(Appendix) Recent surface areas and historical lengths of glaciers from Patagonia and Tierra del Fuego, South America

Fluctuations in the length of 72 glaciers in the Northern and Southern Patagonia Icefield (NPI and SPI, respectively) and the Cordillera Darwin Icefield (CDI) were estimated between 1945 and 2005. The information obtained from historical maps based on 1945 aerial photographs was compared to ASTER and Landsat satellite images and to information found in the literature. The majority of glaciers have retreated considerably, with maximum values of 12.2 km for Marinelli Glacier in the CDI, 11.6 km for O'Higgins Glacier in the SPI and 5.7 km for San Rafael Glacier in the NPI. Among the 20 glaciers that have retreated the most relative to their size, small (less than 50 km**2) and medium (between 50 and 200 km**2) glaciers are the most affected. However, no direct relation between glacier retreat and size was found for the 72 glaciers studied. The highest percentage retreat in the CDI was by the CDI-03 Glacier (37.9%) and Marinelli Glacier (37.6%). In the SPI, relative retreats were heterogeneous and fluctuated between 27.2% (Amelia Glacier) and 0.4% (Viedma Glacier). In the NPI, relative retreat was very high for Strindberg and Cachet glaciers (35.9% and 27.6%, respectively) but for the remaining glaciers in this icefield it ranged between 11.8% (Piscis Glacier) and 3.6% (San Quintin Glacier). In addition to surface area, the surface slope (calculated on the basis of the DEM SRTM) was also related to the relative retreat and no straightforward relation was found. From a global point of view, we suggest that glacier retreat in the region is controlled firstly by atmospheric warming, as it has been reported in this area. Besides the general increase in temperature observed, no signal of a geographical pattern for the fluctuations in glacier length was found. Consequently, glaciers appear to initially react to local conditions most probably induced by their exposition, geometry and hypsometry. The heterogeneity of rates of retreat suggests that differences in basin geometry, glacier dynamics and response time are key features to explain fluctuations of each glacier.

Phytomass and soil carbon storage of different land cover classes in the Usa river basin

This study describes detailed partitioning of phytomass carbon (C) and soil organic carbon (SOC) for four study areas in discontinuous permafrost terrain, Northeast European Russia. The mean aboveground phytomass C storage is 0.7 kg C/m**2. Estimated landscape SOC storage in the four areas varies between 34.5 and 47.0 kg C/m**2 with LCC (land cover classification) upscaling and 32.5-49.0 kg C/m**2 with soil map upscaling. A nested upscaling approach using a Landsat thematic mapper land cover classification for the surrounding region provides estimates within 5 ± 5% of the local high-resolution estimates. Permafrost peat plateaus hold the majority of total and frozen SOC, especially in the more southern study areas. Burying of SOC through cryoturbation of O- or A-horizons contributes between 1% and 16% (mean 5%) of total landscape SOC. The effect of active layer deepening and thermokarst expansion on SOC remobilization is modeled for one of the four areas. The active layer thickness dynamics from 1980 to 2099 is modeled using a transient spatially distributed permafrost model and lateral expansion of peat plateau thermokarst lakes is simulated using geographic information system analyses. Active layer deepening is expected to increase the proportion of SOC affected by seasonal thawing from 29% to 58%. A lateral expansion of 30 m would increase the amount of SOC stored in thermokarst lakes/fens from 2% to 22% of all SOC. By the end of this century, active layer deepening will likely affect more SOC than thermokarst expansion, but the SOC stores vulnerable to thermokarst are less decomposed.

Forest disturbance map for the Carpathian ecoregion, 1984-2012

Detailed knowledge of forest cover dynamics is crucial for many applications from resource management to ecosystem service assessments. Landsat data provides the necessary spatial, temporal and spectral detail to map and analyze forest cover and forest change processes. With the opening of the Landsat archive, new opportunities arise to monitor forest dynamics on regional to continental scales. In this study we analyzed changes in forest types, forest disturbances, and forest recovery for the Carpathian ecoregion in Eastern Europe. We generated a series of image composites at five year intervals between 1985 and 2010 and utilized a hybrid analysis strategy consisting of radiometric change classification, post-classification comparison and continuous index- and segment-based post-disturbance recovery assessment. For validation of the disturbance map we used a point-based accuracy assessment, and assessed the accuracy of our forest type maps using forest inventory data and statistically sampled ground truth data for 2010. Our Carpathian-wide disturbance map achieved an overall accuracy of 86% and the forest type maps up to 73% accuracy. While our results suggested a small net forest increase in the Carpathians, almost 20% of the forests experienced stand-replacing disturbances over the past 25 years. Forest recovery seemed to only partly counterbalance the widespread natural disturbances and clear-cutting activities. Disturbances were most widespread during the late 1980s and early 1990s, but some areas also exhibited extensive forest disturbances after 2000, especially in the Polish, Czech and Romanian Carpathians. Considerable shifts in forest composition occurred in the Carpathians, with disturbances increasingly affecting coniferous forests, and a relative decrease in coniferous and mixed forests. Both aspects are likely connected to an increased vulnerability of spruce plantations to pests and pathogens in the Carpathians. Overall, our results exemplify the highly dynamic nature of forest cover during times of socio-economic and institutional change, and highlight the value of the Landsat archive for monitoring these dynamics.

The Lena River Delta - inventory of lakes and geomorphological terraces

This data set provides a detailed inventory of lakes in the Lena Delta, northern Siberia, with respect to the lakes' association with one of the three geomorphological main terraces of the Lena Delta. The inventory is based on Landsat-7 ETM+ image data and spatial analysis in a Geographical Information System (GIS). Several morphometric lake attributes were determined from the resulting dataset and statistically analyzed. Significant differences in the morphometric lake characteristics allowed the distinction of a mean lake type for each main terrace. The lake types reflect the special lithological and cryolithological conditions and geomorphological processes prevailing on each terrace. In Morgenstern et al. (2008), special focus was laid on the investigation of lake orientation and the discussion of possible mechanisms for the evolution of the second terrace's oriented lakes.

Air photography Samoylov Island

For the qualitative description of surface properties like vegetation cover or land-water-ratio of Samoylov Island as well as for the evaluation of fetch homogeneity considerations of the eddy covariance measurements and for the up-scaling of chamber flux measurements, a detailed surface classification of the island at the sub-polygonal scale is necessary. However, up to know only grey-scale Corona satellite images from the 1960s with a resolution of 2 x 2 m and recent multi-spectral LandSat images with a resolution of 30 x 30 m were available for this region. Both are not useable for the desired classification because of missing spectral information and inadequate resolution, respectively. During the Lena 2003 expedition, a survey of the island by air photography was carried out in order to obtain images for surface classification. The photographs were taken from a helicopter on 10.07.2002, using a Canon EOS100 reflex camera, a Soligor 19-23 mm lens and colour slide film. The height from which the photographs were taken was approximately 600 meters. Due to limited flight time, not all the area of the island could be photographed and some regions could only be photographed with a slanted view. As a result, the images are of a varying quality and resolution. In Potsdam, after processing the films were scanned using a Nikon LS-2000 scanner at maximal resolution setting. This resulted in a ground resolution of the scanned images of approximately 0.3x0.3 m. The images were subsequently geo-referenced using the ENVI software and a referenced Corona image dating from 18.07.1964 (Spott, 2003). Geo-referencing was only possible for the Holocene river terrace areas; the floodplain regions in the western part of the island could not be referenced due to the lack of ground reference points. In Figure 3.7-1, the aerial view of Samoylov Island composed of the geo-referenced images is shown. Further work is necessary for the classification and interpretation of the images. If possible, air photography surveys will be carried out during future expeditions in order to determine changes in surface pattern and composition.

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

Caracterización de clases de ocupación de suelo y usos agrícolas en un área de la provincia de Toledo (Talavera de la Reina) a partir de imágenes de satélite de media resolución.

Con el presente proyecto se ha pretendido realizar una clasificación de los distintos usos del suelo en la provincia de Toledo y de forma más precisa en el municipio de Talavera de la Reina. Se ha profundizado en los conocimientos sobre teledetección adquiridos durante los años de estudio de la titulación de Ingeniero Técnico en Topografía, cubriendo las aplicaciones más importantes. Para ello, en primer lugar se debe recopilar la información, en este caso se han utilizado dos imágenes Landsat 8 - OLI (19/4/2013 - 9/8/2013) y con el software adecuado se realiza la clasificación dividiendo el suelo en los usos más frecuentes de dicha zona. El resultado obtenido nos muestra los distintos usos del suelo en el año de estudio, 2013, y exponer el potencial de las técnicas de teledetección, para así poder interpretar y llegar a conocer temas de gran relevancia como el aprovechamiento del terreno o el desarrollo del sector agrícola en la zona. El procedimiento consta de la elaboración de los correspondientes documentos cartográficos de usos del suelo y vegetación para el año 2013 a partir de las imágenes de satélite.

Flooding through the lens of mobile phone activity

Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The epresentativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.

Flooding through the lens of mobile phone activity

Natural disasters affect hundreds of millions of people worldwide every year. Emergency response efforts depend upon the availability of timely information, such as information concerning the movements of affected populations. The analysis of aggregated and anonymized Call Detail Records (CDR) captured from the mobile phone infrastructure provides new possibilities to characterize human behavior during critical events. In this work, we investigate the viability of using CDR data combined with other sources of information to characterize the floods that occurred in Tabasco, Mexico in 2009. An impact map has been reconstructed using Landsat-7 images to identify the floods. Within this frame, the underlying communication activity signals in the CDR data have been analyzed and compared against rainfall levels extracted from data of the NASA-TRMM project. The variations in the number of active phones connected to each cell tower reveal abnormal activity patterns in the most affected locations during and after the floods that could be used as signatures of the floods - both in terms of infrastructure impact assessment and population information awareness. The representativeness of the analysis has been assessed using census data and civil protection records. While a more extensive validation is required, these early results suggest high potential in using cell tower activity information to improve early warning and emergency management mechanisms.

Aportación de técnicas geoespaciales para la determinación de los niveles de referencia de la deforestación y la degradación de bosques en el ámbito de proyectos REDD. análisis de factores de causalidad subyacentes y directos

La pérdida de bosques en la Tierra, principalmente en ecosistemas amazónicos, es un factor clave en el proceso del cambio climático. Para revertir esta situación, los mecanismos REDD (Reducing Emission from Deforestation and forest Degradation) están permitiendo la implementación de actividades de protección del clima a través de la reducción de emisiones por deforestación evitada, según los esquemas previstos en el Protocolo de Kioto. El factor técnico más crítico en un proyecto REDD es la determinación de la línea de referencia de emisiones, que define la expectativa futura sobre las emisiones de CO2 de origen forestal en ausencia de esfuerzos adicionales obtenidos como consecuencia de la implementación del programa REDD para frenar este tipo de emisiones. La zona del estudio se ubica en la región de San Martín (Perú), provincia cubierta fundamentalmente por bosques tropicales cuyas tasas de deforestación son de las más altas de la cuenca amazónica. En las últimas décadas del siglo XX, la región empezó un acelerado proceso de deforestación consecuencia de la integración vial con el resto del país y la rápida inmigración desde zonas rurales en busca de nuevas tierras agrícolas. Desde el punto de vista de la investigación llevada a cabo en la tesis doctoral, se pueden destacar dos líneas: 1. El estudio multitemporal mediante imágenes de satélite Landsat 5/TM con el propósito de calcular las pérdidas de bosque entre períodos. El estudio multitemporal se llevó a cabo en el período 1998-2011 utilizando imágenes Landsat 5/TM, aplicando la metodología de Análisis de Mezclas Espectrales (Spectral Mixtures Analysis), que permite descomponer la reflectancia de cada píxel de la imagen en diferentes fracciones de mezcla espectral. En este proceso, las etapas más críticas son el establecimiento de los espectros puros o endemembers y la recopilación de librerías espectrales adecuadas, en este caso de bosques tropicales, que permitan reducir la incertidumbre de los procesos. Como resultado de la investigación se ha conseguido elaborar la línea de referencia de emisiones histórica, para el período de estudio, teniendo en cuenta tanto los procesos de deforestación como de degradación forestal. 2. Relacionar los resultados de pérdida de bosque con factores de causalidad directos e indirectos. La determinación de los procesos de cambio de cobertura forestal utilizando técnicas geoespaciales permite relacionar, de manera significativa, información de los indicadores causales de dichos procesos. De igual manera, se pueden estimar escenarios futuros de deforestación y degradación de acuerdo al análisis de la evolución de dichos vectores, teniendo en cuenta otros factores indirectos o subyacentes, como pueden ser los económicos, sociales, demográficos y medioambientales. La identificación de los agentes subyacentes o indirectos es una tarea más compleja que la de los factores endógenos o directos. Por un lado, las relaciones causa – efecto son mucho más difusas; y, por otro, los efectos pueden estar determinados por fenómenos más amplios, consecuencia de superposición o acumulación de diferentes causas. A partir de los resultados de pérdida de bosque obtenidos mediante la utilización de imágenes Landsat 5/TM, se investigaron los criterios de condicionamiento directos e indirectos que podrían haber influido en la deforestación y degradación forestal en ese período. Para ello, se estudiaron las series temporales, para las mismas fechas, de 9 factores directos (infraestructuras, hidrografía, temperatura, etc.) y 196 factores indirectos (económicos, sociales, demográficos y ambientales, etc.) con, en principio, un alto potencial de causalidad. Finalmente se ha analizado la predisposición de cada factor con la ocurrencia de deforestación y degradación forestal por correlación estadística de las series temporales obtenidas. ABSTRACT Forests loss on Earth, mainly in Amazonian ecosystems, is a key factor in the process of climate change. To reverse this situation, the REDD (Reducing Emission from Deforestation and forest Degradation) are allowing the implementation of climate protection activities through reducing emissions from avoided deforestation, according to the schemes under the Kyoto Protocol. Also, the baseline emissions in a REDD project defines a future expectation on CO2 emissions from deforestation and forest degradation in the absence of additional efforts as a result of REDD in order to stop these emissions. The study area is located in the region of San Martín (Peru), province mainly covered by tropical forests whose deforestation rates are the highest in the Amazon basin. In the last decades of the twentieth century, the region began an accelerated process of deforestation due to road integration with the rest of the country and the rapid migration from rural areas for searching of new farmland. From the point of view of research in the thesis, we can highlight two lines: 1. The multitemporal study using Landsat 5/TM satellite images in order to calculate the forest loss between periods. The multitemporal study was developed in the period 1998-2011 using Landsat 5/TM, applying the methodology of Spectral Mixture Analysis, which allows decomposing the reflectance of each pixel of the image in different fractions of mixture spectral. In this process, the most critical step is the establishment of pure spectra or endemembers spectra, and the collecting of appropriate spectral libraries, in this case of tropical forests, to reduce the uncertainty of the process. As a result of research has succeeded in developing the baseline emissions for the period of study, taking into account both deforestation and forest degradation. 2. Relate the results of forest loss with direct and indirect causation factors. Determining the processes of change in forest cover using geospatial technologies allows relating, significantly, information of the causal indicators in these processes. Similarly, future deforestation and forest degradation scenarios can be estimated according to the analysis of the evolution of these drivers, taking into account other indirect or underlying factors, such as economic, social, demographic and environmental. Identifying the underlying or indirect agents is more complex than endogenous or direct factors. On the one hand, cause - effect relationships are much more diffuse; and, second, the effects may be determined by broader phenomena, due to superposition or accumulation of different causes. From the results of forest loss obtained using Landsat 5/TM, the criteria of direct and indirect conditioning that might have contributed to deforestation and forest degradation in that period were investigated. For this purpose, temporal series, for the same dates, 9 direct factors (infrastructure, hydrography, temperature, etc.) and 196 underlying factors (economic, social, demographic and environmental) with, in principle, a high potential of causality. Finally it was analyzed the predisposition of each factor to the occurrence of deforestation and forest degradation by statistical correlation of the obtained temporal series.

Análisis de Mezclas Espectrales para el estudio de la deforestación y establecimiento de la línea de referencia de emisiones en el marco del programa REDD. Aplicación a la región de San Martín, Perú.

La determinación de la línea histórica de deforestación como parte del establecimiento de la línea de referencia de emisiones, en el marco del programa REDD (Reducing Emissions from Deforestation and Forest Degradation), permite medir la evolución de la pérdida de bosque en un periodo definido de tiempo. El objetivo fue calcular la línea histórica de deforestación mediante estudio multitemporal para el periodo 1998-2011, en la región de San Martín (Perú), utilizando la metodología de Análisis de Mezclas Espectrales (Spectral Mixtures Analysis) con imágenes Landsat 5-TM. Palabras clave: teledetección, Landsat 5-TM, análisis de mezclas espectrales, REDD, Protocolo de Kioto, deforestación, Amazonía, SMA Spectral Mixture Analysis for the study of deforestation and establishing reference emissions level within the REDD Program framework. Application to the region of San Martin, Peru. Abstract: Determination of the historical baseline of deforestation as part of establishing the reference emissions level within the REDD (Reducing Emissions from Deforestation and Forest Degradation) Program framework allows for the measurement of the evolution of forest loss over a defined period time. The objective was to estimate the historical baseline of deforestation through a multi-temporal study for the period 1998-2011, in the region of San Martin (Peru), using the methodology of Spectral Mixture Analysis (Mixtures Spectral Analysis) from Landsat 5-TM imagery. Keywords: remote sensing, Landsat 5-TM, spectral mixtures analysis, REDD, Kyoto Protocol, deforestation, Amazon, SMA