Dynamics of land cover and land use in Pernambuco (Brazil): Spatio-temporal variability and temporal trends of biophysical parameters

Bezerra, Alan Cézar; Silva, Jhon Lennon Bezerra da; Moura, Geber Barbosa de Albuquerque; Lopes, Pabrício Marcos Oliveira; Nascimento, Cristina Rodrigues; Ribeiro, Eberson Pessoa; Galvíncio, Josiclêda Domiciano; Silva, Marcos Vinícius da

doi:10.1016/j.rsase.2021.100677

Cited by 6 publications

(8 citation statements)

References 29 publications

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“…The temporal distribution of rainfall has a strong influence on the water balance of the Caatinga biome and, consequently, on the soil moisture, directly influencing the NDVI (Arraes et al, 2012). The reduction of humidity and the reduction of the vegetative canopy result in an increase in the incidence of radiation on the surface, corroborating Bezerra et al (2021aBezerra et al ( , 2021b for the semiarid region of the state of Pernambuco, Northeast of Brazil.…”

Section: Resultssupporting

confidence: 55%

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: Resultssupporting

confidence: 55%

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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“…According to Huete et al [74], the value of 6 is assigned to C1, and the value of 7.5 is assigned to C2; L-adjustment factor for soil conditions, which can vary between 0 and 1 [74,75]. Thus, following the recommendation of various studies conducted in NEB and the BSR, the value of L for this application was 0.5, due to the semiarid conditions in NEB [14,49,51,61]. The numerical values aim to stabilize atmospheric variations, minimizing the residual effects and impacts of aerosols in the study area.…”

Section: Orbital Data From Terra and Aqua Satellites (Modis Sensor)mentioning

confidence: 99%

“…All images were processed digitally in an automated manner in a cloud computing environment using the GEE platform (https://earthengine.google.com/-accessed on 4 January 2021) [63]. GEE features a library with sets of geospatial, climatic, and environmental datasets, standing out for its multiple functions of mathematical analyses, computational modeling, and machine learning operations through specific algorithms to determine, for example, the biophysical parameters of surface energy balance [14,15,61,62], which was the main objective of this research. With that, a methodological script in Python programming language was developed in the GEE, applicable for determining parameters such as albedo and surface temperature, vegetation indices, and actual evapotranspiration.…”

Section: Orbital Data From Terra and Aqua Satellites (Modis Sensor)mentioning

confidence: 99%

“…Overall, these technologies provide effective support in earth sciences research applications. Remote sensing yields results through products extracted from satellite data, such as those obtained from the Moderate Resolution Imaging Spectroradiometer-MODIS [12,15,18,44,50,56,[60][61][62]. For example, the GEE platform of GIS operates with the applicability of cloud-based digital processing, using algorithms, machine learning operations, and computational mathematical modeling [63], offering effective applications at low operational cost [14].…”

Section: Introductionmentioning

confidence: 99%

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Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil

Silva,

Jardim

et al. 2024

Hydrology

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Northeast Brazil (NEB), particularly its semiarid region, represents an area highly susceptible to the impacts of climate change, including severe droughts, and intense anthropogenic activities. These stresses may be accelerating environmental degradation and desertification of soil in NEB. The main aim of this study was to gain geospatial insights into the biophysical parameters of surface energy balance and actual evapotranspiration on a multi-temporal scale, aiming to detect and analyze the spectral behavioral patterns of areas vulnerable to degradation processes, based on thematic maps at the surface, for NEB and mainly the semiarid region of NEB from 2000 to 2019. Geospatial data from 8-day MODIS sensor products were used, such as surface reflectance (Terra/MOD09A1 and Aqua/MYD09A1), surface temperature (Terra/MOD11A2 and Aqua/MYD11A2), and actual evapotranspiration (Terra/MOD16A2 and Aqua/MYD16A2), version 6. Therefore, in this study, pixel-to-pixel values were processed by calculating the average pixel statistics for each year. From the reflectance product, digital processing of the surface albedo and spectral vegetation indices was also carried out, using computational programming scripts and machine learning algorithms developed via the Google Earth Engine (GEE) platform. The study also presents a seasonal analysis of these components and their relationships over 20 years. Through vegetation indices and statistical correlations, a new predictive model of actual evapotranspiration was developed. The quantitative and spatiotemporal spectral patterns of the parameters were assessed through descriptive statistics, measures of central tendency and dispersion, and statistical error analyses and correlation indices. Thematic maps highlighted the pixel-to-pixel results, with patterns of high temperature distribution mainly in the central and northeastern part of NEB and the semiarid region of NEB, highlighting the formation of persistent heat islands over time. Meanwhile, in these areas, the maps of actual evapotranspiration showed a drastic reduction due to the lesser availability of energy. Over time, the semiarid region of NEB presented areas with little and/or no vegetation cover, which were highly well-defined between the years 2012 and 2019, confirming that these areas are extremely vulnerable to degradation and desertification processes due to significant loss of vegetative and water resilience. The components of energy balance were highly interconnected to climatological and environmental conditions, showing the severe results of drought and accentuation of the water deficit in NEB, presenting a greater condition of aridity in the semiarid region of NEB over time.

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“…One of the most commonly used and studied crop health indicators is the Normalized Difference Vegetation Index -NDVI (Rouse et al, 1973). Another index used is the Soil-Adjusted Vegetation Index -SAVI, which is an NDVI correction, aiming to minimize the soil effect (Huete, 1988;Silva et al, 2020;Bezerra et al, 2022;Martins et al, 2022;Silva et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of thermal and optical properties measurements of paspalum in modular green roof in a tropical environment

Ferreira

Lopes²,

Silva³

et al. 2023

Rev. Bras. Geog. Fis.

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Artigo recebido em 01/10/2022 e aceito em 06/02/2023 R E S U M O O objetivo desta pesquisa foi avaliar a utilização de medições de propriedades térmicas e ópticas na caracterização do Gênero Paspalum para utilização em telhados verdes num ambiente tropical. O experimento foi conduzida na Universidade Federal Rural de Pernambuco -UFRPE de Maio a Junho de 2019. Foi avaliado o número de dezesseis acessos de Paspalum com hábitos de crescimento vertical ou prostrado. Em condições simuladas de um telhado verde, foram avaliadas: Índice de Vegetação Ajustada ao Solo (SAVI); temperatura das folhas (L) e do dossel (C) por termometria (TM); e por termografia (TG). O coeficiente de determinação (R 2 ) entre SAVI e a área de cobertura dos acessos foi de 0,84, e com matéria verde (R 2 = 0,73) e matéria seca (R 2 = 0,56). Não foi observada diferença significativa entre os acessos de Paspalum para a medição da temperatura da folha por TM e TG, e para o dossel TM. No entanto, foi observada uma diferença significativa entre a TG do dossel dos acessos de Paspalum, com temperaturas do dossel mais baixas nos acessos com hábito de crescimento vertical do que nos acessos com hábito prostado. A relação entre as técnicas mostrou valores elevados de coeficiente de determinação para TML com TGL (R 2 = 0,85) e TMC com TGC (R 2 = 0,91). Conclui-se que, o SAVI pode ser utilizado para estimar capacidade de área de cobertura vegetal dos acessos. As temperaturas das folhas e das copas podem ser usadas como indicação de déficit hídrico nos acessos de Paspalum, utilizando tanto o termómetro de infravermelhos (mais acessível) como a câmara termográfica. A termografia do dossel também pode ser utilizada para avaliar o desempenho térmico dos telhados verdes. As imagens termográficas indicaram que os acessos de Paspalum de hábitos de crescimento verticais podem proporcionar um melhor conforto térmico. Palavras-chave: Infravermelho; termografia; termometria; SAVI; fenotipagem; melhoramento de plantas. Evaluation of thermal and optical properties measurements of paspalum in modular green roof in a tropical environment A B S T R A C TThe objective was to evaluate the use of thermal and optical properties measurements in the characterization of the genus Paspalum for use on green roofs in a tropical environment. The experiment was conducted at the Universidade Federal Rural de Pernambuco -UFRPE from May to June 2019. The number of sixteen Paspalum accessions with prostrate and upright growth habits was evaluated. In a simulated modular green roof were evaluated: Soil-Adjusted Vegetation Index (SAVI); leaf (L) and canopy (C) temperature by thermometry (TM); and by thermography (TG). The determination coefficient (R 2 ) between SAVI and the coverage area are of 0.84, with green matter (R 2 = 0.73) and dry matter (R 2 = 0.56). No significant difference was observed between the accessions for leaf temperature measure by TM and TG, and for canopy TM. Nevertheless, significant difference to canopy TG were observed, with lower canopy temperatures in accessions w...

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Dynamics of land cover and land use in Pernambuco (Brazil): Spatio-temporal variability and temporal trends of biophysical parameters

Cited by 6 publications

References 29 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

Geospatial Insights into Aridity Conditions: MODIS Products and GIS Modeling in Northeast Brazil

Evaluation of thermal and optical properties measurements of paspalum in modular green roof in a tropical environment

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