Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes.

Wildfires are important natural drivers of forest stands dynamics, strongly affecting their natural regeneration and providing important ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns in the Middle Volga region of the Russian Federation from 2000 to 2022, using remote sensing time series data and considering the influence of climatic factors on forest fires. To assess the temporal trends, the Mann–Kendall nonparametric statistical test and Theil–Sen’s slope estimator were applied using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment revealed a high overall accuracy (>84%) and F-score value (>82%) for forest burnt area detection, evaluated against 581 reference test sites. The results indicate that fire occurrences in the region were predominantly irregular, with the highest frequency recorded as 7.3 over the 22-year period. The total forest BA was estimated to be around 280 thousand hectares, accounting for 1.7% of the land surface area or 4.0% of the total forested area in the Middle Volga region. Coniferous forest stands were found to be the most fire-prone ecosystems, contributing to 59.0% of the total BA, while deciduous stands accounted for 25.1%. Insignificant fire occurrences were observed in young forests and shrub lands. On a seasonal scale, temperature was found to have a greater impact on BA compared with precipitation and wind speed.

Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

Введение. Водоохранные (защитные) леса являются уязвимыми экосистемами с характерными растительными сообществами, произрастающими на относительно богатых пойменных землях, что отличает их от других лесных насаждений. Оценка данных о лесном покрове водоохранных зон представляет собой ключевую информацию для понимания антропогенного и природного влияния на эти экосистемы. Для этих целей широко используются спутниковые изображения, которые доступны на облачной платформе Google Earth Engine (GEE) по обработке глобальных данных. Цель исследования – оценить применение спектральных каналов разносезонных снимков спутника Sentinel-2 для картографирования и оценки лесного покрова водоохранных зон рек в GEE. Объектами исследования явились лесные насаждения, произрастающие в двухсотметровой буферной (водоохранной) зоне участков рек Рутка, Парат, Большая Кокшага, Большой Кундыш, Волга, Малая Кокшага, Малый Кундыш, Илеть, Юшут, Ошла, Большая Ошла Республики Марий Эл в рамках спутниковой сцены T38VPH Sentinel-2. Методы исследования. В работе была проведена управляемая классификация разносезонных спутниковых изображений Sentinel-2 методом «случайного леса» (RF англ. - Random Forest) в GEE по 9 классам наземного покрова (8 классов древесных пород и 1–нелесные территории). На первом этапе исследования созданы и классифицированы четыре разносезонных (зима, весна, лето, осень) изображения Sentinel-2 с использованием нескольких спектральных каналов. После этого выбраны по пять наиболее значимых каналов изображений каждого исследуемого сезона и создано одно комбинированное изображение на их основе. Результаты и выводы. Общая точность проведённой классификации составила 87,4 %, а коэффициент Каппа – 0,84. Наименьшая точность наблюдается по классам «Ольха» и «Осина» – около 64 %. Исследование показало, что для классификации породного состава водоохранных лесов наиболее приемлемы следующие спектральные каналы спутника Sentinel-2 – вегетационный крайний красный летнего и осеннего сезонов, а также коротковолновый инфракрасный диапазон весеннего и осеннего сезона. Использование облачных вычислений значительно упрощает процесс обработки и анализа спутниковых изображений, а применение разносезонных изображений и выбор подходящих каналов могут повысить точность классификации водоохранных лесов. Introduction. Riparian forests are vulnerable ecosystems with specific plant communities growing in relatively rich floodplain soils, which distinguishes them from other forest plantations. The assessment of data on forest cover in riparian zones provides the key information for understanding anthropogenic and natural impacts on these ecosystems. For these purposes, satellite images are widely used, which are available on the Google Earth Engine (GEE) cloud platform for global data processing. The aim of the study is to evaluate the use of spectral bands of Sentinel-2 satellite multi-season images for mapping and assessing the forest cover of riparian zones in GEE. The objects of the study were forest plantations growing in a 200-meter buffer (water protection) zone of the rivers Rutka, Parat, Bolshaya Kokshaga, Bolshoy Kundysh, Volga, Malaya Kokshaga, Maly Kundysh, Ilet, Yushut, Oshla, and Bolshaya Oshla of the Republic of Mari El, within the framework of Sentinel-2 satellite scene T38VPH. Research methods. Supervised classification of Sentinel-2 satellite multi-season images was carried out on the basis of the random forest (RF) method in GEE according to 9 classes of land cover (8 classes of tree species and 1 - non-forest areas). At the first stage of the study, four multi-season (winter, spring, summer, autumn) Sentinel-2 images were created and classified using several spectral bands. After that, five most significant bands of the images for each studied season were selected and one combined image was created based on them. Results and conclusions. The overall accuracy of the classification was 87.4%, and the Kappa coefficient was 0.84. The lowest accuracy rate of approximately 64% was found for the Alder and Aspen classes. The study showed that the following spectral bands of the...