Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid

Silva, Bernardo Barbosa da; Braga, Alexandre; Silva, Madson Tavares; Oliveira, Leidjane Maria Maciel de; Montenegro, Suzana Maria Gico Lima; Junior, Bernardo Barbosa

doi:10.1590/1807-1929/agriambi.v20n1p3-8

Cited by 89 publications

(92 citation statements)

References 21 publications

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“…To estimate albedo (α), the Surface Energy Balance Algorithm for Land (SEBAL) model proposed by Bastiaanssen (1995) was used, as shown in Equation 4. The atmospheric albedo (αatm) was obtained with the radiative transfer model, where the value of this variable recommended for the SEBAL model is 0.03 (LIBERATO, 2011;SILVA et al, 2016). Transmissivity (τoc) for clear sky conditions was described by Allen et al (2007)…”

Section: Surface Albedomentioning

confidence: 99%

“…Listed by the Global Climate Observation System (Global Climate Observation System -GCOS), albedo is one of the key climate variables, considered the key that controls the planetary radiative energy balance and the energy portion between the atmosphere and the Earth's surface (LUKES et al 2014), represented as the integration of reflectance along the solar radiation spectrum. The changes in albedo modify the radiation and energy balance of the surface, which can be detected with its monitoring (SILVA et al, 2016). Hahmann and Dickinson (1997), when developing modeling of climate, they have identified that changes in surface albedo and vegetation roughness correspond to the two most important biophysical properties influencing the local climate.…”

Section: Introductionmentioning

confidence: 99%

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Influence of Forest Coverage in the Surface Albedo

Alba¹,

Marchesan²,

Schuh³

et al. 2019

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The surface albedo controls the energy balance between the surface and the atmosphere, being a primordial variable to identify climatic variations. The objective of this study was to evaluate the changes of the surface albedo in different Land Use and Land Cover in the Atlantic Forest biome from images TM/Landsat 5 and OLI/Landsat 8, verifying its variation in 30 years. The images used were path-row 221-080, which covered the Floresta Nacional de São Francisco de Paula on the dates of 1987 and 2017. The albedo was obtained by the method of the Surface Energy Balance Algorithm for Land, while the mapping of Land Use and Land Cover was performed by the Bhattacharyya algorithm, identifying four thematic classes. Finally, the albedo was crossed with the thematic classes, evidencing their variation in function of the changes in the land cover. The surface albedo ranged from 6 to 22%, but the year 1987 concentrated albedo values higher than in 2017. The native forest presented superior albedo to the Forest Plantations in both dates due to the structure of the canopy of this class. The spatial analysis of the albedo exposes the relation of this climatic variable to the cover of the terrestrial surface. Thus changes in the vegetation cover cause alterations in the albedo, influencing changes in the radiation and atmospheric fluxes.

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Section: Surface Albedomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Influence of Forest Coverage in the Surface Albedo

Alba¹,

Marchesan²,

Schuh³

et al. 2019

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“…(1) onde, λiL é a radiância espectral de cada banda, λik é a irradiância solar espectral de cada banda no topo da atmosfera, Z é o ângulo zenital solar e dr é o quadrado da razão entre a distância média TerraSol e a distância Terra-Sol em dado dia do ano (Silva et al, 2016). (2) onde, segundo Silva et al (2016), o Addrefb corresponde ao fator aditivo de reescalonamento para cada banda, disponível no arquivo metadados da imagem, assim como o Multrefb que corresponde ao fator multiplicativo de reescalonamento para cada banda. ND representa os valores de número digital da imagem, Z é o ângulo zenital solar e também pode ser encontrado no arquivo metadados da imagem.…”

Section: Reflectânciaunclassified

Análise comparativa da umidade da vegetação de áreas de caatinga preservada, agricultura irrigada e sequeiro

Brito

Morais

Ferreira

et al. 2017

J. Env. Anal. Progr.

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A B S T R A C TThe unplanned use of the resources of the caatinga biome has been provoking several environmental degradation processes, which interferes negatively in the physical and biological systems of the semi-arid region. Thus, the present study aimed to analyze the biophysical differences in areas of preserved and degraded caatinga, as well as irrigated and dry farming areas. Two images of the satellite Landsat 8 -Sensor OLI, corresponding to 05/22/2016 and 17/01/2017 were made. In which the reflectance, NDVI and NDWI calculations were applied. The results showed a difference in the values of NDVI and NDWI between the images of 2016 and 2017 according to the different types of vegetation. The areas of caatinga presented predominance of NDVI values from 0.4 to 0.6. Higher NDVI and NDWI characterized irrigated perimeters than in caatinga and dryland areas. Such differences between the analyzed days were the result of the absence and/or low amount of precipitation in the months before the imaging days. As for irrigated agriculture, which has a greater availability of water, than the influence on the nature of plants and in the moisture content present in the soil. Keywords: Semi-arid, biophysical parameters, vegetation. R E S U M OO uso não planejado dos recursos do bioma caatinga vem provocando diversos processos de degradação ambiental, os quais interferem negativamente sobre sistemas físico-naturais e biológicos da região semiárida. Sendo assim, o presente estudo teve como objetivo analisar as diferenças de parâmetros biofísicos em áreas de caatinga preservada e degradada, bem como das áreas de agricultura irrigada e sequeiro. Foram utilizadas duas imagens do satélite Landsat 8 -Sensor OLI, correspondentes aos dias 22/05/2016 e 17/01/2017. Nas quais foram aplicados os cálculos de reflectância, NDVI e NDWI. Os resultados mostraram uma diferença nos valores de NDVI e NDWI entre as imagens de 2016 e 2017 de acordo com os diferentes tipos de vegetação. As áreas de caatinga apresentaram predominância de valores de NDVI de 0,4 a 0,6. Os perímetros irrigados destacaram-se por apresentar NDVI e NDWI maiores do que nas áreas de caatinga e de sequeiro. Tais diferenças entre os dias analisados resultaram da ausência e/ou baixa quantidade de precipitação

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“…The value of 0.03 was adopted in the present study. On the other hand, and were calculated using equations adopted from Silva et al (2016). The calculations were done in ENVI 5.1 software using its Band Math tool.…”

Section: Calculation and Mapping Of Individual Roof Surface Albedomentioning

confidence: 99%

Modelling Mean Albedo of Individual Roofs in Complex Urban Areas Using Satellite Images and Airborne Laser Scanning Point Clouds

Kalantar

Mansor

Khuzaimah

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Knowledge of surface albedo at individual roof scale is important for mitigating urban heat islands and understanding urban climate change. This study presents a method for quantifying surface albedo of individual roofs in a complex urban area using the integration of Landsat 8 and airborne LiDAR data. First, individual roofs were extracted from airborne LiDAR data and orthophotos using optimized segmentation and supervised object based image analysis (OBIA). Support vector machine (SVM) was used as a classifier in OBIA process for extracting individual roofs. The user-defined parameters required in SVM classifier were selected using v-fold cross validation method. After that, surface albedo was calculated for each individual roof from Landsat images. Finally, thematic maps of mean surface albedo of individual roofs were generated in GIS and the results were discussed. Results showed that the study area is covered by 35% of buildings varying in roofing material types and conditions. The calculated surface albedo of buildings ranged from 0.16 to 0.65 in the study area. More importantly, the results indicated that the types and conditions of roofing materials significantly effect on the mean value of surface albedo. Mean albedo of new concrete, old concrete, new steel, and old steel were found to be equal to 0.38, 0.26, 0.51, and 0.44 respectively. Replacing old roofing materials with new ones should highly prioritized.

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Procedures for calculation of the albedo with OLI-Landsat 8 images: Application to the Brazilian semi-arid

Cited by 89 publications

References 21 publications

Influence of Forest Coverage in the Surface Albedo

Influence of Forest Coverage in the Surface Albedo

Análise comparativa da umidade da vegetação de áreas de caatinga preservada, agricultura irrigada e sequeiro

Modelling Mean Albedo of Individual Roofs in Complex Urban Areas Using Satellite Images and Airborne Laser Scanning Point Clouds

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