Características del hábitat que regulan la estructura de las comunidades de macroinvertebrados en ríos tropicales de montaña (Antioquía, Colombia) / Habitat characteristics that regulate the structure of macroinvertebrate communities in tropical mountain rivers (Antioquía, Colombia)

Introducción: Las comunidades de macroinvertebrados son afectadas simultáneamente por la calidad del agua y las características físicas del hábitat acuático, complicando su uso en la bioindicación. Objetivo: Determinar cuáles variables del hábitat condicionan la comunidad de macroinvertebrados acuáticos en algunas corrientes (quebradas) de montaña del Oriente antioqueño (Colombia). Métodos: El muestreo se realizó en febrero 2021 (periodo de transición seco-lluvia), para evaluar variables físicas y químicas en tres tipos de mesohábitats: rápidos, rizos y pozas en corrientes con coberturas vegetales contrastantes. Los macroinvertebrados fueron recolectados en diez sitios de muestreo con red tipo net, pantalla y manual, y preservados en etanol al 70 %. Resultados: Se recolectaron 4 484 macroinvertebrados (16 órdenes, 46 familias y 75 géneros). El mesohábitat rizo presentó mayores valores de diversidad y abundancia, mientras las pozas presentaron los menores. Hubo diferencias en la concentración de oxígeno, profundidad, velocidad y abundancia de macroinvertebrados entre mesohábitats. Las pozas defirieron de los otros mesohábitats en profundidad, velocidad, así como en la composición, abundancia y riqueza de macroinvertebrados, y fue el hábitat de menor preferencia. Conclusión: La velocidad, profundidad y concentración de oxígeno disuelto, desempeñan un papel muy importante en el establecimiento de las comunidades de macroinvertebrados en los diferentes mesohábitats. En el mismo tipo de mesohábitat, la calidad de la cobertura vegetal determinó la diversidad y abundancia de esta comunidad.

Introduction: Macroinvertebrate communities are affected by water quality and physical characteristics of the aquatic habitat, simultaneously, complicating their use as bioindicators. Objective: To determine which habitat variables regulate the macroinvertebrate community in mountain streams in Eastern of Antioquia (Colombia). Methods: Sampling was carried out in February 2021 (dry-rain transition period), to evaluate physical and chemical variables in three types of mesohabitat: ripples, pools, and rapids in streams with contrasting vegetation covers. The macroinvertebrates were collected from ten sampling sites with a net, screen and manual type net preserved with 70 % ethanol. Results: 4 484 macroinvertebrates were collected (16 orders, 46 families and 75 genera). The ripples mesohabitat presented higher values of diversity and abundance, while the pools presented the lowest. There were differences for oxygen concentration, depth, speed, and macroinvertebrate abundance between mesohabitats. Pools differed from the other mesohabitats in depth, speed, as well as in composition, abundance, and richness in macroinvertebrates, and was the least preferred mesohabitat. Conclusion: Speed, depth, dissolved oxygen concentration played a very important role in the establishment of macroinvertebrates community in different mesohabitats. For the same type of mesohabitat, the quality of the plant cover determined both diversity and abundance of this community.

A Comprehensive Review of Application of RS, GIS and GPS in Agriculture India

Food and fibre, two of humanity's most fundamental requirements, are met by agriculture. In the last century, new farming methods have been introduced, such as the Green Revolution, which has enabled agriculture to keep up with the increasing demand for food and other agricultural goods. But population growth, rising income levels, and increased food demand will probably put more stress on the planet's natural resources. As the detrimental effects of agriculture on the environment become more widely acknowledged, new methods and strategies need to be able to meet future food needs while preserving or lessening the environmental footprint of agriculture. Informed management decisions aiming at increasing crop production could be made with the help of emerging technologies like artificial intelligence (AI), Internet of Things (IoT), big data analysis, and geospatial technology. Many scientists, engineers, agronomists, and researchers use a variety of technologies each year to boost agricultural output while minimising pollution, yet these efforts have a negative environmental impact. Precision agriculture examines how technology might be applied to enhance agricultural practises relative to traditional methods while minimising negative environmental effects. Precision agriculture greatly benefits from the deployment of remote sensing technologies, which also presents new chances to enhance agricultural practises. Geographically, latitude and longitude data can be recorded for field data (slope, aspect, nutrients, and yield) using the global positioning system (GPS). Because of its ability to continuously determine and record the right position, it can build a larger database for the user. Geographic Information Systems (GIS), which can handle and store these data, are needed for the additional analysis. This review will offer you an overview of Remote Sensing technology, GPS, and GIS, and how it might be used for precision agriculture.

Remote Sensing as a Management and Monitoring Tool for Agriculture: Potential Applications

Remote sensing technology has revolutionized agriculture management and monitoring by providing valuable information on crop health, soil conditions, weather patterns, and overall land management. The reflectance data are progressively being exploited in agriculture with the momenta gained in ground-based, airborne, and satellite remote sensing. The agriculture systems when managed conventionally don’t facilitate the proper utilization of resources and productivity potential of the soil. However, taking the aid of remote sensing techniques helps in boosting the productivity potential of the soil and optimizing the inputs. This paper aims to review the potential applications of remote sensing in agriculture and its role in improving productivity, resource efficiency, and sustainability. The paper discusses various remote sensing techniques, including satellite imagery, aerial photography, and sensor-based data collection, and their integration with advanced data analysis methods. The applications explored include biomass estimation, yield estimation, global food demand, salinity stress detection, drought monitoring, soil moisture content assessment, and change detection. The paper highlights the benefits and challenges associated with each application and provides insights into future research directions and technology advancements in the field of remote sensing for agriculture.

Remote Sensing Related Tools and their Spectral Indices Applications for Crop Management in Precision Agriculture

World population increased rapidly has increased food demands for human and fulfill the food requirements with limited available resources of the planet is a big challenge for Agriculture. Farmers will need to increase the food production, either the increasing the agricultural land or enhancing crop productivity in agriculture by using different crop management practices and adopting new methods like precision farming. Concept of precision agriculture that involves integrating new technologies and field data to accomplish the right input at the right time in the right place. However, the agricultural sector is yet to adopt remote sensing technologies fully due to lack of knowledge on their sufficiency, appropriateness and techno-economic feasibilities. This study based on the research literature that focused on the application of remote sensing tools in precision agriculture on different aspect of crop management from field preparation to crop harvesting, with the objective of contributing to the scientific understanding on the potential for RS technologies to support decision-making within different production stages. Remote sensing tools and spectral vegetation index (normalized difference vegetation index & others) to support crop management and decisions making at different crop growth stages of crop production in precision agriculture, ranging from field preparation, weather, insect pest management, biotic & abiotic stress management and in-season crop health monitoring to harvest.

Appraisal of Phlebotomus argentipes habitat suitability using a remotely sensed index in the kala-azar endemic focus of Bihar, India

Visceral leishmaniasis, or kala-azar, is recognised as a serious emerging public health problem in India. In this study, environmental parameters, such as land surface temperature (LST) and renormalised difference vegetation indices (RDVI), were used to delineate the association between environmental variables and Phlebotomus argentipes abundance in a representative endemic region of Bihar, India. The adult P. argentipes were collected between September 2009-February 2010 using the hand-held aspirator technique. The distribution of P. argentipes was analysed with the LST and RDVI of the peak and lean seasons. The association between environmental covariates and P. argentipes density was analysed a multivariate linear regression model. The sandfly density at its maximum in September, whereas the minimum density was recorded in January. The regression model indicated that the season, minimum LST, mean LST and mean RDVI were the best environmental covariates for the P. argentipes distribution. The final model indicated that nearly 74% of the variance of sandfly density could be explained by these environmental covariates. This approach might be useful for mapping and predicting the distribution of P. argentipes, which may help the health agencies that are involved in the kala-azar control programme focus on high-risk areas.

Stratification of density in dry deciduous forest using satellite remote sensing digital data—An approach based on spectral indices.

Forest density expressing the stocking status constitutes the major stand physiognomic parameter of Indian forest. Density and age are often taken as surrogate to structural and compositional changes that occur with the forest succession. Satellite remote sensing spectral response is reported to provide information on structure and composition of forest stands. The various vegetation indices are also correlated with forest canopy closure. The paper presents a three way crown density model utilizing the vegetation indices viz., advanced vegetation index, bare soil index and canopy shadow index for classification of forest crown density. The crop and water classes which could not be delineated by the model were finally masked from normalized difference vegetation index and TM band 7 respectively. The rule based approach has been implemented for land use and forest density classification. The broad land cover classification accuracy has been found to be 91·5%. In the higher forest density classes the classification accuracy ranged between 93 and 95%, whereas in the lower density classes it was found to be between 82 and 85%.