# Presentation: 602889

## Testing Biophysical and Land Use Controls on Biodiversity using MODIS and AMSR-E Products

**Montana State University**
**Ruth DeFries**
**University of Maryland**

**Testing Biophysical and Land Use Controls on Biodiversity using MODIS and AMSR-E Products**

**NASA Biodiversity and Ecological Forecasting Team Meeting**
**August 29-31, 2005**

## Long Standing Question 

** ****Difficult to answer because fine-scale detection of species is required over large areas.**
** ****Progress is enabled by convergence of:**
** ****theory on controls of biodiversity**
** ****satellite data for continental scale monitoring and analysis**
** ****continental-scale field monitoring**

**How are species distributed across space and what controls this distribution? **

## Vegetation Structure and Biodiversity

**Habitat Structure – The vertical and horizontal distribution of plant biomass.**
**Habitat structure is known to influence biodiversity at landscape scales and current management is based on this.**

## Energy and Species Richness 

**Biodiversity is often strongly correlated with energy.**
**Energy**
**Heat – e.g., temperature, potential evapotranspiration**
**Ecological productivity – e.g., NPP**
**Why?**
**Abundant food resources or warmer thermal conditions allow higher survival and reproduction of individuals within a population, and larger population sizes reduce the chance of species extinctions (Wright 1983).**

**“****Measures of energy (heat, primary productivity)...[and water balance]...explain spatial variation in richness better than other... variables in 82 of 85 cases”, Hawkins et al. 2003.**

## Energy and Species Richness 

**Energy and Species Richness**** **

**-  Hurlbert and Haskell (2003)**
**- North America**
**- 140 km x 140 km **
**- Terrestrial breeding (BBS)**
**   ****and wintering (CBC) birds**
**- AVHRR NDVI mean monthly**
**   ****(UNEP 1993)**

## MODIS and AQUA 

**Satellite data and products relevant to the question have improved substantially with the MODIS and AQUA sensors.**

## MODIS and AMSR-E Predictors

## MODIS and AQUA 

** ****Finer spatial resolution for NDVI (250 m vs 1 km)**
**Higher radiometric and spectral resolution**
**Addition of EVI, more sensitive to high biomass**
**Addition of vegetation composition products (land cover and continuous fields) and soil moisture.**

**Satellite data and products relevant to the question have improved substantially with the MODIS and AQUA sensors.**

## Questions 

** ****1.  Do MODIS products better explain bird species richness than earlier technologies?  **
**Do MODIS products allow separation of the effects of vegetation productivity and vegetation structure? **
**How much variation in bird species richness is explained by MODIS?**

## USGS Breeding Bird Survey 

** ****Survey unit is a roadside route**
** ****39.4 km in length**
** ****50 stops at 0.8 km intervals**
** ****Birds tallied within 0.4 km**
** ****3 minute sampling period**
** ****One survey per year**
**1966 to 2004, or fewer years**
** ****Water birds, hawks, owls,**
** ****and nonnative species excluded in this analysis**

## BBS Issues: Sampling Biases 

** ****Road-side survey.**
** ****Nonrandom habitats? **
** ****Road-side bird species?**
**Observer effects.**
** ****New observers tend to miss species.**
**Species detectability.**
** ****Ability to detect a bird differs among species and habitats.**
**Representation of species richness.**
** ****Average annual or cumulative.**

**BBS Issues: Sampling Biases**

**Mark recapture statistical methods**
- (estimated but not used here)

**Average annual**

**Uniqueness of data set outweighs bias**

## BBS Issues: Land Use Effects 

**Cropland**

**Urban and built up**

**Cropland and natural vegetation**

**Previous analyses have included all regions, regardless of land use.**
**We used BBS routes >50% natural vegetation.**

## Statistical Techniques

**Model selection techniques based on AIC ****(distance between specified model and reality).**
**Coefficient of determination for amount of variance explained.**
**Bird species richness transformed (log+1) to improve  normality.**** **
**Mixed models will be used to control for spatial and temporal autocorrelations, but ****not done yet****.  **

**Statistical Techniques**

## Landbird Species Richness

**Landbird Species Richness**

## Predictors: MODIS vs Earlier Technologies 

| Theme | Sensor | Spatial resolution | Period | Formulation |
| --- | --- | --- | --- | --- |
| NDVI | MODIS | 1 km | June 26 2004 | Spectral only (ratio of NIR/R); narrower bands than AVHRR
 |
| NDVI | AVHRR | 8 km | Late June 2003 | Spectral only (ratio of NIR/R) |

**MODIS NDVI better than AVHRR NDVI because: ****narrower red and NIR bands**** (which is thought to result in better detection of photosynthetic activity), higher radiometric resolution, improved compositing algorithm? **

## AVHRR NDVI vs MODIS NDVI

**	****	****    **_**AVHRR NDVI**_
**        ****MODIS NDVI          0.91373****	****	**

-    _**Variable      **__**	**__** R-Square           **__**	**__**    AIC    **__**	**_
**AVHRR NDVI      ****	**** 0.5187    ****	****	****-5237.6764    ****	**
**MODIS NDVI****	**** 0.4869    ****	****	****-5105.8716    ****	**

**AVHRR NDVI vs MODIS NDVI**

**Correlation**

**N=2075**

**Best Model**

- Bird richness (log)

- AVHRR NDVI: June 26

## Conclusion

**MODIS and AVHRR NDVI were similar in explaining variation in landbird richness.  **
**Why? **
**8 km vs 1 km??**

## Structural Variables: MODIS NDVI, EVI, LAI

|  | LAI | NDVI | EVI | GPP | PSNnet | NPP
(Annual) |
| --- | --- | --- | --- | --- | --- | --- |
| LAI |  | .837 | .776 | .779 | .530 | .345 |
| NDVI |  |  | .909 | .835 | .512 | .393 |
| EVI |  |  |  | .677 | .315 | .246 |
| GPP |  |  |  |  | .842 | .621 |
| PSNnet |  |  |  |  |  | .624 |
| NPP |  |  |  |  |  |  |

**N=2057**
**P<.0001**

## MODIS Vegetation Indices

**MODIS Vegetation Indices**

**NDVI**

**EVI**

- (Value x 1000)

**Dense forests are scaled to low to moderate EVI values.**

## MODIS Structural Variables

**MODIS Structural Variables**

**LAI**

## MODIS Structural Variables

- Bird richness (log)

- MODIS NDVI – June 26

## Conclusions

**MODIS NDVI produces stronger models than EVI or LAI.**
**NDVI and EVI may be stronger predictors than LAI because they reflect both canopy density and photosynthetic activity, which perhaps birds are responding to.**
**NDVI may stronger than EVI because birds less sensitive to high vegetation density and more influenced by lower vegetation densities.**

## MODIS Productivity Products

**PSNnet: ****GPP- maintenance respiration**
**NPP: ****PSNnet –**** ****annual respiration costs**

**MODIS Productivity Products**

**Birds should be better predicted by NPP than PSNnet or GPP because NPP reflects the proportion of the fixed energy that is available to consumers?**
**However, the MODIS formulation of NPP is annual; growing season may better predict breeding birds.**

## MODIS Productivity Products

**MODIS Productivity Products**

**   **_**Variable      **__**	**__** R-Square           **__**	**__**    AIC    **__**	**_
**GPP****	****      ****	****  0.3307****	****	**** -4558.9579 ****	**** ****	**
**PSNnet****	****	****  0.1049    ****	**** -3960.8136 **
**NPP(ann)****	**** ****	****  0.0887****	****	****-3923.9422 **

**R****2**** = .43**

- Bird richness (log)

- MODIS GPP – June 26
- (gC/m2/day)

## Conclusion

**GPP is the best predictor among productivity variables.  NPP is expected to be better, but annual formulation may reduce utility for breeding season richness.**

## MODIS Structure vs Productivity

- Bird richness (log)

- Bird richness (log)

- MODIS NDVI - Late June

**MODIS Structure vs Productivity**

**R****2**** = .43**

**R****2**** = .49**

**Is NDVI a better predictor because bird richness is driven both by structure and productivity?**

- MODIS GPP – June 26
- (gC/m2/day)

## MODIS Products: Vegetation Structure or Productivity?

**LAI :**** leaf area per unit area**
**NDVI****	****EVI : ****canopy volume and photosynthesis**
**GPP: ****energy fixed**
**PSNnet: ****GPP- maintenance respiration**
**NPP: PSNnet – ****annual respiration costs**

**MODIS Products: Vegetation Structure or Productivity?**

**Vegetation structure**

**Energy fixed for consumers**

## Conclusion

** ****The results are consistent with the hypothesis that landbird diversity is related to both vegetation structure and productivity.  **
**However, MODIS structure and productivity measures are correlated.**
**Radar-based approaches to quantify vegetation structure would seem a means of further testing this hypothesis.  **

## Predictors: Additional Products 

| Theme | Definition | Period |
| --- | --- | --- |
| Continuous Fields 
Vegetation | Proportion cover of plant lifeform, leaf type, and leaf longevity.   | 2001 |
| Land Cover | 17 natural and human cover classes using IGBP scheme. Converted to cover type richness. | 2001  |
| Soil Moisture | Daily measurements of surface soil moisture (top few millimeters) and vegetation water content, from passive microwave measurements.  Not yet included. | June 26 2004  |

## Best Models Using MODIS Products

**Observed landbird richness = **
**	****NDVI LAI GPP NPP NPP****2**** VCF VCF****2**
- 	**R****2**** = .58****	****AIC = -4924****	****	****N=2058**

**BBS Route Level**

**Defined by North American Bird Conservation Initiative**
**BBS routes >50% natural vegetation are included**
**BCRs with >14 routes included.**

## Best Models Using MODIS Products

**Observed landbird richness = NDVI GPP NPP NPP****2**** VCF**
- 	**R****2**** = .82****	****AIC = -111.74****	****	****N=30**

**BBS Ecoregion Level**

## Overall Study and Next Steps

**Human Land Use**
**(Land use,**
**Home  density)**

**Current Biodiversity Value**

**Biophysical Potential**
**(i.e. Energy,**
**Habitat structure)**

**Conservation Priority**

**Overall Study and Next Steps**

** ****Develop predictors for 2000-04 period, including NDVI and GPP phenology.**
**Add topographic, climatic, and other predictors**
** ****Resolve treatment of statistical issues on species detectability and spatial autocorrelation.**

## Thanks

** ****NASA EOS Program**
** ****Woody Turner, Dick Waring, Steve Running**
**Jim Nichols, John Sauer, and colleagues at Patuxent.**
**Curt Flather**

## Study Area 

**Study Area**** **

## Predictors: MODIS vs Earlier Technologies 

| Theme | Sensor | Spatial resolution | Period | Formulation |
| --- | --- | --- | --- | --- |
| NPP | MODIS | 1 km | Annual average, 2004 | Modeled from:
Spectral data
MODIS products
Met data |
| NDVI | MODIS | 1 km | June 26 2004 | Spectral only (ratio of NIR/R); narrower bands than AVHRR
 |
| NDVI | AVHRR | 8 km | Late June 2003 | Spectral only (ratio of NIR/R) |
| PET | Met stations | 10 km (Canadian) | Ave annual | Estimated from met data. |

**MODIS NDVI better than AVHRR NDVI because: narrower red and NIR bands, higher radiometric resolution, improved compositing algorithm? **

## Species Energy Theory 

_**Recent Reviews**_
**Waide, R.B., et al.  1999.  The relationship between productivity and species richness.  69(2):330-339. Annual Rev. Ecol. Syst. 30:257-300.**
**Mittelbach, G.G., C.F. Steiner, S.M. Scheiner, K.L. Gross, H.L. Reynolds, R.B. Waide, M.R. Willig, S.I. Dodson, L. Gough.  2001.  What is the observed relationship between species richness and productivity?  Ecology 82: 2381-2396.**
**Hawkins, B. A., R. Field, H.V. Cornell. D.J. Currie, J. Guegan, D.M. Kaurman, J.T. Kerr, G.G. Mittelbach, T Oberdorff. E.M. O’Brian, E.E. Porter, and J.R.G. Turner.  2003a.  Energy, water and broad-scale geographic patterns of species richness.  Ecology 84(12)3105-3117.**
**Hawkins, B.A., E.E. Porter, and J. A. F. Diniz-Filho.  2003b.  Productivity and history as predictors of the latitudinal diversity gradient of terrestrial birds.  Ecology 84(6):1608-1623.**
**“****Measures of energy (heat, primary productivity)...[and water balance]...explain spatial variation in richness better than other... variables in 82 of 85 cases”, Hawkins et al. 2003.**

## Energy and Species Richness 

**Energy and Species Richness**** **

**- Hawkins et al. (2003)**
**- North and Central America**
**- 2.0****o**** x 2.0****o**
**- Native terrestrial breeding birds**
**- Range maps for birds**
**- 6 climate variables including PET (UNEP 1993).**

## MODIS Productivity vs Structure 

| Theme | Definition | Period | Formulation |
| --- | --- | --- | --- |
| NPP | sum of the cumulative daily PSNnet less the costs associated with annual maintenance and growth respiration.  | Annual 2004 | Modeled from:
Spectral data
MODIS products
Met data |
| PSNnet | equal to GPP less the maintenance respiration.  | June 26 2004 |  |
| GPP | rate at which light energy is converted to plant biomass.  | June 26
2004 |  |
| EVI | radiometeic measures of the amount, structure, and condition of vegetation. | June 26 2004  | Spectral only (based on NIR, Red, Blue) |
| NDVI |  | June 26 2004  | Spectral only (ratio of NIR/R) |
| LAI | one sided green leaf area per unit ground area in broadleaf canopies, or as the projected needleleaf per unit ground area in needle canopies.  | June 26 2004  | Surface reflectance (bands 1-7)
Land cover 
Ancillary data (look-up tables) |

## Correlations Among MODIS Products

|  | LAI | NDVI | EVI | GPP | PSNnet | NPP
(Annual) |
| --- | --- | --- | --- | --- | --- | --- |
| LAI |  | .837 | .776 | .779 | .530 | .345 |
| NDVI |  |  | .909 | .835 | .512 | .393 |
| EVI |  |  |  | .677 | .315 | .246 |
| GPP |  |  |  |  | .842 | .621 |
| PSNnet |  |  |  |  |  | .624 |
| NPP |  |  |  |  |  |  |

**N=2057**
**P<.0001**

## MODIS Structure vs Productivity

**R****2**** = .43**

**R****2**** = .49**

- Bird richness (log)

- MODIS NDVI – June 26

- Bird richness (log)

- MODIS GPP – June 26
- (gC/m2/day)

## MODIS Land Cover Heterogeneity

**MODIS Land Cover Heterogeneity**

** ****	****	****NDVI   ****	****       LAI             GPP             NPP**
**      ****VCF  ****	****0.82046      0.82252     0.84082     0.40322                   **

**N=1824 P<.0001**

**Correlation **

## MODIS Vegetation Continuous Fields

**MODIS Vegetation Continuous Fields**

**R****2**** = .49**

** ****	****	****NDVI   ****	****       LAI             GPP             NPP**
**      ****VCF  ****	****0.82046      0.82252     0.84082     0.40322                   **

**N=1824 P<.0001**

**Correlation **

- Bird richness (log)

- MODIS Vegetation Continuous Fields - % tree

## MODIS Products: Vegetation Structure or Productivity?

**LAI**
**NDVI****	****EVI**
**GPP**
**PSNnet**
**NPP**

**MODIS Products: Vegetation Structure or Productivity?**

**Vegetation structure**

**Energy fixed for consumers**

## MODIS Vegetation Indices

**MODIS Vegetation Indices**

**NDVI**
**EVI**
**Bird Richness**

**Samples high in EVI are also high in NDVI and richness.**

**low**

**high**

## MODIS Vegetation Indices

**Samples high in NDVI are high in richness but scattered in EVI.**

**NDVI**
**EVI**
**Bird Richness**

**low**

**high**

## Correlations Among MODIS Products

|  | LAI | NDVI | EVI | GPP | PSNnet | NPP
(Annual) |
| --- | --- | --- | --- | --- | --- | --- |
| LAI |  | .837 | .776 | .779 | .530 | .345 |
| NDVI |  |  | .909 | .835 | .512 | .393 |
| EVI |  |  |  | .677 | .315 | .246 |
| GPP |  |  |  |  | .842 | .621 |
| PSNnet |  |  |  |  |  | .624 |
| NPP |  |  |  |  |  |  |

**N=2057**
**P<.0001**

## Energy and Species Richness 

**Energy and Species Richness**** **

**R****2 ****= .69**

**- Currie (1991)**
**- North America**
**- 2.5****o**** x 2.5-5.0****o**
**- All birds**
**- Range maps for species**
**- 10 climate variables - Climate atlases**

- PET (mm yr-1)

## Energy and Species Richness 

**Biodiversity is often strongly correlated with energy.**
**Energy**
**Heat – e.g., temperature, potential evapotranspiration**
**Ecological productivity – e.g., NPP**
**Why?**
**Abundant food resources or warmer thermal conditions allow higher survival and reproduction of individuals within a population, and larger population sizes reduce the chance of species extinctions (Wright 1983).**

## Conclusions

**The causes of elevated landbird richness at mid latitudes in North America are not fully understood. **
**MODIS and AVHRR NDVI were similar in explaining variation in landbird richness.**
**Some MODIS products are derived from each other, are highly correlated, and thus do not represent orthogonal predictors of biodiversity.**
**MODIS NDVI produces stronger models than EVI or LAI.**
**GPP is the best predictor among productivity variables.  NPP is expected to be better, but annual formulation likely reduces utility for breeding season richness.**

## Conclusions

**MODIS products in total explain the majority of variation in landbird richness.**
**Strength of the relationship increases with spatial scale of the analysis.**