FACULTY OF AGRICULTUREAND ENVIRONMENT

Quantitative assessment of the relative role of climate change and human

activities in grassland degradation: Application of a satellite tracking system

Inakwu O.A. OdehWith Professor J Li and Team from Nanjing University

Department of Environmental Sciences

Presentation for the Space SyReN (University of Sydney); November 18, 2014

Introduction

Grassland covers approximately 25% of world's natural land surface

It accounts for about 16% of the global terrestrial GNPP

Also, globally, grassland has a major influence on the functioning of the terrestrial biosphere

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Introduction

In China,

Grassland is one of the most important natural resources

It accounts for 42% of the national land area (and 11% of global grassland)

It is home to rich plant and animal diversity

It is the major source of animal products for the teeming population- products such as meat, milk, wool and pelts

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Introduction

However, grassland in China experienced large-scale degradation and desertification in the last 30-40 years due to:

Overgrazing

Large-scale conversion to croplands to feed the teeming population

Drought

And suspiciously climate change

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Introduction

In response, China introduced policies (late 1990s and early 2000s) to restored degraded/ dysfunctional grasslands- extending to northwest

The restoration programs included

Three-North Shelterbelt Forest project,

The Grain-to-Green Project

Grazing Withdrawal Project

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Study Aim

› About 2010, a research project (Funded by Chinese Govt, AusAID, Asia‐Pacific Network for Global Change Research and Usyd IPDF) was initiated

- to quantitatively assess the extent and degree of grassland degradation in response to government restoration programs vis-à-vis the impact of climate change and variability on grassland degradation

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Grassland Types in North-western China

Project Team

› The project was carried out in collaboration with the University of Nanjing's Global Change Institute (GCI-UN).

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• Professor Jianlong Li• Dr S Mu• Dr S Zhou• Dr C Gang• Dr W Ju• Y Chen• Dr Z Wang• Etc.

Methods

The main thrust of the methodology used was the ability to estimate Net Primary Productivity (NPP) from satellite data and using ground data for validation over such a large region; Steps:

‘Actual’ NPP was estimated between 2001 and 2010 using CASA (Carnegie-Ames-Stanford Approach) with MODIS NDVI as the input data

Potential NPP was estimated using Thorntwaite Memorial model based on meteorological data

Differences between potential and actual NPP are hypothesized to be due to either climate change or human activities or both

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Data Requirement

Meteorological data- Including monthly mean temperature and precipitation, total solar radiation were obtained from China Meteorological Data Sharing Service System.

Land cover data: Global Land Cover 2000 dataset

Normalized difference vegetation index (NDVI) data (MODIS)-NDVI data with 1 km spatial resolution from 2001 to 2010,

Field survey to estimate on-ground NPP- We sampled 63 sites across the study area in early April and at the end of August in 2009, to validate the accuracy of the estimated NPP by model.

These datasets are processed within the ArcGIS10.1.

Data required and data processing

Methods- CASA Model for Computing Actual NPP

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Methods- CASA Model for Computing Actual NPP

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Methods- CASA Model for Computing Actual NPP

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The light use efficiency can be estimated as:

where is a coeff.- represents the reduction of NPP caused by biochemical action under extreme temperature conditions; is a coefficient that determines the biomass decline when the temperature deviates from the optimal temperature;

is the moisture stress coefficient which is indicative of the reduction of light-use efficiency caused by moisture factor; is the maximal light-use efficiency under ideal conditions = 0.542 for grasslands

max21 ,,,, txWtxTtxTtx

txT ,1

txT ,2

max

txW ,

Methods- CASA Model for Computing Actual NPP

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In particular, a number of vegetation indices are products of VIS-NIR (satellite) remote sensing systems, e.g.:

Simple ratio (SR);

Normalized difference vegetation index (NDVI)

Fractional vegetation cover

NPP, and hence APAR, is a function of vegetation type and vegetation cover- represented by vegetation indices

The ratio of near-infrared (NIR) to red simple ratio (SR) is the first true vegetation index:

Takes advantage of the inverse relationship between chlorophyll absorption of red radiant energy and increased reflectance of near-infrared energy for healthy plant canopies

Common types of vegetation indices

NIRred

red

NIRSR

Normalized difference vegetation index (NDVI

Used to

identify ecoregions;

monitor phenological patterns of the earth’s vegetative surface, and

assess the length of the growing season and dry periods;

estimate net primary production (NPP)

Common types of vegetation indices

redNIR

redNIRNDVI

NIRred

fv can be computed from NDVI by using a linear mix model with two end members representing fully vegetated land surface and bare ground:

Fractional Vegetation Cover (fv)

Wavelength, nm

400 600 800 1000 1200

Ref

lect

ance

(%

)

0.0

0.1

0.2

0.3

0.4

0.5

very dense vegetation cover (Fv max)

very scant Fv sunlit bare soil

Leaf Versus Canopy

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FAPAR is a function of vegetation type and vegetation cover;

Vegetation type and cover can be modelled by satellite remote sensing data, especially the visible/ near infrared section of EM radiation;

Satellite remote sensing is particularly advantageous because of their archival databases that provide time series records of the earth surface conditions

FAPAR is a function of vegetation type and vegetation cover

CASA Model for Computing Actual NPP

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FPAR can be calculated from NDVI as:

where

NDVImax and NDVImin are respectively 0.634, 0.023;

FAPARmax and FAPARmin are 0.95 and 0.001 respectively

min

min,max,

minmaxmin,),(

)(

)(FPAR

NDVINDVI

FAPARFAPARNDVINDVIFAPAR

ii

itxNDVI

Methods- Thornthwaite Memorial NPP Model for Computing Potential NPP

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Methods- Thornthwaite Memorial NPP Model for Computing Potential NPP

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20 0.000969513000NPP ve

205.111

05.1

Lr

rV

305.0253000 ttL

Thornthwaite Memory model is expressed as:

where v is the average annual actual evapotranspiration (mm), expressed as:

where L is annual average potential evapotranspiration (mm), expressed as:

and r is annual precipitation (mm), t is the annual average temperature (℃)

› Change trend of grassland NPP- whether actual or potential can be obtained from the slope of NPP trend, S, calculated as a linear fit of time/NPP using the ordinary least square estimation:

› Significance test of change trend of grassland NPP can be done using statistic F test.

where, U is regression sum of squares , Q is residual sum of squares, n is the df = 9 years

2

1

2

1 11

）i（

)NPP)((NPP

n

i

n

i

n

i

n

ii

n

ii

in

iinS

Method- Computation of grassland vegetation dynamics vis-à-vis roles of climate and humans

Methods- Flowchart to determine relative roles of climate change vs human activities to grassland dynamics

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MODIS NDVI data (2001-

2010)

Weather station data (2001-2010)

Actual NPP (NPPA) from CASA model

Human appropriation NPP (NPPH) (NPPP -NPPA)

Potential NPP (NPPP) from Thorntwaite memorial model

Compute trend slope of NPPA

(SA) and ΔNPPA

Trend slope of NPPH (SH) and ΔNPPH

Trend slope of NPPP

(SP) and ΔNPPP

Analyse relative roles of climate and humans based on 8 scenarios

Methods- Scenarios of relative roles of climate change vs human activities to restoration/degradation

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ΔNPPj=(n-1)×Sj

Results: Spatial distribution of actual grassland NPP in NW China (2010).

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Actual grassland NPP in NW China (2010).

Results- Validation of Estimated ‘Actual’ and Potential NPP

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The model accuracy of (a) CASA model (Actual NPP) and (b) Thornthwaite Memorial model (Potential NPP)

Actual NPP Potential NPP

Results: Grassland vegetation dynamics

Trending slope of NPP (grassland) dynamics

（ c）

The proportion of different categories of grassland dynamics

The degree of NPP dynamics

Result: Proportion of grassland restoration/degradation by province

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Area percentage of grassland degradation and restoration

Results: The relative roles of climate change versus human activities on grassland degradation

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The proportion of the relative roles of (a) climate change and (b) human activities to grassland degradation.

climate changehuman activities

Results: contribution of climate change/human activities to grassland degradation/ restoration by province

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Contribution of climate change, human activities and the combination of the two factors to (a) grassland degradation; and (b) grassland restoration

Grassland degradation Grassland restoration

Result: Spatial patterns of contributions of climate change and human activities to grassland degradation

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Contributions of climate change (a) and human activities (b) to grassland restoration

climate change Human activities

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Global extension- trend in grassland dynamics

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Global extension- role of climate change vs human activities to grassland degradation

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Global extension- role of climate change vs human activities to grassland restoration

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Conclusions- NW China Study

The mean annual grassland NPP in 2010 was estimated to be about 123 g C/m2/yr and showed obvious spatial heterogeneity.

Between 2001-2010, 62% (1,650,316 km2) of total grassland was degraded

Out of this, 66% of grassland degradation was caused by human activities

Only about 20% was due to climate change

Overall, 38% (1,033,663 km2) showed improvement

Satellite tracking can be useful for elucidating the performance of grassland restoration programs through careful analysis  

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Conclusions

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Pictures

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