中国陆地植被净初级生产力遥感估算

doi:10.17521/cjpe.2007.0050

摘要/Abstract

摘要：

该文在综合分析已有光能利用率模型的基础上,构建了一个净初级生产力(NPP)遥感估算模型,该模型体现了3方面的特色:1)将植被覆盖分类引入模型,并考虑植被覆盖分类精度对NPP估算的影响,由它们共同决定不同植被覆盖类型的归一化植被指数(NDVI)最大值;2)根据误差最小的原则,利用中国的NPP实测数据,模拟出各植被类型的最大光能利用率,使之更符合中国的实际情况;3)根据区域蒸散模型来模拟水分胁迫因子,与土壤水分子模型相比,这在一定程度上对有关参数实行了简化,使其实际的可操作性得到加强。模拟结果表明,1989~1993年中国陆地植被NPP平均值为3.12 Pg C (1 Pg=10¹⁵ g),NPP模拟值与观测值比较接近,690个实测点的平均相对误差为4.5%;进一步与其它模型模拟结果以及前人研究结果的比较表明,该文所构建的NPP遥感估算模型具有一定的可靠性,说明在区域及全球尺度上,利用地理信息系统技术将遥感数据和各种观测数据集成在一起,并对NPP模型进行参数校正,基本上可以实现全球范围不同生态系统NPP的动态监测。

关键词: 生物量, 遥感, 模拟, NPP, NDVI, 中国

Abstract:

Aims Net primary productivity (NPP) is a key component of the terrestrial carbon cycle. Model simulation is commonly used to estimate regional and global NPP given difficulties to directly measure NPP at such spatial scales. A number of NPP models have been developed in recent years as research issues related to food security and biotic response to climatic warming have become more compelling. However, large uncertainties still exist because of the complexity of ecosystems and difficulties in determining some key model parameters.
Methods We developed an estimation model of NPP based on geographic information system (GIS) and remote sensing (RS) technology. The vegetation types and their classification accuracy are simultaneously introduced to the computation of some key vegetation parameters, such as the maximum value of normalized difference vegetation index (NDVI) for different vegetation types. This can remove some noise from the remote sensing data and the statistical errors of vegetation classification. It also provides a basis for the sensitivity analysis of NPP on the classification accuracy. The maximum light use efficiency (LUE) for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The simulated values of LUE are greater than the value used in the CASA model and less than the values simulated with the BIOME-BGC model. The computation of the water restriction factor is driven with ground meteorological data and remote sensing data, and complex soil parameters are avoided. Results are compared with other studies and models.
Important findings The simulated mean NPP in Chinese terrestrial vegetation from 1989-1993 is 3.12 Pg C (1 Pg=10¹⁵ g). The simulated NPP is close to the observed NPP, and the total mean relative error is 4.5% for 690 NPP observation stations distributed in the whole country. This illustrates the utility of the model for the estimation of terrestrial primary production over regional scales.

Key words: biomass, remote sensing, simulation, NPP, NDVI, China

朱文泉, 潘耀忠, 张锦水. 中国陆地植被净初级生产力遥感估算. 植物生态学报, 2007, 31(3): 413-424. DOI: 10.17521/cjpe.2007.0050

ZHU Wen-Quan, PAN Yao-Zhong, ZHANG Jin-Shui. ESTIMATION OF NET PRIMARY PRODUCTIVITY OF CHINESE TERRESTRIAL VEGETATION BASED ON REMOTE SENSING. Chinese Journal of Plant Ecology, 2007, 31(3): 413-424. DOI: 10.17521/cjpe.2007.0050

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2007.0050

https://www.plant-ecology.com/CN/Y2007/V31/I3/413

图/表 8

参考文献 41

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代码 Code	植被类型 Vegetation type	像元数 Pixels	NDVI_max	NDVI_min	SR_max	SR_min
1	落叶针叶林Deciduous needle-leaf forest	4 339	0.738	0.023	6.63	1.05
2	常绿针叶林Evergreen needle-leaf forest	15 104	0.647	0.023	4.67	1.05
3	常绿阔叶林Evergreen broad-leaf forest	6 502	0.676	0.023	5.17	1.05
4	落叶阔叶林Deciduous broad-leaf forest	8 690	0.747	0.023	6.91	1.05
5	灌丛Bush	11 905	0.636	0.023	4.49	1.05
6	疏林Sparse woods	958	0.636	0.023	4.49	1.05
7	海边湿地Seaside wetlands	287	0.634	0.023	4.46	1.05
8	高山、亚高山草甸Alpine and sub-alpine meadow	11 675	0.634	0.023	4.46	1.05
9	坡面草地Slope grassland	4 364	0.634	0.023	4.46	1.05
10	平原草地Plain grassland	7 940	0.634	0.023	4.46	1.05
11	荒漠草地Desert grassland	10 184	0.634	0.023	4.46	1.05
12	草甸Meadow	11 773	0.634	0.023	4.46	1.05
13	城市City	65	0.634	0.023	4.46	1.05
14	河流River	958	0.634	0.023	4.46	1.05
15	湖泊Lake	1 240	0.634	0.023	4.46	1.05
16	沼泽Swamp	1 015	0.634	0.023	4.46	1.05
17	冰川Glacier	1 887	0.634	0.023	4.46	1.05
18	裸岩Bare rocks	4 528	0.634	0.023	4.46	1.05
19	砾石Gravels	13 657	0.634	0.023	4.46	1.05
20	荒漠Desert	12 661	0.634	0.023	4.46	1.05
21	耕地Farmland	30 046	0.634	0.023	4.46	1.05
22	高山、亚高山草地Alpine and sub-alpine plain grassland	10 931	0.634	0.023	4.46	1.05

代码 Code	植被类型 Vegetation type	像元数 Pixels	NDVI_max	NDVI_min	SR_max	SR_min
1	落叶针叶林Deciduous needle-leaf forest	4 339	0.738	0.023	6.63	1.05
2	常绿针叶林Evergreen needle-leaf forest	15 104	0.647	0.023	4.67	1.05
3	常绿阔叶林Evergreen broad-leaf forest	6 502	0.676	0.023	5.17	1.05
4	落叶阔叶林Deciduous broad-leaf forest	8 690	0.747	0.023	6.91	1.05
5	灌丛Bush	11 905	0.636	0.023	4.49	1.05
6	疏林Sparse woods	958	0.636	0.023	4.49	1.05
7	海边湿地Seaside wetlands	287	0.634	0.023	4.46	1.05
8	高山、亚高山草甸Alpine and sub-alpine meadow	11 675	0.634	0.023	4.46	1.05
9	坡面草地Slope grassland	4 364	0.634	0.023	4.46	1.05
10	平原草地Plain grassland	7 940	0.634	0.023	4.46	1.05
11	荒漠草地Desert grassland	10 184	0.634	0.023	4.46	1.05
12	草甸Meadow	11 773	0.634	0.023	4.46	1.05
13	城市City	65	0.634	0.023	4.46	1.05
14	河流River	958	0.634	0.023	4.46	1.05
15	湖泊Lake	1 240	0.634	0.023	4.46	1.05
16	沼泽Swamp	1 015	0.634	0.023	4.46	1.05
17	冰川Glacier	1 887	0.634	0.023	4.46	1.05
18	裸岩Bare rocks	4 528	0.634	0.023	4.46	1.05
19	砾石Gravels	13 657	0.634	0.023	4.46	1.05
20	荒漠Desert	12 661	0.634	0.023	4.46	1.05
21	耕地Farmland	30 046	0.634	0.023	4.46	1.05
22	高山、亚高山草地Alpine and sub-alpine plain grassland	10 931	0.634	0.023	4.46	1.05

代码 Code	植被类型 Vegetation type	样本数 Samples	最小值 Min	最大值 Max	模拟值 Simulated value	NPP实测平均值 Observed NPP (g C·m^-2·a^-1)	NPP实测值标准差 SE of observed NPP	NPP实测值范围 Range of observed NPP (g C·m^-2·a^-1)
代码 Code	植被类型 Vegetation type	样本数 Samples	ε_max (g C·MJ^-1)			NPP实测平均值 Observed NPP (g C·m^-2·a^-1)	NPP实测值标准差 SE of observed NPP	NPP实测值范围 Range of observed NPP (g C·m^-2·a^-1)
1	落叶针叶林 Deciduous needle-leaf forest	39	0.159	2.453	0.485	490	160.9	179~824
2	常绿针叶林 Evergreen needle-leaf forest	110	0.204	2.553	0.389	396	121.2	179~806
3	落叶阔叶林 Deciduous broad-leaf forest	356	0.256	2.521	0.692	672	271.9	114~1 669
4	常绿阔叶林 Evergreen broad-leaf forest	142	0.407	2.194	0.985	1 017	278.9	407~1 913
5	针阔混交林 Needle and broad-leaf mixed forest	21	0.242	0.74	0.475	472	128.3	257~717
6	常绿、落叶阔叶混交林 Evergreen and deciduous broad-leaf mixed forest	22	0.461	1.295	0.768	723	141.4	414~1 098
7	灌丛 Bush	9			0.429	364
8	草地 Grassland				0.542	231	64.9
9	耕地 Farmland				0.542
10	其它 Others				0.542

代码 Code	植被类型 Vegetation type	样本数 Samples	最小值 Min	最大值 Max	模拟值 Simulated value	NPP实测平均值 Observed NPP (g C·m^-2·a^-1)	NPP实测值标准差 SE of observed NPP	NPP实测值范围 Range of observed NPP (g C·m^-2·a^-1)
代码 Code	植被类型 Vegetation type	样本数 Samples	ε_max (g C·MJ^-1)			NPP实测平均值 Observed NPP (g C·m^-2·a^-1)	NPP实测值标准差 SE of observed NPP	NPP实测值范围 Range of observed NPP (g C·m^-2·a^-1)
1	落叶针叶林 Deciduous needle-leaf forest	39	0.159	2.453	0.485	490	160.9	179~824
2	常绿针叶林 Evergreen needle-leaf forest	110	0.204	2.553	0.389	396	121.2	179~806
3	落叶阔叶林 Deciduous broad-leaf forest	356	0.256	2.521	0.692	672	271.9	114~1 669
4	常绿阔叶林 Evergreen broad-leaf forest	142	0.407	2.194	0.985	1 017	278.9	407~1 913
5	针阔混交林 Needle and broad-leaf mixed forest	21	0.242	0.74	0.475	472	128.3	257~717
6	常绿、落叶阔叶混交林 Evergreen and deciduous broad-leaf mixed forest	22	0.461	1.295	0.768	723	141.4	414~1 098
7	灌丛 Bush	9			0.429	364
8	草地 Grassland				0.542	231	64.9
9	耕地 Farmland				0.542
10	其它 Others				0.542

森林类型 Forest type	样本数 Samples	实测值(g C·m^-2·a^-1) Observed NPP						模拟值(g C·m^-2·a^-1) Simulated NPP				平均相对误差(%) Mean relative error
森林类型 Forest type	样本数 Samples	最小值 Min	最大值 Max	平均值 Mean		标准差 SE		最小值 Min	最大值 Max	平均值 Mean	标准差 SE	平均相对误差(%) Mean relative error
1 北方森林 Boreal forest
落叶松Larix forest	39	179	824		490		160.9	152	626	447	115.3	4.9
针阔混交林Coniferous broad-leaf mixed forest	21	257	717		472		128.3	348	559	469	49.7	6.2
冷杉-云杉林Abies-Picea forest	101	179	806		403		123.6	111	585	372	103.8	-0.2
山地杨桦林Montane Populus-Betula forest	72	270	1 314		681		230.1	198	1 135	694	139.2	12.3
樟子松Pinus sylvestris var. mongolica forest	9	258	387		317		37.1	48	425	354	116.9	13.1
2 落叶阔叶林Deciduous broad-leaf forest
典型落叶阔叶林Typical deciduous broad-leaf forest	41	259	704		518		116.5	214	862	663	145.9	36.1
杜加依林Tugai forest	8	114	430		259		118.3	186	426	270	88.9	19.6
3 常绿阔叶林Evergreen broad-leaf forest
典型常绿阔叶林Typical evergreen broad-leaf forest	129	478	1 577		1 041		249.4	401	1 753	927	246.3	-5.0
常绿-落叶阔叶林 Evergreen-deciduous broad-leaf mixed forest	22	414	1 098		722		141.4	493	1 099	688	155.4	-1.2
硬叶常绿阔叶林Sclerophyllous evergreen broad-leaf forest	9	407	651		542		73.3	401	1 221	872	297.8	59.3
4 热带雨林、季雨林Rain forest and monsoon forest	4	904	1 913		1 287		435.4	1 471	1 770	1 628	128.8	36.5
5 温带针叶林Temperate coniferous forest
油松林Pinus tabulaeformis forest	22	269	637		468		112.6	356	828	609	132.1	35.9
6 亚热带针叶林Subtropical coniferous forest
华山松与黄山松林Pinus armandi, P.taiwanensis and P. densata forest	35	260	845		566		157	198	1136	649	201.2	23.7
杉木林Cunninghamia lanceolata forest	86	328	1 669		791		339.9	401	1 115	608	136.8	-10.5
马尾松林Pinus massoniana forest	59	378	1 431		831		249.2	364	904	564	115.5	-27.2
云南松林Pinus yunnanensis and P. khasya forest	19	395	773		604		117.8	425	1 225	836	195.4	44.8
柏林Cupressus forest	14	342	1 023		587		200.7	198	1 136	611	240.6	13.4
合计 Total	690	114	1 913		684		313.9	48	1 770	641	259.2	4.5