Advanced Very High Resolution Radiometer Images Research Articles

Development of a consistent global long-term burned area product (1982–2018) based on AVHRR-LTDR data

This paper presents the generation of a global long-term Burned Area (BA) product based on Advanced Very High Resolution Radiometer (AVHRR) images. The BA product was derived from the Land Long Term Data Record (LTDR), which provides a continuous dataset of geometrically and radiometrically corrected AVHRR images at 0.05° resolution (≈5 km). The BA algorithm improves a Beta version of this dataset (named FireCCILT10) previously released. The new version incorporates an enhanced Random Forest (RF) classification process based on two models, one using a global sample and another one using only Boreal regions. Solar zenith angle (SZA) corrections were introduced to mitigate the impact of satellite orbital drift. Binary classifications were obtained applying probability thresholds, and BA proportions were assigned to each burned pixel. The final product includes the date of detection at 0.05° resolution and the total burned area at 0.05° and 0.25° resolution, both covering the period from 1982 to 2018 (excluding 1994). The resulting product, called FireCCILT11, estimated that 165.26 Mkm2 were globally burned between 1982 and 2018, with an annual average of 4.59 Mkm2. The largest BA was found in 2011 with 5.18 Mkm2 and the lowest was observed in 1991 with 4.09 Mkm2. The month with the highest mean BA was August, with 0.63 Mkm2, and the one with the lowest was March with 0.15 Mkm2. Africa included 66% of total BA. Inter-comparison showed high correlation values with MODIS BA products for annual BA of the common years (r > 0.6, %MAE < 14%). Comparison with national fire statistics of Australia, Canada and Alaska showed also high correlation values (r > 0.8, %MAE < 42%).

Using Satellite Remote Sensing to Study the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshlands, Iraq

The Iraqi Marshes in Southern Iraq are considered one of the most important wetlands in the world. From 1982 to the present, their area has varied between 10,500 km2 and 20,000 km2. The marshes support a variety of plants, such as reeds and papyrus, and are home to many species of birds. These marshes are Al-Hammar, Central or Al-Amarah, and Al-Huwaiza. Freshwater supplies to the marshes come from the Tigris and Euphrates rivers in Iraq and from the Karkha River from Iran. For this analysis, we used the Land Long-Term Data Record Version 5 (LTDR V5) Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) sensor dataset. This dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution to monitor the spatial and temporal variability of vegetation along with other hydrological variables such as land surface temperature, precipitation, and evapotranspiration. In our analysis, we considered three time periods: 1982–1992; 1993–2003; and 2004–2017 due to anthropogenic activities and climate changes. Furthermore, we examined the relationships between various water cycle variables through the investigation of vegetation and water coverage changes, and studied the impacts of climate change and anthropogenic activities on the Iraqi Marshes and considered additional ground observations along with the satellite datasets. Statistical analyses over the last 36 years show significant deterioration in the vegetation: 68.78%, 98.73, and 83.71% of the green biomass has declined for Al-Hammar, The Central marshes, and Al-Huwaiza, respectively. The AVHRR and Landsat images illustrate a decrease in water and vegetation coverage, which in turn has led to an increase in barren lands. Unfortunately, statistical analyses show that marshland degradation is mainly induced by human actions. The shrinkage in water supplies taken by Iraq’s local neighbors (i.e., Turkey, Syria, and Iran) has had a sharp impact on water levels. The annual discharge of the Tigris declined from ~2500–3000 m3/s to ~500 m3/s, and the annual discharge of the Euphrates River declined from ~1500 m3/s to less than 500 m3/s.

Assessment of Surface Urban Heat Island in Three Cities Surrounded by Different Types of Land-Cover Using Satellite Images

Land-cover change is the main factor of Land Surface Temperature (LST) variation in urban areas. The temperature of man-made classes within cities is normally higher than natural classes in the surrounding areas which leads to the Urban Heat Island (UHI) phenomenon. In this study, LST maps of three cities (i.e. Paris, Riyadh and Manama) are analyzed to investigate how SUHI reacts in different cities. The SUHI intensity during summer and winter is also assessed to understand the seasonal changes of SUHI. The Advanced Very High Resolution Radiometer (AVHRR) images have been used as the main data source for investigating temperature variability in the urban areas. However, in order to define different types of land-cover and correlate those with AVHRR pixel values, one of Landsat sensors have also been used in each case. These include Thematic Mapper, Enhanced Thematic Mapper or Operational Land Imager, depending on their synchronization with the AVHRR images. The analysis of the temperature variation showed that the behavior of SUHI is not the same in different cities and is dependent on the land-covers surrounding the city.

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

AbstractSatellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth’s environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

New method and error analysis of lake retrieval with MetOp-A AVHRR images on the Tibetan Plateau

ABSTRACTLand-surface water is an important factor influencing the regional environment and climate and is a key factor in the Tibetan Plateau, which is one of the most sensitive regions to global changes. Because of the high elevation, complex topography, and erratic weather of the Tibetan Plateau, direct measurement of the area of every lake is largely unfeasible. Moreover, complex natural geographic conditions increase the difficulty of image processing and information extraction with remote sensing because they enhance the uncertainty of quantitative data retrieved with satellites. Methods based on spectral features do not generate the expected results of lake area over the Tibetan Plateau due to a lack of distinction between water and other land objects, especially snow, vegetation, and low cloud cover. Therefore, a new method to extract lake area from satellite images in the Tibetan Plateau is needed. In this article, an automatic method was proposed to evaluate lake area during the wet season (from 1 September to 31 October) on the Tibetan Plateau with multi-day Advanced Very High Resolution Radiometer (AVHRR) remote-sensing images on board the Meteorological Operational satellite-A (MetOp-A) satellite. The method considers both spectral and textural features of lakes and does not need a cloud mask as an input. In addition, the Mixture Tuned Matched Filtering (MTMF) algorithm was applied to decompose the mixed pixels to better identify lakes and estimate the lake area. Based on daily lake identifications, the wet season’s lake data were composited with the maximum value composition (MVC) method to determine the lake area. A comparison of our work with the manually interpreted results from Landsat Thematic Mapper (TM) images and observational reports demonstrates the accuracy and reliability of our approach. However, certain factors, i.e. the sensor zenith angle of the polar-orbit satellite and the topography, can affect the lake area extracted from the remote-sensing images.

AVHotRR: near-real time routine for volcano monitoring using IR satellite data

Abstract The AVHotRR routine has been in operation since 2006 to process satellite data for monitoring active volcanoes in the Mediterranean area. Although originally developed to work with advanced very high-resolution radiometer (AVHRR) data, AVHotRR has been developed over the years to adapt to other sensors. In this work we present an improved version of the algorithm for hot-spot detection and effusion rate estimate. The underlying principles upon which the algorithm is based are discussed, focusing on the enhancements. The currently implemented version makes it possible to integrate results from different datasets in order to better constrain the detection of volcanic hot spots. In particular, the high temporal resolution of the SEVIRI instrument aboard MSG is key to reducing false positives in AVHRR and moderate resolution imaging spectroradiometer MODIS images. We propose here a new detection method based on the wavelet transform of SEVIRI data. Results from the application of AVHotRR to a dataset of AVHRR and SEVIRI images from Mt Etna, Italy, are presented and discussed with reference to the advantages and limitations of the algorithm.

Volcanic hot spot detection from optical multispectral remote sensing data using artificial neural networks

SUMMARY This paper describes an application of artificial neural networks for the recognition of volcanic lava flow hot spots using remote sensing data. Satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. In our study a back propagation neural network was used for the recognition of thermal anomalies affecting hot lava pixels. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHRR) sensor as inputs and the corresponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen, and in particular the effusive eruption which took place during the month of 2006 July. The neural network was trained with a time-series of 15 images (12 nighttime images and 3 daytime images) and validated on three independent data sets of AVHRR images of the same eruption and on two relative to an eruption occurred the following month. While for both nighttime and daytime validation images the neural network identified the image pixels affected by hot lava with a 100 percent success rate, for the daytime images also adjacentpixelswereincluded,apparentlynotinterestedbylavaflow.Despitetheseperformance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future work shall address application of the proposed method to data acquired with a high temporal resolution, such as those provided by the spinning enhanced visible and infrared imager sensor on board the Meteosat second generation geostationary satellite.

Effective Compositing Method to Produce Cloud-Free AVHRR Image

The Advanced Very High Resolution Radiometer (AVHRR) series of instruments have been frequently used for land cover change and global environment studies. The availability of more than 30 years' records have made important time-series studies possible. Removing cloud effects from AVHRR images is a critical task when using the data to monitor land cover changes. Considering that the maximum normalized difference vegetation index (NDVI) compositing method is not suitable for land cover types with low NDVI (e.g., water, snow, and bare ground), we propose a new compositing method to remove cloud effects while keeping information of all land cover types. The developed method includes three steps: 1) compositing daily AVHRR images using maximum brightness temperature in Channel 4; 2) replacing water pixels with pixels having maximum Channel 1/Channel 2 ratio; and 3) replacing vegetation pixels with pixels having maximum NDVI. The proposed method was tested on the Land Long-Term Data Record-AVHRR dataset in a region of 37°E-180°E, 3°S-73°N in 1993 to generate monthly minimal cloud-effected images. After compositing, the average ratio of cloud-contaminated and invalid pixels is reduced from 64.8% to 22.0%. Compared with the other two methods (composite using maximum NDVI and composite using cloud masks), our method is shown to be more effective and convenient. Moreover, the compositing process takes consideration of the temporal profile of land surface, which is suited for long time global or continental land cover change studies. Composite images generated in different time periods are also evaluated to identify the proper compositing period.

A Simple Model of Mapping of Land Surface Temperature from Satellite Digital Images in Bangladesh

Land Surface Temperature is a key parameter in energy budget models, estimating soil moisture, forest detection and forecasting, monitoring the state of the crops and many other applications. In this work, a method has been developed to retrieve land surface temperature using the National Oceanic and Atmospheric Administration (NOAA) of USA, Advanced Very High Resolution Radiometer (AVHRR) satellite images. The land surface temperature has been determined taking AVHRR images from February to December in a year. Seasonal variations have also been studied. The results have been presented in the false color composition (fcc) map for each month. Studying the resulting maps it is found that there is a marked spatial and seasonal variations in the surface temperature among different regions of the country. We could not compare our result with the measured data as, till now, no measured data of surface temperature is available for our country. But it is observed that our result is well in conformity with the observed air temperature of the country. This method can thus be used for long term monitoring of surface parameters for the country. The long term study of surface temperature can be very much important for sustainable land resources development and to understand the effect of possible environmental changes.

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.

An Improved High-Resolution SST Climatology to Assess Cold Water Events off Florida

Cloud filters developed for high-resolution (1-km) Advanced Very High Resolution Radiometer (AVHRR) satellite-derived sea surface temperature (SST) observations are generally inadequate to capture extreme cold events. Such events impacted shallow waters in Florida Bay and other coastal regions in January 2010 with fatal consequences for large numbers of corals and associated organisms. Raw AVHRR images were reprocessed to understand whether historical knowledge of daily and interannual SST variations could be used to derive a practical cloud-filtering technique. This approach, however, misidentified valid water temperature pixels in nearly 20% of 2703 images collected during the month of January for each year between 1995 and 2010. To create an improved SST climatology, this cloud-filtering method was combined with manually delineated overrides of falsely masked regions. During the January 2010 cold event, this climatology indicated negative SST anomalies of up to 11.6°C in the Big Bend region and 14°C in Florida Bay, with high spatial heterogeneity throughout. Our findings highlight the need for improved autonomous cloud-masking techniques to detect cold events in near real time.

Transect method for Antarctic cloud property retrieval using AVHRR data

For studies of Antarctic climate change, the Advanced Very High Resolution Radiometer (AVHRR) offers a time series spanning more than two decades, with numerous overpasses per day from converging polar orbits, and with radiometrically calibrated thermal infrared channels. However, over the Antarctic Plateau, standard multispectral application of AVHRR data for cloud optical property retrieval with individual pixels is problematic due to poor scene contrasts and measurement uncertainties. We present a method that takes advantage of rapid changes in radiances at well-defined cloud boundaries. We examine a transect of AVHRR-measured radiances in the three thermal infrared channels across a boundary between cloudy and cloud-free parts of the image. Using scatter diagrams, made from the data along this transect, of the brightness temperature differences between channels 3 and 4, and channels 4 and 5, it is possible to fit families of radiative transfer solutions to the data to estimate cloud effective temperature, thermodynamic phase, and effective particle radius. The major approximation with this method is that along such a transect, cloud water path has considerable spatial variability, while effective radius, phase, and cloud temperature have much less variability. To illustrate this method, two AVHRR images centred about the South Pole are analysed. The two images are chosen based on their differing contrasts in brightness temperature between clear and cloud-filled pixels, to demonstrate that our method can work with varying cloud top heights. In one image the data are consistent with radiative transfer simulations using ice cloud. In the other, the data are inconsistent with ice cloud and are well simulated with supercooled liquid water cloud at 241.5 K. This method therefore has potential for climatological investigation of the radiatively important phase transition in the extremely cold and pristine Antarctic environment.

Drought Monitoring Methodology Based on AVHRR Images and SPOT Vegetation Maps

Many regions of the world are experiencing an increase in the frequency and intensity of droughts. The province of Fars, Iran, has faced particularly severe drought and ground water problems over the course of the last decade. However, previous research on the subject reveals a lack of useful information regarding droughts in this province. This paper presents a fast, efficient and reliable method that can be used to produce drought maps in which Advanced Very High Resolution Radiometer (AVHRR) images are processed and then compared with SPOT vegetation maps. Ten-day maximum Normalized Difference Vegetation Index (NDVI) maps were produced and vegetation drought indices such as the Vegetation Condition Index (VCI) were calculated. Furthermore, a Temperature Condition Index (TCI) was extracted from the thermal bands of AVHRR images in order to produce the Vegetation Health Index (VHI). Remotely sensed data was then compared with hydrological and meteorological data from 1998 to 2007. The Standardized Precipitation Index (SPI) was used to quantify the precipitation deficit while the Standard Water Level Index (SWI) was developed to assess the groundwater recharge deficit. Instead of correlation coefficients, spatial correlation through visual comparison was found to provide better and more meaningful pictures. The highest correlation values were obtained when VHI or Drought Severity Index (DSI) values were correlated with the current month’s SWI data. DSI maps showed strong vegetation conditions existing for the majority of the study period. For most counties in Fars, strong Pearson correlations observed between the DSI and the SWI of the same month reflect high rates of ground water consumption. The results of this study indicate that the proposed method is a potentially promising method for early drought awareness which can be used for drought risk management in semi-arid climates such as in Fars, Iran. This study also recommends that the Iranian government develop programs to help decrease the consumption of ground water resources in the province of Fars to ensure the long term sustainability of the watersheds in this province.

A Global Optimal Solution With Higher Order Continuity for the Estimation of Surface Velocity From Infrared Images

A global optimal solution (GOS) provides surface velocities from Advanced Very High Resolution Radiometer (AVHRR) remote image sequences using bilinear interpolation algorithms. Although an accurate velocity field can be estimated by GOS from a sequence of infrared images, the field has only first-order continuity. Because an actual coastal ocean has a complex irregular coastland and some ocean studies need vorticity and divergence analysis, which must be extracted from the velocity field, the development of generic GOS algorithms with higher order continuity and smoothed cutouts around these edges is very important. This paper addresses the issues of higher order continuity and smoothed cutouts around coastland edges for using GOS to estimate surface velocities. GOS bilinear polynomials, previously applied to square tiles with first-order continuity, are replaced by surface B-spline functions. The new GOS algorithms can be applied to AVHRR images containing complicated coastal land boundaries, even clouds, to yield smooth velocity fields next to land and higher order continuity velocity fields. The velocity fields obtained through the applications of the first- and higher order GOS techniques to a sequence of two National Oceanic Atmospheric Administration AVHRR images, which were taken from the New York Bight fields, are compared with those measured with the CODAR array. The retrieved velocity fields are used directly to calculate the surface divergence and vorticity. It is found that the angular and magnitude errors of the velocity by the first- and third-order GOSs are quite close for both numerical model data and AVHRR image sequences, and the velocity field estimated by the third-order GOS is globally smoothed.

Long-Term Water Temperature Variations in Daya Bay, China Using Satellite and In Situ Observations

Daya Bay is a shallow, semi-enclosed bay in the northern section of the South China Sea. The present study analyzed variations of water temperature in Daya Bay over the past 21 years (1985 - 2005) using Advanced Very High Resolution Radiometer (AVHRR) satellite remote sensing data and in situ observations. Results showed that AVHRR readings of sea surface temperature (SST) increased by 0.07 degrees C y(-1). Linear regression analysis for monthly SST anomalies (SSTA) showed a shift from negative to positive from 1995 - 1996, when the Daya Bay nuclear power station commenced operations in 1994. The slope of linear regression analysis for SSTA nearly doubled from 0.05 (1985 - 1993) to 0.09 (1994 - 2005). Monthly AVHRR images showed a thermal plume from the power station and revealed the increase of SST over 21 years. In situ observations in water temperature also showed an increasing trend for the same period (1985 - 2005). Variations in water temperature in Daya Bay were connected with climatic perturbations and increasing human activity including thermal discharge from nuclear power stations and the rapid economic development around the bay area.

Building an Automated Integrated Observing System to Detect Sea Surface Temperature Anomaly Events in the Florida Keys$^{\ast}$

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993–2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and <formula formulatype="inline"><tex Notation="TeX">$\pm$</tex> </formula>3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent ( <formula formulatype="inline"><tex Notation="TeX">$\pm$</tex></formula>0.5 h) data from the SEAKEYS <emphasis emphasistype="italic"> in situ</emphasis> stations in the Florida Keys showed near-zero bias errors <formula formulatype="inline"><tex Notation="TeX"> $(≪ 0.05\ ^{\circ}\hbox{C})$</tex></formula> in the weekly anomaly for SST anomalies between <formula formulatype="inline"><tex Notation="TeX">$-$</tex></formula>3 <formula formulatype="inline"><tex Notation="TeX">$^{\circ}\hbox{C}$</tex> </formula> and 3 <formula formulatype="inline"><tex Notation="TeX">$^{\circ}\hbox{C}$</tex></formula>, with standard deviations <formula formulatype="inline"><tex Notation="TeX">$≪ 0.5\ ^{\circ}\hbox{C}$</tex></formula>. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential</para>

Building an Automated Integrated Observing System to Detect Sea Surface Temperature Anomaly Events in the Florida Keys

Satellite-derived sea surface temperature (SST) images have had limited applications in near-shore and coastal environments due to inadequate spatial resolution, incorrect geocorrection, or cloud contamination. We have developed a practical approach to remove these errors using Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS) 1-km resolution data. The objective was to improve the accuracy of SST anomaly estimates in the Florida Keys and to provide the best quality (in particular, high temporal and spatial resolutions) SST data products for this region. After manual navigation of over 47 000 AVHRR images (1993-2005), we implemented a cloud-filtering technique that differs from previously published image processing methods. The filter used a 12-year climatology and plusmn3-day running SST statistics to flag cloud-contaminated pixels. Comparison with concurrent ( plusmn0.5 h) data from the SEAKEYS <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in</i> <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situ</i> stations in the Florida Keys showed near-zero bias errors (<0.05degC) in the weekly anomaly for SST anomalies between -3degC and 3degC, with standard deviations <0.5degC. The cloud filter was implemented using Interactive Data Language for near-real-time processing of AVHRR and MODIS data. The improved SST products were used to detect SST anomalies and to estimate degree-heating weeks (DHWs) to assess the potential for coral reef stress. The mean and anomaly products are updated weekly, with periodic updates of the DHW products, on a Web site. The SST data at specific geographical locations were also automatically ingested in near real time into National Oceanic and Atmospheric Administration's (NOAA) Integrated Coral Observing Network Web-based application to assist in management and decision making through a novel expert system tool (G2) implemented at NOAA.

Assessment of Climate Change Impact on the Inflow and Outflow of Two Agricultural Reservoirs in Korea

This study assessed the impact of potential future climate change on the inflow to an agricultural reservoir and its downstream streamflow from reservoir operation for paddy irrigation using SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. For a 366.5 km2 watershed that includes two agricultural reservoirs located in the upper middle part of South Korea, the SLURP model was calibrated using six years (1998-2003) of daily streamflow records and validated using three years (2004-2006) of streamflow data, before the future analysis. The CCCma CGCM2 data of the SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (Intergovernmental Panel on Climate Change) was adopted for the future climate condition, and then the change factor method was used to downscale the future weather data through bias-correction. In addition, the future land covers were predicted with a modified CA (cellular automata)-Markov technique using a time series of Landsat land cover data, and the future vegetation information was predicted by linear regression between the monthly NDVI (normalized difference vegetation index) from NOAA AVHRR (advanced very high resolution radiometer) images and the monthly mean temperature data. The results for the future impact on reservoir storage showed that it would not have recovered to 100% by the end of the year (December) because of a decrease in precipitation and an increase in evaporation from July to September. Therefore, this reduced reservoir storage could bring a spring drought that causes an insufficient guarantee of water for rice paddy transplanting at the end of May. The future impact on the monthly streamflow of the watershed showed that the future streamflow for the three months of April, May, and June would increase because of a future increase in precipitation, and that the future streamflow for the next three months (July, August, and September) would decrease because of a future decrease in precipitation and increase in temperature. Considering the future decrease in the summer and autumn streamflows, the reservoir operation has to be more conservative when supplying water for paddy irrigation during this period and in the decision making process for reservoir releases during storm events.

Estimation of cooling degree days (CDDs) from AVHRR data and an MLF neural network

Cooling degree day (CDD) is the most common and simplest measure used in the ventilating and air-conditioning industry to estimate cooling energy requirements. In this study, multilayer feed-forward (MLF) neural networks with one hidden layer were used to estimate CDDs in Khuzestan Province (in southwest Iran) using Advanced Very High Resolution Radiometer (AVHRR) images. The input variables for the networks were the four bands of the AVHRR image (B1, B2, B4, and B5), altitude, and Julian day. The study examined various combinations of these parameters as inputs to the artificial neural network (ANN) models to evaluate the degree of effectiveness of each of these variables on CDD. Bands 4 and 5, which are used for retrieval of surface temperature, were the most critical components for the estimation. The contribution of Julian day to the precision of estimated CDD was much more superior to that of altitude. The network using four bands, Julian day, and altitude provided the best results, with 12 nodes in the hidden layer. The results of this study showed that daily CDD values can be estimated with acceptable levels of the statistical indicators from AVHRR data and from the two geographic parameters using the ANN model.

Assessing the geometric accuracy of AVHRR data processed with a state vector based navigation system

We evaluate the geometric accuracy of Advanced Very High Resolution Radiometer (AVHRR) data processed using the Common AVHRR Processing System (CAPS) software. Accurate geometric correction, to known standards, of satellite images is crucial for many remote sensing applications, especially those using a time series of images and integrating other spatially referenced datasets. It is critical that data acquired by satellites with short repeat intervals (e.g., daily) are geolocated using methods that are both automatic and reliable. Landsat Enhanced Thematic Mapper Plus (ETM+) high-resolution imagery was used to establish ground control point (GCP) networks for two coastal regions of Australia, one tropical with 40 GCPs and one temperate with 60 GCPs. The geolocation accuracy was assessed for a total of 12 and 13 AVHRR images, respectively, against the GCPs for each region, respectively, totalling 1039 cloud-free GCP comparisons. Results showed that when the view zenith angle was less than 40°, the average (±1 standard deviation) accuracies are 0.36 ± 0.31 and 0.44 ± 0.35 of an at-nadir AVHRR pixel (1.1 km × 1.1 km) in the cross-track direction for the tropical and temperate regions, respectively, and 0.30 ± 0.26 and 0.36 ± 0.29 at-nadir AVHRR pixel in the along-track direction for the tropical and temperate regions, respectively. For higher satellite view zenith angles, the geometric accuracy decreases systematically with an increase in the pixel size towards the edges of the AVHRR swath.