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The article presents a detailed methodology for generating thematic land use cartography in the lower basin of the Luján River for the years 1990 and 2020, using advanced technologies such as satellite imagery, remote sensing, and geographic information systems (GIS). This methodology encompasses everything from image preprocessing to the application of supervised classification techniques to identify different land coverages. The use of supervised classification is emphasized, supported by prior knowledge of the area, fieldwork, and analysis of indices such as NDVI. Through the results of the accuracy matrix, it was demonstrated that the created cartographies are accurate and representative of the study area, validated using the Kappa method. Additionally, detailed analyses of the accuracy of classifications by category are carried out, identifying categories with the highest correlation with reality. [ABSTRACT FROM AUTHOR]

Copyright of Revista Facultad de Ciencias Basicas is the property of Revista Facultad de Ciencias Basicas 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.)

Land use change is one of the main drivers of global change, as well as a cause of biodiversity loss. In northern Ecuador, the montane dry scrub is a characteristic ecosystem of the inter-Andean valleys and is threatened by anthropogenic intervention. This study examined the change in montane dry scrub coverage in the Chota River Valley over a 30-year period and evaluated its conservation status. The supervised classification method was applied to LANDSAT satellite images from 1990, 2007, and 2020 to analyze the rates of coverage variation. The conservation status was determined using an evaluation matrix that considered seven variables and 25 indicators and the overlap of thematic layers with GIS. The results showed a loss of 20% of montane dry scrub coverage, at an annual average of 231.83 ha/ year (-0.75%) due to anthropogenic causes. These causes were responsible for the coverage change of more than half of the 8.34% of the area it occupied, mainly the expansion of the agricultural frontier with 3.96%. The pressure and effect of the identified anthropogenic factors caused the current conservation status to be Regular. Three conservation strategies are proposed: good agroecological practices, comprehensive environmental management, and environmental education. [ABSTRACT FROM AUTHOR]

The acquisition of high-resolution images by drones and their subsequent processing provides valuable information on biophysical variables of grassland vegetation. The objective of this study was to generate a prediction model of the annual productivity dynamics of a semi-arid grassland in northern Mexico through the estimated vegetation cover in digital photographs obtained by drone. A permanent sampling system was designed in the La Cieneguilla cattle ranch. The study variables measured were the direct cut biomass production as the dependent variable and the vegetation cover estimated in digital images acquired by drone as the independent variable. The number of samples collected for both variables was 640 during the year 2020. With 50% of the data, the prediction model was generated and with the other 50% of the data, the validation model, the adjusted regression models were the form Y = ß0 + ß1X + ß2X2 with adjusted R2 = 71.64% and 69.90% respectively. This proposed methodology offers a non-destructive and accurate means for annual monitoring and evaluation of grasslands in dry areas. [ABSTRACT FROM AUTHOR]

Copyright of Ecosistemas y Recursos Agropecuarios is the property of Universidad Juarez Autonoma de Tabasco 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 Cuadernos de Investigación Geográfica is the property of Universidad de la Rioja, Servicio de Publicaciones 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 Terra Latinoamericana is the property of Sociedad Mexicana de la Ciencia del Suelo A.C. 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 Boletin de Geologia is the property of Universidad Industrial de Santander 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 Avances is the property of Instituto de Informacion Cientifica y Tecnologica 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 Boletin de Geologia is the property of Universidad Industrial de Santander 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 present research evaluated spatio-temporal change in the sub-tropical forest of district Malakand through Remote Sensing and GIS techniques. The main objective was to identify different land cover classes and to determine temporal changes in forest cover in the study area. This study was based on the two different classification techniques for the land cover classification. In this study, four Landsat images were acquired with the interval of 10 years from 1988 to 2018. The maximum likelihood classification and Minimum distance classification was applied on all the four images step by step. The total area of district Malakand was recorded as 975.32 km². Regarding percent wise area coverage Forest covered 22.1 % of the total area while other classes' settlements, agriculture, barren land and water have covered 17.7, 23.3, 33 and 3.9 % respectively in 1988. Whereas in 2018, percent wise area coverage forest covered 9.3 % of the total area although other classes' settlements, agriculture, barren land and water have covered 33.8, 22.3, 31.8 and 3.7 respectively. The change in forest area over the time was 60.4 km² which is equal to 6.2 % during 1998 to 2008 and ultimately overall deforestation was 124.4 km² which is equal to 12.7 % loss of forest area lost from 1998 to 2018. Thus, the forest area was changed to settlements and barren lands from 1988 to 2018. Normalized Difference Vegetation Index (NDVI) was calculated for 1988, 1998, 2008 and 2018. Based on NDVI analysis, total deforestation over the time was 166.29 km² in last 30 years (1988-2018) with percent deforestation of 43.66 % area lost. The Landsat images have 30 meters resolution and small forest area changes can not detected by Landsat images, thus high-resolution products can detect more deforestation comparatively. [ABSTRACT FROM AUTHOR]

Copyright of UD y la GEOMÁTICA is the property of UD y la GEOMATICA 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.)

In recent years, there has been major transformations in the landscape from phenomena such as deforestation, erosion, and natural disasters; these changes call for the need of an adequate landscape monitoring system for the integration of adequate territorial management plans. Currently, there exists a commonly utilized land use map often provided by official institutions: remote sensing offers data and techniques for generating land use maps on the dates, however this can only be utilized when satellite images are available. The present investigation reviews the process of generating a land use map by applying a classification process guided by the Maximum Likelihood method to transform Landsat sensor images. The official cartography of the National Geography and Statistics Institute was used as the basis for the training stage of the classification algorithm and the validation polygons of the cartography. The results of the validation show an average omission error of 8.86% and 11.56% commission error, resulting in a Kappa index of 86.90, indicating an acceptable map. [ABSTRACT FROM AUTHOR]

Los datos provenientes de los radares meteorológicos son de suma importancia para el diagnóstico y monitoreo de los sistemas que producen precipitación y sus posibles fenómenos severos asociados. Los ecos causados por objetivos no meteorológicos introducen errores en la información por lo que es necesario detectar la presencia de los mismos previo a la utilización de los datos. Este trabajo presenta cuatro técnicas de clasificación supervisada basadas en diferentes modelos estadísticos que buscan dar una respuesta a este problema. Asimismo como parte importante de este trabajo, se aplicaron técnicas de remuestreo estadísticas sobre los datos de entrenamiento, las que permitieron hacer un análisis más completo sobre los resultados. En la actualidad, las técnicas de remuestreo son herramientas fundamentales en la estadística moderna. Las mismas, a partir de simulaciones sobre los datos, permiten obtener información adicional sobre los modelos planteados. Para este trabajo se realizó un estudio de caso con datos provenientes del radar meteorológico Doppler banda C de doble polarización ubicado en la Estación Experimental Agropecuaria INTA Anguil (La Pampa). Partiendo de la clasificación manual de un experto, se aplicaron cuatro métodos de clasificación supervisada de diferentes grados de flexibilidad en su estructura: Modelo lineal, Modelo Cuadrático, Modelo Logístico y Modelo de Bayes Naive. Luego se compararon los resultados y se evaluó el desempeño de cada uno de ellos. Si bien se encontraron dificultades a la hora de clasificar las zonas de frontera entre clases, los resultados obtenidos fueron adecuados, mostrando el mejor desempeño el modelo menos flexible, el modelo lineal. Se considera necesario seguir avanzando en esta línea de investigación a fin de incorporar una mayor cantidad de casos y tener una mayor significancia de los resultados. [ABSTRACT FROM AUTHOR]

Understanding habitat preferences of grassland birds declining is important for their conservation. Currently, the use of remote sensing technology to describe the habitat of grassland birds is a novel tool in México which may allow for more accurate assessments of grassland habitat. We used both, high-resolution photographs and a protocol established by Bird Conservancy of the Rockies that uses ocular estimation in order to estimate vegetation cover within areas where individual sparrows of the genus Ammodramus were recorded in two sites located in Durango. We radio tracked individuals of A. bairdii (n=33) and A. savannarum (n=23) daily (n=1881) and randomly selected 40 location points. Vegetation metric was obtained and from high-resolution photographs we created an orthomosaic with supervised classification in 4 classes of vegetation cover (%). At each bird location point, we estimated the percentage of each vegetation cover class within a 5 m radius area around the point. We did not find a significant difference between vegetation cover obtained by a high-resolution photographs or ocular estimations (p≤0.05) by species. Both species were found in areas with grass cover similar to those reported using different methods (61.24±4.07%, 62.78±4.24%). These results indicate that the use of remote sensing provide favorable information for the characterization of grassland bird's habitat. [ABSTRACT FROM AUTHOR]

In Mexico, the National Forestry Commission implements the payment for hydrological environmental services, whose objective is to reduce the rate of deforestation in critical areas for water recharging. Although the approved projects are followed up, there is no analysis of the benefits generated by this mechanism. Generating information for these evaluations plays a key role. The main objective of this research was to perform an ecological analysis of payment for hydrological environmental services beneficiary area in the common land La Ciudad, seven years after the allocation of support. The forest cover of the area, the state of the vegetation, the diversity of species and the quality of the water were analyzed. The results showed that the area has a considerable percentage of forest cover and acceptable biodiversity. In the analysis of water quality, contamination can occur, which is why more frequent monitoring is recommended. [ABSTRACT FROM AUTHOR]

Object based image analysis (OBIA) approach has been proved as the best option when working with very high resolution (VHR) satellite imagery. The first step of OBIA, referred to as segmentation, is to delineate objects of interest. Determination of an optimal segmentation is crucial for a good performance of the second stage in OBIA, the classification process. The main goal of this work is to assess the multiresolution segmentation algorithm provided by eCognition software for delineating greenhouses from World- View-2 multispectral orthoimages and to find the relationship between the goodness of the segmentation and the accuracy of the supervised classification. The focus is on finding the optimal parameters of the multiresolution segmentation approach (i.e., Scale, Shape and Compactness parameters) for plastic greenhouses. Assessment of segmentation quality was based on the Euclidean Distance 2 (ED2). Finally, we demonstrated that there was a clear relationship between the goodness of the segmentation on plastic greenhouses and the OBIA classification accuracy attained when features such as spectral, textural and vegetation indices were used. The best overall accuracy attained with the best multiresolution segmentation was slightly better than 95%. [ABSTRACT FROM AUTHOR]

One of the potential distribution areas for tropical dry forest in Colombia is Cauca River Valley. Intensive agricultural activity in this zone has been one of the main factors causing its degradation since the middle of the 20th century. This study identifies and locates the natural areas that have persisted from 1984-2014 in the Cauca River Valley, in Valle del Cauca Department, using cartographic information and satellite images (Landsat TM5 and OLI). The results indicate that in spite of the 50 % reduction in natural areas due to a 72 % increase in cultivated areas, 26 % of them have persisted since 1984, especially in the dry warm forest of the alluvial plain and in the dry warm forest of the alluvial piedmont, ecosystems of the helobiome biomes of the Valle del Cauca and the tropical alternohydric zonobiome of Valle del Cauca, respectively. The persistent natural areas identified in the time frame represents an opportunity to initiate ecological restoration processes and monitoring of dry forest vegetation and its cover in Valle del Cauca, considering that few remnants still retain structural and functional characteristics of this Biome and the conditions of connectivity between them are deficient. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Investigación is the property of Universidad Pedagogica Experimental Libertador 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 Southwest region of the State of Mexico (PSEM as it stands in Spanish) is located in the so called Mexican Transition Mountainous Zone. The forest communities that prevail in this area are pine trees forests (Pinus spp.), oak trees forests (Quercus spp.), mixed ones, mountainous cloud forest and tropical deciduous forest, which are being modified due to the influence of human processes. This research paper establishes a methodology to analyze the changes on the land cover of the PSEM which took place from 1989 to 2009 using a technique of image satellite interpretation (Landsat TM), as well as the use of the maximum authenticity algorithm which leads towards the implementation of soil and vegetation maps whose main purpose is to obtain information on the validity of these study cases. Overlapping those maps, it is possible to come up with a matrix of changes to check annual modifications on the land cover, and based on it the changes rates can be measured. From data obtained through this research, it can be stated that the land cover of this forest has considerably decreased because 19 262 ha disappeared and were taken up by water supplies (105 ha), farming lands (19 113 ha), human settlements (39 ha) and technical and controlled agriculture (5 ha). It can be summed up that this deforestation mainly comes as a result of the practice of agriculture and livestock activities indeed. [ABSTRACT FROM AUTHOR]

Copyright of UD y la GEOMÁTICA is the property of UD y la GEOMATICA 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]

This paper presents an incremental k-most similar neighbor classifier, for mixed data and similarity functions that are not necessarily distances. The algorithm presented is suitable for processing large data sets, because it only stores in main memory the k most similar neighbors processed until step t, traversing only once the training data set. Several experiments with synthetic and real data are presented. [ABSTRACT FROM AUTHOR]