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.

Download Full-text

Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series

Remote Sensing ◽

10.3390/rs11030308 ◽

2019 ◽

Vol 11 (3) ◽

pp. 308 ◽

Cited By ~ 12

Author(s):

Donato Morresi ◽

Alessandro Vitali ◽

Carlo Urbinati ◽

Matteo Garbarino

Keyword(s):

Vegetation Index ◽

Rate Of Change ◽

Tree Canopy ◽

Forest Recovery ◽

Central Apennines ◽

Vegetation Development ◽

Regeneration Dynamics ◽

Tree Canopy Cover ◽

Spectral Recovery ◽

Normalized Burn Ratio

Understanding post-fire regeneration dynamics is an important task for assessing the resilience of forests and to adequately guide post-disturbance management. The main goal of this research was to compare the ability of different Landsat-derived spectral vegetation indices (SVIs) to track post-fire recovery occurring in burned forests of the central Apennines (Italy) at different development stages. Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2) and a novel index called Forest Recovery Index 2 (FRI2) were used to compute post-fire recovery metrics throughout 11 years (2008–2018). FRI2 achieved the highest significant correlation (Pearson’s r = 0.72) with tree canopy cover estimated by field sampling (year 2017). The Theil–Sen slope estimator of linear regression was employed to assess the rate of change and the direction of SVIs recovery metrics over time (2010–2018) and the Mann–Kendall test was used to evaluate the significance of the spectral trends. NDVI displayed the highest amount of recovered pixels (38%) after 11 years since fire occurrence, whereas the mean value of NDMI, NBR, NBR2, and FRI2 was about 27%. NDVI was more suitable for tracking early stages of the secondary succession, suggesting greater sensitivity toward non-arboreal vegetation development. Predicted spectral recovery timespans based on pixels with a statistically significant monotonic trend did not highlight noticeable differences among normalized SVIs, suggesting similar suitability for monitoring early to mid-stages of post-fire forest succession. FRI2 achieved reliable results in mid- to long-term forest recovery as it produced up to 50% longer periods of spectral recovery compared to normalized SVIs. Further research is needed to understand this modeling approach at advanced stages of post-fire forest recovery.

Download Full-text

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-763-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 763-769

Author(s):

H. Tonbul ◽

T. Kavzoglu ◽

S. Kaya

Keyword(s):

Geographical Information Systems ◽

Vegetation Index ◽

Landsat Imagery ◽

Fire Severity ◽

Cost Effective ◽

Geographical Information ◽

Burned Area ◽

Elevation Model ◽

Normalized Burn Ratio ◽

Vegetation Regeneration

Satellite based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Mersin-Gülnar wildfire, which occurred in August 2008 in Turkey, selected as study site. The fire was devastating and continued 55 days. According to Turkish General Directorate of Forestry reports, it caused two deaths and left hundreds of people homeless. The aim of this study is to determine the fire severity and monitor vegetation recovery with using multitemporal spectral indices together with topographical factors. Pre-fire and post-fire Landsat ETM+ images were obtained to assess the related fire severity with using the widely-used differenced Normalized Burn Ratio (dNBR) algorithm. Also, the Normalized Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to determine vegetation regeneration dynamics for a period of six consecutive years. In addition, aspect image derived from Aster Global Digital Elevation Model (GDEM) were used to determine vegetation regeneration regime of the study area. Results showed that 5388 ha of area burned with moderate to high severity damage. As expected, NDVI and SAVI values distinctly declined post-fire and then began to increase in the coming years. Mean NDVI value of burned area changed from 0.48 to 0.17 due to wildfire, whilst mean SAVI value changed from 0.61 to 0.26. Re-growth rates calculated for NDVI and SAVI 57% and 63% respectively, six years after the fire. Moreover, NDVI and SAVI were estimated six consecutive year period by taking into consideration east, south, north and west facing slopes. Analysis showed that north-facing and east-facing slopes have higher regeneration rates in compared to other aspects. This study serves as a window to an understanding of the process of fire severity and vegetation regeneration that is vital in wildfire management systems.

Download Full-text

LANDSAT-BASED DETECTION AND SEVERITY ANALYSIS OF BURNED SUGARCANE PLOTS IN TARLAC, PHILIPPINES USING DIFFERENCED NORMALIZED BURN RATIO (dNBR)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w1-173-2016 ◽

2016 ◽

Vol XLII-4/W1 ◽

pp. 173-179 ◽

Cited By ~ 2

Author(s):

A. B. Baloloy ◽

A. C. Blanco ◽

B. S. Gana ◽

R. C. Sta. Ana ◽

L. C. Olalia

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

The Philippines ◽

Local Economy ◽

Burn Severity ◽

Burned Area ◽

Landsat 8 ◽

Potential Yield ◽

Differenced Normalized Burn Ratio ◽

Normalized Burn Ratio

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.

Download Full-text

Mapping burned areas from landsat-8 imageries on mountainous region using reflectance changes

MATEC Web of Conferences ◽

10.1051/matecconf/201822904012 ◽

2018 ◽

Vol 229 ◽

pp. 04012

Author(s):

Suwarsono ◽

Hana Listi Fitriana ◽

Indah Prasasti ◽

Muhammad Rokhis Khomarudin

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Burned Area ◽

Landsat 8 ◽

Mountainous Region ◽

Mountainous Regions ◽

Burned Areas ◽

Test And Evaluation ◽

Normalized Burn Ratio ◽

D Values

This research tried to detect a burned area that occurred in the mountainous region of Java Island. During this time, forest and land fires mostly occur in lowland areas in Sumatra and Kalimantan. However, it is possible that this phenomenon also occurs in mountainous regions, especially the mountainous regions of Java Island. The data used were Landsat-8, the latest generation of the Landsat series. The research location was on the Northeast slope of Mt. Ijen in East Java. The research methods include radiometric correction, data fusion, sample training retrieval, reflectance pattern analysis, Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) extraction, separability analysis, parameter selection for burned area detection, parameter test, and evaluation. The results show that ρ5 and NBRL parameter shows the highest values of D-values (most sensitive), to detect the burned area. Then, compared to ρ5, NDVI and NBRS, Normalized Burn Ratio long (NBRL) provide better results in detecting burned areas.

Download Full-text

Dinâmica de Queimadas no Município de Cuiabá-MT por Sensoriamento Remoto

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v14.2.p607-618 ◽

2021 ◽

Vol 14 (2) ◽

pp. 607

Author(s):

Noelto da Cruz Teixeira Da Cruz Teixeira ◽

Victor Hugo de Morais Danelichen Hugo de Morais Danelichen

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Kernel Estimator ◽

High Rate ◽

Landsat 8 ◽

Biological Potential ◽

Erdas Imagine ◽

Normalized Burn Ratio

O bioma Cerrado é uma das mais ricas fitofisionomias existentes no Planeta com destaque no elevado índice de ocupação humana direcionada à produção agropecuária. Apesar de seu potencial biológico enfrenta ameaças constantes de queimadas devido à conversão da vegetação em parcelas destinada a agricultura e pastagens. Neste contexto o objetivo deste trabalho é estudar a dinâmica espacial e temporal das queimadas no município de Cuiabá-MT, relacionando com as variáveis microclimáticas, classes de vegetação e declividades do terreno com o uso de recursos, de sensoriamento remoto. Foram utilizados os índices espectrais, NBR, NBR2 e NDVI extraídos das imagens Landsat 8 e focos de calor fornecido pelo Banco de Dados de Queimadas do INPE (Instituto Nacional de Pesquisas Espaciais) no período de 2013 a 2017. Os índices espectrais foram extraídos de 25 imagens referente a órbita 226 e ponto 071, utilizando o programa Erdas Imagine e os mapas de fogo através do estimador de Kernel presente no ArcGis 10.3 a fim de avaliar a distribuição e o padrão das queimadas na área proposta. Os resultados avaliados a partir do conjunto dos índices espectrais e dos mapas de estimativa de Kernel mostraram que o município de Cuiabá apresentou um padrão sazonal de queimadas, evidenciando maiores volumes de queimadas nas formações savânicas e nos terrenos de declividades da classe suave-ondulado em todo o período estudado.Palavras-chave: OLI, precipitação, padrão espacial. Dynamics of Fires in the Municipality of Cuiabá-MT by Remote Sensing A B S T R A C T The Brazilian Cerrado biome has several phytophysiognomies and a high rate of human occupation, with emphasis on agricultural production. Despite its biological potential, it faces constant threats of burning due to the conversion of vegetation into plots for agriculture and pasture. In this context, the objective of this work was to identify and relate the spatial and temporal dynamics of the fires in the municipality of Cuiabá-MT, with the microclimate variables, classes of vegetation and slopes of the land through the use of remote sensing resources. The spectral indexes NBR (Normalized Burn Ratio, NBR2 (Variation of Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) extracted from Landsat 8 images and heat sources provided by the INPE (Instituto Nacional de Space Research) from 2013 to 2017. The spectral indexes were extracted from 25 images referring to orbit 226 and point 071, using the Erdas Imagine program, and the fire maps of the Kernel estimator present in ArcGis 10.3 in order to evaluate the distribution and the pattern of fires in the proposed area. There was a 50.68% coincidence of the total number of hot spots on the reference burned areas, with a higher percentage of 72.12% in 2017 and lower in 2014 of 12.95%. These results made it possible to elaborate maps with a characteristic burning pattern and to highlight the classes most affected by fire throughout the studied period. Keywords: OLI, fire, spatial pattern.

Download Full-text

Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio

Remote Sensing ◽

10.3390/rs13040695 ◽

2021 ◽

Vol 13 (4) ◽

pp. 695

Author(s):

Max J. van Gerrevink ◽

Sander Veraverbeke

Keyword(s):

Spectral Index ◽

Field Data ◽

Vegetation Index ◽

Fire Regime ◽

Fire Severity ◽

Spectral Space ◽

Mid Infrared ◽

Fire Emissions ◽

Differenced Normalized Burn Ratio ◽

Normalized Burn Ratio

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVIMID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVIMID approaches. The results yielded an R2 of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVIMID indices was lower with an R2 = 0.61 for the best performing dNDVIMID index. The dNBR also outperformed the dNDVIMID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVIMID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVIMID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVIMID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.

Download Full-text

SENSORIAMENTO REMOTO APLICADO EM INDICADORES DE DESERTIFICAÇÃO NO MUNICIPIO DE PARELHAS – RN

Revista de Geografia ◽

10.51359/2238-6211.2020.239097 ◽

2020 ◽

Vol 37 (1) ◽

pp. 241

Author(s):

Paulo Igor De Melo Albuquerque ◽

João Paulo Bezerra Rodrigues ◽

Filipe Da Silva Peixoto ◽

Mateus De Paula Miranda

Keyword(s):

Normalize Difference Vegetation Index ◽

Vegetation Index ◽

Landsat 8

Este estudo buscou caracterizar e mapear áreas onde há processo desertificação se encontra mais avançado em Parelhas. Para isso, foram utilizadas imagens de satélite Landsat - 8, sensor OLI. As imagens onde são datadas de 29/09/2016 e 12/06/2017, correspondendo ao período seco, e final do período chuvoso, respectivamente. Foi realizado procedimento de Pré–processamento: correção radiométrica e fusão de bandas para o refinamento da resolução espacial para 15 metros. Na etapa processamento foi aplicado o Normalize Difference Vegetation Index – NDVI para identificar as áreas de maiores atividades fotossintéticas da vegetação, e assim, mensurar o potencial fitoestabilizador no ambiente. Para calibração da classificação da imagem, foram realizadas análises pontuais de 9 pontos de controle que possuiam diferentes valores de NDVI. Foram identificadas áreas em avançado processo de desertificação que permitiu a vetorização e mapeamento. Outros setores onde que a reposta do NDVI foram consideradas mediana a alta (>0,21) foi reconhecido o papel fitoestabiliador da vegetação, impedindo que haja uma alta taxa de erosão e retirada das camadas e horizontes superficiais. A metodologia aplicada pode ser direcionada para áreas diversas para refinar informações sobre a desertificação, além do monitoramento e subsídios para políticas de recuperação ambiental em áreas estratégicas para contenção.

Download Full-text

ASSESSMENT OF FIRE SEVERITY AND POST-FIRE REGENERATION BASED ON TOPOGRAPHICAL FEATURES USING MULTITEMPORAL LANDSAT IMAGERY: A CASE STUDY in MERSIN, TURKEY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-763-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 763-769 ◽

Cited By ~ 2

Author(s):

H. Tonbul ◽

T. Kavzoglu ◽

S. Kaya

Keyword(s):

Geographical Information Systems ◽

Vegetation Index ◽

Landsat Imagery ◽

Fire Severity ◽

Cost Effective ◽

Geographical Information ◽

Burned Area ◽

Elevation Model ◽

Normalized Burn Ratio ◽

Vegetation Regeneration

Download Full-text

Estimating Evapotranspiration in a Post-Fire Environment Using Remote Sensing and Machine Learning

Remote Sensing ◽

10.3390/rs10111728 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1728 ◽

Cited By ~ 7

Author(s):

Patrick Poon ◽

Alicia Kinoshita

Keyword(s):

Machine Learning ◽

Land Surface ◽

Vegetation Index ◽

Hydrological Cycle ◽

Potential Evapotranspiration ◽

Remotely Sensed ◽

Support Vector ◽

Moisture Index ◽

Input Variables ◽

Normalized Burn Ratio

In the hydrological cycle, evapotranspiration (ET) transfers moisture from the land surface to the atmosphere and is sensitive to disturbances such as wildfires. Ground-based pre- and post-fire measurements of ET are often unavailable, limiting the potential to understand the extent of wildfire impacts on the hydrological cycle. This research estimated both pre- and post-fire ET using remotely sensed variables and support vector machine (SVM) methods. Input variables (land surface temperature, modified soil-adjusted vegetation index, normalized difference moisture index, normalized burn ratio, precipitation, potential evapotranspiration, albedo and vegetation types) were used to train and develop 56 combinations that yielded 33 unique SVM models to predict actual ET. The models were trained to predict a spatial ET, the Operational Simplified Surface Energy Balance (SSEBop), for the 2003 Coyote Fire in San Diego, California (USA). The optimal SVM model, SVM-ET6, required six input variables and predicted ET for fifteen years with a root-mean-square error (RMSE) of 8.43 mm/month and a R2 of 0.89. The developed model was transferred and applied to the 2003 Old Fire in San Bernardino, California (USA), where a watershed balance approach was used to validate SVM-ET6 predictions. The annual water balance for ten out of fifteen years was within ±20% of the predicted values. This work demonstrated machine learning as a viable method to create a remotely-sensed estimate with wide applicability for regions with sparse data observations and information. This innovative work demonstrated the potential benefit for land and forest managers to understand and analyze the hydrological cycle of watersheds that experience acute disturbances based on this developed predictive ET model.

Download Full-text

A Statistical and Spatial Analysis of Portuguese Forest Fires in Summer 2016 Considering Landsat 8 and Sentinel 2A Data

Environments ◽

10.3390/environments6030036 ◽

2019 ◽

Vol 6 (3) ◽

pp. 36 ◽

Cited By ~ 9

Author(s):

Ana Teodoro ◽

Ana Amaral

Keyword(s):

Forest Fires ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Burn Severity ◽

Landsat 8 ◽

Fire Event ◽

Burned Areas ◽

Area Burned ◽

Sentinel 2A ◽

Normalized Burn Ratio

Forest areas in Portugal are often affected by fires. The objective of this work was to analyze the most fire-affected areas in Portugal in the summer of 2016 for two municipalities considering data from Landsat 8 OLI and Sentinel 2A MSI (prefire and postfire data). Different remote sensed data-derived indices, such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR), could be used to identify burnt areas and estimate the burn severity. In this work, NDVI was used to evaluate the area burned, and NBR was used to estimate the burn severity. The results showed that the NDVI decreased considerably after the fire event (2017 images), indicating a substantial decrease in the photosynthesis activity in these areas. The results also indicate that the NDVI differences (dNDVI) assumes the highest values in the burned areas. The results achieved for both sensors regarding the area burned presented differences from the field data no higher than 13.3% (for Sentinel 2A, less than 7.8%). We conclude that the area burned estimated using the Sentinel 2A data is more accurate, which can be justified by the higher spatial resolution of this data.

Download Full-text