An improved algorithm for small and cool fire detection using MODIS data: A preliminary study in the southeastern United States

Traditional fire detection algorithms mainly rely on hot spot detection using thermal infrared (TIR) channels with fixed or contextual thresholds. Three solar reflectance channels (0.65 μm, 0.86 μm, and 2.1 μm) were recently adopted into the MODIS version 4 contextual algorithm to improve the active fire detection. In the southeastern United States, where most fires are small and relatively cool, the MODIS version 4 contextual algorithm can be adjusted and improved for more accurate regional fire detection. Based on the MODIS version 4 contextual algorithm and a smoke detection algorithm, an improved algorithm using four TIR channels and seven solar reflectance channels is described. This approach is presented with fire events in the southeastern United States. The study reveals that the T₂₂ of most small, cool fires undetected by the MODIS version 4 contextual algorithm is lower than 310 K. The improved algorithm is more sensitive to small, cool fires in the southeast especially for fires detected at large scan angles.     

A MODIS direct broadcast algorithm for mapping wildfire burned area in the western United States

Improved wildland fire emission inventory methods are needed to support air quality forecasting and guide the development of air shed management strategies. Air quality forecasting requires dynamic fire emission estimates that are generated in a timely manner to support real-time operations. In the regulatory and planning realm, emission inventories are essential for quantitatively assessing the contribution of wildfire to air pollution. The development of wildland fire emission inventories depends on burned area as a critical input. This study presents a Moderate Resolution Imaging Spectroradiometer (MODIS) - direct broadcast (DB) burned area mapping algorithm designed to support air quality forecasting and emission inventory development. The algorithm combines active fire locations and single satellite scene burn scar detections to provide a rapid yet robust mapping of burned area. Using the U.S. Forest Service Fire Sciences Laboratory (FiSL) MODIS-DB receiving station in Missoula, Montana, the algorithm provided daily measurements of burned area for wildfire events in the western U.S. in 2006 and 2007. We evaluated the algorithm's fire detection rate and burned area mapping using fire perimeter data and burn scar information derived from high resolution satellite imagery. The FiSL MODIS-DB system detected 87% of all reference fires > 4 km², and 93% of all reference fires > 10 km². The burned area was highly correlated (R² = 0.93) with a high resolution imagery reference burn scar dataset, but exhibited a large over estimation of burned area (56%). The reference burn scar dataset was used to calibrate the algorithm response and quantify the uncertainty in the burned area measurement at the fire incident level. An objective, empirical error based approach was employed to quantify the uncertainty of our burned area measurement and provide a metric that is meaningful in context of remotely sensed burned area and emission inventories. The algorithm uncertainty is ± 36% for fires 50 km² in size, improving to ± 31% at a fire size of 100 km². Fires in this size range account for a substantial portion of burned area in the western U.S. (77% of burned area is due to fires > 50 km², and 66% results from fires > 100 km²). The dominance of these large wildfires in burned area, duration, and emissions makes these events a significant concern of air quality forecasters and regulators. With daily coverage at 1-km² spatial resolution, and a quantified measurement uncertainty, the burned area mapping algorithm presented in this paper is well suited for the development of wildfire emission inventories. Furthermore, the algorithm's DB implementation enables time sensitive burned area mapping to support operational air quality forecasting.     

Development of a virtual active fire product for Africa through a synthesis of geostationary and polar orbiting satellite data

We explore the ability to enhance landscape fire detection and characterization by constructing a ‘virtual’ fire product from a synthesis of geostationary and polar orbiting satellite data. Active fire pixels detected by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) were spatially and temporally collated across Africa between February 2004 and January 2005. Coincident fire pixels detected by SEVIRI and MODIS were used to populate an empirical database of frequency density (f-D) distributions of fire radiative power (FRP). Frequency density distributions of FRP measured by SEVIRI at 5.0° grid cell resolution and 15-minute temporal resolution were then cross referenced in the database to a set of counterpart f-D distributions of FRP measured by MODIS. This procedure resulted in the first generation of a ‘virtual’ fire product that exhibits the full continental coverage and high temporal resolution of SEVIRI whilst quantifying fire pixel counts and FRP with accuracies approaching those of MODIS. Diurnal cycles extracted from the virtual fire product indicate that SEVIRI measures a greater proportion of the active fire pixels and FRP potentially detectable by MODIS during the day due to the increased prevalence and stronger radiant contribution of highly energetic fire pixels. On a daily basis (sample size n = 365) the peak magnitude in the diurnal cycle of the virtual FRP occurred within the same 15-minute timeslot as in the native SEVIRI fire product. Continental-scale ignition and extinction events, however, were detected on average 44 min earlier (standard deviation s.d. = 40 min) and 137 min later (s.d. = 92 min), respectively. It is anticipated that the methodology developed here can be used to cross-calibrate active fire products between a variety of different satellite platforms.     

Agricultural burning in the Southeastern United States detected by MODIS

The southeastern United States, including the states of Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, and Virginia, had a high occurrence of fire activity as detected by the 1 km Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA Active Fire Product (MOD 14). The analysis of the satellite data from 2001 to 2004 showed that agricultural burning in the southeastern United States accounted for an average of 16% of annual fire activity. The southeastern region contributed an average of 33% of all agricultural burning detected in the contiguous United States. Crop residues that burned in the southeast included rice, winter wheat, sugarcane, soybean and cotton. Much of the agricultural burning occurred in June and from October to January and was related to the harvest of winter wheat and rice in the spring and the harvest of sugarcane, soybean and cotton in the fall and winter. The results showed that cropland burning was spatially dependent on crop type and temporally dependent on management practices (planting/harvesting). Arkansas, Florida, and Louisiana contributed more than 75% of all agricultural burning in the southeast. A 250 m MODIS land cover map cover for 2004 was developed for these three states using a decision tree classification and validation from a field campaign in the fall of 2004. Compared to the standard MODIS 1 km Land Cover Dataset (MOD 12) product ([Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., Schaaf, C. (2002), Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of the Environment, 83, 287-302.]), the 250 m classified images contained on average 50% more cropland area and improved the estimation of cropland area based on validation from ground control sites of croplands. Results from the decision tree classification for each state revealed that in 2004 agricultural burning contributed 73%, 54%, and 33% of total fires for Arkansas, Florida, and Louisiana, respectively.     

Assessing the impact of urban land development on net primary productivity in the southeastern United States

The southeastern United States (SE-US) has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades. Increasing evidence indicates that these changes have impacted mesoscale weather patterns, biodiversity and water resources. Since the Southeast has one of the highest rates of land productivity in the nation, it is important to monitor the effects of such changes regularly. Here, we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function. As an indicator of ecosystem functioning, we chose net primary productivity (NPP), which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS) data. We used the MODIS data, a 1992 Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS) for the years 1992/1993 and 2000 to estimate the extent of urban development and its impact on NPP. The analysis based on the nighttime data indicated that in 1992/1993, urban areas amounted to 4.5% of the total land surface of the region. In the year 2000, the nighttime data showed an increase in urban development for the southeastern United States of 1.9%. Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992-2000 period reduced annual NPP of the southeastern United States by 0.4%. Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study, results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.     

缺少水陆标识数据情况下MODIS图像云检测的实现

云检测中必须进行像元的水陆判识,云的存在给水陆判识带来了困难.因此,云检测中总是利用水陆标识数据辅助云检测.本文讨论了缺少水陆标识数据的云检测方法,并证明了该方法可行、实用.     

Validation of the MODIS snow product and cloud mask using student and NWS cooperative station observations in the Lower Great Lakes Region

NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) snow product (MOD10) creates automated daily, 8-day composite and monthly regional and global snow cover maps. In this study, the MOD10 daily swath imagery (MOD10_L2) and the MODIS cloud mask (MOD35) were validated in the Lower Great Lakes Region, specifically the area to the east of Lake Michigan. Validation of the MOD10_L2 snow product, MOD35 cloud mask and the MOD10_L2 Liberal Cloud Mask was performed using field observations from K-12 student GLOBE (Global Learning and Observations to Benefit the Environment) and SATELLITES (Students And Teachers Evaluating Local Landscapes to Interpret The Earth from Space) programs. Student data consisted of field observations of snow depth, snow water equivalency, cloud type, and total cloud cover. In addition, observations from the National Weather Service (NWS) Cooperative Observing Stations were used. Student observations were taken during field campaigns in the winter of 2001-2002, a winter with very little snow in the Great Lakes region, and the winters of 2000-2001 and 2002-2003, which had significant snow cover. Validation of the MOD10_L2 version 4 snow product with student observations produced an accuracy of 92% while comparison with the NWS stations produced an accuracy of 86%. The higher NWS error appears to come from forested areas. Twenty-five and fifty percent of the errors observed by the students and NWS stations, respectively, occurred when there was only a trace of snow. In addition, 82% of the MODIS cloud masked pixels were identified as either overcast or broken by the student observers while 74% of the pixels the MODIS cloud mask identified as cloudless were identified as clear, isolated or scattered cloud cover by the student observers. The experimental Liberal Cloud Mask eliminated some common errors associated with the MOD35 cloud mask, however, it was found to omit significant cloud cover.     

Operational active fire mapping and burnt area identification applicable to Mexican Nature Protection Areas using MODIS and NOAA-AVHRR direct readout data

Since 1999, the National Commission for the Knowledge and Use of the Biodiversity (CONABIO) in Mexico has been developing and managing the “Operational program for the detection of hot-spots using remote sensing techniques”. This program uses images from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites and from the Advanced Very High Resolution Radiometer of the National Oceanic and Atmospheric Administration (NOAA-AVHRR), which are operationally received through the Direct Readout station (DR) at CONABIO. This allows the near-real time monitoring of fire events in Mexico and Central America. In addition to the detection of active fires, the location of hot spots are classified with respect to vegetation types, accessibility, and risk to Nature Protection Areas (NPA). Besides the fast detection of fires, further analysis is necessary due to the considerable effects of forest fires on biodiversity and human life. This fire impact assessment is crucial to support the needs of resource managers and policy makers for adequate fire recovery and restoration actions. CONABIO attempts to meet these requirements, providing post-fire assessment products as part of the management system in particular for satellite-based burnt area mapping. This paper provides an overview of the main components of the operational system and will present an outlook to future activities and system improvements, especially the development of a burnt area product. A special focus will also be placed on the fire occurrence within NPAs of Mexico.     

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil-vegetation-atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6-22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.     

Evaluating evapotranspiration and water-use efficiency of terrestrial ecosystems in the conterminous United States using MODIS and AmeriFlux data

In this study, we used the remotely-sensed data from the Moderate Resolution Imaging Spectrometer (MODIS), meteorological and eddy flux data and an artificial neural networks (ANNs) technique to develop a daily evapotranspiration (ET) product for the period of 2004-2005 for the conterminous U.S. We then estimated and analyzed the regional water-use efficiency (WUE) based on the developed ET and MODIS gross primary production (GPP) for the region. We first trained the ANNs to predict evapotranspiration fraction (EF) based on the data at 28 AmeriFlux sites between 2003 and 2005. Five remotely-sensed variables including land surface temperature (LST), normalized difference vegetation index (NDVI), normalized difference water index (NDWI), leaf area index (LAI) and photosynthetically active radiation (PAR) and ground-measured air temperature and wind velocity were used. The daily ET was calculated by multiplying net radiation flux derived from remote sensing products with EF. We then evaluated the model performance by comparing modeled ET with the data at 24 AmeriFlux sites in 2006. We found that the ANNs predicted daily ET well (R² = 0.52-0.86). The ANNs were applied to predict the spatial and temporal distributions of daily ET for the conterminous U.S. in 2004 and 2005. The ecosystem WUE for the conterminous U.S. from 2004 to 2005 was calculated using MODIS GPP products (MOD17) and the estimated ET. We found that all ecosystems' WUE-drought relationships showed a two-stage pattern. Specifically, WUE increased when the intensity of drought was moderate; WUE tended to decrease under severe drought. These findings are consistent with the observations that WUE does not monotonously increase in response to water stress. Our study suggests a new water-use efficiency mechanism should be considered in ecosystem modeling. In addition, this study provides a high spatial and temporal resolution ET dataset, an important product for climate change and hydrological cycling studies for the MODIS era.     

欧美网络电话监听的法律规制分析

文章重点考察了欧美网络电话监听的法律制度,包括网络电话协助监听的主体范围、监听程序以及协助监听主体的义务和责任等,并最终进行综合比较与分析。     

美国信息安全政策发展及其启示

对美国信息安全政策法规进行调研,从美国信息安全政策十几年的变化上,可以充分了解美国战略意图的转变和信息时代信息化带来的威胁和挑战.着重介绍了美国信息安全战略的演变轨迹及其特点,以及美国网络安全政策导向,在确立维护网络安全的国家战略,完善维护网络安全的管理机制,优化网络安全的技术组织结构,健全维护网络安全的法制体系等方面...     

Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data

In this study we implemented a comprehensive analysis to validate the MODIS and GOES satellite active fire detection products (MOD14 and WFABBA, respectively) and characterize their major sources of omission and commission errors which have important implications for a large community of fire data users. Our analyses were primarily based on the use of 30 m resolution ASTER and ETM+ imagery as our validation data. We found that at the 50% true positive detection probability mark, WFABBA requires four times more active fire area than is necessary for MOD14 to achieve the same probability of detection, despite the 16× factor separating the nominal spatial resolutions of the two products. Approximately 75% and 95% of all fires sampled were omitted by the MOD14 and WFABBA instantaneous products, respectively; whereas an omission error of 38% was obtained for WFABBA when considering the 30-minute interval of the GOES data. Commission errors for MOD14 and WFABBA were found to be similar and highly dependent on the vegetation conditions of the areas imaged, with the larger commission errors (approximately 35%) estimated over regions of active deforestation. Nonetheless, the vast majority (> 80%) of the commission errors were indeed associated with recent burning activity where scars could be visually confirmed in the higher resolution data. Differences in thermal dynamics of vegetated and non-vegetated areas were found to produce a reduction of approximately 50% in the commission errors estimated towards the hours of maximum fire activity (i.e., early-afternoon hours) which coincided with the MODIS/Aqua overpass. Lastly, we demonstrate the potential use of temporal metrics applied to the mid-infrared bands of MODIS and GOES data to reduce the commission errors found with the validation analyses.     

Remote sensing of phytoplankton community composition along the northeast coast of the United States

Satellite imagery has proven to be a powerful tool for measuring chlorophyll a in surface waters. While this provides an estimate of total phytoplankton biomass, it does not distinguish between phytoplankton groups, many of which have functional differences and therefore affect biogeochemical cycles differently. Phytoplankton pigment analysis has been used to quantify a wide range of photosynthetic and accessory pigments, and chemotaxonomic analysis (e.g. CHEMTAX) has been used to successfully quantify functional taxonomic groups in nature based on pigment distributions. Here, we combine CHEMTAX analysis with satellite-derived distributions of specific phytoplankton pigments to describe the distributions of particular components of the phytoplankton community in the northeast coast of the United States from space. The spatial and seasonal variations in phytoplankton community structure elucidated through satellite remote sensing methods generally agreed with observations of abundance estimates of cell counts. Diatoms were generally the most abundant phytoplankton in this region, especially during Winter-Spring and in the inner shelf, but phytoplankton populations shifted to increasing abundance of other taxa during Summer, especially offshore. While still preliminary, satellite-derived taxa-specific information with proper regional controls holds promise for providing information on phytoplankton abundance to a taxonomic group level which would greatly improve our understanding of the impacts of human activity and climate change on ecosystems.     

MODIS图象的云检测及分析

云一直是遥感图象处理、图象分析的一大障碍.为了解决这一问题,试图探讨利用中分辨率成像光谱仪MODIS检测云的方法,该方法充分考虑到MODIS数据具有36个光谱通道,特别是红外波段细分的特点,先是基于云的波谱特性采用多光谱综合法、红外差值法及指数法来对MODIS图象上的云点进行检测,鉴于这些方法有一定的局限性,因而还运用了一种基于空间结构分析和神经网络的云自动检测算法;最后将各种方法的云检测结果进行相互映证和对照分析,结果表明,这些方法检测到的云互相吻合,说明利用MODIS图象可成功地检测云点像元.这不仅为云的去除奠定了良好基础,而且也可以提高图象识别、图象分类及图象反演的精度.     

Evaluation of the onset of green-up in temperate deciduous broadleaf forests derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data

Vegetation phenology is the chronology of periodic phases of development. It constitutes an efficient bio-indicator of impacts of climate changes and a key parameter for understanding and modelling vegetation-climate interactions and their implications on carbon cycling. Numerous studies were devoted to the remote sensing of vegetation phenology. Most of these were carried out using data acquired by AVHRR instrument onboard NOAA meteorological satellites. Since 1999, multispectral images were acquired over the whole earth surface every one to two days by MODIS instrument onboard Terra and Aqua platforms. In comparison with AVHRR, MODIS constitutes a significant technical improvement in terms of spatial resolution, spectral resolution, geolocation accuracy, atmospheric corrections scheme and cloud screening and sensor calibration. In this study, 250 m daily MODIS data were used to derive precise vegetation phenological dates over deciduous forest stands. Phenological markers derived from MODIS time-series and provided by MODIS Global Land Cover Dynamics product (MOD12Q2) were compared to field measurements carried out over the main deciduous forest stands across France and over five years. We show that the inflexion point of the asymmetric double-sigmoid function fitted to NDVI temporal profile is a good marker of the onset of green-up in deciduous stands. At plot level, the prediction uncertainty is 8.5 days and the bias is 3.5 days. MODIS Global Land Cover Dynamics MOD12Q2 provides estimates of onset of green-up dates which deviate substantially from in situ observations and do not perform better than the null model. RMSE values are 20.5 days (bias -17 days) using the onset of greenness increase and 36.5 days (bias 34.5 days) using the onset of greenness maximum. An improvement of prediction quality is obtained if we consider the average of MOD12Q2 onset of greenness increase and maximum as marker of onset of green-up date. RMSE decreases to 16.5 days and bias to 7.5 days.     

Thematic accuracy of the 1992 National Land-Cover Data for the eastern United States: Statistical methodology and regional results

The accuracy of the 1992 National Land-Cover Data (NLCD) map is assessed via a probability sampling design incorporating three levels of stratification and two stages of selection. Agreement between the map and reference land-cover labels is defined as a match between the primary or alternate reference label determined for a sample pixel and a mode class of the mapped 3×3 block of pixels centered on the sample pixel. Results are reported for each of the four regions comprising the eastern United States for both Anderson Level I and II classifications. Overall accuracies for Levels I and II are 80% and 46% for New England, 82% and 62% for New York/New Jersey (NY/NJ), 70% and 43% for the Mid-Atlantic, and 83% and 66% for the Southeast.     

Comparison of national e‐health implementation in the United States and South Korea

With the development of the health care industry, there has been growing recognition that e‐health implementation is needed to improve the efficiency, quality, and safety of care. In addition, a review of previous studies suggests that much can be learned by investigating the difference in national e‐health implementation in the United States and in South Korea. To this end, the main objective of this article is to compare the national e‐health implementation in the case of the United States and South Korea. The results show that in the United States a decentralized e‐health implementation is being followed by individual‐level e‐health. South Korea is pursuing ubiquitous health (u‐health) after a hospital information system and electronic health were implemented. The United States still lags behind many other countries in the implementation of e‐health, possibly due to its low uncertainty avoidance culture. South Korea has been among the frontrunner groups in e‐health implementation as a consequence of its highly developed telecommunication infrastructures and high uncertainty avoidance culture. On the basis of these results, we recommend that establishing health care policies be done on a national scale that takes into account the countries’ cultures.     

MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous U.S.A.

With the expectation of major shifts in climate, ecologists have focused attention on developing predictive relationships between current climatic conditions and species diversity. Climatic relationships appear best defined at regional rather than local levels. In reference to tree diversity, process-based models that express gross primary production (GPP) as an integrated function of climate seem most appropriate. Since 2000, NASA's MODIS satellite has provided composite data at 16-day intervals to produce estimates of GPP that compare well with direct measurements. The MODIS enhanced vegetation index (EVI), which is independent of climatic drivers, also appears a good surrogate to estimate seasonal patterns in GPP. In this paper we identified 65 out of 84 delineated ecoregions distributed across the contiguous U.S.A., within which sufficient (≥ 200) Federal Inventory and Analysis survey plots were available to predict the total number of tree species, which varied from 17 to 164. Four different formulations of EVI were compared: The annual maximum, the annual integrated, the growing season defined mid-point and growing season averaged values. The growing season mid-point EVI defined the beginning and end of the active growing season. In all formulations of EVI, a polynomial function accounted for about 60% of the observed variation in tree diversity, with additional precision increasing to 80% when highly fragmented ecoregions with < 50% forest cover were excluded. Maps comparing predicted with measured tree richness values show similar patterns except in the Pacific Northwest region where a major extinction of tree genera is known to have occurred during the late Pliocene. The extent that these relationships remain stable under a changing climate can be evaluated by determining if the MODIS climate-driven estimate of GPP continues to match well with EVI patterns and systematic resurveys of forest vegetation indicate that tree species are able to adjust rapidly to climatic variation.     

美国网络监控项目法律保障体系及其启示

“棱镜门”事件的曝光引发全球对国家安全和隐私保护的讨论。在美国网络监控法律框架下去审视美国国家安全局情报监控项目,可发现明确的适用范围、严密的审查监督制度与严格的实施程序要求,让美国政府可在维护国家安全利益的同时力求最大限度保护民众隐私权,做到了“有法可依”。构建政府与互联网公司互利共赢的新关系,完善网络监控法律体系和法律运作程序是从美国网络监控法律保障体系分析中所提炼出对我国具有启发性意义的措施。