| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 2,999 |
| تعداد مقالات | 33,582 |
| تعداد مشاهده مقاله | 44,282,730 |
| تعداد دریافت فایل اصل مقاله | 21,880,487 |
تأثیر توان تفکیک مکانی تصاویر هوایی پهپاد در برآورد ارتفاع درختان بنه (.Pistacia atlantica Desf) | ||
| تحقیقات جنگل و صنوبر ایران | ||
| مقاله 6، دوره 27، شماره 2 - شماره پیاپی 76، تیر 1398، صفحه 169-181 اصل مقاله (1.35 M) | ||
| نوع مقاله: علمی- پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22092/ijfpr.2019.120123 | ||
| نویسندگان | ||
| سیدیوسف عرفانی‌فرد* 1؛ افروز چناری2؛ مریم دهقانی3؛ فرشاد امیراصلانی1 | ||
| 1دانشیار، گروه سنجش‌ازدور و GIS، دانشکده جغرافیا، دانشگاه تهران، تهران، ایران | ||
| 2کارشناس‌‌‌ارشد، بخش منابع طبیعی و محیط‌‌زیست، دانشکده کشاورزی، دانشگاه شیراز، شیراز، ایران | ||
| 3دانشیار، بخش مهندسی عمران و محیط‌‌زیست، دانشکده مهندسی، دانشگاه شیراز، شیراز، ایران | ||
| چکیده | ||
| برآورد ویژگی‌‌های زیست‌‌سنجی مانند ارتفاع تک‌‌درختان که بهطور مستقیم روی تصاویر هوایی پهپادها قابل مشاهده نیستند، دشوار است، بنابراین پژوهش پیش‌رو با هدف معرفی روشی برای برآورد ارتفاع تک‌‌درختان بنه (.Pistacia atlantica Desf) انجام شد. یک محدوده 45 هکتاری از جنگل تحقیقاتی بنه استان فارس با پهپاد فانتوم 4 تصویربرداری شد. سپس، الگوریتمی پیشنهاد شد که روی مدل رقومی سطح (DSM) منطقه، پس از شناسایی خودکار هر درخت، تفاضل ارتفاع بین پیکسل زمین و پیکسل نوک تاج را به‌عنوان ارتفاع درخت درنظر بگیرد. به‌این‌ترتیب، ارتفاع 100 درخت بنه روی DSM با توان‌های تفکیک مکانی 3/47، 10، 20، 40، 60، 80 و 100 سانتی‌‌متر برآورد شد. نتایج نشان داد که بیشترین ضریب تبیین (0/89) و کمترین جذر میانگین مربعات خطای نسبی (11/8 درصد) مربوط به ارتفاع‌های برآوردشده در توان تفکیک مکانی 3/47 سانتی‌‌متر بود. همچنین، بین میانگین‌های ارتفاع برآوردی و واقعی در سه توان تفکیک مکانی 3/47، 10 و 20 سانتی‌‌متر، اختلاف معنی‌‌داری مشاهده نشد. برآورد ارتفاع درختان بنه در توان تفکیک مکانی 3/47 سانتی‌‌متر اندکی بیشتر از مقدار واقعی (امتیاز اریبی 1/15)، در توان تفکیک مکانی 10 سانتی‌‌متر نزدیک به مقدار واقعی (امتیاز اریبی 1/01) و در موارد دیگر، کمتر از مقدار واقعی بود. بهطور کلی، برآورد ارتفاع درختان بنه با استفاده از تصاویر هوایی پهپاد فانتوم 4 امکان‌پذیر است. دراین‌بین، تصاویر هوایی با توان تفکیک مکانی 10 سانتی‌‌متر، توانایی بیشتری در برآورد ارتفاع درختان بنه داشتند. | ||
| کلیدواژه‌ها | ||
| استان فارس؛ جنگل تحقیقاتی بنه؛ مدل رقومی سطح؛ هواپیمای بدون سرنشین | ||
| عنوان مقاله [English] | ||
| Effect of spatial resolution of UAV aerial images on height estimation of wild pistachio (Pistacia atlantica Desf.) trees | ||
| نویسندگان [English] | ||
| Seyyed Yousef Erfanifard1؛ Afrouz Chenari2؛ Maryam Dehghani3؛ Farshad Amiraslani1 | ||
| 1Associate Prof., Deptartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, Iran | ||
| 2M.Sc., Deptartment of Natural Resources and Environment, School of Agriculture, Shiraz University, Shiraz, Iran | ||
| 3Associate Prof., Deptartment of Civil and Environmental Engineering, College of Engineering, Shiraz University, Shiraz, Iran | ||
| چکیده [English] | ||
| Estimation of allometric tree attributes such as heights that are not directly observed on unmanned aerial vehicle (UAV) imagery is challenging. Therefore, this study aimed to introduce a method to estimate the height of wild pistachio (Pistacia atlantica Desf.) single trees in the Zagros region. Therefore, a 45-ha area in Baneh Research Forest of Fars province was captured by a Phantom IV UAV. An algorithm was then suggested to consider the difference between pixels of ground and crown top as tree height on the digital surface model (DSM) following automatic single tree detection. The heights of 100 trees were estimated on DSMs with spatial resolutions of 3.47, 10, 20, 40, 60, 80, and 100 cm. The results showed that the highest coefficient of determination of 0.89 and the lowest relative root mean square error of 11.8% were returned for heights estimated on DSM with 3.47 cm spatial resolution. Moreover, no significant difference was observed among measured and estimated height values on spatial resolutions of 3.47, 10, and 20 cm, respectively. The tree heights were overestimated on DSM with a spatial resolution of 3.47 cm (bias score 1.15), while they were close to the measured values on 10 cm spatial resolution (bias score 1.01) and were underestimated in other spatial resolutions. In general, the results showed the feasibility to estimate heights of wild pistachio trees on Phantom IV imagery, in particular on UAV imagery with a 10 cm spatial resolution. | ||
| کلیدواژه‌ها [English] | ||
| Baneh Research Forest, digital surface model, Fars province, unmanned aerial vehicles | ||
| مراجع | ||
|
- Amini, J. and Sadeghi, Y., 2013. Optical and radar images in modeling the forest biomass in north of Iran. Remote Sensing and GIS, 4(4): 69-82 (In Persian). - Bennet, N.D., Croke, B.F.W, Guariso, G., Guillaume, J.H.A., Hamilton, S.H., Jakeman, A.J., Marsili-Libelli, S., Newham, L.T.H., Norton, J.P., Perrin, C., Pierce, S.A., Robson, B., Seppelt, R., Voinov, A.A., Fath, B.D. and Andreassian, V., 2013. Characterizing performance of environmental models. Environmental Modelling and Software, 40: 1-20. - Birdal, A.C., Avdan, U. and Türk, T., 2017. Estimating tree heights with images from an unmanned aerial vehicle. Geomatics, Natural Hazards and Risk, 8(2): 1144-1156. - Borra-Serrano, I., Peña, J.M., Torres-Sánchez, J., Mesas-Carrascosa, F.J. and López-Granados, F., 2015. Spatial quality evaluation of resampled unmanned aerial vehicle-imagery for weed mapping. Sensors, 15(8): 19689-19708. - Chenari, A., Erfanifard, S.Y., Dehghani, M. and Pourghasemi, H.R., 2017. Estimation of crown area of wild pistachio single trees using DSM of UAV aerial images in Baneh Research Forest, Fars province. Journal of Wood and Forest Science and Technology, 24(4): 117-130 (In Persian). - Deo, R.K., Domke, G.M., Russell, M.B., Woodall, C.W. and Andersen, H.E., 2018. Evaluating the influence of spatial resolution of Landsat predictors on the accuracy of biomass models for large-area estimation across the eastern USA. Environmental Research Letters, 13(5): 055004. - Duncanson, L. and Dubayah, R., 2018. Monitoring individual tree-based change with airborne lidar. Ecology and Evolution, 8(10): 5079-5089. - Feduck, C., McDermidet, G.J. and Castilla, G., 2018. Detection of coniferous seedlings in UAV imagery. Forests, 9(7): 432. - Fisher, J.R.B., Acosta, E.A., Dennedy-Frank, P.J., Kroeger, T. and Boucher, T.M., 2018. Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 4(2): 137-149. - Franklin, S.E., 2001. Remote Sensing for Sustainable Forest Management. CRC Press, Boca Raton, Florida, 424p. - Goodbody, T.R.H., Coops, N.C., Marshall, P.L., Tompalski, P. and Crawford, P., 2017. Unmanned aerial systems for precision forest inventory purposes: A review and case study. The Forestry Chronicle, 93(1): 71-81. - Gulbe, L. and Mednieks, I., 2013. Automatic identification of individual tree crowns in mixed forests using fusion of LIDAR and multispectral data. Technologies of Computer Control, 14(1): 93-99. - Hansen, M.C., Potapov, P.V., Goetz, S.J., Turubanova, S., Tyukavina, A., Krylov, A., Kommareddy, A. and Egorov, A., 2016. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sensing of Environment, 185: 221-232. - Iizuka, K., Yonehara, T., Itoh, M. and Kosugi, Y., 2018. Estimating tree height and diameter at breast height (DBH) from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese cypress (Chamaecyparis obtusa) forest. Remote Sensing, 10(1): 13. - Lee, J.H., Ko, Y. and McPherson, E.G., 2016. The feasibility of remotely sensed data to estimate urban tree dimensions and biomass. Urban Forestry and Urban Greening, 16: 208-220. - Lee, W.J. and Lee, C.W., 2018. Forest canopy height estimation using multiplatform remote sensing dataset. Journal of Sensors: 1593129. - Lin, Y., Jiang, M., Yao, Y., Zhang, L. and Lin, J., 2015. Use of UAV oblique imaging for the detection of individual trees in residential environments. Urban Forestry and Urban Greening, 14(2): 404-412. - Li, W., Guo, Q., Jakubowski, M.K. and Kelly, M., 2012. A new method for segmenting individual trees from the Lidar point cloud. Photogrammetric Engineering and Remote Sensing, 78(1): 75-84. - Li, Z., Zhu, Q. and Gold, C., 2004. Digital Terrain Modeling: Principals and Methodology. CRC Press, Boca Raton, Florida, 340p. - Marvie-Mohadjer, M.R., 2011. Silviculture. University of Tehran Press, Tehran, 418p. - Miri, N., Darvishsefat, A.A., Zargham, N. and Shakeri, Z., 2017. Estimation of leaf area index in Zagros forests using Landsat 8 data. Iranian Journal of Forest, 9(1): 29-42 (In Persian). - Mohan, M., Silva, C.A., Klauberg, C., Jat, P., Catts, G., Cardil, A., Hudak, A.T. and Dia, M., 2017. Individual tree detection from unmanned aerial vehicle (UAV) derived canopy height model in an open canopy mixed conifer forest. Forests, 8(9): 340. - Owji, M.Gh. and Hamzehpour, M., 2012. Vegetation Profile of Wild Pistachio Experimental Forest. Published by Research Institute of Forests and Rangelands, Tehran, 240p (In Persian). - Panagiotidis, D., Abdollahnejad, A., Surový, P. and Chiteculo, V., 2017. Determining tree height and crown diameter from high-resolution UAV imagery. International Journal of Remote Sensing, 38(8-10): 2392-2410. - Pir Bavaghar, M., 2011. Evaluation of capability of IRS-P6 satellite data for predicting quantitative attributes of forests (case study: Northern Zagros forests). Iranian Journal of Forest, 3(4): 277-289 (In Persian). - Puliti, S., Solberg, S. and Granhus, A., 2019. Use of UAV photogrammetric data for estimation of biophysical properties in forest stands under regeneration. Remote Sensing, 11(3): 233. - Rajabpour Rahmati, M., Darvishsefat, A.A., Baghdadi, N., Namiranian, M. and Soofi Mariv, H., 2015. Estimation of forest canopy height in mountainous areas using ICESat-GLAS data. Iranian Journal of Forest and Poplar Research, 23(1): 90-103 (In Persian). - Rezayan, F. and Erfanifard, Y., 2016. Estimating biophysical parameters of Persian oak coppice trees using UltraCam-D airborn imagery in Zagros semi-arid woodlands. Arid Environment, 133: 10-18. - Rokhmana, C.A., 2015. The potential of UAV-based remote sensing for supporting precision agriculture in Indonesia. Procedia Environmental Sciences, 24: 245-253. - Sagheb Talebi, Kh., Sajedi, T. and Pourhashemi, M., 2014. Forests of Iran: A Treasur from the Past, a Hope for Future. Springer, Dordrecht, 160p. - Schowengerdt, R.A., 2007. Remote Sensing: Models and Methods for Image Processing. Third Edition, Academic Press, Burlington, Massachusetts, 560p. - Xiang, H. and Tian, L., 2011. Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform. Biosystems Engineering, 108(2): 104-113. - Yim, J.S., Kim, Y.H., Kim, S.H., Jeong, J.H. and Shin, M.Y., 2011. Comparison of the k-nearest neighbor technique with geographical calibration for estimating forest growing stock volume. Canadian Journal of Forest Research, 41(1): 73-82. - Yousefi, S., Mirzaee, S. and Zeini Vand, H., 2013. Investigation deforestation trends in Zagros mountain with using GIS and RS (Case study: Marivan). Journal of Applied RS and GIS Techniques in Natural Resources Science, 4(2): 15-23 (In Persian). - Zahawi, R.A., Dandois, J.P., Holl, K.D., Nadwodny, D., Reid, J.L. and Ellis, E.C., 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biological Conservation, 186: 287-295. - Zarco-Tejada, P.J., Diaz-Varela, R., Angileri, V. and Loudjani, P., 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. European Journal of Agronomy, 55: 89-99. - Zobeiry, M., 2000. Forest Inventory (Measurement of Tree and Forest). University of Tehran Press, Tehran, 401p (In Persian). | ||
|
آمار تعداد مشاهده مقاله: 703 تعداد دریافت فایل اصل مقاله: 452 |
||