Daljinski indeksi vegetacije izračunati programom SAGA GIS - poređenje vertikalnog i prilagođenog pristupa promena zemljišta za sliku LANDSAT TM

Polina Lemenkova

doi:10.5937/poljteh2103049l

Abstract

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.

Highlights

Vegetation indices computed using various GIS software based on the Landsat TM images have been often used in agricultural sciences to monitor vegetation and crop from space [43, 31]
Indices may be broadly separated into three categories: 1) intrinsic indices, which do not involve any external factor other than the measured spectral reflectances; 2) soil-line related indices, which include soil-line parameters, such as the perpendicular vegetation indices (VIs) (PVI), the weighted difference vegetation index, the soil-adjusted VI or SAVI [23], the transformed SAVI TSAVI [3]
The results of the applied methodology in the presented research is useful to the environmental scientists and agricultural crop studies applying SAGA GIS for analysis of sustainable development, land degradation and resource conservation

Summary

Introduction

Vegetation indices computed using various GIS software based on the Landsat TM images have been often used in agricultural sciences to monitor vegetation and crop from space [43, 31]. The fundamental theoretical properties of this approach consists of a combinations of visible and near-infrared (NIR) spectral reflectance of various land cove types [1]. Asphalt roads) and vegetation of various greenness have different spectral reflectance values [14, 22, 29], it can be used to detect the contours of these land cover types in general, and vegetation in particular using specially developed vegetation indices (VIs). Various bands of the electromagnetic spectrum (red, green, blue, NIR) are being applied to monitor crop cover, health and yield, nitrogen stress, and soil moisture in agricultural mapping. The VIs correlate with the leaf greenness and canopy density. In this way, it indicates at the vegetation health [7]

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Conclusion