This article considers the possibility of using remote sensing to monitor reforestation as exemplified in the Severodvinsk and Onezhsk forestry districts of the Arkhangelsk region of Russia’s Arctic zone. Remote sensing makes use of medium spatial resolution satellite images and high resolution unmanned aerial vehicle (UAV) images. In the course of work on the project, a preliminary method was developed for reforesting land previously subjected to cutting, fire, or windfall. Steps include detecting a reduction in forest cover and collecting field data through the use of UAVs to create a training set, which is used to classify satellite images according to the two classes of ‘restored’ or ‘not restored’. Various data processing tools are used to perform these steps. The Tasseled Cap multi-channel satellite image transformation method is employed as a tool for detecting a reduction in forest cover and analysing reforestation. The k-nearest neighbour algorithm is employed to classify satellite images. This article provides a step-by-step algorithm for monitoring and an assessment is provided of the situation in relation to forest regeneration in the Severodvinsk and Onezhsk forestry districts. The work carried out has shown that it is possible to use UAV images to monitor forest recovery, which is of significant importance for the conditions of the Arctic zone of European Russia.
Оценка лесовосстановления по спутниковым снимкам и создание системы мониторинга является важной задачей на сегодняшний день. Российские и зарубежные ученые проводят исследования в этом направлении, но анализ лесовосстановления является сложной темой исследования в отличие от выявления вырубок и гарей по спутниковым снимкам. Лесовосстановление также является сложным, многофакторным процессом, зависящим от множества факторов. Данная статья описывает мировой опыт создания различных методик для мониторингалесовосстановления, используя различные подходы анализа данных и сенсоры, установленные на спутниках. В рамках статьи рассмотрено применение оптических, радарных снимков и данных, полученных с лидарных сенсоров. Это попытка структурировать накопленный опыт в данной сфере и сгруппировать разработанные методики для анализа их преимуществ и недостатков. Тип сенсора определяет длительность периода мониторинга. Радарные данные позволяют определять процесс лесовосстановления до 60 лет в отличие от оптических сенсоров, которые имеют значительно меньший период оценки лесовосстановления. Применение радарных данных может быть ограничено стоимостью работы и сложностью обработки радарных данных, поэтому использование тех или иных методик может иметь финансовые ограничения. Данный обзор показывает все основные методы оценки лесовосстановления. Assessment of reforestation using satellite images and creation of a monitoring system is an important task today. Russian and foreign scientists are conducting research in this direction, but the analysis of reforestation is a complex topic of research in contrast to the detection of cuttings and burned areas by satellite images. The process of reforestation is a complex, multi-factor process depending on many factors. This article describes the world experience of creating different methods for monitoring forest regeneration and uses different approaches to data analysis and sensors installed on satellites. In the framework of article was considered using of optical, radar images and data obtained from lidar sensors. This is an attempt to structure the accumulated experience in this field and group the developed methods to analyze their advantages and disadvantages. Data from different sensors have different monitoring period. Radar data allow determining the process of reforestation up to 60 years in contrast to optical sensors, which have a much shorter period of reforestation assessment. The using of radar data were limited by the cost of operation and complexity of radar data processing and using of certain techniques may have financial limitations. This review showed all the main methods of assessment of reforestation.
named after M.V. Lomonosov)Currently, a series of multi-temporal satellite images are available, which make it possible to obtain systematic information about objects of observation. The dynamics of changes over time allows you to analyze various scenarios of these changes and compare them with the results of field surveys. The study of dynamic processes over time is an urgent problem of satellite images processing. The dynamic process of overgrowing felling is a process that takes place over many years or even decades.A methodology for thematic processing of multispectral space imagery is proposed and implemented. The algorithm of the work is based on the analysis of different vegetation indices for assessing reforestation at clearings. Анализ возможностей применения разновременных данных дистанционного зондирования Земли(NDVI, SWVI, NBR) was evaluated. In the study, the successful reforestation measure determined the period after felling -8 years and the necessary increase in the index to 83 % of the initial value of the forest before felling index (SWVIpre) in the Arkhangelsk region.
Satellite data becomes an important tool for monitoring global change in forest cover. Further development of remote sensing technologies creates opportunities for solving more complex problems requiring multi-time analysis of satellite data. Assessment of success reforestation after a disturbance in forest cover is such an important task. The traditional method of an assessment of successful reforestation is laying out the ground plots, which task requires significant time and resources. Fieldworks and transfer of land to forest cover land is carried out according to the method, which is developed by the Federal Agency for Forestry of Russia. This method has various criteria of success reforestation for every region. Arkhangelsk region, Vologda region and Republic of Karelia became the territories for research. Forest vegetation of this region belongs to the taiga zone and is divided into five groups: the area of pre-tundra forests and sparse taiga, northern taiga, middle taiga and south taiga. International forest classification relates this area to boreal forest. The task of transfer land to forest cover land can be optimized by using remote sensing data. This research shows analysis of recovery of the normalized difference vegetation index, the shortwave vegetation index and the normalized burn ratio in the framework of reforestation objects. Filed data was collected for every object and this data includes a number of young trees, average height and species composition. Processing of a considerable number of satellite imageries requires significant computing power because of the Google Earth Engine platform using for analysis data. The most suitable index was chosen in the analysis of the obtained data for the development of an automatic method for transfer land to forest cover land. The most suitable index for dividing lands on forest cover and nonforest cover lands is the shortwave vegetation index. Optimal threshold for transferring land is achievement of recovery index of 80 % from initial values before disturbance. The automatic method was developed using unsupervised classification and threshold values of recovery index.
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