Original Russian Text © 2022 D. V. Ershov, E. N. Sochilova published in Forest Science Issues Vol. 5, No. 4, Article 117

© 2023                                        D. V. Ershov^*, E. N. Sochilova

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russian Federation

^*Email: dvershov67@gmail.com

Received: 28.11.2022

Revised: 15.12.2022

Accepted: 18.12.2022

This paper presents the statistics of direct wildfire carbon emission estimates during the wildfires of 2021 on forest lands of Russia using long-term satellite data. In 2021, the area affected by forest wildfires was 9.3 million ha, while carbon emissions amounted to 66.4 MtC. Said values are almost two points higher than the long-term average values. A comparison of similar indicators for twenty years allowed us to conclude that said year was anomalous with respect to the entire time series, similar to the wildfire seasons of 2003 and 2012. A period or interval for recurrence of three anomalous wildfire seasons is nine years. The reason for the recurrence of anomalous wildfire seasons is yet to be found. At the same time, the forest areas affected by wildfires, and direct carbon and other greenhouse gas emissions in anomalous wildfire years decreased from 127.2 MtC (3.7 times) in 2003 to 83.8 MtC (2.4 times) in 2012, and to 66.4 MtC (1.9 times) in 2021.

Keywords: wildfires, pyrogenic emissions, carbon, remote sensing, forest fuels

The task of estimating and monitoring yearly direct wildfire greenhouse gas emissions using remote sensing data from space is being solved by many research teams in Russia. The results and databases with long-term estimates of direct pyrogenic carbon emissions in the 21st century were published in a number of studies, in particular by A. Shvidenko and D. Schepaschenko (Shvidenko, Schepaschenko, 2013), V. Kharuk et al. (Kharuk et al., 2021), and E. Ponomarev et al. (Ponomarev et al., 2021). A. Shvidenko and D. Schepaschenko found that, for the period from 1998 to 2010, on average, the annual values of direct wildfire carbon emissions in Russia were 121 ± 28 MtC, of which 92 ± 18 MtC (2/3 of total emissions) were associated with wildfires on forest lands. V. Kharuk et al. (2021) presented an assessment of areas affected by wildfires in Central Siberia for the period from 1999 to 2019 according to remote sensing data. The authors state that 30% of all satellite data of wildfires detected in Central Siberia are detected on forested cover lands. The average long-term values of direct wildfire pyrogenic carbon emissions in the 21st century in Siberia are estimated at 85 ± 20 MtC per year.

Ponomarev et al. (2021) list even greater amounts of wildfire emissions in Central Siberia for the period of 2002–2020. According to them, average pyrogenic carbon emissions amounted to 80 ± 20 MtC/year in the first decade of the 21st century, and 110 ± 20 MtC/year in the second decade. At the same time, the authors note that in the anomalous fire seasons of 2003, 2012, and 2019, direct wildfire carbon emissions amounted to over 150 MtC/year, 220 MtC/year, and 180 MtC/year, respectively.

Each team used its own techniques to calculate pre-fire forest fuel reserves, models to determine wildfire type and intensity, and methods to estimate direct pyrogenic carbon emissions. For example, E. Ponomarev et al. (Ponomarev et al., 2021) used in their models the values of fire radiative power for each MODIS image pixel when calculating wildfire areas for wildfires of varying (low, medium, and high) degrees of intensity. Areas covered by wildfires were spatially tied to thematic classes of vegetation maps (VEGA-PRO, 2022); based on generalized data on forest fuel (FF) reserves collected from literature sources and ground measurements, total pre-fire FF reserves (in the range from 13.8 to 54.0 t/ha) for different types of woody vegetation were determined. Using empirical estimates, again collected from research papers, the authors defined the ranges of conversion rates and volumes of combustible FF in wildfires of various intensities. Thus, the authors cite the following ranges of biomass reserve consumption for major conductors of forest fuel combustion in low, medium, and high intensity wildfires: 1.1–9.7 t/ha, 8.6–21.5 t/ha, and 22.5–53.6 t/ha, respectively. These ranges are then used to quantify direct wildfire carbon emissions in Siberia.

The purpose of the study presented in this paper is to estimate direct pyrogenic carbon emissions 2021 based on the developed and tested methodology (Ershov et al., 2009), as well as analyze and compare the obtained estimates with long-term satellite monitoring over forest wildfires as well as direct pyrogenic emissions of carbon and other greenhouse gases (2002–2020).

MATERIALS AND METHODS

We used our methodology for assessing annual direct pyrogenic carbon emissions from wildfires for Russian forests at the national level. Carbon emission methodology on pixel level assessment of spatially distributed data (raster maps) on wildfires and pre-fire forest fuel reserves of low spatial resolution MODIS (230 m) is based. For each pixel with a thematic class of forest cover in the vegetation map of terrestrial ecosystems (Bartalev et al., 2016), FF reserves of the upper canopy, new growth, undergrowth, living ground cover, and forest litter (t/ha) were assessed according to the methodology (Sochilova et al., 2009). The obtained digital raster layers form the basis for a spatial framework of pre-fire forest fuel. The ranges of minimum and maximum FF reserves for forest classes on the vegetation map listed in Table 1 show comparable values provided by the article by E. I. Ponomarev et al. (2021) and by other authors (Vonskij, 1957; Kurbatskij, 1970; Sheshukov, 1970; Tsvetkov, 2001; Fedorov, Cykalov, 2002; Furjaev et al., 2007; Matveeva, 2008; Furjaev et al., 2009; Kovaleva et al., 2017). Some underestimations of the average biomass reserves of combustion conductors are probably because the calculations used the 2006 state forest accounting data and the corresponding conversion rates (Zamolodchikov et al., 2003), as well as the database on biomass and forest productivity at test plots, collected from study materials (Utkin et al., 1994). It may be necessary to update the databases and improve the calculation methods. Moreover, the calculation of the biomass FF reserves does not include data for logs and coarse woody debris, which could also affect the total reserves of combustion conductors. In upcoming our papers, significant changes will be made in the calculations of FF biomass reserves based on a number of models the authors published for the following layers: (1) tree biomass (Schepaschenko et al., 2018); (2) lower tree layers —undergrowth and forest shrubs; (3) living ground vegetation cover (Shvidenko et al., 2008); (4) coarse woody debris (Shvidenko et al., 2009); and (5) forest litter (Schepaschenko et al., 2013).

Pyrogenic carbon emissions in forests are determined based on data on the pre-fire reserves of main FF combustion conductors, as well as wildfire type and intensity (Ershov et al., 2016), corresponding consumed forest fuel reserves and corresponding volumes of carbon and greenhouse gases. Spatial wildfire data is generated every year and obtained from Datacenter IKI-Monitoring (Lupjan et al., 2019).

Table 1. Ranges of biomass reserves of main FF combustion conductors by types of vegetation maps of terrestrial ecosystems (Bartalev et al., 2016)

To determine the type and intensity of a forest wildfire, athematic raster product with forest tree condition damaged by wildfire (Stycenko et al., 2013) as well as forest classes of vegetation map (Bartalev et al., 2016) are used. Depending on the degree of damage to coniferous or deciduous forests in each pixel of the vegetation map, the wildfire type (crown fire or ground fire), as well as the degree of ground fire intensity are determined. The obtained raster product of the wildfire type and intensity to determine the proportion of consumed combustion conductor reserves for upper tree canopy, undergrowth, shrub, living ground vegetation cover, and forest litter is then used. At the final stage, the biomass reserves of all layers of FF vertical profile are combined into a common indicator. The total consumed biomass reserves are then reduced by half and converted to direct pyrogenic carbon emissions. To obtain the estimates of greenhouse gases, conversion rates are used that were published in the paper of D. Zamolodchikov et al. (Zamolodchikov et al., 2005).

RESULTS AND DISCUSSION

As a result of applying our methodology we determined the ranges (relative to average values) of the biomass FF consumption for various wildfire types and intensities during the fire season of 2021. The estimates obtained for biomass reserve consumption as a result of low, medium, and high intensity ground fires, as well as crown fires on the forested cover lands of Russia in 2021 range as follows: 0.05–5.46 t/ha (on average 1.62 ± 0.55 t/ha), 1.4–25.33 t/ha (on average 9.51 ± 1.97 t/ha), 0.3–43.25 t/ha (on average 14.37 ± 4.79 t/ha) and 12.20–66.32 t/ha (on average 24.62 ± 2.35 t/ha), respectively. The values of average biomass consumption of main FF combustion conductors are somewhat underestimated as compared with the literature sources cited in the introduction of this paper, as noted above, due to underestimation of pre-fire fuel reserves of the main FF combustion conductors. The scale of this underestimation has yet to be clarified using ground-based data on biomass reserves in forests and other terrestrial ecosystems, whose collection is supported by a research grant as part of development of a national system for monitoring over climatically active substances (Decree …, 2022).

We presented the estimated wildfire emissions over a long period of observations (2002–2021) at the scientific conference “Research Foundations of Sustainable Forest Management” (Ershov et al., 2022). According to the satellite monitoring data (2002–2021), the total area of forests affected by wildfires over 20 years amounted to 100.3 million ha, while direct carbon wildfire emissions amounted to 725.5 MtC (Table 2). For 2002–2020, the average area of forests affected by wildfires per year according to our estimates was 4.79 (±3.05) million ha/year, while direct pyrogenic carbon emissions amounted to 34.69 (±28.27) MtC/year.

The penultimate line of Table 2 shows the difference between the values of areas affected by forest wildfires, direct pyrogenic emissions of carbon and greenhouse gases in 2021, and average long-term values of the same indicators obtained for 2002–2020.

Table 2. Estimates of direct emissions of carbon and other greenhouse gases due to forest wildfires, obtained from 2002–2021 satellite monitoring across the Russian Federation

All indicators, except for specific carbon emissions (column 4), exceed the average long-term values, while the order of numbers is commensurate with the average long-term values. Emissions of carbon and other greenhouse gases in 2021 exceeded the average long-term values by 1.9 times, similar to 2003 (3.7 times) and 2012 (2.4 times). Thus, 2021 can be considered an anomalous year in terms of direct carbon emissions from wildfires, similar to the fire- seasons of 2012 and 2003 (Fig. 1).

Figure 1. Deviation in values of direct pyrogenic carbon emissions relative to the average long-term values from 2002 to 2020. The blue lines indicate the intervals between anomalous years in increments of 9 years, and the numbers indicate the amount and sign of carbon emissions relative to the average long-term values for 19 years

The diagram (Fig. 1) shows three distinguished anomalous years that exceed the total values of pyrogenic carbon emissions relative to the average long-term values by 92.4, 49.1, and 31.8 MtC. The time interval in between is 9 years. In our paper (Ershov, Sochilova, 2020), we noted that, when analyzing the time interval series of 2002 to 2020, two anomalous years were found with 9-years interval in between. We suggested that 2021 might turn out to be anomalous as well if the identified cyclic recurrence exists in the territory of Russia. The reason for such cyclic recurrence in Russia has not yet been established and needs more research. We can only state that direct pyrogenic carbon emissions in anomalous years decreased over 20 years from 127.1 MtC (2003) to 83.8 MtC (2012), and to 66.4 MtC in 2021. Presumably, this is due to the fact that there was a systematic excess of pyrogenic carbon emissions relative to the long-term average value from 2016 to 2020, and the total value of emission exceedances for 2012–2020 has a positive sign, i. e. 57.45 MtC (Fig. 1). Thus, as wildfire intensity increases from year to year, the yearly FF consumption in forest ecosystems increases, which results in decreased emissions during anomalous years. However, this assumption requires additional validation of our model calculations based on ground data in forests affected by wildfires, whose collection will also be sponsored by a research grant as part of development of a national system for monitoring over climatically active substances (Decree …, 2022).

Presumably, 2022 will be a regular wildfire season in Russia that may not exceed the areas of forest wildfires and direct wildfire carbon emissions relative to the average long-term values.

Considering the space of distributed wildfire carbon emissions in 2021 in Russia (Fig. 2), it can be noted that the main contribution is traditionally made by regions of the Urals (Khanty-Mansiysk and Yamal-Nenets Autonomous Okrugs), Siberia (Tomsk Oblast, Krasnoyarsk Krai, Irkutsk Oblast), and the Far East (Republic of Sakha (Yakutia), Zabaykalsky Krai, and Amur Oblast). There is also an increase in flammability and carbon emissions in the northern latitudes of the European part of Russia’s forests compared to 2020 (Ershov, Sochilova, 2022).

Figure 3 shows the deviation of specific pyrogenic carbon emissions in 2021 relative to the long-term average values. In 2021 (as in 2020), excessive carbon emissions relative to the average long-term values was observed in forests of the Republic of Yakutia, in most areas of Magadan Oblast and Chukotka Autonomous Okrug, and in the north of Khabarovsk Krai. Also, the emissions of 2021 exceeded the average long-term values on forest lands in the Volga region and north-western areas. In the European and southern parts of Russia, excessive carbon emissions over the long-term average values are local and fragmented.

Figure 2. Distribution map of direct specific wildfire carbon emissions (t/ha) in 2021 in Russian forests

Figure 3. Map of deviations of direct pyrogenic carbon emissions in 2021 relative to long-term average values

CONCLUSION

The yearly direct pyrogenic emissions of carbon and other greenhouse gases resulting from forest wildfires in Russia in 2021 were estimated by method of the Center of Forest Ecology and Productivity of the Russian Academy of Sciences. The calculations of biomass reserve consumption of forest fuel for various wildfire types and intensities showed underestimated values relative to other studies, which is associated with an underestimation of pre-fire reserves of the main combustion conductors in forests of Russia. We plan to significantly modernize the calculation methodology by using up-to-date models for assessing the biomass of forest fuel layers and new sets of themed satellite products of medium spatial resolution (230 m).

According to the results of the current methodology, the forest areas affected by wildfires amounted to 9.3 million ha in 2021, and direct wildfire carbon emissions were estimated at 66.4 MtC. Our assumptions that 2021 would be an anomalous year in terms of wildfires and greenhouse gas emissions resulting from forest wildfires have been confirmed to be true. We also detected the recurring anomalous wildfire seasons once every nine years over the past 20 years of monitoring. More research is needed to determine the cause and relations of this recurrence. It is important to note that emissions during anomalous years are systematically decreasing, which is probably due to the increase in large high-intensity wildfires in forests and increased consumption of forest fuel during regular fire seasons.

FUNDING

Statistical evaluation of pyrogenic emissions was carried out within the framework of a government assignment for the Center of Forest Ecology and Productivity of the Russian Academy of Sciences AAAA-A18-118052590019-7; development and analysis of satellite products and geographic data maps were supported by Russian Science Foundation (project No 19-77-30015).

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Reviewed by: A. Z. Shvidenko, Doctor of Agricultural Sciences

N.V. Gopp¹, J. L. Meshalkina², A. N. Narykova³, A. S. Plotnikova³, O. V. Chernova⁴

¹Institute of Soil Science and Agrochemistry of the Siberian Branch of the Russian Academy of Sciences pr. Akademika Lavrentieva 8/2, Novosibirsk, 630099, Russian Federation

²Lomonosov Moscow State University
Leninskie Gory 1 bldg. 12, Moscow, 119234, Russian Federation

³Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russian Federation

⁴A. N. Severtsov Institute of Ecology and Evolution

Leninskii pr. 33, Moscow, 119071, Russian Federation

E-mail: gopp@issa-siberia.ru

Received 04.02.2023

Revised: 18.03.2023

Accepted: 20.03.2023

This paper provides an overview of scientific publications in Russia and other countries devoted to the soil organic carbon (SOC) content and stocks mapping at regional and local levels. The analysis showed that the cartographic assessment of the SOC content and stocks was conducted using various approaches that the choice depends on the multiple factors: the size of the territory (continental, national, regional, local levels); the cartographic basis availability (maps of soil types, of landscapes, of vegetation formations, remote sensing data, etc.) and laboratory and field surveys data. Two main approaches were generally used for SOC content and stocks mapping: (1) based on available thematic maps; (2) digital soil mapping. The review also provides the analysis of all spatial predictors that were used in collected papers in concordance with the SCORPAN model widely used in digital soil mapping. Spatial terrain data was one of the most commonly used predictors, followed by the vegetation and climate variables. The accuracy of predictive maps significantly increased by using soil maps. The reviewed studies showed that climate variables had a significant impact on the spatial variation of the SOC content and stocks at the regional level, while at the local level the influence of climatic variables was less significant. The analysis showed that the most common methods used in digital mapping were machine learning algorithms. Random Forest method often showed the best results. Results were cross-validated almost in all studies. Tests of the map’s accuracy using an external independent validation dataset were rare, although this was the most important stage of digital soil mapping. R was the most popular software, that was used for modeling the SOC content and stocks. SAGA GIS, QGIS, ArcGIS, and cloud platform Google Earth Engine (GEE) were most commonly used to prepare predictors.

Key words: digital soil mapping, soil predictors, machine learning, Random Forest, Regression Kriging, Support Vector Machine, cross-validation, bootstrap, Gradient Boosting, monitoring

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]]> https://jfsi.ru/en/6-1-2023-podolskaya/ Thu, 24 Aug 2023 08:48:11 +0000 https://jfsi.ru/?p=5874

Original Russian Text © 2022 E. S. Podolskaia published in Forest Science Issues Vol. 5, No. 4, Article 115.

© 2023                                                           E. S. Podolskaia

Center for Forest Ecology and Productivity of the Russian Academy of Sciences

Profsoyuznaya st. 84/32 bldg. 14, Moscow, 117997, Russian Federation

E-mail: podols_kate@mail.ru

Received: 08.10.2022

Revised: 19.12.2022

Accepted: 20.12.2022

Paper presents an overview of history and current research state on the use of remote sensing data from space to recognize roads for the regional projects in the forestry. We reviewed the principles of road detection on the optical satellite imagery. Group of direct recognition features used in combinations such as brightness and texture, geometry and brightness. Three research directions with examples were identified: visual roads recognition, use of special software and libraries for developers, and neural networks. For the road network detection we have described methods and software, type and spatial resolution of imagery. Road image recognition based on the optical survey from the open and commercial sources, machine learning methods and neural networks. Up-to-date tasks of road recognition are the following: evaluation of road surface condition, modeling of existing roads location, designing and building new roads, roads seasonality. A functional summary of MapFlow plugin for road recognition in Open Source QGIS is given. Paper is a part of regional forestry transport modeling project to access the forest fires and forest resources by ground means.

Key words: remote sensing data from space, road network, image recognition, forestry, neural networks, convolutional neural networks, Open Source QGIS, plugins, MapFlow

Recognition of road network on the satellite imagery is a challenging task both in visual and automated image detection due to the number of difficulties, for instance, combining geometrical and brightness characteristics. We continue research on the modeling the ground movement of firefighters and logging vehicles reaching forest fires and forest resources at the regional level and undertake analysis of scientific papers on the use of data from space to detect road network.

The technology to create access routes, which was developed at the Laboratory of Forest Ecosystems Monitoring of the Center for Forest Ecology and Productivity, the Russian Academy of Sciences (CEPF RAS) collects datasets on routes for an arbitrary period of time, including the fire season (Podolskaia et al., 2020). Up-to-date data on the public and special access roads (forest roads, winter roads) are required to get the correct results in the database.

There is a need for methods and technologies for recognizing and updating data over a road network. Researchers are often focused on how to define presence and location of roads, which are missing in the global and regional datasets. The main source of Open data for the regional roads of entire Russian territory and the world is the vector layers of the Open Street Map project (OSM, https://www.openstreetmap.org/); an example is given in the paper (Podolskaia et al., 2020). Links between OSM data and object recognition at different spatial localization (Muhametshin, Samsonov, 2022), in particular, roads, were studied in, for example (Nachmany, Alemohammad, 2019; Oehmcke et al., 2019; Ayala et al., 2021). The topic of image recognition as a hybrid of Information Technology and Data Science attracts a lot of attention, as evidenced by the review of K. Bhil with colleagues (Bhil et al., 2022).

Infrastructure road projects are getting more complex, require up-to-date and detailed survey data. Present research is devoted to one of the aspects of road image definition and updates — recognition of road geometry on the optical satellite imagery. A separate topic is the use of shooting from unmanned autonomous vehicle (UAV), which is currently developing in Russia in its technical and legal aspects.

Goal of the paper is to overview Russian and foreign scientific publications on the recognition of linear objects of road network infrastructure using remote sensing data obtained from space and then to identify the possibilities for recognizing roads for the regional forestry transport projects. To achieve this goal, it is necessary to solve the following tasks: to characterize the current state of art for the road recognition from space images based on the results from scientific papers for regional projects, to show the features of road recognition in the forestry and to give examples of Open Source tools.

1. Roads recognition on the space imagery: current state of art

Satellite imagery is widely implemented for the different types of transport projects. Road as part of a complex infrastructure facility or project (road construction, oil and gas) is detected taking into account the objects of its surroundings. Space surveys are used in the engineering surveys and design of railways and highways (Bekturov, 2015; Filatova et al., 2017; Andreeva et al., 2019), defining the structure of road network (Mikheeva, Fedoseev, 2016). Roads are one of the elements of general geographic map content, their recognition related to the generalization of a linear object on the imagery (Podolskaia, 2005). For the railways at scale of imagery from 1:25 000 and larger, it is possible to decrypt two rails at a constant distance from each other using border identification tools in the MATLAB system (Zhurkin, Badyshev, 2014). Papers (Dolgopolov et al., 2019; Dolgopolov, 2020) present the analysis of pipeline decryption and accompanying linear infrastructure for the oil and gas industry.

Space images of different types remain the main data source for identifying roads at the regional and local levels. For the scientific research on the transport modeling, it is necessary to understand the possibilities of modern and, most importantly, accessible satellite imagery of different types and resolutions. Non-commercial optical range shooting (Landsat and Sentinel-2 spacecrafts) remains one of the main sources of image analysis in scientific research and also used to recognize roads of different classes. For example, recognition of unpaved roads: papers on narrow dirt roads in southwestern Brazil (Gomes et al., 2015) and unofficial rural roads in the Brazilian Amazon (Botelho et al., 2022) use this satellite optical survey.

Combination of the optical multi-zone and hyperspectral images helps to define changes in the transport network configuration, for which a combined technology of object-oriented analysis for a set of images with cluster and contour processing methods is used (Mikheeva, Fedoseev, 2016). Radar imagery (Henry et al., 2018; Wei et al., 2021) are used less frequently due to the need of pre-processing compared to the optical survey, which is more affordable in cost, but more dependent on meteorological conditions (cloud cover).

In order to characterize modern road recognition, we describe principles for the linear objects. Firstly, roads are extended objects with geometric (shape, size) and brightness (tone, brightness level, color, spectral image) features (Mikheev, Fedoseev, 2016) changing within the image. The geometry of the road in regional projects refers to its shape and size (width), skeleton of the road network geometry is their centerline. An example of joint use of geometric and brightness features is given in the paper (Fedoseev et al., 2018). Secondly, structural features (texture, structure, pattern) and combinations of luminance and structural features are also used for detection, for example, a spectral-texture segmentation algorithm in the paper (Pestunov, Rylov, 2012).

Indirect detection features of the road network are natural (to show the relationship of roads with natural objects), anthropogenic (to identify the functions of roads as objects for communication between settlements, location in the socio-economic infrastructure of the territory), as well as natural and anthropogenic (to indicate the links between landscape and transport development).

In addition to geometric, brightness and structural features, modern research distinguishes topological (connectivity) and functional (use) attributes (Botelho et al., 2022) for the automated road recognition model. To detect the road network, as noted by a number of Russian authors (Miroshnichenko et al., 2013; Tusikova, Vikhtenko, 2019; Ignat’ev et al., 2022), there is a need to select images with a set of key characteristics. They are: significant road length within the image, constant road width, and uniform distribution of the road image’s brightness within the image and road surface clarity. Thus, the main stage of satellite data processing is an image segmentation into thematically homogeneous areas, followed by transformation into a vector format.

Historically road recognition has been started from the example of settlements streets when the satellite imagery of spatial resolution of about several meters were made available (Guindon, 1998). A large review on the automatic road recognition for geoinformatics has been made in the paper (Mena, 2003). It describes the main tasks of recognition: segmentation, vectorization, evaluation and optimization, semantic neural networks, as well as methods and tools of fuzzy logic. This research dated back to 2003 should be continued and updated with a description of present recognition methods and examples of neural networks.

Preparation of image fragments at the beginning of 2000 laid the groundwork for modern road recognition with neural networks method, which is one of the Deep Learning classification algorithms, establishing weight of importance to various regions and objects on the image for the subsequent iterative recognition. Among neural networks for linear objects of the road network, we used convolutional neural networks (CNN) and regional neural networks Region-Based-CNN (RCNN). The decisive advantage of choosing a convolutional network is a shorter, in comparison with other classification algorithms, processing time and far fewer computing resources for images preprocessing (Skripachev et al., 2022). Currently, the best results in semantic image segmentation for road recognition are obtained using the fully connected convolutional neural network Fully CNN, which is a set of connected layers, each neuron of one layer is associated with each neuron of the other one.

An example of RCNN use in Russian publications is Mask-RCNN (Tusikova, Vikhtenko, 2019). Its convolutional principle combins the values of adjacent pixels and detects the most generalized features for the road. Presently quality limitations of network recognition depend on the size of training sample and detail of its annotation (preliminary segmentation). Significant set of data (numbering about thousands of images) is required for the high-quality neural network training. Variants could be found in the ready-made annotated sets; examples of open access are just beginning to appear. We can use space image annotation to automate objects detection, which is assigning a signature or label using keywords.

Researchers attribute the identification of road surface type (Tormozov et al., 2020) and monitoring road technical condition (Chelnokov et al., 2021) as separate tasks of road recognition. A number of roadway identification papers use hyperspectral images and machine recognition methods, noting the need for ultra-high resolution data to determine the road accident ratio (Mikheeva, Fedoseev, 2014; Fedoseev et al., 2016|, 2018). Recent papers published some scientific results of GIS infrastructure project “ITSGIS” (http://itsgis.ru/).

Evolution and current capabilities of road recognition methods and technologies based on the data from space are shown in the Table 1. It groups the characteristics of Russian and foreign papers into the development stages to recognize roads on space images in regional projects. Papers could be grouped into 3 categories, namely: visual detection, use of special software and libraries, and then neural networks. As Table 1 shows, there was a transition from manual expert visual decryption to automated recognition by neural networks based on the expert experience and training sets with a high results accuracy.

The prerequisites for such transitions were the well-known capabilities of information technologies: speed and volume of data processing, especially high-resolution satellite data processing. Advantages of neural network recognition include the speed and availability of image processing; disadvantage is the time spent for the segmented image preparation to be a training sample.

Case studies cover different regions of Russia and the world, used mostly optical imagery. Significant research part dedicated to the use of datasets and neural networks for road recognition is presented by English-language publications. Each block of the Table contains years of publication, which helps to show the historical development of methods and technologies to detect roads on the images stating from the imagery of 1-meter-resolution. The most popular as a Deep Learning library in the studied researches is Keras[1]. One of the most used neural network ResNet101 (https://keras.io/api/applications/resnet/) should be mentioned among the modern examples.

Table 1. Roads recognition on space images at regional level

2. Roads recognition in the forestry

Research experience presented in the Table 1 is useful to recognize a road network on the space images for the forestry transport modeling. Updating of forest road geometry is important in the forestry because forest roads make direct access to the paved roads. There are very few Russian publications on the forest road networks, namely (Orlov, 2006; Shoshina, 2013). First paper is devoted to the connection of road attribute features with pictorial segments and network topological description. Strategy to detect a road depends on its class. About 15 options were mentioned for the roads detection, combining properties of road segments and its location characteristics. Second research describes a system for monitoring forest roads, indicating their main definition signs. This publication focuses on the technical condition of forest roads and possible technologies for detecting defects depending on the season. Tasks of roads detection are to identify the road presence and pavement type, to assess the road state and to identify the segments that require reparation. For the foreign part of the analysis we note a paper (Caliskan, Sevim, 2022), which gives recommendations on the deep learning models for semantic segmentation of forest roads like neural networks ResNet-50 and InceptionResNet-V2.

Forestry peculiarities include the seasonality of road use. In order to model ground access in the forestry, satellite imagery of different year periods are needed, namely: off-season, summer and winter (Shoshina, 2013). Throughout every season of the year, part of the roads will be open; in winter, in addition to public roads, winter roads begin to operate in certain regions; in summer and in the off-season, part of the roads may be inaccessible due to the surface state (Podolskaia, 2022). So, access routes to reach forest resources and forest fires will differ seasonally.

Thus, the current tasks of roads detection in the forestry include assessing their coverage state, modeling the location of existing and designed roads, as well as taking into account the seasonality of road operation.

3. MapFlow plugin for Open Source QGIS

A practical example of an Open Source road recognition tool in the forestry transport projects is the QGIS MapFlow plugin. Nowadays it is one of the image recognition plugins for the roads, which is accessible from the library of Open geoinformation tools (https://plugins.qgis.org/) developed and regularly supported with new versions by GeoAlert (https://geoalert.io/). MapFlow currently uses high spatial resolution optical survey as raw data. Raster road mask is prepared from the image, then it is converted into a set of road centerlines and vectorized to be in GeoJSON format; pavement type and road boundaries (https://geoalert.medium.com/mapflow-ai-new-roads-model-e989557cef26) are also determined. The current version of the plugin dates back to April 2023 (https://plugins.qgis.org/plugins/mapflow/). Plugin has been loaded into the repository since July 2021 and is used for the RuMap platform (http://www.digimap.ru/products) of Geocentre Consulting.

CONCLUSION

Paper provides an overview of available scientific publications on the use of remote sensing data from space for road recognition highlighting features for forestry transport modeling. For implementation in the forestry transport projects we have identified the following research areas.

Firstly, basics of automated road recognition are applied. Roads are being recognized on the images of open and commercial sources, imagery of spatial resolution starting from centimeters to meters are used. For the roads direct detection features are preferably used; they can be better automated in comparison with indirect ones. Researchers over the past 20 years have switched from manual visual decryption of linear objects of road network to the use of deep learning methods and neural networks. Significant time and technical efforts are still being invested in the preparation of training datasets for the neural network, their quality significantly determines recognition result. Thus, the use of artificial intelligence (AI) methods and technologies is an obvious, actively and constantly developing research question.

Secondly, thematic forestry tasks using data from space and AI remain the following: assessment of road state, identification of its accident rate, as well as design modeling and construction of new roads. To detect and to monitor the state of forest roads in the Russian regions it is advisable to shoot high and ultra-high imagery, which can be done using UAV. This shooting technology is of significant interest due to the ultra high spatial resolution (about centimeters), its ability to make the preparation process relatively quicker compare to the shooting from satellites and availability of software processing tools.

As examples of positive forestry outputs, we note several papers (Gulci et al., 2017; Akay, Tas, 2018; Turk et al., 2022). Currently it is difficult to assess the effectiveness of UAVs for road recognition against the use of space images for regional projects. The efficiency assessment will depend on the geographical location of key area, presence and geometrical complexity of road network, terrain, infrastructure and other parameters. The question requires further research and experience gathering from different countries and regions. Due to the present complications to obtain high and ultra-high spatial resolution space imagery in Russia, local UAV surveys even taking into account management, fieldwork and labor costs may be the only option for obtaining data for regular monitoring of road condition.

For the years to come the data obtained from UAVs will be one of the sources used to update the road network image in various GIS projects, including forestry. Definitely, shooting from space and using UAVs will continue to be the two main data sources for roads detection. For various road projects and forestry there are some common patterns like use of mostly accessible open data Landsat and Sentinel. There are still few scientific publications aimed to recognize forest roads on the satellite imagery.

Number of references shows that for the road recognition the most informative and currently used features are direct decryption features (geometric, brightness), then direct texture features applied. Research of last decade implements a combination of direct features, like brightness and texture, geometry and brightness. Papers’ analysis states that tasks of using satellite imagery to recognize road images can be divided into several groups. First of all, there is a need for any imagery in the absence of updated data on the road network, and then the lack of an open access accurate coordinate reference of the linear road infrastructure and finally a need to monitor road coverage for accidents and to carry out subsequent economic assessments.

Regional forestry projects are complex, large-scale and, therefore, costly from the resources point of view. Main technology for their implementation for the road recognition part will remain neural networks, which are constantly improving in their quality and data volume; neural networks have a tendency of becoming open products that collect recognition examples of linear, point and polygon type objects.

ASKNOWLEDGEMENTS

The author thanks the Center for Forest Ecology and Productivity of the Russian Academy of Sciences (CEPF RAS) for supporting this research by the state funding contract “Methodological approaches to estimate structural organization and functioning of forest ecosystems”, registration number N 121121600118-8.

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Reviewer: Candidate of Biological Sciences V. V. Elsakov

[1] Open Source Python-based library which is aimed to interact with neural networks

A. E. Kvashnina^1*, F. K. Vozmitel¹, V. A. Khamedov^2,3

¹“Denezhkin Kamen’” Russian Federal Nature Preserve,

6, Lenina Str., Severouralsk, 624480, Russia

²Siberian State University of Geosystems and Technologies

10, Plakhotnogo Str., Novosibirsk, 630108, Russia

³Saint-Petersburg State University of Aerospace Instrumentation

67, Bolshaya Morskaia Str., Saint-Petersburg, 190000, Russia

^*E-mail: zapov.dk@gmail.com

Received: 01.03.2023

Revised: 20.03.2023

Accepted: 22.03.2023

The study represents spatial and temporal analysis of the consequences of water pollution leading to vegetation destruction, caused by uncontrolled underspoil drain from a copper mine in the north of the Sverdlovsk region, based on GIS methods and satellite remote sensing data. Based on a 1:25 000 topography map DEM with filled sinks we built a flow accumulation model a watershed map. Examining the satellite imagery of the area dated from 2009 to 2023 we have noticed a distinct pattern of the vegetation destruction along the riverbeds located in the affected watersheds. Our ground observations and drone images have confirmed that the dead forest plots are located in the areas of flow accumulation, in the terrain depressions. The total area of forests affected by the uncontrolled underspoil drain from the copper mine gradually grows and has reaches 1140 ha.

Key words: remote sensing, spatial hydrology analysis, runoff model, forest vegetation destruction, copper mine

REFERENCES

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Krylov A. M., Sobolev A. A., Vladimirova N. A., Vyjavlenie ochagov koroeda-tipografa v Moskovskoj oblasti s ispol’zovaniem snimkov Landsat (Identification of foci of bark beetle-typographer in the Moscow region using Landsat images), Forestry Bulletin, 2011, No 4, pp. 54–60.

Kvashnina A. E., Deshifrirovanie kosmosnimkov rajona raspolozhenija Severnogo medno-cinkovogo mestorozhdenija s cel’ju opredelenija vseh vozmozhnyh potokov zagrjaznennyh vod i ih vozdejstvie na okruzhajushhuju sredu (2021 g.) (Interpretation of satellite images of the location of the Northern copper-zinc deposit in order to determine all possible flows of polluted waters and their impact on the environment (2021)), Nauchnye issledovanija v zapovednikah i nacional’nyh parkah Rossijskoj Federacii (2015–2021 gg.), Vol. 5, Simferopol: Business-Inform, 2022, pp. 152–153.

Kvashnina A. E., Vladimirova N. A., Jekspress-ocenka posledstvij nekontroliruemogo podotval’nogo stoka na severnom medno-cinkovom rudnike i problemy zapovednika “Denezhkin kamen’”, s nim svjazannye (Express assessment of the consequences of uncontrolled wastewater runoff at the northern copper-zinc mine and the problems of the Denezhkin Kamen reserve related to it), Geografija i sovremennye problemy geograficheskogo obrazovanija: materialy Vserossijskoj nauchno-prakticheskoj konferencii, posvjashhennoj 100-letiju so dnja rozhdenija Pochetnogo chlena Russkogo Geograficheskogo Obshhestva, doktora geograficheskih nauk, professora Vasilija Ivanovicha Prokaeva, Ekaterinburg: USPU, 2019, pp. 139–145.

Nikitina A. D., Knjazeva S. V., Gavriljuk E. A., Tihonova E. V., Jejdlina S. P., Koroleva N. V., Kartografirovanie dinamiki rastitel’nogo pokrova territorii nacional’nogo parka “Kurshskaja kosa” po materialam kosmicheskoj s’emki Alos i Sentinel-2 (Mapping the dynamics of the vegetation cover of the Curonian Spit National Park on the basis of Alos and Sentinel-2 satellite imagery), Voprosy lesnoj nauki, 2019, Vol. 2, No 3, pp. 1–21.

Vladimirova N. A., Kvashnina A. E., Ocenka masshtabov gibeli lesnyh jekosistem v rezul’tate razrabotki mestorozhdenij Severnogo medno-cinkovogo rudnika po serii kosmicheskih snimkov 2009–2018 gg (Estimation of the extent of the destruction of forest ecosystems as a result of the development of deposits of the Northern copper-zinc mine based on a series of satellite images in 2009–2018), Ajerokosmicheskie metody i geoinformacionnye tehnologii v lesovedenii, lesnom hozjajstve i jekologii: Doklady VII Vserossijskoj konferencii, CEPF RAS, 2019, pp. 29–31.

]]> https://jfsi.ru/en/6-1-2023-khamedov_davydova/ Thu, 06 Jul 2023 08:05:05 +0000 https://jfsi.ru/?p=5744

 V. A. Khamedov^1,2, N. V. Davydova¹

                 ¹Siberian State University of Geosystems and Technologies

10, Plakhotnogo Str., Novosibirsk, 630108, Russia

²Saint-Petersburg State University of Aerospace Instrumentation

67, Bolshaya Morskaia Str., Saint-Petersburg, 190000, Russia

E-mail: khamedov.vladimir@mail.ru

Received: 28.02.2023

Revised: 18.03.2023

Accepted: 20.03.2023

The development and operation of oil fields in Western Siberia has a significant impact on various components of the environment. In particular, natural complexes of existing specially protected natural areas and promising areas that are significant for the conservation of biological diversity and landscapes are affected. In connection with the projected development of a system of specially protected natural areas in the Khanty-Mansiysk Autonomous Okrug, the aim of the work is to assess the likely pollution of municipalities in the region by emissions from the combustion of associated petroleum gas to provide decision support when choosing locations for promising protected areas. The paper presents a general description of chronic thermal and gas-chemical pollution of the atmosphere by products of associated petroleum gas combustion, zones of probable atmospheric pollution as a result of gas combustion are modeled, an assessment is made of the dynamics of the state of vegetation for the territories of municipalities, within the boundaries of which it is planned to create protected natural areas, and recommendations are given for designing such territories.

Keywords: APG, thermal and gas-chemical pollution, atmospheric emissions, VEGA-Science, NDVI, protected areas

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