基于GF-1 WFV影像的丰宁县植被地上碳储量估算

doi:10.18402/resci.2016.06.05

摘要/Abstract

摘要：

陆地植被碳储量的定量估算及其空间分布研究有助于了解区域碳平衡,为增汇减排管理及可持续发展提供科学依据。本文以河北省丰宁县为研究区,结合新型国产高分遥感影像GF-1 WFV 和2015年同步野外实测数据,分别建立基于植被指数的森林、灌丛和草地植被地上生物量一元曲线回归模型及多元线性回归模型,选择最优反演模型计算地上生物量,进一步估算植被地上碳密度和碳储量,并结合GIS技术分析其空间分布特征。结果表明：①所建立的多元回归模型对森林和草地植被生物量有较好的反演精度,R²分别为0.528和0.777,基于RVI的指数方程是估算灌丛生物量最佳模型,R²为0.620;②研究区2015年8月森林、灌丛和草地地上植被碳密度估算值分别为55.406tC/hm²、3.304tC/hm²和0.533tC/hm²,碳储量分别为15.714TgC、1.016TgC和0.080TgC,除农田植被外,植被地上碳储量总量为16.810TgC,森林植被为主要碳库;③丰宁县植被碳密度呈现随海拔升高而先升高后降低的趋势,植被地上碳库主要集中在海拔700~1500m之间。

关键词: GF-1 WFV影像, 碳密度, 植被碳储量, 丰宁县

Abstract:

Quantitative estimation of carbon storage of terrestrial vegetation and its distribution can help to understand the regional carbon balance,and provide scientific basis for the management of emission reduction and sustainable development. According to GF-1 WFV satellite remote sensing images and synchronous field surveys conducted in 2015 in Fengning county,Hebei,China we established single curve regression models and multiple linear regression models of aboveground biomass for forest,shrub and grassland. Optical inversion models were selected to calculate vegetation aboveground biomass,and aboveground carbon density and carbon storage were estimated. Spatial distribution features of the study area were analyzed using GIS. We found that our multiple regression models had good inversion precision,which can be well applied to estimate vegetation aboveground biomass for the forest and grassland （R² values respectively 0.528 and 0.777）. The exponential equation based on RVI was the best inversion model to estimate shrub aboveground biomass and the R² value was 0.620. As at August 2015,the aboveground vegetation carbon densities were respectively 55.406tC/hm²,3.304tC/hm² and 0.533 t C/hm² for forest,shrub and grassland. Aboveground vegetation carbon storages were respectively 15.714TgC,1.016TgC and 0.080TgC for forest,shrub and grassland. The total aboveground vegetation carbon storage was 16.810TgC except for croplands,indicating that forest vegetation represents the main aboveground carbon pool. The vegetation carbon density in Fengning county increased, then decreased with rising of elevation. The vegetation aboveground carbon storage lies mainly at an altitude of 700~1500m.

Key words: GF-1 WFV satellite, carbon density, vegetation carbon storage, Fengning county

孔凡婕, 李晓兵, 白云晓, 李响, 王宏. 基于GF-1 WFV影像的丰宁县植被地上碳储量估算[J]. 资源科学, 2016, 38(6): 1054-1064.

KONG Fanjie,LI Xiaobing,BAI Yunxiao,LI Xiang,WANG Hong. The estimation of aboveground vegetation carbon storage based on GF-1 WFV satellite images in Fengning County[J]. Resources Science, 2016, 38(6): 1054-1064.

图/表 12

图1

表1

表2

研究中用到的植被指数计算方法"

植被指数	计算公式	参考文献
归一化差值植被指数NDVI	$NDVI = (ρ NIR - ρ R) (ρ NIR + ρ R)$	[21]
差值植被指数DVI	$DVI = ρ NIR - ρ R$	[22]
比值植被指数RVI	$RVI = ρ NIR / ρ R$	[23]
增强型植被指数EVI	$EVI = 2.5 × ρ NIR - ρ R ρ NIR + 6.0 ρ R - 7.5 ρ B + 1$	[24]
土壤调整植被指数SAVI	$SAVI = 1.5 (ρ NIR - ρ R) / (ρ NIR + ρ R + 0.5)$	[25]
修正土壤调整植被指数MSAVI	$MSAVI = 0.5 × [2 (ρ NIR + 1) - 2 ρ R + 1) 2 - 8 (ρ NIR - ρ R)]$	[26]
优化土壤调整指数OSAVI	$OSAVI = 1.05 (ρ NIR - ρ R) (ρ NIR + ρ R + 1.05)$	[27]
修正三角植被指数2 MTVI2	$2 MTVI 2 = 1.5 1.2 (ρ NIR - ρ R) - 2.5 (ρ R - ρ G) 2 (ρ NIR + 1) 2 - (6 ρ NIR - 5 ρ R) - 0.5$	[28]
归一化绿波波段差值植被指数GNDVI	$GNDVI = (ρ NIR - ρ G) (ρ NIR + ρ G)$	[29]
考虑绿光改进的植被指数GBNDVI	$GBNDVI = ρ NIR - (ρ B + ρ G) ρ NIR + ρ R + ρ G$	[30]

表2

图2

表3

表4

表5

表6

表7

图3

图4

表 8

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参数	多光谱相机
波长范围/μm	0.45~0.52
	0.52~0.59
	0.63~0.69
	0.77~0.89
空间分辨率/m	16
幅宽/km	800（4台相机组合）
重访周期/d	4

回归模型	显著性	模型决定系数R²	均方根误差RMSE/（t/hm²）	平均相对误差MRE/%
y=2 123.870(GNDVI)²-2 244.564(GNDVI)+620.666	0.002	0.475	33.164	45.25
y=428.107(GNDVI)-200.314	0.002	0.375	35.258	42.99
y=16.829e^0.150(^RVI⁾	0.002	0.382	0.543	38.81
y=9.398(RVI)-2.274	0.003	0.362	35.628	38.73

回归模型	显著性	模型决定系数R²	均方根误差RMSE/（t/hm²）	平均相对误差MRE/%
y=3.019e^0.105(^RVI⁾	0.000	0.620	0.232	33.17
y=88.914(NDVI)²-104.132(NDVI)+34.807	0.000	0.615	1.619	33.57
y=0.014(RVI)²+0.524(RVI)+2.290	0.001	0.608	1.705	32.72
y=0.728(RVI)+1.647	0.000	0.606	1.658	32.53

回归模型	模型决定系数R²	均方根误差RMSE/（t/hm²）	平均相对误差MRE/%
y=0.021(RVI)²+0.005(RVI)+0.748	0.758	0.333	28.91
y=18.750(MSAVI)²-11.767(MSAVI)+2.783	0.723	0.356	30.61
y=30.409(SAVI)²-21.155(SAVI)+4.601	0.723	0.356	30.56
y=0.272(RVI)+0.083	0.719	0.350	31.33

植被类型	显著性	模型决定系数R²	均方根误差RMSE/（t/hm²）	平均相对误差MRE/%
森林	0.001	0.528	31.431	43.96
灌丛	0.000	0.606	1.658	35.72
草地	0.000	0.777	0.319	20.38