Cloud cover determination in polar regions from satellite imagery

The principal objectives of this project are: (1) to develop suitable validation data sets to evaluate the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; (2) to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers; and (3) to compare synoptic cloud data from a control run experiment of the GISS climate model II with typical observed synoptic cloud patterns

Continental land cover classification using meteorological satellite data

The use of the National Oceanic and Atmospheric Administration's advanced very high resolution radiometer satellite data for classifying land cover and monitoring of vegetation dynamics over an extremely large area is demonstrated for the continent of Africa. Data from 17 imaging periods of 21 consecutive days each were composited by a technique sensitive to the in situ green-leaf biomass to provide cloud-free imagery for the whole continent. Virtually cloud-free images were obtainable even for equatorial areas. Seasonal variation in the density and extent of green leaf vegetation corresponded to the patterns of rainfall associated with the inter-tropical convergence zone. Regional variations, such as the 1982 drought in east Africa, were also observed. Integration of the weekly satellite data with respect to time produced a remotely sensed assessment of biological activity based upon density and duration of green-leaf biomass. Two of the 21-day composited data sets were used to produce a general land cover classification. The resultant land cover distributions correspond well to those of existing maps

Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86μm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.Laaja valikoima erilaisia maankäyttöluokkia, pilvisyys ja kosteikkoalueiden suuri määrä luovat erityispiirteet paloalueiden kartoitukselle Kaakkois-Aasian saariston kostean troppisissa olosuhteissa keskiresoluutioisilla (250m-500m) satelliittikuva-aineistoilla. Tämän tutkimuksen tavoitteena oli syventää ymmärrystä keskiresoluutioisen paloaluekartoituksen toteutukseen ja rajoituksiin Kaakkois-Aasian saaristossa vaikuttavista tekijöistä. Tutkimuksen tulokset paljastivat suurta vaihtelua tulipalojen aiheuttamissa heijastussäteilyn muutoksissa riippuen palaneen alueen vehreydestä ennen tulipaloa. Johtopäätöksenä todettiin että keskiresoluutioisten satelliittikuvien koko potentiaalin hyödyntämiseksi paloalueiden kartoituksessa tämä vaihtelu tulisi ottaa huomioon paloalueiden havainnointialgoritmeissa jotka perustuvat heijastussäteilyn muutosten seurantaan. Tähän ajatukseen perustuvaa paloalueiden kartoitusta myös kokeiltiin aineistoilla jotka oli tutkimuksissa todettu parhaiten tarkoitukseen sopiviksi. Paloalueiden muoto- ja kokojakauman analyysiin sekä käytännön testeihin perustuen keskiresoluutioinen paloalueiden kartoitus todettiin käyttökelpoiseksi Kaakkois-Aasian saariston kosteikkoalueilla. Muilla alueilla sen sijaan paloalueiden pieni koko uhkasi vakavasti sen käyttökelpoisuutta. Keskiresoluutioisten satelliittikuva-aineistojen käyttökelpoisuus paloalueiden kartoitukseen kosteikkoalueilla on kuitenkin merkittävää sillä viime aikoina Kaakkois-Aasian kosteikkoalueet ovat monilla tieteenaloilla nousseet kiinnostuksen kohteeksi vuosittain esiintyvien tulipalojen takia. Vuosittaiset tulipalot eivät ainoastaan heikennä näitä ainutlaatuisia ekosysteemejä vaan lähinnä palavan turpeen johdosta myös aiheuttavat pahoja alueellisia savusumuongelmia ja vapauttavat maailmanlaajuisesti merkittäviä määriä hiilidioksidia ilmakehään. Tämän tutkimuksen tulokset osaltaan luovat pohjaa yhä tarkempien alueellisten paloalueiden kartoitusmenetelmien kehittämiselle. Näillä menetelmillä kerättävä tieto paloalueiden laajuudesta ja sijainneista antaa muiden alojen tutkijoille yhä paremmat mahdollisuudet arvioida Kaakkois-Aasian saariston kosteikkoalueiden tulipalojen paikallisia, alueellisia ja maailmanlaajuisia vaikutuksia

Composite front maps for improved visibility of dynamic sea-surface features on cloudy SeaWiFS and AVHRR data

Novel techniques have been developed for increasing the value of cloud-affected sequences of Advanced Very High Resolution Radiometer (AVHRR) sea-surface temperature (SST) data and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) ocean colour data for visualising dynamic physical and biological oceanic processes such as fronts, eddies and blooms. The proposed composite front map approach is to combine the location, strength and persistence of all fronts observed over several days into a single map, which allows intuitive interpretation of mesoscale structures. This method achieves a synoptic view without blurring dynamic features, an inherent problem with conventional time-averaging compositing methods. Objective validation confirms a significant improvement in feature visibility on composite maps compared to individual front maps. A further novel aspect is the automated detection of ocean colour fronts, correctly locating 96% of chlorophyll fronts in a test data set. A sizeable data set of 13,000 AVHRR and 1200 SeaWiFS scenes automatically processed using this technique is applied to the study of dynamic processes off the Iberian Peninsula such as mesoscale eddy generation, and many additional applications are identified. Front map animations provide a unique insight into the evolution of upwelling and eddies

REMOTE SENSING DATA ANALYSIS FOR ENVIRONMENTAL AND HUMANITARIAN PURPOSES. The automation of information extraction from free satellite data.

This work is aimed at investigating technical possibilities to provide information on environmental parameters that can be used for risk management. The World food Program (WFP) is the United Nations Agency which is involved in risk management for fighting hunger in least-developed and low-income countries, where victims of natural and manmade disasters, refugees, displaced people and the hungry poor suffer from severe food shortages. Risk management includes three different phases (pre-disaster, response and post disaster) to be managed through different activities and actions. Pre disaster activities are meant to develop and deliver risk assessment, establish prevention actions and prepare the operative structures for managing an eventual emergency or disaster. In response and post disaster phase actions planned in the pre-disaster phase are executed focusing on saving lives and secondly, on social economic recovery. In order to optimally manage its operations in the response and post disaster phases, WFP needs to know, in order to estimate the impact an event will have on future food security as soon as possible, the areas affected by the natural disaster, the number of affected people, and the effects that the event can cause to vegetation. For this, providing easy-to-consult thematic maps about the affected areas and population, with adequate spatial resolution, time frequency and regular updating can result determining. Satellite remote sensed data have increasingly been used in the last decades in order to provide updated information about land surface with an acceptable time frequency. Furthermore, satellite images can be managed by automatic procedures in order to extract synthetic information about the ground condition in a very short time and can be easily shared in the web. The work of thesis, focused on the analysis and processing of satellite data, was carried out in cooperation with the association ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action), a center of research which works in cooperation with the WFP in order to provide IT products and tools for the management of food emergencies caused by natural disasters. These products should be able to facilitate the forecasting of the effects of catastrophic events, the estimation of the extension and location of the areas hit by the event, of the affected population and thereby the planning of interventions on the area that could be affected by food insecurity. The requested features of the instruments are: • Regular updating • Spatial resolution suitable for a synoptic analysis • Low cost • Easy consultation Ithaca is developing different activities to provide georeferenced thematic data to WFP users, such a spatial data infrastructure for storing, querying and manipulating large amounts of global geographic information, and for sharing it between a large and differentiated community; a system of early warning for floods, a drought monitoring tool, procedures for rapid mapping in the response phase in a case of natural disaster, web GIS tools to distribute and share georeferenced information, that can be consulted only by means of a web browser. The work of thesis is aimed at providing applications for the automatic production of base georeferenced thematic data, by using free global satellite data, which have characteristics suitable for analysis at a regional scale. In particular the main themes of the applications are water bodies and vegetation phenology. The first application aims at providing procedures for the automatic extraction of water bodies and will lead to the creation and update of an historical archive, which can be analyzed in order to catch the seasonality of water bodies and delineate scenarios of historical flooded areas. The automatic extraction of phenological parameters from satellite data will allow to integrate the existing drought monitoring system with information on vegetation seasonality and to provide further information for the evaluation of food insecurity in the post disaster phase. In the thesis are described the activities carried on for the development of procedures for the automatic processing of free satellite data in order to produce customized layers according to the exigencies in format and distribution of the final users. The main activities, which focused on the development of an automated procedure for the extraction of flooded areas, include the research of an algorithm for the classification of water bodies from satellite data, an important theme in the field of management of the emergencies due to flood events. Two main technologies are generally used: active sensors (radar) and passive sensors (optical data). Advantages for active sensors include the ability to obtain measurements anytime, regardless of the time of day or season, while passive sensors can only be used in the daytime cloud free conditions. Even if with radar technologies is possible to get information on the ground in all weather conditions, it is not possible to use radar data to obtain a continuous archive of flooded areas, because of the lack of a predetermined frequency in the acquisition of the images. For this reason the choice of the dataset went in favor of MODIS (Moderate Resolution Imaging Spectroradiometer), optical data with a daily frequency, a spatial resolution of 250 meters and an historical archive of 10 years. The presence of cloud coverage prevents from the acquisition of the earth surface, and the shadows due to clouds can be wrongly classified as water bodies because of the spectral response very similar to the one of water. After an analysis of the state of the art of the algorithms of automated classification of water bodies in images derived from optical sensors, the author developed an algorithm that allows to classify the data of reflectivity and to temporally composite them in order to obtain flooded areas scenarios for each event. This procedure was tested in the Bangladesh areas, providing encouraging classification accuracies. For the vegetation theme, the main activities performed, here described, include the review of the existing methodologies for phenological studies and the automation of the data flow between inputs and outputs with the use of different global free satellite datasets. In literature, many studies demonstrated the utility of the NDVI (Normalized Difference Vegetation Index) indices for the monitoring of vegetation dynamics, in the study of cultivations, and for the survey of the vegetation water stress. The author developed a procedure for creating layers of phenological parameters which integrates the TIMESAT software, produced by Lars Eklundh and Per Jönsson, for processing NDVI indices derived from different satellite sensors: MODIS (Moderate Resolution Imaging Spectroradiometer), AVHRR (Advanced Very High Resolution Radiometer) AND SPOT (Système Pour l'Observation de la Terre) VEGETATION. The automated procedure starts from data downloading, calls in a batch mode the software and provides customized layers of phenological parameters such as the starting of the season or length of the season and many others

Automated Detection of Clouds in Satellite Imagery

Many different approaches have been used to automatically detect clouds in satellite imagery. Most approaches are deterministic and provide a binary cloud - no cloud product used in a variety of applications. Some of these applications require the identification of cloudy pixels for cloud parameter retrieval, while others require only an ability to mask out clouds for the retrieval of surface or atmospheric parameters in the absence of clouds. A few approaches estimate a probability of the presence of a cloud at each point in an image. These probabilities allow a user to select cloud information based on the tolerance of the application to uncertainty in the estimate. Many automated cloud detection techniques develop sophisticated tests using a combination of visible and infrared channels to determine the presence of clouds in both day and night imagery. Visible channels are quite effective in detecting clouds during the day, as long as test thresholds properly account for variations in surface features and atmospheric scattering. Cloud detection at night is more challenging, since only courser resolution infrared measurements are available. A few schemes use just two infrared channels for day and night cloud detection. The most influential factor in the success of a particular technique is the determination of the thresholds for each cloud test. The techniques which perform the best usually have thresholds that are varied based on the geographic region, time of year, time of day and solar angle

Classification of Satellite Time Series-derived Land Surface Phenology Focused on the Northern Fertile Crescent

Land surface phenology describes events in a seasonal vegetation cycle and can be used in a variety of applications from predicting onset of future drought conditions, to revealing potential limits of historical dry farming, to guiding more accurate dating of archeological sites. Traditional methods of monitoring vegetation phenology use data collected in situ. However, vegetation health indices derived from satellite remote sensor data, such as the normalized difference vegetation index (NDVI), have been used as proxy for vegetation phenology due to their repeated acquisition and broad area coverage. Land surface phenology is accessible in the NDVI satellite record when images are processed to be intercomparable over time and temporally ordered to create a time series. This study utilized NDVI time series to classify areas of similar vegetation phenology in the northern Fertile Crescent, an area from the middle Mediterranean coast to southern/south-eastern Turkey to western Iran and northern Iraq. Phenological monitoring of the northern Fertile Crescent is critical due to the area\u27s minimal water resources, susceptibility to drought, and understanding ancient historical reliance on precipitation for subsistence dry farming. Delineation of phenological classes provides areal and temporal synopsis of vegetation productivity time series. Phenological classes were developed from NDVI time series calculated from NOAA\u27s Advanced Very High Resolution Radiometer (AVHRR) imagery with 8 × 8 km spatial resolution over twenty-five years, and by NASA\u27s Moderate Resolution Imaging Spectroradiometer (MODIS) with 250 × 250 m spatial resolution over twelve years. Both AVHRR and MODIS time series were subjected to data reduction techniques in spatial and temporal dimensions. Optimized ISODATA clusters were developed for both of these data reduction techniques in order to compare the effects of spatial versus temporal aggregation. Within the northern Fertile Crescent study area, the spatial reduction technique showed increased cluster cohesion over the temporal reduction method. The latter technique showed an increase in temporal smoothing over the spatial reduction technique. Each technique has advantages depending on the desired spatial or temporal granularity. Additional work is required to determine optimal scale size for the spatial data reduction technique

A Novel Strategy to Reconstruct NDVI Time-Series with High Temporal Resolution from MODIS Multi-Temporal Composite Products

Vegetation indices (VIs) data derived from satellite imageries play a vital role in land surface vegetation and dynamic monitoring. Due to the excessive noises (e.g., cloud cover, atmospheric contamination) in daily VI data, temporal compositing methods are commonly used to produce composite data to minimize the negative influence of noise over a given compositing time interval. However, VI time series with high temporal resolution were preferred by many applications such as vegetation phenology and land change detections. This study presents a novel strategy named DAVIR-MUTCOP (DAily Vegetation Index Reconstruction based on MUlti-Temporal COmposite Products) method for normalized difference vegetation index (NDVI) time-series reconstruction with high temporal resolution. The core of the DAVIR-MUTCOP method is a combination of the advantages of both original daily and temporally composite products, and selecting more daily observations with high quality through the temporal variation of temporally corrected composite data. The DAVIR-MUTCOP method was applied to reconstruct high-quality NDVI time-series using MODIS multi-temporal products in two study areas in the continental United States (CONUS), i.e., three field experimental sites near Mead, Nebraska from 2001 to 2012 and forty-six AmeriFlux sites evenly distributed across CONUS from 2006 to 2010. In these two study areas, the DAVIR-MUTCOP method was also compared to several commonly used methods, i.e., the Harmonic Analysis of Time- Series (HANTS) method using original daily observations, Savitzky–Golay (SG) filtering using daily observations with cloud mask products as auxiliary data, and SG filtering using temporally corrected composite data. The results showed that the DAVIR-MUTCOP method significantly improved the temporal resolution of the reconstructed NDVI time series. It performed the best in reconstructing NDVI time-series across time and space (coefficient of determination (R2 = 0.93 ~ 0.94) between reconstructed NDVI and ground-observed LAI). DAVIR-MUTCOP method presented the highest robustness and accuracy with the change of the filtering parameter (R2 = 0.99 ~ 1.00, bias = 0.001, root mean square error (RMSE) = 0.020). Only MODIS data were used in this study; nevertheless, the DAVIR-MUTCOP method proposed a universal and potential way to reconstruct daily time series of other VIs or from other operational sensors, e.g., AVHRR and VIIRS

Continuity of Landsat Obersvations: Short Term Considerations

As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor\u27s design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community\u27s concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record