Evapotranspiration estimation using vegetation index and surface reflectance SWIR Landsat-8 combination on various land cover

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

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Parametric Methods to Multispectral Image Classification using Normalized Difference Vegetation Index

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b1061.1292s19 ◽

2019 ◽

Vol 9 (2S) ◽

pp. 611-615

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Algorithm ◽

Urban Region ◽

Landsat 8 ◽

Multispectral Images ◽

Barren Soil ◽

Day By Day ◽

Manual Verification

The key to proper governance of the municipal bodies lies in knowing the geography of the region. The land cover of the region changes with respect to time. Also, there are seasonal variation in the layout of the waterbodies. Manual verification and surveying of these things becomes very difficult for want of resources. Remote Sensing Images play a very important role in mapping the land cover. In this paper, we consider such remotely sensed Multispectral Images, taken from Landsat-8. Parametric Machine learning algorithm like Maximum Likelihood Classifier has been used on those images to classify the land cover. Normalized Difference Vegetation Index (NDVI) has been calculated and integrates with the classification process. Four basic land covers have been identified for the purpose namely Water, Vegetation, Built-up and Barren soil. The area of study is Bangalore urban region where we find that the water bodies are decreasing day by day. An overall efficiency of 82% with a kappa hat 0f 0.67 has been achieved with the method. The user and the producer accuracies have also been tabulated in the Results part. The results show the land cover changes in a temporal manner

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Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040257 ◽

2020 ◽

Vol 9 (4) ◽

pp. 257 ◽

Cited By ~ 3

Author(s):

Kiwon Lee ◽

Kwangseob Kim ◽

Sun-Gu Lee ◽

Yongseung Kim

Keyword(s):

Open Source ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Spectral Response ◽

Atmospheric Correction ◽

Urban Forestry ◽

Google Earth ◽

Landsat 8 ◽

Surface Reflectance ◽

Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

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Competing Vegetation Structure Indices for Estimating Spatial Constrains in Carabid Abundance Patterns in Chinese Grasslands Reveal Complex Scale and Habitat Patterns

Insects ◽

10.3390/insects11040249 ◽

2020 ◽

Vol 11 (4) ◽

pp. 249 ◽

Cited By ~ 2

Author(s):

Noelline Tsafack ◽

Simone Fattorini ◽

Camila Benavides Frias ◽

Yingzhong Xie ◽

Xinpu Wang ◽

...

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Spatial Scales ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Insect Communities ◽

Landscape Characteristics ◽

Species Abundances ◽

Negative Impacts ◽

Structure Indices

Carabid communities are influenced by landscape features. Chinese steppes are subject to increasing desertification processes that are changing land-cover characteristics with negative impacts on insect communities. Despite those warnings, how land-cover characteristics influence carabid communities in steppe ecosystems remains unknown. The aim of this study is to investigate how landscape characteristics drive carabid abundance in different steppes (desert, typical, and meadow steppes) at different spatial scales. Carabid abundances were estimated using pitfall traps. Various landscape indices were derived from Landsat 8 Operational Land Imager (OLI) images. Indices expressing moisture and productivity were, in general, those with the highest correlations. Different indices capture landscape aspects that influence carabid abundance at different scales, in which the patchiness of desert vegetation plays a major role. Carabid abundance correlations with landscape characteristics rely on the type of grassland, on the vegetation index, and on the scale considered. Proper scales and indices are steppe type-specific, highlighting the need of considering various scales and indices to explain species abundances from remotely sensed data.

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Land Cover and Forest Type Classification by Values of Vegetation Indices and Forest Structure of Tropical Lowland Forests in Central Vietnam

International Journal of Forestry Research ◽

10.1155/2020/8896310 ◽

2020 ◽

Vol 2020 ◽

pp. 1-18

Author(s):

Hung Nguyen Trong ◽

The Dung Nguyen ◽

Martin Kappas

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Forest Type ◽

Vegetation Indices ◽

Landsat 8 ◽

Tropical Lowland ◽

Central Vietnam ◽

Lowland Forests ◽

Type Classification ◽

Sentinel 2

This paper aims to (i) optimize the application of multiple bands of satellite images for land cover classification by using random forest algorithms and (ii) assess correlations and regression of vegetation indices of a better-performed land cover classification image with vertical and horizontal structures of tropical lowland forests in Central Vietnam. In this study, we used Sentinel-2 and Landsat-8 to classify seven land cover classes of which three forest types were substratified as undisturbed, low disturbed, and disturbed forests where forest inventory of 90 plots, as ground-truth, was randomly sampled to measure forest tree parameters. A total of 3226 training points were sampled on seven land cover types. The performance of Landsat-8 showed out-of-bag error of 31.6%, overall accuracy of 68%, kappa of 67.5%, while Sentinel-2 showed out-of-bag error of 14.3% and overall accuracy of 85.7% and kappa of 83%. Ten vegetation indices of the better-performed image were extracted to find out (i) the correlation and regression of horizontal and vertical structures of trees and (ii) assess the variation values between ground-truthing plots and training sample plots in three forest types. The result of the t test on vegetation indices showed that six out of ten vegetation indices were significant at p<0.05. Seven vegetation indices had a correlation with the horizontal structure, but four vegetation indices, namely, Enhanced Vegetation Index, Perpendicular Vegetation Index, Difference Vegetation Index, and Transformed Normalized Difference Vegetation Index, had better correlations r = 0.66, 0.65, 0.65, 0.63 and regression results were of R2 = 0.44, 0.43, 0.43, and 0.40, respectively. The correlations of tree height were r = 0.46, 0.43, 0.43, and 0.49 and its regressions were of R2 = 0.21, 0.19, 0.18, and 0.24, respectively. The results show the possibility of using random forest algorithm with Sentinel-2 in forest type classification in line with vegetation indices application.

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NDVI Based Assessment of Land Cover Changes Using Remote Sensing and GIS (A case study of Srinagar district, Kashmir)

Sustainability Agri Food and Environmental Research ◽

10.7770/safer-v0n0-art2174 ◽

2020 ◽

Author(s):

Perminder Singh ◽

Ovais Javeed

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Spectral Signature ◽

Landsat 8 ◽

Thematic Mapping

Normalized Difference Vegetation Index (NDVI) is an index of greenness or photosynthetic activity in a plant. It is a technique of obtaining various features based upon their spectral signature such as vegetation index, land cover classification, urban areas and remaining areas presented in the image. The NDVI differencing method using Landsat thematic mapping images and Landsat oli was implemented to assess the chane in vegetation cover from 2001to 2017. In the present study, Landsat TM images of 2001 and landsat 8 of 2017 were used to extract NDVI values. The NDVI values calculated from the satellite image of the year 2001 ranges from 0.62 to -0.41 and that of the year 2017 shows a significant change across the whole region and its value ranges from 0.53 to -0.10 based upon their spectral signature .This technique is also used for the mapping of changes in land use and land cover. NDVI method is applied according to its characteristic like vegetation at different NDVI threshold values such as -0.1, -0.09, 0.14, 0.06, 0.28, 0.35, and 0.5. The NDVI values were initially computed using the Natural Breaks (Jenks) method to classify NDVI map. Results confirmed that the area without vegetation, such as water bodies, as well as built up areas and barren lands, increased from 35 % in 2001 to 39.67 % in 2017.Key words: Normalized Difference Vegetation Index,land use/landcover, spectral signature

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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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SURFACE TEMPERATURE DYNAMICS IN RESPONSE TO LAND COVER TRANSFORMATION

Journal of Civil Engineering Science and Technology ◽

10.33736/jcest.2616.2020 ◽

2020 ◽

Vol 11 (2) ◽

pp. 94-110 ◽

Cited By ~ 1

Author(s):

Syed Riad Morshed Riad Morshed ◽

Md. Abdul Fattah ◽

Asma Amin Rimi ◽

Md. Nazmul Haque

Keyword(s):

Land Cover ◽

Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Moisture Index ◽

Type Transformation ◽

Spatial Environment ◽

Supervised Image Classification

This research assessed the micro-level Land Surface Temperature (LST) dynamics in response to Land Cover Type Transformation (LCTT) at Khulna City Corporation Ward No 9, 14, 16 from 2001 to 2019, through raster-based analysis in geo-spatial environment. Satellite images (Landsat 5 TM and Landsat 8 OLI) were utilized to analyze the LCTT and its influences on LST change. Different indices like Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Buildup Index (NDBI) were adopted to show the relationship against the LST dynamics individually. Most likelihood supervised image classification and land cover change direction analysis shows that about 27.17%, 17.83% and 4.73% buildup area has increased at Ward No 9, 14, 16 correspondingly. On the other hand, the distribution of change in average LST shows that water, vacant land, and buildup area recorded the highest increase in temperature by 2.720C, 4.150C, 4.590C, respectively. The result shows the average LST increased from 25.800C to 27.150C in Ward No 9, 26.840C to 27.230C in Ward No 14 and 26.870C to 27.120C in Ward No 16. Here, the most responsible factor is the transformation of land cover in buildup areas.

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MODELING THE DEVELOPMENTS IN URBANISATION AND RELATIONSHIP WITH VEGETATION COVER IN ALANYA

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

10.5194/isprs-archives-xlvi-4-w5-2021-291-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 291-297

Author(s):

B. İşler ◽

Z. Aslan

Keyword(s):

Land Use ◽

Land Cover ◽

Land Surface ◽

Urban Areas ◽

Vegetation Index ◽

Landsat 8 ◽

World Population ◽

Land Use Land Cover ◽

Temperature And Precipitation ◽

Atmospheric Variables

Abstract. The increase in the world population and the migration of people from rural to urban areas causes an increase in artificial surfaces and causes many negative effects on the ecosystem, regional climate variations and global diversity. Nowadays, as the effects of climate change are felt more and more, it has gained importance in researches on this subject. Therefore, the estimation of the change in the vegetation density for the coming years and the determination of the land use / land cover (LULC) change in cities are very essential for urban planning. In this study, the effects of regional urbanization on vegetation are examined by using satellite data and atmospheric variables. In the vegetation analysis, multi-time index values obtained from TERRA-MODIS satellite, EVI (Enhanced Vegetation Index) and LST (Land Surface Temperature) were taken into account between the years of 2005 and 2018 in Alanya, Turkey. Temperature and precipitation were selected as the atmospheric variables and expected variations in EVI value until 2030 were estimated. In the study employed a wavelet-transformed artificial neural network (WANN) model to generate long-term (12-year) EVI forecasts using LST, temperature and precipitation. The relationship between land use / land cover and urbanization is investigated with NDBI (Normalized Difference Built-up Index) data obtained from the Landsat 8 OLI / TIRS satellite sensor. The simulation results show that The EVI value, which was 0.30 in 2018, will decrease to 0.25 in 2030.

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The Spatial and Temporal Land Cover Patterns of the Qazaly Irrigation Zone in 2003–2018: The Case of Syrdarya River’s Lower Reaches, Kazakhstan

Sustainability ◽

10.3390/su11154035 ◽

2019 ◽

Vol 11 (15) ◽

pp. 4035 ◽

Cited By ~ 3

Author(s):

Kanat Samarkhanov ◽

Jilili Abuduwaili ◽

Alim Samat ◽

Gulnura Issanova

Keyword(s):

Land Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Learning Algorithm ◽

Aral Sea ◽

Landsat 8 ◽

Spatial And Temporal Patterns ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Cover Maps

In this study, the spatial and temporal patterns of the land cover were monitored within the Qazaly irrigation zone located in the deltaic zone of the Syrdarya river in the surroundings of the former Aral Sea. A 16-day MODIS (Moderate Resolution Imaging Spectroradiometer) Aqua NDVI (Normalized Difference Vegetation Index) data product with a spatial resolution of 250 meters was used for this purpose, covering the period between 2003 and 2018. Field survey results obtained in 2018 were used to build a sample dataset. The random forests supervised classification machine learning algorithm was used to map land cover, which produced good results with an overall accuracy of about 0.8. Statistics on land cover change were calculated and analyzed. The correctness of obtained classes was checked with Landsat 8 (OLI, The Operational Land Imager) images. Detailed land cover maps, including rice cropland, were derived. During the observation period, the rice croplands increased, while the generally irrigated area decreased.

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