SITE SPECIES MATCHING FOR COMMERCIAL FORESTRY

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SITE SPECIES MATCHING FOR COMMERCIAL FORESTRY

S OFTWOOD AND H ARDWOOD S PECIES MOST S UITABLE FOR THE RANGE OF S ITE C ONDITIONS IN THE N ORTH E ASTERN C APE OF

S OUTH A FRICA

Talita Cumi van Zyl 16 October 2012

Submitted in part fulfilment of the requirements for the degree of UNIGIS MSc – Master of Science in Geographic Information Science and Systems

Centre for GeoInformatics (ZGIS) Salzburg University Sub-Saharan Africa Office

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SCIENCE PLEDGE

This is to certify that the research and this report is entirely my own work and not of any other person. I have cited all sources used, specified their origin, and acknowledged all published and unpublished sources. The work has not previously been submitted in any form to any University or other institution for assessment for any other purpose.

Name: ____________________________________________

Signature: _________________________________________

Place: ____________________________________________

Date:_____________________________________________

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ABSTRACT

Site species matching is a tool applied by many industries, including agriculture, conservation and forestry, with the purpose of improving productivity. Although much research has been done on the topic of Site Classification and Evaluation, each forestry company has its own soil classification done and develop its own site

evaluation system, suitable for integration into their own GIS milieu. Several research studies have been done in the study area, but no spatial dataset for site classification has been developed. In order to utilise the results of previous studies they have been analysed and incorporated into this study to create a spatial dataset.

The objective for this study is to create a decision-support tool that will enable the user to determine which softwood and hardwood species are most suitable for the range of site conditions in the forestry plantations of North East Cape Forests (NECF). With this tool, suitable sites can be selected to ensure a stipulated mix of different species to supply the recently constructed particle board plant at Ugie.

Once the main criteria were identified, namely Climate, Soils and Topography, these data sets were acquired and their origin and structure were studied from the metadata available. A site classification and evaluation database model was designed and created, which will form the basis to which other applications can be added. Species suitability tables were created from existing site classification reports, which were adjusted by incorporating risk factors.

Classification of the datasets was done based on methods applied in the industry by respective institutions, the climate was classified according to standards set by the Institute of Commercial Forestry and Research, and the soils according to the Soil Classification and Taxonomic System for South Africa, developed by the Soil

Classification Working Group. Evaluation of the classified layers for the purpose of site species matching was created from suitability tables created by the Institute of

Commercial Forestry Research (ICFR) and adjusted by C.W. Smith for the summer rainfall regions of South Africa. The climate classifications and evaluations were further refined according to site specific research reports.

The results of the site species matching were tested by comparing suggested suitability and actual productivity, using the enumeration data available for the study area.

Selections of the compartment data were made where high and low productivity was reported, and compared with the corresponding site suitability. It was found that there were proposed suitable sites where low productivity occurred, while some proposed sub-optimal sites experienced high productivity.

It was concluded that some risk adjustments made to the suitability model may be too general, and should be removed. It was also concluded that other risk factors need to

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be mapped more attentively, while factors such as soil nutrients should be tested and land preparation practices should be documented for each site to have as much information as possible for improving the suitability study.

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ACKNOWLEDGEMENTS

I wish to acknowledge the following persons whose support was essential in completing this thesis:

PG Bison (NECF), who allowed me to use the development of their spatial site classification system as the basis for this study, and for access to the respective datasets and research reports available. Special thanks to Andre Barnard and Pieter de Wet who made this possible; and to Ilse Botman, Dawid Malan and Johan Vermaak, whose inputs were essential in testing the models developed.

Colin Smith of Paperbark Forestry Consultancy, whose Site Classification system report was an essential source of information, and for his assistance.

ICFR, for the use of their bulletin reports, another key source of reliable information during the creation of the datasets. Special thanks to Dr Ilaria Germizhuizen for providing the climatic suitability maps used during testing the new climate models.

The UNIGIS staff, Ann Olivier, of the sub Saharan UNIGIS campus Graaff Reinete, who always responded promptly to my many questions, and continuous motivation towards completing the degree.

My parents, who motivated and supported me during the process - my mother for proof reading, and my dad for his valuable help with understanding the soil data.

Berna Gerber, my valued friend who was always only a phone call away, with inspiration and advice throughout the three years.

To my husband, Theo van Zyl: Thank you for your continued support during the course of this degree

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Figure 4: An example of the ICFR code to be used for each classification unit (Smith et al, 03/2005) 8 Figure 5: System structure for a site classification and evaluation system 9 Figure 6: Conceptual model for data processing and the output variables 24 Figure 7: Weather stations used by DAE in their model calculation 30

Figure 8: TIN_DTM Model of the study area 33

Figure 9: Process model for creating topography and climate layers 34 Figure 10: Concept model for the Classification of climate layers 35 Figure 11: Climate data for study area created with (MAP equation5) 36 Figure 12: Climate data for study area created with (MAP equation4) 36

Figure 13: Slope data for study area 38

Figure 14: Aspect data for study area 38

Figure 15: Vector and raster risk layers created for the study area 40 Figure 16: Extraction of the ‘Tabulate area’ results table of soil class per compartment 56 Figure 17: Example of Pivot Excel table summarizing the soil classes per compartments 56

Figure 18: Location of example area 58

Figure 19: Slope classification layer 59

Figure 20: Aspect classification layer 59

Figure 21 (left): Compartment data as Zone layer and Climate data5 (using equation 5 when calculating

(MAP) as Class layer 60

Figure 22 (right): Compartment data as Zone layer and Climate data4 (using equation 4 when calculating

(MAP) as Class layer 60

Figure 23 (left): Compartment data as Zone layer and Soil raster data as Class layer 61

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Figure 24 (right): Compartment data as Zone layer and Soil vector data as Class layer 61

Figure 25: Slope class majority per compartment 62

Figure 26: Slope class majority per polygon 62

Figure 27: Aspect class majority per compartment 63

Figure 28: Aspect class majority per polygon 63

Figure 29: Climate5 class majority per compartment 64

Figure 30: Climate4 class majority per compartment 64

Figure 31: Soil class majority per compartment 65

Figure 32: Soil class majority per polygon 65

Figure 33:Comparing Climate5 and Climate4 model, determines differences and frequencies 66 Figure 34 (left): Climate 5 for commercial areas before generalization 67 Figure 35 (right): Climate 4 for commercial areas before generalization 67 Figure 36 (left): Climate 5 for commercial areas after generalization 67 Figure 37 (right): Climate 4 for commercial areas after generalization 67

Figure 38: Soil class frequency within the commercial study area 68

Figure 39: Comparing SSCES layers (Climate5 and Climate4), to determine which site combinations occur

most frequent in the study area. 69

Figure 40: Comparison between micro climate model on the left and a macro climate model on the right

(P.pat) 73

Figure 41: Comparison between micro climate model on the left and a macro climate model on the right

(E.nit) 74

List of Tables

Table 1: Illustration of climatic classification levels 2 and 3 of the hierarchical system of the ICFR ... 7

Table 2: Extraction from Herbert’s Table 1 Minimum MAP requirements per Effective Precipitation as influenced by MAT (Herbert, 1998) ... 13

Table 3: Growth days and temperature for Ugie ... 14

Table 4: Soils and described by the Soil Classification Working Group as implemented by the FSD ... 18

Table 5: Example of ground strength classification according with data from FSD dataset (Smith, 2010) 20 Table 6: An example of ground roughness classification according to the FSD dataset ... 20

Table 7: List of Soil Properties that will be used in the System and their Descriptions ... 26

Table 8: Soil Attributes in the FSD and their Class Values (Smith, 2010) ... 27

Table 9: Mean Annual Temperatures as Calculated by DAE) ... 29

Table 10: Classification values for the Climate data ... 35

Table 11: Classification values for slope and aspect ... 37

Table 12: Summary of Optimum Climatic and Soil Critria for Commercial Forestry in Northeastern Cape ... 42

Table 13: Species Tolerance to climatic risk factors (Smith, 2010; Zwolinski, 1998)... 45

Table 14: Link table: Climate_key and Suitability per species ... 45

Table 15: Species climatical suitability adjusted according to site specific criteria... 46

Table 16: Link table: Soil_key and suitability per species ... 47

Table 17: Species suitability ranking according to “Optimal” occurrence in most climate classes ... 51

Table 18: Species suitability ranking according to “Optimal” occurrence in most soil classes ... 52

Table 19: Species Tolerance to climatic risk factors (Smith, 2010; Zwolinski, 1998)... 53

Table 20: Ranking Pines and Eucalyptus most flexible in the range of climatic criteria. ... 54

Table 21: Ranking Pines and Eucalyptus from most flexible in the range of soil criteria. ... 54

Table 22: The selection of climate classes that are found most in the study area... 70

Table 23: The selection of soil classes that are found most in the study area ... 70

Table 24: The results of the low productivity analyses ... 71

Table 25: The results of the high productivity analysis ... 72

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ABBREVIATIONS

CDSM CSIR

Chief Directorate Surveys and Mapping

Council for Scientific and Industrial Research, South Africa

DTM Digital Terrain Model

DAE Department of Agricultural Engineering at the University of Natal

DEM Digital elevation model

DSM Digital surface model

Ep Evaporation potential

ER Entity Relationship

ERD Effective rooting depth

FSD Forestry Soils Database

GD Growth days

GDM Growth days per month

GDY Growth days per year

GIS Geographic Information System

GT Growth temperature

ICFR Institute of Commercial Forestry Research IE

LCI

Information Engineering Laing’s climate index

MAP Mean annual precipitation

MAT Mean annual temperature

MMT Mean monthly temperature

NECF North East Cape Forests, South Africa SCES Site Classification and Evaluation System

SSCES Spatial Site Classification and Evaluation System TIN Triangular irregular network

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CHAPTER 1 – INTRODUCTION

Forestry, as all other businesses, has an end product. Among the various forestry companies in South Africa the products range between saw timber, pulpwood and particle chipboard. This study focuses on particle chipboard. PG Bison purchased North East Cape Forests (NECF) “with a view to supply the recently constructed particle chipboard board plant in Ugie” (Smith, 2010).

In order to achieve the desired product in their board mill, the company requires a stipulated mix of different species. To achieve this will require replanting areas in their forestry estate with particular species better suited to the prevailing site conditions. This will also have the effect of improving plantation productivity (Smith, 2010).

The task for this study was to determine which softwood and hardwood species are most suitable for the range of site conditions at the forestry plantation in the North Eastern Cape of South Africa.

The study area is in the northern part of the Eastern Cape of South Africa, approximately 60km North west from the City of Umtata and surrounds the local towns Ugie, Maclear and Elliot. While the total study area is approximately 70 000 ha, only 30 000 ha is considered for commercial forestry.

Figure 1: Study area

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In order to determine the exact site species matching, a site classification and evaluation system (SCES) was needed. While a site classification document has been compiled in 2010, the application of the recommendations and guidelines needed to be implemented into a Geographic Information System (GIS).

During the literature review, the relevant criteria for a spatial site classification and evaluation system were identified. The datasets needed as inputs for this system were identified and acquired or created.

A suitable data structure was designed and the datasets created and modified according to the design. The input data layers were overlaid, the site classification queries applied and the different site types assigned. Overlaying all the relevant datasets provided a spatial layer that can be used to define homogenous sites. A selection of preferred species was made and their site requirements sourced.

Species can be assigned to these sites based on their individual requirements, showing the optimum location of a range of hardwoods and softwoods.

This Spatial Site Classification and Evaluation System (SSCES) dataset will enable the company to distinguish between those sites better suited for soft wood and those better suited for hardwood species.

A complete site classification and evaluation system includes information to be used for harvesting and other land preparation functions (Strydom, 2000). Although these will not be discussed in detail in this study, the database will be designed to accommodate existing information on these functions. Productivity, growth models and yield prediction will not be discussed in detail.

This study integrated the recommendations and guidelines of previous research reports, project documentation and other literature to assist in the design of a GIS dataset. The design of the dataset was aimed to ensure that the new integrated GIS dataset remains usable and editable, and can easily be applied by future users.

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CHAPTER 2 – LITERATURE STUDY

The literature overview includes different writings on site species matching, site classification and evaluation systems; and its inputs, namely: soil, climate and topography. In this review Site is used synonymously for Land.

The importance of site species matching is stated as “...one of the most important decisions influencing the success of a forestry enterprise.” (Theron, 2000)p79). He identifies the following important site relevant criteria: “mean annual precipitation (MAP), mean annual temperaure (MAT), occurance of frost and soil depth.” He refers to single species plantations which suffer the risk of stress due to ill suited species.

Risk factors such as hail, drought, frost and snow can be managed through species choice. This is confirmed by the (Smith, Pallett, Kunz, & Gardner, 03/2005), whose study showed that optimum growth range for all species are closely related to MAT and MAP. In their opinion, this is to be expected, since MAT relates to three principle risks factors, namely: frost , snow and disease and MAP relates to drought risk.

The site-relevant criteria as stipulated above, is usually stored in a site classification and evaluation system (SCES). According to Ellis (2000), the term site classification and evaluation are often used incorrectly between different disciplines. Data stored during site classification is used in site evaluation studies (Strydom, 2000). In this study, Louw’s explanation of these terms (1995) will be used. (see operational classification systems in 2.2 below).

The development of such a SCESsystem, is therefore imperitive. The design of these systems differ between institutions and although their is no national implemented system, various studies have investigated the different methods applied in the industry e.g. Louw (1995) and Strydom (2000)

2.1 Site Classification and Evaluation Systems

2.1.1 USES

Land classification and evaluation plays an important role in any decision support system, as it organizes the relevant data into an effective framework, useful at the operational, managerial and strategic levels. The decreasing availability of land for afforrestation in South Africa increases the need of productivity per unit area (Louw, 1995).

Site classification can be done either for strategic purposes, which is on a small scale (regional) level, or refined to a larger scale for operational purpose (Louw, 1995).

Although the aim of this study is to apply these studies and resources to develop an

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operational Spatial Site Classification and Evaluation System (SSCES), some strategic level representations will be briefly discussed.

2.1.2 STRATEGIC LEVEL

The classification of forest land at strategic level proved to be an efficient way to supply basic data for evaluating large areas where little information is available. Where a multifactor approach was followed, integrating i.e. parent material, topography, climate and soils, the system proved to be more effective (Louw, 1995).

The two following examples give a brief overview of the different hierarchical levels of classification:

Example 1: Louw (1995) presented a review of a hierarchical approach of

classifications as demonstrated by the South African agricultural industry Figure 2.

Figure 2: Illustration of South African land classification system as described by MacVicar et al (1974).

Land systems are grouped areas on the basis of macro climate, terrain form and soil pattern. They are devided on boundaries where these sites show lesser uniformity between them, i.e. the climate, landuse application and natural vegetation will differ between these systems. (Louw, 1995).

Land type refers to areas where macro climate, terrain form, and soil pattern shows a marked degree of uniformity. The grouping is done on a country-wide scope , and the

Ecotype Scale 1:500 000

Land Type Land System

Scale 1:250 000

Scale 1:20 000 and larger

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sub division stopped where little advantage will be gained by defining the areas in smaller groups. One land type will differ from another in terms of one or more of the three criteria, macro climate, terrain form and soil pattern. (Louw, 1995).

Ecotopes are areas which share macro climate but are then further sub-divided with the use of aspect, soil and soil surface (slope).Ecotopes will differ in production, yield, and species (Louw, 1995).

Example 2: Another hierarchical approach to the classification of land systems for Forestry was created and implemented by the ICFR (Smith et al, 2005/03), Figure 3:

Figure 3: Representation of the hierarchical forestry site classification for the summer rainfall region of southern Africa (Smith et al, 2005/03)

Forest economic zones group magisterial districts, based on political (provincial) boundaries, climate and soil and silvicultural and economic considerations. South Africa is divided into 11 zones. This level is useful in identifying planting windows and growing seasons (Smith et al, 2005/03).

Climate, mean annual temperature (MAT) zones for the summer rainfall region, is divided into three broad climatic zones based on (MAT). Site characteristics that are affected are evaporation demand, radiation, heat units, and min/max temperatures.

Cool Temperate (CT<16°C), Warm Temperate (WT 16 – 19°C) and Sub-Tropical (ST 20 – 22°C). Classification was done mainly based climatic risk factors: snow, frost and frost-free. The three broad zones were each sub-divided into three (1°C) temperature

Level 1 Forest Economic

Zone

Level 2 Climatic Zones

(MAT)

Level 3 Climatic Zones

(MAP)

Level 4 Geological Groupings

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zones. This level is useful, since optimum growth of commercial plantation species are mainly defined by temperature.

Climate, mean annual precipitation (MAP) zones: The three (1°C) MAT temperature zones are each further subdivided into three MAP categories: low, medium and high.

The threshold corresponding to low, medium and high per category will be different for each broad climatic zone, since evaporation varies with temperature increases and decreases. This level is useful “Since rainfall is closely related to growth and drought risk, this level focuses on mean annual precipitation (MAP)” (Smith et al, 2005/03), p12)

Table 1 illustrates the subdivision of Climatic zones according to (Smith et al, 2005/03) Geological zones: Grouping is done according to stratigraphy, main formation and lithology. First classification is based on mode of formation and chemical composition, i.e. sedimentary, acid igneous, intermediate igneous, etc. Groups are further separated based on the relationship between lithology and natural soil bodies called key lithology.

Where less clear separation was found, the other geological zones of similar lithology were assigned to a similar group of the key lithology. (Smith et al, 2005/03)

Once all levels have been classified, they can be annotated in an easily understood code (Figure 4).

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Table 1: Extraction of climatic classification levels 2 and 3 of the hierarchical system of the ICFR

CT1 CT2 CT3 CT4 CT5 CT6 CT7 CT8 CT9 WT1 WT2 WT3 WT4 WT5 WT6 WT7 WT8 WT9 ST1 ST2 ST3 ST4 ST5 ST6 ST7 ST8 ST9

Eastern Cape Eastern Mpumalanga

Zululand KwaZulu- Natal

Midlands MAT^oC MAP(mm) <750

725-

850 >850 <800 800-

900 >900 <825 825-

925 >925 <850 850-

950 >950 <875 875-

975 >975 <900 900-

1000 >1000 <925 925-

1025 >1025 <950 950-

1050 >1050 <975 975- 1075 >1075 Cool temperate (general snow and frost risk)

Forest economic

zones

Warm temperate (frost in hollows /low lying areas) Sub-tropical (frost free)

Climate zones (broad)

1700-1300

1950-1600 1600-1350 1650-1300 2000-1600

2250 -1850 1750-1500 2100-1450

10-14 14-15 15-16

N/A

0700-0450 0550-0100 0300-0000 0950-0650

0850-0550

0150-0000

0900-0600 0750-0450 0550-0300 1300-1050

1300-0950

1300-1000

N/A

0450-0350 0100-0000

N/A

Altitude range (m)

1150-0850 1050-0750

1000-0650 0750-0350

1500-1200

0450-0050

1100-0800 1400-0900

1700-1400 1450-1300 1500-1200

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Figure 4: An example of the ICFR code to be used for each classification unit (Smith et al, 03/2005)

2.2 Operational Level Site Classification

The operational level classification applies the ‘Ecotope level’.(Louw,1995) The applicable classification per site can be done at a scale of between 1:2500 and

1:20000 “a forest site is an area of homogenous silvicultural practice, regarding species choice, amelioration techniques, expected yields, etc” (Louw, 1995, p8).

Louw broke the differences in the systems down to Soil Classification, Land Classification and Land evaluation.

Soil Classification: “The South African soil classification system is described as a taxonomic system and is based on the sequence of diagnostic horizons that occur in soil profiles (Soil Classification Working Group, 1991). Soil Classification in general is thus the scientific division of soils into classes (soil forms and families in the case of the South African system)”(Louw,1995,p9).

Land Classification: “In general land classification involves the grouping of land on the basis of its physical characteristics. A more holistic approach of combining the soils environment with parent material, topography and climate is thus followed. Soil

characteristics are inevitably influenced by the interplay of, for example, parent material, topography and climate, but land classification more explicitly integrate the factors with soil” (Louw,1995,p9).

Land evaluation: “The basis for land evaluation is a comparison between the land and the use of the land, and in a forestry or agricultural context emphasises the interaction between land and vegetation. The procedure for carrying out a land evaluation needs to be adapted to the specific circumstances. Land evaluation can be in the form of an indication of optimal broad land use classes, e.g. softwood plantation, hardwood

woodlots, conservation area, etc. Land evaluation can also be the qualitative evaluation for a specific land use, e.g. optimal or unsuitable for softwood, or it can be quantitative

Forest Economic Zone

Main Lithology (Geological group) Climate

WT2 – Granite (15)

S.E. Mpumalanga

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for a specific land use using productivity classes or exact predicted values of expected yield. If the evaluation of land is focusing on forestry, the term forest potential of a specific piece of land is used” (Louw,1995, p9).

“The purpose of operational forestry site classification studies is as follows:

• To stratify the landscape into homogeneous units with regard to factors such as climate, topography, geology and soils, in order to promote site–specific

silviculture.

• To evaluate sites for the most important commercial tree species, for accurate site species matching to ensure the optimization of yield.

• To provide preliminary site-specific recommendations on site amelioration measures such as fertilizing and site preparation.

• To provide guidelines on the most appropriate harvesting regimes to be

implemented under different terrain conditions, as well as the sensitivity of soils to ersosion and compaction” (Louw (1997),Strydom, 2000, p37).

Figure 5 is an illustration of the inputs and uses of a site classification and evaluation system.

Figure 5: System structure for a site classification and evaluation system

2.2.1 EXAMPLES OF OPERATIONAL FORESTRY SITE CLASSIFICATION SYSTEMS IN

SOUTH AFRICA

2.2.1.1 Forestry Soil Database (FSD) System

The FSD System was created by the Forestry Soils Database Cooperative (FSD, 1995) and funded by theSouth African Forestry Industry. The main aim was to provide soil consultants with a tool to deliver a standardised product. Classification is done by grouping land units based on soils and topography. Climate is not included and this system can be regarded as a soil classification rather than a site classification system, because each land unit does not fit the definition of a forestry site. Data was stored in tables and on paper maps, no GIS database was designed (Strydom, 2000). However,

Site Classification and Evaluation System Climate

Topography Risks

Soil Site preparation and

fertilizing Site species matching

Harvesting Regimes

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some users have utilized the information in a GIS environment and also linked the data to other data bases such as financial systems (Ellis, 2000,pp45-52).

Evaluation for suitable species, fertilisation and land preparation is done with simple rule-based models, surveyors’ expert knowledge and data captured in compartment registers. (Strydom, 2000)

2.2.1.2 CSIR Mpumalanga system:

The system was developed by the Council for Scientific and Industrial Research (CSIR) Forestek division for the Department of Water Affairs and Forestry. This is a coding system, similar to the coding system described above by the ICFR. Six variables are used in this system: climate, broad soils group, effective soil depth, soil depth limiting material, slope gradient and lithology.The sequence of variables uniquely identifies similar land units across plantation boundaries. The system also includes a terrain classification system in accordance with the National Terrain Classification Working Group (Strydom, 2000)

Evaluation to determine site quality is done by the use of statistically developed models per species, and species can be evaluated in classes ranging from “unsuitable” to

“optimal”. The fertilisation and landpreparation recommendation is based on the Institute of Commercial Forestry Research and harvesting prescriptions is based on accessability and site sustainability, by applying rule-based flowcharts proposed by the National Terrain Classification Working Group (Strydom, 2000).

Three GIS coverages (layers) form part of this system: Soil observation points (point), Site classification boundaries (polygons) and terrain classification boundaries

(polygon). The field observations are captured in attribute table format and permanently joined to the spatial dataset (Strydom, 2000).

2.2.1.3 CSIR Cape System:

The system differs from the Mpumalanga system. It is similar to the FSD System, but includes terrain and geology. It qualifies as a site classification system, as variable information that defines a forestry site is captured in a site legend (Strydom, 2000).

Evaluation of site potential is done by assigning productivity classes to each site.

Fertilisation and land preparation are based on foliar and soil analyses, and ICFR research and recommendations (Strydom, 2000).

The spatial database consists of two coverages, soil auger points (point), and site classification boundaries (polygons). The regions use different methods to generate

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their soil point layers. The field obervations are captured in attribute table format and permanently joined to the spatial dataset (Strydom, 2000).

Differences and problems found in these systems by Strydom (2000) include:

• Normalization issues,

• Data are not readily available in electronic format,

• Management units are not linked to the sites, (recommendations cannot be refered back to the units: e.g. compartments),

• Evaluation models are not set up for automated GIS import,

• Climate data are not stored in these systems,

• Classed values of the input datasets are stored in the systems and the measured values, kept in another layer.

Most of these problems identified make the updating and reapplication of the input data difficult. If an evaluation system only provides recommendations to the user without the input values, the user cannot confirm or varify the recommendation (Strydom,2000).

A SCES is designed for a specific purpose, where the input data needs to be in a format that can be utilised by the evaluation component of the system. Once the different variables are collected, classified and stored in the classification system, the evaluation of the data can be done to determine the suitability and management regimes per site (Strydom, 2000). Strydom’s study illustrated how Information Engineering (IE), combined with GIS technology, can be used to integrate the three different systems.

He used Entity Relationship (ER) diagrams to illustrate each step of the process. He started his analysis with a diagram illustrating how the variables attribute to the SCES, followed by the ER diagrams of the soil, climate and terrain classification systems. The processes and their dependancies were analysed and mapped. This design method is very structured and complex, but enables future users to apply similar methods to other datasets.

2.2.2 REVIEW OF THE CLASSIFICATION OF VARIABLES FOR A SCES IN SOUTH

AFRICA

In South Africa, the forestry industry has a wide range of growing conditions with regard to latitude, altitude, climate, lithology, soils, topography and a host of biotic

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factors. “In terms of tree growing , this constitutes such a contrasting diversity of site conditions that no single timber species can survive all of them, let alone yield merchantable roundwood equally well on all sites” (Herbert, 1996).

2.2.2.1 Climate

The classification of climate into climatic zones suitable for a specific landuse, requires a knowledge of the role of climate in such landuse.

“The main atmospheric conditions which determine regional and local climate are light, temperature and moisture. Climatic agencies which cause the most serious damage to plantations are periodic droughts, frost, wind, hail and occational lightning and snow”

(Theron, Climate, 2000, p34).

Temperature: “Optimum growth criteria are mainly defined by temperature due to relationship between species and disease/climatic risk” (Smith et al, 2005/03). Although rainfall is the most limiting factor for tree growth, temperature should be the first

considiration in species selection for a site, according to Herbert (1996). This is

confirmed by various authors and practitioners, who attempted to relate species choice primarily to altitude. The relationship between altitude and temperature is known as the adiabatic lapse rate, and is not consistent as it varies with distance from sea and region to region. The average ‘standard rate’ is - 6.5°C with an increase of 1000m in altitude.

As MAT data are rarely available, users commonly use altitude as an approximation for temperature (Smith et al, 2005/03).

“T(i,j) = a+b(Lat)+c(Long)+d(Ds)+e(Altitude) (after Schulze,1997),)

T = monthly mean of daily maximum or minimum temperature (°C) i = month of year (1...12)

j = lapse rate region (1..12) Lat = latitude (minutes of a degree) Long = longitude (minutes of a degree) DS = shortest distance from the sea (km) Altitude = alitude in meters above sea level (m)

a,b,c,d = regression coefficients “ (Smith, Pallett, Kunz, & Gardner, 2005) Precipitation: Evaporation is one of the factors that determine the water availablity for tree growth, e.g. 800mm in one area may be less effective than 600mm in another area (Theron, Climate, 2000). A specific range of species has been researched over the past century and a range of suitable temperature and percipitation ratios has been determined over this time (Herbert, 1996). Table 2 demonstrates MAP/MAT ratio’s.

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Table 2: Extraction from Herbert’s Table 1 Minimum MAP requirements per Effective Precipitation as influenced by MAT (Herbert, 1998)

MAT EFFECTIVE PRECIPITATION CLASS

VERY LOW LOW MODERATE HIGH VERY HIGH

13 657 689 722 754 787

13.5 675 709 743 776 810

14 693 728 763 798 833

14.5 711 747 783 819 856

15 728 765 803 840 878

15.5 744 783 822 860 899

16. 760 800 840 880 920

16.5 776 817 858 899 941

Moisture Availability: Rainfall, the soil’s water holding capacity and evaporation determine the availability of water to the plant. Drought is often a major limiting factor in Southern African commercial forestry, and should therefore be considered in the species selection site management and preparation techniques. Examples of

accomodating this is: species selection, wider planting space between trees, thinning of trees in established compartments, shorter planting rotations. Deeper ripping can create deeper rooting depth which increases water holding capacity (Theron, Climate, 2000). Effective rooting depth (ERD) is one of the criteria that will be used during the analysis.

Summer rainfall areas in South Africa have been characterized by a marked peak in precipitation and relatively dry winter months of between 10-30 mm per month. As discussed in the strategic classification system of the ICFR (Smith et al, 2005/03), mean annual percipitation can be used as basis of comparison between areas. Based on the different demands for water by different species, it was nessesary to develop a relationship classification system (Effective Precipitation Classes) that indicates the ratio of temperature and minimum water demands for eucalyptus, pine and wattle.

(Herbert, 1996). In this system, each commercial species has been rated in terms of its minimum Effective Precipitation Class. Classes are divided into very low, low,

moderate, high and very high water demand (Herbert, 1996).

“Both thermic conditions and moisture, as well as the interaction between the two variables, affect forest growth in such a way that the inclusion thereof in climatic classification is essential” (Louw et al, 2011). When forest classification based on climatic parameters was investigated, it was said that “the mean number of days per annum on which sufficient water is available to permit plant growth was considered a biologically meaningful index of water availablity.” This index is called ‘growth days’

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(GD), referring to the number of growth days when soil moisture does not limit growth.

Growth Temperature is also a function of this, where growth days per month (GDM) and growth days per year (GDY) is subdivided by the mean monthly temperature (MMT) to derive the growth temperature (GT) for a particular region (Louw et al, 2011).

The results from the study for the area of Ugie is contained in Table 3:

Table 3: Growth days and temperature for Ugie

PARAMETER DESCRIPTION VALUES

MAP Mean annual Percipitation 650mm

MAT Mean annual temperature 15.1°C

Ep Evaporation potential 1675mm

GD Growth days 125

GT Growth temperature 17.31°C

Wind: Wind may cause malformation of the stems, and windfalls create damage and losses in volumes harvested from these sites. Identification of sites that may be more exposed to wind is important during the planning and management of these sites (Theron, Climate, 2000). Aspect and slope are important input factors for identifying these sites, as wind turbulance may cause more damage along crests and ridges, and in gullies and gaps along the escapment (Herbert, 1996).

Cold winds have a high chill factor that may cause necrosis in soft growing tips and death in young trees. Upper slope or crest posistions are most susceptable to cold fronts from the south or snow (Herbert, 1996).

Frost: Frost in valleys may cause damage, especially late frost in the early spring.

Measures to combat these can be to plant frost tolerant species and to avoid planting in concave surfaces (Theron, Climate, 2000). Frost causes more damage in the younger trees as cell walls are damaged due to freezing and thawing therafter, “more likely on north and east aspects since they warm up more rapidly”(Herbert,1996,p7).

Another combat method could be to plant the fast growing trees in early summer to reach a height above the frost layer before the following winter (Herbert, 1996).

Hail and lightning: Although the occurance of hail in the vicinity of escarpments and mountains are expected, it is difficult to identify hail belts. Lightning can cause veld fires, and it was found that circles around the areas that was struck, may only show the evidence after 2 years (Theron, Climate, 2000).

Snow: Snow may occur in areas where MAT is approximately 16°C or colder, where intensity increases with altitude and latitude. Damage include snapping of branches, and crown bending (Herbert, 1996).

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2.2.2.2 Soils

As discussed in the ICFR classification method at regional level, the effect of lithology on forestry is through its relationship to soil properties. Soil classification is the ordering of soils into a hierarchy of classes (Smith et al, 2005/03). The soil properties that are important to tree growth are: usable soil depths, wetness, texture, inherent densities and chemical condition (Ellis, Soils, 2000).

Soil depth: Tree roots need a minimum depth to develop and grow (Effective rooting depth). Factors that may influence this are wetness, soil density (dens clay) and rock layers. (Ellis, Soils, 2000).

Wetness: Wetness refers to free water availble to the roots within normal reach. “Two types of wetness can be distinguished: perched watertables and true ground water tables” (Ellis, 2000, p36). Perched water tables have the water stored in the subsoils due to poor drainage caused by dense clay or impermeable underlying layers. In true water tables, their maximum high is determined by the stream, texture of the soil and the topography of the terrain (Ellis, Soils, 2000). Species have a certain tolerance to wetness at the roots, and others less tolerance to drought. Therefore, wetness should not always be seen as a total limiting factor - in marginal rainfall areas the availability of free water may mean the difference between success and failure. Wetness may also cause chemical alteration of the soil by reducing oxygen and leaching iron. This cause increased concentrations of magnesium and sodium (Ellis, 2000 p36).

Textural gradient: This refers to the textural layering, e.g. sand, gravel, stones, that change with depth. This may influence the effective rooting depth as described above (Ellis,2000,p37).

High inherent soil densities: High soil density occur in almost all soil forms and may be caused by traffic or coarse soil pores being filled with fine soil particles, during site preparation (Ellis, 2000, p38).

Chemical condition: Plant nutrients and the acidity or alkalinity will affect root development and tree growth. During soil sampling, soils may be tested for nutrient deficiencies and pH balance. These can normally be manipulated before planting (Ellis,2000,p38).

‘The Bionomial Soil Classification System’ (Macvicar, 1977) was used until 1991, when it was replaced by ‘Soil Classification: A Taxonomic System for South Africa’ (Soil Classification Working Group, 1991). The Classification is two-part: a high category referring to the soil forms and a lower category referring to soil families (Ellis, 2000,38- 39). These forms are distinguished by the properties mentioned above, i.e. they will

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differ in depth, wetness i.e. clayeyness, structure, texture and densities. Examples of these forms are Oakleaf (Oa), Hutton (Hu) Clovelly (Cv). Each of the soil forms belong to a soil assosiation/group. This is better explained in Table 4 below.

In the South African commercial forestry and indigenous forests areas, five major soil assosiations can be recognized (Ellis,2000,39).

Low base status, apedal red and yellow, well drained clayey soils: “Highly leached (dystrophic), very acid, apedal (i.e. weakly structured), clayey soils, occur in high rainfall areas eastern seaboard (KwaZulu-Natal, Mpumalanga, Northern Province and Swaziland) of Southern Africa” (Ellis, 2000,p39).

Forestry usability: These soils are highly suitable for forestry purposes (Ellis, 2000,p39).

Red, yellow and gray soils in catenary association (red-yellow-grey latosol

plinthic catena): “A catena implies a sequence of soils of about the same age, derived from similar parent material, and occurring under similar climatic conditions but with different characteristics due to variation in relief and drainage” (Ellis, Soils, 2000).The red, yellow and grey soils are gradually found from well drained higher levels (red) to poorly drained lower levels (grey) (Ellis, 2000).

Forestry usability: These soils are considered moderate-high suitable for forestry. Poor drained sites are rated moderate to low (Ellis, 2000,p39).

Duplex an parraduplex soils: “Generally this means that the topsoils differ markedly from the subsoils in texture, structure and consistency, e.g. a relatively coarse – textured, soft, structureless topsoil overlaying a relative clayey, slowly permeable, strong structured subsoil” (Ellis, 2000,p41).

Forestry usability: These soils are seldom used for commercial forestry, but where they are used, they produce low yields and conservation issues due to severe soil erosion.

(Ellis, 2000)

Weakly developed soils on rock (lithosols): “The dominant soils exhibit a profile consisting of only a topsoil overlaying rock or weathered rock” (Ellis, 2000, p41). These soils have the caracterisitics of the rock from which they originated, e.g. they maybe sandy soils formed from sandstone or quartzite and clayey soils maybe formed from shale (Ellis, 2000).

Forestry suitability: In general, due to their shallowness, these soils are not regarded as suitable for forestry. With manipulation during landpreparation these sites may become more suitable (Ellis, 2000).

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Sandy soils: These soils occur mainly along the coast. They are deep and due to a low clay percentage, they have a low water holding capasity. Wind erosion may occur.

Forestry suitability: These soils may be regarded as of moderate to moderate high suitability, depending on the availability of adequate rainfall (Ellis, 2000,p41).

The soil groups/associations are described in Table 4 in accordance to the Soil Classification Working Group (1991) and as implemented by the Forestry Soil

Database (FSD). Table 4 is an example and only indicates the first two descriptions per group.

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Table 4: Soils described by the Soil Classification Working Group Implemented by the FSD

SOIL GROUP/

ASSOCIATION DESCRIPTION SOIL FORMS

A RED AND YELLOW APEDAL SOILS (A)

Aa Undifferentiated humic topsoils

dominant Kp, Ma, Ia, Lu, Sr, No

Ab Red dystrophic Hu

B HIGH CHROMA PLINTHIC SOILS (B)

Ba Red Dystrophic Bv, Bd

Bb Yellow Dystrophic Av, Gc, Pn

C HYDROMORPHIC SOILS (C)

Ca Undifferentiated hydromorphic Ka, Kd, Lo, Wa, We

Cb E-horizon hydromorphic Kd, Lo, Wa

D DUPLEX SOILS (D)

Da Red duplex Sw, Va, Km

Db Non-red duplex Sw, Va, Es, Km, Ss, Se

E MELANIC, VERTIC & RED-STRUCTURED SOILS (E)

Ea Melanic soils Bo, My, Mw, Ik, Wo, Sn, Im

Eb Mesotrophic red-structured Sd

F LITHOSOLS (F)

Fa Undifferentiated lithosols Gs, Ms, Cf, Dr, Cg, Kn

Fb Soft lithocutanic Gs, Cf

G PODZOLIC SOILS (G)

Ga Podzols on wet, unconsolidated

material Lt, Wf

Gb Podzols on non-wet, unconsolidated

material Pg, Cc

H YOUTHFUL SOILS (H)

Ha E-horizon sands with pale topsoils Fw Hb Undifferentiated sands and other

soils Fw, Kd, Vf, Ct, Lo

I MISCELLANEOUS LAND CLASSES (I)

Ia Alluvial or colluvial deposits Du Ib 60 - 80% surface rock, outcrops &

boulders with miscellaneous soils Miscellaneous

J ORGANIC SOILS (J)

Ja Organic soils Ch

2.2.2.3 Topography

The topography modifies the general climatic and edaphic conditions of sites such as exposure (sun, wind and rain), temperature and the effectiveness of rainfall (Howard, 2000).

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The effect of altitude in relation to temperature has already been discussed, but slope and aspect are other variables that play a role in the suitability of sites for specific land uses (Howard, 2000).

Slope

Slope is classified by steepness, shape and position, and affects access, harvesting regimes, soil types, and risk of erosion. (Howard, 2000)

- Steepness: The steeper the slope, the more rapidly it drains and the drier the site.

(Howard, 2000)

- Shape: Slopes can be either concave or convex or a combination. Convex slopes are difficult to access, and are drier. Concave slopes collect runoff water from lower slopes. (Howard, 2000)

- Position: is classified from crest to valley. Lower slopes tend to be wetter and more protected (Howard, 2000).

Aspect

“This refers to the asimuthal (compass) direction that the slope faces. In the Southern hemisphere north and west facing slopes are warmer and drier due to their greater exposure to solar radiation, south and east facing slopes are cooler and wetter”

(Howard, 2000, p44).

Terrain classification is often done while the soil classification is undertaken. There are four main components of terrain classification that is captured for tactical and

harvesting planning. Although we will not be attending to this component of a site classification system in this study, it is briefly discussed.

Ground Strength

In terms of harvesting the “harder the soil is, the higher the ground strength resulting in more grip and traction, whereas soft or wet soils have low ground strength. The most important soil properties that affect ground strength are soil texture, organic carbon content, soil drainage status, clay mineralogy, and soil water content. All of these soil properties are available directly or indirectly from the FSD” (Smith, 2010). See Table 5 for an example of ground strength classification. Ground strength rating is given on a seasonal basis; e.g. in the summer rainfall areas, dry for winter and moist for summer.

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Table 5: Example of ground strength classification with data from FSD dataset (Smith, 2010)

Soil information from FSD

Ground strength Soil sensitivity

Compaction Erosion

Soil form Soil texture

Dry (winter)

Wet (summer)

Dry (winter)

Wet (summer) A. Well drained soils with a humic A horizon

La Kp Ma Salm Good Moderate Low Moderate Moderate

Ground roughness

“Ground roughness describes the size and frequency of surface obstacles which affect machine stability, speed and ease of operation. Surface features that may affect mechanised operations in plantations such are dongas, gullies, outcrops, boulders and rocks. Ground roughness is assessed according to their occurrence in terms of

percentage of area per soil unit. See Table 6. Cultivation factors describe limitations to site preparation according to the presence of rocks or other impediments within the soil profile” (Smith, 2010).

Table 6: An example of ground roughness classification according to the FSD dataset FSD

Category

Ground roughness 1 Even 2 Slightly

uneven

3 Uneven 4 Rough 5 Very rough Surface features (SURFACE)

Dongas (D) 0 D1 – D2 D3 – D4 D5 – D7 D8 – D10

Cultivation factors (CULT_FACT)

Rocks (R) 0 R1 – R2 R3 – R4 R5 – R7 R8 – R10

Notes:

Cultivation factors suffix 1-9 denotes 10% - 90% of soil volume, (FSD v1.3)

Surface factors Assessed for the area surrounding the auger point (75m radius). Suffix 1-9 denotes 10%-90% of surface area coverage (FSD v1.3)

2.3 Summary

The classification of data provides a rational means to interpret and add value to data contained in database systems (Smith, 2010). This review of literature looked at different levels of site classification but concentrated on operational classification methods. The different variables used in a site classification and evaluation system, namely climate, soils, topography and risks were discussed and their relevance and importance explained.

Examples of the classification per variable were given, and will be used and adjusted according to the standards set in the industry. The climate data classification will be

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according to the ICFR Bulletins 03/2005 and 04/2005 and, soils according to the Taxonomic system and FSD systems where applicable. Topography and risks will be mapped and will be classified in accordance with relevance to site species matching.

According to Strydom (2000) one of the difficulties of previous classification systems, is that raw data is not stored in the system, but merely the classes. This makes the editing and updating laboursome. His study addressed this problem by designing a model where the different input datasets, processes and output layers with the final classification values were stored in one database.

In order to avoid similar future difficulties. A similar approach will be attempted in the design of this Spatial Site Classification and Evaluation System (SSCES). A process model will be built in Arcview where the original data, the processes of classification, the classified layers, and the resultant overlays will be stored.

An evaluation phase will be built into this system, where the criteria for site species matching as described by Smith et al (2005/03) and Smith (2010), will be applied.

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CHAPTER 3 – DATA DESIGN AND PROCESSING 3.1 Overview

To determine the most suitable species for the different site conditions found in the study area, a relationship between species and sites should be established in a measurable format.

The sources of information on criteria per species and site have been identified as:

• ICFR Bulletins, 03/2005 and 04/2005 – ‘A strategic site classification for the summer rainfall region of southern Africa based on climate, geology and soils’ and

‘A site evaluation for site species matching in the summer rainfall regions of Southern Africa’.

• Smith, 2010 – ‘A site classification system for landuse management and planning in the north eastern Cape’.

• Herbert, 1997 – ‘Report on a site evaluation study of the estates Glen Cullen and Chillingly, Maclear district’.

The ICFR report was compiled by “conducting a comprehensive study that collates various sources regarding the major commercial species from existing literature. These were presented and ratified at a workshop of the Forest Site Classification Workshop in 2003” (Smith, Gardner, Pallett, Swain, du Plessis, & Kunz, 2005/04).The optimum climatic growth criteria for commercial forestry species grown in the summer rainfall region of South Africa have been summarised in these bulletin reports. The climatic data can be classified into three main categories according to altitude and subdivided into three sub-categories each according to precipitation (see Chapter 2, Example 2).

When assessing the Elliot-Ugie-Maclear study area according to these reports, the area can be classified as Cool temperate due to the high altitude and related temperature.

Smith’s report is the most recent interpretation of various sources, including the ICFR Bulletins and Herbert’s various studies in the study area. A selection of species was made that are regarded as suitable for the climatic variables (Cool temperate) for the region. Soil attributes per species have also been linked in this report. These variables have been created by Smith by adjusting the standard values according to site specific research reports (Zwolinski et al (1998); Swain and Gardner (2003), Smith et al

(2005/03 and 2005/04)).

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Herbert’s site evaluation on two of the estates will be used as tool to create the climate layers by applying the equations created for calculating MAT and MAP.

In the final product, each management unit (compartment) will be regarded as suitable for one or more species based on four variables, namely: CLIMATE_KEY, SOIL_KEY, ASPECT_CLASS, and SLOPE_CLASS.

Referring to Chapter 2’s definition on sites, those areas which have the same

CLIMATE_KEY, SOIL_KEY, ASPECT_CLASS, and SLOPE_CLASS, may be regarded as homogeneous and therefore has the same site classification, and also have the same species suitability. However, it may happen that when the management unit (compartment) boundaries are overlaid, one compartment may have more than one

‘site class’. Such a compartment should be managed according to the site class of the larger area it falls into. Where appropriate, the compartment boundaries may be adjusted to match the suitability site boundaries.

Once the UNITS have been assigned suitability per species, an analysis to determine the most suitable species can be conducted. It is expected that, due to the variety of site types (the study area covers mountainous and plains areas); various species may be suitable for a site. Therefore, the degree of suitability should also be indicated.

3.2 Data Modelling

The conceptual model in Figure 6 maps out the:

1. Identified inputs from the literature study, followed by the 2. Datasets acquired.

3. The datasets are processed / created to prepare the datasets for the classification phase.

4. Classification is done and the outputs are saved as classified layers.

5. During the evaluation phase for each layer a link table is created, listing the different class values and suitability per species linked to each of the values.

Joining these tables to the layers will enable the user to view the suitability of each species in reference to each class/key

.

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Figure 6: Conceptual model for data processing and the output variables

Soils Climate

MAT MAP Altitude

Topography

Digital Terrain Model

Slope Aspect

DTM Contours

Effective rooting depth

Wetness Soil group FSD SOILS DATABASE

v2

Site species matching

Management unit

LANDUSE

DATABASE Compartment

Risks

Slope_class Aspect_class Climate_key Soil_Key

FSD SOILS DATABASE

Classification

CLASS/KEY_SPECIES Suitability link tables

Evaluation

Data identification, acquisition and processing

SITE SPECIES MATCHING FOR COMMERCIAL FORESTRY