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

مدل رقومی زمین برای پردازش‌ اطلاعات مکانی یک مولفه 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 0.9986و خطای 0.204 متر دقیق‌ترین روش برای درون‌یابی و تولید مدل رقومی زمین در منطقه مورد مطالعه است.

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

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 twostep filtering. First, a morphological filter removed most of nonground points, and then a slopebased filter detected remaining points. Inforest areas with roughsurface, 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 nonground 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 GPSRTK. 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 44degree slope is the best threshold for isolation of nonground 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, slopebased filter used for separation of ground and nonground 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. Slopebased filter is suitable for processing of nearsurface vegetation, whilst statistical filter is wellsuited 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 slopebased filter with inverse distance weighting (IDW) interpolation. Based on accurate assessment, this approach can produce reliable DTM in forest areas.