5 Assessment of Non-Timber Functions: Remote Sensing Technologies
5.1 Permanent Plots for Studying the Effects of Non-Wood Forestry
Simo Poso, Mark-Leo Waite, Jyrki Koivuniemi
Summary
Decision making in forestry is usually based on multiple objectives including, in addition to wood production, non-wood issues such as landscape, recreation and biodiversity. Lack of knowledge and experience causes great difficulties when trying to derive forest policies satisfactory for all interest groups.
The authors suggest that permanent sample plots are used together with different long time silvicultural strategies in order to derive necessary data and knowledge about the effects of different strategies.
Keywords: Non-wood forestry, permanent sample plots
5.1.1 Introduction
Forest inventories, either compartmentwise or sample based, have been traditionally carried out for management and national planning of wood production. Other recent types of forest benefits to be taken into consideration are according to FAO/ECE (1993 p. 35):
water, grazing/range, protection (against erosion, avalanches, etc.), hunting (wildlife), nature conservation, and recreation (amenity).
It was concluded that (FAO/ECE 1993 p. 91) "We are still far from being able to apply methodologies allowing valuation, whether in monetary terms or any other units, of the relative importance of different functions or products of the forests" and that "consider
able work would be necessary to develop the concept in practice for benefits other than wood" and recommended (FAOIECE 1993 p. 41) that "forest inventory methods should be strengthened by the introduction of new skills, developed from pilot studies, focusing on the need for fully integrating biodiversity data with traditional forest inventory practices."
The main requirements related to biodiversity were:
- geo-referenced data on micro-types of vegetation surrounding forests under survey, - statistical data on biodiversity indicators ( e.g. richness/abundance/distribution of
species/species groups by age/diameter classes). This should be geo-referenced and set in a large area framework. In addition to surveys of tree species/species groups, plant taxonomists should be included in inventory crews to survey shrubs and herbs. Studies of faunistic diversity are more difficult but might be included following pilot studies and modeling exercises, and
models to estimate species biodiversity at sub-national/national levels using sample survey data.
Two main findings turned out as being common to most industrialized countries in temperate regions (FAO/ECE 1993, p. 92). "They are continuing expansion of the temperate forest resource and the increasing relative and absolute importance of the non
wood functions of the forest."
For example, Finnish studies based on questionnaires and a sample of over 2000 non
industrial private forest owners show that 24% were classified by factorial analysis as
"recreationists" (KARPPINEN 1992). The other three groups were "savers" {17% ), "pro
fessionals" (25% ), and "multi-goal owners" (34% ). Recreationists emphasize the non
wood production values of forests and cut less than the representatives of the other groups.
The average holding size of rccreationists is relatively small. KARPPINEN (1992) concluded that non-wood-production will continue to become more important. However, this will probably not cause a dramatic reduction in roundwood supply, at least in the short run.
According to Stuurman {FAO/ECE 1993, p. 203) it is stated in the Long-Term Forestry Plan that 82% of Dutch forest is multi-functional forest and 18% forest with emphasis on nature development.
The studies referred to do not help in getting information on how multi-goal forestry affects forestry operations, static and dynamic forest variables, forest inventory and growth and development models needed in forest management. Other problems rise from the need to measure and monitor non-wood properties such as biodiversity, landscape, wildlife, and recreation in an appropriate manner for management and decision making.
5.1.2 The Problem
Managing forests in a multi-goal situation requires information on the interrelationships between different input and output combinations. The inventory is a means to assess the current forest status, increment and changes. Information on the relationships will provide the means to develop a management system with growth and change models supporting multiple objectives.
It has been a common practice to simulate alternative sets of treatments as a function of time for forest stands, classes of stands, or individual plots. Optimization methods have then been applied for finding the set of treatments minimizing or maximizing objective functions. A specific simulation and linear programming application, MELA, has been used commonly in Finnish forestry for about ten years now (S I ITONEN 1994).
The application of sophisticated optimizing programs is hampered by the lack of reliable and detailed data. The methods usually apply deterministic models and are one
sidedly sensitive to the deficient quality of growth and other models. The problem is simplified when only one objective function, e.g., maximizing the present value of future net incomes, is applied. In the case of multi-goal optimization weighting can be applied and it may lead to some improvement but does not solve the problem of unreliable data.
A fundamental problem in the case of non-wood production planning is the difficulty of defining the concept of forest function quantitatively. This is understandable as forest conditions vary extensively from one location and time period to another as do man
dependent evaluation criteria.
The purpose of this paper is to describe an outline for studies in order to gain experience and knowledge for future management under multiple use forestry. The
ultimate objective is to derive realistic growth, yield and development (GYD) models for various forest functions under various conditions of forest planning. The task will probably require the use of permanent sample plots and a fairly long time. The suggested approach has its background in Finnish conditions. Conditions elsewhere, however, may not be too different in relation to the problem.
5.1.3 Approach
A forest-owner will probably face three difficulties in carrying out multi-goal forestry planning. He will probably know what the general goals are but not well enough what they imply, what activities or investments they require, and what are their consequences in physical and financial measures. He might be content with a somewhat lower financial outcome than possible but there is a limit and he is keen on knowing the quantities with satisfactory reliability.
The current level of knowledge on multi-goal forestry does not encourage the application of sophisticated methods but rather the acquisition of data having the desirable quality for studying the effects of alternative silvicultural treatments by simple comparisons. Traditional methods, with treatment suggestions based heavily on flexible but subjective judgments become appealing again when plans are made under integrated multi-goal forestry.
The suggested procedure resembles that used when reliable forest growth models were not available. Trees to be cut during the planning period, usually ten years, were marked on sample plots and cutting possibilities were calculated on that basis. It is suggested that in order to carry out multi-goal forestry planning the forest owner first classifies the forest by main forest functions. He then treats each part of the forest according to a certain forest management regime which is dependent on the designated main forest function. He finally monitors the forest and he derives and implements GYD-models in order to determine optimal management regimes.
The suggested approach implies the following five practical items:
1) Classification of the forest according to main forest functions and management regimes,
2) Allocation and establishment of permanent sample plots, 3) Measurement of permanent sample plots,
4) Derivation of GYD-models using permanent sample plot data, and 5) Derivation and implementation of desirable management plans.
As a result, main forest function and management regime is known for every permanent sample plot. It is then the task of forest manager or another professional to identify the needs for treatments on every plot and mark each tree suggested to be removed during the planning period of some five years. This makes it possible to provide information on the quantity and quality of the trees and tree classes suitable for constructing GYD-models.
Marking of trees for cutting includes a subjective element of the system. This can be regarded as harmful and problematic. On the other hand, this subjectivity is difficult to avoid and it offers possibility to link the prevailing ideas on the requirements of non-wood forestry in numerical form. Making concrete suggestions for each plot in relation to area class and existing forest conditions is of central importance for the successful construction of the models needed for multi-function forest planning and, thus, for the whole methodology described in this paper.
The measurement of permanent sample plots and the derivation of GYD-models and desirable management regimes are carried out on a continuous basis. The classification of the forest may be checked and adjusted if necessary.
5.1.4 Forest Classification
The allocation of some area to some forest function class indicates only the principle or direction to which this area should be developed rather than the quantitative and qualitative properties of the future forest. Dividing the forest area into main function classes is based fundamentally on the owner's subjective deliberation. Also the content given to a specific function type is, to a great extent, based on subjective judgments. The actual content is realized afterwards through practicing different types of forestry and analyzing the corresponding results. The definition of a forest function is, thus, statistically derived from experimental data.
Let us assume the forest owner is interested especially in landscape, biodiversity, recreation, and wood production. When defining criteria for deciding which parts of the forest should be allocated to which forest function class, one could assume that, e.g., landscape areas are probably those close to residences, traffic routes, and lakes, and have sufficient amenity properties. Main emphasis to biodiversity may be given for key biotopes, usually exceptional formations, such as creek, swamp, and rocky areas. Key biotopes may be connected together with intermediate areas. Berry, mushroom, and hiking properties may be regarded as important for recreational purposes. Areas which are not of any special importance for other uses can be designated to wood production.
The term management regime is applied here to make forest functions operational. The term has been used e.g. by CLUTTER et al. (1983) for formulating harvest-scheduling problems. "Each management regime defines a strategy involving a series of harvesting/
silvicultural practices that can be implemented during the planning period" (p. 275). In this context management regimes are used to describe, in a rather general manner, sets of silvicultural measures.
As an example, assume that the designated main forest function for a given forest area is biodiversity. One possible management regime related to this specific forest function could be defined as:
1) Cuttings maximize the stand variation in relation to tree species and tree size, 2) Artificial regeneration is not used, and
3) Specific forest improvements ( draining, fertilization) are not used.
The actual operational definition and implementation of a certain management regime is up to the forest owner or forest manager. He decides if any treatments are necessary and he marks the trees to be cut.
5.1.5 Permanent Sample Plots
If the effects of different management strategies on wood production are assessed using field sample plots to generate appropriate data for the construction of GYD-models, the use of permanent plots seems almost compulsory. The plots are placed objectively in the forest area. Each forest function class must, however, be represented well enough. Sample plot measurements should produce information on the quantity and quality of survivor, cut, mortality, and ingrown trees. Since the models will be applied also in structurally
irregular stands. detailed measurements should be carried out on relatively large plots.
Normal inventory plots seem inadequate in this respect because the size of the plots is usually small and the number of measurements quite limited.
When considering the plot type to be used. one must first take into account the type of GYD-models to be constructed. These models may be derived on the plot. tree-group. or single tree levels. Measurements can. likewise. be carried out on these levels. The relative efficiency of different measurement levels is currently still unclear. The single tree level seems. however. recommendable for study purposes. Single tree measurements can also be
used to derive GYD-models of any level.
As an example. basal-area increments were calculated for 20 simulated circular plots of different size in a forest area where all trees were located and enumerated on two occasions. 1982 and 1986 (Tab. 1 ).
Table 1 . Example of calculating basal-area increment by growth components in an experiment area with 20 systematic plots.
- - -
Radius of circular plot, m
Growth Component 3 6 9 12 15
Mean Basal-Area Increment. dm 2/ha. for four years
Survivors 280.7 205.8 227.4 224.8 221.0
Cut 142.7 149.6 151.-1 146.0 137.8
Mortality 0.0 0.2 0.3 0.3 0.2
Ingrowth I 0.0 2.9 3.9 -1.9 5.0
Ongrowth 0.0 -- 0.0 - 0.0 0.0 0.0
Total -123.4 358.5 ---3S2.9 --376.0 364.0
The optimal plot size and tree selection rule will probably vary from case to case and are dependent on the local variances of tree characteristics. the local stand density. and the local spatial distribution of trees. The plot size should be larger the larger the tree characteristic variances. the lower the stand densities. and the higher tree clustering are.
In respect to tree measurements. characteristics related to biological growth theories should be measured with a relatively high accuracy. The following measurements could, e.g., be carried out for each selected tree:
- location.
- species.
- type (living. dead. or cut).
- breast height diameter. and
- treatment proposal (concrete suggestion with marking individual trees).
In addition. the following measurements could be carried out for those trees growing close to the plot center:
- height.
- age.
- height of the lowest live branch. and - damages.
Stem quality assessment can be based on taper functions and on the height of the lowest live branch. The height and age of seedlings under 1 .3 m should also be measured because natural regeneration is an essential part of some management regime strategies.
It is difficult to say what characteristics should be measured in order to assess other main forest function values. It can be, however, stated that a systematic description of the bed-rock, soil, lesser vegetation, bushes, and trees is important for habitat classification.
Habitats may be the key term when assessing, e.g., the biodiversity of a given plot.
5.1.6 Building Production Models for Management
A forest management planning system based on numerical simulation and linear programming. e.g. MELA. will be a useful tool also in multiple use planning if the tree models applied predict growth and yield reliably under various cutting and silvicultural treatments. The empirical ingrowth. growth and mortality models in the MELA system are based on even aged stands. If various management regime strategies are applied, some stands will. however. develop towards an all-aged structure. Existing single tree models must therefore be tested and evidently new models must be constructed for various forest
functions.
5.1.7 Role of Remote Sensing
Remote sensing offers means to divide the forest area or sample of points into homogeneous strata which can then be used for allocating initial permanent field plots (cf.
SCOTT and KOHL 1994). Successive imageries can be used for monitoring changes and adjusting the allocation of field plots.
Remote sensing can also offer possibilities for directly estimating plot and tree characteristics especially if the scale of imaging is large enough. This theme calls, however, for further research and development. Also, new forms of imagery, e.g. imaging spectrometers with an extremely large number of narrow wave-length bands, will probably turn out to be useful in this respect.
5.1.8 Concluding Remarks
It was assumed that the a-priori definition of a forest function in physical measures is not possible or sensible on the current level of knowledge and experience. Hence, the idea of post definition was introduced. First, the forest area is divided into classes labeled according to what is regarded as the main forest function. Then more detailed instructions are given for each main function class. The realization of these instructions is based on subjective judgments by experts or managers. Consequently, the definition of the type of forestry as a basis for production model building will be experimental and statistical in nature.
Permanent sample plots, remote sensing and G IS is recommended for extending this procedure for large areas. The two phase sampling procedure is regarded as recommend
able.
Efficiency studies with systematically sampled field plots {NYYSSONEN et al. 1971) indicate that there is not much difference between plots of 200-1000 m2 in size. Concentric circular and relascope plots were much more efficient than simple circular plots for measuring basal-area and volume of the growing stock. These plot types cause, however, problems when the data are used for constructing GYD-models because the all factors (neighboring trees) effecting the growth of a selected tree are not known completely.
Experiences gained in the application of stratification and the use of sample plot data for model construction suggest that the size of the plot for the largest trees should be at least 500 m2.
This paper does not deal with the influence of different management regimes on non wood properties. This will call for specific studies in which, e.g., different floral and faunal species are identified. Some non-wood properties, e.g. amenity values, must evidently be based on human judgments. The frame described can be regarded as suitable also for these kinds of studies.
5.1.9 References
CLUTTER , J.; FORTSON, J.; BRISTER , G.; BAILEY, R. , 1983: Timber Management. New York, John Wiley & Sons. 329 pp.
FAO/ECE, 1993: Meeting of Experts on Global Forest Resources Assessment (Kotka II). The Finnish Forest Research Institute. Research Papers 469. 214 pp.
KARPPINEN, H., 1992: Metsanomistuksen muuttuvat tavoitteet. Summary: Changing Goals of Private Forest Owners. Tyotehoseuran tiedote 15: 4 pp.
NYYSSONEN , A.; ROIKO-J OKELA, P.; KILKKI, P., 1971: Studies on improvement of the efficiency of systematic sampling in forest inventory. Acta for. fenn. 1 16: 25 pp.
SCOTT, C.T.; KOHL, M., 1994: Sampling with Partial Replacement and Stratification. For. sci. 40, 1 : 30-46.
SIITONEN , M., 1993: Experiences in the use of forest management planning models. Silva fenn. 27, 2:
167-178.
