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

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).

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

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2017001100013 ◽

2017 ◽

Vol 52 (11) ◽

pp. 1072-1079 ◽

Cited By ~ 4

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.

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USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-845-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 845-850 ◽

Cited By ~ 2

Author(s):

H. Bendini ◽

I. D. Sanches ◽

T. S. Körting ◽

L. M. G. Fonseca ◽

A. J. B. Luiz ◽

...

Keyword(s):

Land Use ◽

Time Series ◽

Vegetation Index ◽

Agricultural Land ◽

Vegetation Indices ◽

Agricultural Land Use ◽

Landsat 8 ◽

Land Use Classification ◽

Crop Mapping ◽

Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

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

Advances in Forestry Science ◽

10.34062/afs.v8i2.9875 ◽

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.

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

Nigerian Journal of Environmental Sciences and Technology - March 2018 ◽

10.36263/nijest.2017.02.0046 ◽

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.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w20-11-2019 ◽

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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Monitoring the geodynamic behaviour of earthquake using Landsat 8-OLI time series data: case of Gorkha and Imphal

10.5194/nhess-2017-10 ◽

2017 ◽

Cited By ~ 2

Author(s):

Biswajit Nath ◽

Zheng Niu ◽

Shukla Acharjee ◽

Hailang Qiao

Keyword(s):

Time Series ◽

Time Series Data ◽

Vertical Surface ◽

Series Data ◽

Landsat 8 ◽

Length Variation ◽

Landsat 8 Oli ◽

Research Results ◽

Earthquake Monitoring ◽

Lineament Extraction

Abstract. Prediction of earthquake in advance is really a challenging task for the scientific community till now. But research results from various scientists regarding lineament extraction using satellite imageries help us to way forward for earthquake monitoring study. For the present study, Landsat 8 OLI Time series data analyzed by integrating four different remote sensing and GIS software’s for automatic lineament extraction, its change, including lineament lengths and directions study by creating rose diagrams and finally vertical surface transect profile curve drawing. Two recent major earthquakes (in different geological settings Gorkha of Nepal 7.8 Mw and Imphal, Manipur of Eastern India 6.8 Mw) epicenter based single tile and corresponding same temporal scenes (three for before and one for after quake respectively) were considered for each case to perform lineament extraction, length variation and vertical surface transect profile change analysis. The research results witnessed major variations in lineament number, lineament length and its trends. The major trends found in an ESE-WNW, N-E, N-S, E-W, NNE-SSW directions and ESE-WSW, ESE-WNW, NE-SW on pre-earthquake scenes compared to post earthquake ESE-WNW, NE-SW, NNE-SSW were found for Gorkha, and ESE-WSW for Imphal regions respectively and in both cases, it was observed that the lineation trends return to its earlier status after an earthquake strike. The results obtained using the automated and geo-integrated methods compared cross validation with each other showed our method worked practically for earthquake monitoring and one can apply this new novel combined approach to predict the probable earthquake occurrence in advance just a few days before it strikes.

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