This chapter describes the main vegetation units of Peninsula Valdes at scale 1:250,000 with emphasis on relevant physiognomic and floristic characteristics. Based on photogrammetry (aerial photograph pairs 1:60,000) and ground check, 18 dominant singular plant species arrangements (vegetation units) were identified reflecting the variety of environmental conditions at a mesoscale (1:250,000) within Peninsula Valdes. At sites selected for ground check, floristic-physiognomic census including a complete floristic plant species list with the relative abundance of each species were performed. After that, censuses of species abundance were ordered by principal component analysis. The layer structure, the main life forms and the dominant species for each identified and mapped vegetation unit were described. Among them, we identified shrubby vegetation units at northern and central Peninsula Valdes and, grassy vegetation units at southern Peninsula Valdes. A map of vegetation units and some pictures of the most representative vegetation units complete the vegetation description. Moreover, this chapter includes a detailed description of the plant communities (resolution scale 1:1) characterizing four sites identified as priorities for ecosystem conservation. Priority sites for conservation are located in Salt marshes, Uplands and Plain Systems and Endorheic Basins. Some contrasts between conserved and degraded community states are also exemplified.

In semiarid rangelands where the anthropogenic impact is currently increasing, as occurs in the rangelands of the Peninsula Valdes, the detrimental impacts of soil degradation on land resources became really dramatic. This chapter presents a review on the current knowledge of soil degradation in the Peninsula Valdes rangelands. Section 1 introduces the chapter, Sect. 2 focuses on soil degradation main processes, factors and causes, and Sect. 3 presents a review of soil degradation assessment methods and several soil degradation studies carried out since 1990 in the Peninsula Valdes region. Water and wind erosion are the degradation processes that are most strongly evidenced. Major causes of soil degradation are attributed to a combination of climatic and anthropic factors, with overgrazing being perceived to be a major factor. Four key causes associated with overgrazing in the Peninsula Valdes region rangelands are described: (1) Poor range management with respect to flock distribution and overstocking, (2) Limited access to information, (3) Top-down and largely ineffective government policy, and (4) Overdependence on grazing systems for sustained livelihoods. Assessment methods for assessing soil degradation include: expert judgment, remote sensing, productivity changes, field monitoring, pilot studies at farm level based on field criteria and expert opinion, and modeling.

Our aim was to describe vegetation heterogeneity at a regional scale in northeastern Patagonia and to identify the environmental variables associated to it. The study area encompasses 13 144 km 2 and is characterized by a mixture of species typical of Patagonian steppes and Monte Desert. We performed 48 vegetation relevés, which were randomly assigned to a training set and to a validation set (32 and 16 relevés, respectively). Training set was subjected to cluster analysis, which allowed the identification of two plant communities one related to Patagonian steppes and another to the Monte desert. We derived 3 attributes of the seasonal curve of the NDVI as indicators of ecosystem function: the seasonal amplitude (SA), the date of the maximum (DOM), and the large seasonal integral (LSI). We explored the relationship between NDVI attributes and communities by classification tree analysis. LSI was the strongest predictor among NDVI attributes, separating both communities without misclassification errors. Patagonian steppes occupy areas with higher LSI. The partial RDA analysis explained 38.1% of total data variation, of which 16.5% was ascribed to environment, 7.9% to space, and 13.7% to spatial component of environment. Patagonian steppes are closer to the coast, in areas exhibiting higher annual precipitation and lower annual temperature range than Monte deserts. Our results indicate the occurrence of two plant communities in the study area and highlight the significance of climatic variables to explain their spatial distribution. As most scenarios of future climate predict greater annual thermal amplitude in the study area, the limit between both communities could be displaced eastward.

This paper presents an approach to explicitly determinate optimal stocking rates based on trade-offs between guanaco density and livestock grazing intensity on rangelands of Patagonia, Argentina. As sheep farming becomes less profitable in this region, farmers and ranchers need to focus on sustainable wildlife harvesting as alternative income generation. We developed a methodology for spatially-explicit assessment and mapping of stocking rates based on tradeoffs and synergies between ecosystem services and livestock grazing intensity on rangelands. Forage use for livestock production, carbon sequestration, wildlife conservation and soil erosion are the ecosystem services and disservices evaluated over a gradient of grazing intensity. We show how rangelands stocking rate determination can be converted into a multi-objective optimization problem that can be solved using a Fuzzy Multi-Objective Linear Programing (MOLP).

Understanding spatial and temporal patterns of burned areas at regional scales, provides a long-term perspective of fire processes and its effects on ecosystems and vegetation recovery patterns, and it is a key factor to design prevention and post-fire restoration plans and strategies. Standard satellite burned area and active fire products derived from the 500-m MODIS and SPOT are avail - able to this end. However, prior research caution on the use of these global-scale products for regional and sub-regional applica - tions. Consequently, we propose a novel algorithm for automated identification and mapping of burned areas at regional scale in semi-arid shrublands. The algorithm uses a set of the Normalized Burned Ratio Index products derived from MODIS time series; using a two-phased cycle, it firstly detects potentially burned pixels while keeping a low commission error (false detection of burned areas), and subsequently labels them as seed patches. Region growing image segmentation algorithms are applied to the seed patches in the second-phase, to define the perimeter of fire affected areas while decreasing omission errors (missing real burned areas). Independently-derived Landsat ETM+ burned-area reference data was used for validation purposes. The correlation between the size of burnt areas detected by the global fire products and independently-derived Landsat reference data ranged from R 2 = 0.01 - 0.28, while our algorithm performed showed a stronger correlation coefficient (R 2 = 0.96). Our findings confirm prior research calling for caution when using the global fire products locally or regionally.

Ecological sites are the basic entity used in rangeland health assessment. This study evaluates the synergistic use of multi- and hyper-spectral satellite imagery for sub-pixel classification of ecological sites in semiarid rangelands. Hyperion and Landsat enhanced thematic mapper (ETM) data are included in a two-step procedure to mapping ecological sites in Patagonian rangelands of Argentina. Firstly, mixture tuned matched filtering and logistic regression analyses are used for Hyperion data processing to obtain ecological site probability images in the area covered by hyperspectral imagery. Secondly, artificial neural networks are applied to model the relationships between the spectral response patterns of Landsat and the probability images from Hyperion, and used to map ecological sites over the entire study area. Overall classification accuracy was 81% (kappa=0.77) with relatively high accuracies for all ecological sites demonstrating that their spectral signatures are sufficiently distinct to be detectable. Better accuracies were obtained for shrub steppes with desert pavement (producer's and user's accuracies of 89% and 84%, respectively), and shrub-grass steppes associated to tertiary calcareous outcrops (producer's and user's accuracies of 100% and 86%, respectively), while poorer accuracies resulted for shrub-grass steppes on old alluvial plains (producer's and user's accuracies of 75% and 56%, respectively). Fuzzy maps of ecological sites as presented in this research can provide rangeland managers with a tool to stratify the landscape and organize ecological information for rangeland health assessment and monitoring, prioritizing and selecting appropriate management actions, and promoting the recovery of areas degraded in these environments.

Fire occurrences at the of Parana River Coastal Complex during the 2004-2008 period were studied through hotspot data analysis. Spatial information from different remote sensors (MODIS, ERS, Goes, NOAA) and sources (CONAE, ESA, INPE) was evaluated. The MODIS Aqua sensor (downloaded and processed by CONAE), showed a high hotspot detection performance. For this sensor, an algorithm was developed that allows the interpretation of hotspot data as fire, applying previous knowledge of the fire behavior in the study area. Maps, tables and figures were elaborated. Descriptive statistics, normality (Shapiro Willks) and variance tests (Kruskal Wallis) were carried out. No statistically significant differences (SSD) were founded between hotspots and fire occurrence to different months and seasons. SSD were found between 2004, 2006 and 2008 on the one hand, and 2005 and 2007 on the other. Taking into account these results, fires at the Parana River Coastal Complex are, in general, of short duration and small extent, with a seasonal pattern of occurrence (in spring-summer), and vary from year to year. Exceptional periods (2008) can be identified by the changes of these parameters. This information is presented as a suitable tool for the study and management of fire in the study area.

We focus on Shannon Entropy (SE) for the characterization of polarimetric Synthetic Aperture Radar (PolSAR) images. This approach analyzes the information contribution made by individual pixels to the whole image for assessment of land degradation in the information content of ALOS PALSAR images. Additionally, the performance of other polarization parameters, and polarization decomposition is illustrated and discussed. Dual-Pol and Quad-Pol scenes have been acquired under the SAOCOM (Satelite Argentino de Observacion con Microondas, Spanish for Argentine Microwaves Observation Satellite) project in 2010 and 2011, from northeastern Patagonian coastal desert, Argentina. The accuracy of the SE map was assessed using a set of ground observations based on remotely sensed data that have higher accuracy. The results show that the SE can describe and determine the image features more obviously in the study area, so that it represents an important reference value for land degradation detection and land status characteristics extraction.

Above-ground net primary production (ANPP) in arid and semiarid ecosystems is mainly explained by precipitation (ppt). However, when this relationship is evaluated taking into consideration data from different years in the same site (i.e. temporal models of productivity) the relation is weak, and sometimes it does not exist. In spite of this, the inclusion of previous year's ppt and/or ANPP frequently improves temporal models. In this study we analyze if considering NDVI and mean annual soil water content or transpiration (instead of different combinations of current-year ppt and previous year's ppt or ANPP) improves temporal models of productivity in the Southern Monte (Argentina). Current-year ppt only explained 39.7% of variation in ANPP, while mean soil water content explained 85.3%. The remaining models, which include current-year ppt together with previous-year's ppt or previous-year's ANPP, improve the first model; but the explanatory power of the model based only on mean soil water content is never reached. Our results also show that water losses exceed annual ppt in dry years, whereas the opposite occurs during years with above-average ppt. This carryover effect of soil water indicates that soil acts as a capacitor, accumulating water during wettest years and releasing it during following years. (C) 2013 Elsevier Ltd. All rights reserved.

Land cover maps at different resolutions and mapping extents contribute to modeling and support decision making processes. Because land cover affects and is affected by climate change, it is listed among the 13 terrestrial essential climate variables. This paper describes the generation of a land cover map for Latin America and the Caribbean (LAC) for the year 2008. It was developed in the framework of the project Latin American Network for Monitoring and Studying of Natural Resources (SERENA), which has been developed within the GOFC-GOLD Latin American network of remote sensing and forest fires (RedLaTIF). The SERENA land cover map for LAC integrates: 1) the local expertise of SERENA network members to generate the training and validation data, 2) a methodology for land cover mapping based on decision trees using MODIS time series, and 3) class membership estimates to account for pixel heterogeneity issues. The discrete SERENA land cover product, derived from class memberships, yields an overall accuracy of 84% and includes an additional layer representing the estimated per-pixel confidence. The study demonstrates in detail the use of class memberships to better estimate the area of scarce classes with a scattered spatial distribution. The land cover map is already available as a printed wall map and will be released in digital format in the near future. The SERENA land cover map was produced with a legend and classification strategy similar to that used by the North American Land Change Monitoring System (NALCMS) to generate a land cover map of the North American continent, that will allow to combine both maps to generate consistent data across America facilitating continental monitoring and modeling.

Resumen es: Se ha estudiado las ocurrencias de incendio en el Complejo del Rio Parana costeras durante el periodo 2004-2008 se estudiaron mediante analisis de datos ...

espanolEste trabajo fue desarrollado en el marco de la Red Latinoamericana de Seguimiento y Estudio de los Recursos Naturales (SERENA) en respuesta a la gran necesidad existente de contar con informacion de cobertura del suelo, con una resolucion espacial media, en la region de Latinoamerica y el Caribe (LAC). En el presente articulo se detalla la metodologia y se presentan los resultados preliminares obtenidos para la cartografia de la cubierta super-ficial de LAC para el ano 2008. EnglishThis work was developed in the framework of the Latin American Network of Monitoring and Study of Natural Resources (SERENA) in response to the need for accurate and reliable information on landcover, with a medium spatial resolution in the region of Latin America and the Caribbean (LAC). This article describes the methodology and presents the results obtained for the year 2008.

In many rangeland ecosystems, the role of wildfires has been the object of detailed studies. However, in Argentina and especially in the Monte biome this knowledge is limited. This region is prone to wildfires during late spring and summer, when high winds and temperatures are coupled with low relative humidity. In addition, this region also presents cases of extreme wildfire behaviour. The aim of this study is to develop a probability-based model for estimating wildfire risk at a regional scale based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Hot Spot data. The map presented here covers an area of approximately 246,000 km(2) at a 1: 100,000 scale. It is the result of fitting the 2001-2010 hot spot time series with a logistic regression model to 15 potentially explanatory environmental and anthropogenic variables. At a regional scale, mean net primary production, spring temperature, spring and summer precipitation, elevation, slope, distance to nearest road are the most important factors explaining wildfire risk.