Net Primary Product (NPP) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) in Blue Nile river basin.

Methodology - study site

Clip NDVI to sample land

use

DN NDVI Image From Spot Satellite

Conversion NDVI

Image to Arcinfo grid

and dbf

Decadal NDVI

to monthly NDVI

Determine & stratifiy

Land use types

7 years NDVI

Trends per land use types

Landsat imageries

(1999&2003)

Woody Biomass land cover

Yearly and Monthly

NDVI

Methodology

Algorithms to derive biophysical products

Normalized Difference vegetation Index (NDVI)

REDNIR

REDNIRNDVI

fAPAR = a + b *NDVI fAPAR= -0.025+1.25*NDVI

• Fraction of Absorbed Photosynthetically Active Radiation (fAPAR)

Algorithms……

Net Primary Productivity (NPP)

PARdt*fAPARNPP

12

1

0.798] / 0.087)* (NDVI * NDVI) * 1.25 (-0.025 * [1.5NPP

Results and discussion

The biophysical products are derived from the SPOT vegetation for different LU types which are:

– Dabus Valley park– Farm land– Forest area– Grass land and– Towns

Dabus Valley Park_ NDVI

y = -0.0128x + 0.4758

0.00

0.20

0.40

0.60

1998 1999 2000 2001 2002 2003 2004

Years

ND

VI

MIN

MAX

MEAN

Farm land_ NDVI

y = -0.0076x + 0.4238

0.00

0.20

0.40

0.60

1998 1999 2000 2001 2002 2003 2004

Years

ND

VI

MINMAXMEAN

Forest area_NDVI

y = 0.0002x + 0.4693

-0.10

0.10

0.30

0.50

0.70

1998 1999 2000 2001 2002 2003 2004

Years

ND

VI

MINMAXMEAN

Grassland_NDVI

y = -0.001x + 0.3948

0

0.2

0.4

0.6

0.8

1998 1999 2000 2001 2002 2003 2004

years

ND

VI

MIN

MAX

MEAN

Town-NDVI

y = -0.0045x + 0.4046

0.00

0.10

0.20

0.30

0.40

0.50

0.60

1998 1999 2000 2001 2002 2003 2004

Years

ND

VI

MinMaxMean

NDVI for all land use types

0.30

0.40

0.50

0.60

1998 1999 2000 2001 2002 2003 2004

Dabus

Farm

Forest

Grass

Town

The NDVI for town is appeared at the bottom of all curves followed by that of grass land and forest at the top of all

Overall monthly NDVI for all Land use types

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0.2

0.4

0.6

0.8

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DabusFarmForestGrassTown

Time

NDVI

Summary of fAPAR and NPP

Land Use Mean NDVI fAPAR NPP Remark

Dabus 0.4245 0.5056 5.98

Farm land 0.3936 0.4670 4.64

Forest 0.4702 0.5627 6.36 Highest

Grass land 0.3909 0.4636 4.61

Town 0.3868 0.4585 4.42 Least

Conclusion and Recommendation

The NDVI trend and the derived parameters during the seven periods time showed a declining trend .

Forest area over the study area showed higher value for all the biophysical products and a minimum value is recorded in towns.

Combination of Rain Use Efficiency with NDVI trends will be a good parameter to monitor the status of vegetation cover and good indicator for land degradation.

Field data has to be included for validation and calibration of biophysical products for different land use types

Thank you !

Comments, suggestions and questions are most welcome!

Land cover Land use 15 years ago