Inversion and assimilation methods with applications in geophysical remote sensing

Stratospheric ozone can be measured accurately using a limb scatter remote sensing technique at the UV-visible spectral region of solar light. The advantages of this technique includes a good vertical resolution and a good daytime coverage of the measurements. In addition to ozone, UV-visible limb scatter measurements contain information about NO2, NO3, OClO, BrO and aerosols. There are currently several satellite instruments continuously scanning the atmosphere and measuring the UVvisible region of the spectrum, e.g., the Optical Spectrograph and Infrared Imager System (OSIRIS) launched on the Odin satellite in February 2001, and the Scanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY (SCIAMACHY) launched on Envisat in March 2002. Envisat also carries the Global Ozone Monitoring by Occultation of Stars (GOMOS) instrument, which also measures limb-scattered sunlight under bright limb occultation conditions. These conditions occur during daytime occultation measurements. The global coverage of the satellite measurements is far better than any other ozone measurement technique, but still the measurements are sparse in the spatial domain. Measurements are also repeated relatively rarely over a certain area, and the composition of the Earth’s atmosphere changes dynamically. Assimilation methods are therefore needed in order to combine the information of the measurements with the atmospheric model. In recent years, the focus of assimilation algorithm research has turned towards filtering methods. The traditional Extended Kalman filter (EKF) method takes into account not only the uncertainty of the measurements, but also the uncertainty of the evolution model of the system. However, the computational cost of full blown EKF increases rapidly as the number of the model parameters increases. Therefore the EKF method cannot be applied directly to the stratospheric ozone assimilation problem. The work in this thesis is devoted to the development of inversion methods for satellite instruments and the development of assimilation methods used with atmospheric models.

Geostatistics and remote sensing methods in the classification of images of areas cultivated with citrus

This study compares the precision of three image classification methods, two of remote sensing and one of geostatistics applied to areas cultivated with citrus. The 5,296.52ha area of study is located in the city of Araraquara - central region of the state of São Paulo (SP), Brazil. The multispectral image from the CCD/CBERS-2B satellite was acquired in 2009 and processed through the Geographic Information System (GIS) SPRING. Three classification methods were used, one unsupervised (Cluster), and two supervised (Indicator Kriging/IK and Maximum Likelihood/Maxver), in addition to the screen classification taken as field checking.. Reliability of classifications was evaluated by Kappa index. In accordance with the Kappa index, the Indicator kriging method obtained the highest degree of reliability for bands 2 and 4. Moreover the Cluster method applied to band 2 (green) was the best quality classification between all the methods. Indicator Kriging was the classifier that presented the citrus total area closest to the field check estimated by -3.01%, whereas Maxver overestimated the total citrus area by 42.94%.

Using fraction images derived from modis data for coffee crop mapping

Coffee production was closely linked to the economic development of Brazil and, even today, coffee is an important product of the national agriculture. The State of Minas Gerais currently accounts for 52% of the whole coffee area in Brazil. Remote sensing data can provide information for monitoring and mapping of coffee crops, faster and cheaper than conventional methods. In this context, the objective of this study was to assess the effectiveness of coffee crop mapping in Monte Santo de Minas municipality, Minas Gerais State, Brazil, from fraction images derived from MODIS data, in both dry and rainy seasons. The Spectral Linear Mixing Model was used to derive fraction images of soil, coffee, and water/shade. These fraction images served as input data for the supervised automatic classification using the SVM - Support Vector Machine approach. The best results concerning Overall Accuracy and Kappa Index were obtained in the classification of the dry season, with 67% and 0.41, respectively.

The use of ALOS/PALSAR data for estimating sugarcane productivity

Some models have been developed using agrometeorological and remote sensing data to estimate agriculture production. However, it is expected that the use of SAR images can improve their performance. The main objective of this study was to estimate the sugarcane production using a multiple linear regression model which considers agronomic data and ALOS/PALSAR images obtained from 2007/08, 2008/09 and 2009/10 cropping seasons. The performance of models was evaluated by coefficient of determination, t-test, Willmott agreement index (d), random error and standard error. The model was able to explain 79%, 12% and 74% of the variation in the observed productions of the 2007/08, 2008/09 and 2009/10 cropping seasons, respectively. Performance of the model for the 2008/09 cropping season was poor because of the occurrence of a long period of drought in that season. When the three seasons were considered all together, the model explained 66% of the variation. Results showed that SAR-based yield prediction models can contribute and assist sugar mill technicians to improve such estimates.

Modelling ecological values in heterogeneous and dynamic landscapes with geospatial data

Our surrounding landscape is in a constantly dynamic state, but recently the rate of changes and their effects on the environment have considerably increased. In terms of the impact on nature, this development has not been entirely positive, but has rather caused a decline in valuable species, habitats, and general biodiversity. Regardless of recognizing the problem and its high importance, plans and actions of how to stop the detrimental development are largely lacking. This partly originates from a lack of genuine will, but is also due to difficulties in detecting many valuable landscape components and their consequent neglect. To support knowledge extraction, various digital environmental data sources may be of substantial help, but only if all the relevant background factors are known and the data is processed in a suitable way. This dissertation concentrates on detecting ecologically valuable landscape components by using geospatial data sources, and applies this knowledge to support spatial planning and management activities. In other words, the focus is on observing regionally valuable species, habitats, and biotopes with GIS and remote sensing data, using suitable methods for their analysis. Primary emphasis is given to the hemiboreal vegetation zone and the drastic decline in its semi-natural grasslands, which were created by a long trajectory of traditional grazing and management activities. However, the applied perspective is largely methodological, and allows for the application of the obtained results in various contexts. Models based on statistical dependencies and correlations of multiple variables, which are able to extract desired properties from a large mass of initial data, are emphasized in the dissertation. In addition, the papers included combine several data sets from different sources and dates together, with the aim of detecting a wider range of environmental characteristics, as well as pointing out their temporal dynamics. The results of the dissertation emphasise the multidimensionality and dynamics of landscapes, which need to be understood in order to be able to recognise their ecologically valuable components. This not only requires knowledge about the emergence of these components and an understanding of the used data, but also the need to focus the observations on minute details that are able to indicate the existence of fragmented and partly overlapping landscape targets. In addition, this pinpoints the fact that most of the existing classifications are too generalised as such to provide all the required details, but they can be utilized at various steps along a longer processing chain. The dissertation also emphases the importance of landscape history as an important factor, which both creates and preserves ecological values, and which sets an essential standpoint for understanding the present landscape characteristics. The obtained results are significant both in terms of preserving semi-natural grasslands, as well as general methodological development, giving support to science-based framework in order to evaluate ecological values and guide spatial planning.

Using GPS, GIS & remote sensing to understand Niagara Terroir : Pinot noir in the Four Mile Creek & St. David's Bench sub-appellations

The relationships between vine water status, soil texture, and vine size were observed in four Niagara, Ontario Pinot noir vineyards in 2008 and 2009. The vineyards were divided into water status zones using geographic information systems (GIS) software to map the seasonal mean midday leaf water potential (,P), and dormant pruning shoot weights following the 2008 season. Fruit was harvested from all sentinel vines, bulked by water status zones and made into wine. Sensory analysis included a multidimensional sorting (MDS) task and descriptive analysis (DA) of the 2008 wines. Airborne multispectral images, with a spatial resolution of 38 cm, were captured four times in 2008 and three times in 2009, with the final flights around veraison. A semi-automatic process was developed to extract NDVI from the images, and a masking procedure was identified to create a vine-only NDVI image. 2008 and 2009 were cooler and wetter than mean years, and the range of water status zones was narrow. Yield per vine, vine size, anthocyanins and phenols were the least consistent variables. Divided by water status or vine size, there were no variables with differences between zones in all four vineyards in either year. Wines were not different between water status zones in any chemical analysis, and HPLC revealed that there were no differences in individual anthocyanins or phenolic compounds between water status zones within the vineyard sites. There were some notable correlations between vineyard and grape composition variables, and spatial trends were observed to be qualitatively related for many of the variables. The MDS task revealed that wines from each vineyard were more affected by random fermentation effects than water status effects. This was confirmed by the DA; there were no differences between wines from the water status zones within vineyard sites for any attribute. Remotely sensed NDVI (normalized difference vegetation index) correlated reasonably well with a number of grape composition variables, as well as soil type. Resampling to a lower spatial resolution did not appreciably affect the strength of correlations, and corresponded to the information contained in the masked images, while maintaining the range of values of NDVI. This study showed that in cool climates, there is the potential for using precision viticulture techniques to understand the variability in vineyards, but the variable weather presents a challenge for understanding the driving forces of that variability.

Geochemical, Sedimentological And Remote Sensing Studies Of Kayamkulam Estuary, South West Coast Of India- A Gis Approach

In this study, an attempt has been made to find the textural, geochemical, sedimentological characteristics of sediments and water phases of the kayamkulam estuary located in the Southwest coast of Kerala, besides the impact of gas based thermal power plant located at the northern part of the estuary. Estuaries are an important stage in the transport of the solid weathering product of the earth’s crust. These weathered products or sediments are complex mixtures of a number of solid phases that may include clays, silica, organic matter, metal oxides, carbonates, sulfides and a number of minerals. Studies on the aquatic systems revealed the fact that it posses severe ecological impairments due to heavy discharge of sediments from 44 rivers, the continued disposal of pollutants rich materials from industries, sewage channels, agricultural areas and retting yards

Drought Climatology of Indo-Gangetic Region of India Using Remote Sensing and Crop Growth Simulation Models

Department of Atmospheric Sciences, Cochin University of Science and Technology

Spatio-Temporal Changes in the Wetland Ecosystem of Cochin City using Remote Sensing and GIS

Urban developments have exerted immense pressure on wetlands. Urban areas are normally centers of commercial activity and continue to attract migrants in large numbers in search of employment from different areas. As a result, habitations keep coming up in the natural areas / flood plains. This is happening in various Indian cities and towns and large habitations are coming up in low-lying areas, often encroaching even over drainage channels. In some cases, houses are constructed even on top of nallahs and drains. In the case of Kochi the situation is even worse as the base of the urban development itself stands on a completely reclaimed island. Also the topography and geology demanded more reclamation of land when the city developed as an agglomerative cluster. Cochin is a coastal settlement interspersed with a large backwater system and fringed on the eastern side by laterite-capped low hills from which a number of streams drain into the backwater system. The ridge line of the eastern low hills provides a welldefined watershed delimiting Cochin basin which help to confine the environmental parameters within a physical limit. This leads to an obvious conclusion that if physiography alone is considered, the western flatland is ideal for urban development. However it will result in serious environmental deterioration, as it comprises mainly of wetland and for availability of land there has to be large scale filling up of these wetlands which includes shallow mangrove-fringed water sheets, paddy fields, Pokkali fields, estuary etc.Chapter 1 School 4 of Environmental Studies The urban boundaries of Cochin are expanding fast with a consequent over-stretching of the existing fabric of basic amenities and services. Urbanisation leads to the transformation of agricultural land into built-up areas with the concomitant problems regarding water supply, drainage, garbage and sewage disposal etc. Many of the environmental problems of Cochin are hydrologic in origin; like water-logging / floods, sedimentation and pollution in the water bodies as well as shoreline erosion

Correlated observation errors in data assimilation

Data assimilation provides techniques for combining observations and prior model forecasts to create initial conditions for numerical weather prediction (NWP). The relative weighting assigned to each observation in the analysis is determined by its associated error. Remote sensing data usually has correlated errors, but the correlations are typically ignored in NWP. Here, we describe three approaches to the treatment of observation error correlations. For an idealized data set, the information content under each simplified assumption is compared with that under correct correlation specification. Treating the errors as uncorrelated results in a significant loss of information. However, retention of an approximated correlation gives clear benefits.