Detection and mapping vegetation cover based on the Spectral Angle Mapper algorithm using NOAA AVHRR data

Satellite data, taken from the National Oceanic and Atmospheric Administration (NOAA) have been proposed and used for the detection and the cartography of vegetation cover in North Africa. The data used were acquired at the Analysis and Application of Radiation Laboratory (LAAR) from the Advanced Very High Resolution Radiometer (AVHRR) sensor of 1 km spatial resolution. The Spectral Angle Mapper Algorithm (SAM) is used for the classification of many studies using high resolution satellite data. In the present paper, we propose to apply the SAM algorithm to the moderate resolution of the NOAA AVHRR sensor data for classifying the vegetation cover. This study allows also exploiting other classification methods for the low resolution. First, the normalized difference vegetation index (NDVI) is extracted from two channels 1 and 2 of the AVHRR sensor. In order to obtain an initial density representation of vegetal formation distribution, a methodology, based on the combination between the threshold method and the decision tree, is used. This combination is carried out due to the lack of accurate data related to the thresholds that delimit each class. In a second time, and based on spectral behavior, a vegetation cover map is developed using SAM algorithm. Finally, with the use of low resolution satellite images (NOAA AVHRR) and with only two channels, it is possible to identify the most dominant species in North Africa such as: forests of the Liege oaks, other forests, cereal’s cultivation, steppes and bar soil.     

Environmental degradation analysis using NOAA/AVHRR data

This paper proposes a particular approach to assess information about soil degradation, based on a methodology to calculate soil color from NOAA/AVHRR data. As erosive processes change physical and chemical properties of the soil, altering, consequently, the superficial color, monitoring the change in color over time can help to identify and analyze those processes. A relationship among the soil color (described in the Munsell Color System, i.e., in terms of Hue, Value and Chroma), vegetation indices, surface temperature and emissivity has been established, which is based on the theoretical model. The methodology has three main phases: determination of the regression models among soil color and vegetation indices, emissivity and surface temperature; generation of digital soil color models; and statistical evaluation of the estimated color. The tests showed that the methodology is efficient in determining soil color using the various vegetation indices (i.e., Normalized vegetation index NDVI, Modified soil adjusted vegetation index MSAVI). One vegetation index, i.e., Purified adjusted vegetation index (PAVI) is proposed to subsidies the effect of vegetation over the soil. Best results were obtained for the Hue color component. To further test the methodology, the estimated digital color models were compared with the characteristic color of soil classes in the test area. The results of this application confirmed the methodology’s capacity to determine the soil color from NOAA/AVHRR data. This type of study is quite helpful to know the erosion of soil as well as some abrupt change in soil due to natural hazards by space borne or air-borne sensors.     

Improvements in broadband planetary albedo estimates from narrowband NOAA satellite observations

New and current algorithms for estimating the broadband planetary albedo from NOAA satellite narrowband observations are testes by applying them to retrospective NOAA-9 AVHRR (Advanced Very High Resolution) data of 2 July 1985. For comparison, broadband SW fluxes taken by the Earth Radiation Budget Experiment (ERBE) instrument, also on NOAA-9 are used as reference values. In contrast to the current scene-independent model, the new algorithms are all scene-dependent, but differ in the way they are classified by surface, cloud amount and albedo indices. The current operational model produces a global bias of magnitude 5 W/m² and a strong latitudinal dependence in error. The scene-dependent algorithms yield global biases of less than ±3 W/m² and have little latitudinal dependence over most of the globe.     

Application of SVM on satellite images to detect hotspots in Jharia coal field region of India

The present paper deals with the application of Support Vector Machine (SVM) and image analysis techniques on NOAA/AVHRR satellite image to detect hotspots on the Jharia coal field region of India. One of the major advantages of using these satellite data is that the data are free with very good temporal resolution; while, one drawback is that these have low spatial resolution (i.e., approximately 1.1 km at nadir). Therefore, it is important to do research by applying some efficient optimization techniques along with the image analysis techniques to rectify these drawbacks and use satellite images for efficient hotspot detection and monitoring. For this purpose, SVM and multi-threshold techniques are explored for hotspot detection. The multi-threshold algorithm is developed to remove the cloud coverage from the land coverage. This algorithm also highlights the hotspots or fire spots in the suspected regions. SVM has the advantage over multi-thresholding technique that it can learn patterns from the examples and therefore is used to optimize the performance by removing the false points which are highlighted in the threshold technique. Both approaches can be used separately or in combination depending on the size of the image. The RBF (Radial Basis Function) kernel is used in training of three sets of inputs: brightness temperature of channel 3, Normalized Difference Vegetation Index (NDVI) and Global Environment Monitoring Index (GEMI), respectively. This makes a classified image in the output that highlights the hotspot and non-hotspot pixels. The performance of the SVM is also compared with the performance obtained from the neural networks and SVM appears to detect hotspots more accurately (greater than 91% classification accuracy) with lesser false alarm rate. The results obtained are found to be in good agreement with the ground based observations of the hotspots. This type of work will be quite helpful in the near future to develop a hotspots monitoring system using these operational satellites data.     

气象卫星的现况和发展

<正> 一、引言自从1960年发射第一颗气象卫星以来已经25年了。这25年来气象卫星技术及其应用都有了很大发展,在全球天气预报、灾害性天气监视、海洋和水文环境监测、农业和交通中起了越来越重要的作用。气象卫星的功能可以大致概括为: (一)利用遥感探测仪器对卫星下垫面进行探测。探测器主要有两类:一类是成象仪     

SCIAMACHY solar irradiance observation in the spectral range from 240 to 2380 nm

The SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY) is part of the payload of ESA’s Environmental Satellite ENVISAT which was launched into a sun-synchronous polar orbit on 2002-03-01. It is the first spaceborne instrument covering a wavelength range from 240 to 2380 nm thus including ultraviolet, visible and near infrared spectral regions.The main purpose of SCIAMACHY is to determine the amount and distribution of a large number of atmospheric trace constituents by measuring the radiance backscattered from the Earth. In addition, several solar observations are performed with daily or orbital frequency.The presented results will cover the following topics: (a) comparison of the solar irradiance measured by SCIAMACHY with data from the instruments SOLSPEC/SOLSTICE/SUSIM and a solar spectrum derived by Kurucz; (b) comparison of the SCIAMACHY solar Mg II index with GOME and NOAA data; (c) correlation of the relative change of solar irradiance measured by SCIAMACHY with the sun spot index.The mean solar irradiance for each of the 8 SCIAMACHY channels agrees with the Kurucz data within ±2–3%. The presented analysis proves that SCIAMACHY is a valuable tool to monitor solar irradiance variations.     

A generalised methodology for estimating land surface temperature for non-urban areas of Greece through the combined use of NOAA–AVHRR data and ancillary information

The purpose of this study was twofold: to develop a methodology for the estimation of land surface temperature for non-urban areas and to analyze the sensitivity of the methodology. The key element of the methodology was the development of emissivity maps based on CORINE Land Cover and the ASTER spectral library. Land surface temperatures were derived from NOAA/AVHRR data and the methodology was applied at a national scale in Greece, with emphasis given to non-urban areas. A sensitivity analysis was performed in order to determine the variables that mainly affect the estimation of land surface temperature. A varying propagation error was identified depending on the temperature and humidity of the atmosphere, as well as the land cover type. The methodology was applied to a series of 25 AVHRR images and the results were compared to in-situ measurements from representative stations.     

Satellite measurements of tropospheric aerosols

The ability to measure tropospheric aerosols over ocean surfaces has been demonstrated using several different satellite sensors. Landsat data originally showed that a linear relationship exists between the upwelling visible radiance and the aerosol optical thickness (about 90% of this thickness is generally in the lowest 3 km of the atmosphere). Similar relationships have also been found for sensors on GOES, NOAA-5 and NOAA-6 satellites. The linear relationship has been shown theoretically to vary with the aerosol properties, such as size distribution and refractive index, although the Landsat data obtained at San Diego showed little variability in the relationship. To investigate the general applicability of the technique to different locations, a global-scale ground-truth experiment was conducted in 1980 with the AVHRR sensor on NOAA-6 to determine the relationship at ten ocean sites around the globe. The data for four sites have been analyzed, and show excellent agreement between the aerosol content measured by the AVHRR and by sunphotometers at San Diego, Sable Island and San Juan, but at Barbados, the AVHRR appears to overestimate the aerosol content. The reason for the different relationship at the Barbados site has not been definitely established, but is most likely related to problems in interpreting the sunphotometer data rather than to a real overestimation by the AVHRR. A preliminary analysis of AVHRR Channel 1 (0.65 μm) and Channel 2 (0.85 μm) radiances suggest that useful information on the aerosol size distribution may also be obtained from satellite observations.     

月球车地球敏感器图像地心位置提取算法

地球是月球上可视半径最大的天体,且在天球上运动范围较小,不存在升降现象.当月球车在月球对地面区域活动时,利用地球敏感器对地球成像可实现月球车长期自主天文导航.地球图像地心位置提取是利用地球敏感器进行天文导航的关键技术之一,直接决定了地球敏感器的观测精度.本文通过研究地球敏感器镜头投影模型分析地球成像规律,提出一种不受地相变化约束的地球敏感器图像地心位置提取算法,采用取半搜索法和循环搜索法两步实现地球真实边缘线的充分筛选,并基于此拟合地心位置.半物理仿真实验校验结果表明,本算法能针对不同地相图像有效提取地球中心,外符合平均精度约为9.78"~16.68",在实验条件随机改变的情况下,地心位置外符合精度标准差互差最大不超过0.98".      

卫星软格式化同步器与资源卫星1号02星CCD数据移动窗相关技术研究

处理速度一直是软件帧同步的瓶颈所在,通过引入KMP和Rabin-Karp算法,寻找到一种高效的帧同步处理算法,提高了处理速度,最终设计完成了通用的卫星软件帧同步器,并进行了相关验证测试和分析,给出了分析比较结果,在分析中巴资源卫星1号02星数据格式的基础上,利用所设计的软件帧同步器和DirectX显示技术,实现了一套针对资源卫星双通道CCD传感器的全软件移动窗显示系统,并对该卫星数据的图像交叠等现象作了相应处理,这套软件帧同步器和移动窗显示系统可以作为开发卫星接收记录系统的重要组成部分。     

Radiation budget and cloud properties from METEOSAT observations

Studies of the Earth's radiation budget from polar orbiting satellite systems, such as the forthcoming NASA Earth Radiation Budget Experiment, suffer from errors due to a poor temporal sampling of the diurnal variations in the radiation field. A knowledge of the causes and magnitudes of such variations is of importance in minimising these errors. This paper presents data on daily mean radiation budget parameters, together with their variation over the daylight hours, relating them to physical processes within the earth/atmosphere system. The most significant cause of variability is shown to be persistent high level cloud. The relative magnitude of cloud induced variability in the visible and infrared spectral regions is derived.     

Vegetation cover mapping from NOAA/AVHRR

The visible and near infrared channels, Ch₁ and CH₂ respectively, on the Advanced Very High Resolution Radiometer (AVHRR) provide daily information for monitoring changes in vegetation and crops. Data from these channels are used to create a normalized vegetation index (NVI) that is sensitive to changes in green leaf biomass and is represented mathematically by:

$\text{NVI =}\text{CH}{}_2\text{? CH}{}_1\text{CH}{}_2\text{+ CH}{}_1$

Operational products generated at NOAA include full-scale 1-km resolution images of the NVI covering areas viewed in a single swath of the polar-orbiting NOAA satellite. Global scale NVI images are also produced by compositing over a seven-day period, saving the maximum NVI created daily for each local array (resolution of 15 km at the equator to 30 km at the poles). Such seven-day mapping reduces the effect of cloud contamination. The global vegetation indices are used by foreign and U.S. government agencies for operational and experimental purposes such as assessment of crop conditions, monitoring potential desert locust breeding grounds, forest fire danger models, and monitoring range lands for forage availability. Examples include changes in the NVI in the Lake Chad vicinity, 1981–1982 and 1984; western United States NVI; and seasonal variations of the NVI in the Sahel using the global operational data base, 1982–1983.     

Study of satellite retrieved CO2 and CH4 concentration over India

This paper reports a study of spatial and temporal variations of columnar averaged concentration of CO₂ and CH₄ over India using SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) and Greenhouse gas Observing SATellite (GOSAT) data. Comparison of these data with the global view National Oceanic and Atmospheric Administration (NOAA) land data and also location specific flask data is made. The temporal variation in column averaged global CO₂ is similar to that over India and it is also similar to the NOAA surface flask data and global view. The variation in NOAA surface CH₄ is location dependent and its global view appears to vary seasonally in opposite phase with the column averaged CH₄ values from satellites, reflecting the limited comparability of surface and column averaged data. Over India the CO₂ maximum is in May and minimum in August/September while for CH₄ the maximum is in September and minimum in February/March. The seasonal variation of CH₄ over India is correlated with the eastern coastal rice cultivation.     

Remote sensing of aerosol and radiation from geostationary satellites

The paper presents a high-level overview of current and future remote sensing of aerosol and shortwave radiation budget carried out at the US National Oceanic and Atmospheric Administration (NOAA) from the US Geostationary Operational Environmental Satellite (GOES) series. The retrievals from the current GOES imagers are based on physical principles. Aerosol and radiation are estimated in separate processing from the comparison of satellite-observed reflectances derived from a single visible channel with those calculated from detailed radiative transfer. The radiative transfer calculation accounts for multiple scattering by molecules, aerosol and cloud and absorption by the major atmospheric gases. The retrievals are performed operationally every 30 min for aerosol and every hour for radiation for pixel sizes of 4-km (aerosol) and 15- to 50-km (radiation). Both retrievals estimate the surface reflectance as a byproduct from the time composite of clear visible reflectances assuming fixed values of the aerosol optical depth. With the launch of GOES-R NOAA will begin a new era of geostationary remote sensing. The Advanced Baseline Imager (ABI) onboard GOES-R will offer capabilities for aerosol remote sensing similar to those currently provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on the NASA Earth Observing System (EOS) satellites. The ABI aerosol algorithm currently under development uses a multi-channel approach to estimate the aerosol optical depth and aerosol model simultaneously, both over water and land. Its design is strongly inspired by the MODIS aerosol algorithm. The ABI shortwave radiation budget algorithm is based on the successful GOES Surface and Insolation Product system of NOAA and the NASA Clouds and the Earth’s Radiant Energy System (CERES), Surface and Atmospheric Radiation Budget (SARB) algorithm. In all phases of the development, the algorithms are tested with proxy data generated from existing satellite observations and forward simulations. Final assessment of the performance will be made after the launch of GOES-R scheduled in 2012.     

Early evaluation of thematic mapper data for coastal process studies

Two sets of TM data taken over the ocean off the coast of the Southeastern U.S. Bight were studied for the applicability of TM data to marine environments. First, the results of applying TM and TMS data to determine chlorophyll concentration in the ocean are presented. Chlorophyll quantification in the range of 0.5 to 2.0 mg/M³ was achieved by taking the ratio of TM band-1/band-2. Second, the results of applying TM band-6 data to monitor sea surface temperature are described. A comparison of TM data with AVHRR data shows TM readings coincide with AVHRR data within a scatter of 0.5°C in most of areas studied. Lastly, the results of a technique to map the water depths of coral reefs in the Great Bahama Bank are demonstrated. Depths from 0 to 20 meters, were delineated using TM band-1. The classification accuracy and origins of anomalous depth points are discussed.     

Investigations of the Krasnoyarsk Reservoir waters based on the multispectral satellite data

In this work historical investigations and modern results of classification of the Krasnoyarsk Reservoir are presented. The paper presents results of studying the dynamics of phytopigments and other optically active components, using multispectral satellite data. Several approaches to interpreting satellite data for optically complex inland water bodies are offered. Based on results of historical investigations it is shown that the spatial distribution of phytoplankton in the reservoir stems back to the time of its formation. Color index in the red spectral region (CIR) is introduced. A relationship between the color index and chlorophyll concentration is investigated. The CIR, derived from the AVHRR data, has been found to be related to chlorophyll concentration. Based on MODIS data, the waters of the Krasnoyarsk Reservoir have been classified in accordance with their optical spectral variability, using the technique of unsupervised IsoData classification. An empirical relationship between multispectral MODIS data and the ground-truth measurements of chlorophyll concentration has been found.     

Detection of surface temperature anomaly of the Sea of Marmara

Monitoring sea surface temperature (SST) over a long-term and detecting the anomalies highly contribute to understanding the prevailing water quality of the sea. Earth observation satellite images are the key data sources that offer the long-term SST detection in a cost and time effective way. Since the Sea of Marmara in Türkiye is surrounded by the highly populated provinces, the water quality of the sea has gained importance for scientific and public communities over the years. This article emphasizes on the significance of detecting SST trend and corresponding anomalies of the Sea of Marmara over the past 32 years. To address the SST variations of the Sea of Marmara in time, a comprehensive set of both field and satellite data regarding SSTs were obtained within the context of this study. The SST trend and its anomalies between the years 1990 and 2021 were detected by applying Seasonal-Trend decomposition procedure based on LOESS (STL) method to NOAA OISST V2 data. On the other hand, spatial SST distribution was detected with Landsat-8, Sentinel-3 and NOAA OISST V2 satellite data. SST results were verified with the in-situ data within the scope of accuracy assessment. The results showed that SST time-series data performed an increasing trend and had anomalies mostly during the spring months in the recent years.     

空间碎片天基监测图像仿真研究

空间碎片天基光学监测可以有效弥补地基监测的时空覆盖缺陷.目前天基观测数据不足甚至没有数据，这给监测系统研制、数据处理算法研究及软件系统研发和验证造成了一定困难.因此，研发能够为相关研究提供空间监测图像的空间碎片天基监测图像仿真软件系统具有重要意义.本文根据天基监测图像的特点，研究基本算法，设计仿真流程，给出仿真图像并对结果进行分析.为得到符合实际观测场景的高精度仿真图像，软件系统采用瞬时惯性坐标系；基于Tycho2星表提出了采用基于索引文件的快速星表查询方法来生成背景星图；利用两行根数（Two-Line Element，TLE）数据集仿真空间碎片观测值；并对结果图像中目标的运行轨迹进行分析，得出天基监测空间目标的成像特性.仿真结果表明，该仿真系统能够针对不同观测条件和观测平台，在兼顾精度与时效性的情况下，获得较为真实的空间碎片天基光学系列观测图像.