Accuracy Assessment of LIDAR-Derived Elevation Value Over Vegetated Terrain in Tropical Region

2015 ◽  
Vol 73 (5) ◽  
Author(s):  
Zamri Ismail ◽  
Muhammad Zulkarnain Abdul Rahman ◽  
Mohd Radhie Mohd Salleh ◽  
Abdul Razak Mohd Yusof
Keyword(s):  
Mixed Forest ◽  
Reference Points ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Understorey Vegetation ◽  
Crown Cover ◽  
Class 1 ◽  
Airborne Lidar Data ◽  
Slope Class

Airborne LiDAR has been widely used to generate good quality of Digital Terrain Model (DTM). Normally, good quality of DTM would require high density and quality of airborne LiDAR data acquisition which increase the cost and processing time. This study focuses on investigating the capability of low density airborne LiDAR data captured by the Riegl system mounted on an aircraft. The LiDAR data sampling densities is about 2.2 points per m2. The study area is covered by rubber trees with moderately dense understorey vegetation and mixed forest. The ground filtering procedure employs the adaptive triangulation irregular network (ATIN) technique. A reference DTM is generated using 76 ground reference points collected using total station. Based on this DTM the study area is divided into different classes of terrain slopes. The point clouds belong to non-terrain features are then used to calculate the relative percentage of crown cover. The overall root mean square error (RMSE) of elevation values obtained from airborne LiDAR data is 0.611 m. The slope of the study area is divided into class-1 (0-5 degrees), class-2 (5-10 degrees), class-3 (10-15 degrees) and class-4 (15-20 degrees). The results show that the slope class has high correlation (0.916) with the RMSE of the LiDAR ground points. The percentage of crown cover is divided into class-1 (60-70%), class-2 (70-80%), class-3 (80-90%) and class-4 (90-100%). The correlation between percentage of crown cover and RMSE of the LiDAR ground points is slightly lower than the slope class with the correlation coefficient of 0.663.

scholarly journals Estimation of crown and canopy cover from airborne lidar data

2010 ◽  
Vol 52 (1) ◽  
pp. 5-17 ◽  
Author(s):  
Mait Lang
Keyword(s):  
Norway Spruce ◽  
Scots Pine ◽  
Stand Structure ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Silver Birch ◽  
Lidar Data ◽  
Crown Cover ◽  
Pine Trees ◽  
Airborne Lidar Data

Metsa katvuse ja liituse hindamine lennukilt laserskanneriga Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.

scholarly journals ACCURACY ASSESSMENT OF LIDAR-DERIVED DIGITAL TERRAIN MODEL (DTM) WITH DIFFERENT SLOPE AND CANOPY COVER IN TROPICAL FOREST REGION

2015 ◽  
Vol II-2/W2 ◽  
pp. 183-189 ◽  
Author(s):  
M. R. M. Salleh ◽  
Z. Ismail ◽  
M. Z. A. Rahman
Keyword(s):  
Tropical Forest ◽  
Digital Terrain Model ◽  
Canopy Cover ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Terrain Model ◽  
Digital Terrain ◽  
Terrain Slope ◽  
Airborne Lidar Data

Airborne Light Detection and Ranging (LiDAR) technology has been widely used recent years especially in generating high accuracy of Digital Terrain Model (DTM). High density and good quality of airborne LiDAR data promises a high quality of DTM. This study focussing on the analysing the error associated with the density of vegetation cover (canopy cover) and terrain slope in a LiDAR derived-DTM value in a tropical forest environment in Bentong, State of Pahang, Malaysia. Airborne LiDAR data were collected can be consider as low density captured by Reigl system mounted on an aircraft. The ground filtering procedure use adaptive triangulation irregular network (ATIN) algorithm technique in producing ground points. Next, the ground control points (GCPs) used in generating the reference DTM and these DTM was used for slope classification and the point clouds belong to non-ground are then used in determining the relative percentage of canopy cover. The results show that terrain slope has high correlation for both study area (0.993 and 0.870) with the RMSE of the LiDAR-derived DTM. This is similar to canopy cover where high value of correlation (0.989 and 0.924) obtained. This indicates that the accuracy of airborne LiDAR-derived DTM is significantly affected by terrain slope and canopy caver of study area.

scholarly journals Using low density LiDAR data to map Mediterranean forest characteristics by means of an area-based approach and height threshold analysis

2016 ◽  
pp. 103 ◽  
Author(s):  
J. Guerra-Hernández ◽  
M. Tomé ◽  
E. González-Ferreiro
Keyword(s):  
Regression Models ◽  
Deciduous Forest ◽  
Coniferous Forest ◽  
Mixed Forest ◽  
Forest Stand ◽  
Airborne Lidar ◽  
Mediterranean Forest ◽  
Lidar Data ◽  
Low Density ◽  
Airborne Lidar Data

<p>This study reports progress in forest inventory methods involving the use of low density airborne LiDAR data and an area-based approach (ABA). It also emphasizes the usefulness of the Spanish countrywide LiDAR dataset for mapping forest stand attributes in Mediterranean stone pine forest characterized by complex orography. Lowdensity airborne LiDAR data (0.5 first returns m<sup><span lang="EN-US">–2</span></sup>) was used to develop individual regression models for a set of forest stand variables in different types of forest. LiDAR data is now freely available for most of the Spanish territory and is provided by the Spanish National Aerial Photography Program (Plan Nacional de Ortofotografía Aérea, PNOA). The influence of height thresholds (MHT: Minimun Height Threshold and BHT: Break Height Threshold) used in extracting LiDAR metrics was also investigated. The best regression models explained 61-85%, 67-98% and 74-98% of the variability in ground-truth stand height, basal area and volume, respectively. The magnitude of error for predicting structural vegetation parameters was higher in closed deciduous and mixed forest than in the more homogeneous coniferous stands. Analysis of height thresholds (HT) revealed that these parameters were not particularly important for estimating several forest attributes in the coniferous forest; nevertheless, substantial differences in volume modelling were observed when the height thresholds (MHT and BHT) were increased in complex structural vegetation (mixed and deciduous forest). A metric-by-metric analysis revealed that there were significant differences in most of the explanatory variables computed from different height thresholds (HBT and MHT).The best models were applied to the reference stands to yield spatially explicit predictions about the forest resources. Reliable mapping of biometric variables was implemented to facilitate effective and sustainable management strategies and practices in Mediterranean Forest ecosystems.</p>

scholarly journals Lennukilidari ja spektraalse kaugseireandmestiku kasutamine metsa peamiste takseertunnuste hindamiseks Aegviidu katsealal / Estimation of main forest inventory variables from spectral and airborne lidar data in Aegviidu test site, Estonia

2012 ◽  
Vol 56 (1) ◽  
pp. 27-41 ◽  
Author(s):  
Mait Lang ◽  
Johannes Anniste ◽  
Tauri Arumäe
Keyword(s):  
Standard Error ◽  
Forest Inventory ◽  
Remote Sensing Data ◽  
Test Site ◽  
Airborne Lidar ◽  
Stem Volume ◽  
Lidar Data ◽  
Height Distribution ◽  
Understorey Vegetation ◽  
Airborne Lidar Data

Abstract Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m3 ha-1 standard error. Best species specific stem volume models had 10 m3 ha-1 smaller standard error.

scholarly journals FOREST STEM VOLUME CALCULATION USING AIRBORNE LIDAR DATA

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 265-270
Author(s):  
I. Büyüksalih ◽  
S. Bayburt ◽  
M. Schardt ◽  
G. Büyüksalih
Keyword(s):  
Near Infrared ◽  
Tree Height ◽  
Airborne Lidar ◽  
Stem Volume ◽  
Lidar Data ◽  
Allometric Functions ◽  
Airborne Lidar Data ◽  
The City ◽  
Field Plots

Airborne LiDAR data have been collected for the city of Istanbul using Riegl laser scanner Q680i with 400&amp;thinsp;kHz and an average flight height of 600&amp;thinsp;m. The flight campaign was performed by a helicopter and covers an area of 5400&amp;thinsp;km<sup>2</sup>. According to a flight speed of 80 knot a point density of more than 16 points/m<sup>2</sup> and a laser footprint size of 30&amp;thinsp;cm could be achieved. As a result of bundle adjustment, in total, approximately 17,000 LAS files with the file size of 500&amp;thinsp;m by 700&amp;thinsp;m have been generated for the whole city. The main object classes Ground, Building, Vegetation (medium, high) were derived from these LAS files using the macros in Terrasolid software. The forest area under investigation is located northwest of the city of Istanbul, main tree species occurring in the test site are pine (pinus pinaster), oak (quercus) and beech (fagus). In total, 120 LAS tiles covering the investigation area have been analysed using the software IMPACT of Joanneum Research Forschungsgesellschaft, Graz, Austria. First of all, the digital terrain model (DTM) and the digital surface models (DSM) were imported and converted into a raster file from the original laser point clouds with a spatial resolution of 50&amp;thinsp;cm. Then, a normalized digital surface model (nDSM) was derived as the difference between DSM and the DTM. Tree top detection was performed by multi – resolution filter operations and tree crowns were segmented by a region growing algorithms develop specifically for this purpose. Breast Height Diameter (BHD) was calculated on the base of tree height and crown areas derived from image segmentation applying allometric functions found in literature. The assessment of stem volume was then calculated as a function of tree height and BHD. A comparison of timber volume estimated from the LiDAR data and field plots measured by the Forest Department of Istanbul showed R2 of 0.46. The low correlation might arise either from the low quality of the field plots or from the inadequacy of the allometric functions used for BHD and stem volume modelling. Further investigations therefore will concentrate both on improving the quality of field measurements and the adoption of the allometric functions to the specific site condition of the forests under investigation. Finally stand boundaries of the forest area made available by the forest department of Istanbul were superimposed to the LiDAR data and the single tree measurements aggregated to the stand level. <br><br> Aside from the LiDAR data, a Pleiades multispectral image characterized by four spectral bands (blue, green, red and near infrared) and a GSD of 2.8&amp;thinsp;m has been used for the classification of different tree species. For this purpose the near infrared band covering the spectral range of 0.75&amp;thinsp;μm to 0.90&amp;thinsp;μm has been utilized and the IMPACT software used.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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