Strategies for monitoring tropical deforestation using satellite data

Measuring the aerial extent of tropical deforestation for other than localized areas requires the use of satellite data. We present evidence to show that an accurate determination of tropical deforestation is very difficult to achieve by a 'random sampling' analysis of Landsat or similar high spatial resolution data unless a very high percentage of the area to be studied is sampled. In order to achieve a Landsat-derived deforestation estimate within 20% of the actual deforestation amount 90% of the time, 37 of 40 scenes, 55 of 61 scenes and 37 of 45 scenes were required for Bolivia, Colombia and Peru respectively.     

Sea-ice monitoring over the Caspian Sea using geostationary satellite data

A new technique is proposed for sea-ice mapping using observations from geostationary satellite over the Caspian Sea. A two end-member linear-mixture approach has been employed. A neural-network-based approach was used to simulate water and ice reflectances for all possible sun–satellite geometries. The ice-mapping technique incorporates an advanced cloud-detection algorithm with adaptive threshold values. The average percentage of cloud reduction because of the daily compositing ranged from 22% to 25%. Daily maps of ice distribution and concentration with minimal cloud coverage were produced for the winter seasons of 2007 and 2008. The retrieved ice distribution demonstrated a good agreement with Moderate Resolution Imaging Spectroradiometer (MODIS) images and National Oceanic Atmospheric Administration (NOAA) Interactive Multisensor Snow and Ice Mapping System (IMS) snow and ice charts. The obtained correlation coefficients with IMS charts for 2007 and 2008 were 0.92 and 0.83, respectively. The technique has been proposed as one of the candidate ice-mapping techniques for the future Geostationary Operational Environmental Satellite-R Series (GOES-R) Advance Baseline Imager (ABI) instrument.     

Wildfire monitoring using satellite images,ontologies and linked geospatial data

Advances in remote sensing technologies have allowed us to send an ever-increasing number of satellites in orbit around Earth. As a result, Earth Observation data archives have been constantly increasing in size in the last few years, and have become a valuable source of data for many scientific and application domains. When Earth Observation data is coupled with other data sources many pioneering applications can be developed. In this paper we show how Earth Observation data, ontologies, and linked geospatial data can be combined for the development of a wildfire monitoring service that goes beyond applications currently deployed in various Earth Observation data centers. The service has been developed in the context of European project TELEIOS that faces the challenges of extracting knowledge from Earth Observation data head-on, capturing this knowledge by semantic annotation encoded using Earth Observation ontologies, and combining these annotations with linked geospatial data to allow the development of interesting applications.     

Snowpack monitoring in North America and Eurasia using passive microwave satellite data

Areas of the Canadian high plains, the Montana and North Dakota high plains, and the steppes of central Russia have been studied in an effort to determine the utility of spaceborne microwave radiometers for monitoring snow depths in different geographic areas. Significant regression relationships between snow depth and microwave brightness temperatures were developed for each of these homogeneous areas. In each of the study areas investigated in this paper, Nimbus-6 (0.81 cm) ESMR data produced higher correlations than Nimbus-5 (1.55 cm) ESMR data in relating microwave brightness temperature to snow depth. It is difficult to extrapolate relationships between microwave brightness temperature and snow depth from one area to another because different geographic areas are likely to have different snowpack conditions.     

基于物联网的远程监控系统

随着物联网的发展,越来越多的地方已经开始运用这种基于多种传感器的信息融合、互动的技术.为了使远程监控得到进一步的便利,本文研究了基于物联网的监控、识别、报警等方面的应用.阐述了传感器网络的设计、监控系统的工作流程、数据的传输等功能,并使用远望谷XRF-800型RFID读写器作为人员识别的主要设备.实验结果表明,物联网技术在融合多种信息进行判断方便具有更多的优势,可以明显的减少人在信息处理方面的误差.该方案为远程监控提供了应用思路和解决方案,对于解决实际问题具有一定的参考价值.     

Indonesian rainfall variability observation using TRMM multi-satellite data

It is important to understand the characteristics of Indonesian rainfall within the world’s climate system. The large rainfall in the Indonesian archipelago plays an essential role as a central atmospheric heat source of the Earth’s climate system throughout the year. Monthly rainfall satellite data, measured by the Tropical Rainfall Measuring Mission (TRMM) 3B43 over the course of 13 years, were employed to analyse monthly means, total means, maximum and minimum variability, standard deviation, and the trends analysis of Indonesian rainfall variability. The rainfall estimated from satellite data was then compared to the rain gauge data over the Indonesian region to determine the accuracy level. The results show that oceans, islands, monsoons, and topography clearly affect the spatial patterns of Indonesian rainfall. Most high-rainfall events in Indonesia peak during the December–January–February (DJF) season and the lowest rainfall events occur during the June–July–August (JJA) season. Those conditions are associated and generated with the northwest and southeast monsoon patterns. High fluctuations between maximum and minimum monthly rainfall data of over 400 mm month^?1 occur over Jawa (Java) Island, the Jawa Sea, and southern Sulawesi Island. A high annual and monthly rainfall typically occurs throughout Indonesia over island areas. The trend analysis shows an increasing trend in rainfall from 1998 to 2010 in Kalimantan, Jawa, Sumatra, and Papua. Decreasing rainfall trends occur along the west and south coast of Sumatra, eastern Jawa, southern Sulawesi, Maluku Islands, western Papua, and Bali Island.     

Assessment and improvement of a robust satellite technique (RST) for thermal monitoring of volcanoes

The RST approach (robust satellite technique) is a multi-temporal scheme of satellite data analysis already successfully used to monitor volcanoes at different geographic locations. In this work, the results of a long-term validation analysis of RST-based hot spot products are presented. This study was performed processing fourteen years of NOAA-AVHRR (National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer) records acquired over Mt. Etna area between 1 January 1995 and 31 December 2008, at different overpass times (day/night), and analyzing hundreds of volcano bulletins reporting information on Mt. Etna eruptive activity, provided by visual observations and ground-based measurements. An optimized configuration of the RST approach, named RST_VOLC, is also, for the first time, presented and discussed here. This method, thanks to a better tradeoff between reliability and sensitivity, may be particularly suitable to support operational systems for volcano monitoring and hazard mitigation.     

Remote sensing of vegetation water content from equivalent water thickness using satellite imagery

Vegetation water content (VWC) is one of the most important parameters for the successful retrieval of soil moisture content from microwave data. Normalized Difference Infrared Index (NDII) is a widely-used index to remotely sense Equivalent Water Thickness (EWT) of leaves and canopies; however, the amount of water in the foliage is a small part of total VWC. Sites of corn (Zea mays), soybean (Glycine max), and deciduous hardwood woodlands were sampled to estimate EWT and VWC during the Soil Moisture Experiment 2005 (SMEX05) near Ames, Iowa, USA. Using a time series of Landsat 5 Thematic Mapper, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Advanced Wide Field Sensor (AWiFS) imagery, NDII was related to EWT with R² of 0.85; there were no significant differences among land-cover types. Furthermore, EWT was linearly related to VWC with R² of 0.87 for corn and 0.48 for soybeans, with a significantly larger slope for corn. The 2005 land-cover classification product from the USDA National Agricultural Statistics Service had an overall accuracy of 92% and was used to spatially distribute VWC over the landscape. SMEX05 VWC versus NDII regressions were compared with the regressions from the Soil Moisture Experiment 2002 (SMEX02), which was conducted in the same study area. No significant difference was found between years for corn (P = 0.13), whereas there was a significant difference for soybean (P = 0.04). Allometric relationships relate the size of one part of a plant to the sizes of other parts, and may be the result from the requirements of structural support or material transport. Relationships between NDII and VWC are indirect, NDII is related to canopy EWT, which in turn is allometrically related to VWC.     

A near real-time dual-band-spatial approach to determine the source of increased radiance from closely spaced active volcanoes in coarse resolution satellite data

Bezymianny and Kliuchevskoi volcanoes (Kamchatka) present a danger as both inject ash into North Pacific air routes. Current automated monitoring algorithms do not distinguish them in real time due to their mutual proximity (10 km) and poor geolocation accuracy of Advanced Very High Resolution Radiometer (AVHRR) data. Contrasting mid- and thermal infrared volcanic radiances are influenced by (1) differences in temperature and eruptive style of Bezymianny's andesite and Kliuchevskoi's basalt and (2) different temperatures of the non-volcanic portion of pixels located over their summits, due to different elevations. Data from 571 AVHRR images show the latter is more significant. Discriminant function analysis using summit and regional band 4 pixel-integrated radiant temperatures (pirT) correctly identifies the source volcano of a thermal anomaly in 89% of cases. Weather permitting, a spatial component can be added, leading to improved accuracy. The approach used here can also be applied at other closely spaced volcanoes with substantially different summit elevations.     

Estimating soil wetness using satellite data

Abstract

Improved estimates of soil wetness were obtained using observations from both the NIMBUS-7 Scanning Multichannel Microwave Radiometer (SMMR) and the NOAA-7 Advanced Very High Resolution Radiometer (AVHRR). SMMR 6.6 GHz frequency, horizontal polarization, brightness temperature (T_BH) was first correlated with soil wetness, as computed using an Antecedent Precipitation Index (API) model, for a number of SMMR ground resolution areas involving a fairly wide range of vegetation densities. The API generally accounted for more than 70 per cent of the observed temporal variability in T_BH, with linear correlations being significant at the 1 per cent level. The regression slope of T_BH versus API correlated well, at the 1 per cent level, with a vegetation index derived from AVHRR visible and near-infrared observations. The regression intercept was found to correlate less satisfactorily, but was significant at the 5 per cent level. These linear regression results were used to develop a diagnostic model for soil wetness using SMMR and AVHRR data only. The model was found to be useful in describing four levels of soil wetness as compared to three levels when vegetation was not considered.     

Estimation of the evapotranspiration using surface and satellite data

Abstract

A method to derive evapotranspiration from a combination of satellite and conventional data is investigated. For this purpose NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) infrared images on clear days of various seasons are used to derive surface temperatures over France. These temperatures are then compared to the shelter-height temperatures collected at the WMO (World Meteorological Organization) standard meteorological stations at the time of satellite overpass. The difference between the two temperatures varies both with season and latitude. To analyse those results we use a model of the soil-vegetation interface, forced by a reconstruction of the surface fluxes derived from the WMO data. The model simulates reasonably well the diurnal and seasonal variations in the difference between satellite surface temperature and surface-air temperature. The corresponding latitudinal variations which occur in summer may be interpreted in terms of evapotranspiration. The limitations of this method are determined by a model sensitivity study; in particular they are due to the role played by tall vegetation.     

HJ-1B卫星遥感数据在重庆市城市热环境监测的应用分析

本文以MODIS反演大气透射率,以HJ-1B/CCD分类结果反演地表比辐射率,并基于单窗算法,利用HJ-1B/IRS4数据反演地表温度.在此基础上,提取研究区的热场变异指数来分析重庆热岛空间分布特征,并就NDVI与NDBI对热岛效应的影响进行了分析.其结果如下:1)重庆城市热岛大致位于中梁山、铜锣山之间,呈东北、西南走向分布;2)热岛中心不在市中心,而是集中在大渡口工业园区、江北机场这些能耗大、人口密集区域,热岛强度范围在5?C-10?C之间;3)接近长江、嘉陵江水域的建筑用地密集区域,其热岛效应并不明显;4)NDVI与热岛强度呈负相关关系,NDBI与热岛强度呈现较为明显的正相关关系,二者对热岛都有重要影响,而NDBI的影响更大.因此,利用HJ-1B数据监测城市热环境,能较好地揭示重庆城市热岛空间分布特征,为城市环境监测与改善提供参考.     

Comparative study of the tropospheric ozone derived from satellite data using different interpolation techniques

Tropospheric ozone (TO) has been derived from the Aura/Ozone Monitoring Instrument (OMI) and the Aura/Microwave Limb Sounder (MLS) over the Indian sub-continent region using a tropospheric ozone residual (TOR) technique. The TO was initially retrieved at a horizontal spatial resolution following that of the Aura/MLS (300 km), which has a lower horizontal spatial resolution than that of the Aura/OMI (25 km). To overcome the limitations imposed by data at a lower spatial resolution, we have introduced a 2D rectangular interpolation (RI) algorithm for effective resampling of data to higher horizontal spatial resolutions. The performance of this algorithm has been evaluated by comparison against existing standard techniques such as nearest neighbourhood (NN) and kriging interpolation as well as comparison against in situ ozonesonde observations. Gridded TO estimates were subsequently generated for the region of interest at 25, 50, and 100 km horizontal spatial resolutions for further study.     

Investigation of heavy-mineral deposits using multispectral satellite data

Mineral deposit mapping is essential for sustainable and eco-friendly exploitation of natural resources. The south Tamil Nadu coast of India is rich in minerals. Currently the beach sands are extracted for export entirely in raw form without any value addition. Due to unsustainable sand mining, there are negative environment impacts, which lead to various coastal hazards such as erosion, salinization and sea-water intrusion. In order to initiate the focus on mapping of mineral deposits along this area, standardized hyperspectral analysis has been carried out using Landsat satellite data and Environment for Visualising Images (ENVI) software. The selected endmembers are identified by comparing the spectral signatures with predefined spectral plots from the United States Geological Survey (USGS) spectral library. Finally the endmembers are mapped with ENVI's spectral angle mapper (SAM). The minerals which show significant variation in reflectance at different spectral bands can be effectively mapped by using multispectral data. Ground verifications performed to assess the accuracy of classification were mostly in agreement with the obtained results. This study has opened up new areas for inland heavy mineral exploitation and leads to eco-friendly exploitation of natural resources along the study area. It also illustrates the high potential of multispectral satellite data for exploration and mapping of mineral resources.     

Obelix searches Internet using customer data

One major problem with purchasing through the Web is locating reliable suppliers that offer the exact product or service you need. In the usual approach, you access an indexing based search engine, specify keywords for the purchase, and initiate the search. The outcome is typically a list ranked according to keyword matches; useful, but not always helpful. Keyword matches provide only one ingredient to finding the right Web sites. The ranking should also consider the satisfaction of previous customers purchasing from those sites, customer profiles, and customer behavior. The Obelix search engine uses reconfigurable technology to apply customer satisfaction data obtained from the Internet service provider infrastructure to refine its search criteria. The Obelix system collects data about customer activities, calculates a customer satisfaction index, and updates the search engines with its findings     

Determination of volcanic dust deposition from El Chichon using ground and satellite data

Major volcanic eruptions of EI Chichon on 28 March 1982, 3 April 1982 and 4 April 1982 produced heavy tephra falls in surrounding areas. Data from the NOAA-6 and NOAA-7 satellites were used to determine the extent of the tephra falls. The satellite data showed a larger area of tephra deposition than was reported from conventional ground measurements     

Identifying deforestation in Brazil using multiresolution satellite data

MSS, LAC, GAC and GOES data were used to delineate the extent of deforestation in Rondonia, Brazil, in order to identify those satellite data sources appropriate for monitoring deforestation on a continental/subcontinental scale. These data were processed to differentiate forest from non-forest (cleared, colonized areas) using two different classification procedures. The first procedure utilizes all available spectral bands of data in conjunction with a maximum likelihood classifier to discriminate cleared areas from primary forest. The technique is called probability thresholding. The second employs the red and nearinfrared spectral data to calculate a vegetation index which is subsequently thresholded from forest/non-forest delineation. Ground reference data were not available; the 80m (spatial resolution) MSS digital data products served as the reference data source. The 1·1 km LAC, 4 km GAC and 0·9 km GOES (visible band) images were compared with the MSS imagery. Areal comparisons indicated that (i) the LAC data are capable of adequately delineating colonization clearings in the Amazon; (ii) the spatial resolution of'uhe GAC data is too large to delineate linear clearings of varying length (tens to hundreds of kilometres) up to 2 km wide reliably, (iii) the visible GOES data were of little utility due to excessive data noise and (iv) probability thresholding procedures discriminated forest from non-forest more accurately than vegetation-index thresholding procedures. The results indicate that LAC data used in conjunction with probability thresholding offer the best data-source/classification-procedure combination. MSS data may be used when and where available as a ground reference data source in order to define the AVHRR threshold which most accurately discriminates cleared areas from primary forest.     

Georeferencing and registering satellite data for monitoring vegetation over large areas

Satellite imaging systems do not produce images that are in perfect planimetric accord with the Earth's surface, nor do the images coincide with the deliberately distorted geometrics of various map projections. For many purposes there is a requirement that satellite images be georeferenced or transformed according to the constraints of some convenient map projection. Other requirements may call for one satellite image to be transformed to the geometry of another. In this paper consideration is given to the geodetic correction and registration of polar-orbiting satellite data with particular reference to the monitoring of vegetation over large areas. Attention is given to the variation of the field of view with change in sensor view angle associated with the Advanced Very High Resolution Radiometer (Avhrr) on the Tiros-n series of meteorological satellites. Avhrr data, with a nominal resolution of 1.1 km on the ground, have been compared with multi-spectral scanner (Mss) data from the Landsat series of satellites which have a resolution of approximately 80 m on the ground. As the deterministic correction of image distortions is limited by nonsystematic deviations of the satellite from its known position, methodologies have been developed for emperically determining the appropriate transformation with optimal accuracy.The results presented suggest that polar-orbiting meteorological satellite data enable the monitoring of vegetation over vast areas. Such monitoring, not conceivable previously, could contribute to a timely and appropriate response in the case of calamitous drought, given the experience of Ethiopia and the Sahel in recent years.     

Identification of gas flares in the North Sea using satellite data

This paper describes the identification of oil production platforms responsible for flaring off gas in the North Sea, using data received from the Advanced Very High Resolution Radiometer (AVHRR) carried on the polar-orbiting weather satellites TIROS-N, NOAA-6 and NOAA-7. A selected area of each of several AVHRR scenes of the North Sea and surrounding coastline has been rectified and the geographical co-ordinates of the gas flares ascertained. These have been compared with a map of platform locations. In most cases the identification of the platform responsible for producing the flare is unambiguous.