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3.4 Geographic Information Systems and Remote Sensing Applications in Forest Management

Riyad Ismail,

GIS/Remote Sensing Analyst, Sappi

Mark Norris-Rogers

GIS/Remote Sensing Specialist, Mondi SA

Fethi Ahmed

Associate Professor, University of KwaZulu-Natal

Onisimo Mutanga

Associate Professor, University of KwaZulu-Natal

Introduction

There are approximately 1.5 million hectares of commercial forests in South Africa. Information regarding the location, extent, health, vigour, volume and operational status of these forests is essential for the development of government policy, national and international reporting obligations and commercial decisions relating to investment, planning, harvesting and sales (Coops et al., 2004). It is therefore crucial that forest managers have a good understanding of the spatial technologies used for extracting forest management information and their potential applications. This chapter will attempt to highlight geographic information systems (GIS) and remote sensing technologies, outline their applications in South African forest management, and review current research findings and recommendations.

Geographic Information Systems

Forest management involves the capture, interrogation, update and analysis of forest-related data that has geographic (spatial), as well as attribute (tabular) components. Additionally, forestry-related datasets can be very large (gigabytes or even terabytes) and complex. This inherent data complexity and volume can only be handled effectively through the use of suitable spatial technologies such as GIS (Von Gadow and Bredenkamp, 1992). In fact, GIS has become a fundamental part of forest management systems in many commercial forestry enterprises (Austin and Meyers, 1996; Von Gadow and Bredenkamp, 1992).

GIS has many definitions, but most definitions include a container of maps in digital form or a computerised tool for solving geographic problems (Longley et al., 2005). Early GIS applications in the South African forestry sector focussed on the mapping component. However, more recently, applications have expanded to include decision support systems for site classification, site-species matching, harvest planning and many other analytical applications. GIS has the ability to integrate disparate sources of information such as compartment register details, financial information, area, and volume

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calculations. Consequently, developments within GIS have been driven by a business requirement to provide accurate planning and management information to decision makers at all levels of forest management.

GIS Applications

GIS applications can be grouped into various categories depending on the level of integration with other forest management and financial systems. These categories include: data collection and maintenance, map production, data viewing and query, and decision support systems.

Data collection and maintenance

Sound decision making requires accurate, current and concise information. Forest management information is constantly changing, resulting in a need for timely updates. Updates may include but are not restricted to compartment boundary changes (splits, merges, delineation and excisions), revisions of felling plans, silviculture reporting and current harvesting operations. A key issue, however, is that the data has to be collected in a manner that meets strict predetermined forest management specifications. Recently, remotely sensed and GPS data provide increasingly important sources of data due to their easy integration with GIS. Map production

Maps have long been a basic management tool for foresters. Maps are easily produced using GIS. Depending on the level of systems development, GIS can be used to produce a range of cartographic products, from simple maps (e.g. locality map) to high grade cartographic quality maps (e.g. environmental impact assessment maps). Data viewing and query:

GIS allows for the easy retrieval, accessibility and dissemination of spatial and tabular information. Web-based technologies are increasingly employed by users to browse, query and analyse forestry-related data. Recent developments in GIS technology allows for 3D visualisation, which is far more intuitive than traditional 2D maps. Decision makers (foresters and managers) can now easily “see” objects, phenomena, scenarios or landscapes in a naturally intuitive manner. Decision support systems

Decision support systems (DSS) utilise the full potential of GIS to provide useful management information by integrating and querying very complex, technically detailed spatial datasets or databases. Different “what-if” scenarios can be created and the results analysed thus allowing the user to select the best solution or alternative. The section below provides some forest management examples that use decision support systems.

Introduction

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Forest Management Applications

Map production:

Forest managers require a wide variety of maps to assist with their daily activities. Plantation maps are most commonly used for location purposes and may contain additional useful information such as roads, rivers, compartment boundaries, planted species, and compartment size (ha). Other features such as topographic features (contours), infrastructure, water points, fire breaks, neighbours and conservation areas may be also included in the map.

Harvest planning

Good forest management practice requires detailed planning of harvesting activities. Harvest planning activities include the identification of felling directions, extraction routes, depots and sensitive zones (such as wetlands). Maps constitute a basic planning tool for these activities. Other tactical harvest planning functions utilise maps to identify planned felling over a number of years, and to consolidate felling areas and extraction routes thereby permitting the efficient use of harvesting equipment and other resources.

Roads management:

Roads require particular focus in any forest management system. GIS allows for the roads to be mapped and distances to be accurately calculated. Once the complete road network has been compiled and verified, optimal routing solutions can be derived through network analysis (Stewart, 2005). Ancillary features such as stream crossings, depots and borrow pits may also be captured and managed utilising GIS

Cadastral data management:

An important element in any land management application is legal land tenure. Cadastral boundary maintenance together with the relevant title deed information can be managed with GIS. Temporal changes in ownership details, sub-divisions, consolidations, servitudes, and mineral rights can all be efficiently managed in GIS.

Open area management:

Unplanted areas require as much management consideration as afforested areas. Conservation and biodiversity planning activities such as rotational burning regimes, alien invasive plant control, recording and monitoring of rare and endangered species, grassland and stream quality monitoring can all be effectively managed using GIS.

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Decision support applications:

Various GIS-based decision support applications have been implemented by forest companies in South Africa, including the following:

Slash management: Burning of slash on sensitive sites can cause erosion and site degradation. By integrating slope and soils data it is possible to classify sensitive areas (low or high) based on predetermined criteria. Foresters can then make informed decisions based on the classification map, without having to examine in situ soil and slope conditions.

Site classification: Site classification is a complex process that integrates spatial data (geology, soils, climate, terrain) as well as attribute data based on management prescriptions (Smith et al., 2005). By applying analytical GIS techniques, it is possible to derive a practical site classification system that defines forested areas as good, medium or poor sites. Alternatively, GIS can also be used to develop complex and detailed site classification systems (Strydom, 2000).

Site-species matching: Researchers have expanded site classification systems to incorporate specific recommendations for the optimal growth of species (or clone). Matching a particular commercial tree species to a site involves combining the species site requirements with the site characteristics in terms of soil type and depth, climate and altitude (Smith et al., 2005).

Site specific silviculture: Site specific silviculture is analogous to the concept of “precision agriculture” and involves defining the optimal site preparation techniques, fertiliser recommendations and optimal planting dates for individual compartments. The level of complex and varied data integration required for this process can only be effectively achieved using GIS.

Viewshed analysis: Determining the optimal location of fire towers and fire detection cameras is an important GIS application. By applying viewshed analysis techniques that utilise digital terrain models, it is possible to calculate which areas are visible from any point across a landscape, and identify optimal locations that provide maximum visibility. By carrying out a viewshed analysis it is possible to substantially reduce costs when setting up a fire tower or an automated fire detection camera.

Thus far, a few pertinent GIS applications for forest management have been

highlighted. However, it is possible to develop and implement numerous GIS applications for forest management provided that suitable data, software, hardware and expertise are available.

Global Positioning Systems and Mobile GIS

With greater precision inherent in modern GIS, it has also become necessary to capture spatial data more accurately and precisely. For example, it is no longer

Introduction

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sufficient to draw a line on a map to indicate compartment boundary changes. Global positioning systems (GPS) are now more affordable and available in the country. Spatial changes can be easily surveyed in the field and subsequently used to update existing GIS datasets at the required degree of accuracy. However, attempting to capture GPS data under dense canopy cover could introduce positional inaccuracies.

Mobile GIS integrates both GIS and GPS technologies and allows GIS technology to be taken to the field, as opposed to being limited to an office computer. Norris-Rogers (2008) showed that it is possible to utilise personal digital assistants (PDAs) to display maps and compartment data while additionally allowing for in-field data capture and assessments. The technology reduces the necessity of having multiple paper-based maps and compartment lists and expedites the data capture and maintenance workflows.

GIS into the future

With forest management becoming increasingly complex in future, due to greater environmental and social involvement and pressures, GIS is likely to play an increasingly central role. Developments in greater band-width, web based technology and wireless communication will provide much greater opportunities for information access (Longley et al., 2005), even in more remote areas. This will allow real-time online data capture and query in the field.

Remote Sensing

Remote sensing is defined as obtaining information about an object without being in physical contact with it. This technology involves the analysis and interpretation of the electromagnetic radiation reflected or emitted by an object as recorded by a sensor, usually mounted on board a satellite or aircraft (Mather, 2004). Different objects on the earth’s surface reflect or emit electromagnetic energy in different proportions, and it is this difference that is utilised in remote sensing analyses (Mather, 2004). Traditionally, panchromatic aerial photography provided the major source of remotely sensed information for forest management (Kätsch and Vogt, 1999). When combined with manual interpretation methods the high spatial resolution of aerial photographs provided valuable information for forestry related applications in South Africa. Panchromatic aerial photography is still used in forest mapping applications and for the production of contours or digital elevation models (DEM) data from stereo pairs.

Panchromatic aerial photography contains limited amounts of spectral information, especially in vegetation applications, whereas optical remote sensing systems (multispectral or hyperspectral) measure the amount of electromagnetic energy reflected from the tree canopy in a series of wavelengths, which can range from 350 to 2600 nanometers (nm). When used within a GIS framework, remotely sensed datasets allow for integrated visualisation and modelling across a range of operational scales. Consequently,

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considerable research work is focussed on developing remote sensing applications that have practical application for commercial forestry operations.

Remote Sensing Applications

Internationally, researchers have investigated the potential benefit of utilising remote sensing for forestry applications (Franklin, 2001; Kalacska and Sanchez-Azofeifa, 2008; Treitz and Howarth, 1999; Wulder and Franklin, 2007; Wulder, 1998) and within the South African context useful reviews are provided by Ahmed (2006), Kätsch and Vogt (1999), Roberts et al. (2007) and Thompson and Whitehead (1992). In this chapter, the focus is on the more recent (circa 2000 - 2009) remote sensing applications, with the intention of giving the reader a synoptic overview of current research findings and recommendations. Forest health

Traditionally, forest health assessments were conducted using broad scale aerial reconnaissance followed by ground truthing exercises to verify the results (Ismail et al., 2007). The effectiveness of visual assessments used for the monitoring and detection of diseased or stressed trees is questionable, because it is qualitative, subjective and dependent on the skill of the surveyor (McConnell et al., 2000; Stone and Coops, 2004). Recently, researchers have focussed on the remote detection and mapping of Sirex noctilio infestations in pine plantations using either high spatial or spectral resolution data. Results have shown that normalised difference vegetation index (NDVI) derived from high spatial resolution image data (0.5 m) has the potential to accurately detect and map the later stages of S. noctilio infestations (Ismail et al., 2007; Ismail et al., 2008b), while it is possible to discriminate the early symptoms of S. noctilio infestation using specific wavelengths located in the visible and near infrared region (Ismail et al., 2008a). In addition, a number of combined texture measures derived from high resolution image data (0.5 m) have accurately predicted the severity of S. noctilio infestations in commercial pine plantations (Dye et al., 2008). Presently, the Council for Scientific and Industrial Research (CSIR), Sappi, University of KwaZulu-Natal (UKZN) and the Department of Science and Technology (DST) are investigating the potential to map S. noctilio infestations using airborne (AISA Eagle) hyperspectral image data. Forest structure

The relationship between forest structural attributes and remotely sensed data differs depending on the species studied, geographic location and management of the site and the image data that is used (Gebreslasie et al., 2008). Initially, Gebreslasie et al. (2008) assessed the utility of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to estimate the structural attributes of E. grandis and E. nitens. ASTER covers the visible (15 m), near infrared (15 m), shortwave infrared (60 m) and the thermal infrared regions (90 m) of the electromagnetic spectrum. Empirical relationships

Introduction

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between stems per hectare (SPHA), diameter at breast height (DBH), mean tree height (MTH), basal area, volume, and the ASTER datasets were calculated using canonical correlation analysis (CCA). The results indicated weak relationships between the studied forest structural attributes and several ASTER derived datasets (i.e. individual bands, CCA bands, vegetation indices, CCA vegetation indices, log transformed bands and principal components). The authors concluded that the ASTER image data was not suitable for estimating forest structural attributes in commercially managed Eucalyptus forests.

Subsequently, Gebreslasie (2009) assessed the utility of texture measures derived from multispectral (4 m) and panchromatic (1 m) IKONOS image data to improve the estimation of forest structural attributes. The researcher states that strong and significant relationships between the estimated and actual measures of SPHA, DBH, MTH, basal area, and volume were predicted using artificial neural network models. Results showed that correlation coefficients based on multispectral IKONOS image data were greater than 0.8 while correlation coefficients based on the panchromatic IKONOS image data were higher than 0.9. However, further research is required to document the performance of the method and techniques developed by Gebreslasie (2009) under different environmental conditions and topographical changes, as well as for other species.

More recently, Tesfamichael (2009) assessed the structural attributes of even-aged E. grandis forest plantations using small-footprint discrete return lidar data. It was concluded that multiple forest structural attributes can be assessed using lidar data only. The study has taken a significant step towards determining if lidar data can be used as a stand-alone remote sensing data source for assessment of structural plantation parameters. Not only does such an approach seem viable, but the lower required point densities will help to reduce acquisition costs significantly.

Forest productivity

Ghebremicael et al. (2004) investigated the potential of Landsat ETM+ (30 m) to predict the leaf area index (LAI) of A. mearnsii as a means to rapidly assess forest productivity. LAI is a key input variable into process based models such as 3-PG. Results from the study indicated that NDVI is a more robust predictor of LAI (R2 = 0.62) than ratio vegetation index (RVI), transformed vegetation index (TVI) and vegetation index 3 (VI3). The techniques developed by Ghebremicael et al. (2004) have the potential to estimate LAI without the expense of ground measurements. The authors recommended implementing texture and spectral mixture analysis to improve the prediction of LAI. Research carried out by Mzinyane (2007) also successfully used Landsat data and 3-PGS to model productivity and water use of E. grandis in Zululand.

More recently, Cho et al. (2008) evaluated the stability of remote sensing indicators of leaf water, chlorophyll and nutrients to discriminate between E. grandis growing on different site qualities (defined by the total available soil water) during the winter and early summer growing seasons. The analysis showed that the discriminatory capabilities of leaf water, chlorophyll, and

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nutrient concentrations for E. grandis growing on different site qualities (good, medium and poor), are seasonal by nature. Leaf water and chlorophyll indices were better indicators of site quality in winter than foliar nutrient concentrations of N, P, and K, which in turn performed better during the summer season. Overall, the results of the study provide a remote sensing timeframe for monitoring E. grandis growth in commercially managed forests in KwaZulu-Natal.

Forest species composition

Previously, studies have used multispectral satellite image data to successfully carry out genus level classifications in forestry environments (Kätsch and Vogt, 1999). More recently, van Aardt and Norris-Rogers (2008) used airborne hyperspectral image data to discriminate even aged Eucalyptus (E. dunnii, E. grandis, and E. grandis x nitens hybrid) and Acacia (A. mearnsii) compartments in KwaZulu-Natal. Results showed that by using the compact airborne spectral imager (CASI) image data (36 bands with a spectral range of 426 to 952 nm and spatial resolution of 1 m resolution) it is possible to firstly, discriminate between Eucalyptus and Acacia species and secondly, differentiate among the age classes (i.e. 1 to 3 years, 4 to 6 years, and 7 to 11 years) of the same species. More specifically, results showed that the transformed data (minimum noise fraction) provided the highest overall classification accuracies (85.03%) as opposed to using the radiance values which provided an overall accuracy of 47.59%. Spectral regions that are important for discrimination of the species were mainly related to the red edge. van Aardt and Norris-Rogers (2008) also noted that, due to the high correlations between bands in the CASI dataset, using a broader band sensor could be more beneficial. Forest change detection

Using a series of Landsat 7 ETM + (medium resolution) and QuickBird (high resolution) images, Norris-Rogers et al. (2006) investigated whether clear-felling, re-establishment and weed control could be monitored using change detection techniques. Results from the study indicated that the medium resolution image data was successful in accurately identifying clear-felled stands, but the high resolution image data (QuickBird panchromatic, 0.6 m) was required to identify replanted compartments, and the weed status of those compartments. Overall, the study recommended standard procedures and techniques that could be applied in an operational plantation forest monitoring environment.

Foliar biochemistry

Mthembu (2007) used field-based hyperspectral data to determine foliar and wood lignin concentrations of Eucalyptus clones. The relationship between the laboratory-measured wood lignin concentrations and predicted wood lignin concentrations was highly significant with an R2 of 0.91. The study concluded that it was possible to predict wood lignin using hyperspectral data.

Introduction

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More recently, Oumar (2009) examined the ability of the proposed SumbandilaSat to estimate plant water content in a Eucalyptus plantation. By integrating neural networks and simulated SumbandilaSat data, plant water content was successfully (r = 0.74; RMSE =1.41) estimated on an independent dataset. According to Oumar (2009) the ability of SumbandilaSat to estimate plant water content provides the impetus for the large scale monitoring of forest health and productivity with a locally built sensor. The xanthophyll, blue and near infrared bands were ranked as the most important bands to estimate plant water content in E. grandis (Table 3.4.1). Overall, the study showed the potential of the SumbandilaSat bands in predicting plant water content in a commercial Eucalyptus forest.

Table 3.4.1 SumbandilaSat bands and potential applications (van Aardt,

2007).

Wavelength Range Intended Application 440-510 nm (blue) Water bodies, soil/vegetation,

deciduous/coniferous. 520-540 nm (xanthophyll) Silt in water and deforested lands, urban areas 520-590 nm (green) Green reflectance peak for plant vigour 620-680 nm (red) Chlorophyll absorption, roads, bare soil 690-730 nm (red-edge) Plant stress 840-890 (near-infrared) Plant-biomass estimates, water bodies,

vegetation

Remote sensing into the future

The increased availability of several new high spatial resolution commercial airborne and spaceborne sensors such as GeoEye 2 and WorldView 1 and 2, as well as the scheduled launch of the SumbandilaSat multispectral sensor in 2009 will herald a new era for remote sensing in South Africa. With the future availability and accessibility of remotely sensed data, together with the potentially cost-effective operational applications, it is envisaged that forest companies will embrace the technology more readily and that remotely sensed data will provide crucial information on the status and condition of commercial plantations in South Africa into the future.

Conclusions

This chapter has highlighted many applications where GIS and remote sensing technologies can enhance forest managers’ ability to manage forestry operations in an increasingly complex and challenging environment. These spatial technologies hold promise in allowing forest managers to make sense of a multitude of controlling factors by presenting forest information in a manner conducive to effective decision making.

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