Estimation of leaf area index using WorldView-2 and Aster satellite image: a case study from Turkey

Abstract

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images.

Modeling forest stand attributes using Landsat ETM+ and QuickBird satellite images in western Turkey

“…The spectral response of pixels within a 1x1 pixel window corresponding to each sample plot was extracted after overlapping the sample plots on the resampled Landsat ETM+ satellite image. In QuickBird satellite image, to analyze spectral values in sample plots, the average digital number of pixels within a 35×35 pixel window (for the size of 400 m 2 ), 41x41 pixel window (for the size of 600 m 2 ) and 48x48 pixel window (for the size of 800 m 2 ) centered on the GPS location of each field plot was extracted from the QuickBird spectral band values (Günlü et al 2017).…”

“…Noorian et al (2016) indicated the relationship between different satellite images and forest structural attributes in Hyrcanian forests. In recent years, new high-resolution satellite images such as QuickBird and WorldView have been used in estimating forest structural parameters (Özdemir and Karnieli 2011, Noorian et al 2016, Günlü et al 2017.…”

Modeling leaf area index using time-series remote sensing and topographic data in pure Anatolian black pine stands

“…However, remote sensing data have been utilized to calculate LAI, which is more efficient than field measurements, especially for large forest areas. Because of the capability of remote sensing data to cover a broad area, it reduces field measuring activity (Gray and Song 2012 ; Pu and Cheng 2015 ; Günlü et al 2017 ; Zhou et al 2017 ). In the last two decades, many studies have been conducted to estimate LAI using various satellite images with different spatial and temporal resolutions (Tian et al 2017 ; Lu and He 2019 ; Brede et al 2020 ; Brown et al 2021 ; Wang et al 2022 ).…”

“…Furthermore, there are studies in which Landsat and Sentinel-2 images are evaluated together in the estimation of LAI (Ganguly et al 2012 ; Korhonen et al 2017 ; Meyer et al 2019 ). In studies on the estimation of LAI in forest ecosystems, band reflectance (Stenberg et al 2008 ; Günlü et al 2017 ; Vafaei et al 2021 ), vegetation indices (Kodar et al 2008 ; Vafaei et al 2021 ; Kinane et al 2021 ), texture (Zhou et al 2017 ), or combined (Wang et al 2022 ) data are mainly used as remote sensing data.…”

“…Many statistical models are used to estimate LAI based on remote sensing data, and they can be classified into two groups: (1) parametric linear models (Günlü et al 2017 ; Meyer et al 2019 ) and nonlinear models (Zhang and Song 2021 ), and (2) nonparametric models such as artificial neural networks (Xie et al 2021 ), random forest (Vafaei et al 2021 ), and support vector machines (Verrelst et al 2012 ; Cohrs et al 2020 ). Multiple linear regression (MLR) is the most commonly used method in LAI estimation studies (Soudani et al 2006 ; Günlü et al 2017 ; Guo et al 2021 ; Wang et al 2022 ). The MLR analysis is often utilized to estimate future values using features of a particular time series or other relevant time-series data (Chatfield 2004 ).…”