利用遥感数据优化物候模型时样本选择的新方法

doi:10.17521/cjpe.2015.0026

植物生态学报 ›› 2015, Vol. 39 ›› Issue (3): 264-274.DOI: 10.17521/cjpe.2015.0026

所属专题：遥感生态学

利用遥感数据优化物候模型时样本选择的新方法

马勇刚^1,^2,³, 张弛^1,^*(), 陈曦¹

¹中国科学院新疆生态与地理研究所荒漠与绿洲生态国家重点实验室, 乌鲁木齐 830011
²新疆大学资源与环境科学学院, 乌鲁木齐 830046
³新疆科技发展战略研究院, 乌鲁木齐 830011

收稿日期:2014-05-04 接受日期:2014-12-17 出版日期:2015-03-01 发布日期:2015-03-17
通讯作者: 张弛
作者简介:
# 共同第一作者
基金资助:
国家重点基础研究发展计划(2014CB- 954204)、国家自然科学基金国际(地区)合作与交流项目(41361140361)和中国科学院百人计划(张弛)

A new method of sample selections for optimizing phenology model based remote sensing data

MA Yong-Gang^1,^2,³, ZHANG Chi^1,^*(), CHEN Xi¹

¹State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, China
²College of Resources and Environment Science, Xinjiang University, Ürümqi 830046, China
³Xinjiang Academy of Science and Technology Development Strategy, Ürümqi 830011, China

Received:2014-05-04 Accepted:2014-12-17 Online:2015-03-01 Published:2015-03-17
Contact: Chi ZHANG
About author:
# Co-first authors

摘要/Abstract

摘要：

植被物候模型是生态系统模型的重要组成部分, 其精度对准确地模拟陆面和大气之间的能量和物质交换具有重要意义。利用遥感获取空间物候信息并与气候数据进行耦合分析是在中亚干旱区等地面物候观测数据缺乏的地区构建物候模型的重要方法。为减小混合植被像元和气候数据资料的内在误差及二者在空间尺度的不匹配对物候模型构建产生的影响, 该研究提出一种在气象站点周围选取满足规定规则集的“代表植被类型像元”作为样本点的选择方法, 以代表植被类型像元的遥感物候数据和气象站点数据为基础, 结合经典物候模型和改进物候模型, 在粒子群优化算法支持下, 分别以独立的拟合与评价样本数据, 完成了荒漠草原植被与落叶阔叶林的模型拟合与评价。研究发现中亚干旱区荒漠草原植被的最优模型为温度-降水修正模型, 落叶阔叶林的最优模型为替代模型。通过此方法模型总体精度在8-10 d左右。结果表明此方法在气候数据和植物物候空间匹配方面有改进, 有助于提高物候模型精度。

关键词: 干旱区, 物候模型, 遥感, 中亚

Abstract: Aims

Phenology model is considered as the most efficient tool to assess the phonological responses of plants to future climate change. Furthermore, as an important component in dynamic ecological models, the performance of phenology model is of significance for the precision in simulating mass and energy exchanges between land and atmosphere. Combining long time series remote sensing data and climate data to construct regional phenology model may be the only way to solve the problem of deficiency in lacking in-situ observational data on phenology and species-specific phenology models. The objective of this study was to develop a new method of sample selections for constructing phenology in the arid zone of Central Asia where only sparse observational data are available.

Methods

Based the phenology data retrieved from 250 m-resolution MODIS images for the period 2000-2010, a new method is developed for constructing the vegetation phenology model in arid zone. A set of rules were built to select the representative pixels of PFTs (plant function types) surrounding the climate station, making sure that the climate of the representative pixels of PFTs could be represented by the observed meteorological data at the station, and then the phenology data on the representative pixels of PFTs were extracted from the MODIS images and the corresponding climatic data were set as the sample for model fitting and assessment. Forty-six representative pixels of PFTs for desert grassland vegetation and broadleaved deciduous forests were selected under the rules. The phenology model parameters were estimated using data from odd-numbered years and the simulation accuracy was assessed with the independent even-year data. Particle swarm optimization algorithm was used to parameterize the pre-selected model. The root-mean-square error and coefficient of determination were used to examine the performance of model with independent data.

Important findings

The best model for desert steppe vegetation was the modified temperature-precipitation model and the best model for deciduous broadleaved forest was the alternative model. Compared with other documented findings, the new method was proven feasible, and the results also suggested that this new method may improve the spatial match of climatic data and vegetation phenology data, and therefore contribute to improvement of the phenology model accuracy.

Key words: arid zone, phenology model, remote sensing, Central Asia

马勇刚, 张弛, 陈曦. 利用遥感数据优化物候模型时样本选择的新方法. 植物生态学报, 2015, 39(3): 264-274. DOI: 10.17521/cjpe.2015.0026

MA Yong-Gang,ZHANG Chi,CHEN Xi. A new method of sample selections for optimizing phenology model based remote sensing data. Chinese Journal of Plant Ecology, 2015, 39(3): 264-274. DOI: 10.17521/cjpe.2015.0026

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2015.0026

https://www.plant-ecology.com/CN/Y2015/V39/I3/264

图/表 6

参考文献 42

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模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型 Model	参数T₀Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型 Model	固定型/游动型 Fixed or moved (mobile)	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	70	11.18	0.55	79	15.42	0.10
Chuine模型 Chuine model	固定 Fixed		11.94	0.51		18.25	0.15
SW模型 SW model	游动 Moved		10.75	0.57		9.68	0.55
SW模型 SW model	固定 Fixed		13.24	0.42		16.27	0.61
Seq模型 Seq model	游动 Moved		14.01	0.37		18.31	0.12
Seq模型 Seq model	固定 Fixed		14.02	0.37		12.06	0.69
Par模型 Par model	游动 Moved		9.38	0.61		10.72	0.69
Par模型 Par model	固定 Fixed		19.36	-		-	-
Al模型 Al model	游动 Moved		9.20	0.74		19.6	0.37
Al模型 Al model	固定 Fixed		-	0.20		-	-
Al-P模型 Al-P model	游动 Moved		6.60	0.68		16.57	0.51
Al-P模型 Al-P model	固定 Fixed		12.40	0.37		-	0.12
T-P修正模型 Modified T-P model	游动 Moved		7.70	0.83		9.87	0.70
T-P修正模型 Modified T-P model	固定 Fixed		10.25	0.77		14.19	0.64
Chuine-P模型 Chuine-P model	游动 Moved		12.32	0.39		14.13	0.27
Chuine-P模型 Chuine-P model	固定 Fixed		15.25	0.33		16.93	-
T-D模型 T-D model	游动 Moved		9.79	0.67		11.77	0.66
T-D模型 T-D model	固定 Fixed		10.64	0.68		15.18	0.68
T-P-D1模型 T-P-D1 model	游动 Moved		7.78	0.69		10.01	0.48
T-P-D1模型 T-P-D1 model	固定 Fixed		8.60	0.77		11.34	0.71
T-P-D2模型 T-P-D2 model	游动 Moved		8.00	0.47		17.21	0.47
T-P-D2模型 T-P-D2 model	固定 Fixed		10.54	0.64		14.50	0.41

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

模型名称 Model name	参数 T₀ Parameter T₀	拟合程度 Fitting result			精度评价 Accuracy assessment
模型名称 Model name	固定型/游动型 Fixed or moved	样本个数 Sample size	均方根误差 RMSE	决定系数 R²	样本个数 Sample size	均方根误差 RMSE	决定系数 R²
Chuine模型 Chuine model	游动 Moved	15	9.70	0.71	18	18.30	0.46
Chuine模型 Chuine model	固定 Fixed		11.70	0.42		16.20	0.41
SW模型 SW model	游动 Moved		12.80	0.22		13.95	0.56
SW模型 SW model	固定 Fixed		13.25	0.13		13.73	0.51
Seq模型 Seq model	游动 Moved		12.60	0.31		17.50	0.21
Seq模型 Seq model	固定 Fixed		12.56	0.31		15.88	0.59
Par模型 Par model	游动 Moved		11.90	0.40		12.80	0.57
Par模型 Par model	固定 Fixed		11.90	0.44		12.63	0.58
Al模型 Al model	游动 Moved		8.00	0.82		12.10	0.63
Al模型 Al model	固定 Fixed		14.29	0.62		-	0.29
Al-P模型 Al-P model	游动 Moved		-	-
Al-P模型 Al-P model	固定 Fixed		-	-
T-P修正模型 Modified T-P model	游动 Moved		-	0.44		-	-
T-P修正模型 Modified T-P model	固定 Fixed		-	0.10		-	-
Chuine-P模型 Chuine-P model	游动 Moved		-	-
Chuine-P模型 Chuine-P model	固定 Fixed		-	-
T-D模型 T-D model	游动 Moved		12.66	0.34		17.75	0.33
T-D模型 T-D model	固定 Fixed		13.10	-		19.32	-
T-P-D1模型 T-P-D1 model	游动 Moved		-	0.44		-	-
T-P-D1模型 T-P-D1 model	固定 Fixed		-	-		-	-
T-P-D2模型 T-P-D2 model	游动 Moved		-	0.22		-	-
T-P-D2模型 T-P-D2 model	固定 Fixed		-			-	-

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利用遥感数据优化物候模型时样本选择的新方法

A new method of sample selections for optimizing phenology model based remote sensing data

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