تهیه مدل رقومی زمین مناطق جنگلی با استفاده از داده های لایدار هوایی - مطالعه موردی: درود، لرستان

نوع مقاله: مقاله پژوهشی

نویسندگان

1 کارشناس ارشد، اداره کل منابع طبیعی و آبخیزداری استان چهارمحال و بختیاری

2 استادیار گروه سنجش از دور و GIS، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی

3 دانشیار مرکز سنجش از دور و GIS دانشگاه شهید بهشتی

10.22131/sepehr.2020.40477

چکیده

مدل رقومی زمین برای پردازش اطلاعات مکانی یک مؤلفه اصلی محسوب میشود و در علوم زمین کاربردهای فراوانی دارد. برای تولید مدل رقومی زمین از دادههای لایدار بایستی نقاطی که متعلق به عوارض غیرزمینی هستند از مجموعه دادهها حذف شوند و سپس با روشی مناسب اقدام به درونیابی نقاط زمینی شود تا مدل رقومی زمین بصورت یک شبکه رستر با ابعاد مناسب از این نقاط تولید گردد. در تحقیق حاضر برای تولید مدل رقومی زمین از دادههای لایدار در بخشی از مناطق جنگلی شهرستان درود، ابتدا فیلتر مورفولوژیک شیبمبنا برای جداسازی نقاط مربوط به پوشش جنگلی (نقاط مربوط به عوارض غیرزمینی) استفاده شد و آستانه شیب مناسب برای فیلتر شیبمبنا تعیین گردید. این فیلتر بر پایه مفاهیم مورفولوژیک ریاضی طراحی شده است. الگوریتم فیلترینگ شیب مبنا دو پارامتر ورودی شعاع همسایگی و آستانه شیب دارد. پس از اجرای الگوریتم شیبمبنا بر ابر نقاط لایدار برای اطمینان از دقت فیلترکردن دادهها، بخشی از ابر نقاط منطقه (5 درصد سطح منطقه) انتخاب و نقاط آن بصورت دستی فیلتر شد. نتایج فیلتر دستی با نتایج فیلترکردن شیبمبنا (با در نظر گرفتن آستانه شیبهای مختلف) مقایسه شد. آستانه شیبهای پیشنهادی براساس شرایط منطقه انتخاب شدند و در نهایت بهترین آستانه شیب برای فیلترینگ دادهها انتخاب گردید. سپس دو روش  عکس فاصله وزنی و کریجینگ برای درونیابی و تولید مدل رقومی زمین بکار گرفته شدند. نتایج نشان داد شیب 44 درجه بهترین آستانه برای جداسازی نقاط عوارض غیرزمینی از زمینی است و روش عکس فاصله وزنی با توان سوم با ضریب همبستگی 9986/0و خطای 204/0 متر دقیقترین روش برای درونیابی و تولید مدل رقومی زمین در منطقه مورد مطالعه است.

کلیدواژه‌ها


عنوان مقاله [English]

Generating Digital Terrain Model for forest areas using aerial LiDAR data Case study: Dorood, Lorestan

نویسندگان [English]

  • Asghar Hosseini 1
  • Zahra Azizi 2
  • Saeed Sadeghian 3
1 Expert of general bureau of natural resource and watershed management organization, Chaharmahal and Bakhtiari, Shahrekord
2 Zahra Azizi* Assistant professor, Department of remote sensing and GIS, Science and Research Branch, Islamic Azad University
3 Associate professor, Shahid Beheshti University
چکیده [English]

Introduction
LiDAR (Light Detection and Ranging) employs pulse models which penetrates vegetation cover easilyand provides the possibility of retrieving data related to Digital Terrain Model (DTM).Pulses sent by the Lidar sensorhitdifferent geographical features on the surfaceground and scatter inall directions. Distance to the object is determined by recording the time between transmitted and backscattered pulses and by using the speed of light to calculate the distance traveled by the small portion of pulses backscattered. Most LiDAR receivers at least record the first and last backscattered pulses. The first backscattered pulses are used to produce Digital Surface Models (DSMs) and the last ones are used to produce DTMs. Despite the fact that these data can provide a valuable source for DTM generation, the volume of vegetation (vegetation density) in forest areas reducesthe accuracyof DTMs. Onthe other hand, ground surveying of forest areas is rather expensive and time consuming, especially in largerforests. Aerial images are also used as a source for DTM generation, but this approach requires a 60–80% overlap between images which along with canopy height reduce the potential of this method for DTM generation. Also, low spatial resolution of satellite images collected from forest areas increases errors in DTM generation to a large degree. The present study investigates the accuracy and precision of DTMsproduced from LiDAR data in a forest area. Furthermore, the effect of different methods of filtering and DTM interpolation was explored. Different methods of DTM generation were also closely analyzed and evaluated.
 
Materials & Methods
 The case study area is located in Doroodforests, a part of Zagros forests, in the southeastern regions of Lorestan province in Iran (48°51’19’’E to 48°54’31’’E and 33°19’21’’N to 33°21’15’’N). Minimum and maximum altitude above sea level were 1143 and 2413m, respectively. The study area covers 100 hectares of mountains with an average slope of 38%. Approximately 50% of the area is covered by forests in which Brant’s oak (Quercusbrantii Lindley) is the most frequent species. LiDAR data were collected by the National Cartographic Center of Iran (NCC) in 2012 using a Laser scanner system (Litermapper 5600) fixed on an aircraft flying at an average altitude of 1000m. LiDAR data consisted of the first and last returns (backscattered pulses), distance and their intensity value. Collected data had an irregular structure and included an average of more than four points per square meter. A DTM was produced using a two-step filtering. First, a morphological filter removed most of non-ground points, and then a slope-based filter detected remaining points. Inforest areas with rough-surface, DTM was producedthrough processing ofLiDAR data with statistical methods likekriging and inverse distance weighting (IDW). These methods apply third and fourth power to detect and remove non-ground points. To assess the accuracy of DTMs produced by different approached, 5 percent of the LiDAR point cloudswererandomly separated as the test data. Amongst these data sets, 62 points with a suitable dispersion were selected and measured using a GPS-RTK. An error matrix, along with accuracy indices (including correlation and Root Mean Square Error (RMSE)) were calculated based on these data.
 
Results & Discussion
Results indicated that 44-degree slope is the best threshold for isolation of non-ground points and inverse distance weighting (IDW) is the best third power interpolation method with the highest correlation (0.9986) and the lowest RMSE (0.204 meter). Amongst the filtering methods, slope-based filter used for separation of ground and non-ground points had the best performance. Since this filter combines two parameters of slope and radius, it can remove cloud points related to the vegetation cover and results in high efficiency for steep forest areas. Slope-based filter is suitable for processing of near-surface vegetation, whilst statistical filter is well-suited for vegetation cover consisting of tall trees.
 
Conclusion
The present study proposed and investigated different scenarios for the production offorest areas’ DTM using LiDAR data and two interpolation methods. These algorithms were practicallyassessed using LiDAR data collected from Dorood forest areas. The best scenario was slope-based filter with inverse distance weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.

کلیدواژه‌ها [English]

  • LiDAR
  • Filtering
  • Slope Base
  • Interpolation
  • inverse distance weighting
1. امینی امیرکلایی، عنایتی، ویسی؛ حامد، حمید، مریم (1396). استخراج مدل رقومی زمین از ابرنقاط با ارائه یک روش پیش رونده مورفولوژی مبنا. فصلنامه علمی - پژوهشی اطالاعات جغرافیایی (سپهر)، دوره 26، شماره 102، صفحه 53-65.

2. شفیعی خورشیدی، متین فر، علوی پناه، فرخی؛ فاطمه، حمیدرضا، سیدکاظم، مهدی (1390). تولید مدل رقومی ارتفاع با استفاده از زوج تصویر سنجنده ASTER. نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی، دوره 2، شماره 3، صفحه 1-11.

3. متکان، حاجب؛ علی‌اکبر، محمد (1392). مقایسه توانایی طبقه‌بندی کننده‌های ماشین بردار پشتیبان، شبکه عصبی مصنوعی و حداکثر احتمال در بهبود نتایج الگوریتم‌های فیلترینگ داده‌های لیدار، سنجش از دور و GIS ایران، سال 5، شماره2: 1-16.

4. Andersen, Hans-Erik; McGaughey Robert J.; Reutebuch Stephen E. (2005). Estimating forest canopy fuel parameters using lidar data. Remote Sensing of Environment. Vol.94, No.4, pp. 441-449.

5. Axelsson, Peter. (2000). DEM generation form laser scanner data using adaptive TIN models. International Archives of Photogrammetry, Remote Sensing and Spatial Information Science. Vol. 33(4): 110-117.

6. Azizi, Zahra; Najafi, Akbar; Sadeghian, Saeed (2014). Forest Road Detection Using LiDAR Data. Forestry Research Journal. Vol.25, No. 4, pp. 975-980.

7. Bilskie, Matthew V; Hagen, Scott C. (2013). Topographic accuracy assessment of bare earth LiDAR-derived unstructured meshes.  Advances in Water Resources. Vol. 52 pp. 165–177.

8. Childs, Colin (2004). Interpolation surfaces in ArcGIS spatial analyst. ArcUser July-September, pp. 32-35.

9. Gomroki1, Masoomeh; Jafari, Marzieh; Sadeghian, Saeed; Azizi, Zahra (2017). Application of Intelligent Interpolation Methods for DTM Generation of Forest Areas Based on LiDAR Data, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol.85:227–241.

10. Gonçalves-Seco, Luis; Miranda, David; Crecente, Rafael; Farto, Javier (2006). Digital terrain model generation using airborne LiDAR in a forested area Galicia, Spain. Proceedings of 7th International symposium on spatial accuracy assessment in natural resources and environmental sciences, Lisbon, Portugal,pp. 169-180.

11. Habib, Ayman; Ghanma, Mwafag; Morgan, Michel; Al-Ruzouq, Rami (2005). Photogrammetric and LiDAR data registration using linear features. Photogrammetric Engineering and Remote Sensing. Vol.71, No.6, pp. 699-707.

12. Hinz, Stefan; Baumgartner, Albert (2000). Road extraction in urban areas supported by context objects. International Archives of Photogrammetry and Remote Sensing. Vol.33, Vol.3. 405-412.

13. Hodgson, Michael.E; Bresnahan, Patrick (2004). Accuracy of airborne LiDAR-derived elevation: empirical assessment and error budget. Photogrammetric Engineering and Remote Sensing. Vol. 70, No.3, pp. 331-339.

14. Kobler, Andrej; Pfeifer, Norbert; Ogrinc, Peter; Todorovski, Ljupčo; Oštir, Krištof; Džeroski, Sašo (2007). Repetitive interpolation:  A robust algorithm for DTM generation from aerial laser scanner data in forested terrain. Remote Sensing of Environment. Vol.108(1): 9–23.

15. Kraus, Karl; PfeiferKraus, Norbert (1998). Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry & Remote Sensing. Vol.53, No.4, pp. 193-203.

16. Liu, Xiaoye (2008).  Airborne LiDAR for DEM generation: Some critical issues.” Progress in Physical Geography: an international review of geographical work in the natural and environmental sciences. Vol. 32, No.1, pp. 31-49.

17. Meng, Xuelian; Currit, Nate; Zhao, Kaiguang (2010). Ground filtering algorithms for airborne LiDAR data: A review of critical issues. Remote Sensing journal. Vol. 2: 833–860.

18. Meng, Yu Song; Hui, Lee Yee; Chong Ng Boon (2009). Study of propagation loss prediction in forest environment. Progress in Electromagnetic Research B. Vol.17, pp. 117-133.

19. Montané, Juana M.; Torres, Raymond (2006). Accuracy assessment of lidar saltmarsh topographic data using RTK GPS. Photogrammetric Engineering & Remote Sensing. Vol. 72, No.8, pp. 961-967.

20. Sithole, George; Vosselman, George (2001). “Filtering of laser altimetry data using a slope adaptive filter.” International Archives of Photogrammetry and Remote Sensing. Vol.34, No.(3/W4), 203–210.

21. Sithole, George; Vosselman, George (2005). Filtering of airborne laser scanner data based on segmented point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol.36(19): 66-71.

22. Umasuthan, Martin; Wallace, Andrew (1996). Outlier removal and discontinuity preserving smoothing of range data. IEE Proceedings on Visual Imaging & Signal Processing. Vol.143(3):191-200.

23. Vosselman, George (2000). Slope based filtering of laser altimetry data. International Archives of Photogrammetry and Remote Sensing, Amsterdam. Vol, 11: 935- 942.

24. Wack, Robert; Wimmer, Anna (2002). Digital terrain models from airborne laser scanner data-A grid based approach. International Archives of Photogrammetry and Remote Sensing, Graz, Austria. Vol.34, Vol.3B: 293–296.

25. White, Russell A.; Dietterick, Brian C.; Mastin, Thomas; Strohman Rollin (2010). Forest roads mapped using LiDAR in steep forested terrain. Remote Sensing Journal. Vol.2, pp. 1120-1141.

26. Wierzbicki, Damian; Nienaltowski, Marcin (2019). Accuracy Analysis of a 3D Model of Excavation, created from Images Acquired with an Action Camera from Low Altitudes. ISPRS Internanal Journal of Geo-Information, Vol.8(2): 83, 1-26.

27. Zhang, Keqi; Chen, Shu-Ching; Whitman, Dean; Shyu, Mei-Ling; Yan, Jianhua; Chengcui Zhang (2003). A progressive morphological filter for removing non-ground measurements from airborne lidar data.  IEEE Transaction on Geoscience and Remote Sensing. Vol. 41, No.4, pp. 872–882.

28. Zhou, Qiming (2017). Digital Elevation Model and Digital Surface Model, The International Encyclopedia of Geography, John Wiley & Sons, Ltd.P:1-12.