Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: a case study from Caijiachuan watershed

doi:10.3969/j.issn.1004-1524.2021.08.18

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (8): 1505-1518.DOI: 10.3969/j.issn.1004-1524.2021.08.18

• Biosystems Engineering • Previous Articles Next Articles

Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: a case study from Caijiachuan watershed

WU Ningshan¹^,²(), WANG Jiaxi¹^,², ZHANG Yan¹^,²^,^*(), YUAN Mutian¹^,², ZHANG Qi¹^,², GAO Chiyu¹^,²

1. School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083,China
2. Jixian Forest Ecosystem Studies, National Observation and Research Station, Beijing 100083,China).

Received:2021-01-14 Online:2021-08-25 Published:2021-08-27
Contact: ZHANG Yan

Abstract

Abstract:

In the present study, Caijiachuan Watershed in the hilly loess region of west Shanxi was selected as the study area to examine the feasibility of unmanned aerial vehicle (UAV) remote sensing in extracting forest species and stand characteristics. The information of forest species and crown width were identified and extracted from visible images of UAV using the object-based nearest neighbor classification method, and the accuracy was evaluated by comparison with field investigation. The presented method was also used in the watershed level to investigate forest species and canopy characteristics in the farmland sub-watershed and plantation sub-watershed. The results showed that the Kappa coefficient of the confusion matrix for forest species classification was 0.898 and 0.728 in the farmland sub-watershed and in the plantation sub-watershed, respectively, suggesting higher accuracy under the condition of low canopy density than that under multiple vegetation types with high canopy density. According to the determination coefficient between the extracted and measured crown width, the accuracy of crown width extraction of planted forests and economic forest (R²>0.7) was higher than that of secondary forests (R²=0.422 3). Based on the UAV remote sensing and the presented method, it was found that the mixed forest of Robinia pseudoacacia L., Pinus tabulaeformis Carr. and Platycladus orientalis(L.) Franco was dominant, along with the economic forest, Malus domestica Borkh. in the plantation sub-watershed, as the stand density of Pinus tabulaeform was 1 744 hm ^-2, with the average crown width of 2.24 m, ad the stand density of Malus domestica was 382 hm ^-2 with the average crown width of 4.26 m. There were 912 Malus domestica, with a stand density of 439 hm ^-2 and an average crown width of 3.84 m in the farmland sub-watershed. These results indicated that the proposed method coupled with UAV remote sensing would improve the efficiency and increase the accuracy of forest resource surveys with the capability in forest species classification, tree number counting and crown width estimation.

Key words: object-oriented classification, tree species classification, canopy characteristics, unmanned aerial vehicle imagey, loess region

CLC Number:

K903

WU Ningshan, WANG Jiaxi, ZHANG Yan, YUAN Mutian, ZHANG Qi, GAO Chiyu. Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: a case study from Caijiachuan watershed[J]. Acta Agriculturae Zhejiangensis, 2021, 33(8): 1505-1518.

Figures/Tables 12

References 43

[1]	何远梅, 姚文俊, 张岩, 等. 黄土高原区植被恢复的空间差异性分析[J]. 中国水土保持科学, 2015, 13(2):63-69.
	HE Y M, YAO W J, ZHANG Y, et al. Spatial variability of vegetation restoration on the Loess Plateau based on MODIS/NDVI[J]. Science of Soil and Water Conservation, 2015, 13(2):63-69.(in Chinese with English abstract)
[2]	HILKER T, LYAPUSTIN A I, HALL F G, et al. On the measurability of change in Amazon vegetation from MODIS[J]. Remote Sensing of Environment, 2015, 166:233-242. DOI URL
[3]	陈宝强, 张建军, 赵荣玮, 等. 晋西黄土区陡坡植被自然恢复评价[J]. 生态学杂志, 2018, 37(1):17-25.
	CHEN B Q, ZHANG J J, ZHAO R W, et al. Evaluating natural restoration of vegetation on steep slopes in the Loess Plateau, Shanxi Province[J]. Chinese Journal of Ecology, 2018, 37(1):17-25.(in Chinese with English abstract)
[4]	张含玉, 方怒放, 史志华. 黄土高原植被覆盖时空变化及其对气候因子的响应[J]. 生态学报, 2016, 36(13):3960-3968.
	ZHANG H Y, FANG N F, SHI Z H. Spatio-temporal patterns for the NDVI and its responses to climatic factors in the Loess Plateau, China[J]. Acta Ecologica Sinica, 2016, 36(13):3960-3968.(in Chinese with English abstract)
[5]	范建忠, 李登科, 董金芳. 陕西省重点生态建设工程区植被恢复状况遥感监测[J]. 农业工程学报, 2012, 28(7):228-234.
	FAN J Z, LI D K, DONG J F. Remote sensing analysis of vegetation restoration in key ecological construction areas of Shaanxi Province[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(7):228-234.(in Chinese with English abstract)
[6]	颜梅春. 高分辨率影像的植被分类方法对比研究[J]. 遥感学报, 2007, 11(2):235-240.
	YAN M C. Research and contrast on several vegetation-classification methods of high-resolution satellite image data[J]. Journal of Remote Sensing, 2007, 11(2):235-240.(in Chinese with English abstract)
[7]	BRYSON M, REID A, RAMOS F, et al. Airborne vision-based mapping and classification of large farmland environments[J]. Journal of Field Robotics, 2010, 27(5):632-655. DOI URL
[8]	汪小钦, 王苗苗, 王绍强, 等. 基于可见光波段无人机遥感的植被信息提取[J]. 农业工程学报, 2015, 31(5):152-157.
	WANG X Q, WANG M M, WANG S Q, et al. Extraction of vegetation information from visible unmanned aerial vehicle images[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5):152-157.(in Chinese with English abstract)
[9]	杜凤兰, 田庆久, 夏学齐, 等. 面向对象的地物分类法分析与评价[J]. 遥感技术与应用, 2004, 19(1):20-23.
	DU F L, TIAN Q J, XIA X Q, et al. Object-oriented image classification analysis and evaluation[J]. Remote Sensing Technology and Application, 2004, 19(1):20-23.(in Chinese with English abstract)
[10]	刘萌萌, 刘亚岚, 孙国庆, 等. 结合纹理特征的SVM样本分层土地覆盖分类[J]. 遥感技术与应用, 2014, 29(2):315-323.
	LIU M M, LIU Y L, SUN G Q, et al. SVM land cover classification based on spectral and textural features using stratified samples[J]. Remote Sensing Technology and Application, 2014, 29(2):315-323.(in Chinese with English abstract)
[11]	李莹, 于海洋, 王燕, 等. 基于无人机重建点云与影像的城市植被分类[J]. 国土资源遥感, 2019, 31(1):149-155.
	LI Y, YU H Y, WANG Y, et al. Classification of urban vegetation based on unmanned aerial vehicle reconstruction point cloud and image[J]. Remote Sensing for Land & Resources, 2019, 31(1):149-155.(in Chinese with English abstract)
[12]	袁慧洁. 基于无人机遥感和面向对象法的简单地物分类研究[J]. 测绘与空间地理信息, 2020, 43(3):113-117.
	YUAN H J. Research on simple object classification based on UAV remote sensing and object-oriented method[J]. Geomatics & Spatial Information Technology, 2020, 43(3):113-117.(in Chinese with English abstract)
[13]	朱恩泽, 宋伟东, 戴激光. 改进支持向量机的高分遥感影像道路提取[J]. 测绘科学, 2016, 41(12):224-228.
	ZHU E Z, SONG W D, DAI J G. Road extraction of high-resolution remote sensing images based on improved SVM[J]. Science of Surveying and Mapping, 2016, 41(12):224-228.(in Chinese with English abstract)
[14]	DAI P Q, DING L X, LIU L J, et al. Tree species identification based on FCN using the visible images obtained from an unmanned aerial vehicle[J]. Laser & Optoelectronics Progress, 2020, 57(10):36-45. (in Chinese with English abstract)
[15]	耿仁方, 付波霖, 蔡江涛, 等. 基于无人机影像和面向对象随机森林算法的岩溶湿地植被识别方法研究[J]. 地球信息科学学报, 2019, 21(8):1295-1306. DOI
	GENG R F, FU B L, CAI J T, et al. Object-based Karst wetland vegetation classification method using unmanned aerial vehicle images and random forest algorithm[J]. Journal of Geo-Information Science, 2019, 21(8):1295-1306.(in Chinese with English abstract)
[16]	温小乐, 钟奥, 胡秀娟. 基于随机森林特征选择的城市绿化乔木树种分类[J]. 地球信息科学学报, 2018, 20(12):1777-1786. DOI
	WEN X L, ZHONG A, HU X J. The classification of urban greening tree species based on feature selection of random forest[J]. Journal of Geo-Information Science, 2018, 20(12):1777-1786.(in Chinese with English abstract)
[17]	王熊, 胡兵, 韩泽民, 等. 基于GF-2号影像的森林优势树种分类[J]. 湖北林业科技, 2020, 49(1):1-7.
	WANG X, HU B, HAN Z M, et al. Dominant tree species specific classified by GF-2 imagery[J]. Hubei Forestry Science and Technology, 2020, 49(1):1-7.(in Chinese with English abstract)
[18]	DE PINHO C M D, FONSECA L M G, KORTING T S, et al. Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis[J]. International Journal of Remote Sensing, 2012, 33(19):5973-5995. DOI URL
[19]	PACIFICI F, DEL FRATE F, SOLIMINI C, et al. An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(9):2940-2952. DOI URL
[20]	ZHANG C Y, XIE Z X. Combining object-based texture measures with a neural network for vegetation mapping in the Everglades from hyperspectralimagery[J]. Remote Sensing of Environment, 2012, 124:310-320. DOI URL
[21]	徐军. 多尺度分割与案例推理的高分辨率遥感影像信息提取方法[D]. 武汉: 武汉大学, 2017.
	XU J. High resolution remote sensing image information extraction method for multi-scale segmentation and case-based reasoning[D]. Wuhan: Wuhan University, 2017. (in Chinese with English abstract)
[22]	GARZON-LOPEZ C X, BOHLMAN S A, OLFF H, et al. Mapping tropical forest trees using high-resolution aerial digital photographs[J]. Biotropica, 2013, 45(3):308-316. DOI URL
[23]	PANAGIOTIDIS D, ABDOLLAHNEJAD A, SUROVý P, et al. Determining tree height and crown diameter from high-resolution UAV imagery[J]. International Journal of Remote Sensing, 2017, 38(8/9/10):2392-2410. DOI URL
[24]	HERNÁNDEZ J G, FERREIRO E G, SARMENTO A, et al. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal[J]. Forest Systems, 2016, 25(2): eSC09.
[25]	李丹, 张俊杰, 赵梦溪. 基于FCM和分水岭算法的无人机影像中林分因子提取[J]. 林业科学, 2019, 55(5):180-187.
	LI D, ZHANG J J, ZHAO M X. Extraction of stand factors in UAV image based on FCM and watershed algorithm[J]. Scientia Silvae Sinicae, 2019, 55(5):180-187.(in Chinese with English abstract)
[26]	王枚梅, 林家元, 林沂, 等. 基于无人机可见光影像的亚高山针叶林树冠参数信息自动提取[J]. 林业资源管理, 2017(4):82-88.
	WANG M M, LIN J Y, LIN Y, et al. Subalpine coniferous forest crown information automatic extraction based on optical UAV remote sensing imagery[J]. Forest Resources Management, 2017(4):82-88.(in Chinese with English abstract)
[27]	孙钊, 潘磊, 孙玉军. 基于无人机影像的高郁闭度杉木纯林树冠参数提取[J]. 北京林业大学学报, 2020, 42(10):20-26.
	SUN Z, PAN L, SUN Y J. Extraction of tree crown parameters from high-density pure Chinese fir plantations based on UAV images[J]. Journal of Beijing Forestry University, 2020, 42(10):20-26.(in Chinese with English abstract)
[28]	郭宝妮, 张建军, 黄明, 等. 吉县蔡家川流域不同树龄刺槐林和油松林土壤微团聚体分形特征研究[J]. 土壤通报, 2012, 43(4):787-792.
	GUO B N, ZHANG J J, HUANG M, et al. Study on fractal features of soil micro-aggregates under the Robinia pseudoacacia and Pinus tabulaeformis of different ages in Caijiachuan watershed of Ji County[J]. Chinese Journal of Soil Science, 2012, 43(4):787-792.(in Chinese with English abstract)
[29]	李敏敏, 魏天兴, 李信良, 等. 黄土区蔡家川流域刺槐人工林林下物种多样性[J]. 浙江农林大学学报, 2018, 35(2):227-234.
	LI M M, WEI T X, LI X L, et al. Species diversity in the understory of a Robinia pseudoacacia plantation in the Caijiachuan Watershed of the Loess Plateau[J]. Journal of Zhejiang A & F University, 2018, 35(2):227-234.(in Chinese with English abstract)
[30]	周龙君, 陈晓芬, 杨利娟. 面向对象标准最邻近分类法在地理国情监测中的应用[J]. 测绘与空间地理信息, 2016, 39(5):155-157.
	ZHOU L J, CHEN X F, YANG L J. Object-based standard nearest neighbor classification used in national geomaticsmonitoring[J]. Geomatics & Spatial Information Technology, 2016, 39(5):155-157.(in Chinese with English abstract)
[31]	郑明国, 蔡强国, 秦明周, 等. 一种遥感影像分类精度检验的新方法[J]. 遥感学报, 2006, 10(1):39-48.
	ZHENG M G, CAI Q G, QIN M Z, et al. A new approach to accuracy assessment of classifications of remotely sensed data[J]. Journal of Remote Sensing, 2006, 10(1):39-48.(in Chinese with English abstract)
[32]	李巍岳, 胡志斌, 闫巧玲. 基于ETM+影像的森林资源信息提取: 以黄土高原丘陵沟壑区水土保持林为例[J]. 生态学杂志, 2009, 28(9):1737-1742.
	LI W Y, HU Z B, YAN Q L. Information extraction of forest resources based on ETM+ image: a case study of water and soil conservation forest in a hilly and gully area of Loess Plateau[J]. Chinese Journal of Ecology, 2009, 28(9):1737-1742.(in Chinese with English abstract)
[33]	井然, 邓磊, 赵文吉, 等. 基于可见光植被指数的面向对象湿地水生植被提取方法[J]. 应用生态学报, 2016, 27(5):1427-1436.
	JING R, DENG L, ZHAO W J, et al. Object-oriented aquatic vegetation extracting approach based on visible vegetation indices[J]. Chinese Journal of Applied Ecology, 2016, 27(5):1427-1436.(in Chinese with English abstract)
[34]	张安定. 遥感原理与应用题解[M]. 北京: 科学出版社, 2016.
[35]	张俊, 朱国龙, 李妍. 面向对象高分辨率影像信息提取中的尺度效应及最优尺度研究[J]. 测绘科学, 2011, 36(2):107-109.
	ZHANG J, ZHU G L, LI Y. Scale effect and optimal scale in object-oriented information extraction of high spatial resolution remote sensing image[J]. Science of Surveying and Mapping, 2011, 36(2):107-109.(in Chinese with English abstract)
[36]	张正健, 李爱农, 雷光斌, 等. 基于多尺度分割和决策树算法的山区遥感影像变化检测方法: 以四川攀西地区为例[J]. 生态学报, 2014, 34(24):7222-7232.
	ZHANG Z J, LI A N, LEI G B, et al. Change detection of remote sensing images based on multiscale segmentation and decision tree algorithm over mountainous area: a case study in Panxi region, Sichuan Province[J]. Acta Ecologica Sinica, 2014, 34(24):7222-7232.(in Chinese with English abstract)
[37]	DRǍGUT L, CSILLIK O, EISANK C, et al. Automated parameterisation for multi-scale image segmentation on multiple layers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 88:119-127. DOI URL
[38]	刘凯. 基于多源数据的黄土高原(重点流失区)侵蚀沟提取及区域差异性研究[D]. 南京: 南京师范大学, 2017.
	LIU K. Gully features extraction and the regional difference analysis in the severe soil erosion region of Loess Plateau of China based on multisource data[D]. Nanjing: Nanjing Normal University, 2017. (in Chinese with English abstract)
[39]	万红梅, 李霞, 董道瑞, 等. 塔里木河下游林地树冠QuickBird影像信息提取与分析[J]. 西北植物学报, 2011, 31(9):1878-1885.
	WAN H M, LI X, DONG D R, et al. Abstraction and analysis of tree-crown of forest land based on QuickBird image in the lower reaches of Tarim River[J]. Acta Botanica Boreali-Occidentalia Sinica, 2011, 31(9):1878-1885.(in Chinese with English abstract)
[40]	杨柳, 陈延辉, 岳德鹏, 等. 无人机遥感影像的城市绿地信息提取[J]. 测绘科学, 2017, 42(2):59-64.
	YANG L, CHEN Y H, YUE D P, et al. Information extraction of urban green space based on UAV remote sensing image[J]. Science of Surveying and Mapping, 2017, 42(2):59-64.(in Chinese with English abstract)
[41]	牛利伟. 基于无人机倾斜摄影测量的行道树特征提取与分类研究[D]. 北京: 北京林业大学, 2020.
	NIU L W. Research on feature extraction and classification of street trees based on UAV incline photogrammetry[D]. Beijing: Beijing Forestry University, 2020. (in Chinese with English abstract)
[42]	乔正年, 马骏, 徐雁南. 无人机遥感在林木冠幅提取中的应用[J]. 林业资源管理, 2019(1):78-84.
	QIAO Z N, MA J, XU Y N. Application of UAV remote sensing in measuring canopy[J]. Forest Resources Management, 2019(1):78-84.(in Chinese with English abstract)
[43]	曾霞辉, 王颖, 曾掌权, 等. 无人机影像树冠信息提取研究[J]. 中南林业科技大学学报, 2020, 40(8):75-82.
	ZENG X H, WANG Y, ZENG Z Q, et al. Research on extraction of tree crown information from UAV images[J]. Journal of Central South University of Forestry & Technology, 2020, 40(8):75-82.(in Chinese with English abstract)

样地类型 Plot type	经度 Longitude/(°)	纬度 Latitude/(°)	海拔高度 Altitude/m	坡度 Slope/(°)	坡向 Aspect
油松Pinus tabuliformis	110°45'32″E	36°16'30″N	1 117	26	S195°
侧柏Platycladus orientalis	110°45'54″E	36°16'24″N	1 120	28	SE145°
刺槐Robinia pseudoacacia	110°44'29″E	36°16'33″N	1 173	16	N17°
次生林Secondary forest	110°44'10″E	36°16'02″N	1 044	21	S166°
油松侧柏混交林Pinus tabuliformis & Platycladus orientalis mixed forest	110°45'34″E	36°16'25″N	1 120	28	W275°
刺槐油松混交林	110°45'33″E	36°16'31″N	1 120	35	W254°
Robinia pseudoacacia & Pinus tabuliformis mixed forest
刺槐侧柏混交林	110°44'23″E	36°16'34″N	1 195	33	SE126°
Robinia pseudoacacia & Platycladus orientalis mixed forest
梨Pyrus spp.	110°44'52″E	36°16'37″N	1 171	8	SE175°
苹果Malus domestica	110°45'08″E	36°16'20″N	1 109	13	NE56°
山杏Armeniaca vulgaris	110°44'57″E	36°16'38″N	1 170	12	NE37°

样地类型 Plot type	经度 Longitude/(°)	纬度 Latitude/(°)	海拔高度 Altitude/m	坡度 Slope/(°)	坡向 Aspect
油松Pinus tabuliformis	110°45'32″E	36°16'30″N	1 117	26	S195°
侧柏Platycladus orientalis	110°45'54″E	36°16'24″N	1 120	28	SE145°
刺槐Robinia pseudoacacia	110°44'29″E	36°16'33″N	1 173	16	N17°
次生林Secondary forest	110°44'10″E	36°16'02″N	1 044	21	S166°
油松侧柏混交林Pinus tabuliformis & Platycladus orientalis mixed forest	110°45'34″E	36°16'25″N	1 120	28	W275°
刺槐油松混交林	110°45'33″E	36°16'31″N	1 120	35	W254°
Robinia pseudoacacia & Pinus tabuliformis mixed forest
刺槐侧柏混交林	110°44'23″E	36°16'34″N	1 195	33	SE126°
Robinia pseudoacacia & Platycladus orientalis mixed forest
梨Pyrus spp.	110°44'52″E	36°16'37″N	1 171	8	SE175°
苹果Malus domestica	110°45'08″E	36°16'20″N	1 109	13	NE56°
山杏Armeniaca vulgaris	110°44'57″E	36°16'38″N	1 170	12	NE37°

类型 Type	苹果 Malus domestica	刺槐 Robinia pseudoacacia	灌木 Shrub	其他乔木 Other tree species	建设用地 Construction land	旱地 Dry land	合计 Total	用户精度 User accuracy/%
苹果Malus domestica	51	0	3	0	0	2	56	91.1
刺槐Robinia pseudoacacia	0	67	2	3	0	0	72	93.1
灌木Shrub	0	0	33	2	1	0	36	91.7
其他乔木Other tree species	1	4	0	43	0	0	48	89.6
建设用地Construction land	0	0	0	0	9	0	9	100
旱地Dry land	0	0	0	0	0	8	8	100
合计Total	52	71	38	48	10	10	—	—
生产者精度Producer accuracy/%	98.1	94.4	86.8	89.6	90.0	80.0	—	—

类型 Type	苹果 Malus domestica	刺槐 Robinia pseudoacacia	灌木 Shrub	其他乔木 Other tree species	建设用地 Construction land	旱地 Dry land	合计 Total	用户精度 User accuracy/%
苹果Malus domestica	51	0	3	0	0	2	56	91.1
刺槐Robinia pseudoacacia	0	67	2	3	0	0	72	93.1
灌木Shrub	0	0	33	2	1	0	36	91.7
其他乔木Other tree species	1	4	0	43	0	0	48	89.6
建设用地Construction land	0	0	0	0	9	0	9	100
旱地Dry land	0	0	0	0	0	8	8	100
合计Total	52	71	38	48	10	10	—	—
生产者精度Producer accuracy/%	98.1	94.4	86.8	89.6	90.0	80.0	—	—

类型 Type	侧柏 Platycladus orientalis	刺槐 Robinia pseudoacacia	油松 Pinus tabuliformis	梨 Pyrus spp.	苹果 Malus domestica	灌木 Shrub	其他乔木 Other tree species	裸地 Bare land	道路 Road	合计 Total	用户精度 User accuracy/%
侧柏Platycladus orientalis	30	9	0	0	0	2	3	0	0	44	68.2
刺槐Robinia pseudoacacia	2	47	4	0	1	0	7	0	1	62	75.8
油松Pinus tabuliformis	1	4	31	0	0	0	3	0	0	39	79.5
梨Pyrus spp.	0	0	0	29	3	1	0	0	0	33	87.9
苹果Malus domestica	0	0	0	0	32	5	0	0	0	37	86.5
灌木Shrub	1	2	1	2	3	22	0	1	0	32	68.8
其他乔木	1	4	2	0	0	3	22	0	0	32	73.3
Other tree species
裸地Bare land	0	0	0	0	0	0	0	11	3	14	78.6
道路Road	0	0	0	0	0	0	0	1	4	5	80.0
合计Total	35	66	38	31	39	33	35	13	8	—	—
生产者精度	85.7	71.2	81.6	93.6	82.1	66.7	62.9	84.6	50.0	—	—
Producer accuracy/%

Determining tree species and crown width from unmanned aerial vehicle imagery in hilly loess region of west Shanxi, China: a case study from Caijiachuan watershed

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子流域 Sub-watershed	树种 Tree species	株数 Plant number	林分密度 Stand density/hm^-2	郁闭度 Canopy density/%	平均冠幅 Average crown width/m
人工林子流域Plantation sub-watershed	油松Pinus tabuliformis	8 268	1 744	68.70	2.24
	苹果Malus domestica	491	382	50.40	4.26
	刺槐Robinia pseudoacacia	14 339	997	97.00	3.52
	侧柏Platycladus orientalis	941	1 862	55.58	1.95
	梨Pyrus spp.	107	502	63.36	4.01
农牧复合子流域Agro-pastoral sub-watershed	油松Pinus tabuliformis	2 155	1 051	61.90	2.74
农地子流域Farmland sub-watershed	苹果Malus domestica	912	439	50.90	3.84