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

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.

scholarly journals Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

2019 ◽  
Vol 11 (6) ◽  
pp. 649 ◽  
Author(s):  
Koffi Noumonvi ◽  
Mitja Ferlan ◽  
Klemen Eler ◽  
Giorgio Alberti ◽  
Alessandro Peressotti ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Eddy Covariance ◽  
Vegetation Index ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Information Criterion ◽  
Growing Season ◽  
Coefficient Of Determination ◽  
Regression Equations ◽  
Eddy Covariance Method

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

scholarly journals Effect of Image Fusion on Vegetation Index Quality—A Comparative Study from Gaofen-1, Gaofen-2, Gaofen-4, Landsat-8 OLI and MODIS Imagery

2020 ◽  
Vol 12 (10) ◽  
pp. 1550 ◽  
Author(s):  
Prakash Ghimire ◽  
Deng Lei ◽  
Nie Juan
Keyword(s):  
Image Fusion ◽  
Vegetation Index ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pass Filter ◽  
Modis Imagery

In recent years, the use of image fusion method has received increasing attention in remote sensing, vegetation cover changes, vegetation indices (VIs) mapping, etc. For making high-resolution and good quality (with low-cost) VI mapping from a fused image, its quality and underlying factors need to be identified properly. For example, same-sensor image fusion generally has a higher spatial resolution ratio (SRR) (1:3 to 1:5) but multi-sensor fusion has a lower SRR (1:8 to 1:10). In addition to SRR, there might be other factors affecting the fused vegetation index (FVI) result which have not been investigated in detail before. In this research, we used a strategy on image fusion and quality assessment to find the effect of image fusion for VI quality using Gaofen-1 (GF1), Gaofen-2 (GF2), Gaofen-4 (GF4), Landsat-8 OLI, and MODIS imagery with their panchromatic (PAN) and multispectral (MS) bands in low SRR (1:6 to 1:15). For this research, we acquired a total of nine images (4 PAN+5 MS) on the same (almost) date (GF1, GF2, GF4 and MODIS images were acquired on 2017/07/13 and the Landsat-8 OLI image was acquired on 2017/07/17). The results show that image fusion has the least impact on Green Normalized Vegetation Index (GNDVI) and Atmospherically Resistant Vegetation Index (ARVI) compared to other VIs. The quality of VI is mostly insensitive with image fusion except for the high-pass filter (HPF) algorithm. The subjective and objective quality evaluation shows that Gram-Schmidt (GS) fusion has the least impact on FVI quality, and with decreasing SRR, the FVI quality is decreasing at a slow rate. FVI quality varies with types image fusion algorithms and SRR along with spectral response function (SRF) and signal-to-noise ratio (SNR). However, the FVI quality seems good even for small SRR (1:6 to 1:15 or lower) as long as they have good SNR and minimum SRF effect. The findings of this study could be cost-effective and highly applicable for high-quality VI mapping even in small SRR (1:15 or even lower).

scholarly journals Comparison between Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Assessment of Vegetation Indices

2017 ◽  
Vol 1 (2) ◽  
pp. 355-366
Author(s):  
E. O. Makinde ◽  
A. D. Obigha
Keyword(s):  
Statistical Analysis ◽  
Land Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Water Index ◽  
Landsat 7 ◽  
Index 2

The Landsat system has contributed significantly to the understanding of the Earth observation for over forty years. Since May 2013, data from Landsat 8 has been available online for download, with substantial differences from its predecessors, having an extended number of spectral bands and narrower bandwidths. The objectives of this research were majorly to carry out a cross comparison analysis between vegetation indices derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) and also performed statistical analysis on the results derived from the vegetation indices. Also, this research carried out a change detection on four land cover classes present within the study area, as well as projected the land cover for year 2030. The methods applied in this research include, carrying out image classification on the Landsat imageries acquired between 1984 – 2016 to ascertain the changes in the land cover types, calculated the mean values of differenced vegetation indices derived from the four land covers between Landsat 7 ETM+ and Landsat 8 OLI. Statistical analysis involving regression and correlation analysis were also carried out on the vegetation indices derived between the two sensors, as well as scatter plot diagrams with linear regression equation and coefficients of determination (R2). The results showed no noticeable differences between Landsat 7 and Landsat 8 sensors, which demonstrates high similarities. This was observed because Global Environmental Monitoring Index (GEMI), Improved Modified Triangular Vegetation Index 2 (MTVI2), Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), Leaf Area Index (LAI) and Land Surface Water Index (LSWI) had smaller standard deviations. However, Renormalized Difference Vegetation Index (RDVI), Anthocyanin Reflectance Index 1 (ARI1) and Anthocyanin Reflectance Index 2 (ARI2) performed relatively poorly because their standard deviations were high. the correlation analysis of the vegetation indices that both sensors had a very high linear correlation coefficient with R2 greater than 0.99. It was concluded from this research that Landsat 7 ETM+ and Landsat 8 OLI can be used as complimentary data.

scholarly journals Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

2018 ◽  
Vol 8 (9) ◽  
pp. 1435 ◽  
Author(s):  
Xiaochen Zou ◽  
Iina Haikarainen ◽  
Iikka Haikarainen ◽  
Pirjo Mäkelä ◽  
Matti Mõttus ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Imaging Spectroscopy ◽  
Leaf Angle ◽  
Field Crops ◽  
Leaf Chlorophyll ◽  
Simple Ratio ◽  
The Relationship

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

scholarly journals Spectral variables, growth analysis and yield of sugarcane

2005 ◽  
Vol 62 (3) ◽  
pp. 199-207 ◽  
Author(s):  
Maurício dos Santos Simões ◽  
Jansle Vieira Rocha ◽  
Rubens Augusto Camargo Lamparelli
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Lower Amount ◽  
Total Biomass ◽  
Area Index ◽  
Spectral Vegetation Indices ◽  
Green Vegetation ◽  
Highly Correlated ◽  
Simple Ratio

Spectral information is well related with agronomic variables and can be used in crop monitoring and yield forecasting. This paper describes a multitemporal research with the sugarcane variety SP80-1842, studying its spectral behavior using field spectroscopy and its relationship with agronomic parameters such as leaf area index (LAI), number of stalks per meter (NPM), yield (TSS) and total biomass (BMT). A commercial sugarcane field in Araras/SP/Brazil was monitored for two seasons. Radiometric data and agronomic characterization were gathered in 9 field campaigns. Spectral vegetation indices had similar patterns in both seasons and adjusted to agronomic parameters. Band 4 (B4), Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI), and Soil Adjusted Vegetation Index (SAVI) increased their values until the end of the vegetative stage, around 240 days after harvest (DAC). After that stage, B4 reflectance and NDVI values began to stabilize and decrease because the crop reached ripening and senescence stages. Band 3 (B3) and RVI presented decreased values since the beginning of the cycle, followed by a stabilization stage. Later these values had a slight increase caused by the lower amount of green vegetation. Spectral variables B3, RVI, NDVI, and SAVI were highly correlated (above 0.79) with LAI, TSS, and BMT, and about 0.50 with NPM. The best regression models were verified for RVI, LAI, and NPM, which explained 0.97 of TSS variation and 0.99 of BMT variation.

STAND AGE MODEL FOR MAPPING SPATIAL DISTRIBUTION OF RUBBER TREE USING REMOTELY SENSED DATA IN KEDAH, MALAYSIA

2016 ◽  
Vol 78 (5) ◽  
Author(s):  
Iqbal Putut Ash Shidiq ◽  
Mohd Hasmadi Ismail
Keyword(s):  
Spatial Distribution ◽  
Vegetation Indices ◽  
Rubber Tree ◽  
Remote Sensing Data ◽  
Stand Age ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remotely Sensed Data ◽  
Age Model ◽  
The Relationship

This study attempt to develop stand age model of rubber tree by using remote sensing data. Rubber tree is one of the important biomass that has been considered as the essential part in global warming reduction plan due to its beneficial carbon sequestration capability. The spatial distribution of rubber tree based on different age was most highlighted as the focus of this study. Felda Lubuk Merbau in state of Kedah has been selected as a study area and Landsat 8 OLI-TIRS data was utilized to map rubber tree and differentiate them based on age group. The relationship between vegetation indices namely NDVI, SAVI and EVI to different age stages of rubber tree were discussed.

scholarly journals PENGEMBANGAN PENGGUNAAN PENGINDERAAN JAUH UNTUK ESTIMASI PRODUKSI PADI (STUDI KASUS KABUPATEN BEKASI)

2019 ◽  
Vol 19 (1) ◽  
pp. 6-12
Author(s):  
Eka Rudiana ◽  
Ernan Rustiadi ◽  
Muhammad Firdaus ◽  
Dede Dirgahayu
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Rice Yield ◽  
Equation Model ◽  
Rice Production ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Enhanced Vegetation Index ◽  
The One ◽  
The Relationship

The utilization of remote sensing imagery such Landsat-8 (OLI) to estimate harvested area and yield using Enhanced Vegetation Index (EVI) parameter is a new approach to estimate regional rice production. Based on the analysis of the satellite imagery acquisition during May-August 2015, the estimation of rice harvested area in Bekasi District during July-October 2015 is 15.86 thousand ha or 7.74 thousand ha (32.79%) lower than BPS figures in the same period. Based on the relationship between yield (from the crop cutting survei, BPS) and EVI maximum, the equation model for rice yield estimation is: Yield (qu ha-1) = 36.818 + 44.965 EVImax. R2 value is 0.809. Based on the model, the estimation of rice yield in Bekasi District during July-October 2015 is 47.40 qu ha-1. Compared to the data published by BPS, the result is 12.66 qu ha-1 lower than the yield figure in subround I 2015, 6.77 qu ha-1 lower than the one in subround II 2015, 10.15 qu ha-1 lower than the one subround III 2015, and 6.62 qu ha-1 lower than the one in January-December 2015. Meanwhile, based on satellite imagery analysis, the estimation of rice production in the period of July-October 2015 is 75.16 thousand tons of GKG or 55.35 thousand tons of GKG (42.41%) lower than BPS figures during the same period. Keywords: Enhanced Vegetation Index, Landsat-8 (OLI), rice production estimation

scholarly journals Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

2021 ◽  
Vol 14 (2) ◽  
pp. 869
Author(s):  
João Pedro Ocanha Krizek ◽  
Luciana Cavalcanti Maia Santos
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Environmental Parameters ◽  
Biomass Productivity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
De Se ◽  
Correct Estimation

A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa.  Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação.   Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

scholarly journals ANÁLISE DO IMPACTO DA CORREÇÃO ATMOSFÉRICA NO CÁLCULO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA A PARTIR DE IMAGEM LANDSAT 8/OLI

2020 ◽  
Vol 13 (1) ◽  
pp. 076
Author(s):  
Cristiane Nunes Francisco ◽  
Paulo Roberto da Silva Ruiz ◽  
Cláudia Maria de Almeida ◽  
Nina Cardoso Gruber ◽  
Camila Souza dos Anjos
Keyword(s):  
Rio De Janeiro ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat 8 Oli ◽  
Arithmetic Operations

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação.  Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

scholarly journals ACCURACY ANALYSIS OF SENTINEL 2A AND LANDSAT 8 OLI+ SATELLITE DATASETS OVER KANO STATE (NIGERIA) USING VEGETATION SPECTRAL INDICES

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 65-72
Author(s):  
O. A. Isioye ◽  
E. A. Akomolafe ◽  
U. H. Ikwueze
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Stress Index ◽  
Landsat 8 ◽  
Agricultural Practice ◽  
Landsat 8 Oli ◽  
Area Index ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2A

Abstract. This study explores the capabilities of Sentinel-2 over Landsat-8 Operational Land Imager (OLI) imageries for vegetation monitoring in the vegetated region of Minjibir LGA in Kano State. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. Vegetation indices, comprising the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Leaf Area Index (LAI) and Moisture Stress Index (MSI) were determined for each year. The findings showed an increase in Sentinel 2A value of the vegetation indices with respect to Landsat 8 throughout the time of the study (2015–2019). The best average performance over the supervised classification was obtained using Sentinel-2A bands, which are dependent on the training sample and resolution. While the spectral consistency of the data was inferred by cross-calibration analysis using regression analysis. The spatial consistency was assessed by descriptive statistical analysis of examined variables. Regarding the spatial consistency, the mean and standard deviation values of all variables were steady for all seasons excluding for the mean value of the LAI and MSI. Based on this finding, it is recommended that Sentinel-2A data could be used as a complementary data source with Landsat 8 OLI in vegetation assessment.

Sign in / Sign up

Export Citation Format

Share Document