Mapping of understory infested boxwood trees using high resolution imagery

Journal Pre-proof Mapping of understory infested boxwood trees using high resolution imagery Rohollah Esmaili, Shaban Shataee, Javad Soosani, Hamed Naghavi PII:

S2352-9385(19)30252-6

DOI:

https://doi.org/10.1016/j.rsase.2020.100289

Reference:

RSASE 100289

To appear in:

Remote Sensing Applications: Society and Environment

Received Date: 26 July 2019 Revised Date:

17 January 2020

Accepted Date: 18 January 2020

Please cite this article as: Esmaili, R., Shataee, S., Javad Soosani, , Naghavi, H., Mapping of understory infested boxwood trees using high resolution imagery, Remote Sensing Applications: Society and Environment (2020), doi: https://doi.org/10.1016/j.rsase.2020.100289. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier B.V.

Author Statement: Conceptualization, Rohollah Esmaili and Shaban Shataee, Javad, Soosani, and Hamed Naghavi; Methodology, Rohollah Esmaili and Shaban Shataee; Software, Rohollah Esmaili and Shaban Shataee and Hamed Naghavi; Validation, Rohollah Esmaili and Shaban Shataee; Formal analysis, Rohollah Esmaili, Shaban Shataee, and Javad, Soosani; Resources, Rohollah Esmaili and Shaban Shataee; Writing—original draft preparation, Rohollah Esmaili; Writing—review and editing, Rohollah Esmaili and Shaban Shataee; Supervision, Shaban Shataee, Javad, Soosani; Project administration, Javad, Soosani.

Rohollah Esmaili1, Shaban Shataee2*Javad, Soosani3, Hamed Naghavi4 1

PhD Graduated student, Faculty of Natural Resources, Lorestan University, Lorestan, Iran.

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Professor, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, [email protected]

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Associate Professor, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan, Iran.

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Assistant Professor, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan, Iran. *Corresponding Author: Dr. Shaban Shataee Jouibary, Professor of Forestry, Forest Sciences Faculty, Gorgan University of Agricultural Science and Natural Resources, 386, Gorgan, Iran, Postal code: 49189-43464 Tel: +98 17 32427050 Fax: +98 17 32427176 Email: [email protected]; [email protected]

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Mapping of Understory Infested Boxwood Trees Using High Resolution Imagery

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The Buxus Hyrcania stands in the Hyrcanian forests have been infested by Buxus blight pathogen in recent years. Determination of the rate, and finding the spatial distribution of infested trees, is very important for control and treatment management. In a case study, the capacity of leaf-off (winter) Pleiades high-resolution satellite imagery was examined using pixel and object-based classification algorithms to map the different infestation intensity classes in the reserved area of Anjilsi-Khiboos, a small part of the Hyrcanian forest. Ground truth of five infested classes including healthy, semi-healthy, full-infested, forest stands without boxwood understory and, the non-forest area was prepared using accurate positioning using RTK-DGPS. The images were geometrically orthorectified using DGPS ground control points and a DEM. Using suitable vegetation indices, the separability of classes on main and processed bands was evaluated using a transformed divergence index. Image classifications were carried out using appropriate algorithms and assessed using unused ground truth points. In the pixel-based classification, the best results were achieved using maximum likelihood algorithms on the original VNIR bands. In the object-based classification, the Bayes algorithm on VNIR bands had better performance among other used algorithms; however, the object-based classifiers had slightly the same results compared with pixel-based classifiers. The results showed that due to the similarity of spectral responses of healthy and semi-healthy boxwood trees and the heterogeneous distribution of boxwood blight disease in the study area, detection of the healthy trees from infested trees was slightly impossible. However, the fully infested and dead trees could be detected well from healthy boxwood trees and other stands and classes. Keywords: Understory detection; Boxwood blight disease; High-resolution imagery; Leafless season; Pleiades; Hyrcanian forests

1. Introduction: The forests in Iran, with an area of about 14.3 million hectares, comprise approximately 7.8% of the country (FAO, 2016). These forests have a range of geographic conditions, producing different forests of various tree and shrub species and production capacity in different edaphoclimatic conditions (FAO, 2002). Among five large vegetation regions throughout Iran, the most important vegetation region according to density, canopy cover, and diversity, is the Hyrcanian forest, covering an area of approximately 2 million ha, extending throughout the south coast of Caspian Sea. The Hyrcanian vegetation zone is a greenbelt stretching over the northern slopes of the Alborz mountain range (Sagebtalebi, et al. 2003). It has a high production capacity because of the humid, temperate climate, and suitable soil. Hyrcanian forests extend for 800 km in length from east to west. These natural, mixed-hardwoods and broad-leaved forests have rich diversity based on tree and understory species (Sageb-talebi, et al. 2003). Forest understory communities play many different roles in forest ecosystems (Suchar & Crookston, 2010) and provide habitat for wildlife, which are important factors in nutrient cycling and fire behavior (Falkowski, et al. 2009). Thus, understory communities are often proper ecological indicators of forest health (Kerns & Ohmann, 2004). Understory vegetation plays a key role in sustaining soil microbial biomass and extracellular enzyme activities (Yang, et al., 2018). Boxwood (Buxus sempervirens subsp. hyrcana) is an evergreen shrub or tree and understory species that spreads as compact colonies in the Hyrcanian forests of the Caspian Sea region of Iran, particularly in the reserved area (Rezaee, et al. 2012). The genus Buxus has several species on the IUCN threatened species list (IUCN, 2017) and B. sempervirens is one of the important species for protection in the world and Iran. It is entered in the European red list, too (Rivers et al., 2019). During the summer of 2012, a sudden leaf and twig blight disease by Caloenectria pseudonaviculata has been observed on the boxwood shrubs and trees throughout the northern forests of Iran. This disease rapidly spread from west to east throughout the Hyrcanian boxwood stands and approximately 40,000 hectares of the original boxwood stands were infested and damaged by this disease at different intensities. Disease symptoms included circular dark spots on leaves leading to defoliation, and longitudinal brown-black streaks on the shoots (Mirabolfathy, et al., 2013). This infestation leads to severe leaf decline and then tree dieback and mortality in some zones of the Hyrcanian forests with different extent and severity (Samavat, 2017). Dieback of B. sempervirens subsp. hyrcana trees caused by the boxwood blight has been reported in the Hyrcanian forest of Iran, and the spread of the disease has been reported in neighboring countries such as Azerbaijan and Georgia (FAO, 2016). Another pest of boxwood trees is the box tree caterpillar (Cydalima perspectalis), which causes defoliation. Diseases and insect infestations on the forest plants have been documented to be a serious threat to the economic and, to a certain extent, ecological value of the forest ecosystems (Jactel and Vodde, 2011). Knowledge of the location and intensity of the infestations and the resulting dead trees is important. Survey methods for mapping and gathering spatial information on insect or disease infestations extend from local to regional scales. Several researches (Wulder, et al. 2004; 2005; 2006; 2006; 2006a; 2008; Rullán-Silva, et al. 2015; Senf, et al. 2017; Nasi, et al. 2018) have reported details on the variety of relevant ground and remote sensing-based survey methods for insects and disease infestation monitoring and mapping. These methods mainly include field campaigns, sketch mapping from helicopters or airplanes, and digital airborne or space-borne remote sensing. The forest attributes are traditionally gathered by ground-based field measurements using handheld equipment. These measurements are expensive, timeconsuming, and labor-intensive, especially in mountainous and dense forests with understory trees and shrub vegetation (Mohammadi, et al. 2010).

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Satellite remote sensing is an alternative source and a new tool for forest inventory and measuring, especially in large areas (Noorian et al., 2016). Rapid improvements in remote sensing technology have led to various types of sensors, such as multispectral, hyperspectral, ultraviolet, thermal sensors, light detection and ranging (LiDAR), radio detection and ranging (radar), and other sensors (Shataee, et al., 2012). Each sensor has been designed for specialized purposes, tasks, and different applications (Kalbi, et al. 2014). These new potential sources have been shown to be effective tools to assess and monitor forest features with reasonable accuracy levels (Hyyppa, et al. 2000). Compared with the other described methodologies, digital remote sensing data has the advantage of being compatible with forest inventory databases while delivering high positional accuracy. The further benefits of remote sensing data combined with automatic processing algorithms; leads to reduced interpreter bias, which therefore offers greater consistency and reliability among different areas or dates (Wulder, et al., 2005). Since the early 1980s, satellite imagery has been used to map and predict insect and disease infestations (Skakun et al. 2003). Most of these studies (i.e. Franklin et al., 2003; Wulder et al., 2006; Meddens et al., 2013) used the medium spatial resolution (5-30 m) imagery, because of large and complete spatial and temporal coverage and proper spectral resolutions as well as to be free and available. For instance, Franklin et al. (2003), Skakun et al., (2003), Wulder et al. (2006), Meigs et al. (2011), and Meddens et al. (2013) have tried to map the infested forest stands in the "red stage" of a mountain pine beetle attack with an overall accuracies with ranging from 67% to 78% using traditional classifier and multi temporal indices. Launching recent satellites with very high spatial resolution optical space-born sensors such as IKONOS, QuickBird, OrbView or airborne digital and UAVbased cameras, together with developing new classification algorithms could prepare the new potential ways and tools for small area level and tree-level infested mapping (Taghuchi, et al. 2005; White, et al. 2005; Nasi, et al. 2015; Dash, et al. 2017). The ability to benefit from this information relies heavily on the ability to get frequent measurements of land surfaces at various spatial and temporal resolutions and scales. For understory cover detection and surveying for conditions such as insect or disease infestations, the leave-on dates of optical remote sensing images is not be useful because of reflectance of tree leaves on overstory canopy covers. Therefore, use of leafless (winter) images can be applied to detect evergreen understory vegetation (Morain 1986; Linderman et al. 2004; Wang et al. 2009; Naphidkar, 2017). On the other hand, understory vegetation cover surveying has proved to be difficult inherently using traditional methods such as field methods or aerial photo interpretation because of cost and time-consuming methods; therefore, using satellite data and automated methods could be a suitable solution. Remote sensing techniques have shown high potential for providing information on forest characteristics, with lower costs than traditional field inventories. In optical (i.e. images from visible and infrared wavelengths) remote sensing applications, the sensors with any special wavelength get reflectance from the target surface and its environment. Thus, in forest ecosystems, there are less desired signals from the inside and the forest floor (soil and understory vegetation), particularly in high dense forests. However, fewer investigators have dealt with the understory, although differences in understory composition are known to have a significant effect on the forest reflectance (Chen & Cihlar, 1996; Spanner, et al., 1990) in low dense canopy covers. Accurate analysis of understory reflectance is needed to separate the spectral signals of understory plants from that of the forest canopy (overstory) and subsequent infestation condition from insect or pathogen. One of the solutions to overcome this problem is using leafless season (winter) images (Liang, 2004; Meng, et al. 2018) in different scales from stand to tree level. In tree-level detection and mapping, the use of very high-resolution images is suggested by some studies using airborne digital imagery (Ciesla, 2000; Coggins, et al. 2011) or space-borne imagery (Coops, et al. 2006; Murfitt, et al. 2016). Particularly in a different situation of infestations on trees or shrubs, quantifying spectral traits, its variations, and their interactions in tree-level, need to use very high-resolution images. The spatial resolution and the density of infestations on a tree will determine the characteristics that are contained in one pixel (Lausch, et al. 2016). One of the major approaches, which are affecting the infestation analysis results, is selecting appropriate classification approaches and algorithms. Many classification algorithms have been developed since the first satellite images were acquired in the early 1970s (Hall, et al. 1995). Based on classification units, the classification methods can be subdivided into two sections of pixel-based (pixels are classification units) and object-based (objects which are segmented by grouping homogeneous or similar pixels based on spectral or other characteristics in different segmentation scale levels). The most important pixel-based classifiers are the hard and parametric classifiers such as maximum likelihood classifier (MLC), and new learning-based or machine-learning as nonparametric algorithms such as artificial neural network classifiers (ANN), Support vector machine (SVM), decision tree (DT) classifiers like random forest (RF) and other variants. The MLC is a parametric classifier based on statistical theory, which has a critical limit or restriction because of its assumption of a normal distribution of class signature (Swain and Davis, 1978). Despite this limit, it is perhaps one of the most widely used classifiers (Hansen et al. 1996). The support vector machine (SVM) classifier is a theoretically superior machine learning algorithm with high performance (Shataee et al., 2012). The SVM algorithm uses optimization techniques to find the optimal boundaries between classes. Statistically, the optimal boundaries should be generalized to unseen samples with the lowest errors among all possible boundaries separating the classes, therefore reducing the confusion between classes and improving the classification results. In this study, we used only two well-known and performance pixel-based parametric (MLC) and nonparametric (SVM) classifiers reported by researchers to map the dead trees and semi infested boxwood trees by Buxus blight from healthy boxwood trees and hardwood trees without any boxwood understory. However, some researches (Shataee et al. 2004; Niphadkar et al. 2017) have shown that the object-based classification approach by different algorithms can be more useful to map the forest attributes. Therefore, three machine learning object-based algorithms implemented in

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eCognition software including parametric classifiers of Bayes, and two nonparametric SVM with RBF kernel and decision trees (DT) classifiers were examined to map and compare their ability with pixel-based classifiers. These algorithms have superiority over machine learning algorithms such as KNN or CART algorithms based on some reports (Tzotsos, 2006; Amami, et al. 2012; Qian, et al. 2015). Previous studies have shown the different classifiers may lead to different classification results. Therefore, many studies have been conducted to study the effectiveness and efficiency of different classifiers (Shataee, 2004; Qian, et al. 2015). However, efficiency and performance of the algorithms and accuracy of classification results are dependent on some factors such as segmentation setting parameters in object-based algorithms, setting the tuning parameters in the machine learning algorithms and selecting the best features (main and processed bands). Therefore, the main aim of the study is the examination of the potential of very high-resolution leaf-off space-borne imagery to recognize infested boxwood trees by blight disease on the boxwood understory shrubs and trees in the complex and mixed hardwood forest stands at the Hyrcanian forests. Incidentally, a parametric and nonparametric performance test of the pixel and object-based classifiers was the second aim of this study to map the full and semi infested and damaged boxwood trees by Buxus blight from healthy boxwood trees and hardwood trees without any boxwood understory. 2. Materials and methods 2.1. Study area The study area is located in the Hyrcanian forests, on Khiboos & Anjilsi boxwood reserved area, North part of Iran (Fig.1). The study area is approximately 1,500 ha, and consists of natural, temperate and uneven-aged stands. The main tree species are Fagus orientalis (oriental beech), Quercus castaneifolia (chestnut-leaved oak), Carpinus betulus (hornbeam), Acer velutinum (velvet maple), Alnus subcordata (Caucasian alder), Tilia begonifolia (linden tree), Parrotia persica (Persian ironwood), Ulmus glabra (elm), Acer platanoides (Norway maple), Diospyros lotus (date palm), Zelkova carpinifolia (Siberian elm) and Acer cappadocicum (Coliseum maple). This region is a dense habitat of F. orientalis with an uneven-aged structure understory dominated by evergreen boxwood shrubs or trees (Buxus sempervirens subsp. Hyrcana) (Esmaili et al., 2018). Figure 1 2.2. Remotely sensing data: A small window of Level-A Pleiades image on 13th April 2014 from the study area was ordered and prepared from Digital Globe archives with cloud-free (less than 3%). The study area was snow free during this acquisition; and trees and shrubs in canopy cover were leafless due to altitude and time of year. The Pleiades sensors operate in two different spectral wavelength regions including the visible (Blue (0.43-0.55µm), Green (0.51-0.58µm), and Red (0.62-0.70µm)) and near-infrared (NIR) (0.775-0.915µm) bands with 2.5 meters spatial resolution and a panchromatic (0.48-0.82µm) band with 82 centimeters spatial resolution, respectively. The quantization level of images was 12 byte. 2.3. Field surveys and ground truth map: The boxwood blight infestation occurred in the summer of 2012 and the infested intensities on boxwood trees or shrubs classified as three general categories including healthy, semi-healthy and dead or full-infested trees after a one-year attack. In January 2014, in any patch of classes, 250 points (50 points for each class), depending on extending each class of boxwood blight presence or absence was randomly sampled (Table 1 and Figure 2). The place of each point was settled proportionally based on actual distribution of infested boxwood classes, non-damaged boxwood trees, and other classes inside the study area. Table 1 Figure 2 To prepare a ground truth map, 50-point samples from any five definite classes (250 total samples) where classes have fixed and homogeneous conditions about five meters distance from the center of samples were selected throughout the study area. The geographic coordinates of the center of point samples were recorded with a Differential Geographical Positioning System (DGPS) through Real-time kinematic (RTK) positioning method (figure 3). The sample plots were divided into two training areas (75 % of samples from any classes) for classification and test sets (25 % samples) for accuracy assessment. Figure 3 2.4. Image preprocessing In the preprocessing stage, at first, the quality of the images was evaluated for radiometric noise so that any considerable radiometric distortion was not observed on the images. These images were then orthorectified with UTM projection using 20 ground control points taken from DGPS, a Digital Elevation Model (DEM) with 10 m resolution. By applying the nearest neighbor resampling technique, the images were registered with RMSe of 0.37 pixels. The atmosphere affects the ability of a given sensor to quantify visible and near-infrared signals in several ways (Forester, 1984). The purpose of atmospheric corrections in the visible and near-infrared wavelengths for a satellite-imaging

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sensor is the conversion of image digital numbers (DNs) to surface radiance/reflectance values (Teillet and Fedosejevs, 1995). This is especially important in forestry mapping as the atmosphere modifies strength and spectral distributing the electromagnetic energy received by a sensor (Lillesand and Kiefer, 1999). The general atmospheric COST model has used for the atmospheric correcting of the VNIR bands because of to be mountainous of the study area and effect of the atmosphere on the visible bands.

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4. Discussion:

2.5. Image processing and separating analysis In order to enhance the vegetation classes, based on previous research, some suitable different vegetation indices were created using the original bands (Table 2). After applying the effective vegetation indices for classification, all the synthetic bands and 4 original VNIR bands were examined using transformed divergence index (DTI) to select the best bands for classification. In addition, the separability of classes on these bands was assessed using a transformed divergence index. . Table 2 2.6. Image Classification 2.6.1. Pixel-based classification Firstly, the signature generation analysis was done using 75 % of the ground truth field samples as training sites. Then, the supervised and pixel-based classification was performed on two different data sets (original bands and best bands selected by separability analysis) by appropriate non-parametric (Support Vector Machine learning algorithm with four types of kernels including linear, polynomial, sigmoid and radial basis function), and parametric (Maximum likelihood) classifiers (Mohammadi et al. 2010; Shataee et al. 2012; Shataee et al. 2014). In carrying out of each classifier, the tuning parameters were set to achieve the optimal classification based on previous experiences. 2.6.2. Object-based classification Given the capabilities of object-based approaches for mapping, the contextual cover types on the VHR data for detection of details of understory seems to be an effective methodology (Naphidkar et al. 2017) in the Hyrcanian forests. To compare the ability of object-based classifiers against pixel-based classifiers, the segmentation was performed after setting and tuning up the parameters of compactness, scale, and shape for object demarcation based on previous experiences (Shataee et al. 2004). Then, the object-based classifications were done on the original bands and the best bands individually using appropriate algorithms (Bayes, SVM-RBF, SVM-Linear, and Decision trees classifiers) by Nearest Neighbor membership functions approach and selecting the sample segments from each class and the mean and standard deviation feature statistics. The nearest neighbor classifier algorithm in eCognition searches for sample image objects that are closest to the feature space characteristics of each segmented object, assigning the objects to the closest classes with similar feature space characteristics (Naphidkar et al. 2017). 2.7. Accuracy assessment The 25 % of remaining ground truth data was used to perform an accuracy assessment of the classification results. The accuracy metrics including Kappa coefficient and overall accuracy were calculated based on error matrices for all classification results. 3. Results Among the synthetic and original bands, the bands of VNIR, AVI, GDVI, and DVI vegetation indices were selected as the best bands. The separability test showed that the classes could be separated from these bands (Table 3). Table 3 The results of applying the pixel-based classifiers on the main and the best bands were shown on figures 4 and 5 using the used algorithms on the original and the best-selected bands. The accuracy assessment results of the classifiers are shown in Table 4. Tables 5 and 6 show the detailed user and producer accuracies of carrying out the pixel-based algorithms on the best bands as the best results according to Kappa and overall accuracy measures. In addition, the accuracy assessment of the object-based classifiers showed that the Bayes algorithm had a higher performance against other used classifiers. Table 4 Table 5 Table 6 Table 7 Figure 4 Figure 5 Figure 6

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Several studies have been carried out using multispectral imagery to characterize insect infestations (Meddens et al., 2013). Although several studies (Cheng et al., 2010; Fassnacht et al., 2014) have focused on the detection of vegetation damage using hyperspectral information, one of the key challenges in quantifying forest health and infestations using VHR optical imagery with low spectral bands (such as Pleiades images), is overcoming the problem of detecting the understory reflectance in high-density canopy cover area damaged by insect defoliation. Until now, the potential of those data for accurately mapping dead understory trees has not been intensively assessed, the main aim of this study was to explore the efficiency and capability of the Pleiades images in predicting boxwood blight damages in the mixed hardwood forests of Iran and performance assessment using the pixel-based and the object-based classifiers. Accurate and timely quantification of boxwood damages by blight or any pest or diseases is necessary for managing infested areas. Therefore, this research was mainly designed to assess whether integrating high-resolution imagery with different algorithms has the potential to raise the accuracy of damage estimates. To evaluate the separability of the classes, the signature analysis was performed with a transformed divergence index. The separability results showed distinct separations on some classes except for weak separability between healthy boxwood trees (HBT) and semi-healthy boxwood trees (SBHT) classes, also between dead boxwood trees (DBT) and non-forest area (NFA) classes because of similar spectral responses. The minimum separability founded between dead boxwood trees and non-forest area classes (1.27), because of similarity of spectral signatures of leave-less stems and background of hardwood stands which are generally low density canopy cover due to being mature trees and low frequency of generation. On the other hand, the maximum separability was found to be similar between healthy boxwood trees (HBT) and non-forested area (NFA), due to different reflectance of two classes (Table 3), which is a normal expectation. However, the separability of dead boxwood trees and evergreen or healthy boxwood trees is at an acceptable rate and showing that leafless VNIR images can successfully detect the evergreen understory plants. The accuracy assessment of the pixel-based classified images (Figures 5 and 6) meant that the classification process with maximum likelihood classifier on VNIR original bands provided better overall accuracy and kappa values of 54.54 % and 0.54, respectively (Table 4). The higher accuracy of the VNIR original bands is coming from this point that they had more useful information to detect the evergreen boxwoods from dead trees and other classes, so that the vegetation indices could improve the separability. In SVM, the RBF kernel on original bands and sigmoid kernel on best bands had second-order results, but slightly weaker results compared with parametric maximum likelihood classifier. This low accuracy results from weak separability of healthy boxwoods from semi-healthy boxwoods. The semi-healthy boxwoods generally kept their greenness on the canopy crown of the tree and declining leaves occurred on the bottom of trees and on lower stems and branches because of rapid spreading of fungal spores in the ground. Since the spectral reflectance is generally reflected from the top surface of trees, therefore, the reflectance of healthy and semi-healthy boxwoods is slightly similar. However, the dead boxwood trees by blights could be distinguished from healthy and semi-healthy trees/shrubs (Tables 5 and 6). In object-based classification (Table 7), the Bayes algorithm had better performance compared with SVM and Decision tree algorithms, but the results of Bayes classifier on both the VNIR and the best bands were slightly similar to objectbased classification by maximum likelihood classifier. These results show that since the boxwood decline lacks any definite shape and has heterogonous spatial distribution, the segmentation of images into special objects; so, the objectbased classifiers could not improve the classification results despite other researches (Shataee et al. 2014; Niphadkar et al. 2017). 5. Conclusion: Understory vegetation mapping in both healthy and infested conditions using remote sensing data is a challenge for forest managers. In this study, several chains of band selection, training, and classification were used here to find the possibility of using the VHR imagery for distinguishing and mapping the infested boxwood trees and shrubs in mixed hardwood stands in the Hyrcanian forest. Based on our findings, it can be concluded that leafless very high-resolution imagery may improve results in boxwood blight-infested mapping efforts. However, misclassification, for example, the conflict between semi-healthy and healthy boxwood trees, could potentially be obviated through improvements in techniques or data fusion, such as integrating Lidar or Radar data with optic imagery. Despite the suitable results supporting the proposed methodology for classifying dead boxwood trees, the attempt accurately to map the damage classes of boxwood blight was not slightly successful. This study highlighted the importance of the leafless season of very high-resolution images such as Pleiades imagery for having a relatively suitable capability to separate forest understory in the mixed hardwood Hyrcanian forests. The results also exposed that we are not able to distinguish successfully between the two healthy boxwood trees and semihealthy boxwood trees because of spectral similarity. After merging these two similar classes into one category, classification accuracies reached to a passable and comparable level to those performed for other understory classification. The results also showed that vegetation indices could be suitable for classification. Our results agreed with recent researchers, which are exporting the usage of vegetation indices from Pleiades images in distinguishing understory forest cover types (Yuksel et al. 2008). Acknowledgment: This research has been done under the financial support of the Iranian National Science Foundation (INSF), Science deputy of the presidency on the code number of 93041553, and we are thankful for this support.

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References: Amami, R., Ben Ayed, D., Ellouze, N. 2012. An Empirical Comparison of SVM and Some Supervised Learning Algorithms for Vowel recognition. International Journal of Intelligent Information Processing (IJIIP), 3(1): 1-8. DOI: 10.4156/IJIIP.vol3.issue1.6 Baret, F., Guyot, G. 1991. Potentials and limits of vegetation indices for LAI & APAR assessment. Remote Sensing of Environment, 35, 161-173. Broge, N. H. Leblanc, E. 2000. Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density. Remote Sensing of Environment, 76, 156-172. Cheng, T., Rivard, B., Sanchez-Azofeifa, G. A., Feng, J., Calvo-Polanco, M. 2010. Continuous wavelet analysis for the detection of green attack damage due to mountain pine beetle infestation. Remote Sensing of Environment, 114, 899–910. Ciesla, W. M. 2000. Remote sensing in forest health protection. USDA Forest Service, Fort Collins, CO., FHTET; Report No. 00-03. p. 266. Coggins, S. B., Coops, N.C., Wulder, M. A. 2011. Estimates of bark beetle infestation expansion factors with adaptive cluster sampling. International Journal of Pest Management, 57: 11-21 Coops, N.C., Johnson, M., Wulder, M.A., White, J. C. 2006. Assessment of QuickBird high spatial resolution imagery to detect red-attack damage due to mountain pine beetle infestation. Remote Sensing of Environment,103 (1):67–80. Crippen, R.1990. Calculating the Vegetation Index Faster. Remote Sensing of Environment Vol. 34 pp. 71-73. Dash, J. P., Watt, M. S., Pearse, G. D., Heaphy, M., Dungey, H. S. 2017. Assessing very high-resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131: 1-14 Esmaili, R., Shataee, Sh., Soosani, J., Naghavi, H., Poorshakori, F. 2018. Capability investigation on spectral images of Ikonos from leave less season for Buxus understory distribution mapping in the Hyrcanian forest (Case study: Khiboos Anjilsi Buxus reserved area, Mazandaran). Iranian Journal of Forest and Poplar Research, 26 (1): 12-23. (Persian) Falkowski, M. J., Evans, J. S., Martinuzzi, S., Gessler, P. E., Hudak, A. T. 2009. Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA. Remote Sensing of Environment, 113, 946–956 FAO, 2016. Iran’s Oak and Buxus Forests Face Great Threats. http://www.fao.org/iran/news/detail-events/en/c/383086. FAO, 2002. www.fao.org/forestry/site/23747/en/irn, visited on 21 December 2017. Franklin, S. E., Wulder, M.A., Skakun, R. S., Carroll, A. L. 2003. Mountain pine beetle red-attack forest damage classification using stratified Landsat TM data in British Columbia, Canada. Photogrammetric Engineering and Remote Sensing, 69(3): 283-288. Fassnacht, F. E., Latifi, H., Ghosh, A., Joshi, P. K., Koch, B., 2014. Assessing the potential of hyperspectral imagery to map bark beetle-induced tree mortality. Remote Sensing of Environment, 140, 533–548. http://dx.doi.org/10.1016/j.rse.2013.09.014. Gitelson, A. A., Kaufman, Y. J., Merzlyak, M. N., 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298. Gitelson, A. 2004. Remote estimation of leaf area index and green leaf biomass in maize canopies. Journal of Plant Physiology, Vol. 161, pp. 165–173. Goel, N. Qin, S. 1994. Influences of canopy architecture on relationships between various vegetation indices and LAI and FPAR: A computer simulation. Remote Sensing Reviews Vol. 10, pp. 309– 347. Hyyppä, J., Pyysalo, U., Hyyppä, H., Haggren, H., Ruppert, G. 2000. Accuracy of laser scanning for DTM generation in forested areas. Proceedings of SPIE. 4035, 119-130. IUCN, 2017. The IUCN Red List of Threatened Species. Version 2017-1. www.iucnredlist.org. Downloaded on 18 May 2017. Jactel, H., Vodde, F. 2011. Prevalence of biotic and abiotic hazards in European forests. EFI Technical Report, 66pp. Kalbi, S., Fallah, A., Shataee, Sh. 2014. Forest type classification using tree-based algorithms and SPOT-HRG Data. International journal of environmental resources research. 1(2): 263-274. Kaufman, Y., Tanre, D. 1992. Atmospherically Resistant Vegetation Index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience & Remote Sensing, 30, 261-270. Kerns B. K., Ohmann, J. L. 2004. Evaluation and prediction of shrub cover in coastal Oregon forests (USA). Ecological Indicators, 4, 83–98. Lausch, A., Erasmi, S., Douglas J. K., Magdon, P., Heurich, M. 2016. Understanding forest health with remote sensing Part I—A review of spectral traits, processes, and remote-sensing characteristics. Remote Sensing, 8 (12), 1029. Lillesand, T. M., Kiefer, R.W. 1999. Remote Sensing and Image Interpretation. 4th Ed. New York, John Wiley & Sons, Inc. Linderman, M., Liu, J., Qi, J., An, L., Ouyang, Z., Yang, J., Tan, Y. 2004. Using artificial neural networks to map the spatial distribution of understory bamboo from remote sensing data. International Journal of Remote Sensing, 25(9):1685–1700. Meddens, A. J. H., Hicke, J. A., Vierling, L. A. 2013. Evaluating methods to detect bark beetle-caused tree mortality using single-date and Multi-date Landsat imagery. Remote Sensing of Environment, 115, 1632–1642.

6

371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431

Meigs, G. W., Kennedy, R. E., Cohen, W. B. 2011. A Landsat time series approach to characterize bark beetle and defoliator impacts on tree mortality and surface fuels in conifer forests Remote Sensing of Environment, 115, 37073718. Meng, R., Dennison, Ph. E., Zhao, F., Shendryk, I., Rickert, A., Hanavan, R. P., Cook, B. D., Serbin, S. P. 2018. Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements. Remote Sensing of Environment, 215: 170–183 Mirabolfathy, M., Ahangaran, Y., Lombard, L., Crous, P. W. 2013. Leaf blight of Buxus sempervirens in northern forests of Iran caused by Calonectria pseudonaviculata. Plant Disease, 97 (8): 1121-1122. (Persian) Mohammadi, J., Shataee, Sh., Yaghmaee, F., Mahiny, A. 2010, Modeling forest stand volume and tree density using Landsat ETM+ data. International journal of remote sensing, 31(11), pp. 2959 - 2975 Morain, S. A. 1986. Surveying China’s agricultural resources: Patterns and progress from space, Geocarto International, 1:15–24. Murfitt, J., He, Y., Yang, J., Mui, A., De Mille, K. 2016. Ash Decline Assessment in Emerald Ash Borer Infested Natural Forests Using High Spatial Resolution Images. Remote Sensing, 8(3), 256. Näsi, R., Honkavaara, E., Lyytikäinen-Saarenmaa, P., Blomqvist, E., Litkey, P., Hakala, T., Viljanen, N., Kantola, T., Tanhuanpää, T., Holopainen, M. 2015. Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree-level. Remote Sensing, 7, 15467-15493 Näsi, R., Honkavaara, E., Blomqvist, M., Lyytikäinen-Saarenmaa, P., Hakala, T., Viljanen, N., Kantola, T., Holopainen, M. 2018. Remote sensing of bark beetle damage in urban forests at individual tree level using a novel hyperspectral camera from UAV and aircraft. Urban Forestry & Urban Greening, 30:72-83 Niphadkar, M., Nagendra, H., Tarantino, C., Adamo, M. Blonda, P. 2017. Comparing pixel and object-based approaches to map an understory invasive shrub in tropical mixed forests. Front Plant Science, 8: 892 Noorian, N., Shataee, Sh., Mohammadi, J. 2016. Assessment of different remote sensing data for forest structural attributes estimation in the Hyrcanian forests. Forest Systems, 25(3):1-11. http://dx.doi.org/10.5424/fs/2016253-08682 Qian, Y., Zhou, W., Yan, J., Li W., Han, L. 2015. Comparing machine learning classifiers for object-based land cover classification using very high-resolution imagery. Remote Sensing, 7, 153-168; DOI:10.3390/rs70100153. Rezaee, S., Kia-Daliri, H., Sharifi, K., Ahangaran, Y., Hajmansoor, S. 2012. Boxwood blight caused by Cylindrocladium buxicola in Tonekabon forest. Plant Pests & Disease, 80(2):197-198. Richardson, A., Wieg, C. 1977. Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, 43, 1541–1552. Rivers, M.C., Beech, E., Bazos, I., Bogunić, F., Buira, A., Caković, D., Carapeto, A., Carta, A., Cornier, B., Fenu, G., Fernandes, F., Fraga, P., Garcia Murillo, P.J., Lepší, M., Matevski, V., Medina, F.M., Menezes de Sequeira, M., Meyer, N., Mikoláš, V., Montagnani, C., Monteiro-Henriques, T., Naranjo Suárez, J., Orsenigo, S., Petrova, A., Reyes-Betancort, J.A., Rich, T., Salvesen, P. H., Santana López, I., Scholz, S., Sennikov, A., Shuka, L., Silva, L.F., Thomas, P., Troia, A., Villar, J. L. and Allen, D. J. 2019. European Red List of Trees. Cambridge, UK and Brussels, Belgium: IUCN. viii + 60pp. Roujean, J., Breon, F.1995. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sensing of Environment, 51, 375–384. Rouse, J. Haas, R., Schell, J., Deering, D., Harlan, J. 1974. Monitoring the vernal advancement of retrogradation of natural vegetation. NASA/GSFC, Type III, final report, Greenbelt, MD. Rullán-Silva, C., Olthoff, A. E., Pando, V., Pajares, J. A., Delgado, A. J. et al. 2015. Remote monitoring of defoliation by the beech leaf-mining weevil Rhynchaenus fagi in northern Spain. Forest Ecology and Management, 347, 200208 Sageb-talebi, Kh., Sajedi, T., Yazdian, F. 2003. Forests of Iran, Technical Publication. No. 339-2003. Samavat, S. 2017. Hyrcanian boxwood blight disease. Plant Pathology Sciences, 6(2): 89-96. Senf, C., Seidl, R., Hostert, P. 2017. Remote sensing of forest insect disturbances: Current state and future directions. International Journal of Applied Earth Observation Geoinformation, 60:49-60 Shataee, Sh., Darvishsefat, A. A., Kellenberger, T. 2004. Forest types classification using ETM+ data in the north of Iran/ comparison of object-oriented with pixel-based classification techniques, XXth ISPRS Congress, 12-23 July 2004, Istanbul, Turkey. Shataee, Sh., Kalbi, S., Fallah, A. A. Pelz, D. 2012. Forest attributes imputation using machine learning methods and ASTER data, comparison of Knn, SVR and random forest regression algorithms. International Journal of Remote Sensing, 33(19): 6254 - 6280. Skakun, R. S.; Wulder, M.A.; Franklin, S.E. 2003. Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of Environment, 86, 433-443. Suchar, V. A., Crookston, N. L. 2010. Understory cover and biomass indices predictions for forest ecosystems of the Northwestern United States. Ecology Indicators, 10, 602–609. Taghuchi, H., Usuda, Y., Fukui, H., Furtani T., Furukawa, K. 2005. Forest Damage Detection Using High Resolution Remotely Sensed Data. https://www.researchgate.net/publication/267848396, [accessed Mar 18 2019]. Teillet, P. M., Fedosejevs, G. 1995. On the dark target approach to atmospheric correction of remotely sensed data. Canadian Journal of Remote Sensing. 4:374-387

7

432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461

Tucker, C. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150. Tzotsos, A. 2006. A support vector machine approach for the object-based image. 1st International Conference on Object-based Image Analysis, (OBIA 2006) July 4-5, 2006. Salzburg University, Austria Wang, T., Skidmore, A., Toxopeus, A. 2009. Understory bamboo discrimination using a winter image. Photogrammetric Engineering & Remote Sensing, 75 (1): 37–47. White, J. C., Wulder, M. A., Brooks, D., Reich, R., Wheate, R. D. 2005. Detection of red attack stage mountain pine beetle infestation with high spatial resolution satellite imagery. Remote Sensing of Environment, 96, 340-351. Wing, B. M., Ritchie, M. W., Boston, K., Cohen, W. B., Gitelman, A., Olsen, M. J. 2012. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment, 124:730741. Wulder, M. A., Skakun, R. S., Dymond, C. C., Kurz, W. A., White, J. C. 2005. Characterization of the diminishing accuracy in detecting forest insect damage over time. Canadian Journal of Remote Sensing, 31:421–431. Wulder, M. A.; Dymond, C. C.; White, J. C. 2005. Remote sensing in the survey of mountain pine beetle impacts: Review and recommendations. Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, Victoria, BC. Information Report BC-X-401. 56 p. Wulder, M. A.; Dymond, C. C.; White, J. C. Leckie, D. G., Carroll, A. L. 2006. Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. For Ecol Manage, 221:27–41. Wulder, M. A., White, J. C., Bentz, B., Alvarez, M.F., Coops, N.C. Estimating the probability of mountain pine beetle red-attack damage. Remote Sensing of Environment. 2006b;101:150–166. Wulder, M.A., Ortlepp, S. M., White, J.C., Coops, N.C., Coggins, S. B. 2008. Monitoring tree-level insect population dynamics with multi-scale and multi-source remote sensing. J Spat Sci, 53:49–61. Yang, Y., Zhang, X., Zhang, C., Wang, H., Fu, X., Chen, F., Wan, S., Sun, X., Wen, X., Wang, F. 2018. Understory vegetation plays a key role in sustaining soil microbial biomass and extracellular enzyme activities. Biogeosciences, 15, 4481–4494 Yuksel, A., Akay, A. E. Gundogan, R. 2008. Using ASTER imagery in land use/cover classification of Eastern Mediterranean landscapes according to the CORINE land cover project. Sensors. 8:1237-1251

8

1

Tables

2

Table.1. Description of five different classes on sampling and inventory Classes Hardwood trees (MHT) Healthy boxwood trees (HBT) Semi-healthy boxwood trees (SHBT) Dead boxwood trees (full infested) (DBT) Non- forest area (NFA)

Description Mixed Hardwood trees without boxwood understory Mixed Hardwood trees with healthy boxwood understory Mixed Hardwood trees with semi-healthy boxwood in understory infested by boxwood blight in some part of trees (generally in the bottom and middle and have green leaves on the top part) Dead boxwood trees understory with dried leaves or leafless. Areas without forest vegetation cover

3 4

Table.2. Vegetation Indices Used Ratio bands Normalized Difference Vegetation Index (NDVI) Difference Vegetation Index (DVI) Green Difference Vegetation Index (GDVI) Green Normalized Difference Vegetation Index (GNDVI) Normalized Ratio Vegetation Index (NRVI) Ratio Vegetation Index (RVI) Greenness Index (GI) Nonlinear Vegetation Index (NLVI) Renormalized Difference Vegetation Index (RDVI) Transformed Vegetation Index (TVI) Infrared Percentage Vegetation Index (IPVI) Atmospherically Resistant Vegetation Index(ARVI)

Formula (NIR- ED) /(NIR+NIR) NIR-RED NIR-GREEN (NIR-GREEN) /(NIR+GREEN)

Reference Rouse et al. (1973) Tucker (1979) Tucker (1979) Gitelson et al. (1996)

(RVI-1)/(RVI+1) NIR / RED

Baret and Guyot (1991) Richardson and Wiegand (1977) NIR/Green-1 Gitelson et al. (2004) 2 (NIR - RED) / (NIR2 + RED) Goel and Qin (1994) 0.5 (NIR-RED)/ (NIR+RED) Roujean and Breon (1995) (NIR − RED)/(NIR + RED) + 0.5 Broge and Leblanc (2000) NIR/(NIR+RED) Crippen (1990) (NIR-BLUE)/(NIR+BLUE) Kaufman and Tanre (1992)

5 6

Table.3. Separability results obtained by transformed divergence index MHT DBT HBT NFA SHBT MHT 2 DBT 1.45 2 HBT 1.98 1.85 2 NFA 1.832 1.27 1.986 2 SHBT 1.55 1.65 1.31 1.69 2

7 8

Table (4). Accuracy assessment results of the pixel-based algorithms Data set

Original bands

The best bands

Algorithms

SVM

Maximum

Linear

Polynomial

Sigmoid

RBF

likelihood

Overall accuracy

50

51.29

53.24

52

54.54

Kappa

0.37

0.38

0.41

0.39

0.54

Overall accuracy

51.29

53.24

50

53.24

49.56

Kappa

0.39

0.41

0.37

0.41

0.34

9 10 11 12 13 14 1

1

Table (5). User accuracies of classes on the best bands SVM/Kernels

Classes

Maximum

Linear

Polynomial

Sigmoid

RBF

likelihood

MHT

88.89

96.30

100

96.30

40.74

DBT

70

70

70

70

94.12

HBT

30.77

29.26

26.92

26.92

35.5

NFA

64.86

67.57

48.65

67.57

70.27

SHBT

5.88

8.82

11.76

8.82

11.76

2 3

Table (6). Producer accuracies of classes on the best bands

4 5

Classes

6 7

SVM/Kernels

Maximum

Linear

Polynomial

Sigmoid

RBF

likelihood

MHT

85.71

83.87

71.05

83.87

91.67

DBT

38.18

37.50

40.38

37.50

31.37

8

HBT

50

43.75

38.89

43.75

43.25

9

NFA

61.54

65.79

54.55

65.79

92.86

10

SHBT

12.50

23.08

30.77

23.08

16.67

11 12

Table (7). Accuracy assessment results of the object-based classifications Algorithm Accuracy measures

Decision tree Kappa

Overall accuracy (%)

SVM_Linear Kappa

Overall accuracy (%)

SVM_RBF Kappa

Overall accuracy (%)

Bayes Kappa

Overall accuracy (%)

Original bands

0.34

36

0.31

36.35

0.36

39.90

0.51

55.2

The best bands

0.32

32.92

0.30

36.30

0.36

38.16

0.46

48.4

13 14 15

2

Figures

Figure 1. The geographical location of the study area (left), and a true colour composite of the study area (right). The green places are showing the boxwood understory (healthy and semi-healthy).

E Figure 2. Different classes on study area: (A): Mixed Hyrcanian hardwood trees without boxwood understory (MHT), (B): Mixed Hyrcanian hardwood trees with healthy boxwood understory (HBT), (C): Mixed Hyrcanian hardwood trees

with healthy boxwood understory that that are infested by boxwood blight but only has green crown on top of the trees (SHBT) and (D): Mixed hardwood trees with dead boxwood on understory (DBT); and (E): Non-forested area such as roads, cattle farms or caw keeping buildings and their blanked surroundings (NFA).

Figure 3. The Field surveys with Real-time kinematic method, base station receiver and ground truth map.

A

B

C

D

E Figure 4. The boxwood blight damaged map the study area got from applying he pixel-based classifiers on the original bands using; (A): SVM-Linear, (B): SVM-Polynomial (C): SVM-Sigmoid, (D): SVM-RBF, (E): Maximum likelihood classifiers

A

B

C

D

E

Figure 5. The boxwood blight infested map of the study area got from the pixel-based classification on the best bands using (A): SVM-Linear, (B): SVM- polynomial, (C): SVM- sigmoid, (D): SVM- RBF, (E): Maximum likelihood classifiers.

A

B

D C

E

G

F

H

Figure 6. The boxwood blight infested map of study area, got from object-based classification on the original bands using: (A): Bayes, (B): Decision tree, (C) SVM-Linear, (D): SVM-RBF; and on the best-selected bands using (E): Bayes (F): Decision tree, (G): SVM-Linear, and (H): SVM-RBF kernel classifiers.

We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing we confirm that we have followed the regulations of our institutions concerning intellectual property. We further confirm that any aspect of the work covered in this manuscript that has involved either experimental animals or human patients has been conducted with the ethical approval of all relevant bodies and that such approvals are acknowledged within the manuscript. We understand that the Dr. Shaban Shataee as Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the Corresponding Author and which has been configured to accept email from ([email protected]) Signed by all authors as follows: [email protected] [email protected] [email protected] [LIST AUTHORS AND DATED SIGNATURES ALONGSIDE]

Mapping of Understory Infested Boxwood Trees Using High Resolution Imagery Shaban Shataee*1, Rohollah Esmaili2, Javad, Soosani3, Hamed Naghavi4 1

Professor, Faculty of Forest Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan, Iran, [email protected] 2

3

PhD, Faculty of Natural Resources, Lorestan University, Lorestan, Iran.

Associate Professor, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan, Iran.

4

Assistant Professor, Faculty of Agriculture and Natural Resources, Lorestan University, Lorestan, Iran.

*Corresponding Author: Dr. Shaban Shataee Jouibary, Professor of Forestry, Forest Sciences Faculty, Gorgan University of Agricultural Science and Natural Resources, 386, Gorgan, Iran, Postal code: 49189-43464

Tel: +98 17 32427050 Fax: +98 17 32427176 Email: [email protected]; [email protected]

We wish to submit a new manuscript entitled “Mapping of Understory Infested Boxwood Trees Using High Resolution Imagery" for possible evaluation. The Corresponding author of this manuscript is Dr. Shaban Shataee and contribution of the authors as mentioned below with their responsibility in the research: 1. Rohollah Esmaili 2. Javad, Soosani 3. Hamed Naghavi With the submission of this manuscript, I would like to undertake that: • All authors of this research paper have directly participated in the planning, execution, or analysis of this study; • All authors of this paper have read and approved the final version submitted; • The contents of this manuscript have not been copyrighted or published previously; • The contents of this manuscript are not now under consideration for publication elsewhere; • The contents of this manuscript will not be copyrighted, submitted, or published elsewhere, while acceptance by the Journal is under consideration; • There are no directly related manuscripts or abstracts, published or unpublished, by any authors of this paper.