Variabilidade Temporal da Cobertura das Terras nos Estados da Paraíba e Rio Grande do Norte

Oliveria, Verônica Gabriella de; Silva, Madson Tavares; Santos, Carlos Antônio Costa dos; Serrão, Edivaldo Afonso de Oliveira; Silva, Bruce Kelly da Nóbrega; Santos, Marcos Ronielly da Silva; Corrêa, Irene Cristina Pereira

doi:10.1590/0102-77863610011

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Despite the significant reduction in NDVI values from the wet to the dry season, the maximum values are approximate. mainly in the month of the dry period (December) due to the variability of species losing their leaves, indicating low photosynthetic activity, a phenological characteristic of the caatinga biome (Oliveira et al, 2021;Oliveira and Costa, 2020). Associated with this, the spatial heterogeneity of NDVI found in the hydrographic basin, both in the dry and rainy seasons, is due to the great diversity of native botanical families in the Caatinga, which present different growth behavior and sizes (Rodrigues et al, 2020;Bezerra et al, 2014).…”

Section: Resultsmentioning

confidence: 99%

Spatiotemporal Variation of the Vegetation Index by Normalized Difference in the Hydrographic Basin of Pernambuco Semiarid, Brazil

Silva

Lopes

2021

Jour. Hypers. Rem. Sensg.

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Remote sensing is an important mechanism for spatiotemporal monitoring of vegetation. Vegetation indexes have been widely used in monitoring vegetated areas, to determine and estimate the index of leaf area, biomass, and photosynthetically active radiation, among others. In these indexes, the Normalized Difference Index Vegetation (NDVI) stands out, which indicates the health status of a vegetative canopy. Considering the proven applicability of this index, in addition to the relevant importance of the Caatinga biome, related to the need for monitoring at the expense of desertification processes, the article aimed to analyze temporal variations of the NDVI in a hydrographic basin of the Brazilian semiarid region, in the municipality of Itacuruba/PE, Brazil. The study area covers a hydrographic basin belonging to the São Francisco River. Remote sensing techniques were used to estimate the NDVI in four orbital images provided by NASA/USGS in the year 2020, with less than 10% clouds. Then, the data were subjected to descriptive statistical analysis to obtain minimum, maximum, mean, standard deviation, and coefficient of variation values. The results showed that the NDVI presented values ranging from -0.30 to 0.57. The means found ranged from 0.16 to 0.23 for the months studied, as well as in the standard deviation values, values between 0.06 and 0.09 were found, corroborating other correlated studies in the region. The coefficient of variation data showed average variability, according to the Warrick & Nielsen classification criteria. It is noteworthy that, in addition to the dry climate of the region, the shrubby vegetation presents physiological defense characteristics such as the fall of foliage. It was concluded that the use of remote sensing was effective in evaluating the spatial-temporal dynamics of the vegetation index in the watershed, managing to measure environmental variations at the expense of climate change.

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Section: Resultsmentioning

confidence: 99%

Spatiotemporal Variation of the Vegetation Index by Normalized Difference in the Hydrographic Basin of Pernambuco Semiarid, Brazil

Silva

Lopes

2021

Jour. Hypers. Rem. Sensg.

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“…Furthermore, with 36 spectral bands between 0.405 and 14.385 μm and spatial resolution ranging from 250 m to 1 km, MODIS offers products related to the atmosphere, surface, cryosphere, and oceans (Bisht & Bras 2011). These characteristics make it a good option for the study of surface Rn in the semiarid region of Brazil, because of its proximity to the Atlantic Ocean, cloud cover is frequent throughout the day in this region, considerably reducing the possibilities of obtaining cloudless satellite images from satellites with higher spatial resolution and lower temporal resolution, for example, the Landsat satellite with a 16-day orbital cycle (Santos et al 2017;Santos et al 2020;Oliveria et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Net Radiation in the Semiarid Region of the States of Paraíba and Rio Grande do Norte Using the MODIS Sensor

Silva

Oliveira

Santos

et al. 2022

Anu. Inst. Geociênc.

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This study aimed to estimate the surface net radiation (Rn) for the States of Paraíba and Rio Grande do Norte and validate the results with data measured in validate the results with measurements in experimental recovering (CRec), degraded (CDeg), and preserved (CPres) areas of Caatinga. Seventeen images of the MODIS sensor on board the Terra satellite were selected for CRec and CDeg in the State of Paraíba and twenty-eight images for CPres in the state of Rio Grande do Norte, corresponding to the period of January to December 2014. The MODIS-Terra MOD09A1 and MOD11A2 products were used. Parametric (Shapiro-Wilk) and non-parametric (Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney) tests were applied to understand the patterns. Statistical error indices were applied to verify the accuracy of the estimates in relation to the observed values. Only the measured albedo (α_meas) met the assumption of normality. According to mean absolute percentage error statistics, the model used to estimate the albedo was classified as inappropriate for CPres and CRec ( 25%), while it was satisfactory for CDeg (15-25%). Significant differences between observed and estimated values were absent only for the variable Rn in the three observation sites. The good performance of Rn estimates confirms the efficiency of the application of the method in semiarid regions.

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Application of Remote Sensing for Climatic Vulnerability Analysis of the Urban Area of Lages/SC through the Use of Radiometric Index and Surface Temperature

Coelho,

Campos,

Sá

et al. 2024

Rev. Bras. Geog. Fis.

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The growth of cities caused by the increase in population and human activities directly resulted in an increase in air temperature in the urban area, which can lead to changes in microclimatic patterns and consequently impact on the quality of life of the population. To mitigate these effects, the need for climate studies in urban areas becomes essential, as it is a measure that can guide public managers to make effective decisions aimed at the pursuit of sustainable urban planning. The objective of the present study was, through remote sensing, to verify possible alterations in the microclimatic patterns of the mountainous region of Santa Catarina, in the long term, through the analysis of data referring to the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST). These data were obtained through the use of orbital images from the LANDSAT satellite (centered in 1997, 2009 and 2021) together with the processing carried out by the QGIS software. The results obtained from the NDVI show that there is a significant increase in the exposed soil class of 16.88% (Winter) and 11.63% (Summer) over the analyzed period (1997-2021). The LST also showed a significant increase for both winter and summer, especially the average temperature. Therefore, the results of this study demonstrate the importance of the contribution of Remote Sensing, as a tool that allows the monitoring and spatial analysis of long decades, which can serve as subsidies for public managers to act effectively in their communities, in the issue of urban planning.

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Variabilidade Temporal da Cobertura das Terras nos Estados da Paraíba e Rio Grande do Norte

Cited by 3 publications

References 39 publications

Spatiotemporal Variation of the Vegetation Index by Normalized Difference in the Hydrographic Basin of Pernambuco Semiarid, Brazil

Spatiotemporal Variation of the Vegetation Index by Normalized Difference in the Hydrographic Basin of Pernambuco Semiarid, Brazil

Net Radiation in the Semiarid Region of the States of Paraíba and Rio Grande do Norte Using the MODIS Sensor

Application of Remote Sensing for Climatic Vulnerability Analysis of the Urban Area of Lages/SC through the Use of Radiometric Index and Surface Temperature

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