Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height

doi:10.11947/j.JGGS.2021.0302

Abstract

Abstract:

Reliable and prompt information on forest above-ground biomass (AGB) and tree diameter at breast height (DBH)are crucial for sustainable forest management. Remote sensing technology, especially the Light Detection and Ranging (LiDAR) technology, has been proven to estimate important tree variables effectively. This study proposes predicting DBH and AGB from tree height and other LiDAR data extracted metrics. In the suggested DBH prediction, we developed a nonlinear estimation equation using the total tree height. As for the AGB prediction approach, we used regression methods such as multiple linear regression (MLR), random forest (RF) and support vector machine for regression (SVR). We conducted the study for the Gudao forest area dominated by Robinia Pseudoacacia trees, located in the Yellow River Delta (YRD), China. For our developed approaches, we used Unmanned Aerial Vehicle (UAV) and Backpack LiDAR point cloud datasets obtained in June 2017, and three field data measurements gathered in June 2017 and 2019 and October 2019, all from the same study area. The results demonstrate that: ① The LiDAR data individual tree segmentation (ITS) from which we extracted individual tree information like tree location and tree height, was carried out with an overall accuracy F=0.91; ② We used the ITS height data from the field stand in 2019 as a fit and developed a nonlinear DBH estimation equation with Root Mean Square Error (RMSE)=3.61cm, later validated by the 2017 dataset; ③ Forest AGB at stand level was estimated with the MLR, RF and also SVR regression methods, and results show that the SVR method gave higher accuracy with R²=0.82 compared to the R²=0.72 of RF and the R²=0.70 of the MLR. Calculated AGB at plot level using the 2017 LiDAR data was used to validate both models’ accuracy. Combining the UAV LiDAR data and the Backpack LiDAR significantly improved the overall ITS. The UAV LiDAR ability to provide high accuracy tree height abstraction, the DBH of the regression equation and other extracted LiDAR metrics showed high accuracy in estimating the forest AGB. This study shows that being cost-free is not the only advantage of free available software. In the performance of ITS and the LiDAR, metrics extraction proved to be as good as the commercially available software.

Key words: forest AGB; DBH estimation; UAV LiDAR; Backpack LiDAR

Petar DONEV,Hong WANG,Shuhong QIN,Pengyu MENG,Jinbo LU. Estimating the Forest Above-ground Biomass Based on Extracted LiDAR Metrics and Predicted Diameter at Breast Height[J]. Journal of Geodesy and Geoinformation Science, 2021, 4(3): 13-24.

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Data collected	Tree number	Height range/m	Mean height/m	DBH range/cm	Mean DBH/cm
June	540	4.2—16.3	10.41	6.05—38.73	18.38
October	144	6.4—15.6	10.37	37.3—96.0	55.87

Metrics	Description
Elev kurtosis	Kurtosis of the heights of all points above 2m
Elev AAD	The absolute average deviation of the heights of all points above 2m
Elev MAD median	Median absolute deviation of the median height
Elev percentile #	Height Percentile (01, 05, 10, 20, 25, 30, 40, 50, 60, 70, 75, 80, 90, 95, 99)
Elev maximum	Maximum height of all points above 2m
Elev mean	Mean height of all points above 2m
Elev stddev	The standard deviation of the heights of all points above 2m
Elev variance	A variance of the heights of all points above 2m
Elev CV	Coefficient of variation of the heights of all points above 2m
Elev IQ	An interquartile distance of the height of all points above 2m
Elev skewness	The skewness of the heights of all points above 2m
Elev L #	L-moments of height (L1, L2, L3, L4)
Elev MAD mode	Mode deviation of the median height
Elev L CV	L-moment Coefficient of Variation of height
Elev L skewness	L-moment Skewness of the height
Elev L kurtosis	L-moment Kurtosis of the height
Elev SQRT mean SQ	Height quadratic mean
Elev CURT means CUBE	Height cubic mean
LP	Percentage of last returns above ground
Canopy cover above 2m	Percentage first returns above 2.00
Canopy relief ratio	The proportion of the Canopy and the relief

Plot ID	1	2	3	4	5	6	7	8
Number of trees	43	70	47	98	91	87	46	47
H mean/m	7.38	11.74	12.64	11.22	8.42	10.90	10.21	13.42
SD	1.13	3.29	1.66	2.25	1.47	1.75	1.49	2.22
DBH mean/cm	14.19	17.65	18.40	17.87	12.55	13.18	15.88	17.29
SD	3.85	4.80	5.03	7.68	5.39	4.21	5.51	6.67
AGB mean/(mg/hm²)	19.64	68.84	50.57	92.63	39.85	49.18	33.42	50.70
SD	7.49	23.18	20.47	43.55	14.26	18.24	11.71	25.03

Dataset	Number of trees	MAD /cm	RMSE /cm	rRMSE /(%)	Error<10% /(%)
2019 fit dataset	684	2.82	3.61	20	92
2017 validation dataset	488	3.68	4.00	26	72

Plot ID	1	2	3	4	5	6	7	8	Error≤20% /(%)
Number of trees	43	70	47	98	91	87	46	47
MLR-AGB/(mg/hm²) mean	17.44	85.71	61.62	94.58	47.71	68.91	38.81	69.63	53
SD	13.87	19.13	23.58	31.11	22.82	17.22	24.22	24.55	53
RF-AGB/(mg/hm²) mean	18.09	81.96	60.98	90.58	47.08	68.16	39.59	63.30	60
SD	10.55	19.99	21.48	26.91	14.52	17.09	13.25	16.10	60
SVR-AGB/(mg/hm²) mean	17.93	80.48	61.39	75.28	47.35	68.75	39.05	59.09	62
SD	7.04	21.81	21.09	19.93	13.34	17.82	11.93	13.48	62