利用多树种立地形指数对林地质量进行综合评价

doi:10.12302/j.issn.1000-2006.202103007

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南京林业大学学报（自然科学版） ›› 2021, Vol. 45 ›› Issue (6): 81-89.doi: 10.12302/j.issn.1000-2006.202103007

利用多树种立地形指数对林地质量进行综合评价

李清顺¹^,²(), 金万洲¹, 王得军¹^,², 孙景梅¹^,², 李宏韬¹^,²

1. 国家林业和草原局西北调查规划设计院,陕西西安 710048
2. 旱区生态水文与灾害防治国家林业和草原局重点实验室,陕西西安 710048

收稿日期:2021-03-03 接受日期:2021-05-31 出版日期:2021-11-30 发布日期:2021-12-02
基金资助:
青海省基础科研项目(2020-ZJ-713)

A comprehensive evaluation of forest land quality using multi tree site form index

LI Qingshun¹^,²(), JIN Wanzhou¹, WANG Dejun¹^,², SUN Jingmei¹^,², LI Hongtao¹^,²

1. Northwest Surveying,Planning and Designing Institute of National Forestry and Grassland Administration,Xi’an 710048, China
2. Key Laboratory of National Forestry and Grassland Administration on Ecological Hydrology and Disaster Prevention in Arid Regions,Xi’an 710048, China

Received:2021-03-03 Accepted:2021-05-31 Online:2021-11-30 Published:2021-12-02

摘要/Abstract

摘要： 目的采用青海省湟水规模化林场3种常见乔木树种桦木、杨树和青海云杉的立地形指数,通过分析其与生长域的地貌、坡向、坡度、土壤类型、土壤厚度以及年平均气温、年降雨量、年日照辐射等因子的关系,对规模化林场进行立地质量评价,构建林地质量评价综合数据库,指导林场科学造林和科学营林,发挥林地生产力和生态服务价值的目标。方法根据建模区域范围内的一类清查数据,获取每个样地的优势高,求出该样地的立地形指数。分析立地形指数与地貌、坡向、坡度、土壤类型、土壤厚度以及年平均气温、年降雨量、年日照辐射等因子的关系,利用因子分析法选择影响立地形指数的主导因子,并建立回归模型;根据计算出的3个常见树种的立地形指数的平均值,进行聚类分析,把林地分为Ⅰ、Ⅱ、Ⅲ、Ⅳ、Ⅴ5个等级,并作出林地质量分布图;最后,根据分类结果进行了验证,并提出了相应的经营措施。结果 Ⅰ级林地3 409.37 hm²,占整个林地面积的1.39%;Ⅱ级林地38 765.07 hm²,占15.77%;Ⅲ级林地78 989.17 hm²,占32.14%;Ⅳ级林地77 102.74 hm²,占31.38%;Ⅴ级林地47 477.50 hm²,占19.32%。从整个分类结果来看,规模化林场林地质量等级较低,以Ⅲ、Ⅳ级林地为主;现场验证质量等级正确率为88%,正确率较高。结论通过一类清查数据中的标准木、样木库、样地库获取优势高的方法可行;评价因子选择了地形因子、土壤因子、气象因子,多因子的引入使评价的结果更为客观,提高了评价的准确性;采用多树种立地指数进行综合评价,特别是对非林地的立地质量进行评价是一种更为有效的方法。

关键词: 多树种, 立地形指数, 立地质量, 经营措施, 综合评价

Abstract:

【Objective】 The site form index of three common tree species birch, poplar and Picea crassifolia in the Huangshui Large-scale Forest Farm in Qinghai Province was used to analyze the landform, aspect, slope, soil type, soil thickness, and annual average temperature, annual rainfall, annual solar radiation, and other factors of the growing area. The site quality evaluation of large-scale forest farms was carried out, and a comprehensive database of forestland quality evaluation was constructed to achieve the goal of guiding the scientific afforestation and management of forest farms and maximizing the value of forest land productivity and ecological services. 【Method】 First, according to the type of inventory data within the modeling area, the advantage of each plot was obtained, and the site form index of the plot was obtained. Following this, to analyze its relationship with landform, aspect, slope, soil type, soil thickness, annual average temperature, annual rainfall, annual solar radiation, and other factors, the factor analysis was used to select the dominant factors affecting the topographic index, and a regression model was established. According to the calculated average of the site form index of the three common tree species, the cluster analysis was carried out, and the forest land was divided into five grades: Ⅰ, Ⅱ, Ⅲ, Ⅳ and Ⅴ, and the forest land quality distribution map was prepared. Finally, according to the classification, the results were verified, and corresponding management measures were proposed. 【Result】 Grade Ⅰ forest land area was 3 409.37 hm ², accounting for 1.39% of the total forest land area; Grade Ⅱ forest land area was 38 765.07 hm ², accounting for 15.77%; Grade Ⅲ forest land area was 78 989.17 hm ², accounting for 32.14%; Grade Ⅳ forest land area was 77 102.74 hm ², accounting for 31.38%; and Grade Ⅴ forest land area was 47 477.50 hm ², accounting for 19.32%. Based on the overall classification results, the forest land quality grade of large-scale forest farms was low, mainly Ⅲ and Ⅳ forest land. The corrected rate of on-site verification quality grade was 88%, and the corrected rate was high. 【Conclusion】 It is feasible to obtain high advantages through the standard wood, sample wood bank, and sample base bank in the first type of inventory data. Following this, the topographic factors, soil factors, and meteorological factors can be selected as the evaluation factors. The introduction of multiple factors makes the evaluation results more objective and improves the accuracy of the evaluation. Using multi-tree species site form indexes for a comprehensive evaluation, especially the evaluation of site quality of non-forest land is a more effective method.

Key words: multi-tree species, site form index, site quality, management measure, comprehensive evaluation

中图分类号:

S757

李清顺,金万洲,王得军,等. 利用多树种立地形指数对林地质量进行综合评价[J]. 南京林业大学学报（自然科学版）, 2021, 45(6): 81-89.

LI Qingshun, JIN Wanzhou, WANG Dejun, SUN Jingmei, LI Hongtao. A comprehensive evaluation of forest land quality using multi tree site form index[J].Journal of Nanjing Forestry University (Natural Science Edition), 2021, 45(6): 81-89.DOI: 10.12302/j.issn.1000-2006.202103007.

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参考文献 21

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统计量 statistic	桦木 birch					杨树 poplar					青海云杉 Picea crassifolia
统计量 statistic	D_a/cm	H_a/m	D_w/cm	D_p/cm	H_p/m	D_a/cm	H_a/m	D_w/cm	D_p/cm	H_p/m	D_a/cm	H_a/m	D_w/cm	D_p/cm	H_p/m
最小值 min.	5.5	3.0	5.0	5.3	4.3	5.2	4.0	5.0	5.7	4.6	5.8	3.7	5.0	2.5	1.5
最大值 max.	37.2	23.0	59.2	37.1	28.5	51.0	21.7	59.2	36.9	18.6	42.5	94.0	76.6	37.1	28.5
平均值 average	15.0	10.4	11.6	16.1	11.3	16.6	11.1	11.6	16.9	11.2	19.5	13.8	17.9	17.3	11.8
标准差 SD	5.8	3.3	5.7	6.3	4.3	6.8	3.9	5.7	6.0	3.8	8.8	8.3	10.2	7.3	5.2
变动系数/% CV	38.8	31.7	48.9	39.4	37.6	40.8	35.1	48.9	35.8	33.7	45.0	60.0	57.0	42.1	44.1

方程 equation	a	b	c	a%	b%	c%	决定系数 R²	均方根误差 RMES	赤池信息准则 AIC
Logistic	15.952	4.703	0.121	9.006	11.646	15.574	0.553	2.08	406.48
Mitscherlich	20.400		0.040	11.240		15.930	0.547	2.09	407.87
Gompertz	17.969	2.063	0.074	14.095	6.951	23.240	0.551	2.08	407.56
Korf	2 108.349	7.744	0.130	977.283	118.606	184.773	0.548	2.09	409.87
Richard	28.851	0.019	0.826	80.981	154.600	25.409	0.548	2.09	409.20

变量 variable	因子载荷 factor load		因子得分 factor score
变量 variable	F₁	F₂	F₁	F₂
土壤类型 soil type	-0.07	0.87^*	0.04	0.28
土壤厚度 soil thickness	-0.18	0.89^*	0.01	0.28
坡向 aspect	-0.13	0.93^*	0.03	0.29
坡度 slope	-0.08	0.88^*	0.04	0.28
年平均气温 annual average air temperature	0.97^*	-0.05	0.27	0.05
年最高气温 annual maximum air temperature	0.97^*	-0.05	0.27	0.05
年最低气温 annual minimum air temperature	0.99^*	-0.06	0.27	0.05
年降雨量 annual rainfall	-0.77^*	0.27	-0.19	0.03
年日照时数 annual sunshine hours	0.54	-0.11	0.14	0
特征值 eigenvalue	3.79	3.26
方差贡献率/% variance contribution rate	42.16	36.18
累积贡献率/% cumulative contribution rate	42.16	78.34

计算过程 calculation process	杨树 poplar		青海云杉 Picea crassifolia
树高-胸径回归模型 tree height and DBH regression model	Logistic H=1.3+15.6/[1+7.101exp(-0.165 D)]		Logistic H=1.3+19.619/[1+17.228 1exp(-0.184D)]
树高-胸径回归模型 tree height and DBH regression model	95%显著水平下的t值为1.216,平均总相对误差-2.22%,相对总误差0.01%,预估精度98.56%		95%显著水平下的t值为1.518,平均总相对误差-0.79%, 相对总误差0.09%,预估精度98.56%
导向曲线 guide curve	Logistic H=1.3+15.221/[1+6.707exp(-0.251D)]		Logistic H=1.3+19.550/[1+22.273exp(-0.319D)]
生长量方程 growth equation	连年生长量	Z(D)=25.64exp[(-0.251D)]/ [1+6.707exp(-0.251D)]²	连年生长量	Z(D)=138.84exp[(-0.319D)]/ [1+22.273exp(-0.319D)]²
生长量方程 growth equation	平均生长量	θ(D)=15.221[1/[1+6.707× exp(-0.251D)]-0.130]/D	平均生长量	θ(D)=19.55[1/[1+22.273exp(-0.319D)]- 0.043]/D
立地形方程 site form equation	基准胸径 12 cm	S_I=(H-1.3)[1+6.707 exp(-0.251D₀)]/[1+6.707exp(-0.251D)]+1.3	基准胸径 16 cm	S_I=(H-1.3)[1+19.55exp(-0.319D₀)]/ [1+19.55exp(-0.319D)]+1.3
主导因子的选择 selection of dominant factors	年最低气温(T_zdw)、年降雨量(L_nj)、地貌(D_m)、土壤类型(X_tr)、土壤厚度(h_tr)、坡向(P_x)和坡度(P_d)		年最高气温(T_zgw)、年降雨量(L_nj)、海拔(H_b)、土壤类型(X_tr)、土壤厚度(h_tr)、坡向(P_x)和坡度(P_d)
立地形回归方程 regression equation of site form	S_杨=-0.393T_zdw-0.032L_nj+0.537D_m-2.659X_tr- 1.21h_tr-2.874P_x-0.296P_d+45.532		S_云=-0.373T_zgw+0.004L_nj-0.636H_b-3.363X_tr-1.092h_tr- 2.682P_x-1.312P_d+34.462

利用多树种立地形指数对林地质量进行综合评价

A comprehensive evaluation of forest land quality using multi tree site form index

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小班编号 sub class No.	立地形指数 SF index	级别 level	验证立地形指数 verification SF	验证级别 verification level	是否跳级 skip grade	立地质量现场评价 site qualityon- side evaluation
1	13.90	Ⅳ	13.90	Ⅳ		差
2	20.21	Ⅰ	21.56	Ⅰ		良
3	12.87	Ⅳ	12.78	Ⅳ		良^*
4	19.07	Ⅱ	20.26	Ⅰ	√	优
5	10.63	Ⅴ	13.01	Ⅳ	√	差
6	10.56	Ⅴ	10.56	Ⅴ		差
7	17.77	Ⅱ	20.16	Ⅰ	√	优
8	18.77	Ⅱ	18.77	Ⅱ		优
9	16.44	Ⅲ	16.44	Ⅲ		中
10	19.02	Ⅱ	21.40	Ⅰ	√	优
11	18.16	Ⅱ	17.88	Ⅱ		良
12	21.23	Ⅰ	20.32	Ⅰ		优
13	18.54	Ⅱ	19.74	Ⅱ		优^*
14	19.74	Ⅱ	19.74	Ⅱ		良
15	18.38	Ⅱ	19.57	Ⅱ		良
16	18.49	Ⅱ	19.68	Ⅱ		良
17	19.72	Ⅱ	20.91	Ⅰ	√	优
18	18.51	Ⅱ	18.51	Ⅱ		良
19	11.18	Ⅴ	12.38	Ⅳ	√	差
20	16.74	Ⅲ	16.74	Ⅲ		良
21	19.79	Ⅱ	19.79	Ⅱ		良
22	13.43	Ⅳ	13.43	Ⅳ		劣^*
23	14.33	Ⅳ	14.33	Ⅳ		差
24	14.34	Ⅳ	14.34	Ⅳ		差
25	14.32	Ⅳ	14.32	Ⅳ		差

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