The development of stand volume estimation model using airborne LiDAR for Hinoki (Chamaecyparis obutsusa) and Sugi (Cryptomeria japonica)

In the last two decades there has been significant leap on the spatial resolution of the satellite digital images which may be very useful for estimating stand parameter required for forest as well as environment management. This paper describes development of stand volume estimator models using SPOT 6 panchromatic and multispectral images with an object-based digital image analysis (OBIA) and conventional pixel-based approaches. The data used include panchromatic band with1.5m spatial resolution, and multispectral band with6m spatial resolution. The proposed OBIA technique with mean-shift algorithm was functioned to derive a canopy cover variable from the fusion of the panchromatic and multispectral, while the pixel-based vegetation index was used to develop model with an original pixel-size of 6 m. The estimator models were established based on 65 sample plots both measured in the field and images. The study found that the OBIA provides more accurate identification with Kappa Accuracy (KA) of 71% and Overall Accuracy (OA) of 86%. The study concluded that the best stand volume estimation model is the model that developed from the canopy cover (C) derived from OBIA i.e., v = 13.47e0.032C with mean deviation of only 0.92%, better than the model derived from conventional pixel-based approach, i.e., v = 0.0000067e16.48TNDVI with a mean deviation of 5.37%.

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Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [Picea abies (L.) Karst.] Test Site in Romania

Notulae Botanicae Horti Agrobotanici Cluj-Napoca ◽

10.15835/nbha44110155 ◽

2016 ◽

Vol 44 (1) ◽

pp. 313-323 ◽

Cited By ~ 2

Author(s):

Bogdan APOSTOL ◽

Adrian LORENT ◽

Marius PETRILA ◽

Vladimir GANCZ ◽

Ovidiu BADEA

Keyword(s):

Picea Abies ◽

Norway Spruce ◽

Volume Estimation ◽

Airborne Lidar ◽

Stem Volume ◽

Lidar Data ◽

Individual Tree ◽

Stand Volume ◽

Empirical Modelling ◽

The Individual

The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m3).

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A study of the generalization of the stand volume estimation method based on airborne LiDAR using models of spatial volume and stem volume －A case study of Sugi and Hinoki in Gifu prefecture－

Journal of the Japan society of photogrammetry and remote sensing ◽

10.4287/jsprs.56.70 ◽

2017 ◽

Vol 56 (3) ◽

pp. 70-80

Author(s):

Kiyoshi TAKEJIMA

Keyword(s):

Estimation Method ◽

Volume Estimation ◽

Airborne Lidar ◽

Stem Volume ◽

Stand Volume ◽

Spatial Volume ◽

Gifu Prefecture

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Variance reduction in Ueno's method and cylinder sampling for forest volume estimation

Canadian Journal of Forest Research ◽

10.1139/x95-193 ◽

1995 ◽

Vol 25 (11) ◽

pp. 1783-1794 ◽

Cited By ~ 3

Author(s):

Thomas B. Lynch

Keyword(s):

Computer Simulation ◽

Variance Reduction ◽

Volume Estimation ◽

Critical Height ◽

Volume Estimate ◽

Individual Tree ◽

Stand Volume ◽

Reduction Techniques ◽

Antithetic Variates ◽

Volume Equation

Three basic techniques are proposed for reducing the variance of the stand volume estimate provided by cylinder sampling and Ueno's method. Ueno's method is based on critical height sampling but does not require measurement of critical heights. Instead, a count of trees whose critical heights are less than randomly generated heights is used to estimate stand volume. Cylinder sampling selects sample trees for which randomly generated heights fall within cylinders formed by tree heights and point sampling plot sizes. The methods proposed here for variance reduction in cylinder sampling and Ueno's method are antithetic variates, importance sampling, and control variates. Cylinder sampling without variance reduction was the most efficient of 12 methods compared in computer simulation that used estimated measurement times. However, cylinder sampling requires knowledge of a combined variable individual tree volume equation. Of the three variance reduction techniques applied to Ueno's method, antithetic variates performed best in computer simulation.

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Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

Remote Sensing ◽

10.3390/rs10111691 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1691 ◽

Cited By ~ 1

Author(s):

Xuebo Yang ◽

Cheng Wang ◽

Sheng Nie ◽

Xiaohuan Xi ◽

Zhenyue Hu ◽

...

Keyword(s):

Surface Characteristics ◽

Airborne Lidar ◽

Estimation Accuracy ◽

Estimation Model ◽

Large Area ◽

Waveform Data ◽

Terrain Slope ◽

Lidar Measurements ◽

Slope Estimation ◽

Study Sites

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596°, reduced by ~0.6°, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180°, reduced by ~1.8°, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421°, reduced by ~1.1°, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

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Effects of vegetation and soil on evapotranspiration, flow regime, and basin storage in three nearby catchments in northeast Japan

10.5194/hess-2016-616 ◽

2016 ◽

Author(s):

Shoji Noguchi ◽

Tomonori Kaneko ◽

Shin'ichi Iida ◽

Wataru Murakami ◽

Takanori Shimizu

Keyword(s):

Flow Regime ◽

Cryptomeria Japonica ◽

Soil Depth ◽

Forested Catchments ◽

Sapwood Area ◽

Stand Volume ◽

Breast Height ◽

Soil Storage ◽

Order Of Magnitude ◽

Soil Measurements

Abstract. Vegetation and soil determine evapotranspiration, flow regime, and basin storage in forested catchments. We conducted hydrological observations at three nearby catchments (catchments nos. 1, 2, and 3) in the Nagasaka experimental watershed located on the green tuff region in northeast Japan. Diameter-at-breast height (DBH) of all trees > 3 cm DBH was recorded. In addition, we measured soil depth at 170 locations and investigated 45 soil pits. Based on these detailed vegetation and soil measurements, we examined evapotranspiration, flow regime, and basin storage during the no-snow-cover period (May–November). More than 80.9 % of stands in the catchment were comprised of Cryptomeria japonica. Stand volume (122.0 m3 ha−1) and sapwood area (10.7 m2 ha−1) in catchment no. 3 were smaller than those in the other two catchments (no. 1: 255.7 m3 ha−1; 16.0 m2 ha−1, no. 2: 216.5 m3 ha−1; 14.2 m2 ha−1). Consequently, evapotranspiration was lower in catchment no. 3 than that in catchments nos. 1 and no. 2. In addition, low and scanty runoffs in catchment no. 3 were larger than those in nos. 1 and 2. The order of magnitude for soil storage was catchments no. 1 (104.2 mm)

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