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.     

Development of digital interactive processing system for NOAA satellites AVHRR data

The paper discusses the digital image processing system for NOAA/AVHRR data including Land applications — configured around VAX 11/750 host computer supported with FPS 100 Array Processor, Comtal graphic display and HP Plotting devices; wherein the system software for relational Data Base together with query and editing facilities, Man-Machine Interface using form, menu and prompt inputs including validation of user entries for data type and range; preprocessing software for data calibration, Sun-angle correction, Geometric Corrections for Earth curvature effect and Earth rotation offsets and Earth location of AVHRR image have been accomplished. The implemented image enhancement techniques such as grey level stretching, histogram equalization and convolution are discussed. The software implementation details for the computation of vegetative index and normalized vegetative index using NOAA/AVHRR channels 1 and 2 data together with output are presented; scientific background for such computations and obtainability of similar indices from Landsat/MSS data are also included. The paper concludes by specifying the further software developments planned and the progress envisaged in the field of vegetation index studies.     

U.S. operational space program for climate observations

Satellites provide two important characteristics to earth climate studies not available from other, conventional sources: (1) full global coverage, and (2) consistency within the data set. This latter arises from the fact that the satellite data are usually derived from one instrument (or at least from a small number) whereas other sources involve large numbers of separate instruments and hence exhibit a substantial standard deviation. Satellite data, of course, are more subject to bias and must therefore be carefully validated, usually via ground truth.The ISCCP and ISLSCP are examples of the increasing reliance on satellite data for climate studies. In addition to the multispectral images, quantitative products of importance are: (1) atmospheric temperature structure, (2) snow cover, (3) precipitation, (4) vegetation index, (5) maximum/minimum temperature, (6) insolation, and (7) earth radiation balance. The U.S. civil space program is presently committed to its current geostationary (GOES) and polar (NOAA) programs through this decade and to continue both programs into the next decade with spacecraft carrying improved and augmented instrumentation. GOES VISSR Atmospheric Sounder (VAS) data, presently in research status and available only for special observation periods, will become available operationally in 1987 from the current spacecraft series. GOES-Next will provide additional spectral channels, simultaneous imaging, atmospheric soundings, and possibly increased resolution starting in 1990. The NOAA follow-on spacecraft, in the same time frame, is expected to provide additional spectral channels, improved passive microwave radiometry, and possibly increased spatial resolution. The Landsat program is expected to be continued by a commercial operator following the useful life of Landsat-5. All three follow-on programs are presently at various stages of definition and procurement. Final definition may not be completed until late in 1984. However, their status as of the time of this presentation will be reviewed in detail.     

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.     

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.     

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     

Satellite monitoring of surface temperatures in semi-desert: Differences between anthropogenically impacted terrain and protected natural vegetation

The northern Sinai is a sandy semi-desert. Severe overgrazing and other anthropogenic pressures contribute to an extremely sparse vegetative cover. A 6×6 km area was fenced off in the summer of 1974, constituting an exclosure from the grazing herds and from harvesting of plants for firewood. The vegetation in this exclosure recovered rapidly. In this study, radiances and surface temperatures of the vegetated exclosure and of the surrounding anthropogenically impacted terrain were monitored for the period March–September 1981, using NOAA-6 satellite. This satellite carries the Advanced Very High Resolution Radiometer (AVHRR) that measures visible and solar infrared radiances and also radiation temperatures at 11 μm band. In the digital images, the exclosure forms an easily recognized square, darker in the visible and solar infrared AVHRR channels than the surroundings. We concentrated on the corner in which there was no anthropogenic activity. Based on the ratio of the radiance over the exclosure to that over the surrounding terrain, the protrusions parameter s (vertical projection of the protrusions per unit area) has been estimated. The average value of s for the various satellite passes is 0.20 as measured in the visible channel and 0.18 as measured in the solar infrared. The radiation temperatures of the exclosure and of the surrounding terrain were analyzed. The radiation temperatures of the vegetated exclosure (sand with protruding bushes) are higher (by up to 2.5°K) than those of the surrounding terrain (that can be approximately regarded as bare sand). It is concluded that in an arid climate, under the semi-dormant conditions of vegetation (which prevail at all times except for the desert-bloom period, after a rain) the evapotranspiration is low, so that its effect on the surface temperatures is very small. Under these conditions, the surface temperatures are controlled by the surface albedo and the air flow at the surface.     

Characteristics of recent dust storms over the Indian region using real time multi-satellite observations from the direct broadcast receiving system at IMD

In this study, observations from microwave satellites, visible and infrared instruments have been analyzed to detect dust storm over north and north–west part of India during 18–23 March 2012. This study investigated the approach to utilize the multi satellite data of Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the Terra and Aqua satellite and the Advanced Microwave Sounding Unit (AMSU) on-board NOAA satellite to study the characteristics of dust storms from real time direct broadcast (DB) receiving system installed at three places of India Meteorological Department (IMD). The dust storm detection is based on the infrared brightness temperature (BT) difference between channels at 11 and 12 μm and polarized BT difference between two channels of 89 and 23.8 GHz. It is found that the significant differences between the BT of channel 89 and 23.8 can be used as a discriminator of identifying dust storm. The Total Ozone Mapping Spectroradiometer (TOMS) Aerosol Index (AI) and AMSU-A 23 GHz channel BT from NOAA satellite over the north and north-west part of India have also been analyzed. The result indicated the characteristic behavior between BT and AI during the different phases of the dust storm. Finally, the occurrence of dust outbreaks has also been validated with sky radiometer of IMD, which confirms the presence of a dust storm over the Indian region. Further, the findings of the study and its approaches apply to the other dust storm cases which occurred during the months of April and June 2012. The integrated approach suggested the potential to use high resolution data of microwave as well as thermal–infrared using multi-satellite observations from real time direct broadcast system for the detection of severe, moderate or weak dust storms very well. The approach is found to be promising for operational application.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

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.     

Assessment of the sensitivity of vegetation to El-Niño/Southern Oscillation events over China

This research explores the sensitivity of vegetation in China to El-Niño/Southern Oscillation (ENSO) events from 1982 to 2006. The ENSO events are defined by the Multivariate ENSO Index (MEI), and variation in vegetation cover is captured by the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI). Pearson’s χ2 test was used to identify the areas where the variation in vegetation was sensitive to El Niño and La Niña events. The difference in the sensitivity of various ecosystems was investigated using the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product in 2000. Composite NDVI graphs during El Niño, La Niña and non-ENSO years were also produced to investigate the ENSO relationship with the six vegetation ecosystems during El Niño, La Niña and normal phases. The results show that most of the ENSO-sensitive land in China is only affected by one of the two phases of ENSO events, and the area of El Niño-sensitive vegetation is much larger than that of La Niña-sensitive vegetation. North China and the Hengduan Mountains are the two cores of the El Niño-sensitive areas, while the La Niña-sensitive areas are mainly distributed in the central, northwest and northeast regions of China. The sensitivity of vegetation varies across ecosystems: grassland and shrubland had the largest share of El Niño-sensitive areas, and sparse vegetation and savanna were the most sensitive to La Niña events. Overall, the impacts of El Niño events on vegetation in China had regular seasonal variation, while the impacts of La Niña events had regular zonal distribution.     

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.     

Effects of concurrent snow and cloud cover on planetary albedo

The effects of snow and cloud cover on planetary albedo are examined using observations from NOAA polar orbiting satellites. Reflected radiation was measured in the visible range (0.5 – 0.7 μm). Planetary albedos resulting from different cloud/snow cover conditions are compared using Northern Hemisphere snow cover maps, surface weather charts, satellite photos and data on land surface types. None of the cases studied show that concurrent cloud and snow cover produces significantly different planetary albedos than cloud cover alone. Cloud cover alone is found to yield higher planetary albedos than snow cover alone; the difference being greatest over forested areas. With and without snow cover present, clear-sky planetary albedos over farming and grazing lands (snow(0.45), no snow(0.15)) are found to be significantly higher than those over forested regions (snow(0.33), no snow(0.11)). Variations in satellite zenith angle are not found to produce significant effects in most cases studied.     

Study of changes in the lineament structure,caused by earthquakes in South America by applying the lineament analysis to the Aster (Terra) satellite data

The region between Southern Peru and Northern Chile is one of the most seismically and volcanically active regions in South America. This is caused by a constant subduction of the South American Plate, converging with the Nazca Plate in the extreme North of Chile. We used the 15 and 30 m resolution satellite images, provided by the ASTER (VNIR and SWIR) instrument onboard the Terra satellite to study changes in the geological faults close to earthquake epicenters in southern Peru. Visible and infrared spectral bands were analysed using “The Lineament Extraction and Stripes Statistic Analysis” (LESSA) software package to examine changes in the lineament features and stripe density fields caused by seismic activity. We used the satellite images 128 and 48 days before and 73 days after a 5.2 Richter scale magnitude earthquake. The fact that the seasonal variations in the South of Peru and North of Chile are very small, and the vegetation is very limited, allowed us to establish substantial changes in the lineament and the stripe density field features. We develop a methodology that allows to evaluate the seismic risk in this region for the future.     

Development and testing of a statistical texture model for land cover classification of the Black Sea region with MODIS imagery

A statistical model is proposed for analysis of the texture of land cover types for global and regional land cover classification by using texture features extracted by multiresolution image analysis techniques. It consists of four novel indices representing second-order texture, which are calculated after wavelet decomposition of an image and after texture extraction by a new approach that makes use of a four-pixel texture unit. The model was applied to four satellite images of the Black Sea region, obtained by Terra/MODIS and Aqua/MODIS at different spatial resolution. In single texture classification experiments, we used 15 subimages (50 × 50 pixels) of the selected classes of land covers that are present in the satellite images studied. These subimages were subjected to one-level and two-level decompositions by using orthonormal spline and Gabor-like spline wavelets. The texture indices were calculated and used as feature vectors in the supervised classification system with neural networks. The testing of the model was based on the use of two kinds of widely accepted statistical texture quantities: five texture features determined by the co-occurrence matrix (angular second moment, contrast, correlation, inverse difference moment, entropy), and four statistical texture features determined after the wavelet transformation (mean, standard deviation, energy, entropy). The supervised neural network classification was performed and the discrimination ability of the proposed texture indices was found comparable with that for the sets of five GLCM texture features and four wavelet-based texture features. The results obtained from the neural network classifier showed that the proposed texture model yielded an accuracy of 92.86% on average after orthonormal wavelet decomposition and 100% after Gabor-like wavelet decomposition for texture classification of the examined land cover types on satellite images.     

Evaluation of MODIS GPP over a complex ecosystem in East Asia: A case study at Gwangneung flux tower in Korea

Moderate Resolution Imaging Radiometer (MODIS) gross primary productivity (GPP) has been used widely to study the global carbon cycle associated with terrestrial ecosystems. The retrieval of the current MODIS productivity with a 1 × 1 km² resolution has limitations when presenting subgrid scale processes in terrestrial ecosystems, specifically when forests are located in mountainous areas, and shows heterogeneity in vegetation type due to intensive land use. Here, we evaluate MODIS GPP (MOD17) at Gwangneung deciduous forest KoFlux tower (deciduous forest; GDK) for 2006–2010 in Korea, where the forests comprise heterogeneous vegetation cover over complex terrain. The monthly MODIS GPP data overestimated the GDK measurements in a range of +15% to +34% and was fairly well correlated (R = 0.88) with the monthly variability at GDK during the growing season. In addition, the MODIS data partly represented the sharp GPP reduction during the Asian summer monsoon (June–September) when intensive precipitation considerably reduces solar radiation and disturbs the forest ecosystem. To examine the influence of subgrid scale heterogeneity on GPP estimates over the MODIS scale, the individual vegetation type and its area within a corresponding MODIS pixel were identified using a national forest type map (∼71-m spatial resolution), and the annual GPP in the same area as the MODIS pixel was estimated. This resulted in a slight reduction in the positive MODIS bias by ∼10%, with a high degree of uncertainty in the estimation. The MODIS discrepancy for GDK suggests further investigation is necessary to determine the MODIS errors associated with the site-specific aerodynamic and hydrological characteristics that are closely related to the mountainous topography. The accuracy of meteorological variables and the impact of the very cloudy conditions in East Asia also need to be assessed.     

Remote sensing of clouds

The extraction of information on cloud cover from present-day multispectral satellite images poses a challenge to the remote sensing specialist. When approached one pixel at a time, the derived cloud cover parameters are inherently nonunique. More information is needed than is available in the radiances from each channel of an isolated pixel. The required additional information can be obtained for each scene, however, by analyzing the distribution of pixels in the multi-dimensional space of channel radiances. The cluster patterns in this space yield statistical information that points to the most likely solution for that scene. The geostationary and polar orbiting meteorological satellites all have, at a minimum, a solar reflection channel in the visible spectrum and a thermal infrared channel in the 8–12 micron window. With the information from the cluster patterns and application of the equations of radiative transfer, the measurements in those channels will yield cloud cover fraction, optical thickness, and cloud-top temperature for an assumed microphysical model of the cloud layer. Additional channels, such as the 3.7 micron channel on the AVHRR of the polar orbiting meteorological satellites, will will yield information on the microphysical model—e.g., distinguishing small liquid liquid droplets (typical of low level clouds) from large ice particles (typical of cirrus and the tops of cumulonimbus). New channels to be included in future satellite missions will provide information on cloud height, independent of temperature, and on a particle size and thermodynamic phase, independently of each other. A proposed STS mission using lidar will pave the way for the use of active sensors that will provide more precise information on cloud height and probe the structure of thin cirrus and the top layer of of the thicker cloud.     

Sea surface temperature measurement from satellites: Validation and accuracy

A review of the latest published results concerning the accuracy of satellite derived sea surface temperature (SST) estimation is presented. Two types of platforms are considered : orbiting satellites and geosynchronous satellites and the accuracies that may now be expected from such systems are reported. This review emphasizes the impressive improvement in global mapping of SST obtained from the Advanced Very High Resolution Radiometer (AVHRR) on NOAA's operational polar satellites. Tests of the AVHRR SST's against a high reliability data set consisting of buoys, bathythermographs and research ship reports indicate biases of < 0.1°C and RMS differences of < 0.75°C (McClain [1]). Particular attention is also paid to a method adding along track scanning capability to the present multichannel AVHRR technique. This method is demonstrated owing to the coupling of an orbiting satellite (TIROS-N) and a geosynchronous satellite (METEOSAT). Another type of coupling of two such platforms is also presented in connection with the advent of geostationary satellites equipped with a vertical sounding capability, such as GOES-4.     

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.     

Vegetation biomass change of the Bosoh Peninsula: Impacted by the volcano fumes from the Miyakejima

Since the 2001 eruption, the volcanic fumes from the Oyama of Miyakejima Island, located approximately 200 km southwest from the mainland of Japan, have affected the vegetation health and biomass of the Tokyo metropolitan area. In this study, we evaluated the potential for measuring/mapping relative forest damage/recovery in forests in the Bosoh Peninsula, Chiba Prefecture, Japan, using LANDSAT Enhanced Thematic Mapper data and ASTER Level-1B data. The simple ratio which is derived from the near-infrared and red reflective response was found to correlate well with ground-based measurements of forest damage caused by continuous SO₂ contained in the volcanic fumes. Accumulated data by the synchronized field campaign observation of the Japanese cedar field spectra and by satellite analysis indicates the following interesting features: (1) regional differences of SO₂ levels in the forestal area can be estimated by the simple spectrum ratio of the near-infrared and red bands of multispectral satellite data; (2) time-series images using the simple ratios indicated the location of still damaged and recovered forests between the 2001 eruption and 2003; (3) if the vegetations under study are homogeneous single-species with a similar leaf area index and a similar age, the simple ratio was useful to evaluate vegetation damage or recovery among individual forests throughout many of the forests in the Bosoh Peninsula; (4) the damage level of the Tokyo Bay side was found to be consistently higher than the Pacific Ocean side of the peninsula. This obvious difference is likely caused, not only by SO₂ from Miyakejima Island, but also by long term and chronic discharged gas from heavy industrial complexes.