UNIVERSITI PUTRA MALAYSIA

APPLICATION OF REMOTE SENSING TECHNIQUE TO ESTIMATE LEAF AREA INDEX IN AYER HITAM FOREST RESERVE, PUCHONG,

SELANGOR, MALAYSIA

WONG ZEE YENG.

FH 2005 5

APPLICATION OF REMOTE SENSING TECHNIQUE TO ESTIMATE LEAF AREA INDEX IN AYER EUTAM FOREST RESERVE, PUCHONG,

SELANGOR, MALAYSIA

By

WONG ZEE YENG

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirement for the Degree of Master Science

May 2005

Abstract of thesis presented to the Senate of the Universiti Putra Malaysia in fulfilment of the requirements for the degree of Master of Science

APPLICATION OF REMOTE SENSING TECHNIQUE TO ESTIMATE LEAF AREA INDEX IN AYER BITAM FOREST RESERVE, PUCHONG,

SELANGOR, MALAYSIA

WONG ZEE YENG

May 2005

Chairman: Associate Professor Ahmad Ainuddin bin Nnmddin, PhD

Facutty: Forestry

Leaf Area Index (LAI), quantifies the total foliage per unit ground surface area. It is

the "driving" biophysical variable and is therefore an important input parameter to

many hydrological, ecological and climate models. In this study, we develop LAI

estimation model for obtaining LAI value using LANDSAT TM data image analysis.

The use of vegetation indices acquired h m remotely sensed data considered as mean

of estimating LAX. A study was carried out to estimate LA1 using Normalised

Difference Vegetation Index (NDVI) using Landsat 'Rvl data. The objective of this

study was to estimate LAI in Air Hitam Forest Reserve (AHFR) using NDVI, in

which, the empirical relationship between LAI (NDVI) and measured LAI (LICOR

2000) was developed.

The AHFR, located in Puchong, Selangor, Malaysia was chosen for this study.

Landsat TM was digitally processed and enhanced using ERDAS IMAGINE 8.4

which produced the NDVI map. Based on this NDVI values, 12 sample plots of 20 m

x 40 m were established to measure the LAI (using LICOR 2000) along with six

stand parameters namely the dbh, height, crown volume, stand density, basal area and

light intensity. The measurements were analysed to determine the descriptive statistic

of each plot using SPSS Software. LA1 was later regressed against the stand variables

as well as the NDVI value, derived earlier from the Landsat TM image, to establish

the regression fit. Once the regression fit model between NDVI and LA1 was

obtained, the NDVI image data was converted to the mapping of LAI. The accuracy

of LAI was evaluated using the accuracy assessment in ERDAS IMAGINE 8.4.

Stand characteristic variables such as dbh, height, crown volume, basal area and light

intensity showed correlation with LAI except stand density. It also produced

correlation with the NDVI which was derived fiom Landsat TM. Linear relationship

described the relationship between LA1 and NDVI with the regression equation of

LAI = 7.0861 x N D V 1 + 1.0929 with high R~ of 0.81 with significance level of 0.05.

The range of LAI observed (LICOR 2000) was similar to the overall range in LAI

from the NDVI. Through the models of LAI value estimation, LAI mapping on

AHFR can be done with each pixel holding its own LAI value. This study suggests

that remote sensing can be used to estimate LA1 in forest stands and produce LAI

map in AHFR subsequently provide predictive models of LAI specific for forest

stands.

iii

Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk ijazah Master Sains

APPLIKASI TEKNIK PENDERIAAN JAUH BAG1 MENGANGGAR INDEX KELUASAN DAUN DI WTAN SIMPAN AYER HITAM, PUCHONG,

SELANGOR, MALAYSIA

Oleh

WONG ZEE YENG

Mei 2005

Pengerusi: Associate Professor Ahmad Ainuddin bin Nnmddin, PhD

Facutti. Perhutanan

Index Keluasan Dam (LA) mengukur jumlah dam per unit keluasan permukaan atas

tanah. Ia merupakan pengerak pembolehubah biofuikal maka adalah input parameter

yang penting bagi model seperti hidrologi, ekologi dan model-model iklim. Kajian ini

merupakan pembentukkan model anggaran nilai LA1 dengan menggunakan imej

Landsat TM data. Bagi menganggar LAI, adalah perlu untuk membina kaedah untuk

menepati nilainya. Penggunaan vegetation indices yang dipolehi dari data

penderiaan jauh dianggap sebagai cam untuk menganggar LA1 secara tepat. Oleh itu,

satu kajian telah dijalankan bagi menganngar LAI menggunakan Normalised

DzFerence Vegetation Index (NDVI) dari data Landsat TM. Objektif kajian ini adalah

bertujuan untuk menganggar LA1 di Hutan Simpan Ayer Hitam (HSAH), Puchong

dengan menggunakan NDVI di mana hubungan empirical antara LA1 (NDVI) dan

LAI diukur (LICOR 2000) dibina.

Hutan Simpan Ayer Hitam (HSAH) yang terletak di Puchong, Selangor, Malaysia

telah dipilih untuk kajian hi. Landsat TM telah diproses secara digital dan proses

peninggian dengan menggunakan ERDAS IMAGINE 8.4 yang menghasilkan peta

W A K A A N SULTAN A W J L S A W UYVERglTi WTRA MALAYSIA

NDVI. Berasaskan nilai-nilai NDVI, 12 sampel plot bemkman 20 m x 40 m telah

disediakan untuk mengukur LAI (menggunakan LiCOR 2000) serta enam

pemborehubah dirian iaitu dbh, height, isipadu silara, kepadatan dirian, keluasan

perempang dan intensity cahaya. Pengukuran dianalisis dengan Perisian SPSS bagi

menentukan statistic desknptif bagi setiap plot. Kemudian LAI diplot dengan

rnenggunakan pembolehubah dirian serta nilai NDVI yang didapati terlebih dahuIu

dari imej Landsat TM. Setelah itu, menggunakan model regresi, nilai-nilai NDVI

telah ditukar ke LAI bagi pemetaamya. Ketepatan LAI telah dirujuk b g a n kaedah

Pmbdehbah cii-ciri d- s e e dbh, kehggim, isipabn silara, keluasan

perempang dan intensiti cahaya menunjukkan kolerasi dengan LAI kecuaIi kepadatan

dirian. Kajian ini juga mengZlasi1ka.n korelasi di antara LA. and NDW yang

diperolehi dari Landsat l%4. Hubungan linear yang tinggi antara LAI dan NDW

digambarkan menerusi persamaaan LA1 = 7.0861 xNDVI + 1.0929 dengan R' of 0.81

d e q p tahap siguifikag 0-05- Julat LU yang diperhati (LICOR 2000) a&&& sama

dengan julat k e s e l h LAI darI imej NDVI. Menemi model penganggaraa nilai

LA& pemkm LAI di HSAH bdeh dijalaakan dengan setiap pixel memegang nilai

LAI yang t-iri.

Kajian ini r n e n m j a penderiaan jmh boleh digcmak-an bagi menganggar LAI di

hutan dirian dan mengbasikan peta LAJ[ yang memberi model penganggaran spesifik

LA1 di hutan dirian.

ACKNOWLEDGEMENTS

First of all I would like to express my heartfelt gratitude and appreciation to my

supervisor, Assoc. Prof. Dr Ahmad Ainuddin Bin Nuruddin for his invaluable help,

dedicated efforts, guidance, suggestions and constructive criticisms throughout this

study. I am also very grateful indeed to my two other supervisors, En Ismail Adnan

bin Abdul Malek and En Ibrahim Selamat for their kind assistance, knowledge and

advice. I am also gratefbl to the staff of Photogrammetry Lab; Cik Fauziah Abu

Bakar, whom had helped and advised me in carrying out this project suecessful~y.

Finally, I would like to express my deepest love and gratitude to my father, mother,

brothers, sister and brother in law for their support and encouragement during the

course of this study. Last but not the least, I would like thanks to all my housemates

and fiiends for their moral support and most of all their invaluable fiendship.

I certify that an Examination Committee met on 1 6 ~ May 2005 to conduct the final examination of Wong Zee Yeng on her Master of Science thesis entitled "Application of Remote Sensing Technique to Estimate Leaf Area Index in Ayer Hitam Forest Reserve, Puchong, Selangor, Malaysia" in accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the candidate be awarded the relevant degree. Members of the Examination Committee are as follows:

Capt. Kamaruzaman Jusoff, PhD Professor Faculty of Forestry Universiti Putra Malaysia (Chairman)

Anuar Abdul Rahim, PhD Associate Professor Faculty of Agriculture Universiti Putra Malaysia (Internal Examiner)

Lim Meng Tsai, PhD Associate Professor Faculty of Forestry Universiti Putra Malaysia (Internal Examiner)

Hamirdin Ithin, PhD Professor Faculty of Art and Social Science Universiti Malaya (External Examiner)

School of ~raduateftudies Universiti Putra Malaysia

Date: 2 2 AUG 2005

vii

This thesis submitted to the Senate of University Putra Malaysia has been accepted as fulfillment of the requirement for the degree of Master of Science. The members of the Supervisory Committee are as follows:

AHMAD AINUDDIN BIN NURUDDIN, PhD Associate Professor Faculty of Forestry Universiti Putra Malaysia (Chairman)

ISMAIL ADNAN ABDUL MALEK Lecturer Faculty of Forestry Universiti Putra Malaysia (Member)

IBRAHIM SELAMAT Senior Research Officer Malaysian Centre for Remote Sensing (MACRES) (Member)

AINI IDERIS, PhD Professor/Dean School of Graduate Studies Universiti Puim Malaysia

viii

DECLARATION

I hereby declare that the thesis is based on my original work except for quotations and citations which have been duly acknowledge. I also declare that it has not been previously or concurrently submitted for any other degree at UPM or other institutions.

Wong Zee Yeng

Date: #,/C us7 C

TABLE OF CONTENTS

Page

ABSTRACT ABSTRAK ACKNOWLEDGEMENTS APPROVAL DECLARATION LIST OF TABLES LIST OF FIGURES LIST OF ABBREVIATIONS

CHAPTER

1 INTRoDUmON 1.1 General Review 1.2 Justification of the Study 1.3 Objectives of the Study

LITERATURE REVIEW 2.1 Forest Coverage in Malaysia 2.2 Applications of Remote Sensing 2.3 Remote Sensing in Forestry

2.3.1 Leaf Structure and Spectral Response 2.3.2 Detection of Mature Cover and Senescence

2.4 Leaf Area Index 2.4.1 Deflaitions of Leaf Area Index 2.42 Importance of LAI 2.4.3 Considerations in Determination of LAI 2.4.4 Methods for Determining LA1

2.4.4.1 Ground Based Estimation 2.4.4.2 Remotely Based Estimation

2.5 Vegetation Indices. 2.5.1 Introduction 2.5.2 Factors Affecting Vegetation Indices 2.5.3 Applications of Vegetation Indices

3 MATERIALS AND METHODS 3.1 Description of Study Area 3.2 Types of Data 3.3 Digital Image Processing

3.3.1 Preprocessing 3.3.2 Image Enhancement

3 -32.1 Brightness/Contrasts Enhancement 3 -3.2.2 Band Combination 3 -3.2.3 Vegetation Indices

. . i1

iv vi vii ix xii xiii XV

3.4 Field Measurement 3.4.1 Diameter at breast height 3.4.2 Height 3.4.3 Crown Volume 3.4.4 Stand Density 3.4.5 Basal Area 3.4.6 Light Intensity 3.4.7 Leaf Area Index

3.5 Statistical Analysis 3.6 Unsupervised Classification 3.7 Accuracy Assessment

4 RESULTS AND DISCUSSIONS 4.1 Visual Intepretation of Simulation Images

4.1.1 Image Preprocessing Visualisation 4.1 2 Image Enhancement Visualisation. 4.1 3 NDVI Image

4.2 Field Measurement 4.3 Regression Analysis 4.4 Leaf Area Index Regression Model and Mapping 4.5 Unsupervised Classication Visualisation 4.6 Accuracy Assessment

5 CONCLUSIONS AND RECOMMENDATIONS 5.1 Conclusions 5.2 Recommendations

REFERENCES APPENDICES BIODATA OF THE AUTECOR

LIST OF TABLES

Table Page

2.1 Distribution and extent of natural forests by major forest types in Malaysia, 1995 (million ha) 8

2 2 Previous studies conducted for estimating LAI

2.3 Previous studies estimation of Leaf Area Index (LAI) fiom vegetation indices

4.1 Location (coordinate-x, coordinate-y) corresponding with LA1 value 59

4.2 Stand characteristics in plot A1 to F2 with the size of 0.08 ha each at Compartment 12,14 and 15 in Ayer Hitam Forest Reserve

4.3 Regression equations for estimating LA1 ffom stand parameters

4.4 Summary of stand parameters for simulated Landsat TM images for fixed LAI values of range 2-4

4.5 Mean comparison between parameters fiom ground data and calculated data

4.6 Regression models reported in literature to relate Leaf Area Index (LAI) and NDVI derived fiom Landsat TM data

4.7 Area coverage of LA1 in AHFR, Puchong

4.8 LAI estimates from LICOR 2000 anx d linear regression equation from NDVI

4.9 Error matrix cordhion table of unsupervised classification

xii

LIST OF FIGURES

Figure Page

7 2.1 Forest cover map in Peninsular Malaysia

3.1 Map of natural and physical resource in Ayer Hitam Forest Reserve, Puchong, Selangor

3.2 Landsat TM image year 2001 (pathhow 127/58) of Ayer Hitarn Forest Reserve, Puchong

3.3 Flow chart of procedures involving the digital image processing and field measurement

3.4 Schematic diagram of NDVI model constructed using model maker module in ERDAS Environment

3.5 Twelve sample plots with each contained two subplots on Ayer Hitarn Forest Reserve, Puchong

3.6 Diameter tape

3.7 Suunto clinometer.

The trigonometry of tree heights measurements

Light meter

Sample plot layout and transect lines.

LICOR LAI-2000

Landsat TM image normalisation of Ayer Hitam Forest Reserve, Puchong

Spectral reflectance of four targets of land cover in Landsat TM image

Landsat TM image with (a) 4,3,2 (before) and (b) 4,5,3 (after) band combinations enhancement

Landsat TM image with band combinations enhancement of 4,5, 3 (red, green, blue) where D=developed area, F=forested area, B=bare soil and W=water body

NDVI map for Ayer Hitam Forest Reserve, Puchong

xiii

4.6 Reclassification of NDVI value (ranged from 1 50-209)

4.7 Overlaid image with vector files h m Forestry Department (rivers, contours, roads and boundaries)

4.8 Photographs of the canopy from ground where plot A = NDVI 0.16-0.19, B = NDVI 0.20-0.23, C = NDVI 0.24-0.27, D = NDVI 0.28-0.31, E = NDVI 0.32-0.35, and F =NDVI 0.36-0.40

4.9 Relationship between LA1 with Dbh

4.10 Relationship between LA1 with height

4.1 1 Relationship between LAI with crown volume

4.12 Relationship between LAI with basal area

4.13 Relationship between LAI with light intensity

4.14 Relationship between LAI with NDVI

4.15 Leaf Area Index mapping on Ayer Hitam Forest Reserve, Puchong

4.16 LA1 estimates from LICOR 2000 and NDVI

4.17 Relationship between ground LAI (LICOR 2000) and calculated LA1 (NDW

4.18 Unsupervised classification on Ayer Hitam Forest Reserve, Puchong 85

xiv

LIST OF ABBREVIATIONS

AHFR

DVI

ERDAS

GEM

GPS

IR

Ayer Hitarn Forest Reserve

Difference Vegetation Index

Earth Resource Data Analysis System

Global Environmental Modified Index

Global Positioning System

Inbred

Landsat TM Landsat Thematic

LA1 Leaf Area Index

MACRES Malaysia Centre for Remote Sensing

MSAVI Modifies Soil Adjusted Vegetation Index

NDVI Normalised Difference Vegetation Index

NIR

PVI

R~

RS

Rso

RVI

SAVI

SE

TSAVI

VI

WDVI

Near Iafrared

Perpendicular Vegetation Index

Root Square

Remote Sensing

Rectified Skewed Orthomorphic

Ratio Vegetation Index

Soil Adjusted Vegetation Index

Standard Error

Transformed Soil Adjusted Vegetation Index

Vegetation Index

Weighted Difference Vegetation Index

CHAPTER ONE

INTRODUCTION

General Review

The Leaf Area Index (LAI) provides information on the structure of plant canopy,

showing how much surface area is covered by green foliage relative to total land

surface area. It determines a stand capacity to intercept light and precipitation,

exchange the carbon dioxide with the atmosphere, and store carbohydrates and

nutrients. It is considered as a major variable in ecosystem understanding and a

relevant input to productivity models. Due to the importance of LAI for several

ecological processes, considerable research has focused on developing tools to

measure LAI.

Many methods are available to measure LAI directly and are in variations of either

leaf sampling or litterfall collection technique. These methods tend to be difficult to

be carried out in the field, extremely labour extensive, require expensive with many

replicates to account for spatial variability in the canopy and are thus costly in terms

of time and money. Consequently, many indirect methods of measuring LAI have

been developed such as destructive harvesting and direct determination (Nel and

Wessman, 1993).

Remotely sensed imagery can also be used to estimate LAI and spatial information to

indicate plant biophysical attributes. LAI can be determined remotely, relatively,

cheaply and easily using imagery fiom various satellites, as different vegetation

indices can be applied to transform the imagery into LAI estimation (Kauhan et al.,

1997).

The Normalised Difference Vegetation Index (NDVI) is probably the most

commonly used index for analysing vegetation on a continental scale (Gamon et al.,

1995; Rouse et al., 1973). Such an approach should provide satisfactory estimates of

LAI as this techniques are based on reflectance in the red (R) and near infixed (NIR)

spectral bands which are widely used in airborne and spaceborne remote sensing

studies. This spectral index makes use of radiances or reflectances in R and NIR

spectral regions because they enhance the contrast between soil and vegetation.

Hence, the LAI estimation could be made concurrently in this study by applying the

NDVI.

1.2 Justification of the Study

The AHFR has been intensively logged its forest since the late 1930's to 1960's.

Since then, it was designated as a forest reserve to safeguard the last remaining

lowland forest in Selangor. Nevertheless, at present; the compartments in the AHFR

are either at earlier stage of succession or recovered well fiom its past disturbance,

and therefore, require consistent data fiom forest assessment to understand its

condition.

The assessment of forest characteristics on a regular and systematic was not

institutionalised. Most assessment carried out on ad hoc basis for specific purposes

and with the support of external assistance. Thus, there is no organized system to

collect, analyse and distribute forest resource data.

A proper forest assessment requires ecological information such as primary

productivity which forms the basis for landuse planning and forest activities such as

silvicultural treatment. Nevertheless, the actual productivity and its dynamic of

AHFR are not clearly known. Hence, the role in sequestering the LAI and stand

variables fiom AHFR is believed to be important in understanding its forest growth.

Leaf Area Index is a major variable in ecosystem understanding and a relevant input

to productivity models. Whatever method used, LAI estimation is time consuming

and fairly inaccurate. For long term study sites, LAI has to be determined routinely

and nondestructively. A number of methods can be applied, each with advantages

and disadvantages. Consequently, estimating LAI over large area like AHFR, is

problematic.

Remote sensing techniques, particularly the use of satellite imagery, may offer a

practical indirect means to measure LAI at the landscape scale or even global scale

(Running et al., 1989). With remote sensing techniques, studies have made progress

in developing methods that correlate remotely sensed data with regional estimates of

a number of forest ecosystem variables, which in this study, including LAI, where,

the empirical regression model to predict pixel-based LA1 value for LAI mapping is

applied by using vegetation index as quantitative indicators of vegetation amount.

Vegetation index reduces the multidimensional spectral space of vegetation scene to

one dimension in order to sense variability in such properties as LAI. In this study,

Normalised Difference Vegetation Index, NDVI (ratio between the difference in the

i d b e d and red bands and the sum of bands) is used as it commonly preferred

because it reduces the effects of atmospheric conditions and topographical variations.

This approach is required for the collection and maintenance of geographic data and

information in remote sensing for AHFR The approach provides the ability to access

and readily available and up-to-date sources of forest status assessment for the

collection of LA1 data as well its variables which include diameter at breast height

and crown area, closure and height in this forest.

1 3 Objectives of this Study

Therefore, the main objective of this study is to estimate LAI in Ayer Hitam Forest

Reserve, Puchong while else the specific aims are as follows:

1. To develop statistical relationship between stand characteristics and LAI

2. To estimate LAI in Ayer Hitam Forest Reserve using Normalised Difference

Vegetation Index 0.

3. To develop statistical relationships between LA1 of derived NDVI and

ground measured LAI.

4. To develop LA1 map for Ayer Hitam Forest Reserve.

CHAPTER TWO

LITERATURE REVIEW

2.1 Forest Coverage in Malaysia

The total forested area in Malaysia as at the end of 2001 was estimated to be 18.91

million hectares or 57.5% of the total land area. Of this total, it is estimated that some

16.41 million hectares are the inland dipterocarp forests, with the remaining 1.69,

0.62 and 0.19 million hectares being fresh water swamp, mangrove swamp and

plantation forests, respectively. The distribution and extent of forest areas by major

forest types and regions is as shown in Figure 2.1 and Table 2.1. This amount does

not include agriculture tree crops, which covers 4.8 million hectares. Thus, if

inclusive of the agriculture tree crops, the total tree cover in Malaysia in 2001

amounted to 23.71 million hectares or 72 per cent of the total land area (Anon,

2o0la).

Forest assessment is a procedure for obtaining information on the quantity and

quality of forest resources and in the characteristics of the land areas (Husch et al.,

1982). It deals with the methods of obtaining information on forest resources

estimation of growing stock, growth and health of the forest. That information is a

basis for decisions of the forest industry, the forestry officers and the forest owners

(Torma and Hyyppa, 1999).

Figure 2.1 : Forest cover map in Peninsular Malaysia (Anon)

Table 2.1: Distribution and extent of natural forests by major forest types in

Land Diprerocaip Swamp Mangrove Plantation Total Percentage Region Area Forest Forest Forest Forest Forested Total of

Land Forested Land

Peninsular 13.16 5.38 0.30 0.10 0.07 5.85 44.5 Malaysia Sabah 7.37 3.83 0.19 0.32 0.1 1 4.45 60.4 Sarawak 12.33 7.20 1.20 0.20 0.01 8.61 69.8 Malaysia 32.86 16.41 1.69 0.62 0.19 18.91 57.5

Source: Anon, 2001"

2.2 Applications of Remote Sensing

For the last few years, satellite remote sensing has been used intensively to monitor

fundamental changes in Earth surface properties as a major contribution to studies of

environmental change. Much attention has been directed at detecting major land

cover changes such as deforestation and desertification (Cochrane et al., 1999; Foody

and Boyd, 1999; Lambin, 1 999; Nepstad et al., 1999).

Apart fi-om that, remotely sensed data also has been used for a number of agricultural

applications as well, it includes the determination of yield and dry matter in durum

wheat (Aparicio et al., 2000) measuring the rate of dunun wheat senescence

(Adamsen et al., 1999) and estimation of grain yield for corn (Shanahan et al., 2001).

Another important issue in today's operational remote sensing is to assess the

potentialities of the joint use of multi-sensor satellite imagery to improve the

discrimination between forest and non-forest, between forest types, between crops

and clear cuts and between crop types. In addition, several complementary issues,

9

relative to terrain topography or include studies on detection and monitoring of man-

made infrastructures and natural structures has been also addressed.

Study from Nezry et al. (2000) reported that the operational implementation of new

techniques for the exploitation of RS data (SAR and optical) in the framework of

forestry applications. In particular, it presented a new technique for standing timber

volume estimation. This technique is based on remote sensing knowledge (SAR and

optical synergy) and forestry knowledge (forest structure models) proves fairly

accurate. To illustrate the application of these techniques, an operational commercial

case study regarding forest concessions in Sarawak was done. Validation of this

technique by comparison of the remote sensing results and the database of the

customer has shown that this technique is fairly accurate.

In addition, remote sensing has considerable potential as a source of information on

biodiversity as well. Remote sensing may be able to provide useful information on

biodiversity such as number of species or species richness. In the study by Foody and

Culter (2002), the potential of remote sensing to provide information on species

richness and composition in a tropical forest environment was evaluated in Sabah.

New method with applications of two types of neural network was used to derive

measures of biodiversity from Landsat TM data of tropical rainforest. A feed forward

neural network was used to provide information on species composition. The result

indicated the potential of remote sensing as a source of maps of biodiversity.

Disturbance to forest ecosystem due to drought and fire may affect biodiversity. A

monitoring system to highlight areas of severe drought stress would be advantageous

for the management of threatened ecosystem. Hence, one approach to achieving this