The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping

Umarhadi, Deha Agus; Danoedoro, Proio; Wicaksono, Pramaditya; Widayani, Prima; Nurbandi, Wahvu; Juniansah, Anwar

doi:10.1109/icstc.2018.8528670

Cited by 8 publications

(7 citation statements)

References 10 publications

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“…As for the negative response of the canopy closure to Band 3 (Green) other studies also showed that green biomass had the strongest negative correlation with reflectance in the green band of all visible wavebands (Lorenzen & Jensen 1988). Umarhadi et al (2018) also found the negative correlation between Sentinel B3 and canopy closure measured using hemispherical photography. The green band might also act as a variable that best minimizes shade fraction or saturation effects on NIR and SWIR.…”

Section: Discussionmentioning

confidence: 78%

Estimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus)

Mikeladze¹,

Gavashelishvili²,

Akobia³

et al. 2020

iForest

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Biogeosciences and Forestry Biogeosciences and ForestryEstimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus) Giorgi Mikeladze (1) , Alexander Gavashelishvili (1) , Ilia Akobia (2) , Vasil Metreveli (1) Our objective was to use Sentinel-2A multispectral data in order to cost-effectively detect change in forest cover in Georgia (the Caucasus). Generalized additive models (GAMs) were used to fit forest cover measures to Sentinel-2A spectral band values modified using different topographic correction methods. Canopy closure (calculated from upward-looking fisheye photographs taken beneath forest canopy) was the best forest cover measure accounted for by the Sentinel-2 spectral data that were topographically corrected using the Minnaert Correction (R 2 = 0.882). Spectral bands best explaining canopy closure were Band 3 (Green), Band 8 (NIR) and Band 12 (SWIR). Our model is able to reasonably detect spatial and temporal changes in canopy closure, even in highly rugged terrain and diverse vegetation cover, and it has potential to be improved to the extent that it can be applied by managers of natural resources. Based on free open source applications in combination with cheap gadgets our approach might play an important role in monitoring the forests of countries with low economic indicators.

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Section: Discussionmentioning

confidence: 78%

Estimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus)

Mikeladze¹,

Gavashelishvili²,

Akobia³

et al. 2020

iForest

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“…UAV method was chosen because this indirect measurement is easily conducted over the mountainous area, and it provides good results quite similar to the hemispherical photography method that has been widely used [19]. We equipped Color-Infrared (CIR) camera on DJI Phantom quad-copter.…”

Section: B Canopy Density Measurementmentioning

confidence: 99%

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

Umarhadi¹,

Danoedoro²

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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One of the main factors contributing to radiometric distortion on remote sensing data is a topographic effect, but it can be reduced by applying the topographic correction. This study identifies the effect of topographic correction on canopy density mapping in the Menoreh Mountains, Indonesia. Topographic correction methods examined in this research are C-Correction, Minnaert, and Sun-Canopy-Sensor+C (SCS+C). Canopy density estimation was approached using vegetation indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI), Aerosol Free Vegetation Index (AFRI) 1.6, and AFRI 2.1 derived from Landsat-8 OLI imagery. We evaluated the performance of topographic correction by visual and statistical analysis before comparing the accuracy of canopy density estimation of different vegetation indices and correction methods. The results showed that topographic normalization could increase the accuracy of canopy density mapping. The most significant improvement is the model using MSAVI, which increases by 1.207% using the Minnaert method to reach 86.692% accuracy. Meanwhile, NDVI, AFRI 1.6, and AFRI 2.1 have less significant improvement with the maximum increase of 0.241%, 0.057%, and 0.032% using the SCS+C method, reaching the accuracy of 88.980%, 83.303%, and 82.308%, respectively. The accuracies were slightly improved since the algorithms have already reduced the effect of topography, which are categorized as ratio vegetation indices. SCS+C is the best topographic correction method because of not only the appropriate assumption of canopy normalization but also its consistency and better accuracy in canopy density estimation, among other methods.

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“…Various approaches to extracting vegetation parameter information have been carried out with optical digital images, for example through multispectral classification of various land cover classes or multispectral classification combined with GIS to identify ecosystem units (Danoedoro, 2019), transformation of vegetation indices for study of vegetation density and biomass estimation (Huete 1988;Dewa and Danoedoro, 2017), and also classification of vegetation structural composition through forest cover density or FCD (Himayah et al, 2016;Ismail et al, 2017;Salsabila and Danoedoro, 2021;Danoedoro and Gupita, 2022). Vegetation studies using vegetation indices in Menoreh Mountain have been carried out by Umarhadi et al (2018) and Umarhadi and Danoedoro (2019).…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

Danoedoro

Gupita

Afwani

et al. 2022

IJG

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Digital surface model (DSM) has been widely available for mapping and was also sometimes used for mapping vegetation height. The authors conducted a preliminary study to evaluate the potential use of DSMs derived from ASTER, ALOS, and SRTM for estimating vegetation cover density in mountainous area. This study used NDVI and SAVI vegetation indices, in addition to forest cover density (FCD) model as references for evaluation. A DSM-based volume index (Volindex) concept is introduced, which is the product of the canopy height model (CHM) and the pixel area. CHM was derived from the value difference between the DSM and the reference DEM. The Volindex model was then compared with the NDVI, SAVI and FCD. We found that all DSM-based Volindex models are not accurate enough to represent the vegetation cover density, although the ALOS Palsar-based Volindex could reach 41.53% accuracy and was finally used to predict the vegetation cover density.

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The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping

Cited by 8 publications

References 10 publications

Estimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus)

Estimation of forest cover change using Sentinel-2 multi-spectral imagery in Georgia (the Caucasus)

The Effect of Topographic Correction on Canopy Density Mapping Using Satellite Imagery in Mountainous Area

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

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