Mapping water quality and substrate cover in optically complex coastal and reef waters: an integrated approach

Sustainable management of coastal and coral reef environments requires regular collection of accurate information on recognized ecosystem health indicators. Satellite image data and derived maps of water column and substrate biophysical properties provide an opportunity to develop baseline mapping and monitoring programs for coastal and coral reef ecosystem health indicators. A significant challenge for satellite image data in coastal and coral reef water bodies is the mixture of both clear and turbid waters. A new approach is presented in this paper to enable production of water quality and substrate cover type maps, linked to a field based coastal ecosystem health indicator monitoring program, for use in turbid to clear coastal and coral reef waters. An optimized optical domain method was applied to map selected water quality (Secchi depth, Kd PAR, tripton, CDOM) and substrate cover type (seagrass, algae, sand) parameters. The approach is demonstrated using commercially available Landsat 7 Enhanced Thematic Mapper image data over a coastal embayment exhibiting the range of substrate cover types and water quality conditions commonly found in sub-tropical and tropical coastal environments. Spatially extensive and quantitative maps of selected water quality and substrate cover parameters were produced for the study site. These map products were refined by interactions with management agencies to suit the information requirements of their monitoring and management programs. (c) 2004 Elsevier Ltd. All rights reserved.

Evaluation of remote sensing for the detection of landfill gas and leachate in an urban environment

The technique of remote sensing provides a unique view of the earth's surface and considerable areas can be surveyed in a short amount of time. The aim of this project was to evaluate whether remote sensing, particularly using the Airborne Thematic Mapper (ATM) with its wide spectral range, was capable of monitoring landfill sites within an urban environment with the aid of image processing and Geographical Information Systems (GIS) methods. The regions under study were in the West Midlands conurbation and consisted of a large area in what is locally known as the Black Country containing heavy industry intermingled with residential areas, and a large single active landfill in north Birmingham. When waste is collected in large volumes it decays and gives off pollutants. These pollutants, landfill gas and leachate (a liquid effluent), are known to be injurious to vegetation and can cause stress and death. Vegetation under stress can exhibit a physiological change, detectable by the remote sensing systems used. The chemical and biological reactions that create the pollutants are exothermic and the gas and leachate, if they leave the waste, can be warmer than their surroundings. Thermal imagery from the ATM (daylight and dawn) and thermal video were obtained and used to find thermal anomalies on the area under study. The results showed that vegetation stress is not a reliable indicator of landfill gas migration, as sites within an urban environment have a cover too complex for the effects to be identified. Gas emissions from two sites were successfully detected by all the thermal imagery with the thermal ATM being the best. Although the results were somewhat disappointing, recent technical advancements in the remote sensing systems used in this project would allow geo-registration of ATM imagery taken on different occasions and the elimination of the effects of solar insolation.

The investigation of vegetation change using remote sensing to detect and monitor migration of landfill gas

Decomposition of domestic wastes in an anaerobic environment results in the production of landfill gas. Public concern about landfill disposal and particularly the production of landfill gas has been heightened over the past decade. This has been due in large to the increased quantities of gas being generated as a result of modern disposal techniques, and also to their increasing effect on modern urban developments. In order to avert diasters, effective means of preventing gas migration are required. This, in turn requires accurate detection and monitoring of gas in the subsurface. Point sampling techniques have many drawbacks, and accurate measurement of gas is difficult. Some of the disadvantages of these techniques could be overcome by assessing the impact of gas on biological systems. This research explores the effects of landfill gas on plants, and hence on the spectral response of vegetation canopies. Examination of the landfill gas/vegetation relationship is covered, both by review of the literature and statistical analysis of field data. The work showed that, although vegetation health was related to landfill gas, it was not possible to define a simple correlation. In the landfill environment, contribution from other variables, such as soil characteristics, frequently confused the relationship. Two sites are investigated in detail, the sites contrasting in terms of the data available, site conditions, and the degree of damage to vegetation. Gas migration at the Panshanger site was dominantly upwards, affecting crops being grown on the landfill cap. The injury was expressed as an overall decline in plant health. Discriminant analysis was used to account for the variations in plant health, and hence the differences in spectral response of the crop canopy, using a combination of soil and gas variables. Damage to both woodland and crops at the Ware site was severe, and could be easily related to the presence of gas. Air photographs, aerial video, and airborne thematic mapper data were used to identify damage to vegetation, and relate this to soil type. The utility of different sensors for this type of application is assessed, and possible improvements that could lead to more widespread use are identified. The situations in which remote sensing data could be combined with ground survey are identified. In addition, a possible methodology for integrating the two approaches is suggested.

Consulting Services to Determine the Effectiveness of Vegetation Classification Using WorldView 2 Satellite Data for the Greater Everglades

The purpose of this project was to evaluate the use of remote sensing 1) to detect and map Everglades wetland plant communities at different scales; and 2) to compare map products delineated and resampled at various scales with the intent to quantify and describe the quantitative and qualitative differences between such products. We evaluated data provided by Digital Globe’s WorldView 2 (WV2) sensor with a spatial resolution of 2m and data from Landsat’s Thematic and Enhanced Thematic Mapper (TM and ETM+) sensors with a spatial resolution of 30m. We were also interested in the comparability and scalability of products derived from these data sources. The adequacy of each data set to map wetland plant communities was evaluated utilizing two metrics: 1) model-based accuracy estimates of the classification procedures; and 2) design-based post-classification accuracy estimates of derived maps.

The sensitivity of mapping methods to reference data quality:training supervised image classifications with imperfect reference data

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.

USO Y COBERTURA DE LA TIERRA EN COSTA RICA PARA 1992: UNA APLICACIÓN DE TELEDETECCIÓN ESPACIAL Y SISTEMAS DE INFORMACION GEOGRÁFICA

En el presente artículo se describe el procedimiento utilizado para actualizar el mapa de uso y cobertura de la tierra de Costa Rica para el año 1992. En el proceso se integró la cartografía análoga existente para 1985 a escala 1:200.000 e imágenes digitales del Mapeador Temático de LANDSAT para los años 1991 y 1993. Para comprobar la exactitud de la clasificación se usaron 1.372 puntos obtenidos de fotos aéreas de 1992 y trabajo de campo empleando un Sistema de Posicionamiento Global (SPG). Los resultados obtenidos indican que existe un 46.6% del país bajo pastos, un 32,9 bajo bosques y un 8,5% bajo uso agrícola. Un 7,8% del área se incluyó en una categoría denominada <<no clasificada, usos mezclados, deforestada>>. La exactitud global de la clasificación fue de un 74%; con una confusión entre pasto y bosque de un 19%.  SUMMARY The objective of this paper is to describe the process used by the authors to update the preliminary 1985 land use-land cover map of Costa Rica. Paper maps of 1985 at scale 1:200.000 we digitized, rasterized and use to label the output of a nonsupervised classification carried out using 1991-93 digital data from LANDSAT 5 (Thematic Mapper). Aerial photography and field work aided by a Global Positional System (GPS) was used to gather ground-truth data. A total of 1372 stratified points were used to test the accuracy of the final map. Our results showed that 46,6% of the country is under pasture, 32,9% under forest and 8.5% under agriculture. The nonclassified areas were lumped into one category that accounted for 7,8% of the country. Global accuracy of the classification was 74% with the confusion between forest and pasture accounting for 19% of this error.

Avaliação da dinâmica do carbono na fitomassa de cana-de-açúcar e pastagem.

A avaliação na alteração dos estoques de carbono na fitomassa agrícola ocorreu em uma área de 51.650 km2, compreendendo 125 municípios das regiões, central, norte e nordeste do Estado de São Paulo. Essas regiões possuem as cadeias de produção especializadas da cana-de-açúcar e das pastagens que estão presentes em praticamente quase todos os municípios da região e competem por área. Por meio da investigação do sensor Moderate Resolution Imaging Spectroradiometer (MODIS) e da interpretação de imagens do sensor Thematic Mapper (TM), avaliou-se a mudança de uso e cobertura da terra nos anos de 1988 e 2015. A expansão a área de cana-de-açúcar acelerou-se significativamente em toda a região e nos últimos 27 anos a área cultivada passou de 1.085.900 ha (21% da área de estudo) para 1.966.445 ha (38% da área de estudo). As áreas de pastagens reduziram-se de 1.397.724 ha (26% da área de estudo) para 684.323 ha (13% da área de estudo). A análise dos dados revelou que a cana-de-açúcar é capaz de acumular 107,2 t.ha.-1.ano-1 de carbono na fitomassa, enquanto as pastagens cultivadas somente 11,7 t.ha.-1.ano-1 de carbono. Em 1988 toda a área de cana-de-açúcar retinha na fitomassa 116 milhões de toneladas de CO2 e em 27 anos esse acúmulo passou para 211 milhões de toneladas de CO2 .ano-1. Constata-se com isso que o carbono pode, ao menos em parte, ser recomposto pelos agroecossistemas durante o subsequente uso do solo. Dos 125 municípios avaliados, 118 deles apresentaram elevação do carbono acumulado na fitomassa devido a incorporação de áreas de pastagens por cana-de-açúcar, num total de 592 mil ha. Somente nas áreas de pastagens que foram substituídas por cana-de-açúcar nesses 27 anos, promoveu-se a remoção de 54 milhões de toneladas de CO2 da atmosfera.

Analysis of Thematic Classified Aerial Images Trough Multispectral and LIDAR Data

The application of thematic maps obtained through the classification of remote images needs the obtained products with an optimal accuracy. The registered images from the airplanes display a very satisfactory spatial resolution, but the classical methods of thematic classification not always give better results than when the registered data from satellite are used. In order to improve these results of classification, in this work, the LIDAR sensor data from first return (Light Detection And Ranging) registered simultaneously with the spectral sensor data from airborne are jointly used. The final results of the thematic classification of the scene object of study have been obtained, quantified and discussed with and without LIDAR data, after applying different methods: Maximum Likehood Classification, Support Vector Machine with four different functions kernel and Isodata clustering algorithm (ML, SVM-L, SVM-P, SVM-RBF, SVM-S, Isodata). The best results are obtained for SVM with Sigmoide kernel. These allow the correlation with others different physical parameters with great interest like Manning hydraulic coefficient, for their incorporation in a GIS and their application in hydraulic modeling.

Communicating thematic data quality with web map services

Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium’s Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new “quality-enabled” profile of WMS, which we call “WMS-Q”. This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall also describe new open-source implementations of the new specifications, which include both clients and servers.

Atlas of the Songwe River Basin: Thematic Maps with Explanatory Text Sheets

This atlas presents a comprehensive set of thematic maps depicting a wide range of aspects of the Songwe river area. It includes baseline maps (such as topographic overview, hillshade), satellite images (years 1991, 2001, 2004), land cover and land cover change, biomass and biomass change, priority conservation areas, resource management (watershed classification, watershed classification combined with biomass, soil erosion), accessibility and special maps (such as historical river course). Map clippings of the most important maps facilitate the readability of the maps. The accompanying explanatory text sheets contain graphics and information about material, methods, results and interpretation.

Classification of Satellite Images by means of Fuzzy Rules generated by a Genetic Algorithm

The data acquired by Remote Sensing systems allow obtaining thematic maps of the earth's surface, by means of the registered image classification. This implies the identification and categorization of all pixels into land cover classes. Traditionally, methods based on statistical parameters have been widely used, although they show some disadvantages. Nevertheless, some authors indicate that those methods based on artificial intelligence, may be a good alternative. Thus, fuzzy classifiers, which are based on Fuzzy Logic, include additional information in the classification process through based-rule systems. In this work, we propose the use of a genetic algorithm (GA) to select the optimal and minimum set of fuzzy rules to classify remotely sensed images. Input information of GA has been obtained through the training space determined by two uncorrelated spectral bands (2D scatter diagrams), which has been irregularly divided by five linguistic terms defined in each band. The proposed methodology has been applied to Landsat-TM images and it has showed that this set of rules provides a higher accuracy level in the classification process