A robust approach to generate canopy cover maps using UltraCam-D derived orthoimagery classified by support vector machines in Zagros woodlands, West Iran

Erfanifard, Yousef; Khodaei, Zohre; Shamsi, Rashid Fallah

doi:10.5721/eujrs20144744

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…Percentage canopy cover (PCC), which in other studies is equivalent to 'percent canopy cover' (PCC; Weishampel et al (1996), Díaz and Blackburn (2003), Schreuder, Bain, and Czaplewski (2003), Korhonen et al (2006), Walton, Nowak, and Greenfield (2008), Coulston et al (2012), McIntosh, Gray, and Garman (2012), Erfanifard, Khodaei, and Shamsi (2014), and Ozdemir (2014)), is a commonly used forestry concept (Erfanifard, Khodaei, and Shamsi 2014) and is important in predicting fire behaviours, air quality and estimating carbon sequestration amounts (Coulston et al 2012). As discussed in Ozdemir (2014), percentage canopy cover is often used to assess the suitability of wildlife habitat, to manage watershed, and to control erosion.…”

Section: Percentage Canopy Covermentioning

confidence: 99%

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

Kim

Popescu

Lopez

et al. 2020

International Journal of Remote Sensing

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LIght Detection And Ranging (lidar) data have been widely used in the areas of ecological studies due to lidar's ability to provide information on the vertical structure of vegetation in wildlife habitats. The overall objective of this project was to map the vegetation on No Name Key, Florida where endangered wildlife species reside using publicly available remote sensing data such as lidar data and high resolution aerial images (including National Agricultural Imagery Program (NAIP) images). The methods involved the use of 4 different classification algorithms (Support Vector Machine, Random Forest, Maximum Likelihood, and Mahalanobis Distance), different classification settings (default and custom settings), and normalization (original and normalized input bands) on 2 different input stacked images, NAIP image alone and NAIP combined with lidar data. A majority filter was applied to each classification output before performing the accuracy assessment. The result of performing the image classifications showed the following: across all inputs and classification algorithms, the highest overall accuracy (OA) and kappa coefficient (к) were achieved by the Random Forest classification on the NAIP-lidar stacked image with an applied majority filter and original input bands. The result also showed the airborne data combined with the lidar data resulted in a higher classification accuracy than the airborne data alone; and when normalization was applied to all the input bands or layers, the classification accuracies were not increased in most cases compared to when original bands were used in the classification. In most cases, the application of the majority filter increased the accuracy of the classification results as opposed to when no majority filter was applied. Instead of using default values, when the new parameter values (i.e., custom settings) for the penalty parameter (C) and the gamma (ɤ) were used, the accuracies of the Support Vector Machine (SVM) classified images were slightly increased compared to using the default values for the 2 parameters. With increased availability of public lidar data, combining the aerial image with lidar data would enhance the accuracy of vegetation mapping, which would lead to more effective and accurate wildlife management.

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Section: Percentage Canopy Covermentioning

confidence: 99%

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

Kim

Popescu

Lopez

et al. 2020

International Journal of Remote Sensing

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“…SVM gives better results than the traditional classifiers like maximum likelihood (Melgani and Bruzzone 2004;Huang et al 2002;Pal and Mathur 2005). Support vector machines (SVMs) are superior image classification techniques in airborne and satellite imagery, applying a set of machine learning algorithms and having their roots in statistical learning theory (Erfanifard et al 2014). Due to its ability to handle the nonlinear classifier problem, SVM is able to go beyond the limitations of linear learning machines by implementation of the kernel function, which paves the way to find a nonlinear decision function (Aghababaee et al 2013).…”

Section: Data Preparationmentioning

confidence: 99%

Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

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“…The GIS and vegetation classi cation systems in conjunction with computer-based automated eld mapping techniques have been prepared the appropriate tools in the process of creating or up-to-dating the vegetation maps (Ismail 2010). In a research which was done on the Zagros forest (Erfanifard et al, 2014), the capability of GIS tools is used to map the forest canopy cover density classes as a robust or novelty method to improve the classi cation results.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

Naseri

Shataee

2021

Preprint

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Background: Accurate mapping and monitoring canopy cover using remote sensing data as an alternative way for field surveys are very important for forest managers, particularly in the spare and low dense forests. Due to being area-based of canopy cover density and mixing spectral responses of tree crowns and soil in the thin and semi-dense forests, finding the high-performance method of classification is a challenge particularly on high-resolution imagery. In this study, we compared produced maps of canopy cover using direct remote sensing and indirect (RS-GIS-based) methods in two forest sites on the Quickbird and WorldView-2 images using the Artificial Neural Network (ANN) algorithm. Also, the optimal plot area was examined by different plot areas.Results: In the direct method and based on the obtained results, in the Dashte Barm using Quickbird image, the best classification was for plots of 7500 m2 with an overall accuracy of 56.57% and kappa coefficient of 0.32. In the Ilam site and on the WorldView-2 image, the best result is obtained by the plots of 5,000 m2 area with an overall accuracy of 45.71% and the kappa coefficient of 0.263. The results of accuracy assessment of maps of indirect method in the Dashte Barm site for grids with different areas showed that the best classifications obtained from sample plot areas of 10000 m2 with overall accuracy of 82.69% and Kappa coefficient of 0.744; but in the Ilam sites the best result was obtained using sample area of 1000 m2 with overall accuracy of 74.27% and the Kappa coefficient of 0.690. Conclusions: The results exposed that use of the RS-GIS based method could considerably improve the results compare to direct classification. Also, the results showed concerning the conditions of canopy cover density of forest stands, plots with different areas can be used to map of forest canopy cover density; however, for direct classification the use of plots with areas of 5000 m2 and more are suitable in sparse forests. For RS-GIS based method, the plot areas of 1000 m2 are optimal due to time and cost saving.

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A robust approach to generate canopy cover maps using UltraCam-D derived orthoimagery classified by support vector machines in Zagros woodlands, West Iran

Cited by 12 publications

References 37 publications

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

Vegetation mapping of No Name Key, Florida using lidar and multispectral remote sensing

Urban sprawl assessment and modeling using landsat images and GIS

Improvement of Forest Canopy Density Mapping of Spare Forests Using a Novel RS-GIS Based Classification Method

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