SAGA GIS for Computing Multispectral Vegetation Indices by Landsat TM for Mapping Vegetation Greenness

Summary The study presents a comparative analysis of eight Vegetation Indices (VIs) used to examine vegetation greenness over the northern coasts of Iceland. The geographical extent of the study area is set by the coordinates of the two fjords, Eyjafjörður and Skagafjörður, notable for their agricultural significance. Vegetation in Iceland is fragile due to the harsh climate, climate change, overgrazing and volcanic activity, which increase soil erosion. The study was conducted on a Landsat TM image using SAGA GIS as a technical tool for raster bands calculations. The NDVI dataset shows a range from -0.56 to 0.24, with 0 indicating ‘no vegetation’, and negative values – ‘other surfaces’ (e.g. rocks, open terrain). The DVI, compared to the NDVI, shows statistically non-normalized values ranging from -112 to 0, with extreme negative values while the coastal vegetation areas are badly distinguished from the water areas. The NRVI shows an extent from -0.24 to 0.48 with higher values for vegetation. The NRVI reduces topographic, solar and atmospheric effects and creates a normal data distribution. RVI shows a range in a dataset from 0.2 to 3.2 with vegetation in the river valleys clearly visible and depicted, while the water areas have values 0.8 to 1.0. The CTVI shows corrected TVI, in a data range -0.10 to 1.10, as the dataset of NDVI were negative. The TVI dataset ranges from 0.44 to 0.80 with the ice-covered areas and glaciers distinguishable and water values within a range from 0.60 to 0.64 and the vegetation from 0.60 to 0.44. The TTVI dataset ranges from 0.40 to 0.80 performing similarly to the TVI, but more refined with vegetation values 0.64 to 0.68. SAVI dataset ranges from -0.80 to 0.30 with minimized effects of soil on the vegetation through a constant soil adjustment factor added into the NDVI formula. The paper presents a comparison of eight VIs for Arctic vegetation monitoring. The overall behavior of SAGA GIS in calculation and mapping of the VIs is effective in terms of their use for vegetation mapping of the region.

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Towards a snow-depth distribution model in a heterogeneous subalpine forest using a Landsat TM image and an aerial photograph

Annals of Glaciology ◽

10.3189/172756402781817923 ◽

2002 ◽

Vol 34 ◽

pp. 65-70 ◽

Cited By ~ 6

Author(s):

Manfred Stähli ◽

Jesko Schaper ◽

Andreas Papritz

Keyword(s):

Regression Model ◽

Snow Depth ◽

Aerial Photograph ◽

Depth Distribution ◽

Vegetation Indices ◽

Landsat Tm ◽

Subalpine Forest ◽

Landsat Tm Image ◽

Tm Image ◽

Forest Mosaic

AbstractFor landscapes with a complex topography and a heterogeneous forest mosaic it is not feasible to map the snow depth directly from optical satellite images. In this paper, an indirect method to predict the snow-depth distribution is presented and applied to a 0.7 km2 subalpine catchment in central Switzerland. The method consists of (a) a parsimonious linear regression model which includes the attributes of topography and vegetation indices (derived from a Landsat Thematic Mapper (TM) image) as explanatory variables, and (b) geostatistical interpolationtechniques. A previous analysis of the forest mosaic revealed two main scales showing up in the Landsat TM image and an aerial photograph. This discrepancy in scale was assumed to be the major reason why the vegetation indices derived from the Landsat TM image were only weak explanators of the snow-depth variation measured at 100–200 locations within the catchment. Surprisingly, the geostatistical interpolation (universal kriging) was not able to improve the prediction of the snow-depth distribution significantly. The residuals of the regression model showed hardly any spatial dependence for single snow-measurement dates.

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SAGA GIS for Information Extraction on Presence and Conditions of Vegetation of Northern Coast of Iceland Based on the Landsat TM

Acta Biologica Marisiensis ◽

10.2478/abmj-2020-0007 ◽

2020 ◽

Vol 3 (2) ◽

pp. 10-21

Author(s):

Polina Lemenkova

Keyword(s):

Vegetation Index ◽

Methodological Approach ◽

Remote Sensing Data ◽

Livestock Grazing ◽

Landsat Tm ◽

Vegetation Coverage ◽

Northern Coast ◽

Landsat Tm Image ◽

Harsh Climate ◽

The Impact

AbstractThe paper aims to evaluate the presence and condition of vegetation by SAGA GIS. The study area covers northern coasts of Iceland including two fjords, the Eyjafjörður and the Skagafjörður, prosperous agricultural regions. The vegetation coverage in Iceland experience the impact of harsh climate, land use, livestock grazing, glacial ablation and volcanism. The data include the Landsat TM image. The methodology is based on computing raster bands for simulating Tassel Cap Transformation (wetness, greenness and brightness) and Enhanced Vegetation Index (EVI) sensitive to high biomass. The results include modelled three bands of brightness, greenness and wetness. Greenness variation shows the least values in ice-covered areas (-56.98 to -18.69). High values (-23.48 to 9.12) are in the valleys with dense vegetation, correlating with the geomorphology of the river network, the vegetation-free areas and ocean which corresponds to the peak of 30.87 to 41.19. The bell-shaped data distribution shows frequency 43.19–141.74 for vegetation indicating healthy state and canopy density. Maximal values are in ice-covered regions and glaciers (64°N-65°N). Very low values (0 to -20) show desertification and mountainous rocks. Moderate values (20-40) indicate healthy vegetation. The most frequent data: -28,17 to 11,8. The EVI shows data variations (-0.14 to 0.04). The study contributes both to the regional studies of Arctic Iceland and methodological approach of remote sensing data processing by SAGA GIS.

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Hyperspectral Vegetation Indices Calculated by Qgis Using Landsat Tm Image: a Case Study of Northern Iceland

Advanced Research in Life Sciences ◽

10.2478/arls-2020-0021 ◽

2020 ◽

Vol 4 (1) ◽

pp. 70-78

Author(s):

Polina Lemenkova

Keyword(s):

Vegetation Indices ◽

Health Condition ◽

Landsat Tm ◽

Hyperspectral Reflectance ◽

Arctic Ecosystems ◽

Landsat Tm Image ◽

Raster Calculator ◽

Spectral Bands ◽

Green Vegetation ◽

Landsat Tm Imagery

Abstract The vegetation indices (VIs) derived from the hyperspectral reflectance of vegetation are presented in this study for monitoring live green vegetation in the northern ecosystems of Iceland, along the fjords of Eyjafjörđur and the Skagafjörđur. The comparative analysis of the following VIs was performed: the NDVI, RVI, NRVI, TVI, CTVI, TTVI and SAVI. The methodology is based on the raster calculator band in a QGIS. The dataset includes a Landsat TM scene of 2013, UTM Zone 53, WGS84 captured from the GloVis. The computed bands include the NIR and R spectral bands and their combinations according to the algorithms of each of the seven VIs. The hyperspectral reflectance and crop canopy computations were applied to generate various scales of VIs and demonstrated following data range: NDVI: -0.91 to 0.65, RVI: 0.22 to 19.65, NRVI: 0.63 to 0.90, TVI: 0 to 1.12, CTVI: -0.64 to 1.07, TTVI: 0.70 to 1.18 and SAVI: -1.36 to 0.99 (roughly to 1.00). Of these, the RVI, NRVI, TVI and TTVI are adjusted to the positive values while the NDVI, CTVI and SAVI do include the negative diapason in the dataset due to the computing algorithm. The algorithms of the seven VIs are described and visualized in form of maps based on the multispectral remote sensing Landsat TM imagery identifying vegetated areas, their health condition and distribution of green areas against the bare soils, rocks, ocean water, lakes and ice-covered glaciers. The paper contributes both to the technical presentation of the QGIS functionality for the Landsat TM data processing by a raster calculator, and to the regional geographic studies of Iceland and Arctic ecosystems.

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Distance-based vegetation indices computed by SAGA GIS: A comparison of the perpendicular and transformed soil adjusted approaches for the LANDSAT TM image

Poljoprivredna tehnika ◽

10.5937/poljteh2103049l ◽

2021 ◽

Vol 46 (3) ◽

pp. 49-60

Author(s):

Polina Lemenkova

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Landsat Tm ◽

Area Index ◽

Gis Application ◽

Landsat Tm Image ◽

Tm Image ◽

First Case ◽

Background Line ◽

The Difference

Landsat-TM of 2001 covering Iceland (15.5°W-21°W, 64.5°N-67°N) was processed using SAGA GIS for testing distance-based Vegetation Indices (VIs): four approaches of Perpendicular Vegetation Index (PVI) and two approaches of Transformed Soil Adjusted Vegetation Index TSAVI. The PVI of vegetation from the soil background line indicated healthiness as a leaf area index (LAI). The results showed that the reflectance for vegetation has a linear relation with soil background line. Four PVI models and two TSAVI shown coefficients of determination with LAI. The dataset demonstrate variations in the calculated coefficients. The mode in the histograms of the PVI based on four different algorithms show the difference:-7.1,-8.36, 2.78 and 7.0. The dataset for the two approaches of TSAVI: first case ranges in 4.4.-80.6 with a bell-shape mode of a histogram (8.09 to 23.29) for the first algorithm and an irregular shape for the second algorithm with several modes starting from 0.11 to 0.2 and decreasing to 0.26. SAGA GIS permits the calculation of PVI and TSAVI by computed NDVI based on the intersection of vegetation and soil background. Masking the NIR and R, a linear regression of grids was performed using an equation embedded in SAGA GIS. The advantages of the distance-based PVI and TSAVI consists in the adjusted position of pixels on the soil brightness line which refines it comparing to the slope-based VIs. The paper demonstrates SAGA GIS application in agricultural studies.

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QVigourMap: A GIS Open Source Application for the Creation of Canopy Vigour Maps

Agronomy ◽

10.3390/agronomy11050952 ◽

2021 ◽

Vol 11 (5) ◽

pp. 952

Author(s):

Lia Duarte ◽

Ana Cláudia Teodoro ◽

Joaquim J. Sousa ◽

Luís Pádua

Keyword(s):

Open Source ◽

Precision Agriculture ◽

Vegetation Indices ◽

Data Interpretation ◽

Vegetation Monitoring ◽

Distribution Maps ◽

Monitoring Process ◽

Multi Temporal ◽

The Creation ◽

The Right

In a precision agriculture context, the amount of geospatial data available can be difficult to interpret in order to understand the crop variability within a given terrain parcel, raising the need for specific tools for data processing and analysis. This is the case for data acquired from Unmanned Aerial Vehicles (UAV), in which the high spatial resolution along with data from several spectral wavelengths makes data interpretation a complex process regarding vegetation monitoring. Vegetation Indices (VIs) are usually computed, helping in the vegetation monitoring process. However, a crop plot is generally composed of several non-crop elements, which can bias the data analysis and interpretation. By discarding non-crop data, it is possible to compute the vigour distribution for a specific crop within the area under analysis. This article presents QVigourMaps, a new open source application developed to generate useful outputs for precision agriculture purposes. The application was developed in the form of a QGIS plugin, allowing the creation of vigour maps, vegetation distribution maps and prescription maps based on the combination of different VIs and height information. Multi-temporal data from a vineyard plot and a maize field were used as case studies in order to demonstrate the potential and effectiveness of the QVigourMaps tool. The presented application can contribute to making the right management decisions by providing indicators of crop variability, and the outcomes can be used in the field to apply site-specific treatments according to the levels of vigour.

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Using k-nn and discriminant analyses to classify rain forest types in a Landsat TM image over northern Costa Rica

Remote Sensing of Environment ◽

10.1016/j.rse.2007.11.015 ◽

2008 ◽

Vol 112 (5) ◽

pp. 2485-2494 ◽

Cited By ~ 25

Author(s):

Sirpa Thessler ◽

Steven Sesnie ◽

Zayra S. Ramos Bendaña ◽

Kalle Ruokolainen ◽

Erkki Tomppo ◽

...

Keyword(s):

Costa Rica ◽

Rain Forest ◽

Landsat Tm ◽

Forest Types ◽

Landsat Tm Image ◽

Tm Image ◽

Discriminant Analyses

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THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

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

10.5194/isprs-archives-xlii-3-1623-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1623-1627

Author(s):

S. Talebi ◽

J. Shi ◽

T. Zhao

Keyword(s):

Optical Depth ◽

Vegetation Index ◽

Vegetation Indices ◽

Soil Surface ◽

Single Scattering ◽

Single Scattering Albedo ◽

Theoretical Modelling ◽

Vegetation Monitoring ◽

Scattering Effects ◽

The Relationship

This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

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