Implicaciones del filtrado de calidad del índice de vegetación EVI para el seguimiento funcional de ecosistemas

Reyes-Díez, Andrés; Alcaraz‐Segura, Domingo; Cabello, Javier

doi:10.4995/raet.2015.3316

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…Within this framework, the intention is to contribute to understanding AI through a calculation methodology comprising an index of land use intensity based on the assessment of the main agricultural uses in the territory, and the linking of this index with the behavior of aboveground net primary production (ANPP), depending on the land's productive potential. Since the ANPP is a variable of the behavior of the environmental system comprising structural and/or functional attributes [23][24][25][26] and AI is a process of (self) transformation of these attributes, the analysis of this variable allows us to approach the operation of environmental systems [25].…”

Section: Introductionmentioning

confidence: 99%

Agricultural Transformations in the Southern Cone of Latin America: Agricultural Intensification and Decrease of the Aboveground Net Primary Production, Uruguay’s Case

2019

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The agri-exporting enclaves of the current corporate food regime intensively exploit natural assets in global strategies that govern the local processes. Their multidimensional impacts contribute to the environmental and civilizational crisis. Linked to the agrarian metabolism in its appropriation phase, land use has impacts on local systems. To understand this process, it is necessary to identify the systemic and spatial expression of environmental transformation. The objective of this work was to analyze the dynamic adjustment of aboveground net primary production (ANPP) to agricultural intensification between the years 2000 and 2017, using a land use intensity index and the soils’ productive potential. Agricultural expansion and consolidation are observed, as well as the significant intensification throughout the period in 65% of the country’s area—with differences between regions—associated with soil types. ANPP is higher in areas of low land use intensity and lower in high intensity areas, varying from high to low in soils with low to high productive potential, respectively, and growing throughout the period—depending on the area, with less growth in areas of greater intensity. The appropriation of edaphic wealth puts the systemic functionality at risk and challenges these transforming dynamics, with a strong impact on southern systems.

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Section: Introductionmentioning

confidence: 99%

Agricultural Transformations in the Southern Cone of Latin America: Agricultural Intensification and Decrease of the Aboveground Net Primary Production, Uruguay’s Case

2019

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“…The variables used in this study that contributed to predicting fuel load are related to primary productivity, such as the EVI. Vegetation indices reflect the spatiotemporal variation in primary productivity [21,49,67] and are better predictors than variables that remain constant over long periods of time, such as topography [12]. Vegetation indices, which can be freely obtained, are variables that can reduce economic and logistical costs in the evaluation of forestry resources.…”

Section: Discussionmentioning

confidence: 99%

“…The EVI reduces both atmospheric interference and soil saturation, is more sensitive to canopy structural variations and is more reliable under high-biomass conditions [49].…”

Section: Prediction Fuel Loadmentioning

confidence: 99%

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Aradillas-González

Vargas-Tristán

Azuara-Domínguez

et al. 2022

Forests

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The prediction of fuel load areas and species associated with these events reduces the response time to fight forest fires. The objective of this study was to estimate the annual fuel load from 2009–2013, predict the annual fuel load in the rest of the ecosystem, identify species that contribute most to this load and compare the percentage of area by risk category in the temperate forests of Tamaulipas. Fuel load was estimated with inventory data using three models. Fuel load was predicted with elevation, total annual precipitation, mean annual temperature, and enhanced vegetation index from satellite scenes using partial least squares regression. The highest concentration of fuel load was associated with the oak, oak-pine, pine forest and mountain mesophyll forest ecosystems. The contribution of genera to fuel load was different. Quercus contributed the most variation among clusters, and the contribution among Quercus species was similar. The results highlight the importance of focusing fuel management programs on this type of ecosystem, emphasizing actions in particular Quercus, and the results can also serve as a basis for future research, such as carbon sequestration and forest management programs.

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“…On their basis, ideas about the significance of individual factors of vegetation are formed; the conformity of management decisions to changes in environmental conditions is assessed; the dynamics of the state of agro-landscapes is forecasted. An important source of information on farmland productivity comes from multi-zone satellite filming (Ambika and Mishra, 2019;Jaafar and Ahmad, 2015;Reyes-Díez et al, 2015;Rokni and Musa, 2019). The "specialization" of satellite images as a source of information is associated with wide possibilities for analyzing the state and dynamics of agrocenoses of large regions with an area of tens of thousands of square kilometers, with a distraction from local variability of vegetation factors.…”

Section: Introductionmentioning

confidence: 99%

Seasonal and Long-Term Changes of Vegetation Index of Arable Lands of Bryansk Region (Central Russia): Regularities and Dynamics Factors

Лобанов¹,

Avramenko²,

Protasova³

et al. 2020

PTQ

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There are a number of different factors that affect the sustainable development of agriculture. Efficient use of land and water resources, taking into account the threats that may arise due to climate change, as well as the dynamics of plant growth and development. Vegetation indices play an important role in monitoring vegetation variations. This article provides information on seasonal and long-term changes in the EVI (Enhanced Vegetation Index) of arable land in the Bryansk region. The purpose of the article is to discern the course of the seasonal dynamics of the index, the ranges of its variability, probable causes of differences between 2000-2018. In this article, physicochemical research methods were used to calculate the humus content, surface topography characteristics, and agricultural use features based on cartographic and stock materials, and the soil composition and density were determined through fieldwork. Summarized material on changes in EVI values for a snowless period as a whole and for individual filming intervals (18 per year) in 2000-2018 is presented. The mechanism of the influence of a variety of edaphic vegetation conditions on smoothing the differences in EVI over a series of years with different meteorological conditions has been described. The results of the analysis of the factors of the long-term dynamics are presented, the role of short-term climatic fluctuations, a progressive decrease in the amount of precipitation, and changes in the species composition of grain crops in the long-term dynamics of EVI are demonstrated.

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Implicaciones del filtrado de calidad del índice de vegetación EVI para el seguimiento funcional de ecosistemas

Cited by 7 publications

References 49 publications

Agricultural Transformations in the Southern Cone of Latin America: Agricultural Intensification and Decrease of the Aboveground Net Primary Production, Uruguay’s Case

Agricultural Transformations in the Southern Cone of Latin America: Agricultural Intensification and Decrease of the Aboveground Net Primary Production, Uruguay’s Case

Spatial and Temporal Variations of Predicting Fuel Load in Temperate Forests of Northeastern Mexico

Seasonal and Long-Term Changes of Vegetation Index of Arable Lands of Bryansk Region (Central Russia): Regularities and Dynamics Factors

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