Biomass and vegetation index by remote sensing in different caatinga forest areas

ABSTRACT: Continued unsustainable exploitation of natural resources promotes environmental degradation and threatens the preservation of dry forests around the world. This situation exposes the fragility and the necessity to study landscape transformations. In addition, it is necessary to consider the biomass quantity and to establish strategies to monitor natural and anthropic disturbances. Thus, this research analyzed the relationship between vegetation index and the estimated biomass using allometric equations in different Brazilian caatinga forest areas from satellite images. This procedure is performed by estimating the biomass from 9 dry tropical forest fragments using allometric equations. Area delimitations were obtained from the Embrapa collection of dendrometric data collected in the period between 2011 and 2012. Spectral variables were obtained from the orthorectified images of the RapidEye satellite. The aboveground biomass ranged from 6.88 to 123.82 Mg.ha-1. SAVI values were L = 1 and L = 0.5, while NDVI and EVI ranged from 0.1835 to 0.4294, 0.2197 to 0.5019, 0.3622 to 0.7584, and 0.0987 to 0.3169, respectively. Relationships among the estimated biomass and the vegetation indexes were moderate, with correlation coefficients (Rs) varying between 0.64 and 0.58. The best adjusted equation was the SAVI equation, for which the coefficient of determination was R² = 0.50, R2aj = 0.49, RMSE = 17.18 Mg.ha-1 and mean absolute error of prediction (MAE) = 14.07 Mg.ha-1, confirming the importance of the Savi index in estimating the caatinga aboveground biomass.

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The accuracy of species-specific allometric equations for estimating aboveground biomass in tropical moist montane forests: case study of Albizia grandibracteata and Trichilia dregeana

Carbon Balance and Management ◽

10.1186/s13021-019-0134-8 ◽

2019 ◽

Vol 14 (1) ◽

Cited By ~ 2

Author(s):

Damena Edae Daba ◽

Teshome Soromessa

Keyword(s):

Forest Ecosystem ◽

Aboveground Biomass ◽

Forest Type ◽

Model Performance ◽

Allometric Equation ◽

Allometric Equations ◽

Coefficient Of Determination ◽

Biomass Estimation ◽

Moist Forest ◽

Species Specific

Abstract Background Application of allometric equations for quantifying forests aboveground biomass is a crucial step related to efforts of climate change mitigation. Generalized allometric equations have been applied for estimating biomass and carbon storage of forests. However, adopting a generalized allometric equation to estimate the biomass of different forests generates uncertainty due to environmental variation. Therefore, formulating species-specific allometric equations is important to accurately quantify the biomass. Montane moist forest ecosystem comprises high forest type which is mainly found in the southwestern part of Ethiopia. Yayu Coffee Forest Biosphere Reserve is categorized into Afromontane Rainforest vegetation types in this ecosystem. This study was aimed to formulate species-specific allometric equations for Albizia grandibracteata Tuab. and Trichilia dregeana Sond. using the semi-destructive method. Results Allometric equations in form of power models were developed for each tree species by evaluating the statistical relationships of total aboveground biomass (TAGB) and dendrometric variables. TAGB was regressed against diameter at breast height (D), total height (H), and wood density (ρ) individually and in a combination. The allometric equations were selected based on model performance statistics. Equations with the higher coefficient of determination (adj.R2), lower residual standard error (RSE), and low Akaike information criterion (AIC) values were found best fitted. Relationships between TAGB and predictive variables were found statistically significant (p ≤ 0.001) for all selected equations. Higher bias was reported related to the application of pan-tropical or generalized allometric equations. Conclusions Formulating species-specific allometric equations is found important for accurate tree biomass estimation and quantifying the carbon stock. The developed biomass regression models can be applied as a species-specific equation to the montane moist forest ecosystem of southwestern Ethiopia.

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Assessing vegetation response to drought in the Laohahe catchment, North China

Hydrology Research ◽

10.2166/nh.2011.134 ◽

2012 ◽

Vol 43 (1-2) ◽

pp. 91-101 ◽

Cited By ~ 7

Author(s):

Xiaofan Liu ◽

Liliang Ren ◽

Fei Yuan ◽

Jing Xu ◽

Wei Liu

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Correlation Coefficients ◽

Meteorological Drought ◽

Growing Season ◽

Severity Index ◽

Drought Severity ◽

Seasonal Effect ◽

Vegetation Types ◽

The Relationship

In order to better understand the relationship between vegetation vigour and moisture availability, a correlation analysis based on different vegetation types was conducted between time series of monthly Normalized Difference Vegetation Index (NDVI) and Palmer Drought Severity Index (PDSI) during the growing season from April to October within the Laohahe catchment. It was found that NDVI had good correlation with PDSI, especially for shrub and grass. The correlation between NDVI and PDSI varies significantly from one month to another. The highest value of correlation coefficients appears in June when the vegetation is growing; lower correlations are noted at the end of growing season for all vegetation types. The influence of meteorological drought on vegetation vigour is stronger in the first half of the growing season, before the vegetation reaches the peak greenness. In order to take the seasonal effect into consideration, a regression model with seasonal dummy variables was used to simulate the relationship between NDVI and PDSI. The results showed that the NDVI–PDSI relationship is significant (α = 0.05) within the growing season, and that NDVI is an effective indicator to monitor and detect droughts if seasonal timing is taken into account.

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Satellite-Derived Estimation of Grassland Aboveground Biomass in the Three-River Headwaters Region of China during 1982–2018

Remote Sensing ◽

10.3390/rs13152993 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2993

Author(s):

Ruiyang Yu ◽

Yunjun Yao ◽

Qiao Wang ◽

Huawei Wan ◽

Zijing Xie ◽

...

Keyword(s):

Spatial Resolution ◽

Aboveground Biomass ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growth Period ◽

Spatiotemporal Variation ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Ancillary Data

The long-term estimation of grassland aboveground biomass (AGB) is important for grassland resource management in the Three-River Headwaters Region (TRHR) of China. Due to the lack of reliable grassland AGB datasets since the 1980s, the long-term spatiotemporal variation in grassland AGB in the TRHR remains unclear. In this study, we estimated AGB in the grassland of 209,897 km2 using advanced very high resolution radiometer (AVHRR), MODerate-resolution Imaging Spectroradiometer (MODIS), meteorological, ancillary data during 1982–2018, and 75 AGB ground observations in the growth period of 2009 in the TRHR. To enhance the spatial representativeness of ground observations, we firstly upscaled the grassland AGB using a gradient boosting regression tree (GBRT) model from ground observations to a 1 km spatial resolution via MODIS normalized difference vegetation index (NDVI), meteorological and ancillary data, and the model produced validation results with a coefficient of determination (R2) equal to 0.76, a relative mean square error (RMSE) equal to 88.8 g C m−2, and a bias equal to −1.6 g C m−2 between the ground-observed and MODIS-derived upscaled AGB. Then, we upscaled grassland AGB using the same model from a 1 km to 5 km spatial resolution via AVHRR NDVI and the same data as previously mentioned with the validation accuracy (R2 = 0.74, RMSE = 57.8 g C m−2, and bias = −0.1 g C m−2) between the MODIS-derived reference and AVHRR-derived upscaled AGB. The annual trend of grassland AGB in the TRHR increased by 0.37 g C m−2 (p < 0.05) on average per year during 1982–2018, which was mainly caused by vegetation greening and increased precipitation. This study provided reliable long-term (1982–2018) grassland AGB datasets to monitor the spatiotemporal variation in grassland AGB in the TRHR.

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Semideciduous Seasonal Forest Production of Leaves and Deciduousness in Function of the Water Balance, LAI, and NDVI

International Journal of Ecology ◽

10.1155/2014/923027 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 4

Author(s):

Thomaz Correa e Castro da Costa ◽

João Herbert Moreira Viana ◽

Juliana Leite Ribeiro

Keyword(s):

Water Balance ◽

Vegetation Index ◽

Carbon Sink ◽

Quadratic Functions ◽

Permanent Plots ◽

Forest Fragments ◽

Leaf Production ◽

Semideciduous Forest ◽

Area Index ◽

The Relationship

This study investigated the relationship between leaf production, litterfall, water balance, Leaf Area Index (LAI), and Normalized Difference Vegetation Index (NDVI) in semideciduous forests. The goal was to model this phenomenon to obtain the estimates of this component as an additional compartment of the ecosystem carbon sink. The tests were conducted in eight semideciduous forest fragments. Twenty-four permanent plots were monitored monthly and LAI measurements and weighing of litterfall deposited in nets were conducted for a period of thirteen months. In this period, Landsat 5 and IRS satellite images were obtained and processed for generation of NDVI. The water balance was calculated for each day. The relationship among the variables “leaf dry weight,” “LAI,” “NDVI,” and “water balance” was verified and a regression model was built and evaluated. The deciduous phenomenon can be explained by hydric balance, and LAI and NDVI are ancillary variables. The tendency of the variables in the period of 13 months was explained by quadratic functions. The varied behavior among the monitoring sites helped to know differences in the deposition of leaves. This study showed that only the leaf component of the litterfall of a semideciduous forest in tropical climate can capture 4 to 8 Mg·ha−1·yr−1of CO2and this amount can be estimated using climate, biophysics, and vegetation index variables.

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A APLICAÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) PARA ANÁLISE DA DEGRADAÇÃO AMBIENTAL DA ÁREA DE INFLUÊNCIA DIRETA DO AÇUDE CASTANHÃO

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.602 ◽

2019 ◽

Vol 21 (2) ◽

pp. 674-685

Author(s):

Amanda Menezes De Albuquerque ◽

José Robério Cabral Ribeiro ◽

Marta Celina Linhares Sales

Keyword(s):

Remote Sensing ◽

Environmental Degradation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Soil Cover ◽

Spatial Location ◽

Biophysical Parameters ◽

Spectral Image ◽

Vegetation Indexes ◽

The Relationship

O aumento da degradação ambiental de terras secas vem conduzindo à erosão dos solos e desertificação, o uso intenso e predatório dos recursos naturais nessas áreas acaba impossibilitando a sobrevivência das comunidades que vivem nessas regiões. O estado do Ceará tem cerca de 92% de seu território inserido no semiárido, a pesquisa foi desenvolvida na Área de Influência Direta do Açude Castanhão – AIC. A através do registro de imagens, tornou-se possível às análises de relacionamento entre localização espacial de alvos do meio ambiente, variação espectral da imagem e variação da cobertura vegetal dos solos. A utilização do sensoriamento remoto e de índices de vegetação como o Índice de Vegetação da Diferença Normalizada (NDVI), facilita a obtenção e modelagem de parâmetros biofísicos das plantas, como a área foliar, biomassa e porcentagem de cobertura do solo, fornecendo importantes informações sobre a Degradação Ambiental da área.Palavras-chave: Degradação; Sensoriamento Remoto; Cobertura Vegetal. ABSTRACTThe increased environmental degradation of dry lands has led to soil erosion and desertification, the intense and predatory use of natural resources in these areas makes it impossible to survive the communities living in these regions. The state of Ceará has about 92% of its territory inserted in the semi-arid, the research was developed in the Area of Direct Influence of Castanhão - AIC. A through image registration, it became possible to analyze the relationship between spatial location of environmental targets, spectral image variation and variation of soil cover. The use of remote sensing and vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) facilitates the obtaining and modeling of plant biophysical parameters such as leaf area, biomass and percentage of soil cover, providing important information on the Environmental Degradation of the area.Keywords:Degradation; Remote Sensing; Vegetal Cover.

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Application of geo-informatics engineering to estimate above-ground carbon sequestration

Engineering review ◽

10.30765/er.38.3.7 ◽

2018 ◽

Vol 38 (3) ◽

pp. 303-308

Author(s):

Teerawong Laosuwan ◽

Yannawut Uttaruk ◽

Tanutdech Rotjanakusol ◽

Kusuma Arsasana

Keyword(s):

Carbon Sequestration ◽

Vegetation Index ◽

Vegetation Indices ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Regression Equations ◽

Landsat 8 Oli ◽

Green Vegetation ◽

Simple Ratio ◽

The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

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Local Allometric Equations for Estimating Above-Ground Biomass (AGB) of Mangroves (Rhizophora spp. and Avicennia germinans) from the Komo, Mondah and Rio Mouni Estuaries in Gabon

European Scientific Journal ESJ ◽

10.19044/esj.2021.v17n34p172 ◽

2021 ◽

Vol 17 (34) ◽

pp. 172

Author(s):

Igor Akendengue Aken ◽

Okanga-Guay Marjolaine ◽

Ondo Assoumou Emmanuel ◽

Ajonina Gordon Nwutih ◽

Mombo Jean-Bernard

Keyword(s):

Aboveground Biomass ◽

Tree Height ◽

Avicennia Germinans ◽

Allometric Equations ◽

Local Context ◽

Coefficient Of Determination ◽

Mean Square ◽

Above Ground Biomass ◽

Independent Variable ◽

Mean Square Errors

The aboveground biomass (AGB) of Gabonese mangroves is commonly estimated from equations calibrated in other countries, and is generally adapted poorly to the local context. This paper focuses on developing local allometric equations for the AGB estimation and to evaluate their accuracy compared to other general equations. The local equations for Rhizophora spp and Avicennia germinans were performed with tree volume, bark and wood densities, and are used with the diameter as an independent variable. The heights and diameters of 408 trees (314 Rhizophora spp and 94 Avicennia germinans) were measured at 13 sites in Estuaire Province. Sixty-four aliquots were taken from the trunks of both species at the Mondah site. This site has tree diameters ranging from 2 to 127 cm for Avicennia and from 1.4 to 75.8 cm for Rhizophora. The tree height ranges from 0.9 to 24 m for Avicennia, and from 1.1 to 53 m for Rhizophora. Avicennia has an overall trunk density of 0.88 g/cm3 and Rhizophora has 1.17 g/cm3. The coefficient of determination (R2) of the equations are 0.98 for Rhizophora spp, 0.97 for Avicennia germinans, and 0.99 for the general equation. The seven equation display biases that are less than 1% and the root mean square errors vary between 0.073 and 1.68. Compared to other equations generally used, these local equations improve the accuracy of aboveground biomass estimations of Gabonese mangroves.

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SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR

ENERGIA NA AGRICULTURA ◽

10.17224/energagric.2019v34n2p263-270 ◽

2019 ◽

Vol 34 (2) ◽

pp. 263-270

Author(s):

Victor Costa Leda ◽

Aline Kuramoto Golçalves ◽

Natalia da Silva Lima

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Sao Paulo ◽

Coefficient Of Determination ◽

Landsat 8 ◽

São Paulo ◽

Vegetation Indexes ◽

Production Area ◽

Remote Sensing Techniques ◽

Sugarcane Productivity

SENSORIAMENTO REMOTO APLICADO A MODELAGEM DE PRODUTIVIDADE DA CULTURA DA CANA-DE-AÇÚCAR VICTOR COSTA LEDA1, ALINE KURAMOTO GOLÇALVES2, NATALIA DA SILVA LIMA3 1 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 2 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. 3 Departamento de Solos e Recursos Ambientais, Universidade Paulista “Júlio de Mesquita Filho” – Unesp, Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP 18610-034, Botucatu, São Paulo, Brasil, [email protected]. RESUMO: O trabalho objetivou modelar as correlações de produtividade da cana-de-açúcar com índices de vegetação obtidos por meio de análise de imagens orbitais. Para análise, foram elaborados modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, SP. A área ocupa aproximadamente 6000 ha, com altimetrias variando entre 600 e 700 m. Foi constatado que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97, sendo que, em períodos de colheita com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto áreas que apresentaram coeficientes de determinação baixos, podem ser explicadas devido a fatores como, dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos. Palavras-chaves: índices de vegetação, Landsat 8, regressão linear múltipla. REMOTE SENSING FOR THE SUGARCANE PRODUCTIVITY MODELING ABSTRACT: The aim of this study was to model the sugarcane productivity correlations with vegetation indexes obtained through orbital image analysis. From the analysis was elaborated mathematical models to explain sugarcane productivity through geoprocessing and remote sensing techniques. The experiment was carried out in the commercial production area of Agrícola Rio Claro, a partner of the Zilor group, located in the municipalities of Lençóis Paulista and Pratânia, SP, with approximately 6,000 hectares, with altimetry varying between 600 and 700 meters. It was verified that the modeling was satisfactory, varying the coefficient of determination between 0,15 and 0,97. Once in periods with high determination coefficients, areas of agglomerated form can usually be found, which suggests a lower incidence of variables. While, in periods with low determination coefficients, can be explain due to listed factors that occurred as dispersion of the stands in the area, classes of soil, precipitation and probably different varieties of the crop. Keywords: vegetation index, landsat8, multiple linear regression.

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Retrieving wheat Biomass by using a hyper-spectral device on UAV

Advances in Animal Biosciences ◽

10.1017/s2040470017001182 ◽

2017 ◽

Vol 8 (2) ◽

pp. 833-836

Author(s):

L. Xia ◽

R. R. Zhang ◽

L. P. Chen ◽

Y. Wen ◽

F. Zhao ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Vegetation Indexes ◽

Spectral Device ◽

Dimensional Distribution ◽

Aerial Vehicle

In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R2 was used to draw the two-dimensional distribution of R2 values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R2 higher than 0.88 by using the linear regression model in the study.

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ANÁLISE DA TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O NDVI DA ZONA URBANA DO MUNICÍPIO DO CRATO/CEARÁ 2017

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.566 ◽

2019 ◽

Vol 21 (2) ◽

pp. 334-347

Author(s):

Mayra Alves Pinheiro ◽

Juliana Maria Oliveira Silva

Keyword(s):

Surface Temperature ◽

Vegetation Index ◽

Plant Density ◽

Vegetation Indexes ◽

Higher Temperature ◽

Lower Temperature ◽

The Relationship ◽

The City ◽

Surface Temperature Variation ◽

Chapada Do Araripe

O objetivo do trabalho é analisar a relação existente entre os dados de temperatura de superfície com o índice de vegetação (NDVI) na zona urbana do Município do Crato para o mês de agosto de 2017. A distribuição da temperatura de superfície na cidade do Crato apresentou uma variação entre 22.1°C a 35.4°C. Os bairros com maiores valores de temperatura entre 32.4°C e 35.8°C, configuram-se em locais com baixa densidade vegetal, referente aos bairros do Centro, Vila Alta, Seminário, Barro Branco, Mirandão e Santa Luzia, diferente da área com menores temperaturas, entre 22.1°C a 25°C. Os valores de NDVI apresentam uma relação com a temperatura, quanto menor o índice de vegetação, maior será a temperatura. Com isso, os bairros que apresentaram menores índice de vegetação (entre 0.03 a 0.24) foram o Centro, Vila Alta, Seminário e Santa Luzia. Os bairros que tem os maiores índices de vegetação (calculado entre 0.64 a 0.85) estão os bairros mais próximos a encosta da Chapada do Araripe, como o Granjeiro, Coqueiro, Lameiro e São Gonçalo. Com relação aos resultados encontrados nos mapas de Temperatura e NDVI, pode-se dizer que área centrais da cidade do Crato apresentaram maiores valores de temperatura de superfície e menores índices de vegetação.Palavras-chave: sensoriamento remoto; NDVI e temperatura de superfície. ABSTRACTThe objective of this work is to analyze the relationship between the surface temperature data and the NDVI vegetation index of the urban area of the Municipality of Crato / Ceará for the month of August 2017. The surface temperature distribution in the city of Crato, has a surface temperature variation between 22.1 ° C and 35.4 ° C. The neighborhoods with thighs surface temperature values between 32.4 ° C and 35.8 ° C, are located in places with low plant density, referring to the neighborhoods of Vila Alta, Seminary, Barro Branco, Mirandão and Santa Luzia, different from the area with lower temperature, between 22.1 ° C to 25 ° C. The NDVI values influence the temperature increase, the lower the vegetation index, the higher the temperature. Thus, the neighborhoods with the lowest vegetation index were between 0.03 and 0.24 were the Center, Vila Alta, Pimenta and Santa Luzia. The neighborhoods that have the highest vegetation indexes (calculated from 0.64 to 0.85) are the neighborhoods closest to the hillside of Chapada do Araripe, such as Granjeiro, Coqueiro, Lameiro and São Gonçalo. With respect to the results found in the maps of Temperature and NDVI, it can be said that the most central areas of the city of Crato presented higher temperature values.Keywords: remote sensing, NDVI and surface temperature.RESUMENEl objetivo de este trabajo es analizar la relación entre los datos de temperatura de la superficie y el índice de vegetación NDVI del área urbana del Municipio de Crato / Ceará para el mes de agosto de 2017. La distribución de la temperatura de la superficie en la ciudad de Crato, tiene un variación de la temperatura de la superficie entre 22.1 ° C y 35.4 ° C. Los barrios con valores de temperatura de la superficie de los muslos entre 32.4 ° C y 35.8 ° C, se encuentran en lugares con baja densidad de plantas, en referencia a los barrios de Vila Alta, Seminario, Barro Branco, Mirandão y Santa Luzia, diferentes del área con temperatura más baja, entre 22.1 ° C a 25 ° C. Los valores de NDVI influyen en el aumento de la temperatura, cuanto menor es el índice de vegetación, mayor es la temperatura. Así, los barrios con el índice de vegetación más bajo estuvieron entre 0.03 y 0.24 fueron el Centro, Vila Alta, Pimenta y Santa Luzia. Los barrios que tienen los índices de vegetación más altos (calculados de 0,64 a 0,85) son los más cercanos a la ladera de Chapada do Araripe, como Granjeiro, Coqueiro, Lameiro y São Gonçalo. Con respecto a los resultados encontrados en los mapas de Temperatura y NDVI, se puede decir que las áreas más céntricas de la ciudad de Crato presentaron valores de temperatura más altos.Palabras clave: teledetección, NDVI y temperatura superficial.

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