The state of forests has always been of great importance from the point of view of ecology, nature conservation, forestry and forestry industry due to their unique economic, environmental and social functions. In recent years, according to the ICP-Forests monitoring system [1], there has been a deterioration in the state of forests in Europe, for example, almost a third of the test areas of the system are moderately or severely damaged, and the dynamics over time of mean defoliation of tree crowns shows its increase, on the one hand, and a decrease in the number of completely healthy trees, on the other.

The main damaging forest factors are forest fires, insect attacks, pests and diseases, unfavorable weather conditions and different kinds of anthropogenic influences. It is shown that in Europe visible signs of tree damage were presented on almost half of the trees monitored in 2019. The most frequently reported biotic factors causing damage to trees were insects and drought became the main abiotic factor.

In Russia, forest fires are the main factor damaging forests, the areas of forests that they damage have increased substantially in recent years and amount to many millions of hectares [2].

Thus, it is confirmed that forest damage occurs over vast areas of millions of hectares. To counteract this, it is necessary to organize continuous monitoring of forests. The condition of forests is assessed as a result of their survey on networks of test areas, for example, according to the ICP-Forests program or by special expeditionary research. In both cases, the share of the surveyed area is a small part of the entire controlled territory, which makes it necessary to transfer information from the surveyed test areas or routes to the entire territory. Such a task must be solved with the help of remotely sensed data of the Earth, data from ground test areas and routes, as well as special statistical methods for processing combined information from these two sources.

The close to border forests health status is of large issue of both countries Russian Federation and Finland first due to the fact of possible chances for сross-border transport of pollutants, forest fires, pests and diseases. This territory is relatively dense populated. There is a well-developed road network from the both sides of the border. This area intensively used for different kinds of economic and social activities. Some of them such as forestry, recreation, tourism, sport, hunting, fishing, ecological farming, schooling and training are sensitive to forest health status. That is why authorities at regional and municipal levels, including forest authorities and nongovernmental groups of interests representing local people, are interested in the information on study area forests status. It is practical to use for the tree stands health status estimations on study area the level 1 regular grid of PSP established according ICP-Forests methodologies. At least two motives may also be referred to in want of such a decision: first, ICP-Forests program used to be applied for a long time in both countries, so about the identical grids of sample plots are already existed in each country, second, the methodology of field assessment of sample plots is exactly the identical and the results of all estimations and assessments will be comparable, which is very essential for between countries comparisons and conclusions.

The main objectives of this study were to test the utility of the k-NN method to assess the state of forests throughout the whole studied territory by joint statistical processing of data from ground sample plots and Sentinel-2B imagery, as well as examine the possibility of using the short-wave vegetation index (SWVI) calibrated on ground-based data to assess the state of forests of study area. In the course of the study, the idea of mandatory joint use of Earth remote sensing materials and ground-based sample areas was considered. To obtain reliable characteristics of the state of forests, it is necessary to calibrate and verify remotely sensed data based on field sample plots. Otherwise, the picture of the state of forests may be distorted.

The k-NN method of remotely sensed data classification occupies a special place among other methods for researchers in the field of forest management and forest monitoring. This method makes it possible to combine in one procedure the use of Earth remotely sensed data and data obtained from ground-based polygons. This possibility may be of interest for assessing the state of forests. The k-NN method for studying forests was first proposed and then put into practice by Finnish researchers [3] [4] [5] [6]. In the early 2000s, many studies were published aimed at a comprehensive analysis of the possibilities and limitations of the k-NN method [7] - [12].

Part of the publications related to k-NN is devoted to the results of the experimental use of the method to solve the problems of the national forest inventory (NFI) in individual countries that differ in natural and economic conditions, forest management features, as well as tasks solved within the framework of the NFI [13] [14] [15]. Experiments on the use of the k-NN method were carried out in the NFI system of Sweden, USA, Norway, China, Germany and some other countries [16].

In recent decades, the study of the use of various vegetation indices for classifying and assessing the characteristics and health of vegetation, soils, and crops has become one of the most popular areas of remote methods. Additional reasons for the development of this direction are substantial technical progress associated with an increase in the number of satellites, improvement in the characteristics of remotely sensed materials, increasing their availability for numerous users, and continuous improvement of software and processing algorithms.

The basic foundations of the definition and interpretation of vegetation indices are set out in a number of textbooks and monographs [17] - [23]. Remotely sensed data processing programs, geoinformation systems and special applications can serve as tools for determining and studying vegetation indices based on remotely sensed materials [24] [25].

Brief overviews of spectral characteristics of vegetation as properly as 27 vegetation indices which might also be determined by software ENVI are performed in [26]. It is mentioned that the predominant advantage of vegetation indices is the ease of obtaining them and a broad vary of tasks solved with their help. It is also noted that for particular natural conditions and number tasks, some indexes can provide more accurate predictions than others. The determination of an index that displays the investigated property as precisely as possible is carried out via comparing the results of index calculations with field data. It is additionally noted that any vegetation indices provide only relative estimates of the properties of vegetation cover, which can be interpreted and recalculated into absolute ones with the involvement of field data. The index values are influenced by using the characteristics of the sensor and shooting conditions [18] [27].

A large number of studies are devoted to solving the problems of identifying and analyzing the relationship between vegetation indices and forest characteristics, as well as developing and improving forest monitoring methodology based on satellite data [28] - [33].

The studies became carried out on the territory of the Vyborg district of the Leningrad region. The trial regions are organized as a regular network with a step of 4 - 8 km. Every trial area was implemented as permanent sample plot (PSP) and is a 4-element cluster North-South and West-East orientated, including 4 subplots on each of which became evaluated the state of 6 specially selected sample trees. The methodology of undertaking field work is defined in detail in [34] and effectively was extensively used in some of different special researches [35] [36] [37] [38].

A total of 99 sample plots were surveyed, during the field work and then included in the database for further processing. In Table 1 data on the predominant tree species in the surveyed sample areas are given. Six species of woody plant became tested on PSP: Scots pine (Pinus sylvestris), Norway spruce (Picea abies), Birch (Betula pendula), Aspen (Populus tremula), Black alder (Alnus glutinosa) and Gray alder (Alnus incana). Four of them Scots pine, Norway spruce, Birch and Aspen occupied a dominant position on PSP even as two species Black and Grey alder have been presented through single trees.

From the data in Table 1 and Figure 1, it can be seen that Scots pine and Norway spruce predominate on the territory—they occupy 87.9% of the total number of sample plots. The health status of 2416 trees was assessed using the ICP-Forests methodology based on estimates of defoliation and discoloration of trees crowns assessments during field work. As a result, every sample tree become attributed via health status grade: 0—complete health tree, 1—slightly damaged, 2—moderately damaged, 3—severely damaged and 4—dead tree. Species distribution of the trees is provided in Table 2.

Table 2 data demonstrate that in the total number of model trees evaluated in the study area in the Vyborgsky district of the Leningrad region, Scots pine and Norway spruce predominate.

The developed method of field observations at each PSP included the registration in a circle with a radius of 100 m from the central tree of a set of additional characteristics related to the tree stand health and vitality: occurrence of infrastructural elements, garbage dumps, footprints of cuttings, thinning, forest fires, wind withdrawals, snow and frost damage to trees, recreation footprints (fire places, campfire, walking trail) and other signs of forest damage. With the help of maps and satellite data, the proximity of the PSP to quarries and settlements was determined.

To characterize the forest health status, mean damage class was calculated for each sample plots (see Table 3). This variable is considered as a quantitative measure of forest health status at tree stands level and as initial data for spatial analysis in geographic information systems (GIS).

To be able to interpret Table 3 data statistics the subsequent scale was used: full healthy tree stands were those with a mean damage class in the range of 0 - 0.5, slightly damaged 0.51 - 1.50, moderately 1.51 - 2.50, severely damaged 2.51 - 3.50, dying and dead 3.51 - 4.00. Table 3 information says that stands of all tree species are either healthy (spruce, birch) or slightly damaged (pine), broadleaves are in better condition than coniferous ones, and pine stands are most damaged. In general, the state of stands in the border region of Russian Federation and Finland on the part of Russia, within the Vyborg district of the Leningrad region, may be assessed as good: 56 sample plots were assessed as completely healthy and 43 as slightly damaged. To increase the statistical representation of the tree stands with negative health status, forest allotments with severe

damaged, dying and dead tree stands were added to the ground based data. To solve this problem, forest management data from standard geoinformation databases of the North-Western forestry enterprise were used. With the help of WinGIS and PLP-2015 programs, a set of forest parcels was selected from databases, for which the degree of damage was marked as severely damaged, drying out, as well as dead stands. Then, a set of polygons with the boundaries of forest parcels with severely damaged, drying and dead stands was exported from the forest management databases to the GIS. The suitability of selected plots for further analysis was assessed using fine spatial resolution images from the public domain (Bing, Google and Yandex). In total, 153 ground sample areas were used during processing, which were divided into two equal groups. Training trial plots were used to classify the territory according to the degree of vegetation damage, and control plots were used to assess the quality of the classification.

The k-NN approach of forest classification based totally on the joint use of remotely sensed data and ground-based sample areas has been broadly used in many countries for national forest inventories, mainly in Finland. With its help, objective data for large areas have been acquired during the forest inventory and the method demonstrated its practical effectiveness. In this study the k-NN approach was used to predict the condition and extent of damage to tree stands, which are also of great significance for solving some specific problems of sustainable forest management. The results of the application of developed methodology to assess the state of forests over large areas based on the k-NN approach were positive. A very important result is that the approach does not give systematic errors, and a random error creates uncertainty only when determining neighboring classes of damage to tree stands, which is not critical for its application. This method makes it possible to extrapolate data on the health status of tree stands on PSP, which in the geographical sense are points, to the entire territory represented through the satellite image. Applicability of the k-NN approach for estimation of forest health in assembles with ICP-Forest ground PSP is of significance due to the fact this network covers all the Europe and similar monitoring grids exist in the number of countries.

Some other end results of this study consist in revealed high sensitivity of short-wave vegetation index SWVI on tree stand health state as reflected by using its mean damage class. The value of SWVI varies as much as 4 times for healthy and dead tree stands. The higher the index value, the better the health status of plantings. If in analysis use the 3 aggregated damage classes instead of 5 preliminaries usually used the resolution ability of vegetation index SWVI elevates as much as twice and relationship between index value and aggregated damage classes became linear with large determination coefficient. This feature may be successively used for thematic mapping by means of GIS technology for visualization and evaluation of forest health state at the regions of interest. The practical application of this approach may be related to monitoring the condition of forests in remote areas. Thematic maps that replicate the state of plantings may be interesting to various fascinated organizations and agencies of citizens, such as state forestry management bodies, environmental organizations, hunters, fishermen and different recreational workers.

Generally, the methods primarily based on k-NN classification and use of vegetation index SWVI can be recommended for solving many issues of forestry related to monitoring the state of forests. Mainly, it could be used to organize nonstop monitoring of the health status of forests on the basis of acquiring updated materials of remote sensing for constantly informing involved companies of the population about the consequences.

This study was carried out with the financial support of the project No 21-16-00065: “The role of insects and pathogens in the weakening and death of coniferous stands in the North-West of the Russian Federation: quantification and monitoring” funded by the Russian Science Foundation.

The authors declare no conflicts of interest regarding the publication of this paper.

Alekseev, A.S. and Chernikhovskii, D.M. (2022) Assessment of the State of Forests Based on Joint Statistical Processing of Sentinel-2B Remote Sensing Data and the Data from Network of Ground-Based ICP-Forests Sample Plots. Open Journal of Ecology, 12, 513-528. https://doi.org/10.4236/oje.2022.128028