Forest fire risk assessment in parts of Northeast India using geospatial tools

Puri, Kanchan; Areendran, G.; Raj, Krishna; Mazumdar, Sraboni; Joshi, P. K.

doi:10.1007/s11676-011-0206-4

Cited by 43 publications

(22 citation statements)

References 8 publications

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“…Collar tags placed on animals can be considered mobile devices [19], [20]. On the other hand, sensors were placed statically in cases of monitoring some phenomenon or potential disaster, such as landslides [17], flooding [14], [30], tsunami [18], [28], forest fire [25] and earthquakes [26]. Static sensors were also used to measure air quality [16].…”

Section: Mobile Vs Static Iot Devicesmentioning

confidence: 99%

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Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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Geospatial analysis offers large potential for better understanding, modelling and visualizing our natural and artificial ecosystems, using Internet of Things as a pervasive sensing infrastructure. This paper performs a review of research work based on the IoT, in which geospatial analysis has been employed in environmental informatics. Six different geospatial analysis methods have been identified, presented together with 26 relevant IoT initiatives adopting some of these techniques. Analysis is performed in relation to the type of IoT devices used, their deployment status and data transmission standards, data types employed, and reliability of measurements. This paper scratches the surface of this combination of technologies and techniques, providing indications of how IoT, together with geospatial analysis, are currently being used in the domain of environmental research. IntroductionThe Internet of Things (IoT) includes technologies and research disciplines that enable the Internet to reach out into the real world of physical objects [1]. The vision of IoT involves seamless integration of physical devices to the internet/web, by means of well-understood, accepted and used protocols, technologies and programming/description languages, for efficient human-to-machine or machine-to-machine communication [2]. Internet-enabled things are equipped with sensors, measuring the physical world and its phenomena such as temperature, humidity, radiation, electromagnetism, noise, chemicals etc. Moreover, the rise of multi-sensory mobile phones offers advanced sensing capabilities, such as measuring proximity, acceleration and location, recording audio/noise, sensing electromagnetism or capturing images and videos [3]. An important characteristic of the measurements performed by Internet-enabled things (i.e. sensors, mobile phones, cameras etc.) is the geo-location where each measurement was done. As the physical or artificial environments are generally quite complex, being characterized by a wide variety of parameters, a rich collection of measurements in space and time is necessary in order to model and understand these ecosystems [4]. Hence, sensory-based IoT information needs to be aggregated, stored and analyzed for more elaborate and holistic reasoning and inference. To exploit this variety of IoT measurements in type, space and time, geospatial analysis is used in order to provide high-quality analytics and insights [5].The contribution of this paper is to survey IoT applications that employ analytic operations that focus on location, examining the various geospatial analysis techniques employed. It is noted that we refer to the IoT as used in environmental informatics, i.e. sensing equipment and measurement systems recording

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Section: Mobile Vs Static Iot Devicesmentioning

confidence: 99%

“…Besides geo-located information, a variety of different data sources. were used for enriching the geospatial analysis performed, such as mobile phone-based crowdsourcing [12], or satellite-based imagery [23], [25], [28], [21]. Data acquired from previous projects was used for recording soil fertility data [35].…”

Section: Data Sources and Typesmentioning

confidence: 99%

Geospatial Analysis and the Internet of Things

Kamilaris

Ostermann

2018

IJGI

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“…We use the developed spatial model (2) and plot the fire occurrence probability maps in the period from the 153rd day in 2000 to the 91st day in 2009 with ArcGIS software. The probability varies from 0 to 1 and the bigger the value of a grid is, the higher the probability of fire occurrence is.…”

Section: F the Fire Occurrence Probability Mapsmentioning

confidence: 99%

Fire Occurrence Probability Mapping of Northeast China With Binary Logistic Regression Model

Zhang

Han

Dai

2013

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fire occurrence probability mapping provides a detailed understanding of the spatial distribution of the fire occurrence probability and it is useful in fire management. The binary logistic regression (BLR) can combine continuous and categorical variables together in the analysis. Here we use BLR analysis to map the fire occurrence probability of Northeast China which has the largest forest area in China. Ten predictor variables including altitude (Alt), slope (Sl), aspect (As), distance to the nearest village (Dv), distance to the nearest path (Dp), distance to the nearest water bodies (Dw), land cover (LC), Fuel Moisture Content (FMC), land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) are employed and multi-temporal random sampling methodology is used to create the training subset, and then the training subset is utilized to build the fire occurrence probability spatial model. Here, a backwards stepwise procedure based on the likelihood ratio estimation is used in the model development. Assessed by the area under a relative operating characteristic (ROC) curve (AUC-area under curve) procedure, the model's fitness accuracy is 84.2%. The interpretations of the estimated coefficients show that NDVI best explain fire occurrence in the region. Evaluated by the inner testing and independent validation, better reliability and discrimination capacity of the developed spatial model can be concluded from 17 fires among the total 18 fires. Good performance suggests that the developed model is valuable to fire managers or can be directly applied to fire management in Northeast China.Index Terms-Fire danger, fire occurrence probability, logistic regression, Northeast China.

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“…Geospatial representations of risk are an important way to identify hazard hotspots [7]. They have been used successfully to identify high risk areas for many spatially dependent hazards, including forest fires [8,9], landslides [10,11], flooding [12,13] and radioactivity propagation [14]. In this work, similar geospatial models have been developed for three spatially dependent areas of risk found on railways: passenger slips, trips and falls (STFs); suicides (struck by train) and train derailments.…”

Section: Geospatial Risk Modelsmentioning

confidence: 99%