Forest health assessments and multi-scale conversion methods

doi:10.12302/j.issn.1000-2006.201911007

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2021, Vol. 45 ›› Issue (3): 206-216.doi: 10.12302/j.issn.1000-2006.201911007

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Forest health assessments and multi-scale conversion methods

DONG Lingbo(), LIU Zhaogang^*()

Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, School of Forestry, Northeast Forestry University, Harbin 150040, China

Received:2019-11-04 Revised:2020-03-15 Online:2021-05-30 Published:2021-05-31
Contact: LIU Zhaogang E-mail:farrell0503@126.com;lzg19700602@163.com

Abstract

Abstract:

【Objective】 Forest ecosystems have an obvious hierarchical structure; thus, the concept of health assessment results from the sample points or sample areas at the regional scale is currently an important issue in sustainable forest management. Therefore, the goal of this study was to achieve the efficient conversion of forest health assessment results among trees, stands and regional levels using statistical methods. It may provide a theoretical basis and technical support for forest health management in northeastern China. 【Method】 Based on the datasets of 51 sample plots and forest resource inventories in the Pangu Forest Farm in the Greater Khingan Mountains, a tree-level health assessment model was constructed using the entropy-AHP method. Then, five statistical indicators, namely, mean value (H_m), standard deviation (H_sd), coefficient of variation (H_cv), skewness (H_pd) and kurtosis (H_fd), were generated for each plot, which were treated as the results of a stand-level health assessment. The error-in-variable regression model was applied to develop a comprehensive evaluation model of stand-level health status. Finally, the spatial distributions of regional-level forest health scores were mapped using the estimated regression model and forest management inventory datasets. The basic characteristics and patterns of the five statistical indicators were analyzed. 【Result】 The prediction accuracy of the estimated forest health comprehensive evaluation model was relatively high, in which the values of determination coefficients (R²) of the H_m, H_sd, H_cv, H_pd and H_fd models were as high as 0.464 3, 0.305 6, 0.909 6, 0.298 1 and 0.448 5, respectively, meeting the potential demands of forest health assessment. The results of tree-, stand-, and regional level health assessments indicated that the forests within the study area mainly belonged to the category of sub-health, in which the stand (height of individual tree, height of dominant tree, number of tree species, number of trees per hectare, stand basal area and stand volume) and topographic factors (elevation, slope and slope position) affected the forest health scores and their distributions. The average values of forest health were 0.623 4, which belonged to the sub-health level. With respect to the five statistical indicators, they all exhibited obvious spatial patterns, in which the stands with larger H_m, H_cv, H_pd and H_fd values were mainly concentrated in the northern part of the forest farm, in particular being close to settlements and roads. However, the values were significantly lower in the southern portion where transportation was usually not convenient. Furthermore, the spatial distribution pattern of H_sd was completely opposite to that of the other indicators, revealing that the adaptive management is meaningful in the improvement of forest health levels. 【Conclusion】 The statistical methods used in this study could efficiently achieve scale conversions of forest health assessments at the trees, stands and regional levels.

Key words: forest health assessment, scale transformation, forest canopy, error-in-variable regression model, spatial distribution

CLC Number:

S718.5

DONG Lingbo, LIU Zhaogang. Forest health assessments and multi-scale conversion methods[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(3): 206-216.

Figures/Tables 10

Fig.1

Table 1

Fig.2

Fig.3

Fig.4

Table 2

Results of stand-level health assessments and their corresponding distribution patterns"

样地 plot	统计值 statistical values										健康等级比例/% percent of health grades
样地 plot	H_m		H_sd		H_cv		H_pd		H_fd		不健康 unhealth		中健康 mid-health	亚健康 sub-health	健康 health
P1	0.798 8		0.055 3		6.930 0		-0.720 0		0.720 0		0.00		0.00	15.00	85.00
P2	0.797 0		0.065 7		8.240 0		-0.610 0		-0.350 0		0.00		0.00	24.24	75.76
P3	0.732 0		0.097 9		13.380 0		-0.950 0		0.060 0		0.00		2.86	42.86	54.29
P4	0.556 1		0.125 1		22.500 0		-0.430 0		1.470 0		2.27		30.68	63.64	3.41
P5	0.762 4		0.073 0		9.570 0		-0.630 0		0.010 0		0.00		0.00	45.98	54.02
P6	0.678 8		0.073 6		10.840 0		-0.540 0		-0.300 0		0.00		1.00	84.00	15.00
P7	0.683 0		0.085 4		12.510 0		-0.820 0		2.910 0		0.00		1.43	77.14	21.43
P8	0.683 8		0.078 0		11.400 0		-0.800 0		2.120 0		0.00		3.48	81.74	14.78
P9	0.661 2		0.076 9		11.630 0		-0.170 0		0.590 0		0.00		3.14	84.30	12.56
P10	0.675 2		0.089 0		13.190 0		-0.950 0		1.460 0		0.00		4.64	72.19	23.18
P11	0.601 0		0.083 5		13.890 0		-0.570 0		0.560 0		0.00		10.53	86.84	2.63
P12	0.659 3		0.093 1		14.120 0		-0.700 0		1.350 0		0.00		3.54	79.65	16.81
P13	0.635 6		0.077 7		12.230 0		-0.900 0		1.600 0		0.00		6.02	89.16	4.82
P14	0.694 3		0.094 0		13.540 0		-1.830 0		3.710 0		0.00		5.79	66.94	27.27
样地 plot		统计值 statistical values										健康等级比例/% percent of health grades
样地 plot		H_m		H_sd		H_cv		H_pd		H_fd		不健康 unhealth	中健康 mid-health	亚健康 sub-health	健康 health
P15		0.724 3		0.075 5		10.430 0		-0.980 0		1.190 0		0.00	0.96	56.73	42.31
P16		0.687 0		0.104 8		15.260 0		-0.490 0		0.870 0		0.00	1.75	71.93	26.32
P17		0.650 1		0.074 4		11.450 0		-0.620 0		0.180 0		0.00	2.86	92.86	4.29
P18		0.660 1		0.093 2		14.120 0		-0.790 0		0.870 0		0.00	6.88	80.63	12.50
P19		0.659 6		0.087 5		13.260 0		-0.070 0		-0.150 0		0.00	2.17	83.70	14.13
P20		0.629 4		0.082 9		13.170 0		-0.200 0		-0.050 0		0.00	7.32	86.18	6.50
P21		0.669 9		0.086 0		12.840 0		-0.370 0		-0.170 0		0.00	3.00	78.00	19.00
P22		0.634 8		0.077 4		12.200 0		-0.330 0		0.300 0		0.00	4.65	88.95	6.40
P23		0.688 8		0.094 8		13.760 0		-0.470 0		0.930 0		0.00	5.00	70.00	25.00
P24		0.725 0		0.101 1		13.950 0		-1.010 0		1.540 0		0.00	4.84	53.23	41.94
P25		0.661 0		0.098 6		14.910 0		-0.360 0		-0.190 0		0.00	8.62	70.69	20.69
P26		0.535 4		0.122 7		22.930 0		-0.520 0		-0.180 0		2.10	31.47	65.03	1.40
P27		0.595 5		0.083 1		13.950 0		-0.450 0		-0.310 0		0.00	11.96	88.04	0.00
P28		0.683 3		0.058 4		8.550 0		-0.370 0		0.740 0		0.00	0.00	87.23	12.77
P29		0.675 9		0.084 0		12.420 0		-0.210 0		-0.250 0		0.00	2.38	76.19	21.43
P30		0.594 2		0.083 4		14.030 0		-0.620 0		1.320 0		0.00	10.30	89.09	0.61
P31		0.632 5		0.089 1		14.090 0		-0.150 0		0.110 0		0.00	5.21	84.38	10.42
P32		0.659 2		0.094 4		14.320 0		-0.490 0		0.200 0		0.00	5.60	79.20	15.20
P33		0.658 2		0.072 2		10.970 0		-0.270 0		0.160 0		0.00	2.22	86.67	11.11
P34		0.616 6		0.093 2		15.120 0		-0.740 0		0.940 0		0.00	9.17	87.16	3.67
P35		0.644 5		0.103 4		16.030 0		-0.670 0		1.130 0		0.00	6.45	79.03	14.52
P36		0.734 2		0.073 3		9.990 0		-0.600 0		-0.260 0		0.00	0.00	51.06	48.94
P37		0.644 3		0.082 3		12.770 0		-0.260 0		1.130 0		0.00	3.33	83.33	13.33
P38		0.646 3		0.090 9		14.060 0		-0.950 0		2.040 0		0.00	4.76	85.71	9.52
P39		0.644 2		0.079 5		12.350 0		-0.790 0		0.940 0		0.00	4.44	87.78	7.78
P40		0.618 6		0.099 1		16.030 0		-0.560 0		0.630 0		0.00	13.21	78.30	8.49
P41		0.659 0		0.072 9		11.060 0		-0.420 0		0.230 0		0.00	2.54	88.98	8.47
P42		0.581 1		0.074 1		12.740 0		-0.070 0		0.340 0		0.00	14.77	83.52	1.70
P43		0.621 3		0.093 1		14.990 0		-1.080 0		2.660 0		0.87	6.96	84.35	7.83
P44		0.649 2		0.083 0		12.790 0		-0.870 0		1.210 0		0.00	5.19	84.44	10.37
P45		0.642 3		0.094 2		14.660 0		-0.330 0		-0.280 0		0.00	6.59	81.32	12.09
P46		0.723 4		0.058 1		8.030 0		-0.790 0		1.690 0		0.00	0.64	63.46	35.90
P47		0.694 6		0.068 0		9.790 0		-0.540 0		1.550 0		0.00	1.23	77.50	21.27
P48		0.652 5		0.073 9		11.320 0		-0.710 0		1.220 0		0.00	3.40	91.06	5.53
P49		0.650 7		0.074 4		11.440 0		-0.330 0		1.420 0		0.00	3.03	90.63	6.34
P50		0.670 1		0.074 7		11.140 0		-0.700 0		1.330 0		0.00	2.47	85.32	12.21
P51		0.693 0		0.068 0		9.820 0		-0.650 0		2.230 0		0.00	1.21	79.55	19.24
平均 mean		0.663 3		0.084 1		12.837 5		-0.597 1		0.846 5		0.10	5.48	75.78	18.63
标准差 SD		0.051 0		0.014 1		2.868 3		0.308 9		0.912 1		0.44	6.20	16.35	18.11

Table 2

Fig.5

Table 3

Table 4

Fig.6

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变量 variables	平均胸径/ cm mean DBH	平均树高/ m mean tree hight	林分密度/ (株·hm^-2) density	郁闭度 canopy density	落叶松比例 proportion of larch	平均冠幅/ m mean crown width	平均冠长/ m mean crown length	缺陷木株数比例 proportion of defective wood	单位蓄积量/ (m³·hm^-2) volume
最小值min.	6.30	6.66	475.00	0.40	0.00	0.92	3.27	0.00	55.67
最大值max.	14.94	17.86	3 917.00	0.80	1.00	1.73	8.86	0.50	185.16
平均值mean	10.20	11.04	1 730.90	0.60	0.68	1.29	5.83	0.06	100.22
标准差SD	2.12	2.00	662.40	0.12	0.25	0.21	1.16	0.11	31.01
变异系数/%CV	20.76	18.10	38.27	19.78	36.93	16.28	19.9	183.33	30.94

编号 No.	模型 model	F	P	确定系数 R²	均方根误差 RMSE	平均绝对误差 Bias
1	H_m=1.019 8-0.006 7S_BA-0.038 6H_T+0.026 1H_DT- 0.000 3E_LE+0.002 5S_LO-0.008 2P_OS	8.104 2	<0.000 1	0.465 2	0.037 7	0.028 8
2	H_sd=0.170 3-0.106 6H_m+0.003 8N_TS-0.000 1E_LE	8.377 0	0.000 2	0.361 1	0.007 4	0.005 4
3	H_cv=15.913 0+170.797 0H_sd-26.239 0H_m	248.100 0	<0.000 1	0.909 8	0.876 4	0.531 3
4	H_pd=-1.242 5-4.153 1H_sd +0.003 8V_OL+0.001 9E_LE- 0.014 9S_LO-0.000 1N_UM	4.415 0	0.002 4	0.308 4	0.187 4	0.131 2
5	H_fd=1.847 5-2.213 4H_pd-3.432 3H_m+0.112 4S_BA- 0.019 3V_OL-0.105 8P_OS	8.984 0	<0.000 1	0.449 0	0.689 6	0.528 4

模型 model	拟合优度 goodness-of-fit			检验结果 validation result
模型 model	确定系数 R²	均方根误差 RMSE	平均绝对误差 Bias	确定系数 R²	均方根误差 RMSE	平均绝对误差 Bias
H_m	0.464 3	0.037 7	0.028 8	0.492 6	0.032 5	0.024 5
H_sd	0.305 6	0.009 5	0.007 6	0.290 1	0.009 6	0.007 8
H_cv	0.909 6	0.877 5	0.546 9	0.907 8	0.838 6	0.533 3
H_pd	0.298 1	0.207 4	0.150 7	0.304 9	0.201 4	0.141 8
H_fd	0.448 5	0.689 9	0.528 6	0.438 1	0.695 0	0.530 4

Forest health assessments and multi-scale conversion methods

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