Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia

Panuju, Dyah Retno; Trisasongko, Bambang H.; Susetyo, Budi; Raimadoya, M.; Lees, Brian

doi:10.5614/itbj.sci.2010.42.1.5

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…The principle of most geostationary satellite fire detection algorithms is essentially the same as that of Earth orbit satellite sensors [35][36][37][38], most of which depend on the threshold setting. In current fire products, the absolute brightness temperature or reflectivity value is typically used as the main recognition condition [39][40][41][42][43][44][45]. The important theoretical component behind the algorithm investigated here is to compare the difference between the radiation value and the background radiation of the point to be measured at two different times, combining space and time for overall analysis, and considering that the comparison between the central pixel and the surrounding pixels is affected by outliers.…”

Section: Calculation Of Time Series 1) Concept Of Algorithm Designmentioning

confidence: 99%

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

Zhang

Sun

2022

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

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The GF-4 satellite has a moderate spatial resolution and is capable of high temporal frequency observation. Thus, it can play an important role in the acquisition of information regarding fires. In this study, the characteristics of brightness temperature difference between fire point area and non-fire point area are comprehensively analyzed. Employing the advantages of high-frequency continuous observation of these data, an improved algorithm for joint spatial attribute detection using multi-temporal data is proposed. This method is based on the traditional Contextual Method and incorporates the concept of time sequence, and uses the changes in time phase before and after the fire to reduce the false alarm caused by the high heterogeneity of pixels in a single spatial scene in the traditional method. In order to evaluate the accuracy of the algorithm, the perfect MODIS thermal anomaly product is used as the verification data to evaluate the feasibility of the algorithm. At the same time, it is necessary to compare the algorithm in this study with the fire algorithm currently applied to GF-4 with the four typical regions. The improved algorithm shows better drawing ability, and the consistency of fire description of biomass combustion for many days is improved. In the case of the Heilongjiang region on October 30, 2017, the accuracy rate is improved by 62%, compared with the Contextual Method.

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Section: Calculation Of Time Series 1) Concept Of Algorithm Designmentioning

confidence: 99%

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

Zhang

Sun

2022

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

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“…Goetz et al (2006), for instance, presented useful graphical representations of NDVI time-series data to assess post-fire forest regrowth in Canada. Present advance using X12-ARIMA demonstrated that time-series data were indispensable to characterize fire spot in tropical region (Panuju et al, 2010).…”

Section: Time-series Analysismentioning

confidence: 99%

Seasonal Pattern of Vegetative Cover from NDVI Time-Series

Panuju¹,

Trisasongko²

2012

Tropical Forests

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“…Compared with geostationary satellites, polar-orbiting satellites can provide good resolution. At present, researchers have also carried out studies on the temporal information of polar-orbiting satellites [30][31][32], but how to integrate the spatial information still needs further exploration.…”

Section: Introductionmentioning

confidence: 99%

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

Dong

et al. 2022

Forests

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As one of China’s new generation polar-orbiting meteorological satellites, FengYun-3D (FY-3D) provides critical data for forest fire detection. Most of the existing related methods identify fire points by comparing the spatial features and setting thresholds empirically. However, they ignore temporal features that are associated with forest fires. Besides, they are difficult to generalize to multiple areas with different environmental characteristics. A novel method based on FY-3D combining the genetic algorithm and brightness temperature change detection is proposed in this work to improve these problems. After analyzing the spatial features of the FY-3D data, it adaptively detects potential fire points based on these features using the genetic algorithm, then filters the points with contextual information. To address the false alarms resulting from the confusing spectral characteristics between fire pixels and conventional hotspots, temporal information is introduced and the “MIR change rate” based on the multitemporal brightness temperature change is further proposed. In order to evaluate the performance of the proposed algorithm, several fire events occurring in different areas are used for testing. The Moderate-Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies/Fire products (MYD14) is chosen as the validation data to assess the accuracy of the proposed algorithm. A comparison of results demonstrates that the algorithm can identify fire points effectively and obtain a higher accuracy than the previous FY-3D algorithm.

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Historical Fire Detection of Tropical Forest from NDVI Time-series Data: Case Study on Jambi, Indonesia

Cited by 9 publications

References 36 publications

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

GF-4 Satellite Fire Detection With an Improved Contextual Algorithm

Seasonal Pattern of Vegetative Cover from NDVI Time-Series

Forest Fire Detection of FY-3D Using Genetic Algorithm and Brightness Temperature Change

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