GEODESY, CARTOGRAPHY AND AERIAL PHOTOGRAPHY

Earth remote sensing and using the satellite images play an important role when monitoring the effects of forest fires and assessing damage. Applying different methods of multispectral space images processing, we can determine the risk of fire distribution, define hot spots and determine thermal parameters, mapping the damaged areas and assess the consequences of fire. The purpose of the work is the severity assessment connected with the post-fire period on the example of the forests in the Chornobyl Exclusion Zone. The tasks of the study are to define the area of burned zones using space images of different time which were obtained from the Sentinel-2 satellite applying the method of a normalized burn ratio (NBR) and method of supervised classification. Space images taken from the Sentinel-2 satellite before and after the fire were the input data for the study. Copernicus Open Access Hub service is a source of images and its spatial resolution is 10 m for visible and near infrared bands of images, and 20 m for medium infrared bands of images. We used method of Normalized Burn Ratio (NBR) and automatically calculated the area damaged with fire. Using this index we were able to identify areas of zones after active combustion. This index uses near and middle infrared bands for the calculations. In addition, a supervised classification was performed on the study area, and signature files were created for each class. According to the results of the classification, the areas of the territories damaged by the fire were also calculated. The scientific novelty relies upon the application of a method of using the normalized combustion coefficient (NBR) and supervised classification for space images obtained before and after the fire in the Chernobyl Exclusion Zone. The practical significance lies in the fact that the studied methods of GIS technologies can be used to identify territories and calculate the areas of vegetation damaged by fires. These results can be used by local organizations, local governments and the Ministry of Emergency Situations to monitor the condition and to plan reforestation. The normalized burned ratio (NBR) gives possibility efficiently and operatively to define and calculate the area which were damaged by fires, that gives possibility operatively assess the consequences of such fires and estimate the damage. The normalized burned ratio allows to calculate the area of burned forest almost 2 times more accurately than the supervised classification. The calculation process itself also takes less time and does not require additional procedures (set of signatures). Supervised classification in this case gives worse accuracy, the process itself is longer, but allows to determine the area of several different classes.

Impact of Forest Fires on Air Quality in Wolgan Valley, New South Wales, Australia—A Mapping and Monitoring Study Using Google Earth Engine

Forests are an important natural resource and are instrumental in sustaining environmental sustainability. Burning biomass in forests results in greenhouse gas emissions, many of which are long-lived. Precise and consistent broad-scale monitoring of fire intensity is a valuable tool for analyzing climate and ecological changes related to fire. Remote sensing and geographic information systems provide an opportunity to improve current practice’s accuracy and performance. Spectral indices techniques such as normalized burn ratio (NBR) have been used to identify burned areas utilizing satellite data, which aid in distinguishing burnt areas using their standard spectral responses. For this research, we created a split-panel web-based Google Earth Engine app for the geo-visualization of the region severely affected by forest fire using Sentinel 2 weekly composites. Then, we classified the burn severity in areas affected by forest fires in Wolgan Valley, New South Wales, Australia, and the surrounding area through Difference Normalized Burn Ratio (dNBR). The result revealed that the region’s burnt area increased to 6731 sq. km in December. We also assessed the impact of long-term rainfall and land surface temperature (LST) trends over the study region to justify such incidents. We further estimated the effect of such incidents on air quality by analyzing the changes in the column number density of carbon monoxide and nitrogen oxides. The result showed a significant increase of about 272% for Carbon monoxide and 45% for nitrogen oxides. We conclude that, despite fieldwork constraints, the usage of different NBR and web-based application platforms may be highly useful for forest management to consider the propagation of fire regimes.

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public.

Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R2 = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.

Identification of Lignite as One of Usable Alternative Energy Using Difference Normalized Burn Ratio (dNBR) Calculated from Landsat-8 Data and Gravity Method in Lam Apeng Village, Aceh Besar District, Aceh Province

One of the alternative energy that exists in Indonesia, including in Aceh is coal energy. In 2016, a fire happened caused of coal in Lam Apeng Village, proven by coal’s landscape formed after the fire. The research purposes to identify the coal existence as one alternative energy in Indonesia using difference Normalized Burn Ratio (dNBR) calculated from Landsat-8 data. The difference between NBR’s which is able to show the only burn location based on its index with high severity level representes the burnt location. Gravity survey validates the results and proves that the low-density zone indicated coal existence has the same pattern as the high severity level. Both of the results give information about lignite dissemination in Lam Apeng Village.

Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data

Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.

Characterization of vegetation structural changes using multi-temporal LiDAR and its relationship with severity in Calcena wildfire

La caracterización de los cambios estructurales y presencia de huecos tras el fuego puede proporcionar información muy relevante para comprender los efectos ecológicos de los incendios en ecosistemas mediterráneos. En el presente estudio se caracterizan estas variables tras el incendio de Calcena en masas forestales de pinar y encinar, y su relación con la severidad del mismo. Dicho incendio calcinó 4.573 hectáreas en 2012 afectando de forma parcial al Parque Natural de la Dehesa del Moncayo localizado en Aragón (España). Para ello se hace uso de información multi-temporal Light Detection and Ranging (LiDAR) de las coberturas de 2011 y 2016 del Plan Nacional de Ortofotografía Aérea (PNOA), así como imágenes Landsat 7 para estimar la severidad del incendio mediante el índice differenced Normalized Burn Ratio (dNBR). Se evalúan los cambios estructurales producidos utilizando métricas LiDAR pre y post-incendio, así como la distribución de los huecos en el dosel forestal, su tamaño, número y frecuencia, y se analizan sus correlaciones con la severidad del incendio. La severidad fue predominantemente baja (42.32 %) o mediabaja (30.38 %), y produjo una disminución de la altura, de la densidad del dosel forestal y de la diversidad estructural. El tamaño de los huecos se incrementó tras el incendio, reduciéndose el número de huecos pequeños e incrementándose aquellos de tamaño intermedio en torno a 0.2 ha. Los cambios en las métricas LiDAR relacionadas con la altura, variabilidad de la altura en el perfil vertical, y densidad del dosel forestal presentaron las mayores correlaciones, indicando que son las que sufren mayores modificaciones. Los resultados muestran el interés de utilizar los datos LiDAR para caracterizar cambios estructurales y apoyar decisiones en la gestión silvícola.

ОЦЕНКА ВОССТАНОВИТЕЛЬНОЙ ДИНАМИКИ РАСТИТЕЛЬНОГО ПОКРОВА ЛЕСНЫХ ГАРЕЙ С ИСПОЛЬЗОВАНИЕМ ДАННЫХ СО СПУТНИКОВ LANDSAT

Актуальность. До 400 тысяч лесных пожаров, ежегодно возникающих на Земле, ведут к попаданию в атмосферу до четырех миллиардов тонн углерода и выгоранию до 0,5 % площади лесов. Лесные пожары уничтожают древесные ресурсы, снижают эффективность их использования, наносят экономике гигантский урон. Оперативная и объективная информация об их последствиях востребована для решения комплекса теоретических и практических задач в области землеустройства, кадастра и мониторинга земель лесного фонда, а также для научного обоснования использования, восстановления, охраны и защиты лесов. Объект: земли лесного фонда, подвергшиеся пожарам. Предмет: пост-пирогенная динамика растительного покрова на примере лесных гарей Томской области. Методы: тематическое картирование территории по состоянию растительности; оценка значений NDVI (Normalize Difference Vegetation Index) и нормализованного индекса гарей NBR (Normalized Burn Ratio) по данным дистанционного зондирования Земли; анализ информации со спутников Landsat 5 (камера TM), 7 (ETM+) и 8 (OLI) с использованием геоинформационных технологий и статистической обработки полученных данных. Результаты. Произведена оценка состояния растительного покрова гарей в сравнении с тестовым лесным участком сходного породного состава (46 % – сосна сибирская, 36 % – береза повислая, 11 % – осина обыкновенная, 7 – % сосна обыкновенная и лиственница сибирская). Степень повреждения растительного покрова изученных гарей охарактеризована как низкая. Для гарей и фонового участка рассчитаны нормализованный вегетационный индекс (NDVI) и индекс гарей (NBR). Выявлены резкие перепады их значений и аномальный ход годовой динамики для гарей. Значения NDVI для гарей и тестового участка различались на 3–56 %, значения NBR на 20–198 %. Различия сохранялись и спустя 17 лет после пожара. Корреляционный анализа выявил достоверную связь между значениями индексов NBR и NDVI гарей и средними значениями температуры воздуха и количества осадков в пожароопасный сезон. Она оказалась отрицательной средней и слабой силы для мая; сильной и средней для июля и слабой для августа. Осадки связаны со значениями индексов NBR и NDVI гарей со средней силой: в мае и июне отрицательно, в августе положительно. Это свидетельствует о достаточном увлажнении экотопов в начале вегетационного периода, последующем просыхании почвы, оптимальном для жизнедеятельности деревьев, и ее иссушении, предопределяющем возможность возникновения лесных пожаров. Отмечена явная территориальная изменчивость значений NDVI и NBR в границах гари.

Remote Sensing Calculation of the Influence of Wildfire on Erosion in High Mountain Areas

Soil erosion is one of the most important environmental problems of the moment, especially in areas affected by wildfires. In this paper, we study pre-fire and post-fire erosion using remote sensing techniques with Sentinel-2 satellite images and LiDAR. The Normalized Burn Ratio is used to determine the areas affected by the fire that occurred on 18 August 2016 in the Natural Reserve of Garganta de los Infiernos (Cáceres). To calculate the erosion, the multi-criteria analysis is carried out from the RUSLE. Once all calculations were performed, there was a considerable increase in sediment production from 16 June 2016 (pre-fire) with an erosion of 31 T/ha·year to 16 June 2017 of 74 T/ha·year for areas of moderate fire severity, and an increase from 11 T/ha·year in 2016 to 70 T/ha·year for areas with a very high severity. From the NDVI, it was possible to verify that this also affected the recovery of post-fire vegetation, decreasing the NDVI index 0.36 in areas of moderate severity and 0.53 in areas of very high severity.

Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests

Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R2 = 0.42, p < 0.001). The dNBR performed better to predict GeoCBI in open mature larch plots (R2 = 0.56, p < 0.001). The GeoCBI provides a holistic field assessment of fire severity yet is dominated by the effect of fire on vegetation. No significant relationships were found between GeoCBI components (overall and substrate stratum) and burn depth within our fires (p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R2 = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.