Exploring spatially varying relationships between forest fire and environmental factors at different quantile levels

2020 ◽  
Vol 29 (6) ◽  
pp. 486
Author(s):  
Qianqian Cao ◽  
Lianjun Zhang ◽  
Zhangwen Su ◽  
Guangyu Wang ◽  
Futao Guo
Keyword(s):  
Environmental Factors ◽  
Quantile Regression ◽  
Forest Fire ◽  
Vegetation Index ◽  
Model Fitting ◽  
Fire Occurrence ◽  
Road Density ◽  
Forest Fire Management ◽  
Normalised Difference Vegetation Index ◽  
Spatially Varying

The effect of driving factors on forest fire occurrence at various risk levels beyond average fire risk is of great interest to forest fire managers in practice. Using forest fire occurrence data collected in Fujian province, China, global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to investigate the spatially varying relationships between forest fire and environmental factors at different quantiles (e.g. 0.50, 0.75, 0.90 and 0.99) of fire occurrence. These results indicated that: (1) at each quantile, the regression coefficients of both global QR and GWQR were negative for elevation, slope and Normalised Difference Vegetation Index, and positive for settlement density, national road density and grass cover; (2) low number of pixels with high fire occurrence in space might dramatically affect the analysis and modelling of the relationship between fire occurrence and a specific environmental factor; (3) according to GWQR, the relationships between forest fire and environmental factors significantly varied across the study area at different quantiles of fire occurrence; and (4) the GWQR models performed better in model fitting and prediction than the QR models at all quantiles. Therefore, the GWQR models could help decision makers to better plan for forest fire management and prevention strategies.

scholarly journals Static Risk Mapping of Forest Fires in the Case of the Province of Chefchaouen (Morocco)

2019 ◽  
Vol 2 ◽  
pp. 1-7
Author(s):  
Malak Alasli
Keyword(s):  
Wind Speed ◽  
Environmental Factors ◽  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Structure ◽  
Fire Management ◽  
Hazard Maps ◽  
Probability Map ◽  
Forest Fire Management ◽  
Static Risk

<p><strong>Abstract.</strong> Forest fire is responsible for a lot of problems as it destroys the landscape. Such spaces are valuable and take very long to recover. Hence, the risk of forest fire consists primarily of both the risk of an outbreak and of propagation which depend, in addition to the weather, to a number of environmental factors including: the type of vegetation (structure and composition), its state of desiccation as well as the slope and exposure to prevailing winds. Therefore, the goal is to develop static hazard maps of a 100&amp;thinsp;m resolution related to the province of Chefchaouen where the focus is on three maps; Surface threatened; Annual pressure of fire; Probability map. The production of these maps is based on various data including statistics on the fire, meteorological references, and flammability. In addition, several data were generated, namely, wind direction, wind speed, humidity, the slope in percent, aspect, etc. The production of these maps will make it possible to orient and optimize the means of investment, in particular with regard to infrastructures, equipment and forest fire management operations.</p>

scholarly journals Methodology for mapping the probability of fire occurrence in the Brazilian cerrado biome based on the danger of fire propagation variables

2021 ◽  
pp. 89-98
Author(s):  
Marcos César Ferreira ◽  
Cassiano Gustavo Messias
Keyword(s):  
Vegetation Index ◽  
Agricultural Land ◽  
Drainage Density ◽  
Wind Effect ◽  
Fire Occurrence ◽  
Brazilian Cerrado ◽  
Conservation Units ◽  
Road Density ◽  
Fire Propagation ◽  
Cerrado Biome

The area covered by the Brazilian cerrado biome has been greatly reduced in recent years due to the expansion of agricultural land and the increased number of fire outbreaks. The objective of this paper is to propose a methodology based on geospatial analysis and logistic regression analysis (LRA) for mapping the probability of fire occurrence in Brazilian cerrado conservation units. This model was applied in the Serra da Canastra National Park (SCNP) in the Southeast of Brazil. The methodology uses the maps of the following environmental variables, which are related to the danger of fire propagation: wind effect (WIN), terrain convexity (CVX), slope (SLO), drainage density (DRD), altitude (ELV), vegetation index (NDVI), and road density (ROD). The results of the LRA showed that the variables SLO, ELV, NDVI, ROD (p<0.0001), DRD (p=0.0005) and WIN (p=0.0007) contributed significantly to the occurrence of fire outbreaks. The model correctly classified 94.26% of cases. We conclude that this methodology can be used to inform the planning of firefighting actions in the Brazilian cerrado biome.

A Satellite Index of Risk of Forest Fire Occurrence in Summer in the Mediterranean Area

1998 ◽  
Vol 8 (4) ◽  
pp. 173 ◽  
Author(s):  
V Prosper-Laget ◽  
A Douguedroit ◽  
JP Guinot
Keyword(s):  
Forest Fire ◽  
Vegetation Index ◽  
Fire Risk ◽  
Mediterranean Area ◽  
Soil Water Deficit ◽  
Fire Occurrence ◽  
Risk Class ◽  
Noaa Avhrr ◽  
Vegetation Stress ◽  
Forest Fire Risk

An index of forest fire risk has been determined by using the vegetation index NDVI and the surface temperature Ts, computed from NOAA-AVHRR over 21 Mediterranean French forests. Those 2 satellite parameters can be interpreted in terms of soil water deficit and vegetation stress in summer. An inverse linear correlation between their values for each forest pixel of 10 dates in 1990 was used to establish the index which has been divided into 5 equal classes. Those classes correspond with 5 risk classes of forest fire occurrence which were mapped for several forests. Periods and areas in the highest risk class correspond with those where the most important number of fires appeared in that year for the studied forests. A statistical model of the period of highest fire risk has also been constructed for each forest.

scholarly journals Análise multitemporal da regeneração natural da candeia após ocorrência de incêndio florestal

2017 ◽  
Vol 12 (2) ◽  
pp. 192
Author(s):  
Ivy Mayara Sanches de Oliveira ◽  
Alex Donizeti Sales ◽  
Eduarda Martiniano de Oliveira Silveira ◽  
Fausto Weimar Acerbi Júnior ◽  
José Marcio De Mello
Keyword(s):  
Forest Fire ◽  
Natural Regeneration ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Minas Gerais ◽  
Landsat Tm ◽  
Fire Occurrence ◽  
Regeneration Pattern ◽  
Serra Do Cipó ◽  
Before And After

<p class="SemEspaamento1">Os sensores de satélites têm a capacidade de fornecer informações sobre regiões afetadas pela atividade do fogo, sendo uma ferramenta eficiente para a detecção e quantificação destas áreas. Objetivou-se avaliar o comportamento da regeneração natural da candeia <em>Eremanthus incanus </em>(Less.) Less<em>,</em> após a ocorrência de incêndio florestal por meio do índice de vegetação da diferença normalizada (NDVI) de forma a identificar a capacidade de resiliência da espécie. O incêndio ocorreu em 1999, ao lado do Parque Nacional da Serra do Cipó no município de Morro do Pilar, Minas Gerais. Foi selecionada uma série temporal de quatro imagens adquiridas entre os anos de 1999 a 2005 do satélite Landsat (TM e ETM<sup>+</sup>). Foram geradas as imagens NDVI e em seguida foram obtidos seus valores de reflectância nas diferentes datas para analisar o comportamento espectral das áreas em regeneração. Posteriormente esses parâmetros foram utilizados para analisar as alterações na cobertura vegetal. Ao comparar os valores de NDVI antes e pós-incêndio, observou-se que num período de 6 anos a candeia apresenta valores de reflectância próximos àqueles encontrados antes do incêndio, o que sugere que a cobertura vegetal está num estágio similar à antes da ocorrência do fogo. O índice aplicado mostrou-se eficiente na análise da capacidade de resiliência da espécie após o fogo.<strong> </strong></p><p align="center"><strong><em>Multitemporal analysis of natural regeneration of Candeia after occurrence of forest fire</em></strong></p><pre><strong>Abstract:</strong> Satellite sensors have the ability to provide information on areas affected by fire activity, being an efficient tool for detection and quantification of these areas. The aim of this study was to analyze the natural regeneration pattern of the <em>Eremanthus incanus </em>(Less.) Less, after the occurrence of forest fire, using the normalized difference vegetation index (NDVI) in order to identify its resilience capacity. The fire occurred in 1999, next to the Serra do Cipó National Park in Morro do Pilar city, Minas Gerais. A time series of four Landsat (TM e ETM</pre><sup>+</sup><pre>) images acquired between the years 1999-2005 were selected. The NDVI images were generated and their reflectance values were obtained at the different dates to analyze the spectral pattern of regenerating areas. Later, these parameters were used to analyze the vegetation cover changes. Comparing the NDVI values before and after the fire, it was observed that, over a period of 6 years the reflectance values were close to those found before the fire, which suggests that the vegetal cover is at a similar stage before the fire occurrence. The applied index proved to be efficient in the analysis of the species capacity of resilience after the fire occurrence.</pre><p><strong> </strong></p><br /><strong></strong>

Modelling and mapping dynamic variability in large fire probability in the lower Sonoran Desert of south-western Arizona

2014 ◽  
Vol 23 (8) ◽  
pp. 1108 ◽  
Author(s):  
Miranda E. Gray ◽  
Brett G. Dickson ◽  
Luke J. Zachmann
Keyword(s):  
Sonoran Desert ◽  
Vegetation Index ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Large Fire ◽  
Native Plant ◽  
Negative Consequences ◽  
Road Density ◽  
Generalised Linear Mixed Model ◽  
Normalised Difference Vegetation Index

In the lower Sonoran Desert of south-western Arizona, climate change and non-native plant invasions have the potential to increase the frequency and size of uncommon wildfires. An understanding of where and why ignitions are more likely to become large fires will help mitigate the negative consequences of fire to native ecosystems. We use a generalised linear mixed model and fire occurrence data from 1989 to 2010 to estimate the relative contributions of fuel and other landscape variables to large fire probability, given an ignition. For the 22-year period we examined, a high value for the maximum annual Normalised Difference Vegetation Index was among the strongest predictors of large fire probability, as were low values of road density and elevation. Large fire probability varied markedly between years of moderate and high fine fuel accumulation. Our estimates can be applied to future periods with highly heterogeneous precipitation. Our map-based results can be used by managers to monitor variability in large fire probability, and to implement adaptive fire mitigation at a landscape scale. The approaches we present have global applications to other desert regions that face similar threats from changing climate, altered fuels and potential punctuated changes in fire regimes.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals Multi-Year NDVI Values as Indicator of the Relationship between Spatiotemporal Vegetation Dynamics and Environmental Factors in the Qaidam Basin, China

2021 ◽  
Vol 13 (7) ◽  
pp. 1240
Author(s):  
Junpeng Lou ◽  
Guoyin Xu ◽  
Zhongjing Wang ◽  
Zhigang Yang ◽  
Sanchuan Ni
Keyword(s):  
Environmental Factors ◽  
Vegetation Dynamics ◽  
Vegetation Index ◽  
Qaidam Basin ◽  
Elevation Gradient ◽  
Practical Significance ◽  
Elevation Gradients ◽  
Relative Contribution ◽  
Significant Difference ◽  
Different Altitudes

The Qaidam Basin is a unique and complex ecosystem, wherein elevation gradients lead to high spatial heterogeneity in vegetation dynamics and responses to environmental factors. Based on the remote sensing data of Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS), we analyzed the spatiotemporal variations of vegetation dynamics and responses to precipitation, accumulative temperature (AT) and soil moisture (SM) in the Qaidam Basin from 2001 to 2016. Moreover, the contribution of those factors to vegetation dynamics at different altitudes was analyzed via an artificial neural network (ANN) model. The results indicated that the Normalized Difference Vegetation Index (NDVI) values in the growing season showed an overall upward trend, with an increased rate of 0.001/year. The values of NDVI in low-altitude areas were higher than that in high-altitude areas, and the peak values of NDVI appeared along the elevation gradient at 4400–4600 m. Thanks to the use of ANN, we were able to detect the relative contribution of various environmental factors; the relative contribution rate of AT to the NDVI dynamic was the most significant (35.17%) in the low-elevation region (< 2900 m). In the mid-elevation area (2900–3900 m), precipitation contributed 44.76% of the NDVI dynamics. When the altitude was higher than 3900 m, the relative contribution rates of AT (39.50%) and SM (38.53%) had no significant difference but were significantly higher than that of precipitation (21.97%). The results highlight that the different environmental factors have various contributions to vegetation dynamics at different altitudes, which has important theoretical and practical significance for regulating ecological processes.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

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