Using Normalized Difference Vegetation Index (NDVI) to Assess Vegetation Cover Change in Mining Areas of Tubay, Agusan del Norte and Claver, Surigao del Norte

A Proposed Thesis by

Kendel P. Bolanio

Submitted to the Engineering Sciences DepartmentCollege of Engineering and Information Technology

Caraga State University

In Partial Fulfilment of the RequirementsFor the Bachelor of Science in Geodetic Engineering

College of Engineering and Information TechnologyCaraga State UniversityAmpayon, Butuan City

September 2014

ii

Table of Contents

Title Page

Table of Contents...............................................................................................................ii

List of Figures...................................................................................................................iv

List of Equations................................................................................................................v

Chapter 1............................................................................................................................6

Introduction........................................................................................................................6

1.1 Statement of the Problem...........................................................................................8

1.2 Objectives of the Study..............................................................................................8

1.3 Significance of the Study...........................................................................................9

1.4 Scope and Limitation of the Study...........................................................................10

Chapter 2..........................................................................................................................11

Review of Related Literature..........................................................................................11

2.1 Vegetation: An Overview........................................................................................11

2.2 Change Detection and Analysis Concepts...............................................................12

2.3 Remote Sensing for Quantifying Vegetation Change..............................................13

2.4 Understanding the NDVI.........................................................................................14

2.5 Related Studies.........................................................................................................15

Chapter 3..........................................................................................................................18

Methodology.....................................................................................................................18

3.1 Site Description........................................................................................................18

3.2 Data Procurement.....................................................................................................21

3.2.1 Landsat Imagery................................................................................................21

3.2.2 Spectral Measurement of Pseudo-Invariant Features........................................22

3.3 Data Processing and Analysis..................................................................................23

3.3.1. Integration of Ground and Remotely-Sensed Measurements through Spectral

Calibration..................................................................................................................23

3.3.2 Normalized Difference Vegetation Index (NDVI) Calculation and NDVI Maps....................................................................................................................................24

iii

3.3.3 Creating Vegetation Cover Change Maps.........................................................24

3.3.4 Mining Tenement Map......................................................................................25

3.4 Methodological Framework.....................................................................................27

Bibliography.....................................................................................................................29

iv

List of Figures

Figure 1. Map Showing the Municipal Boundary of Tubay..............................................20

Figure 1. Map Showing the Municipal Boundary of Claver.............................................21

Figure 2. 2014 Caraga Mining Tenement Control Map....................................................26

Figure 3. Work Flow of the Study.....................................................................................28

v

List of Equations

Equation 1. NDVI Calculation..........................................................................................24

Chapter 1

Introduction

The human impact on the biosphere has altered natural vegetation cover into a

modified one. Over the last two centuries, economic progress and population growth have

produced fast variations to the Earth’s vegetation and there is a probable prediction that

the pace of these changes will accelerate in the posterity.

Studies have shown that there remain only few landscapes on the Earth that is still in

their natural state due to anthropogenic activities1. Generally, land use/land cover pattern

of a particular area is a result of natural and socio-economic factors and their utilization

by man in time and space. Such activities include industry, agriculture, construction,

transportation, deforestation, habitation, and most especially, mining.

Nowadays, mining industry is prevalent anywhere due to its continuing investments

among interested public and private sectors. It supplies materials that are needed to

progress the socio-economic well-being of the people. However, this undertaking

encumbers the agricultural and forested production in which no one can oppose nor repel

its overwhelming momentum.

According to Mercado2, in the basic reality, land resources are limited and finite,

majority under deplorable and unsustainable use. Fast growth of mining operations can

7

also be ascribed as one of the causes of land degradation; hence, it is essential to discern

proper protocols to achieve the sustainable development and ecological protection.

Commonly, valuable mineral ores exist below the surface of the vegetation area and

mining processes are to be conducted which result into depression of the said extent.

Thus, it is indispensable to oversee such alterations on the ground surface.

In this paper, the proponent aims to assess quantitatively the ability of Normalized

Difference Vegetation Index (NDVI) to extract meaningful vegetation abundance

information from Landsat 8 OLI/TIRS data in mining areas of Tubay, Agusan del Norte

and Claver, Surigao del Norte. It also discusses the methodologies necessary to

incorporate ground-based measurements with satellite-based measurements. GIS

technique is applied for generating vegetation cover change maps for spatial analysis.

8

1.1 Statement of the Problem

This study aims to assess the vegetation cover change in mining areas of Tubay,

Agusan del Norte and Claver, Surigao del Norte. Specifically, it attempts to answer the

following questions found relevant in the investigation:

1. How do Normalized Difference Vegetation Index (NDVI) and spectral

measurements become a great help in assessing the vegetation change in mining

areas of Tubay, Agusan del Norte and Claver, Surigao del Norte?

2. What are the techniques involved in merging the ground-based and satellite-based

measurements?

3. From the findings acquired in the study, does the existence of mining activity

yields a great impact towards the change in vegetation of the study sites?

1.2 Objectives of the Study

The general objective of the study is to develop practical techniques for the assessment

of vegetation cover change in mining areas of Tubay, Agusan del Norte and Claver,

Surigao del Norte. This study is also intended to recognize the following specific

objectives:

9

1. To assess the vegetation cover change in mining areas of Tubay, Agusan del

Norte and Claver, Surigao del Norte using Normalized Difference Vegetation

Index (NDVI) and spectral measurements.

2. To find out the techniques in integrating the ground-based and satellite-based

measurements.

3. To assess if the presence of mining activity is related to the vegetation cover

change of the study areas.

1.3 Significance of the Study

This study would be useful to the following individuals and organizations involved as

the basis for sustainable development of its community and environment.

The assessment of vegetation cover change in mining areas will help the local

government of Tubay and Claver to acquire pertinent information with regards to the

existing physical conditions in their locality. Perhaps, this investigation shall convince

them to undertake activities in relation to land management plans, executing the decision-

support mechanism, proper monitoring and taking necessary measures and regulations.

It provides proper evaluation of facts which shows reliable acquisition for the

competent authorities when deciding over investments in dealing mining ventures. This

study provides a spatial vision among the people within the study area so that they will be

10

aware with the changes occurred in their locality and can give prior recommendations

contributing for solving the problem.

This study is in coordination with the ICT Support for Responsible Mining in

Mindanao Project which aims to derive different vegetation indices using multi-spectral

satellite imagery and in-situ spectral measurements data of the different vegetation cover

within and outside key mining areas with different ore type (e.g. gold, nickel, etc.).

Furthermore, this study will be commendable to the teachers and students particularly

in Mining Engineering and Environmental Science Programs since it intertwines with

their field of expertise.

1.4 Scope and Limitation of the Study

This study is limited only on using NDVI to assess vegetation cover change in mining

areas of Tubay, Agusan del Norte and Claver, Surigao del Norte. The proponent aims to

gather Landsat images during the time periods 1995, 2007, and 2014 for the creation of

vegetation cover maps. For the calibration of data, conducting spectral measurement of

pseudo-invariant features find relevant to integrate with the satellite imagery for the

derivation of vegetation indices for the analysis of change detection in vegetation. GIS

technique is employed to come up with vegetation change maps for visual results from

the investigation. Along with, tenement maps are overlaid for the final evaluation of the

study goal.

11

Chapter 2

Review of Related Literature

This chapter presents all the related studies and concepts which gave the proponent the

insights that guided him in the implementation of the proposed study. It also discusses the

written information about a certain subject of interest.

2.1 Vegetation: An Overview

Lexically, vegetation refers to assemblages of plant species and the ground cover they

provide3. It is wider than the term flora which refers to the composition of species. It is

synonymous with the term plant community; but vegetation often refers to a broader

range of spatial scales, including scales as large as the global. Primeval redwood forests,

coastal mangrove stands, sphagnum bogs, desert soil crusts, roadside weed patches,

wheat fields, cultivated gardens and lawns, all of these are encompassed by the word

vegetation4.

In the other context, vegetation is distributed throughout the world in definable

patterns. The parameters controlling these patterns are climatic, physical, and biological

12

and include light, temperature, soil, moisture, wind, competition with other plants,

parasites and disease, plant dispersal and adaptability, and many others. All these factors

interact and hence cannot truly be isolated and measured alone5.

Generally, in maps, vegetation is pictured as a scale, or index of greenness. Greenness

is based on several factors: the number and type of plants, how leafy they are, and how

healthy they are. In places whose foliage is dense and plants are growing quickly, the

index is high, represented in dark green. Regions where few plants grow have a low

vegetation index, shown in tan6.

2.2 Change Detection and Analysis Concepts

Change detection is the process of identifying differences in the state of an object or

phenomenon by observing it at different times78. Timely and accurate change detection

of Earth’s surface features provides the foundation for better understanding relationships

and interactions between human and natural phenomena to better manage and use

resources7.

Vegetation change detection identifies vegetative land cover changes over time.

Majumder 7 stressed that changes in land cover by land use did not necessarily imply

degradation of the land. However, due to a shift in land use patterns, land cover changes

that affects biodiversity, water and other processes that come together to affect climate

and biosphere.

13

Change detection analysis is a versatile tool that can be used in mapping the

differences land cover pattern in many fields like urban area, forests, river, lakes and

mining9. It comprises a wide range of methods used to identify, describe, and quantify

differences between images of the same scene at different times or under different

conditions10.

2.3 Remote Sensing for Quantifying Vegetation Change

As defined by Lillesand11, remote sensing refers to the science and art of obtaining

useful information about an object, area or phenomenon through the analysis of data

acquired by a device that is not in contact with the object, area, or phenomenon under

investigation. Sabins12 stated it as a science of acquiring processing and interpreting

images that record the interaction between electromagnetic energy and matter.

Before the use of satellite imagery and computers land use and land cover maps were

prepared using tracing paper over topographic sheets. Land measurements and surveys

were carried out using measuring tapes, graph sheets, ropes and drawing boards. These

methods were not only tiresome and labor intensive but were also flawed due to human

errors9.

Later aerial photographs were used for the land cover and land use pattern study. The

principal advantage of aerial photographs in vegetation and land-use surveys lies in the

fact that they provide a permanent record of conditions which is available in the office for

detailed analysis13. But they also had there disadvantages like identification of ground

14

features were difficult without landmarks, location and scale were approximate. They

were black and white; hence distinguishing features from one another was difficult9.

But now a days with availability of satellite imagery, that are multispectral (that

acquire image data at specific frequencies across the electromagnetic spectrum), multi-

temporal (images that have been taken over a period of time) and have very high spatial

resolution, change detection studies of inaccessible places have also become much more

accurate, efficient and can be frequently carried out9. Moreover, field observations were

also used for the purpose of sustaining the difficulties encountered in analysing satellite-

based measurements. Ground-based remotely sensed devices are as follows: field

spectroscopy (i.e. ocean optics spectrometer in SpectraSuite Software, and FieldSpec®

spectroradiometer systems) are ideal for rapid collection of spectra in just about any

environment14; RADAR, which stands for Radio Detection and Ranging, emits a signal

in the radio frequency; SODAR, Sound Detection and Ranging Device, emits signal in

the audible spectrum; LIDAR, Light Detection and Ranging, emits signal of visible

wavelengths; and RASS, which conveniently stands for Radio-Acoustic Sounding

System, combines the radar and sodar technologies15.

2.4 Understanding the NDVI

The Normalized Difference Vegetation Index (NDVI) is a numerical indicator that

uses the visible and near-infrared bands of the electromagnetic spectrum, and is adopted

15

to analyze remote sensing measurements and assess whether the target being observed

contains live green vegetation or not16.

NDVI has found a wide application in vegetative studies as it has been used to

estimate crop yields, pasture performance, and rangeland carrying capacities among

others. It is often directly related to other ground parameters such as percent of ground

cover, photosynthetic activity of the plant, surface water, leaf area index and the amount

of biomass17.

Generally, healthy vegetation will absorb most of the visible light that falls on it, and

reflects a large portion of the near-infrared light. Unhealthy or sparse vegetation reflects

more visible light and less near-infrared light. Bare soils on the other hand reflect

moderately in both the red and infrared portion of the electromagnetic spectrum18.

NDVI information can be derived by focusing on the satellite bands that are most

sensitive to vegetation information (near-infrared and red). The bigger the difference

therefore between the near-infrared and the red reflectance, the more vegetation there has

to be. The NDVI algorithm subtracts the red reflectance values from the near-infrared and

divides it by the sum of near-infrared and red bands. (See Chapter 3, Equation 1)

Theoretically, NDVI values are represented as a ratio ranging in value from -1 to 1 but

in practice extreme negative values represent water, values around zero represent bare

soil and values over 6 represent dense green vegetation19.

2.5 Related Studies

16

Several studies about the assessment of vegetative land cover change in mining areas

were conducted in various places with different approaches used.

One of the studies was made by Elmore et al 20 about the precision and accuracy of

two techniques in quantifying vegetation change: Spectral Mixture Analysis (SMA) and

NDVI applied to Landsat TM data, were assessed quantitatively using high-precision in-

situ data. In Owens Valley, California, they conducted 6 years of continuous field data

(1991-1996) for 33 sites acquired concurrently with six cloudless Landsat TM images.

The multitemporal remotely sensed data were coregistered to within 1 pixel,

radiometrically calibrated using temporally invariant surface features, and geolocated to

within 30 meters. Results show that SMA absolute percent live cover (%LC) estimates

are accurate to within ±4.0%LC and estimates of change in live cover have a precision of

±3.8%LC. Furthermore, even when applied to areas of low vegetation cover, the SMA

approach correctly determined the sense of change (i.e., positive or negative) in 87% of

the samples. SMA results are superior to NDVI, which, although correlated with live

cover, is not a quantitative measure and showed the correct sense of change in only 67%

of the samples.

Another study was undertaken by Choudhury 9 in which he employed the remote

sensing and GIS as a tool for change detection and analysis at the huge coal mining in

Talcher Region, Odisha. The land cover changes in Talcher area was analysed for the

three different years – 1973, 1990 and 2009. The Landsat MSS Imagery for 1973 and

LANDSAT TM Imagery for the years 1990 and 2009 were used for the study. Along

with land use and land cover maps, NDVI maps of the area for the given time period have

been created. The results show a tremendous increase in the mines area and settlements.

17

Though the water body has not suffered much change but the forest area has drastically

decreased.

In the thesis duly authorized by Majumder7, he desired to monitor the changes in the

mining activities of the Sukinda Valley which was known for chromite deposit and to

regulate them. His study was therefore undertaken to analyse the land cover and land use

change that has taken place in the said area between 1975 and 2005 using remote sensing

data and GIS by mapping land cover and land use. The land cover and land use study was

conducted by mapping Landsat data of three different years (1975, 1992, 2005) with the

help of ERDAS and Quantum GIS. The result of the work had shown a rapid growth in

mining between 1975 and 2005 at the cost of decrease in forest area.

18

Chapter 3

Methodology

This chapter presents the study area, data procurement, data processing and analysis,

and methodological framework which will be employed in conducting the study. The

systematic processes on how the researcher established this study were further explained

in this chapter.

3.1 Site Description

The study will be conducted in the Municipality of Tubay, Province of Agusan del

Norte and Claver, Province of Surigao del Norte, Philippines.

Tubay, a fourth class municipality, is located between 09°10′N   and 125°32′E. It

covers an area of 138.09 km 2 (53.32 mi 2 ). According to 2010 census, Tubay has a total

population of 20,426 people 21 , in which about half of the population lives in urban areas

and the remaining half in rural areas. In terms of land cover type, the eastern part of the

place constitutes most of vegetation areas (i.e. agricultural and forested areas) while the

western part comprises with built-up areas and mining areas wh ich are near to the Butuan

19

Bay. Mine companies situated in Tubay were the Mindoro Resources Ltd. (MRL), Alston

Mining Company, and San Roque Metals Inc. (SRMI), all of which were known typically

for gold and nickel mining operations.

Claver, a fourth class municipality, is located between 09°34′N   and 125°44′E. It

covers a total land area of 322.60 km 2 (124.56 mi 2 ), the largest municipality of the

province. According to 2010 census, Claver has a total population of 23,702 people 22 .

In terms of its location boundary, in the northeast, it is bounded by the Pacific Ocean, to

the west by the town of Gigaquit, and the south by the adjoining town of Carrascal,

Surigao del Sur. It is approximately 60 kilometers (37 mi.) southeast of the provincial

capital, Surigao City.

Economically, Claver is mainly a town of trading, farming, fishing, and mining in

which the latter is the major source of livelihood among residents due to large iron

deposits on its mountains. The well-known mining companies situated in the area are the

Platinum Group Metals Corporation (PGMC), Adnama Mining Resources Inc. (AMRI),

Tag-anito High-Pressure Acid Leaching (THPAL-Sumitomo), and Taganito Mining

Corporation (TMC).

20

Figure 1. Map Showing the Municipal Boundary of Tubay

21

Figure 2. Map Showing the Municipal Boundary of Claver

3.2 Data Procurement

3.2.1 Landsat Imagery

Eventually, there were only limited resources of free satellite imagery to acquire.

However, as cited in the previous chapter, the proponent aims to collect satellite images

at the time periods 1995, 2007, and 2014 using Landsat 8 OLI (Operational Land Imager)

22

and TIRS (Thermal Infrared Sensor) since several literatures utilized this type of

imagery. Then all satellite images obtained will undergo coregistration, spectral

calibration of pseudo-invariant features, and georeferencing. These will be employed to

produce vegetation cover change maps in the study site and also for the validation of data

acquired from field measurement.

3.2.2 Spectral Measurement of Pseudo-Invariant Features

In order to derive with vegetation index, conducting spectral measurement of pseudo-

invariant features is deemed appropriate. This assumes that the spectral properties of

pseudo-invariant objects do not change significantly over time23. Their spectral values

are used to normalize scenes to a base image using linear regression techniques. The

examples of these objects are roads, buildings, bridges, and the like. This approach has to

be identified manually by locating predetermined sampling points through Global

Positioning System (GPS). After locating the GPS points, spectral measurements are to

be made by employing USB4000 Fiber Optics Spectrometer, Reflection Probe, and

SpectraSuite Software Application.

23

3.3 Data Processing and Analysis

3.3.1. Integration of Ground and Remotely-Sensed Measurements through Spectral

Calibration

After gathering the necessary data, the next itinerary of working out the study is to

integrate the field observations and satellite-based measurements through the process of

spectral calibration.

It is critical that the radiometric response of a given scene is analogous to that of the

other scene. However, there are strategies available to achieve a common multitemporal

spectral response. The proponent applies the technique of pseudo-invariant features as

stated from the preceding subtopic. This time, each image is calibrated from the spectral

signatures procured from the fieldwork. Afterwards, the expected outcome is to generate

a calibrated result of each image which can be utilized later on NDVI computation as

explained in the succeeding method of this paper.

Furthermore, it is significant to contemplate the graphs in every bands of a scene; that

is, each band from each image is to be regressed against the corresponding band from the

selected base image, a line is fit to the points, and all images are to be adjusted to have

the same spectral response with the given base dataset. The preference in choosing the

base dataset, according to Elmore et al20, is that it must be cloudless and temporally near

the middle of the total dataset.

24

3.3.2 Normalized Difference Vegetation Index (NDVI) Calculation and NDVI Maps

After collecting the spectral values from the ground-based measurements, Normalized

Difference Vegetation Index (NDVI) is a simple numerical indicator that can be used to

measure the vegetation change. NDVI is calculated by the equation:

NDVI = (NIR – RED) / (NIR + RED)

Equation 1. NDVI Calculation

where NIR and RED stand for the spectral measurements acquired in the near infrared

and visible red regions24.

Subsequently, NDVI maps shall be generated with NDVI values ranging from -1 to 1.

3.3.3 Creating Vegetation Cover Change Maps

The vegetation cover change maps of mining areas in Tubay, Agusan del Norte and

Claver, Surigao del Norte will be created using Geographic Information System (GIS) for

spatial assessment. The softwares to be used are the ENVI 5 for visual interpretation

which is subject to classification based on the alterations of a specified landscape cover

due to mining operations and ArcGIS 10.1 for the digitization of areas categorized

through comprehensive classification and calculating its area.

25

3.3.4 Mining Tenement Map

Generically, mining tenement is a permit, claim, license or lease that may be granted

by a Mining Registrar, the Minister of Mines and Energy, or by the Governor in Council

25. A permit, claim, license or lease may authorize prospecting, exploration, mining,

processing or transport of minerals and may be applied by an eligible person.

In the country, tenement maps were generated based from the existing tenement type

code in a particular region. In Figure 3, the illustration entails the Mining Tenements

Control Map of Caraga, as of May 2014. In the said region, the lists of approved and

applied mining rights are the following: Mineral Production Sharing Agreement (MPSA),

Application for Production Sharing Agreement (APSA), Exploration Permit (EP), and

Exploration Permit Application (EXPA).

Directing to the study area, under MPSA are North Dinagat Mineral Resources

Corporation, Taganito Mining Corporation, Adnama Mining Resources Inc., Ludgoron

Mining Corporation, Kepha Mining Exploration Company, Claver Mineral Development

Corporation, C.T.P. Construction and Mining Corporation, and Case Construction and

Development Corporation; for APSA is the Real Mining Corporation; for EP is the

Oriental Synergy Mining Corporation; and the holder for EXPA are Joselito R. Carino,

Goldamier Planeta M. Millare, and Rapid City Realty and Development Corporation.

26

Figure 3. 2014 Caraga Mining Tenement Control Map

27

3.4 Methodological Framework

This paper is sought to create an assessment of vegetation cover change in mining

areas of Tubay, Agusan del Norte and Claver, Surigao del Norte. The diagram in Figure 4

shows the systematic processes of undertaking the study. The phase represented in the

figure starts with Data Gathering, explicitly the fieldwork and remotely sensed data and

terminates with the mathematical analysis result from vegetation change maps and the

overlaid tenement maps for comparative assessment which serve as outputs of the study.

28

Figure 4. Work Flow of the Study

29

Bibliography

[1] A. O. Zubari, Change Detection in Land Use and Land Cover Using Remote Sensing and GIS (A case study of Ilorin and its environs in Kwara State). 2006.

[2] E. Mercado, Framework for a Sustainable National Land Use Policy in the Philippines. 2011.

[3] C. J. Burrows, Processes of Vegetation Change, U. Hyman, Ed. London.

[4] en.wikipedia.org/wiki/Vegetation.

[5] E. E. Rosenblum, Grolier International Encyclopedia. Danbury, Connecticut, United States of America: Grolier Incorporated, 1997.

[6] http://earthobservatory.nasa.gov/GlobalMaps/view.php?d1=MOD13A2_M_NDVI.

[7] B. Majumder, Land Use and Land Cover Change Detection Study at Sukinda Valley Using Remote Sensing and GIS. 2010-2011.

[8] N.P. Singh, T.K. Mukherjee, and B.B.P. Shrivastava , "Monitoring The Impact of Coal Mining and Thermal Power Industry on Land Use Pattern in and Around Singrauli Coalfield Using Remote Sensing Data and GIS," Journal of the Indian Society of Remote Sensing, vol. xxv, pp. 61-72, 1997.

[9] S. Choudhury, Change Detection Analysis of Talcher Coalfield Using Remote Sensing and GIS. 2010-2011.

[10] http://www.exelisvis.com/docs/ChangeDetectionAnalysis.html.

[11] T. M. Lillesand and W. K. Ralph, Remote Sensing and Image Interpretation. John Wiley and Sons Inc., 1979.

[12] F. F. Sabins, Remote Sensing - Principles and Interpretation, 3rd ed., W. H. Freeman, Ed. New York, 1997.

[13] S. H. Spurr. Aerial Photography. [Online]. http://www.fao.org/docrep/x5345e/x5345e04.htm

[14] Field Spectrometry. [Online]. http://www.asdi.com/applications/remote-sensing/field-spectroscopy-and-field-spectroradiometry-research

[15] (1996) Remote Sensing Techniques. [Online]. http://san.hufs.ac.kr/~gwlee/session3/remote.html

[16] Understanding the Normalized Difference Vegetation Index. [Online].

30

http://www.fsnau.org/downloads/Understanding_the_Normalized_Vegetation_Index_NDVI.pdf

[17] J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring Vegetation Systems in the Great Plains with ERTS," in Third ERTS Symposium, vol. I, 1973, pp. 309-317.

[18] A. M. Holme, D. G. Burnside, and A. A. Mitchell, "The Development of a System for Monitoring Trend in Range Condition in the Arid Shrublands of Western Australia," Australian Rangeland Journal, no. 9, pp. 14-20, 1987.

[19] M. Roderick, R. C. G. Smith, and G. Ludwick, "Calibrating Long Term AVHRR-Derived NDVI Imagery," Remote Sensing and Environment, no. 58, pp. 1-12, 1996.

[20] A. J. Elmore, J. F. Mustard, S. J. Manning, and D. B. Lobell, "Quantifying Vegetation Change in Semiarid Environments: Precision and Accuracy of Spectral Mixture Analysis and the Normalized Difference Vegetation Index," Remote Sensing and Environment, vol. 73, no. 1, pp. 87-102, Jul. 2000.

[21] [Online]. http://en.wikipedia.org/wiki/Tubay,_Agusan_del_Norte

[22] [Online]. http://en.wikipedia.org/wiki/Claver,_Surigao_del_Norte.

[23] C. Huang, et al., "At-Satellite Reflectance: A First Order Normalization of Landsat 7 ETM+ Images," USGS.

[24] Dingtian Yang and Chaoyu Yang, Detection of Seagrass Distribution Changes from 1991 to 2006 in Xincun Bay, Hainan, with Satellite Remote Sensing, p. 835, Feb. 2009.

[25] (2008, Aug.) Mineral Development Licenses.