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APPLICATION OF DATA MODELING

In natural resources and forest management

Yujia Zhang and Bruce E. Borders

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INTRODUCTION

A database is a collection of

related data. In forestry, Data

are typically stored in electronic

files with additional information

stored on field tally sheets.

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During data collection, data

formats may be changed, or files

may be revised by users

individually without informing

others, which causes problems in

data storage and analysis.

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The traditional data storage

approach results in redundancy

in data storage. For example,

stand information stored in a

tree level file takes large

storage space and increases

storage cost.

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Relational database management

systems (RDBMS) provide a

powerful tool to store and update

forest data, review relationships

among individual components,

and model forest dynamics.

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Data modeling is a conceptual or

logical design of a database. A data

model is a set of concepts that

refers to types, relations,

constraints, and operations of data.

DATA MODELING

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The basic operations of a data

model include accessing and

updating the database. We use

an entity-relation (ER) data model

for conceptual analysis and

implementation of database.

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The concepts involved in the ER

model are entities, attributes,

and relationships. An entity is

an object, attributes are

descriptions of the properties of

the entity, and relationships are

interactions among entities.

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Data are stored in tables. A row in a

table is called a tuple, a column

header is called an attribute, and a

table is called a relation. The data

illustrated here are from the

Consortium for Accelerated Pine

Plantation Studies (CAPPS).

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The CAPPS plantations were

established in 1987. The applied

silvicultural treatments are

herbicide (H), fertilization (F),

herbicide and fertilization (HF),

and control (C).

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A relation schema R of degree n

can be denoted as:

R (A1, A2, … , An)

where R is the name of the

relation and A1, A2, … , An are

attributes.

RELATION SCHEMA

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The relation schemas in our data model are:

STAND (PlOT_ID, Location, Block, Plot, FirstGrowingSeason,

Treatment);

TREE (ID, PlantationAge, TreeNumber, DBH, Height, CrownHeight, CronartiumQuartileCode, TipMothCode, DamageCode, Plot_ID);

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GROUNDCOVER (ID, SubPlot, PlantationAge,

PercentAndropogon, HeightAndropogon, PercentGrass, HeightGrass, PercentBroadLeaf, HeightBroadLeaf, Plot_ID)

SMALLCOMPETITOR (ID, SubPlot, PlantationAge, Species, TreeHeight, CrownLength, CrownWidth, Plot_ID)

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LARGECOMPETITOR (ID, Subplot, PlantationAge, Species, TreeHeight, DBH, CrownHeight, BaseHeight, Plot_ID));

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A relation is a set of tuples. An attribute with distinct value can be used as a primary key to identify a tuple. The value of the primary key must not be null, which is called the entity integrity constraint.

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A foreign key is needed to maintain the consistency among relations. For example, tree level information and stand level information can be combined together using a foreign key, PLOT_ID.

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The operations for a relational

database include select, project,

and join. The notation for operation

select is:

<selection condition>(<relation name>)

To select trees with DBH larger than

10 cm, we use:

DBH>10(TREE)

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Operation project selects certain

columns from a table:

<attribute list>(<relation name>)

To list tree number, plantation

age, and tree height, we use:

TreeNumber, PlantationAge, Height(TREE)

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Operation join combines two

tuples from two relations

together:

R||<join condition>S

To access all trees from location

Athens, we use:

(TREE)|| LOCATION=ATH (STAND)

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The RDBMS installed on the server is Oracle8 Enterprise. Each user can access the database over a network. Data files used by each user can be stored either in the server or PC, or in other external devices.

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The implementation of a data model includes establishing relations and queries. The following query is used to obtain some stand level information from relations STAND and TREE.

DATA MODEL IMPLEMENTATION

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Assuming a forester wants to

know the number of trees per

plot, average DBH and average

height for each age in stands

located in Athens that have

accepted herbicide and

fertilization, the regarding query

is:

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select s.Location, s.Block, s.Plot, s.FirstGrowingSeason, s.Treatment, t.PlantationAge, count(t.TreeNumber), avg(t.DBH), avg(t.Height) fromSTAND s,TREE twheres.Plot_ID = t.Plot_ID and s.Location = 'ATH'and s.Treatment = 'HF'group by s.Location, s.Block, s.Plot, t.PlantationAge;

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THE OUTPUT PRODUCED IS:==============================================================================

LOC BLK PLT FGS TRT AGE TREE DBH(cm) HT(m)ATH 1 1 89 HF 1 81 0.0 0.7ATH 1 1 89 HF 2 81 0.0 0.2ATH 1 1 89 HF 3 81 5.3 3.9ATH 1 1 89 HF 4 81 8.9 5.2ATH 1 1 89 HF 5 81 10.7 7.0ATH 1 1 89 HF 6 80 13.0 8.3ATH 1 1 89 HF 7 78 14.7 9.8ATH 1 1 89 HF 8 78 15.7 11.5ATH 1 1 89 HF 9 77 17.0 13.0ATH 1 1 89 HF 10 75 18.0 14.3…………………………………………………………………………………………

=============================================================================

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where LOC = Location, BLK = Block, PLT= Plot, FGS = First Growing Season, TRT= Treatment, AGE = Plantation Age,TREE = Tree/plot, DBH = AverageDiameter at Breast Height/plot (cm), andHT = Average Total Tree Height/plot (m).

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Forest data are characterized with large size of records, complicated relationships, and a diversity of data types. The traditional approach is far from satisfactory for data storage and manipulation.

SUMMARY

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In our database, The relations

among data files eliminate

redundancy in data storage. All

files are stored in a server, which

ensures updated data available

for each user.

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For data safety, the redundant hard disks in the server store the mirrored data that can be recovered when the server is down. Also, Oracle8 backup manager can backup the whole database to external storage devices.

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Users can be granted privileges at different levels by the database administrator (DBA) to view, revise, and transfer files. The database is protected from unauthorized access.

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Data modeling helps establish a comprehensive database including tree, soil, hydrology, GIS, and wildlife data, which facilitates natural resources and forest management.