Advances in active fire detection using a multi-temporal method for next-generation geostationary satellite data

ABSTRACT

A vital component of fire detection from remote sensors is the accurate estimation of the background temperature of an area in fire's absence, assisting in identification and attribution of fire activity. New geostationary sensors increase the data available to describe background temperature in the temporal domain. Broad area methods to extract the expected diurnal cycle of a pixel using this temporally rich data have shown potential for use in fire detection. This paper describes an application of a method for priming diurnal temperature fitting of imagery from the Advanced Himawari Imager. The BAT method is used to provide training data for temperature fitting of target pixels, to which thresholds are applied to detect thermal anomalies in 4?μm imagery over part of Australia. Results show the method detects positive thermal anomalies with respect to the diurnal model in up to 99% of cases where fires are also detected by Low Earth Orbiting (LEO) satellite active fire products. In absence of LEO active fire detection, but where a burned area product recorded fire-induced change, this method also detected anomalous activity in up to 75% of cases. Potential improvements in detection time of up to 6?h over LEO products are also demonstrated.     

基于MODIS影像的森林火灾火线检测方法

结合归一化火灾差异指数NDBR(normalized difference burn ratio)和MODIS多波段影像梯度边缘分析手段检测火线, 应用B样条函数拟合火线并确定火势蔓延方向。为对比验证, 基于火线的Kriging插值实现火灾外推预测, 与30min后的火灾参考数据目视对比与统计：火线的预测变化与参考影像基本保持一致, 火灾外推影像的均值和熵约为参考影像的86%和81%, 火迹地检测的Kappa系数达80.2%。试验表明, 提出的森林火线特征自动检测方法在动态火灾监测中是可行、有效的。     

基于VIIRS火点数据和FARSITE系统的森林火灾蔓延模拟

针对森林火灾模型模拟、预测火灾蔓延结果精度随时间增加而降低的问题,提出了使用卫星遥感火监测数据重初始化火灾模型降低模拟结果误差的方法。使用FARSITE (Fire Area Simulator)火灾模拟系统模拟内蒙古自治区一场森林火灾,并使用375 m的VIIRS (Visible infrared Imaging Radiometer Suite)火监测数据对FARSITE重初始化,探究该方法在中国林火蔓延模拟中的应用。使用SC (S?rensen’s Coefficient)值,辅以多源卫星遥感数据,评估该方法模拟结果精度。结果显示,各模拟过程中,模拟结果精度均随模拟时间的增加而降低,重初始化的模拟结果与实际过火区一致性更高,VIIRS重初始化模拟结果 SC值最高提高56.89%,最终SC值提高了45.45%;最终模拟结果的SC值从最初的54.14%经过VIIRS火点修正后提高到78.76%;模拟过程中的VIIRS火点重初始化的模拟结果 SC值最高为87.8%。使用VIIRS火监测数据重初始化FARSITE火灾模拟系统,将火灾分为多次进行模拟,缩短模拟时间,有效控制模拟误差的传递...     

三温模型与MODIS影像反演蒸散发

提出三温模型结合MODIS数据反演区域蒸散发的方法,在内蒙古草原开展案例研究,以2008年植被生长季(7—10月)的波文比系统观测数据为标准,对该方法进行检验。结果表明:三温模型反演的蒸散发量,平均值、最大、最小值分别为4.58mm/d、9.03mm/d、1.28mm/d;蒸散发反演结果在空间上分布较均匀,与草原的均一性相吻合,在时间上蒸散发的数值先逐渐增大,8月后逐渐减小,与观测结果相一致;三温模型反演的蒸散发量与观测值之间的最小、最大绝对误差分别为0.11mm/d、1.64mm/d,平均绝对误差为0.58mm/d、平均相对误差为17.10%。三温模型在1km空间尺度的反演精度较理想。     

A hierarchical model for estimating methane emission from wetlands using MODIS data and ARIMA modeling

A three-step hierarchical Semi Automated Empirical Methane Emission Model (SEMEM) has been used to estimate methane emission from wetlands and waterlogged areas in India using Moderate Resolution Imagine Spectroradiometer (MODIS) sensor data onboard Terra satellite. Wetland Surface Temperature (WST), methane emission fluxes and wetland extent have been incorporated as parameters in order to model the methane emission. Analysis of monthly MODIS data covering the whole of India from November 2004 to April 2006 was carried out and monthly methane emissions have been estimated. Interpolation techniques were adopted to fill the data gaps due to cloudy conditions during the monsoon period. AutoRegressive Integrated Moving Average (ARIMA) model has been fitted to estimate the emitted methane for the months of May 2006 to August 2006 using SPSS software.     

Methane emission modelling using MODIS thermal and optical data: A case study on Gujarat

Wetlands are one of the most important sources of atmospheric methane (CH₄) contributing about 22% to the global methane budget. But to improve estimates of CH₄ emission at regional and global scales there is a need to observe the sources such as wetlands frequently and develop process-based models. In this regard, wetland inventory using satellite remote sensing data has conventionally been carried out by analysis of optical data. Due to thermal inertia differences emittive thermal channels data has shown promise to provide highly critical information about wetlands such as water spread, aquatic vegetation and mud flats etc. Thermal channels data of MODIS (Moderate Resolution Imaging Spectroradiometer) sensor with a spatial resolution of 1km and swath of 2330 km is emerging as the key source of remote sensing data for global/ regional wetland estimation and assessment of green house gas emission. In the present study MODIS thermal channels (31 and 32) and optical channels (1,2, and 3) data have been used for evaluating methane emission from wetlands in Gujarat. An empirical model based on temperature and productivity has been used to investigate the response of methane emission from different sources. Model has the potential to estimate country level methane emission based on satellite remote sensing in conjunction with collateral data/information. In this study. MODIS data of two dates pertaining to Gujarat have been analyzed and results compared with respect to methane emission.     

Assessment and development of algorithms to detection of oil spills using MODIS data

Oil spill detection and subsequent monitoring are of major concern for coastal zone management as they form potential marine pollutants. The present study is based on examining the feasibility of Moderate Resolution Imaging Spectroradiometer (MODIS) high resolution bands (250 and 500 m) for detection of oil spill. The MODIS — Aqua for 18, 19 and 20^th January 2003 were used to study the oil spill in Lake Maracaibo, Venezuela. The examination of L1B and L2 data revealed that L2 products such as SST, Rrs and BRDF were not very useful due to erroneous atmospheric corrections. Visual examination of raw radiance data i.e. L1B data in the 250 and 500 m spatial resolution was found to be the simplest yet feasible method for spill detection. The study further revealed that atmospherically uncorrected radiances at 469, 555 and 645 nm were showing significant signature of oil spill. Therefore an attempt was made to perform ratio operations to enhance the feature. The study concluded that the ratio of difference and sum at 645 and 555 nm, normalized by 469 nm, provides the best result. The result was validated by comparing with the previous published literature. The result clearly indicates the potential of MODIS-Aqua high resolution data in oil spill monitoring. Therefore, MODIS-Aqua data with daily coverage and high resolution can be reliable and cost-effective for such events.     

Multi-year MODIS active fire type classification over the Brazilian Tropical Moist Forest Biome

ABSTRACT

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km² and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presented and are similar to patterns reported in the available recent literature.     

Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products

ABSTRACT

The AHI-FSA (Advanced Himawari Imager - Fire Surveillance Algorithm) is a recently developed algorithm designed to support wildfire surveillance and mapping using the geostationary Himawari-8 satellite. At present, the AHI-FSA algorithm has only been tested on a number of case study fires in Western Australia. Initial results demonstrate potential as a wildfire surveillance algorithm providing high frequency (every 10 minutes), multi-resolution fire-line detections. This paper intercompares AHI-FSA across the Northern Territory of Australia (1.4 million km²) over a ten-day period with the well-established fire products from LEO (Low Earth Orbiting) satellites: MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite). This paper also discusses the difficulties and solutions when comparing high temporal frequency fire products with existing low temporal resolution LEO satellite products. The results indicate that the multi-resolution approach developed for AHI-FSA is successful in mapping fire activity at 500?m. When compared to the MODIS, daily AHI-FSA omission error was only 7%. High temporal frequency data also results in AHI-FSA observing fires, at times, three hours before the MODIS overpass with much-enhanced detail on fire movement.     

Diurnal and seasonal impacts of urbanization on the urban thermal environment: A case study of Beijing using MODIS data

Beijing has experienced rapid urbanization and associated urban heat island effects and air pollution. In this study, a contribution index was proposed to explore the effect of urbanization on land surface temperature (LST) using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived data with high temporal resolution. The analysis indicated that different zones and landscapes make diurnally and seasonally different contributions to the regional thermal environment. The differences in contributions by the three main functional zones resulted from differences in their landscape compositions. The roles of landscapes in this process varied diurnally and seasonally. Urban land was the most important contributor to increases in regional LSTs. The contributions of cropland and forest varied distinctly between daytime and nighttime owing to differences in their thermal inertias. Vegetation had a notable cooling effect as the normalized vegetation difference index (NDVI) increased during summer. However, when the NDVI reached a certain value, the nighttime LST shifted markedly in other seasons. The results suggest that urban design based on vegetation partitions would be effective for regulating the thermal environment.     

局部统计活动轮廓模型的SAR图像海岸线检测

首次将局部统计活动轮廓模型引入SAR图像海岸线检测问题中,提出了一种基于局部统计活动轮廓模型的SAR 图像海岸线检测方法。首先利用C-V模型进行粗分割,消除局部统计活动轮廓模型对初始轮廓线设置要求严格的限制,然后提出了一种基于G⁰分布的局部统计活动轮廓模型,进行精细分割。该模型采用G⁰分布对轮廓线上每一点的邻域进行统计建模,增强了模型数据拟合能力,提高了海岸线检测精度,加入水平集函数惩罚项,消除了重新初始化过程。实测SAR图像实验表明,本文方法可用于精确海岸线检测。     

利用改进型MODIS火点探测算法实现河北省秸秆焚烧火点识别

为准确地了解河北省秸秆焚烧火点的空间分布,为秸秆焚烧监测的实现、禁烧工作的开展、环境质量改善提供支持.基于MODIS L1B数据、MODIS标准火点产品MOD14、全国秸秆焚烧火点日报数据为基础,采用改进型MODIS火灾探测算法,并通过IDL语言实现,得到秸秆焚烧火点空间分布信息,并进行空间与定量精度分析.研究表明:火点大部分位于河北省南部的一些地区,其中尤以邢台、石家庄、邯郸火点数量最为突出;该算法运算速度快,获取的秸秆焚烧火点数据具有一定检测精度和可靠性,对秸秆焚烧的监测具有一定的实用价值.     

利用中国通量网和MODIS数据评估MISR地表反照率产品

地表反照率是反映地表能量平衡的重要参数.本文通过中国陆地生态系统通量观测研究网络的实测反照率和MODIS的地表反照率产品对MISR的短波反照率数据进行验证和分析:提取了中国通量网中的8个站点的数据和对应的MODIS、MISR的反照率产品用于验证.验证的结果显示,在多数站点,MISR短波反照率能与地面数据相吻合,大部分的反演误差都集中在0.04以内;MISR与MODIS短波反照率的吻合度更高,总体的误差为0.018,均方根误差在0.04左右.总的来说,MISR地表反照率产品具有较高的反演质量.     

Study of thermal IR phenomena associated with 27 February 2010 Chile Mw 8.8 earthquake using MODIS data

Stresses acting on fault systems before major earthquakes can produce thermal anomalies; these abnormalities can be observed using multi-sensor satellite data. Moderate resolution spectro-radiometer on board the terra and aqua satellites can provide thermal infrared (TIR) imaging data for land and ocean. These TIR data have recorded short-lived thermal anomaly prior to major earthquakes. It is suggested by others that these electromagnetic (EM) phenomena relate to stress build up before earthquakes. The objective of this study is to find an association between spatial extent and temporal evolution of thermal anomalies and known major earthquakes near the boundary of Nazca plate and South American plate. Our approach is to map the TIR transient fields from polar orbiting satellites and analysing those data using time series temperature plots to detect the abnormal thermal trends before the earthquake. This study concentrated on marine earthquakes to detect the changes in both land and ocean before seismic activity.     

Detection of sub-surface waterlogging using Terra-1 MODIS data

Waterlogging due to rising ground watertable, being a sub-surface phenomenon, is not amenable to detection by optical remote sensing. Microwave and thermal sensor data have, however, shown some promise in the detection of sub-surface waterlogging. The present study was taken up to evaluate the potential of near-IR, short-wave IR (SWIR) and thermal-IR data from Moderate Resolution Imaging Spectrometer (MODIS) aboard Terra-1 acquired during day-and-night time postmonsoon data for detection of sub-surface waterlogging. The approach involves retrieval of day-and-night land surface temperature (LST), generation of normalized difference of channel-2 and 6 (ND₂₆); 2 and 7 (ND₂₇); ground truth collection involving concurrent ground water table observations to satellite date of pass, thresholding of normalized differences (NDs) and correlating the NDs with depth of ground water table. Amongst various spectral indices, day and night-time LST difference (DLST) and night-time LST have been found to correlate well with the incidence of waterlogging (water table depth < 2m), followed by normalized difference of band-2 (841–876 nm) and band-7 (2105–2155 nm). The sensitivity of threshold limits for these indices was maximum for DLST followed by ND₂₆ and ND₂₇. Poor accuracy of detecting sub-surface waterlogging with thermal bands during day time is attributed to the non-corresponding of the time of Terra MODIS data acquisitions with thermal maxima of the terrain. Though the ND₂₇ gave better accuracy to detect subsurface waterlogging, it is very sensitive to threshold limits.     

利用POLDER数据验证MODIS BRDF模型参数产品及Ross-Li模型

将MODIS BRDF模型参数产品(MCD43A1)模拟的近红外波段反射率与从POLDER-3/PARASOL BRDF全球数据集中筛选的9961个像元的BRDF观测数据进行对比,验证了MCD43A1所采用的RossThick-LiSparseR BRDF 模型(Ross-Li模型)拟合二向反射的能力。结果表明,Ross-Li模型总体上可以有效地模拟地物的二向反射,所有像元的近红外波段反射率模拟值与POLDER-3观测数据之间的R²达到0.943,RMSE为0.016,模拟反射率比POLDER-3数据总体偏低5.2%。但Ross-Li模型明显低估了热点反射率,热点模拟结果比POLDER-3数据平均偏低14%,模拟值的R2为0.824,RMSE为0.07。热点反射率模拟误差与地表覆盖类型有关,针叶林热点反射率模拟值偏低最严重,其次是阔叶林、草地与农田,灌木与裸地热点反射率模拟值偏低相对较小。通过修正Ross-Li模型中的体散射核,可以明显改善热点反射率的模拟效果(R²＝0.839,RMSE＝0.043)。Ross-Li模型对天底、暗点等特征方向反射率的模拟较为准确。Ross-Li模型的模拟精度随太阳天顶角和观测天顶角的增大而降低。对于农田与草地而言,Ross-Li模型的模拟精度随NDVI的增加而降低;但在森林与灌木覆盖条件下,当NDVI约为0.5时,Ross-Li模型的模拟效果最差。     

Predicting forest fire in the Brazilian Amazon using MODIS imagery and artificial neural networks

The presented work describes a methodology that employs artificial neural networks (ANN) and multi-temporal imagery from the MODIS/Terra-Aqua sensors to detect areas of high risk of forest fire in the Brazilian Amazon. The hypothesis of this work is that due to characteristic land use and land cover change dynamics in the Amazon forest, forest areas likely to be burned can be separated from other land targets. A study case was carried out in three municipalities located in northern Mato Grosso State, Brazilian Amazon. Feedforward ANNs, with different architectures, were trained with a backpropagation algorithm, taking as inputs the NDVI values calculated from MODIS imagery acquired during five different periods preceding the 2005 fire season. Selected samples were extracted from areas where forest fires were detected in 2005 and from other non-burned forest and agricultural areas. These samples were used to train, validate and test the ANN. The results achieved a mean squared error of 0.07. In addition, the model was simulated for an entire municipality and its results were compared with hotspots detected by the MODIS sensor during the year. A histogram analysis showed that the spatial distribution of the areas with fire risk were consistent with the fire events observed from June to December 2005. The ANN model allowed a fast and relatively precise method to predict forest fire events in the studied area. Hence, it offers an excellent alternative for supporting forest fire prevention policies, and in assisting the assessment of burned areas, reducing the uncertainty involved in currently used methods.     

Reconstruction of time series MODIS EVI data using de-noising algorithms

Long-term Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data have inherent noise due to clouds and poor atmospheric conditions that limit its applicability for environmental applications. This study was carried out with an objective of noise removal and reconstruction of time series MODIS EVI data (16 day) for the period 2010–2014 using de-noising algorithms. Relative evaluation of de-noising algorithms for smoothing temporal data with ideal noise free data is not possible in actual scenario. Hence, synthetic signals were generated and introduced Gaussian noise at different variance levels for evaluation purpose. Spatial analysis was carried out by introducing noise at different variance levels into the noise free EVI images from the raw EVI stacked image. Spatio-temporal analyses of noise signals in the reconstructed EVI images were evaluated in terms of performance indicators, namely Peak Signal-to-Noise Ratio and Mean Square Error.     

Water quality classification of lakes using 250-m MODIS data

The traditional method used in the water quality classification of Finnish lakes includes the collection of water samples from lakes and their analysis in laboratory conditions. The classification is based on statistical analysis of water quality parameter values and on expert opinion. It is possible to acquire similar information by using radiance values measured with the Earth Observing System Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS). In this letter, the classification accuracy with MODIS data is about 80%. Only about 0.2% of the 20 391 pixels were misclassified by two or more classes, as a four-class classification system is used.     

利用MODIS数据识别水稻关键生长发育期

利用遥感方法提取中国范围内的水稻关键生长发育期。首先, 对时间序列Terra MODIS-EVI(Enhanced Vegetation Index)进行傅里叶和小波低通滤波平滑处理, 然后, 根据水稻在移栽期、分蘖初期、抽穗期和成熟期的EVI变化特征, 实现对各个生长发育期的识别。通过将利用2005年MODIS数据识别的结果与当年气象台站的地面观测资料进行比较, 采用本研究中的识别方法得出的水稻各个生长发育期的绝对误差大部分小于16d, 经过F检验表明提取的结果与地面观测资料在0.05水平下具有显著一致性。研究中的信息提取方法可被用于其他年份的水稻生长发育期识别, 根据其他作物的生长发育特点, 也可能适合于提取其他作物的生长发育期。