FACULTY OF AGRICULTURE AND 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. Odeh With 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 2

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 3

Introduction  However, grassland in China experienced large-scale degradation and desertification in the last years due to: Overgrazing Large-scale conversion to croplands to feed the teeming population Drought And suspiciously climate change 4

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 5

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 6 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). 7 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 8

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 10

Methods- CASA Model for Computing Actual NPP 11

Methods- CASA Model for Computing Actual NPP 12 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 = for grasslands

Methods- CASA Model for Computing Actual NPP 13

14  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

Phenological Cycles of Cotton Crop as Grown in Northern NSW June July Aug Sept Oct Nov Dec Jan Feb Mar April May Field preparation Planting Emergence Square Blooming Ball Harvest setting opening, defoliation Relative Greenness Infrared Colour Composite Actual NPP is a function of phenological cycles of vegetation and crops

 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 NIR red

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 NIR red

 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 Reflectance (%) very dense vegetation cover (Fv max) very scant Fv sunlit bare soil Leaf Versus Canopy

Canopy Reflectance Vegetation Index Leaf Area Index (or Fv, for example) Dominated by a change in percent vegetation cover Complete vegetation cover Differences are due to increasing canopy density 100% vegetation cover

21  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 22 FPAR can be calculated from NDVI as: where NDVI max and NDVI min are respectively 0.634, 0.023; FAPAR max and FAPAR min are 0.95 and respectively

Methods- Thornthwaite Memorial NPP Model for Computing Potential NPP 23

Methods- Thornthwaite Memorial NPP Model for Computing Potential NPP 24 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 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 26

Methods- Scenarios of relative roles of climate change vs human activities to restoration/degradation 27 ΔNPP j =(n-1)×S j

Results: Spatial distribution of actual grassland NPP in NW China (2010). 28 Actual grassland NPP in NW China (2010).

Results- Validation of Estimated ‘Actual’ and Potential NPP 29 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）（c） The proportion of different categories of grassland dynamics The degree of NPP dynamics

Result: Proportion of grassland restoration/degradation by province 31 Area percentage of grassland degradation and restoration

Results: The relative roles of climate change versus human activities on grassland degradation 32 The proportion of the relative roles of (a) climate change and (b) human activities to grassland degradation. climate change human activities

Results: contribution of climate change/human activities to grassland degradation/ restoration by province 33 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 34 Contributions of climate change (a) and human activities (b) to grassland restoration climate changeHuman activities

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Global extension- trend in grassland dynamics 36

Global extension- role of climate change vs human activities to grassland degradation 37

Global extension- role of climate change vs human activities to grassland restoration 38

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Conclusions- NW China Study  The mean annual grassland NPP in 2010 was estimated to be about 123 g C/m 2 /yr and showed obvious spatial heterogeneity.  Between , 62% (1,650,316 km 2 ) 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 km 2 ) showed improvement  Satellite tracking can be useful for elucidating the performance of grassland restoration programs through careful analysis 40

Conclusions 41

Pictures 42