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Copyright of Revista Chapingo Serie Ciencias Forestales is the property of Universidad Autonoma Chapingo and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The shallow lakes of the Pampa region in Argentina are highly prone to variability in temperature and precipitation regime. These ecosystems are fundamental in environmental processes, but their presence and environmental services are unknown. Climate variability and agricultural activities have threatened them in recent decades by intervening and modifying the regional hydrography. This study seeks to monitor the evolution of shallow lakes and their relationship with climate variability over a period of 35 years using satellite imagery and meteorological data. It aims to study the dynamics in accordance with the change in temperature and precipitations. The historical collection of Landsat for 1986-2021 related to climate data through an ANCOVA analysis and a climate index of evapotranspiration-precipitation have been used. The results allow us to identify the distribution in the basin of the shallow lakes as semi-permanent, emphasizing that temperatures have a more substantial effect than the precipitations that would determine their presence or drying out. The proposed methodology frames the need to generate this type of information about the response of the hydrographic system to potential hazard and the identification of vulnerabilities. [ABSTRACT FROM AUTHOR]

Copyright of Tecnología y Ciencias del Agua is the property of Instituto Mexicano de Tecnologia del Agua (IMTA) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Agriscientia is the property of Revista AgriScientia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Prevention and early warning of risks related to climate variability are essential to minimize the manifestation of a certain risk. However, given the impossibility of preventing this from occurring, it is equally important to monitor the threat, both spatially and temporally. In this sense, extreme drought events in the northeastern region of Argentina during 2020, added to cultural practices and poor fire management, have generated significant sources of fires, affecting vast areas of the region. Given the above, the objective of the work is to measure and analyze the areas affected by the manifestation of fires in the periurban area of the city of Corrientes (Argentina), applying fire assessment tools with Google Earth Engine. It was possible to corroborate the veracity of the results while proposing a complimentary analysis methodology that allows obtaining information with greater precision. [ABSTRACT FROM AUTHOR]

Copyright of Revista Mexicana de Biodiversidad is the property of Universidad Nacional Autonoma de Mexico, Instituto de Biologia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The "pajonal" is a typical grassland of the central region of Buenos Aires, a province of Argentina, being dominated by Paspalum quadrifarium, a tall-tussock grass. During the past five centuries, this grassland has been used as a feeding resource for domestic cattle. This traditional usage, which is often accompanied by the practice of periodic burning, gives way to impacts on various aspects of ecosystem structure and dynamics at different scales. Remote sensing has proved to be a powerful tool able to monitor land cover changes at regional scale and assess landscape fragmentation. This work was carried out in the central region of the original distribution of "pajonal" in the Flooding Pampa of Buenos Aires pro-vince. A Landsat TM image, radiometrically and atmospherically calibrated, was used. The supervised classification of the reconstructed images was done by the maximum likelihood method. Assessment of landscape structure by spatial metrics of "pajonal" grassland was performed by FRAGSTATS 4.1 computer programme. Classification was accomplished with a very good accuracy, and evaluation of key metrics allowed the identification of a gra-dient related to the "pajonal" conservation condition. [ABSTRACT FROM AUTHOR]

Copyright of Revista Espanola de Antropologia Americana is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The feasibility to obtain quality agricultural products, irrigated with saline water, makes it necessary to assess areas peripheral to the irrigated oasis in order to determine their suitability for cropping. The goal is to combine diverse methodologies for mapping and assessing ecologically homogeneous units in marginal lands of Mendoza, using plant communities as an indicator of these units. A pilot area was surveyed using LANDSAT/TM image processing, and vegetation, soil and water table were analysed. Assessed vegetation units and their respective salinity ranges (dS m-1), up to 50 cm depth, were: steppe of Heterostachys ritteriana, 91-83; shrubland of Allenrolfea vaginata with H. ritteriana, 83-48; open shrubland of A.vaginata con Prosopis strombulifera, 62-55; open steppe of Suaeda divaricata with Atriplex argentina, 52-83; shrubland of S. divaricata con Lycium tenuispinosum, 40-43; open woodland of Prosopis flexuosa, 37-26; dense shrubland of Prosopis alpataco, 7-6. The concentration of soluble salts exceeds the critical levels tolerated by cultivated plants. No water tables were detected within 15 m from the surface. It is considered that soils have good drainage conditions, so they can be recovered without high investment. This methodological approach showed good correspondence among plant communities, their spectral expression, and soil salinity and drainage conditions. [ABSTRACT FROM AUTHOR]

Land cover data represent environmental information for a variety of scientific and policy applications, so its classification from satellite images is important. Since neural networks (NN) do not require a hypothesis about data distribution, they are valuable tools to classify satellite images. The objectives of this work were to develop NN models to classify land cover data from information from satellite images and to evaluate them when different input variables are used. MODIS-MYD13Q1 satellite images and data of 85 plots in Córdoba, Argentina, were used. Five NN models of multi-layer feed-forward perceptron were designed. Four of these received NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), red (RED) and near infrared (NIR) reflectance values as input patterns, respectively. The fifth NN had RED and NIR reflectances as input values. By comparing the information taken in the field and the classification made during the validation phase, it can be concluded that all models presented good performance in the classification. The model that shows better performance is the one that jointly considers RED and NIR reflectance as input; this model shows an overall classification accuracy of 93% and an excellent Kappa statistic. The networks constructed with NDVI and EVI values have a similar performance (86 and 83% accuracy, respectively). The Kappa statistics correspond to the categories of very good and good, respectively. The networks including only RED or NIR reflectance values get the lowest accuracy results (76 and 81%, respectively) and Kappa values within fair and good ranks, respectively. [ABSTRACT FROM AUTHOR]