Jan Verbesselt - Assessing severity of tree mortality in Pinus radiata plantations with MODIS time series

Detecting tree mortality severity using MODIS

satellite data

Jan Verbesselt

14 ARSPC Conference

30 September 2008

Tree mortality

• Severe Ips bark beetle damage

CSIRO. Detecting tree mortality severity

Tree mortality

• Caused by drought stress


Precipitation mm/year

Current monitoring systems

• Aerial sketch mapping and field surveys


May 2007

Current monitoring systems

• Aerial and field surveys

• Few highly skilled individuals in the country

• Expensive and labour demanding

• Infrequent coverage (aircraft hire, cloud coverage)

• -> only annually

• A critical need for a forest health monitoring system


• A critical need for a forest health monitoring system

• MODIS satellite imagery

• Frequent coverage over large areas

• Spatial resolution: 250 - 1000m

Detecting tree mortality

• Aim

• Assess sensitivity of MODIS satellite data

• Which VI’s are most sensitive to tree mortality?

• When are VI’s most sensitive?

• Using aerial ADS 40 digital imagery

• 15 cm spatial resolution


• 15 cm spatial resolution

• Visual dead tree detection

Data


MODIS satellite data

• MOD13Q1 product

• 250 m spatial resolution

• 16 daily composited images

• 2000-2008

• Vegetation indices

• Normalized Difference Vegetation Index (NDVI)

• ~ “greenness”

NIR redNDVI

NIR red

−=

+


• ~ “greenness”

• Leaf chlorophyll, leaf area, canopy cover and architecture

• Enhanced Vegetation index (EVI)

• Improved sensitivity in high biomass regions

• Minimizing soil and atmosphere influence

* red (620-670 nm), near-Infrared (NIR; 841 - 876 nm), blue (459-479 nm)

6 7.5 1

NIR redEVI

NIR red blue

−=

+ × + × +

Time series analysis







• Apply the Relative Index (RI)• To remove seasonality

• Reduce general atmospheric noise

• Increase sensitivity

( , )1

x tVIRI

VI= −


• VI median is median at time t ( t: 2000 -> 2007) for current selected pixels within an area of 10 by 10 km.

( )

1median t

RIVI

= −



Statistical analysis

• Tree Mortality

• VI2005 ~ amount of alive trees in 2005

• VI2007 ~ amount of alive trees in 2007

• Analysis

• 4 indices: NDVI, EVI, RI.NDVI, RI.EVI

2005 2007TreeMortality VI VI= −


• 4 indices: NDVI, EVI, RI.NDVI, RI.EVI

• 3 periods: Median VI of whole year, Feb – Mar (a), and Jun – Aug (b)

• 2 manifestations: Difference approach, 2007 value

• Modelling

• OLS model, step-wise selection of variables based on AIC value

• correct for over-estimation by resampling of the OLS model

Preliminary results

• 4 VI based models

model R2 (*)

NDVI 0.32

RI.NDVI 0.32

EVI 0.13

RI.EVI -0.07


(*) index corrected for over-estimation

RI.EVI -0.07

Discussion

• Remote sensing challenges

• Tree Mortality only up to maximally 30%

• Drought stress caused overall decline in forest health in 2007

(~ defoliation)

• Understorey influence

(e.g. weeds)


Conclusions & further work

• NDVI is more sensitive then EVI

• Sensitivity changes within a year

• NDVI : Feb-Mar

• EVI: Jun-Aug

⇒Development of specific change detection metrics for each VI


• Further work

• Verify EVI

• Include SWIR based indices

• Derive amount of alive trees in 2005 and 2007 from Quickbird data

• Tree density estimation using local Maxima and image segmentation

Sustainable Ecosystems

Dr. Jan Verbesselt, Dr. Darius Culvenor

Phone: 03/95452265

Email: [email protected]

Contact Us

Phone: 1300 363 400 or +61 3 9545 2176

Email: [email protected] Web: www.csiro.au

Thank you

Acknowledgements This work was undertaken within the CRC for Forestry

Program 1: Monitoring Measuring. Discussions with Andrew Robinson,

Christine Stone, and Angus Carnegie contributed to this study.

Documents

Jan Verbesselt - Assessing severity of tree mortality in Pinus radiata plantations with MODIS time series