Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources

Remote-sensing based assessment of post-fire changes in land surface temperature in Arctic-Boreal permafrost regions

In recent years, wildfires became more predominant in northern high latitude permafrost regions. Arctic warming, as a consequence of climate change, causes drying of vegetation being more flammable and promotes lightning incidents. Hence, the Arctic wildfire season extents accompanied by an increase in wildfire frequency as well as burn severity (BS). By now, boreal forests are known as carbon sink but will become a carbon source, further enhancing climate change. Within loss in surface organic layer due to wildfires, the thermal conductivity of the soils changes, impacting the underlying permafrost. Thawing permafrost again releases greenhouse gasses, resulting in a positive feedback, further accelerating climate warming. Regarding these global consequences of wildfires, a better understanding of small regional scale processes is necessary for reliable future predictions. Therefore, the aim of this study is to assess post-fire impacts on permafrost in the north-eastern Siberian Yana river catchment using remote sensing data. As previous studies announced a future spread of wildfires northward from Taiga to Tundra ecosystems, both will be considered in the study analysis to distinguish between their influence quantity. In order to answer the research question, the effects on permafrost after wildfire were investigated using 9 Siberian fire sites including their accompanied control sites, along the Yana river. The yearly mean land surface temperature (LST), calculated from Landsat images over a time period from 2006- 2020 for the summer months (June, July, August) serves therefore as data basis. Based on that, the Permafrost_CCI products including the yearly mean ground surface temperature (GST) and active layer thickness (ALT) between 1997-2018, were consulted for comparison purposes. Created time series of LST, GST and ALT were individually analyzed by visual interpretation, descriptive statistics and trend analysis. Finally, GST and ALT time series were correlated against LST time series. Additionally, the normalized burn ratio (NBR) was calculated from Landsat images to get supportive information about the BS and vegetation recovery, as these factors play a very important role in influencing the magnitude of permafrost variations due to wildfires. The main findings show a correlation between LST and ALT resulting in a decrease of permafrost as the ALT increases within increasing LST after a wildfire. The coherence between LST and GST does not show unique results though, but result in increasingly warmer temperatures in the soil as well. Regarding differences between Taiga and Tundra ecosystems, impacts are causing a greater threat for permafrost in Tundra regions, especially in context with future predicted increase of wildfire frequency and BS. Nevertheless, studying permafrost remains still challenging due to the remoteness of the study area, resulting in a lack of in-situ data, as well as remote sensing data

Estudo sobre incêndios florestais na Floresta de Miombo Reserva do Niassa-Moçambique, com base em dados de sensoriamento remoto

Os incêndios florestais são um dos principais fatores recorrentes de danos ambientais, sociais e econômicos na Reserva do Niassa. A presente pesquisa teve como objetivo entender os fatores causais e os padrões de ocorrência dos incêndios florestais na Reserva do Niassa-Moçambique, com base no conhecimento prévio do padrão de distribuição espacial e da dinâmica temporal da cobertura vegetal, utilizando dados MODIS, entre o período de 2001 a 2015. Para tal foram utilizadas bases de dados, de diferentes produtos do sensor MODIS (Moderate Resolution Imaging Spectroradiometer): produto MOD13Q1 (índice de vegetação NDVI - Normalized Difference Vegetation Index); produto MCD14ML (Fogo ativo); produto MCD64A1 (Área queimadas). Os dados meteorológicos de precipitação pluvial foram obtidos do Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), a temperatura média do ar do ERA Interim; e umidade relativa foi calculada com base na equação da FAO-Penman-Monteith. Foram utilizados ainda dados topográficos (Modelo Digital de Terreno) do Shuttle Radar Topographic Mission (SRTM) para o cálculo de elevação, declividade e exposição solar. Os dados de vias de acesso e dos assentamentos populacionais foram produzidos pela CENACARTA (Centro Nacional de Cartografia e Teledeteção-Moçambique). O uso de séries temporais de dados NDVI /MODIS permitiu obter informações sobre a fenologia da vegetação, identificar diferentes tipos de cobertura vegetal da Reserva e analisar a sua dinâmica e variabilidade espaço-temporal. A sazonalidade da vegetação da Reserva apresenta ciclos bem marcados com baixos valores na estação seca e valores altos na estação chuvosa. Para análise dos padrões espaço-temporais dos incêndios florestais foram utilizados os produtos MCD14ML (Fogo ativo) e MCD64A1 (Área queimada), utilizando a estatística descritiva, análise de tendência e a densidade de Kernel. Foi observado que os incêndios florestais ocorrem entre os meses de agosto a outubro, período de maior ocorrência, e com uma dinâmica espacial que inicia a leste e se desloca para o oeste. Os incêndios ocorrem predominantemente nas florestais decíduas e de montanha. Foi também utilizada à regressão logística para a modelagem de ocorrência de incêndio florestal, com vista à identificação de locais de maior ocorrência de incêndios florestais, e identificação de fatores determinantes para a sua ocorrência. Os resultados revelaram que os principais fatores determinantes para ocorrência dos incêndios florestais na Reserva do Niassa, entre 2001 e 2015, foram fundamentalmente: cobertura vegetal, seguida de temperatura do ar e da elevação. A área de maior ocorrência de incêndios é a zona leste da Reserva. Os resultados obtidos permitiram concluir que a cobertura vegetal é um dos fatores fundamentais da ocorrência de incêndio nas florestais de Miombo. O uso dos dados MODIS, índice de vegetação, focos de incêndio e áreas queimadas demonstrou potencial no estudo de incêndios florestais na Reserva do Niassa.Forest fires are one of the main recurring factors of environmental, social and economic damages in Niassa Reserve. The objective of the present research was to understand the causal factors and patterns of occurrence of forest fires in Niassa-Mozambique Reserve, based on prior knowledge of the spatial distribution pattern and temporal dynamics of the vegetation cover using MODIS data between the period from 2001 to 2015. For this purpose, the following databases were used: MODIS sensor (Moderate Resolution Imaging Spectroradiometer): MOD13Q1 (Normalized Difference Vegetation Index); MCD14ML product (active fire); product MCD64A1 (burned area). Meteorological data on rainfall were obtained from the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), and the average air temperature of ERA Interim; The relative humidity was calculated based on FAO-Penman-Monteith equation. Topographic data (Digital Terrain Model) of the Shuttle Radar Topographic Mission (SRTM) were used for the calculation of elevation, slope and sun exposure. Data on access routes and population settlements were produced by CENACARTA (National Center for Cartography and Teledetection-Mozambique). The use of NDVI / MODIS temporal data series allowed us to obtain information on vegetation phenology, to identify different types of vegetation cover of the Reserve and to analyze its dynamics and spatio-temporal variability. The seasonality of the Reserve vegetation shows well marked cycles with low values in the dry season and high values in the rainy season. For the analysis of spatiotemporal patterns of forest fires, the products MCD14ML (Active fire) and MCD64A1 (Burned area) were used, using descriptive statistics, trend analysis and Kernel density. It was observed that forest fires occur between August and October, the period of greatest occurrence, and with a spatial dynamics that begins in the east and moves to the west. Fires occur predominantly in deciduous and mountain forests. It was also used logistic regression for modeling of occurrence of forest fire, with a view to the identification of sites of greater occurrence of forest fires, and identification of factors determining its occurrence. The results showed that the main determining factors for the occurrence of forest fires in Niassa Reserve between 2001 and 2015 were fundamentally: vegetation cover, followed by air temperature and elevation. The area with the highest occurrence of fires is the eastern zone of the Reserve. The results obtained allowed to conclude that the vegetation cover is one of the fundamental factors of the fire occurrence in Miombo forest. The use of MODIS data, vegetation index, active fire and burned areas showed potential in the study of forest fires in Niassa Reserve