Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

2016 ◽  
Vol 53 (3) ◽  
pp. 337-359 ◽  
Author(s):  
Nima Ahmadian ◽  
Sahar Ghasemi ◽  
Jean-Pierre Wigneron ◽  
Reinhard Zölitz
Keyword(s):  
Vegetation Indices ◽  
Landsat 8 ◽  
Agricultural Crops ◽  
Landsat 8 Oli ◽  
Biophysical Parameters ◽  
Landsat 7 ◽  
Comprehensive Study

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 Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

2015 ◽  
Vol 7 (10) ◽  
pp. 13485-13506 ◽  
Author(s):  
Xiaojun She ◽  
Lifu Zhang ◽  
Yi Cen ◽  
Taixia Wu ◽  
Changping Huang ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Landsat 8 ◽  
Vegetation Types ◽  
Landsat 8 Oli ◽  
Landsat 7

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 Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

scholarly journals Estimation of rice phenology date using integrated HJ-1 CCD and Landsat-8 OLI vegetation indices time-series images

2015 ◽  
Vol 16 (10) ◽  
pp. 832-844 ◽  
Author(s):  
Jing Wang ◽  
Jing-feng Huang ◽  
Xiu-zhen Wang ◽  
Meng-ting Jin ◽  
Zhen Zhou ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Indices ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Time Series Images ◽  
Rice Phenology

scholarly journals DINÂMICA DO VOLUME, BIOMASSA E CARBONO NA MATA ATLÂNTICA POR FERRAMENTA DE DETECÇÃO DE MUDANÇAS

Nativa ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 437
Author(s):  
Ayrton Machado ◽  
Ana Paula Marques Martins ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Jaime Wojciechowski ◽  
...  
Keyword(s):  
River Basin ◽  
Atlantic Forest ◽  
Landsat 8 ◽  
South American ◽  
Landsat 8 Oli ◽  
Mata Atlântica ◽  
Landsat 7 ◽  
Mata Atlantica ◽  
Year 2000 ◽  
Iguaçu River

A Mata Atlântica é reconhecida internacionalmente como uma das maiores e mais importantes florestas tropicais do continente sul-americano e além de sua importância para a biodiversidade, esse Bioma exerce importante função no ciclo de carbono. O objetivo deste trabalho foi desenvolver e aplicar uma rotina de detecção de mudanças dos estoques de volume, biomassa e carbono de 2000 a 2015 na Bacia do Rio Iguaçu, Estado do Paraná. Foram utilizadas imagens Landsat-7 ETM+ para o ano 2000 e Landsat-8 OLI para o ano de 2015 totalizando dez cenas para cada período. Foi desenvolvido uma rotina em Python e implementado no Software ArcGIS 10.4 para realizar a automatização de um processo de cálculo de estimativa de volume, biomassa e carbono para os remanescentes de vegetação natural. Houve acréscimo de 15,21% em volume, 14,95% em biomassa, 14,96% em carbono não considerando os estágios sucessionais nem subdivisão por fitofisionomia na bacia do Rio Iguaçu.  Desta forma, concluiu-se que a região de estudo está colaborando de forma positiva para a remoção de dióxido de carbono da atmosfera.Palavras-chave: bacia do rio Iguaçu; mudanças climáticas; sequestro de carbono. DYNAMICS OF VOLUME, BIOMASS AND CARBON IN THE ATLANTIC FOREST BY A CHANGE DETECTION TOOL ABSTRACT: The Atlantic Forest is recognized internationally as one of the largest and most important tropical forests in the South American continent and besides its importance for biodiversity, this biome plays important role in the carbon cycle. The objective of this work was to develop and apply a routine of detection of changes in volume, biomass and carbon stocks from 2000 to 2015 in the Iguaçu River Basin, State of Paraná. They were used Landsat-7 ETM+ images for the year 2000 and Landsat-8 OLI images for the year 2015 totaling ten images for each period. A routine was developed in Python and implemented in ArcGIS 10.4 Software to perform the automation of a calculation process of volume, biomass and carbon estimation for the remnants of natural vegetation. There was an increase of 15.21% in volume, 14.95% in biomass, 14.96% in carbon, not considering successional stages nor subdivision by phytophysiognomy in the Iguaçu River basin. Thus concludes that the region of study is collaborating in a positive way for the removal of carbon dioxide from the atmosphere.Keywords: Iguaçu river basin; climate changes; carbon sequestration.

scholarly journals Prediction of dendrometric variables by optical data in Pinus taeda L. in the south of Brazil

2021 ◽  
Vol 8 (2) ◽  
pp. 1433-1443
Author(s):  
Carla Talita Pertille ◽  
Marcos Felipe Nicoletti ◽  
Larissa Regina Topanotti ◽  
Luís Paulo Baldiserra Schorr
Keyword(s):  
Pinus Taeda ◽  
Forest Inventory ◽  
Vegetation Indices ◽  
Basal Area ◽  
Optical Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Pinus Taeda L ◽  
South Of Brazil ◽  
Pinus Spp

The objective of this work was to estimate the basal area and volume of a Pinus taeda L. settlement located in Santa Catarina, correlating data from an orbital image of the Landsat-8 / OLI sensor and forest inventory. In this sense, a forest research was carried out, with a random sampling process using the fixed area method. 20 circular parcels of 400 m² were allocated. An orbital image of the Landsat-8 / OLI sensor was used and 10 average vegetation indices per plot were calculated. These were correlated as variables of volume and basal area per plot, decorative by the forest inventory. The index with the best correlation for the volume was GNDVI with 0.47 and for a basal area, the MVI with 0.51. The adjustment of the regression models showed adjusted R² indices of 0.5639 and Syx of 13.31% for volume, and 0.5213 and 11.93% for the basal area. It was possible to estimate the volume and basal area of the stands through the spectral data, however, it is recommended that this same technique be tested in other species of the genus Pinus spp. and with high spatial resolution media.

scholarly journals ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 11-19
Author(s):  
T. T. Cat Tuong ◽  
H. Tani ◽  
X. F. Wang ◽  
V.-M. Pham
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat Data ◽  
Bamboo Forest ◽  
Landsat 8 Oli ◽  
L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

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