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Quantifying Spatial and Temporal Vegetation Recovery Dynamics Following a Wildfire Event in a Mediterranean Landscape using EO Data and GIS
... -Gully control works [40] These works consist of various methods, such as e.g. tree spurs, branch layering, or vegetated chase. ...
... PhD Thesis 2022 16 -Walls, sills, and weirs [40] As post-fire measures transversal erosion barriers can be implemented at watershed scale to retain the sediment. The choice of the measure employed depends on available material, labor, accessibility, and time. ...
... The control of natural hazards using herbaceous species remains a challenge in areas where technical, socioeconomic and ecological issues are hindering factors [40], especially in rural areas. Nevertheless, success can be achieved when compromises such as longer construction times or manual excavations are taken into consideration. ...
The present thesis describes the theoretical transfer of soil and water bioengineering (SWBE) as a measure for erosion mitigation to a fire-prone Take-Up Site in the temperate Andes in southern Ecuador. A fire-prone area in the Pisan Mountains/Italy, where SWBE measures are frequently used, served as Leading Site. The Transferability Analysis aims to estimate key barriers or key support factors appearing with the transfer. Various analyses were carried out to support the findings: Autonomous vegetation recovery capacity at the Take-Up Site in post- fire conditions was analyzed using multitemporal vegetation indices from Sentinel 2 images. A soil data base at the Take-Up Site elaborated from field study and laboratory gave information regarding soil properties. To estimate soil loss at both sites the Revised Universal Soil Loss Equation (RUSLE) was used to simulate pre- and post-fire conditions. Further, the fire induced change of the runoff coefficient was estimated for one unburned and one burned basin at the Leading Site. The results showed a high vegetation recovery of grasslands at the Take-Up Site to the level of pre-fire conditions within one year. One key result from the soil analysis was the high infiltration rate in post-fire conditions, probably influencing the subsurface flow. The comparison of erosion behavior showed a moderate mean annual erosion at the Leading Site in pre-fire conditions (33.89 t/ha^-1/yr^-1), with an increase of 285% in post-fire conditions. The mean annual erosion at the Take-Up Site was already high in pre- fire conditions (116.14 t/ha^-1/yr^-1) and showed an increase of 7.16 % in post fire conditions. The runoff coefficient at the Leading Site changed with the fire event from 0.2 to 0.5. Regarding the take-up of SWBE measures to the fire prone area in the temperate Andes probable constraints resulted to be qualified labor and equipment/mechanical instruments. Key support factors for the transfer were Botany and Materials as a variety of plants shows important characteristics for SWBE measures, able to compensate constraints in certain cases.
... Risk models have been applied to determine ecological roles and to protect fragile ecosystems. Statistical methods (Logistic regression, Frequency ratio model, Bivariate statistical analysis) have been utilized to assess ecological risks (Sahana and Ganaie, 2017;Krejci et.al., 2018;Setras et al., 2018;Petropoulos et al., 2014;Rosa and Martinico, 2013). Risk estimation can be used as a decision support tool in order to prevent the loss of important habitats in landscape ecology. ...
... Risk estimation can be used as a decision support tool in order to prevent the loss of important habitats in landscape ecology. In addition, Analysis of Earth Observation data can be integrated with GIS to monitor the change of land cover and reduce natural disasters (Petropoulos et al., 2014). Sub-headings of studies under ecological mapping are: effects of landscape variables; ecological integration; green corridor planning; environmental restoration; mapping of ecologically valuable trees; investigation of landscape metabolism; ecological security; and the landscape-ecological concept (Fig. 5). ...
... The advent of technology and its useful tools will provide a better understanding of landscape ecology in the coming years. Freely accessible datasets (CORINE maps, Earth Observation data, etc.) or tools that can be used in the GIS/RS interface (SDMtoolbox, r.diversity, etc.) are particularly valuable for landscape ecologists and researchers (Steiniger and Hay, 2009;Rocchini et al., 2013;Petropoulos et al., 2014;Brown et al., 2017). ...
... The effect of fuel structure on fire severity can also vary with weather conditions at the time of burning, such that extreme temperatures, humidity levels, and wind speed are more likely to result in faster fire spread and complete combustion of fuels [39,41,42]. Additionally, topographic factors influence fire behavior (e.g., fire intensity may be greater on steeper slopes or at high slope positions) [43] and spatial patterns in vegetation recovery (e.g., faster recovery on north-facing slopes due to greater moisture availability, or at lower elevations due to warmer temperatures and longer growing season) [44,45]. ...
... To determine whether spruce beetle outbreak severity shows an effect on short-term vegetation recovery from fire, we used the Landsat-derived Normalized Difference Vegetation Index (NDVI) to assess understory vegetation recovery two years after a large, high-severity wildfire. NDVI provides an indicator of grass and herbaceous cover in early recovery stages [44,45,56]. We chose the West Fork Complex fire in southwestern Colorado, USA, as a case study because this event exemplifies an extreme wildfire event co-occurring with severe spruce beetle disturbance. ...
... Previous wildfire recovery studies have shown that NDVI tends to increase rapidly in the two years following fire occurrence [44,45]. NDVI in the West Fork Complex also increased rapidly, and overall NDVI values are correlated with pre-disturbance values. ...
Spruce beetle (Dendroctonus rufipennis) outbreaks are rapidly spreading throughout subalpine forests of the Rocky Mountains, raising concerns that altered fuel structures may increase the ecological severity of wildfires. Although many recent studies have found no conclusive link between beetle outbreaks and increased fire size or canopy mortality, few studies have addressed whether these combined disturbances produce compounded effects on short-term vegetation recovery. We tested for an effect of spruce beetle outbreak severity on vegetation recovery in the West Fork Complex fire in southwestern Colorado, USA, where much of the burn area had been affected by severe spruce beetle outbreaks in the decade prior to the fire. Vegetation recovery was assessed using the Landsat-derived Normalized Difference Vegetation Index (NDVI) two years after the fire, which occurred in 2013. Beetle outbreak severity, defined as the basal area of beetle-killed trees within Landsat pixels, was estimated using vegetation index differences (dVIs) derived from pre-outbreak and post-outbreak Landsat images. Of the seven dVIs tested, the change in Normalized Difference Moisture Index (dNDMI) was most strongly correlated with field measurements of beetle-killed basal area (R² = 0.66). dNDMI was included as an explanatory variable in sequential autoregressive (SAR) models of NDVI2015. Models also included pre-disturbance NDVI, topography, and weather conditions at the time of burning as covariates. SAR results showed a significant correlation between NDVI2015 and dNDMI, with more severe spruce beetle outbreaks corresponding to reduced post-fire vegetation cover. The correlation was stronger for models which were limited to locations in the red stage of outbreak (outbreak ≤ 5 years old at the time of fire) than for models of gray-stage locations (outbreak > 5 years old at the time of fire). These results indicate that vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing wildfire. © 2017, Public Library of Science. All rights reserved. This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
... Several studies also investigate the effects of NDVI on the urban heat island (UHI) (e.g. Li et al., 2009;Petropoulos et al. 2014;Kumar and Shekhar, 2015;Jamei et al. 2019;Malik et al., 2019;Maskooni et al. 2021). Negative NDVI values correspond to the water surface, and the values close to zero to the soil, bare lands, or residential areas. ...
... Negative NDVI values correspond to the water surface, and the values close to zero to the soil, bare lands, or residential areas. As the Petropoulos et al. (2014) stated that the values between 0.1 and 0.75 generally indicate vegetation cover. ...
This study analyzed the changes in the urban heat island effect in the 30 years (from 1990 to 2021) in the central district of Osmaniye. In this sense, there were two primary goals. Firstly, Land use/land cover change (LULC), land surface temperature (LST), normalized difference built-up index (NDBI), and normalized difference vegetation index (NDVI) were analyzed by using remote sensing methods between 1990 and 2021. Secondly, a linear regression analysis was conducted to determine the factors associated with LST, NDVI, and NDBI. The study results revealed increases in urban surfaces and the average land surface temperature values in the past 30 years and showed a decline in the vegetation with low, medium, and high NDVI values. The regression analysis results indicated a strong negative relationship between LST and NDVI and a strong positive relationship between LST and NDBI. It was also found a robust negative relationship between NDBI and NDVI. In light of the findings, it was stated that the amount of open and green areas should be increased in order to prevent the negative effects of the urban heat island in the central district of Osmaniye. For this purpose, it has been proposed to encourage green roof systems throughout the city, to create city parks and to create a green belt system. In addition, as a result of the study, the importance of preventing forest destruction caused by over-settlement in the Amanos Mountains, which is one of the rare habitats of the world with different plant species, was emphasized. In this sense, legal sanctions should be employed to protect those areas and prevent construction.
... The reference image was taken in December or January (pre-fire) and the post-fire image was taken during the spring months (April and May). Finally, using Google Earth Engine (Petropoulos, Griffiths, & Kalivas, 2014) maps were produced ( Figure 3) representing the presence of wildfires for the last six years (2017-2022). Fire severity was classified based on criteria established by the United States Geological Survey (USGS) from the differential NBR (dNBR) (Keeley, 2009). ...
... La imagen de referencia se tomó en diciembre o enero (preincendio) y la imagen posincendio se tomó durante los meses de primavera (abril y mayo). Finalmente, con el uso de Google Earth Engine (Petropoulos, Griffiths, & Kalivas, 2014) se elaboraron mapas (Figura 3) que representaron la presencia de incendios forestales durante los últimos seis años (2017-2022). La severidad de los incendios se clasificó con base en los criterios establecidos por el Servicio Geológico de los Estados Unidos (USGS) a partir del diferencial del NBR (dNBR) (Keeley, 2009). ...
... Moreover, three scenes were chosen for each of the following years (2020: Nr.5-7 and 2021: Nr.8-10; Table 3), starting from the end of the rainy season (April/May) until one and two years after the fire event. Spectral indices derived from satellite images are widely used to map burned areas [36][37][38], as well as to monitor the vegetation's development after a fire event [23,37,39,40]. For this study, 23 VIs (Appendix A, Table A1) were calculated for every S2 scene and databases were created by extracting the VI-values according to the FS class (unburned, low severity, moderate low severity, moderate high severity) derived from the RBR at the El Saco basin. ...
... When classifying the FS using Random Forest for every single scene of the used dates, before and after the fire event, widely used VIs for time series monitoring, such NDVI, or Soil Adjusted Vegetation Index (SAVI) [8,40,43,44], were not part of the final models. The NBR, which is frequently used for post-fire vegetation monitoring [22], was part of one model (29 September 2019), which questions the application of this index for grasslanddominated areas. ...
In wildfire areas, earth observation data is used for the development of fire-severity maps or vegetation recovery to select post-fire measures for erosion control and revegetation. Appropriate vegetation indices for post-fire monitoring vary with vegetation type and climate zone. This study aimed to select the best vegetation indices for post-fire vegetation monitoring using remote sensing and classification methods for the temperate zone in southern Ecuador, as well as to analyze the vegetation's development in different fire severity classes after a wildfire in September 2019. Random forest classification models were calculated using the fire severity classes (from the Relativized Burn Ratio-RBR) as a dependent variable and 23 multitemporal vegetation indices from 10 Senti-nel-2 scenes as descriptive variables. The best vegetation indices to monitor post-fire vegetation recovery in the temperate Andes were found to be the Leaf Chlorophyll Content Index (LCCI) and the Normalized Difference Red-Edge and SWIR2 (NDRESWIR). In the first post-fire year, the vegetation had already recovered to a great extent due to vegetation types with a short life cycle (sea-sonal grass-species). Increasing index values correlated strongly with increasing fire severity class (fire severity class vs. median LCCI: 0.9997; fire severity class vs. median NDRESWIR: 0.9874). After one year, the vegetations' vitality in low severity and moderate high severity appeared to be at pre-fire level.
... After three years, N. alessandrii forests have recovered in terms, at least, of the greenness analyzed by the NDVI. This is consistent with that reported by Petropoulos et al. (2014), who observed that the spatial pattern NDVI after a fire in a Mediterranean environment generally shows a gradual but systematic return to the prefire conditions. In our study, this was confirmed when analyzing those sectors where the fire severity was low-medium ("El Desprecio", "El Porvenir", and "El Fin"). ...
... If it is considered that the forests of N. alessandrii are distributed naturally only on shaded slopes (Santelices et al., 2012), the relatively rapid recovery could be explained by this factor. Petropoulos et al. (2014) suggested that hillsides with shady exposures show a slightly faster recovery rate compared to sunny slopes. This could be due to more favorable microclimatic and hydrological conditions for vegetation growth in these areas in Mediterranean environments; on shady slopes, finer and deeper soils have been observed, and these conditions are more favorable for a greater growth of vegetation than on sunny slopes (Fox et al., 2008). ...
Nothofagus alessandrii is an endangered species that is naturally distributed in a Mediterranean environment in central Chile. In recent years, this territory has been subject to the effects of climate change, especially an increase in summer temperatures and prolonged periods of drought. In the summer of 2017, there was a fire of great magnitude consuming 184,000 ha, which affected the forests of N. alessandrii. This study assessed the severity and recovery dynamics of postfire vegetation by using spectral indices from Sentinel-2 images. The differenced normalized burn ratio (dNBR), relative differenced normalized burn ratio (RdNBR), and relativized burn ratio (RBR) were calculated before and after the fire and, later, the normalized difference vegetation index (NDVI) before the fire and during three consecutive years after the fire was utilized. The accuracy of the fire severity classifications was estimated using the kappa test (p<0.05). The three severity indices showed a similar classification in severity assessment and postfire response. The low-medium burn area in N. alessandrii forests ranged between 111.2 ha (RdNBR) and 130.3 ha (dNBR), and the high effect was between 46.1 ha (dNBR) and 66.0 ha (RdNBR), which was equivalent in both cases, approximately 11% of the total. Regarding the NDVI, vegetation recovery after three years of the fire showed a systematic return to prefire conditions. The assessment of the effect of a mega forest fire on the remaining forests of N. alessandrii based on Sentinel-2 images offers the opportunity for a better understanding of the severity of damage and the behavior of vegetation after the fire. All this information will help in a better recovery of these forests.
... Several studies also investigate the effects of NDVI on the urban heat island (UHI) (e.g. Li et al., 2009;Petropoulos et al. 2014;Kumar and Shekhar, 2015;Jamei et al. 2019;Malik et al., 2019;Maskooni et al. 2021). Negative NDVI values correspond to the water surface, and the values close to zero to the soil, bare lands, or residential areas. ...
... Negative NDVI values correspond to the water surface, and the values close to zero to the soil, bare lands, or residential areas. As the Petropoulos et al. (2014) stated that the values between 0.1 and 0.75 generally indicate vegetation cover. ...
Purpose
In Turkey, where the environmental impact assessment (EIA) has been applied since 1993, there have been numerous amendments in the legal and administrative process of the EIA. This study aims to evaluate the effectiveness of those amendments to the EIA process.
Design/methodology/approach
This paper evaluated EIA system performance in the context of procedural effectiveness in Turkey from the day implementation was begun. From its beginning to the present day, the positive and negative developments at the EIA process in Turkey caused by the amendments were evaluated and at which stages. Measures recommended increasing the effectiveness of each of the EIA systems were also identified.
Findings
As the EIA Directive first came into force in the USA in 1970, EIA procedures have been widely adopted throughout the world. Although it has been implemented for many years, expectations regarding the EIA process have still not been realized which has forced countries to conduct studies to increase the effectiveness of the EIA process. Turkey, like other countries that are implementing the EIA, acknowledges that the EIA is a significant impact assessment tool and continues its studies to implement this system effectively. In this respect, in Turkey, where the EIA has been applied since 1993, there have been numerous amendments in the legal and administrative process of the EIA.
Originality/value
The results obtained from this study were expected to facilitate the evaluation of the EIA process in Turkey and to guide other similar countries.
... For thousands of years, fires have been one of the most important factors shaping Mediterranean landscape where rates of post-fire recovery dynamics are usually spatio-temporal variable and contingent upon a number of factors -eg. landscape complexity, range responses and fire regimes (Mayor et al., 2007;Petropoulos et al., 2014). Mediterranean regions are responsible for approximately 90% of fires in the European Union where landscapes, recently, have undergone dramatic land abandonment (Mouillot et al., 2005). ...
... The aspect map was created by separating the slopes into two groups -based on the direction they face (ie. North and South) and by identifying which ones have greater regeneration rates (according to Mouillot et al. (2005) and Petropoulos et al. (2014). The slope steepness map was created by utilising the elevation (altitude of each pixel) attribute. ...
Obtaining post-fire information from a burnt region is of paramount importance in applications such as examining the disturbance of natural ecosystems and in providing crucial information to local authorities that have control on policymaking. This study uses freely available data from the European Space Agency's (ESA) Sentinel-2 satellite to create a Red-Edge Normalised Difference Vegetation Index (NDVI705) and combines the resulting layer with 30 m Digital Elevation Model (DEM) from the Japanese Aerospace Exploration Agency (JAXA) to assess topographical parameters (ie. slope steepness and aspect) which may have influenced the revegetation process. Additionally, weather data is combined with the aforementioned datasets to study the revegetation dynamics. A fire event which occurred in June 2016 in Evrychou, Cyprus, was chosen, as it was one of the largest fire events in the island and happened when the Sentinel-2 was already operational, hence a period of time spanning 14 months has been studied. The results have indicated an inconsistent NDVI705 change throughout the period. However, a significant improvement in NDVI705 values was observed in the months of spring 2017. The improvement in vegetation health was mostly observed on north-facing and less-steep slopes, something which corresponds with previous studies in northern-hemisphere Mediterranean climates. The results have also highlighted the ability to conduct a rapid and cost-effective post-fire assessment which can be scaled up or down depending on the fire size and which can be applied to any other environment where post-fire management is required.
... Others introduced this index in drought studies while detecting changes in vegetation trends (e.g. Faour, Mhawej, & Abou Najem, 2015;Faour, Mhawej, & Fayad, 2016;Gu, BrownVerdin, & Wardlow, 2007;Liu & Kogan, 1996;Mwaniki & Möller, 2015;Peters et al., 2002;Petropoulos, Griffiths, & Kalivas, 2014;Riva, Daliakopoulos, Eckert, Elias, & Liniger, 2017;Shalaby & Tateishi, 2007;Van Hoek, Jia, Zhou, Zheng, & Menenti, 2016). Moreover, the NDVI was used in different discipline, such as forestry and wildfire managements (e.g. ...
... More recent studies (e.g. Brovkin et al., 1997;Mwaniki & Möller, 2015;Neilson, 1993;Petropoulos et al., 2014;Prentice, Guiot, Huntley, Jolly, & Cheddadi, 1996;Running et al., 1995) have added diverse ecological-based factors (e.g. specific physiological responses to cold tolerance, drought stress, aboveground live biomass and leaf longevity) in relation to the geographic distribution of different vegetation types. ...
In remote sensing studies, the photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies is measured using Red and Near-Infra Red bands. The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices that are generally obtained from a calculation of the above mentioned bands; it presents a decent surrogate measures of the physiologically functioning surface greenness level. In this study, the latest version of the GIMMS NDVI data set, between the period of January 1982 and December 2015, were used to classify the global vegetation areas into five main categories (i.e. Agriculture Areas, Boreal Forests, Deciduous Forests, Evergreen and Tropical Forests, and Other Vegetation), using a simple and straight-forward method of classification, sumamed Global Vegetation Types Classification (GVTC). The total accuracy of the model reached 90.4% with a kappa value of 87.1%. In each category, a trend analysis has been carried out at both global and continental levels. The objective was to highlight the changes within each category, throughout the past thirty-four years. Results show that Agriculture Areas are increasing worldwide, with a huge upsurge observed since 2011 coinciding with a remarkable decrease in Boreal Forests. Changes in vegetation's classes, between 1982 and 2015, were more pronounceable in continents such as Asia, America and Africa; Europe and Oceania showed limited variations throughout this same period. Following these results, regional policies should be reformed and mitigation plans should be established in order to maintain a sustainable development of the global vegetation lands. The GVTC could be implemented with higher spatial resolution imageries for more local-based assessments.
... In recent decades, the earth-observing techniques as an effective remote sensing approach, has been widely applied to study the vegetationclimate relationships at different scales (Coppin et al. 2004;Verbesselt et al. 2010;Zewdie et al. 2017). The normalized difference vegetation index (NDVI) is an effective indicator to quantify the spatiotemporal characteristics of vegetation states (Petropoulos et al. 2014;Cao et al. 2018). For example, in the global scale, the vegetation in Northern Hemisphere has an obvious upward trend since the 1980s, while the vegetation in Southern Hemisphere has significantly degraded (Jong et al. 2012). ...
Rapid change of climate in vertical and considerable geomorphologic features form a typical diversity and distribution of biota in mountain ecosystems, i.e., the subalpine forest zone (SFZ), the valley savanna zone (VSZ), and the transition zone between them. The arid hot valley in the middle and lower reaches of Jinsha River, China represents a well target area to study distribution and the driving factors in these typical mountain ecosystems. Therefore, this study selects four sub-sample areas in the arid-hot valley to explore the distinctive changes of vegetation during 1990 to 2020, and their driving factors in the three different vegetation zones on spatiotemporal scales. On the spatial scale, the Moran’s index was applied to identify the transition zone between the SFZ and the VSZ. Results show that the VSZ at low altitudes (less than 600–1000 m from the valley bottom) is mainly affected by geomorphologic features, especially the slope aspect. With increase in altitude, the climate factors (e.g., humidity, temperature, etc.) play a more significant role in the development of the SFZ, while the effect of geomorphologic features gradually weakens. On the time scale, The SFZ at higher altitudes experienced more rapid changes in temperature (temperature increase of 1.41°C over the last 60 years) than the VSZ at lower altitudes (temperature increase of 0.172°C over the past 60 years). It caused the forest cover increase faster than that of savanna grassland. Humidity and heat conditions are altered by topography and climate conditions, which shapes the development and physiology of plants as they adapt to the different climatic zones. Furthermore, according to the driving factors (geomorphologic and climate factors) of vegetation distribution found in this study, it suggests that suitable tree species should be planted in the transition zone to evolve into the forest zone and making the forest zone to recover from high to low altitudes gradually.
... Indeed, the 2021/2022 hydrological year (until May) was the fourth driest year since 1931 [46]. These results are consistent with other studies that indicate it as the cause for the NDVI decrease in the impact of fire and/or low precipitation (NDVI anomalies) [3,15,21,24,25]. ...
Wildfires are a major environmental issue that have an impact on land degradation. Remote sensing spectral indices provide valuable information for short-term mitigation and rehabilitation after wildfires. A study area in the Centre inland of Portugal occupied with Maritime pine and Eucalypts forests and affected by wildfires in 2003, 2017 and 2020 was used. The aims of the study were twofold: (1) to compute the Normalized Difference Vegetation Index (NDVI) and with forest inventory data derivate a Maritime pine production model, differentiate evergreen coniferous forests (e.g., Maritime pine), evergreen broadleaved forests (e.g., Eucalypts), and shrubland, and monitor vegetation and its post-fire recovery; and (2) to compute the Normalized Burn Ratio (NBR) difference between pre-fire and post-fire dates for burn severity levels assessment. The plots of a previous forest inventory were used to follow the NDVI values in 2007 and from 2020 to 2022. An aerial coverage in 2007 and the Sentinel-2 imagery in 2020–2022 were used. Linear models fitted maritime pine production with the transformed NDVI by age, showing a fitting efficiency of 60%. The stratification of cover types by stand development stage and fire occurrence was possible using the NDVI time curve, which also showed the impact of fire and of low precipitation. Cover types were ranked by decreasing NDVI values as follows: mature Eucalypts plantations, young Maritime pine regeneration, mature Maritime pine, young Eucalypts plantations, Strawberry tree shrubland, Eucalypts plantations post-fire, Maritime pine post-fire, tall shrubland, and short shrubland. Vegetation post-fire recovery was lower in higher burn severity level areas. Maritime pine areas have lost their natural regeneration capability due to the wildfires’ short cycles. Spectral indices were effective tools to differentiate cover types and assist in the evaluation of forest and shrubland conditions.
... The NDVI is another widely utilized index that is an indicator of plant greenness, measuring plant type, and amount on land surfaces. Many studies have used NDVI to monitor post-fire vegetation dynamics in the Mediterranean region (Mitri & Gitas, 2010;Petropoulos et al., 2014;Veraverbeke et al., 2010). ...
Satellite-based remote sensing technologies and Geographical Information Systems (GIS) present operable and cost-effective solutions for mapping fires and observing post-fire regeneration. Elwasita wildfire, which occurred during April and May in 2013 in Libya, was selected as a study site. This study aims to monitor vegetation recovery and investigate the relationship between vegetation recovery and topographic factors by using multi-temporal spectral indices together with topographical factors. Landsat 8 (OLI and TIRS) images from different data were obtained which were for four years; April 2013, June 2014, July 2015, and July 2016, to assess the related fire severity using the widely-used Normalized Burn Ratio (NBR). Normalized difference Vegetation Index (NDVI) was used to determine vegetation regeneration dynamics for four consecutive years. Also, the state of damage, vegetation recovery and, damage dimensions about the burned area were capable of being effectively detected using the result of supervised classification of Landsat satellite images. In addition, aspect, slope, and altitude images derived from Digital Elevation Model (DEM) were used to determine the fire severity of the study area. The results have found that it could be possible to figure out the degree of vegetation recovery by calculating the NDVI and NBR using Landsat 8 OLI and TIRS images. Analysis showed that it mainly oriented towards the northwest (47%), north (29%), and northeast (12%). The statistical analysis showed that fire was concentrated on the incline by 76%, and the most affected areas are those between 200 m-450 m above sea level, with a percentage of 80%. It is expected that the information can be acquired by various satellite data and digital forests. This study serves as a window to an understanding of the process of fire severity and vegetation recovery that is vital in wildfire management systems.
... De nombreuses ont porté sur la péninsule ibérique 9 , la zone européenne qui a historiquement le plus connu d'incendies de forêt et de superficies brûlées 10 , alors que relativement moins d'études ont porté sur le sud de la France 11 , l'Italie 12 et la Grèce 13 . 5 La plupart des incendies de forêt en zone méditerranéenne se produisent entre juillet et août, lorsque des températures élevées et une faible humidité, tant dans l'air que dans la végétation couvrant le sol, créent les conditions nécessaires à l'allumage et la propagation du feu 14 . Les valeurs climatiques 15 et la fréquence des pluies estivales 16 en sont un prédicteur d'occurrence. ...
La région méditerranéenne est sujette aux incendies de forêt alors que les préoccupations écologiques génèrent un engagement institutionnel important dans la lutte. Toutefois, la question de la prévention reste ouverte : s’il est convenu que la majeure part des incendies de forêt est causée par l’homme, il n’existe pas un modèle consensuel permettant de localiser cette relation. Une voie est d’estimer le rôle que joue l’utilisation des terres dans le nombre ou l’intensité des incendies passés. Dans cette perspective, nous examinons l’impact de l’agriculture sur les incendies via une analyse quantitative de l’effet de l’usage agricole des terres sur leur occurrence et leur étendue. Nos résultats permettent de dresser le profil des municipalités qui sont plus exposées au risque d’incendie de forêt eu égard à la présence de certains types de cultures ou d’activités agricoles. Ils témoignent toutefois d’une relation complexe pour ce qui est de l’espace agricole dans son ensemble, qui peut être caractérisée à partir d’une classification raisonnée des productions (le maraichage minore les occurrences et les parcours extensifs les accroissent) ou des dynamiques spatiales (les espaces abandonnés minorent les occurrences et majorent les étendues).
https://journals.openedition.org/cdlm/14660
... De nombreuses ont porté sur la péninsule ibérique 9 , la zone européenne qui a historiquement le plus connu d'incendies de forêt et de superficies brûlées 10 , alors que relativement moins d'études ont porté sur le sud de la France 11 , l'Italie 12 et la Grèce 13 . 5 La plupart des incendies de forêt en zone méditerranéenne se produisent entre juillet et août, lorsque des températures élevées et une faible humidité, tant dans l'air que dans la végétation couvrant le sol, créent les conditions nécessaires à l'allumage et la propagation du feu 14 . Les valeurs climatiques 15 et la fréquence des pluies estivales 16 en sont un prédicteur d'occurrence. ...
... Satellite sensor derived data has become a phenomenal tool to study and analyze Land Use and Land Cover (LULC) changes for the researchers, and there is an increasing importance to detect coastal LULC using multi-spectral imagery (Szuster et al., 2011). In this present world, changes in land cover is considered as the most important variable of worldwide change influencing ecological systems (Petropoulos et al., 2014). Studies revealed a significant relation between changes in land cover classes and LST (Sahana et al., 2019;Weng et al., 2004;Yue et al., 2007). ...
St. Martin's Island, one of the most visited tourist destinations of Bangladesh, is greatly famous for its diverse coral ecosystem. Because of the frequent tourist visits, the island has faced gradual changes in terms of land use and land cover alteration. The subsequent change in surface temperature impacts the coral reef and local microclimate. The current study aims to pin point the locations in St. Martin's Island where tourism-related activities are mostly seen and quantify the locations in terms of tourist pressure index (TPI). Furthermore, the study sought to address and explain relationships among TPI, land use land cover change (LULC) and land surface temperature (LST) from year 2005 to year 2019. It was observed that TPI was largely dependent on the availability of tourism facilities. The changes in LULC were found to be more frequent in the locations where TPI was relatively high. Rise in LST was intensified by the changing pattern of LULC. The study found that agricultural lands were reduced within the last decade and significant number of resorts and hotels were constructed. Jetty Beach and North Beach attained the highest TPI ranking, whereas Chera Dwip and South Beach scored the lowest. Observed reduction in coral reef footprint were 38% during the study period which indicates that anthropogenic activity had an adverse effect on the local environment. To conserve the corals, stern law enforcement is essential to alleviate adverse environmental impact on the biodiversity of the island.
... For instance, Kumar and Shekhar [30] investigated the correlation between vegetation parameters and LST using line transect in the south East-West, and North-South direction, and found that the normalized difference vegetation index (NDVI) can weaken LST, while some indices showed a positive effect in the urban landscape. The LST and energy balance can be affected by vegetation cover, which brings changes through the exchange of water content between the land surface and air [31]. ...
The urban thermal environment is closely related to landscape patterns and land surface
characteristics. Several studies have investigated the relationship between land surface characteristics and land surface temperature (LST). To explore the effects of the urban landscape on urban thermal environments, multiple land-use/land-cover (LULC) remote sensing-based indices have emerged. However, the function of the indices in better explaining LST in the heterogeneous urban landscape has not been fully addressed. This study aims to investigate the effect of remote-sensing-based LULC indices on LST, and to quantify the impact magnitude of green spaces on LST in the city built-up blocks. We used a random forest classifier algorithm to map LULC from the Gaofen 2 (GF-2) satellite and retrieved LST from Landsat-8 ETM data through the split-window algorithm. The pixel values of the LULC types and indices were extracted using the line transect approach. The
multicollinearity effect was excluded before regression analysis. The vegetation index was found to have a strong negative relationship with LST, but a positive relationship with built-up indices was found in univariate analysis. The preferred indices, such as normalized difference impervious index (NDISI), dry built-up index (DBI), and bare soil index (BSI), predicted the LST (R2 = 0.41) in the multivariate analysis. The stepwise regression analysis adequately explained the LST (R2 = 0.44) due to the combined effect of the indices. The study results indicated that the LULC indices can be used to explain the LST of LULC types and provides useful information for urban managers and planners for the design of smart green cities.
... There is therefore a strong correlation between LST and land use types as confirmed by [10][11][12]. It has also been established in [13] and [14] that vegetation cover affects LST and the land-air exchange of energy and water. The relationship between LST and different vegetation parameters like; Soil Adjusted Vegetation index (SAVI), Vegetation Indices (VIs), Ratio Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) have been widely studied. ...
... Therefore, NDVI analysis is regarded as one of the important indicators used in the evaluation of ecosystem dynamics (Petropoulos et al. 2014). Many studies conducted on the urban ecosystem services have highlighted the significant role of urban forest, urban trees, or urban vegetation in reducing the air pollution (e.g., Nowak et al. 2006;Jim and Chen 2008;Manes et al. 2016;Marando et al. 2016;Bottalico et al. 2016;Li et al. 2016;Jayasooriya et al. 2017;Valeria Sacchi et al. 2017;Nowak et al. 2018;Viippola et al. 2018;Hein et al. 2018;Mexia et al. 2018;Gopalakrishnan et al. 2018;De Carvalho and Szlafsztein 2019;Tian et al. 2019). ...
The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O3, NO2, CO, SO2, PM10, and PM2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.
... As far as the former are concerned, the analysis lead to interesting results, showing how the herbaceous vegetation, even in montane and sub-alpine environments, can rapidly recover, similarly to the Mediterranean communities. This might also (partly) be due to a rapid vegetation recovery in the first two years after the fire, becoming more gradual in the following years (Petropoulos et al. 2014). ...
Wildfires are currently one of the most important environmental problems, as they cause disturbance in ecosystems generating environmental, economic and social costs. The Sentinel-2 from Copernicus Program (Sentinel satellites) offers a great tool for post-fire monitoring. The main objective of this study is to evaluate the potential of Sentinel-2 in a peculiar mountainous landscape by measuring and identifying the burned areas and monitor the short-term response of the vegetation in different ‘burn severity’ classes. A Sentinel-2 dataset was created, and pre-processing operations were performed. Relativized Burn Ratio (RBR) was calculated to identify ‘burn scar’ and discriminate the ‘burn severity’ classes. A two-year monitoring was carried out with areas identified based on different severity classes, using Normalized Difference Vegetation Index (NDVI) to investigate the short-term vegetation dynamics of the burned habitats; habitats refer to Annex I of the European Directive 92/43/EEC. The study area is located in ‘Campo Imperatore’ within the Gran Sasso – Monti della Laga National Park (central Italy). The first important result was the identification and quantification of the area affected by fire. The RBR allowed us to identify even the less damaged habitats with high accuracy. The survey highlighted the importance of these Open-source tools for qualitative and quantitative evaluation of fires and the short-term assessment of vegetation recovery dynamics. The information gathered by this type of monitoring can be used by decision-makers both for emergency management and for possible environmental restoration of the burned areas.
... In recent years, several studies have focused on fire severity mapping and burned area differentiation, mostly in the Mediterranean area, by combining spectral information acquired in the red, Near Infrared (NIR) and thermal (TIR) bands, with successful results (Petropoulos et al. 2014;Mallinis and Koutsias 2012). The most common indices used have been based on Red and NIR bands, for both pre-and post-fire mapping using the Normalized Difference Vegetation Index (NDVI) (Illera et al. 1996;Chuvieco et al. 2004). ...
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management.This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and robability area based on the NBR differencebetween pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, intowhich other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to mapforest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired tosupport a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in anarea based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found tobe the most suitable option, since it required less computational time and resources in comparison to the GIS-based modelingapproach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provideinformation that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
... Methodologies such as the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithms that perform time series analysis of this rich image archive have become popular for vegetation trend analyses (Huang et al. 2010, Kennedy andYang 2010;Verbesselt et al. 2010;Banskota et al. 2014). Numerous studies have described post-fire vegetation recovery with multispectral time series analysis in Mediterranean ecosystems (Viedma et al. 1997;Díaz-Delgado 2001;Díaz-Delgado et al. 2002;Riaño et al. 2002;Díaz-Delgado and Lloret 2003;Malak 2006;Hope and Tague 2007;Wittenberg et al. 2007;Röder et al. 2008;Minchella et al. 2009;Gouveia and DaCamara 2010;Solans Vila 2010;Vicente-Serrano and Pérez-Cabello 2011;Veraverbeke et al. 2012;Fernandez-Manso and Quintano 2016;Lanorte et al. 2014;Meng et al. 2014;Petropoulos et al. 2014;Yang et al. 2017) and boreal ecosystems (Hicke et al. 2003;Epting 2005;Goetz and Fiske 2006;Cuevas-González et al. 2009;Jin et al. 2012;Frazier and Coops 2015;Bartels et al. 2016;Liu 2016;Pickell et al. 2016;White et al. 2017;Yang et al. 2017;Frazier et al. 2018); other forest types have been less studied (Idris and Kuraji 2005;Lhermitte et al. 2011;Sever and Leach 2012;Chen et al. 2014;Chompuchan 2017;Yang et al. 2017;Hislop et al. 2018), with only a few studies conducted in ponderosa pine and mixed conifer forests of western North America (White et al. 1996;van Leeuwen 2008;van Leeuwen et al. 2010;Chen et al. 2011;Meng et al. 2015). Among these studies, the Normalized Difference Vegetation Index (NDVI) has most frequently been applied to indicate vegetation greenness. ...
Few studies have examined post-fire vegetation recovery in temperate forest ecosystems with Landsat time series analysis. We analyzed time series of Normalized Burn Ratio (NBR) derived from LandTrendr spectral-temporal segmentation fitting to examine post-fire NBR recovery for several wildfires that occurred in three different coniferous forest types in western North America during the years 2000 to 2007. We summarized NBR recovery trends, and investigated the influence of burn severity, post-fire climate, and topography on post-fire vegetation recovery via random forest (RF) analysis. NBR recovery across forest types averaged 30 to 44% five years post fire, 47 to 72% ten years post fire, and 54 to 77% 13 years post fire, and varied by time since fire, severity, and forest type. Recovery rates were generally greatest for several years following fire. Recovery in terms of percent NBR was often greater for higher-severity patches. Recovery rates varied between forest types, with conifer−oak−chaparral showing the greatest NBR recovery rates, mixed conifer showing intermediate rates, and ponderosa pine showing slowest rates. Between 1 and 28% of patches had recovered to pre-fire NBR levels 9 to 16 years after fire, with greater percentages of low-severity patches showing full NBR recovery. Precipitation decreased and temperatures generally remained the same or increased post fire. Pre-fire NBR and burn severity were important predictors of NBR recovery for all forest types, and explained 2 to 6% of the variation in post-fire NBR recovery. Post-fire climate anomalies were also important predictors of NBR recovery and explained an additional 30 to 41% of the variation in post-fire NBR recovery. Landsat time series analysis was a useful means of describing and analyzing post-fire vegetation recovery across mixed-severity wildfire extents. We demonstrated that a relationship exists between post-fire vegetation recovery and climate in temperate ecosystems of western North America. Our methods could be applied to other burned landscapes for which spatially explicit measurements of post-fire vegetation recovery are needed.
... Vegetation indices have a strong relation with biomass and leaf area index, thus suitable for vegetation evaluation both prior and after fire events, whether spatially or temporally [52,154,[158][159][160][161][162][163][164][165][166]. One of the most frequently used indices is Normalized Difference Vegetation Index (NDVI) [52,[167][168][169]. Other vegetation indices are also widely used, such as Soil Advanced Vegetation Index (SAVI) and Transformed Soil Advanced Vegetation Index (TSAVI) [52,154,157,170]. ...
Forest fires are a common disturbance in many forest systems in the world and in particular in the Mediterranean region. Their origins can be either natural or anthropogenic.
The effects in regard to the time trends, vegetation, and soil will be reflected in the species distribution, forest composition, and soil potential productivity. In general, it can be said that the larger the fire and the shorter the time between two consecutive occurrences,
the higher the probability to originate shifts in vegetation and soil degradation. In the Mediterranean region, the number of fire ignitions does not reflect the burnt area due to the occurrence of very large fires. The latter occur in a very small proportion of the number of ignitions, but result in very large burnt areas. Also there seems to be an increasing trend toward larger fires in the Mediterranean region due mainly to climatic and land use changes. This case study highlights the importance of vegetation regrowth a short time after the fire to maintain both forest systems and soil conservation.
... In recent years, several studies have focused on fire severity mapping and burned area differentiation, mostly in the Mediterranean area, by combining spectral information acquired in the red, Near Infrared (NIR) and thermal (TIR) bands, with successful results (Petropoulos et al. 2014;Mallinis and Koutsias 2012). The most common indices used have been based on Red and NIR bands, for both pre-and post-fire mapping using the Normalized Difference Vegetation Index (NDVI) (Illera et al. 1996;Chuvieco et al. 2004). ...
Information on fire probability is of vital importance to environmental and ecological studies as well as to fire management. This study aimed at comparing two forest fire probability mapping techniques, one based primarily on freely distributed EO (Earth observation) data from Landsat imagery, and another one based purely on GIS modeling. The Normalized Burn Ratio (NBR) computed from Landsat data was used to detect the high fire severity and probability area based on the NBR difference between pre- and post-fire conditions. The GIS-based modeling was based on a multi criterion evaluation technique, into which other attributes like anthropogenic and natural sources were also incorporated. The ability of both techniques to map forest fire probability was evaluated for a region in India, for which suitable ancillary data had been previously acquired to support a rigorous validation. Subsequently, a conceptual framework for the prediction of high fire probability zones in an area based on a newly introduced herein data fusion technique was constructed. Overall, the EO-based technique was found to be the most suitable option, since it required less computational time and resources in comparison to the GIS-based modeling approach. Furthermore, the fusion approach offered an appropriate path for developing a forest fire probability identification model for long-term pragmatic conservation of forests. The potential fusion of these two modeling approaches may provide information that can be useful to forest fire mitigation policy makers, and assist at conservation and resilience practices.
... EO data has become extremely valuable for wildfire management and has made it possible to assess wildfire risk over large areas with relative ease (Brown et al., 2018). EO has many other applications including detection of active fires (Giglio et al., 2008), as well as calculating burn severity (Amos et al., in press) or mapping post-fire vegetation re-growth (Petropoulos et al., 2014). ...
In this study, the development of a suitable methodology for establishing and monitoring indicators of Ecosystem Health (EH) and its responses to wildfire using Earth Observation (EO) data synergistically with Geographical Information Systems (GIS) is investigated. The proposed methodology combined GIS and Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data to assess ecosystem characteristics, including its vigor, organization and resilience, for a case study in Central Greece. These parameters were quantified primarily by utilizing EO-based techniques focusing on the analysis of the Normalized Difference Vegetation Index (NDVI). Topographic features, including slope, aspect and a Compound Topographic Index (CTI) were also derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM), and integrated in a modeling scheme to assess EH. The developed modeling scheme illustrates the effect of wildfires on EH accurately, demonstrating correlations between areas of past wildfires and their associated recovery. Our findings thus provide useful information to land managers and policy makers of fire affected regions alike, and could provide important contributions to the potential development of an operational estimation of EH recovery after wildfire.
... Also, the ecosystem should be regarded as a comprehensive and overall complicated system instead of simple independent components (Dolan et al. 2000;Patten et al. 2002;. In order to ensure the examination objectives, ecosystem health assessment should put additional thought on the temporal and spatial dynamics of regional ecosystem health rather than subjectively analyze ecosystem health in a certain time or place (Peng et al. 2007;Petropoulos et al. 2014). ...
Developing new and practical methodologies in order to assess ecosystem health based on physical, ecological and socio-economic indicators is an essential field of environmental studies. Nowadays, because of the considerable importance of the spatiotemporal dynamics of ecosystem variables, scientists utilize technologies such as geospatial information systems and remote sensing to achieve different indicators. In this paper, the Taleghan watershed, in Alborz province in Iran was selected as a study area, which has been exposed to many stresses including the construction of a dam in 2007. First, an indicator system based on Driving force-Pressure-State-Impact (DPSI) model was established. Indicators were quantified before and after construction of the dam. At the end, the assessment was carried out by development of a spatial decision support system based on fuzzy analytical hierarchy process method. This system was made for calculating the weights of indicators, compositing the maps of various indicators, producing and displaying maps of DPSI indicators and regional ecosystem health, and identifying critical areas in terms of ecosystem health. The results show that ecosystem health values in the eastern (especially northeast) parts of the watershed (upstream of the dam) and the areas adjacent to the river have been lower in comparison with other areas before dam construction. However, after the dam construction, critical areas in terms of ecosystem health shifted to the downstream region in the western parts. 29.79% of the region in the first period and 23.37% in the second period had a very low and low level of ecosystem health.
... Wildfires can affect humans in several ways. Some of the most common include the damage to property, loss of crops, destruction of infrastructure, and the possible loss of life (Keeley et al. 2009;Petropoulos, Griffiths, and Kalivas 2014). ...
Accurate, reliable, and timely burn severity maps are necessary for planning, managing and rehabilitation after wildfires. This study aimed at assessing the ability of the Sentinel-2A satellite to detect burnt areas and separate burning severity levels. It also attempted to measure the spectral separability of the different bands and derived indices commonly used to detect burnt areas. A short investigation into the associated environmental variables present in the burnt landscape was also performed to explore the presence of any correlation. As a case study, a wildfire occurred in the Sierra de Gata region of the province of Caceres in North-Eastern Spain was used. A range of spectral indices was computed, including the Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR). The potential added value of the three new Red Edge bands that come with the Sentinel-2A MSI sensor was also used. The slope, aspect, fractional vegetation cover and terrain roughness were all derived to produce environmental variables. The burning severity was tested using the Spectral Angle Mapper (SAM) classifier. European Environment Agency’s CORINE land cover map was also used to produce the land cover types found in the burned area. The Copernicus Emergency Management Service have produced a grading map for the fire using 0.5 m resolution Pleiades imagery, that was used as reference. Results showed a variable degree of correlation between the burning severity and the tested herein spectral indices. The visible part of the electromagnetic spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (short-wave infrared 2 – SWIR2) produced the best results for detecting burnt areas. SAM resulted in a 73% overall accuracy in thematic mapping. None of the environmental variables appeared to have a significant impact on the burning severity. All in all, our study result showed that Sentinel-2 MSI sensor can be used to discern burnt areas and burning severity. However, further studies in different regions using the same dataset types and methods should be implemented before generalizing the results of the current study.
... EO has been recognized as being essential for landscape level assessments of wildland fires (Tanase et al., 2015). Some of the main advantages of using EO data when exploring wildland fires is that large areas can be assessed with relative ease and cost (Cohen and Goward, 2004;Petropoulos et al., 2014), as well as assessing regions that are inaccessible at regular time intervals (Tanase et al., 2015). ...
... The estimated product accuracies for vertical and horizontal data were 20 m and 30 m, respectively, at a 95% confidence level [41]. During an aspect analysis in ArcGIS, orientations between NW (315) and NE (45) were classified as north-facing slopes and those between SE (135) and SW (225) were classified as south-facing slopes [42,43]. We mainly discuss the vegetation restoration effects on pixels within the burn scar areas on north-and south-facing slopes; pixels that did not fall within these ranges were excluded. ...
The increasing frequency of fires inhibits the estimation of carbon reserves in boreal forest ecosystems because fires release significant amounts of carbon into the atmosphere through combustion. However, less is known regarding the effects of vegetation succession processes on ecosystem C-flux that follow fires. This paper describes intra- and inter-annual vegetation restoration trajectories via MODIS time-series and Landsat data. The temporal and spatial characteristics of the natural succession were analyzed from 2000 to 2016. Finally, we regressed post-fire MODIS EVI, LST and LSWI values onto GPP and NPP values to identify the main limiting factors during post-fire carbon exchange. The results show immediate variations after the fire event, with EVI and LSWI decreasing by 0.21 and 0.31, respectively, and the LST increasing to 6.89 °C. After this initial variation, subsequent fire-induced variations were significantly smaller; instead, seasonality began governing the change characteristics. The greatest differences in EVI, LST and LSWI were observed in August and September compared to those in other months (0.29, 6.9 and 0.35, respectively), including July, which was the second month after the fire. We estimated the mean EVI recovery periods under different fire intensities (approximately 10, 12 and 16 years): the LST recovery time is one year earlier than that of the EVI. GPP and NPP decreased after the fire by 22-45 g C·m⁻²·month⁻¹ (30-80%) and 0.13-0.35 kg C·m⁻²·year⁻¹ (20-60%), respectively. Excluding the winter period, when no photosynthesis occurred, the correlation between the EVI and GPP was the strongest, and the correlation coefficient varied with the burn intensity. When changes in EVI, LST and LSWI after the fire in the boreal forest were more significant, the severity of the fire determined the magnitude of the changes, and the seasonality aggravated these changes. On the other hand, the seasonality is another important factor that affects vegetation restoration and land-surface energy fluxes in boreal forests. The strong correlations between EVI and GPP/NPP reveal that the C-flux can be simply and directly estimated on a per-pixel basis from EVI data, which can be used to accurately estimate land-surface energy fluxes during vegetation restoration and reduce uncertainties in the estimation of forests' carbon reserves.
... Many scholars examined the effect of LST on energy system (Zhang, Harris, & Balzter, 2015), urban heat island (Sekertekin, Kutoglu, & Kaya, 2016), surface air temperature (Hao et al., 2016) and morphology of green space (Weng & Lu, 2008). Some other studies revealed strong correlation between land transformation and LST (Petropoulos, Griffiths, & Kalivas, 2014;Wei & Zhou, 2011;Yokohari, Brown, Kato, & Yamamoto, 2001;Yue, Xu, Tan, & Xu, 2007). Weng et al. (2004) attempted to analyze relationship of different vegetation indices (NDVI, SAVI, RVI, etc.) with LST. ...
Land transformation as a result of unprecedented urbanization has introduced changes in local climate and surface energy budget. Land surface temperature (LST) is an important factor influencing local climate and ecology. Mumbai being second largest populated city is experiencing significant changes in land use/land cover (LULC) and surface energy fluxes. Hence, the main objective of the study is to assess the spatial variation in land surface temperature due to land use/land cover change. Several indices like; Normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), modified normalized difference water index (MNDWI) and normalized difference built up index (NDBI) were derived to validate the spatial variability of LST in different land use/land cover classes. The study utilized Landsat5/TM and Landsat8/TIRS data for assessing land transformation and its relation with LST in Mumbai city. January, June and October months of three time series 1990, 2000 and 2015 were chosen as representative of three seasons to analyze variation in LST. Pixel to pixel overlay analysis for different indices and LST was carried out to examine the relation of LST with different indices. The study revealed the maximum change in LST was recorded during the month of June over the study period. Land transformation from vegetation and agricultural land to urban built up has been found to be the main cause of increased LST in the study area. The finding of the study may help in promulgating sustainable urban land use policies and avoiding the effect of urban heat island.
KEYWORDS: Land transformation, land surface temperature (LST), mono window algorithm, remote sensing, Mumbai city
... Pinus pinaster and Eucalyptus. Petropoulos, Griffiths and Kalivas (2014) compared the recovery progress of different slope aspects and found north-facing slopes to enjoy a faster vegetation recovery rate. ...
Wildfires are a major natural hazard with tremendous implications for the Earth’s ecosystems. Investigating fire regimes and fire–vegetation dynamics using remote-sensing techniques is becoming increasingly common because of their large-scale coverage and data availability. However, there is still scarce study to compare vegetation regeneration between different ecosystems after wildfires due to lack of data. This study used time series of Landsat images to explore and compare post-fire vegetation recovery in a Mediterranean (Witch Creek Fire) and tundra (Anaktuvuk River Fire) ecosystem. After 8 years of disturbance, the vegetation in the Mediterranean ecosystem had still not yet recovered, whereas the tundra ecosystem recovered in just 3 years. Higher degree burning leads to quicker vegetation recovery rate. However, ecological retrogression was also detected. Spatial heterogeneity in post-fire vegetation recovery observed in both sites can be attributed to topographic factors, soil water availability and the thermokarst process.
... Vegetation can 47 Fig. 8. Spatial distribution of changes in (a,b) summer and (c,d) winter daily maximum temperature from (a,c) stage 1 to stage 2 and (b,d) stage 2 to stage 3. Other details as in Fig. 2 affect the LST and the land surface energy balance by altering the exchange of energy and water between the land surface and the air (Li et al. 2009, Petropoulos et al. 2014, and the vegetation index presented triangular and trapezoidal relationships with the LST under different conditions (Moran et al. 1994, Carlson et al. 1995, Kustas et al. 2003. The LST is a vital parameter in the physics of land surface processes on regional (a) and (b), respectively; but for winter EVI and LST. ...
The Three Gorges Dam (TGD) has caused hydrological regime changes in the region downstream of the dam. Based on meteorological station data, this study focused on regional climate changes by evaluating several climatic factors in the middle reaches of the Yangtze River and investigated the mutation time of the number of high and low temperature days using the Mann-Kendall (MK) test. We also examined the vegetation response to regional climate change and variations induced by this in the land surface temperature, utilizing the enhanced vegetation index (EVI) and land surface temperature (LST) images, respectively. The study defined 3 stages related to the construction and commissioning time of the TGD. The regional climate before and after the commissioning of the TGD displayed opposite trends in temperatures, including daily mean, maximum and minimum temperatures. Temperatures tended to decrease in the Northern portion of the study area, and increase in the southern portion of the study area. MK test results indicated that the mutation times of the number of high and low temperature days occurred around the time that the dam began commissioning to regulate the water flow. Precipitation decreased in the study area, particularly in the Dongting Lake region and its surrounding areas. Vegetation coverage generally increased in most of the southern study area in response to the change in climate. Moreover, the LST trends in the different regions were affected by the changes in vegetation.
... Based on different spatial sampling methods, Sahana et al. (2016) and Rhee et al. (2014) analyzed the relationship between LST and land use changes. To investigate the driving mechanisms of energy exchange and LST, increasing emphasis has been placed on research into the vegetation-LST relationship (Petropoulos et al., 2014). As important indicators of land use and vegetation, vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), and Soil-Adjusted Vegetation Index (SAVI), have been used widely to investigate the vegetation-LST relationship (Yue et al., 2007;Wei and Zhou, 2011). ...
This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.
Wildfire burn severity has important implications for postfire vegetation recovery and boundary-layer climate. We used a collection of Moderate Resolution Imaging Spectroradiometer (MODIS) datasets to investigate the impact of burn severity (relative differenced Normalized Burn Ratio, RdNBR) on vegetation recovery (Enhanced Vegetation Index, EVI), albedo change, and land surface temperature in seven California ecoregions, including: Southern California Mountains (SCM), Southern California Coast (SCC), Central California Foothills (CCF), Klamath (K), Cascades (C), Eastern Cascades (EC), and Sierra Nevada (SN). A statewide MODIS-derived RdNBR dataset was used to analyze the impact of burn severity on the five-year postfire early-summer averages of each biophysical variable between the years 2003–2020. We found that prefire EVI values were largest, and prefire albedo and temperature were lowest in the K, C, EC, and SN ecoregions. Furthermore, the largest changes between prefire and first-year postfire biophysical response tended to occur in the moderate and high burn severity classes across all ecoregions. First-year postfire albedo decreased in the K, C, EC, and SN but increased in the SCM, SCC, and CCF ecoregions. The greatest decreases, but most rapid recovery, of EVI occurred after high severity fires in all ecoregions. After five-years post-fire, EVI and land surface temperature did not return to prefire levels in any burn severity class in any ecoregion.
The environmental effects of wildfires are a hot issue in current research. This study examines the effects of the 2021 wildfires in the Attica region in Greece based on Earth observation and GIS-based techniques for the development of a web app that includes the derived knowledge. The effects of wildfires were estimated with the use of Sentinel-2 satellite imagery concerning burned area extent and burn severity using a NBR-based method. In addition, the erosion risk was modeled on a pre-fire and post-fire basis with the RUSLE. This study highlights the importance of assessing the effects of wildfires with a holistic approach to produce useful knowledge tools in post-fire impact assessment and restoration.
Reporte técnico que considera el análisis de severidad de un incendio forestal ocurrido en un predio en la provincia de Ultima Esperanza en la región de Magallanes y propuestas de restauración ecológica acorde a la clasificación y zonificación de daño.
Monitoring of the vegetation change trajectory in mining areas is crucial to understand ecosystem degradation induced by mining activities and to evaluate the effectiveness of ecological restoration. Beijing, the capital of China, is rich in mineral resources and has had many small-scale mine sites. In this study, we aimed to propose a new method to automatically characterize vegetation degradation and recovery trajectory for multiple mine sites. First, the method constructs temporal composites of the Normalized Differential Vegetation Index (NDVI) on a pixel-by-pixel basis using Landsat satellite observations on the Google Earth Engine platform; second, the Pettitt test and Sen+Mann–Kendall analysis are used to identify the year of change and pre- and post-change trends; then, the time-series NDVI is classified into five vegetation trajectory types [recovery (R), degradation (D), degradation–recovery (D–R), recovery–degradation (R–D), and no change (NC)] and 13 subtypes; and finally, the recovery status of the mine sites is analyzed. The method was applied to track vegetation change in 500 mine sites in the Beijing mountainous area and compared to widely used the LandTrendr algorithm. The results showed that our method achieved satisfactory accuracies in trajectory type classification with an overall accuracy of 91.10%. The year of change detected by the Pettitt test was generally consistent with historical Google Earth high resolution imagery. Compared to the LandTrendr algorithm, our method yielded much less omission and commission errors of R, D, R–D, and D–R types and had better capability in identifying gradual recovery or degradation. As the method required few parameters, it is suitable for automatic trajectory monitoring of multiple mine sites. In the study area, 1469.07 ha out of 3746.25 ha of the mine sites have been recovered from 2000 to 2019. The recovery mainly occurred during 2009–2013, around the same time when the Green Mine Construction Campaign was launched in Beijing. 622.62 ha of the mine sites have experienced degradation probably due to illegal mining activities. Our results are expected to support evaluation of mine restoration effects and detection of illegal mine sites.
Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984–1989 and 2014–2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-metric predictor of recovery, contributing 34–43% of explanatory power in multivariate regressions (R² = 0.16–0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale predictor of recovery and explained the greatest amount of variance in a multivariate regression (R² = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.
Different forest fires causing different degrees of effects occur in fire-sensitive forests due to various reasons such as climate change. Useful as well as harmful aspects of forest fires are a multi-disciplinary research topic. Geographical information systems (GIS) and remote sensing (RS) methods offer a number of benefits for researchers and operators in the field of forest fire research. The present study analyses timber pricing based on forest contractor demands of post-salvage logging processes. The effect of timber obtained from compartment units on producers’ pricing policy was modelled. Sapadere forest fire area (2500 ha) located in Antalya in Turkey was selected as the main study area. Topography parameters (aspect, slope and slope position), stand types (diameter class and crown closure), and burn severity were analyzed together using GIS and R software packages. A multi-linear regression model (R2 = 0.752) demonstrated that factors that had the most impact on pricing were slope position, aspect, stand age, crown closure and burn severity. This model can be used to estimate salvage logging prices in Calabrian pine (Pinus brutia Ten.) stands with similar parameters. Forest administrators and contractors may readily address the unit price of timber by estimating approximate costs in a given forest area for which they are going to bid. This will help reduce operational planning times of harvesting procedures in burned stands.
Remote sensing is increasingly being used as a cost effective and practical solution for the rapid evaluation of impacts from wildfires. The recent launch of the Sentinels offers a unique opportunity to assess the impacts of wildfires at both greater spatial and spectral resolutions provided by those Earth observing systems. In this study, an assessment of the Sentinels to map burnt areas is conducted by initially exploring the use of Sentinel‐2 to detect burnt areas. The investigation attempted in particular to evaluate the use of different bands and derived indices that are commonly used to detect burnt areas. A range of spectral indices was used, including Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR) for both the SWIR1 and SWIR2 portions of the EM spectrum. The three new Red Edge bands that come with the Sentinel‐2A MSI sensor were also used. The Slope, Aspect, and Fractional Vegetation Cover and Terrain Roughness were all derived to produce environmental variables. The Copernicus Emergency Management Service has produced a grading map for the fire using 0.5 m resolution Pleiades imagery, which was used as reference. The visible part of the EM spectrum was not well suited to discern burned from unburned land cover. The NBRb12 (SWIR2) produced the best results for detecting burnt areas. The SAM classification resulted in a 73% overall accuracy. All in all, our study contributes to the understanding of Mediterranean landscape dynamics. It also provides further evidence that use of Sentinel‐2 technology, combined with GIS analysis techniques, can offer an effective tool in mapping wildfires.
In the era of climate change, disasters are among the events of concern in the research field due to their devastating effects worldwide. Satellite‐based Earth observation (EO) is an established technology for mapping and monitoring spatial information about disasters at frequent intervals in all weathers at real time. Further, the information from past and present conditions gathered and stored by EO enables identifying the changes occurring in Earth's cover, and helps in framing management and mitigation strategies. Chapter 3 already introduced various types of disasters and their management briefly, and in this chapter we review some commonly used sensors on board EO satellites and spatial analysis approaches with their application in disaster impact assessment and modeling. It provides a holistic view of EO techniques in disaster prediction, management, and mitigation.
The purpose of this study is to investigate the spatial and temporal changes in land use and patterns of vegetation and its impacts on land surface temperature (LST) in two Indian cities. Specifically the motivation behind this study is to examine whether a correlation exists between these parameters for the two cities. Indian cities are facing tremendous pressures of rapid urbanization altering the country’s land use patterns. This in turn has significantly altered the country’s land surface temperature over the years. This study investigates the changes in the land use, land cover and surface temperature in two Indian cities of Surat and Bharuch over a period of 2 decades using Landsat 5 Thematic Mapper and Landsat 8 OLI/TIRS datasets. The study also examines changes in vegetation pattern during this period using a normalized difference vegetation index (NDVI) and investigates the correlation between LST and NDVI. Additionally, the study examines the spatial patterns of LST by mapping the directional profiles of LST. Results of the study reveal that over time both the cities have witnessed a dramatic growth in built-up area, systematic reduction in green space and increase in LST. There is 85% increase in built-up area in Surat in the past 2 decades and 31% increase in built-up area in Bharuch during the same period. At the same time, mean surface temperature in Surat has shown an increase of 2.42 °C per decade while in Bharuch the mean surface temperature has increased by 2.13 °C per decade. Moreover, examination of correlation between LST and NDVI showed a negative relation between the two parameters. Directional profiles showed a continued increase in temperature from 2008 to 2016 from North to South direction Surat indicating an increased urbanization in that direction. Also, new peaks were observed in the profile of Surat for 2008 and 2016 in the north–south direction indicating urban expansion particularly in the southern part of the city. Moreover, substantial growth has taken place in the central part of the city and along the banks of the rivers Tapi and Narmada. This study will be helpful in investigations that address the challenges of urbanization in Surat and Bharuch by assisting local government officials, land management professionals and planners to determine areas where growth must be curbed to avoid further environmental degradation thereby assisting in systematic urban planning practices.
Human and natural activities are considered one of several factors that lead to global environmental changes. Urbanization and population growth are the most prevailing issues facing scientists around the world which further affect the environment and temperature. According to statistics, the population in Gaza Strip, a small and besieged area, will be over 2.4 million and the land demands will exceed the sustainable capacity of land use by 2023. In this study, geographic information system (GIS) and remote sensing (RS) techniques were applied to estimate temporal change detection of land use/land cover (LULC) and land surface temperature (LST) for Gaza Strip between 2000 and 2017 and to indicate the relationship between land changes and demography changes and LST in the same period. In this study, two kinds of pixel-based classifiers, i.e., maximum likelihood (ML) and support vector machine (SVM), have been performed to extract LULC changes. While for LST, it was calculated by using Normalized Difference Vegetation Index (NDVI) and Surface Emissivity equations. The results show clear decrease between 2000 and 2017 in bare land (67.19%) compared to an increase in urban (13.12%) and crop and vegetation (4.95%). Furthermore, the increase in population is directly proportional to the increase in urban area through this period. In addition to that, LST in bare lands has the highest temperature in July and September (summer, autumn) and the lowest in January (winter) due to seasonal effects.
En este estudio se analizan el daño y los cambios que se han producido en la vegeta-ción forestal por el incendio forestal de Sierra Seca y Donceles de Hellín (Albacete), ocu-rrido en 2012. Mediante el empleo de nuevas tecnologías y el ineludible trabajo de campo se evalúa el impacto ocasionado por el incendio y se analiza la estructura y composición de la vegetación unos meses tras el fuego. Para la evaluación del daño se ha desarrollado la meto-dología recientemente publicada por el Ministerio de Agricultura, Medio Rural y Marino basada en la vulnerabilidad del medio y la severidad del fuego. Para el análisis del cambio de la vegetación se han llevado muestreos de vegetación durante la primavera posterior al in-cendio, en 30 parcelas representativas del grado severidad del fuego, en las que se identifica-ron las especies y se midieron otras variables ambientales. Los resultados obtenidos indican que a medio plazo el incendio ha causado un daño bajo en la superficie afectada gracias a la capacidad de respuesta de las especies vegetales que, si bien es alta en esta zona, se ve mermada por factores como la climatología, la topografía o la recurrencia de incendios. La respuesta de la vegetación en términos de composición de las comunidades vegetales difiere según la severidad del fuego, al menos varios meses tras la perturbación; momento en el que todas las especies preexistentes antes del incendio, y características de las etapas poste-riores de sucesión, se encuentran presentes en la zona, si bien con claro dominio de las her-báceas y del esparto (Stipa tenacissima). El esparto se consolida como especie con un papel protagonista en la conservación de suelos y de las etapas primo-colonizadoras tras el fuego en esta zona.
Air temperature (T2m or Tair) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (p = 0.0001) with T2mDay (daytime air temperature) and T2mNight (night-time air temperature). The correlation (r) between LSTDay and TDay is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T2mNight.
The effectiveness of winery operations in a wine cellar and their impact on wine quality depend closely on the technology used. A correct application of refrigeration systems is perhaps the best guarantee of a correct processing process. In this work, a review of the refrigeration engineering in warehouse is carried out, calculating the refrigeration needs of each one of the main stages of elaboration, according to the different winemakings. The energy requirements for the cold maceration and debourbage in white winemaking, the cooling of the crushed-grapes in the elaboration of red wine, as well as for the temperature control during fermentation and physical-chemical stabilization of the finished wine are calculated. The main cold production techniques in the winery are also addressed to respond to those needs.
Keywords: winemaking, refrigeration, fermentation, tartaric stabilization, wine refrigeration exchanger
Fire used to be a rather rare and localised disturbance for high altitude coniferous forests in Greece. However, during the last decade an increasing number of fire events are recorded across landscapes covered by such forests, a possible result of the global climate change. For Greece, Abies cephalonica Loudon (Greek fir) and Pinus nigra J.F Arnold (black pine) form the most affected forest types of this group. Both species have not been evolved under the selective force of fire and thus do not have any active post-fire regeneration mode. In summer 2007, 7 large fires burned approximately 70% of the total burned area of 270.000 ha. At least in two cases, fire burned over extended mountainous ridges, affecting both dry Mediterranean and high altitude forest ecosystems. In the case of Mount Parnitha, 2,180 ha of fir forest have been burned, whereas in the case of Mount Taygetos, 4,500 ha of Greek fir and Black pine have been affected. In both cases, permanent transects have been established during the 2 nd post-fire year, so as to monitor plant community composition and seedling emergence of both tree species. In all cases, plant communities recovered rapidly and the number of plant taxa found within the burned areas was much higher than that found within the neighboring unburned patches, with annual herbs being the richest growth form. Compositae was the richest family in terms of species number, and consisted of taxa bearing reproductive units of remarkable ability for long distance dispersal. Nevertheless, in the case of burned fir forests the second richest family was that of Leguminosae, a family with typical seeding species, the germination of which is greatly promoted by fire. A relatively high number of black pine seedlings was recorded, most of which located within a zone of 30 meters from unburned patches, while fir showed almost no regeneration. It is concluded that besides successful regeneration of most understorey plant taxa the recovery of the dominant tree
Fire is an integral part of many ecosystems, including the Mediterranean ones. However, in recent decades the general trend in number of fires and surface burnt in European Mediterranean areas has increased spectacularly. This increase is due to: (a) land-use changes (rural depopulation is increasing land abandonment and consequently, fuel accumulation); and (b) climatic warming (which is reducing fuel humidity and increasing fire risk and fire spread). The main effects of fire on soils are: loss of nutrients during burning and increased risk of erosion after burning. The latter is in fact related to the regeneration traits of the previous vegetation and to the environmental conditions. The principal regeneration traits of plants are: capacity to resprout after fire and fire stimulation of the establishment of new individuals. These two traits give a possible combination of four functional types from the point of view of regeneration after fire, and different relative proportions of these plant types may determine the post-fire regeneration and erosion risk. Field observations in Spain show better regeneration in limestone bedrock type than in marls, and in north-facing slopes than in south-facing ones. Models of vegetation dynamics can be built from the knowledge of plant traits and may help us in predicting post-fire vegetation and long-term vegetation changes under recurrent fires.
Burned forested areas have patterns of varying burn severity as a consequence of various topographic, vegetation, and meteorological factors. These patterns are detected and mapped using satellite data. Other ecological information can be abstracted from satellite data regarding rates of recovery of vegetation foliage and variation of burn severity on different vegetation types. Middle infrared wavelengths are useful for burn severity mapping because the land cover changes associated with burning increase reflectance in this part of the electromagnetic spectrum. Simple stratification of Landsat Thematic Mapper data define varying classes of burn severity because of changes in canopy cover, biomass removal, and soil chemical composition. Reasonable maps of burn severity are produced when the class limits of burn severity reflectance are applied to the entire satellite data. Changes in satellite reflectance over multiple years reveal the dynamics of vegetation and fire severity as low burn areas have lower changes in reflectance relative to high burn areas. This results as a consequence of how much the site was altered due to the burn and how much space is available for vegetation recovery. Analysis of change in reflectance across steppe, riparian, and forested vegetation types indicate that fires potentially increase biomass in steppe areas, while riparian and forested areas are slower to regrow to pre-fire conditions. This satellite-based technology is useful for mapping severely burned areas by exploring the ecological manifestations before and after fire.
With the support of new technologies such as of remote sensing, today's
societies have been able to map and analyse wildland fires at large
observational scales. With regards to burnt area mapping in particular,
two of the most widely used operational products are offered today by
the United States National Aeronautics and Space Administration (NASA)
and the European Forest Fires Information System (EFFIS) of the European
Commission. In this study, a rigorous intercomparison of the burnt area
estimates derived by these two products is performed in a geographical
information system (GIS) environment for the Greek fires that occurred
from 2005 to 2007. For the same temporal interval, the relationships of
the burnt area estimates by each product are examined with respect to
land use/cover and elevation derived from CORINE 2000 and the ASTER
global digital elevation model (GDEM), respectively. Generally,
noticeable differences were found in the burnt area estimates by the two
products both spatially and in absolute numbers. The main findings are
described and the differences in the burnt area estimates between the
two operational datasets are discussed. The lack of precise agreement
between the two products which was found does not necessarily mean that
one or the other product is inaccurate. Rather, it underlines the
requirement for their calibration and validation using high-resolution
remote sensing data in future studies. Our work not only builds upon a
series of analogous studies evaluating the accuracy of the same or
similar operational products worldwide, but also contributes towards the
development of standardised validation methodologies required in
objectively evaluating such datasets.
Multitemporal Principal Component Analysis (MPCA) was used for processing Landsat TM/ETM+ satellite images. MPCA was able to merge spectral data corresponding to TM-1996 (pre-fire in 1997), ETM-2000 (post-fire 1997 and pre-fire 2002), and ETM-2003 (post-fire in 2002), which was crucial for detecting the fire impact and vegetation recovery. Results indicate that the burnt areas of 1997 and 2002 were 89,086 ha (16.5%) and 31,859 ha (5.9%), respectively, within the study area of 540,000 ha. SPOT-VEGETATION 10-days Maximum Value Composite (MVC) data were also used and compared with Normalized Difference Vegetation Index (NDVI) from ground-based NDVI. Our research demonstrates the strong relationship between Landsat TM/ETM+, SPOT-VEGETATION data and ground-based NDVI to identifying land cover changes and vegetation recovery over the tropical peat swamp forest area in Central Kalimantan, Indonesia that is affected by forest fires occurred in 1997 and 2002.
Due to climate changes, the interest in the post-fire recovery of forest communities not adapted to wildfires, such as Greek fir (Abies cephalonica) forests, has increased. In this study, the post-fire recovery of the burned A. cephalonica forest of Parnitha National Park (central Greece) was investigated after a stand-replacing fire occurred in summer 2007, as well as the performance of A. cephalonica plantings in the post-fire conditions. The research focused on the estimation of the A. cephalonica stand reproductive capacity without fire, the evaluation of the post-fire regeneration of the burned stands, and the monitoring of the plantations performance after the fire in the area. Then, based on the field and laboratory data, the post-fire recovery process of A. cephalonica was evaluated by application of a simplified form of the comprehensive causal framework for ecological succession estimation in open site, developed by Pickett et al. ([30]), adapted to the study conditions. According to the findings of the study, stand seed crop without fire was high, while seed quality was found extremely low. In the burned area, no A. cephalonica seedling recruitment was observed during the three years after the fire. A. cephalonica plantings exhibited a medium overall survival rate (65.3%), while seedlings growth was very slow. Thus, we can suppose that an ecological succession process may occur in the burned area, if no human interventions applied, and species adapted to wildfires (mainly shrubs and herbaceous) will dominate in the area. However, planting of A. cephalonica seedlings could contribute to the species participation in the post-fire communities.
A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.
Accurate production of regional burned area maps are necessary to reduce uncertainty in emission estimates from African savannah fires. Numerous methods have been developed that map burned and unburned surfaces. These methods are typically applied to coarse spatial resolution (1 km) data to produce regional estimates of the area burned, while higher spatial resolution (65% at the MODIS scale, presumably because of the decrease in signal-to-noise ratio as compared to the Landsat scale. At the MODIS scale the Mid-Infrared Bispectral Index (MIRBI) using a fixed threshold of >1.75 was determined to be the optimal regional burned area mapping index (slope = 0.99, r 2 = 0.95, SE = 61.40, y = Landsat burned area, x = MODIS burned area). Application of MIRBI to the entire MODIS temporal series measured the burned area as 10 267 km2 during the 2001 fire season. The char fraction map and the MIRBI methodologies, which both produced reasonable burned area maps within southern African savannah environments, should also be evaluated in woodland and forested environments.
Vegetation recovery from fire has been widely studied at the stand level in many types of terrestrial ecosystems, but factors controlling regeneration at the landscape scale are less well known. Over large areas, fire history, climate, topography, and dominant type of vegetation may affect postfire response. Increased fire frequency, as is occurring in some mediterranean-type ecosystems, may reduce ecosystem resilience, i.e., the ability to recover the pre-disturbance state. We used the Normalized Difference Vegetation Index (NDVI) from Landsat imagery to monitor vegetation recovery after successive fires in a 32 100-km 2 area of Catalonia (northeastern Spain) between 1975 and 1993. In areas burned twice, NDVI patterns indicated that regrowth after 70 mo was lower after the second fire than after the first. This trend was observed several years after burning, but not immediately following fire. Green biomass after the second fire significantly increased with longer intervals of time between fires. There was also a positive correlation between postfire NDVI and mean rainfall, whereas a negative correlation was found between NDVI and solar radiation. Forests dominated by resprouting Quercus spp. were more resilient to fire, but they showed a larger decrease in resilience after the second fire than did forests dominated by Pinus spp. that regenerate from seed. We conclude that the use of time series satellite images may help to gain further insights in postfire vegetation dynamics over large regions and long time periods.
Due to the large volume of carbon currently stored in boreal regions and the high frequency of wildfire, the prospects of a warming climate would have important implications for the ecology of boreal forests which in turn would have significant feedbacks for carbon cycling, fire frequency, and global climate change. Since ecological studies and climate change models require routine information on surface soil moisture, the ability to remotely sense this variable is highly desirable. Toward this end research was conducted on developing methods for the retrieval of spatially and temporally varying patterns of soil moisture from recently burned boreal forest ecosystems of Alaska using C-band satellite radar data. To do this we focused on both individual date and temporal SAR datasets to develop techniques and algorithms which indicate how moisture varies across a recently burned boreal forest. For each of the methods developed we focused on reducing errors of SAR-derived soil moisture estimates due to confounding factors of variations in vegetative biomass and surface roughness. For the individual date soil moisture monitoring, we grouped test sites by a measurable biophysical variable, burn severity, and then developed algorithms relating moisture to SAR backscatter for each burn severity group. The algorithms developed had high coefficients of determination (0.56-0.82) and the moisture maps produced had high accuracy (3.61 rms error) based on the minimal validation conducted. For the seasonal soil moisture mapping we used principal component analysis to capture the time-variant feature of soil moisture and minimize the relatively time-invariant features that confound SAR backscatter. This resulted in good agreement between the drainage maps produced and our limited in situ observations and weather data. However, further validation, with larger sample sizes, is needed. While this study focuses on Alaska, research indicates that the techniques developed should be applicable to boreal forests worldwide.
In this paper we analyse the interactions between fire severity (plant damage) and plant regeneration after fire by means of remote sensing imagery and a field fire severity map. A severity map was constructed over a large fire (2692 ha) occurring in July 1994 in the Barcelona province (north-east of Spain). Seven severity classes were assigned to the apparent plant damage as a function of burning intensity. Several Landsat TM and MSS images from dates immediately before and after the fire were employed to monitor plant regeneration processes as well as to evaluate the relationship with fire severity observed in situ . Plant regeneration was monitored using NDVI measurements (average class values standardized with neighbour unburned control plots). Pre-fire NDVI measurements were extracted for every plant cover category (7), field fire severity class (7), and spatial cross-tabulation of both layers (33) and compared to post-fire values. NDVI decline due to fire was positively correlated with field fire severity class. Results show different patterns of recovery for each dominant species, severity class and combination of both factors. For all cases a significant negative correlation was found between damage and regeneration ability. This work leads to a better understanding of the influence of severity, a major fire regime parameter on plant regeneration, and may aid to manage restoration on areas burned under different fire severity levels.
Fire occurrence in Mediterranean landscapes has been studied widely. Despite this, a specific monitoring of vegetation recovery after recurrent fires by means of satellite images has been developed to a lesser extent. With the use of Satellite Remote Sensing (SRS) techniques and multi-temporal Landsat images of the area of Ayora (287 700 ha) in Valencia (Eastern Spain), between the years 1984 and 1999, we studied the post-fire regeneration of the Normalized Difference Vegetation Index (NDVI) in areas subjected to different fire recurrences. Emphasis is given to the effect of time since fire, precipitation, and bedrock types on post-fire NDVI changes. Results suggest that for the first 7 years after a single fire, NDVI depends mainly on the time since fire (post-fire regeneration), whereas environmental parameters (precipitation and bedrock type) are of little relevance. After this period, precipitation begins to have a direct influence on the NDVI. In patches burned twice, with fire intervals of 8 and 9 years, NDVI is also controlled by the time since fire. Furthermore, NDVI recovery is faster after the first fire than after the second fire, suggesting that fire recurrence has a negative impact on the resilience of these communities. Bedrock type did not show any effect on NDVI after fire. These findings contribute to the understanding of Mediterranean landscape dynamics and provide evidence for the usefulness of NDVI in post-fire regeneration assessment, and the possible negative effects of the increasing fire recurrences observed in the last decades.
A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral- temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km x 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, 1 km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.
Runoff and erosion rates are known to increase substantially after a major forest fire. Erosion control measures therefore need to be put into place quickly after a large fire, and determining where to locate the measures requires accurate mapping of post fire erosion risk. Burn severity can be determined from field observations, but these are costly and time consuming. Satellite imagery is an alternative to quickly map burn severity for erosion mapping purposes. Post fire erosion decreases as forest vegetation recovers and this is related to both pre fire vegetation characteristics and soil properties. The objectives of this study were to test the use of SPOT multispectral images for mapping burn severity, pre fire vegetation density, and longer term (2.5 years) vegetation recovery. Indices tested include the Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Differenced Normalized Burn Ratio (dNBR), and Differenced Normalized Difference Vegetation Index (dNDVI). Indices were compared to field data gathered immediately after the fire and about 2.5 years later. The multi-temporal indices (dNBR and dNDVI) were more useful for burn severity mapping in the heterogeneous forest–scrubland–vineyard environment where bedrock and vineyard surfaces were confused with burned areas when using single images (NBR and NDVI). All of the post fire indices showed traces of the fire 2.5 years later; this was confirmed using Analysis of Variance where differences in indices were related to original ground cover (pine forest, mixed forest, mostly bedrock and vineyards) and the burn scar. Pre fire vegetation cover was less successfully mapped using the NDVI according to the initial field observations. Similarly, none of the post fire indices were able to distinguish differences in N–S vegetation recovery revealed by field measurements of understory vegetation height and cover; N facing slopes had deeper finer soils and these more favourable conditions led to greater vegetation growth than on S facing slopes and topslope convexities. It is suggested that relationships between topography and soil properties can be useful for mapping both soil erodibility and post fire vegetation recovery.
Post-fire regeneration of Pinus halepensis, the most abundant tree in the Mediterranean Basin, can vary largely. In the present work, we aim to study the parameters determining this variability in the eastern Iberian Peninsula. For this reason we sampled in 2002 the sapling pine density on 22 plots that burned in 1993 and 71 plots that burned in 1994. Pre-fire vegetation (tree density and basal area) were obtained from the Spanish Forest Inventory. The regeneration ranged from 0.006 to 20.4pines/m2 (mean=1.24, S.D.=3.22). The statistical analysis suggested that the most important variables explaining this variability were the amount of branches found on the forest floor (branches collapsed from burned trees or branches left by foresters), the aspect of the plot, the pre-fire basal area, and whether the slope was terraced or not. High regeneration was observed in forests with large amounts of branches on the floor (which create appropriate microclimatic conditions), with northern aspects, with high pre-fire basal area, and on terraced slopes. Furthermore, other water-related variables (annual precipitation and slope) also had some (although lower) importance. These results have direct implications for forest managers in the study area.
The main aim of this study was to evaluate the usefulness of spectral mixture analysis (SMA) for mapping forest areas burned by fires in the Mediterranean area using low and medium spatial resolution satellite sensor data. A methodology requiring only one single post-fire image was used to carry out the study (uni-temporal techniques). This methodology is based on the contextual classification of the fraction images obtained after applying SMA to the original post-fire image. The results showed that the proposed method, using only one image acquired post-fire, could accurately identify the burned surface area (Kappa coefficient>0.8). The spatial resolution of the satellite images had practically no influence on the accuracy of the burned area estimate but did affect the possibility of detecting areas inside the perimeter of the burned area which were only slightly damaged.
Restoration of Mediterranean-type forest ecosystems is of great importance and a main environmental issue in Greece and other countries of the Mediterranean basin as well. For the effective implementation of proper restoration practices, a thorough knowledge is required as regards post-fire regeneration processes, namely changes that occur in the vegetation composition and the evolution of ecosystem resilience rate, after one or more fire incidents. The research work presented in the paper, took place at the National Park of Sounio (Greece), where an intensive forest land survey was performed. Forest-ecosystem resilience and vegetation composition was studied under various post-fire regimes by the use of Remote Sensing and GIS techniques. For that purpose, Landsat (TM and ETM+) satellite images spanning a 20-year period were used for the calculation of NDVI time-series. Classification maps depicting the pre- and post-fire situation were also considered. Results showed that for the single-fire case, high resilience post-fire rates accompanied by minimum changes in the composition of the initial vegetation are established. In the case where a second fire recurs within a time period of 19 years, results showed that severe changes in vegetation composition are induced and also lower resilience rates are finally established. As regards reforestation efficiency, in the latter case, a satisfactory contribution with higher evolutionary vegetation schemes was found.
Spectral properties of recent burns are characterised, in the visible, near infrared, mid-infrared, thermal infrared, and microwave spectral domains. Fire-induced reflectance changes are also compared for various ecosystems and biomes, and discussed in terms of the ecological effects of phytomass combustion. The spectral signatures of combustion products and of burnt areas are compared with those of various plant material and land cover types, in order to graphically represent relevant aspects of burnt area spectral discrimination. A series of colour composite images, based on Landsat Thematic Mapper data is used to illustrate the appearance of burnt surfaces in various tri-spectral spaces, and in contrast with healthy forests, agricultural fields, and urban areas. The temporal evolution of the spectral properties of burns is also demonstrated, with a five-year time series of Thematic Mapper images of two conifer forest burns in central Portugal. Finally, a series of conclusions is proposed, concerning the distinctive spectral properties of burnt surfaces, and implications for discrimination and mapping of such areas.
Large scale, high-severity fires are increasing in the western United States. Despite this trend, there have been few studies investigating post-fire tree regeneration. We established a study in the footprint of the 2000 Storrie Fire, a 23,000 ha wildfire that occurred in northern California, USA. We used a stratified sampling design to quantify post-fire vegetation dynamics across four levels of burn severity and three forest types on the Lassen National Forest nine and ten years following fire. Within each sampled stand, we recorded tree seedlings, forest overstory, shrub cover, and abiotic factors hypothesized to influence growth and establishment.Median conifer seedling densities varied substantially by burn severity: 1918 seedlings ha−1 in the Unchanged units; 4838 seedlings ha−1 in the Low-severity units; 6484 seedlings ha−1 in the Medium-severity units; and 710 seedlings ha−1 in the High-severity units. Increased burn severity was associated with greater shrub coverage: shrub cover in High-severity burns was more than three times those of lower burn severities. We calculated Shannon’s Species Diversity (H′) and Pielou’s Evenness (EH) indices to examine woody shrub and tree diversity. Abies spp. were by far the most abundant regenerating conifer species, which may be a concern for land managers; shrub cover after High-severity burns was dominated by Ceanothus spp. Although fir regeneration was prolific, the Storrie Fire generated diverse vegetative responses, potentially aiding in the reintroduction of the diverse landscape mosaic homogenized by a century of landscape-scale fire exclusion.
It is challenging to detect burn severity and vegetation recovery because of the relatively long time period required to capture the ecosystem characteristics. Multitemporal remote sensing data can provide multitemporal observations before, during and after a wildfire, and can improve the change detection accuracy. The goal of this study is to examine the correlations between multitemporal spectral indices and field-observed burn severity, and to provide a practical method to estimate burn severity and vegetation recovery. The study site is the Jasper Fire area in the Black Hills National Forest, South Dakota, that burned during August and September 2000. Six multitemporal Landsat images acquired from 2000 (pre-fire), 2001 (post-fire), 2002, 2003, 2005 and 2007 were used to assess burn severity. The normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized burn ratio (NBR), integrated forest index (IFI) and the differences of these indices between the pre-fire and post-fire years were computed and analysed with 66 field-based composite burn index (CBI) plots collected in 2002. Results showed that differences of NDVI and differences of EVI between the pre-fire year and the first two years post-fire were highly correlated with the CBI scores. The correlations were low beyond the second year post-fire. Differences of NBR had good correlation with CBI scores in all study years. Differences of IFI had low correlation with CBI in the first year post-fire and had good correlation in later years. A CBI map of the burnt area was produced using regression tree models and the multitemporal images. The dynamics of four spectral indices from 2000 to 2007 indicated that both NBR and IFI are valuable for monitoring long-term vegetation recovery. The high burn severity areas had a much slower recovery than the moderate and low burn areas.
The relationship between vegetation greenness and the North Atlantic Oscillation (NAO) is assessed over Europe. The study covers the 21-year period from 1982 to 2002 and is based on monthly composites of the Normalised Difference Vegetation Index (NDVI) and Brightness Temperature from the Global Inventory Monitoring and Modelling System (GIMMS) as well as on monthly precipitation from the Global Precipitation Climatology Centre (GPCC).
A systematic analysis is first performed of point correlation fields over the 21-year period between the winter NAO index and spring and summer NDVI, followed by an assessment of the vegetation response to precipitation and temperature conditions in winter, over two contrasting regions, namely the Iberian Peninsula and Northeastern Europe. Finally, the impact of NAO on vegetation dynamics over the two regions is evaluated by studying the corresponding annual cycles of NDVI and comparing their behaviour for years associated with opposite NAO phases.
Over the Iberian Peninsula there is strong evidence that positive (negative) values of winter NAO induce low (high) vegetation activity in the following spring and summer seasons. This feature is mainly associated with the impact of NAO on winter precipitation, together with the strong dependence of spring and summer NDVI on water availability during the previous winter. Northeastern Europe shows a different behaviour, with positive (negative) values of winter NAO inducing high (low) values of NDVI in spring, but low (high) values of NDVI in summer. This behaviour mainly results from the strong impact of NAO on winter temperature, associated with the critical dependence of vegetation growth on the combined effect of warm conditions and water availability during the winter season. Copyright
Vegetation regeneration in post-fire environments varies across the landscape of a burned area. Variations are caused by interacting
factors, including soil properties, vegetation characteristics, hydrology, land management history, and burn severity. While
many of these factors have been explored previously, few studies have investigated the combination of multiple factors. A
time-series of the remotely sensed enhanced vegetation index data has been analyzed to estimate rates of regeneration across
a burn in central Arizona. We used regression trees to evaluate post-fire vegetation response as a function of multiple factors.
Regeneration was a function of elevation (likely a proxy for moisture availability), burn severity, pre-burn vegetation, and
post-burn management activities. Both time-series vegetation data and regression trees were valuable tools for determining
dominant interacting factors responsible for variations in post-fire regeneration. Evaluation of the time-series data and
modeled post-fire vegetation permitted the interpretation of management actions across the burned area.
KeywordsEVI-MODIS-Regression trees-Time-series-Wildfire
Two hundred years of landscape changes were studied on a 3,760 ha area of central Corsica (France) representing a typical Mediterranean environment. Different historical sources, including an accurate land-cover map from 1774 and statistics on land cover from 1848 and 1913, were used. Three additional maps (1960, 1975 and 1990) were drawn, and a complete fire history from 1957 to 1997 was created. Forests expanded slowly by a border effect. Forest expansion was more rapid in unburnt sites (0.59% per year) than in burnt sites (0.23% per year), mostly because the initial amount of forests was greater. Because of the border effect, the combination of past landscape pattern and short distance colonization abilities of forest species may have allowed the shrublands to persist in some places after land abandonment. This persistence may explain the pattern of fire in the landscape, since shrubland burn more readily than forests.
Vegetation recovery from fire has been widely studied at the stand level in many types of terrestrial ecosystems, but factors controlling regeneration at the landscape scale are less well known. Over large areas, fire history, climate, topography, and dominant type of vegetation may affect postfire response. Increased fire frequency, as is occurring in some mediterranean-type ecosystems, may reduce ecosystem resilience, i.e., the ability to recover the pre-disturbance state. We used the Normalized Difference Vegetation Index (NDVI) from Landsat imagery to monitor vegetation recovery after successive fires in a 32 100-km2 area of Catalonia (northeastern Spain) between 1975 and 1993. In areas burned twice, NDVI patterns indicated that regrowth after 70 mo was lower after the second fire than after the first. This trend was observed several years after burning, but not immediately following fire. Green biomass after the second fire significantly increased with longer intervals of time between fires. There was also a positive correlation between postfire NDVI and mean rainfall, whereas a negative correlation was found between NDVI and solar radiation. Forests dominated by resprouting Quercus spp. were more resilient to fire, but they showed a larger decrease in resilience after the second fire than did forests dominated by Pinus spp. that regenerate from seed. We conclude that the use of time series satellite images may help to gain further insights in postfire vegetation dynamics over large regions and long time periods.
This chapter analyzes the post-fire management of non serotinous pine forests. These pine species do not regenerate after fire and depend on the arrival of seeds from the unburned vegetation to recover after wildfires. The chapter starts with an overview of the ecological context of these forests, from their distribution, vegetation composition and importance to their response after fire. Then it introduces the main issues and alternatives of post-fire management, and concludes with two case studies, one in the western and the other in the eastern Mediterranean Basin.
This research focused on determining the degree to which differences in burn severity relate to post-fire vegetative cover within a Michigan pine forest. Landsat MSS data from June 1973 and TM data from October 1982 were classified using an unsupervised approach to create pre-fire and post-fire cover maps of the study area. Using a raster-based geographic information system (GIS), the maps were compared and a map of vegetation change was created. Classification comparisons of pine and deciduous forest classes (1973 to 1982) revealed that the most change in vegetation occurred in areas subjected to the most intense burn. -from Authors
In the history of research of the learning problem one can extract four periods that can be characterized by four bright events: (i) Constructing the first learning machines, (ii) constructing the fundamentals of the theory, (iii) constructing neural networks, (iv) constructing the alternatives to neural networks.