=Paper= {{Paper |id=Vol-2131/paper4 |storemode=property |title=Development of the Control System for State Forest Inventory |pdfUrl=https://ceur-ws.org/Vol-2131/paper4.pdf |volume=Vol-2131 |authors=Alexander V. Suslov,Regina Z. Mullagalieva }} ==Development of the Control System for State Forest Inventory== https://ceur-ws.org/Vol-2131/paper4.pdf
          Development of the Control System for State Forest Inventory

            Alexander V. Suslov                                         Regina Z. Mullagalieva
 Ural State Forestry Engineering University                   Ural State Forestry Engineering University
       620100, Russia, Ekaterinburg,                                620100, Russia, Ekaterinburg,
          890287166886@mail.ru                                            reginamz@mail.ru


                                                         Annotation

                          A work presents questions of the possibility of using the system of
                       state forest inventory for the purpose of forest plantations monitoring on
                       of the territory of the forest park named after Foresters of Russia as some
                       example. Stratification of forest plantations of the forest park was carried
                       out. The number of trial plots is calculated. 25 trial plots in light
                       coniferous ripe and over mature high-productive plantations have been
                       laid and an assessment of their condition has been made.



  1.   Introduction

   Urban forests of Yekaterinburg and its environs are an integral part of the city-building structure and the most
important part of its ecological framework. They are included in the life support system as a fundamental environment-
forming and environmental protection factor. At the same time, Yekaterinburg with a large population and developed
industry is one of the largest and most dirty cities in Russia. Currently, one of the most acute problems of the city is the
state of the environment.
   Forest ecosystems are a powerful stabilizer of the ongoing destructive anthropogenic processes. City forests perform
important environment regulative, environment formative and recreational functions. They can be used as one of the
most suitable objects for accounting, estimating and forecasting the impact of human activity on the environment. By
analyzing the state of forest ecosystems, it is possible to reliably monitor and predict the environmental consequences of
anthropogenic influences. Environmental monitoring is the most effective way to solve such problems [4].
   In our country, basic information about forests is obtained by carrying out a forest management, because of
differentiated taxation. In recreational forests, the sanitary and hygienic assessment, the stage of recreational digression,
and the biological resistance of forest plantations are additionally indicated. For these indicators, a general assessment
of the state of forests can be made. Taking into account that the main taxation is carried out in a visual manner with a
relatively low accuracy of the determination of taxation indicators, forest inventory materials cannot provide reliable
information about the forests and their condition.
   With the adoption of the Forest Code (Article 90), in addition to forest management, a new type of forest inventory
work was introduced — the State Forest Inventory (SFI). When organizing SFI, quantitative and qualitative
characteristics of forests are determined by mathematical-co-statistical method by laying circular trial plots (TP). One of
the main goals of the SFI is the timely identification and prediction of the development of processes that have a
negative impact on forests. As a result of the State forest inventory, more reliable data are obtained in comparison with
the forest management inventory (the accuracy of the forest reserve determining the in the Middle Urals taiga region is
2%). In the field, 117 indicators are determined, taking into account, among other things, the state of the plantations.
Thus, SFI activities are a full-scale forest monitoring system, while their object is the forest area of the Russian
Federation.
   Currently monitoring of the state of urban forests in our country has not yet been developed. The need for its
organization is conditioned by the existing need to restore and improve the sustainability of forests in a more
complicated ecological situation. Scientifically conditioned monitoring of the state of urban forests can be included in
the general system of monitoring and the environment state control [3].
   We propose to use the developed SFI activities as the main means of monitoring the state of the city forests of
Yekaterinburg. The city is unique by the presence of natural forest plantations, which represent the so-called "green
ring" of 15 forest parks with a total area of 12097.5 hectares of forest inventory in 2007 and urban forests with a total
area of about 11274 hectares. No other city of Russia has such preserved natural forest plantations. Therefore, a
scientifically based system for urban forests monitoring in Yekaterinburg can be the basis for developing a common
methodological concept of these systems in large cities of Russia, including the creation of so-called "ecological
skeletons".
   The purpose of the work is the organization of monitoring forest plantations of the forest park named after Foresters
of Russia because of the use of the State Forest Inventory (SFI).
   Forest Park named after Foresters of Russia is located in the southeastern part of the city of Yekaterinburg. Its area is
882.6 hectares, of which 763 hectares are covered with forest vegetation.

   2. Technique
   According to the "Methodological Recommendations for the State Forest Inventory" approved by the order of
Rosleskhoz of November 10, 2011, No. 472, a single scheme of stratification of forests of the Russian Federation in
determining the quantitative and qualitative characteristics of forests is applied. Stratification is based on the use of
high-level forest management databases by grouping forest plantations into homogeneous groups (forest strata). 49
strata were singled out in methodical recommendations, depending on the age group, bonitet group and stock groups
[2].
   When stratification is carried out, the variation of reserves within strata is reduced (in the mathematical-statistical
sense), and homogeneous types of groups covered and not covered by forest vegetation of lands (a typical type of
sample by qualitative characteristic) are distinguished by qualitative characteristics. With this approach, the greatest
effect is achieved from stratification of forests with the achievement of the planned accuracy of accounting for land
areas and timber reserves with an optimal selective population (the number of trial plots).
   On the territory forest park named after Foresters of Russia. We identified 18 strata in forest areas covered by forest
vegetation. As a result, the data presented in Table 1 were obtained. Their analysis shows that the territory of the forest
park named after Foresters of Russia are significantly dominated by 10 strata (light coniferous ripe and over-productive
high yielding) — 77%. This indicates a low stability of forests. The carrying out of monitoring and assessment of the
state of plantations will be of particular importance here.
   The number of trial plots for each stratum is determined by the formula:
                                                                       ,
   where, S is the variance of wood reserves; t² — the value of the Student's test; X — mean value of wood stock m² /
ha; g — target accuracy (in units of units) 1%, 5% and 10% [1].
   The calculation of the number of trial plots is given in Table 1. The data show that with the increase in the accuracy
index, the number of trial plots decreases, so when the basic accuracy (g = 1%) is taken, their number is 13179 pieces,
with g = 5% — 527 pieces, g = 5% — 132 pcs. It should be noted that according to the formula above-mentioned the
number of TP does not depend on the area of the object. For assessing the state of the forest park in the forest park
named after Foresters of Russia, we consider it sufficient to lay 132 trial plots at 10% accuracy of determining the
reserves. For the study of all urban forests, it is necessary to include their entire area.
   To determine the locations of the trial plot laying, a digital basis was created because of differentiated data of forest
inventory materials. The created digital base is used to locate the centers of trial plots, by a method combining a
systematic and random sampling. Trial areas were located in the corresponding forest strata in a random manner while
simultaneously striving for an equal number of their location throughout the work site. For this, a systematic sampling
over a virtual network with a random spread is used. Placing the required number of trial plots is done with the
determination of the coordinates of their centers.
   In our work, when creating a digital basis, we used the software Quantum GIS and MapInfo Pro. On the territory of
the forest park on a digital basis, only 132 PPs were placed in the WGS-84 coordinate system, and in each stratum, their
number corresponds to the calculated index in Table 1. In the 10 strata, 28 trial plots were placed. Thus, a bio indication
ecological monitoring network was established on the territory of the forest park named after. Foresters of Russia,
shown in Figure 1.
   It should be noted that the established bio indication-monitoring network in case of random placement does not take
into account the intensity of anthropogenic impact. It is obvious that road transport, recreational loads, industrial
emissions and others will affect the forest plantations depending on their location with different degrees of influence.
To solve this problem, it may be expedient to double stratify these factors. In our work, we did not set out to carry out
such an analysis.
                                   Table 1: Stratification of forest plantations and calculation of trial plots in the territory of the forest park named after Foresters of Russia
                                                                                                          total weight of




                                                                                                                              Specific weight of strata
                                                                                number of allotments in
                                                                                                                                                                                      area, ha                                                                      No. of trial plots
                                                                                                               strata
Number of strata




                                                                                                                                                                                                                                    variability,%
                                                                                                                                                                                        maximum value




                                                                                                                                                                                                                                                     dispersion
                                                                                                                                                                      minimum value




                                                                                                                                                                                                                        deviation
                                                                                                                                                                                                        average value
                                                                                                                                                           area, ha
                                                                                       stratum
                                      Name of stratum




                                                                                                           area, ha
                                                                                                                       %                                                                                                                                          1%       5%      10 %



   2               young litters of natural origin                                      5                   3         0.4     0.09                         231        50               110               77             26          34               680           375      15       4
   3               artificial litter                                                    8                  6.8        0.9     0.14                         992        10               330              146             93          64              8686          2137      85       21
                   light middle-aged medium-productive and high-
   4                                                                                  43                  47.7        6.3     0.73                        15895       110              540              333             111         33              12230         3100     124       31
                   productive
7                  light coniferous ripening high- productive                       4                      8.7         1.1    0.07                         3710       260              480              426             96          22              9158           132      5        1
10                 light coniferous ripe and over mature high- productive          129                    593.2       77.7    2.20                        228305      250              590              385             70          18              4861          2771     111       28
                   light coniferous ripe and over-productive medium-
11                                                                                      6                  5.8        0.8     0.10                        1298        110              420              224             103         46              10710         840       34       8
                   productive
26                 soft-leaved middling medium-productive                             3                    2          0.3     0.05                        577         150              370              289             124         43              15433          364      15       4
29                 soft-leaved ripe and over-productive medium-productive             22                  13.6        1.8     0.37                        3725        150              460              274              84         31              6999          1343      54       13
                   small-leaved middle-aged medium-productive and high-
31                                                                                      4                  3.4        0.4     0.07                         469        30               180              138             68          49              4600          633       25       6
                   productive
                   small-leaf ruminant ripe and over-mature high-
37                                                                                    18                   47         6.2     0.31                        12370       160              380              290             75          26              5612          788       32       8
                   performance
                   small-leaved ripe and over-productive medium-
38                                                                                    13                  26.4        3.5     0.22                        4140        70               210              157             42          27              1758          608       24       6
                   productive
                   hard-leafed medium-aged and mating medium-
41                                                                                      3                  3.1        0.4     0.05                         231        60                90               75             18          25              233            83        3       1
                   productive
                   solid hard maturing, ripe and over mature high-
43                                                                                      2                  2.3        0.3     0.03                         243        100              110              106               7            7             50            6         0       0
                   performance total
                   Subtotal                                                        260                    763         100.0   4.43                        272186                                        2918                                                      13179    527      132
    Fig. 1. Bio indication network of ecological monitoring on the territory of the forest park named after Foresters of
                                                        Russia.


   To assess the application of the system (SFI) for monitoring forest plantations, we conducted field studies with the
insertion of PP in the most predominant stratum. The test area was laid at a point with predetermined coordinates of the
center of the trial plot. To do this, the TP network was loaded into the navigator. We used the GARMIN OREGON
650T navigator. Work began with the binding and fixation of the armored peg center of the TP.
   The test area, with a total area of 500 m², is a concentric circular platform of a constant radius of 12.62 m. within the
limits of which measurements and mapping of trees were carried out. The scheme of the trial plot is shown in Figure 2.
   For field data collection, we used the Field-Map program-measuring complex (PMC Field-Map), which allows to
measure and map trees in real time. The PMC Field-Map used consists of a field table computer with installed software,
a laser rangefinder with Bluetooth, a circular reflector, an electronic measuring plug.
   The position of the tree on the trial plot is mapped using a laser distance number with the capabilities of an electronic
goniometer, an electronic compass, and a reflector placed on the surface of the trunk. Field-Map automatically checks
whether the tree is inside or outside the trial plot, so you do not need to mark the area boundary on the terrain.
   In the process of measuring by the laser rangefinder, PMC Field-Map automatically numbers the trees and assigns
them to the sides of the world in automatic mode. In attributive data of the program, in addition to taxation indicators,
information was included on the evaluation of the state of trees. The use of modern information technologies, with the
ability to determine accurate data on the location of trees and their condition is important in monitoring forest
plantations.
   In the course of the work, we laid 25 trial plots in 10 strata of light coniferous ripe and over mature high-productive
plantations.
                                                 Fig.2. Scheme of the trial plot
   In the basis of assessing the state of trees, we followed the methodology of European monitoring of forests [5, 6],
according to which the conditions of the surveyed plants were determined on the basis of the use of the bio indication
method, in which morphological changes in trees were taken into account. The most important bio indication signs of
tree damage are de-foliation (loss of needles and foliage) and dechromation (discolouration) of tree crowns. Deflation
and dechromation of tree crowns were estimated as a percentage with an accuracy of 5-10%. Based on these indices, the
integral classes of tree damage are given in Table 2.

                                         Table 2: Integral class of damage to trees
                                 Degree of defoliation, % Degree of needle color change, %

                                                        Degree of needle color change, %
   Degree of defoliation, %
                                       < 10               11-25                26-60                    > 60
              < 10                      0                   0                    1                       2
             11-25                      0                   1                    2                       2
             26-60                      1                   2                    3                       3
             61-99                      3                   3                    3                       3
              100                       4                   4                    4                       4


  2.     Results and its analysis

   To assess the vital state of the tree stands, the state index (I, scores) is used, which is a weighted average damage
class of tree stand components:

                                                             ∑         W,


   where i — numbers of classes of damage of trees, points; wi — number of trees of the i-th class of damage in the
given plantation; W is the total number of trees.
   By the value of the index of the state, the stands are classified as follows: healthy 0-0.5; weakened by 0.6-1.5;
damaged by 1.6-2.5; dying off 2.6-3.5; deadly > 3.6 points.In addition, the category of the state of each tree was
determined.
   Data on defoliation, dechromation and index of coniferous stands assessment for all trial plots

   Table 3: Data on t of defoliation, dechromation and index of coniferous stands assessment in the forest park named
after Foresters of Russia.
   Number of trees         Number of dead                                     Mean values
   examined, pcs.           trees, pcs. /%          defoliation, %      dechromation, %     state index, score

         392                     18/5                     37                    22                 1.76


   According to the European method, light coniferous ripe and over mature pine stands are damaged (I = 1.76). The
maximum values of defoliation and dechromatization are 100%; this is due to the presence of dead trees, the smallest —
5%. The total number of examined trees was 392 pieces, of which about 5% are deadwood.
   For the purpose of spatial analysis of forest plantation monitoring, indicators of the state of pine plantations were
separately determined for each trial plot. The results are shown in Figure 3. The histogram data showed an uneven
distribution of the TP by the state index. Its minimum value is marked by 6 TP and is 0.61; the maximum value on
sample 24 is 2.92. The coefficient of variation for these indicators is about 40%, which indicates a heterogeneous
population with a wide range of values. Perhaps such dispersion is associated with the location of trial plots, depending
on the degree of impact of anthropogenic factors. In this paper, we did not conduct such studies, TP were located
randomly through the forest park in 10 strata.




                                        Fig.3. Distribution of trial plots by state index

   Generalized data on the distribution of PP by the state index are shown in Fig. 4. In the laid 10 stratum in the light
coniferous ripe and over mature high-productive plantations, PP with a state index from 1.6 to 2.5 (damaged)
predominate. The weakened stands are represented by 9 TP; the proportion of dying off is 20% (5 TP). In general, given
that the TPs were laid in pine outposts with an average age of about 180 years, this distribution indicates a relatively
good state of the plantations.
                                                    5
                                                                  9

                                                     11


                                         weakened           damaged           dying out
                                      Fig.4. Distribution of TP according to their state

   The main parameters of the bio indication network for terrestrial monitoring of forests of a certain area is the total
number of model trees that are located on the PP. In monitoring, first of all, it is necessary to justify the number of
surveyed trees required for reliable conclusions about the state of forests [1]. To this end, we used the Chebyshev
equation:
                                                       N = s2 / a2 P,
   where, s² is the variance of the distribution of trees by damage classes, and is the admissible error in determining the
average damage class and P is the probability of deviation of the true mean value of the damage to trees from the
calculated one. To calculate this formula, you need to specify the values of the values in its right-hand side. The
admissible error in determining the average damage class (a) should be put equal to 0.1, and the error probability P =
0.05 [1].
   In our case, when examining 392 trees, the variance is 1.5. Substituting the values into equality, the required number
of trees to evaluate the state is 300 pieces. Thus, the methodological recommendations for conducting the state forest
inventory of Russia with the stratification carried out on the territory of the forest park named after Foresters of Russia
were used as a basis for demonstrating the possibility of using them to create a monitoring of the city's forests in
Ekaterinburg.


          3.      Conclusions

     Stratification of forests on the territory of the forest park named after Foresters of Russia showed a significant
predominance of light coniferous ripe and over mature high-productive plantations. In conditions of high anthropogenic
factors, monitoring of forests is of particular importance here.
   The use of (SFI) activities to monitor urban forests has given:
   1. The random placement of trial plots in space does not take into account the anthropogenic impact. It is necessary
to improve the algorithm for determining the places of TP insertion.
   2. A field survey showed a damaged state of plantations in 10 countries.
   3. The state of plantations in trial plots is not uniform.
   4. A sufficient number of examined trees.
   The State Forest Inventory has a well-developed methodology based on modern information technologies. Its use and
improvement with the aim of creating monitoring of urban forests can have broad practical application

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