北部湾沿海红树林生物量和碳贮量的遥感估算
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摘要
红树林是一种潮汐类沼泽,分布于海岸潮间带,其地面调查极其艰难,极大地限制了对红树林生物量的研究。为提高红树林生物量研究的效率,本文在北部湾沿海红树林分布区进行了大量的生物量样地调查,结合TM遥感影像,研究了5种红树林群落(红海榄Rhizophora stylosa、木榄Bruguiera gymnoihiza、白骨壤Vicennia mariana、桐花树Aegiceras corniculatum、秋茄Kandelia candel)及混合树种群落地上部分生物量、碳贮量的遥感估算方法。研究分为多元逐步回归分析和KNN(K-Nearest Neighbor)两类方法,并对两类方法进行比较评价。
研究结果如下:
(1)红树林生物量与多个纹理特征存在较好的相关关系,而与一些常被用于生物量遥感估算的变量NDVI、TM3、TM4等相关性不大。
(2)得到像元尺度上红海榄、木榄、白骨壤、桐花树4种群落以及混合群落的地上生物量遥感估算模型,秋茄群落未能得到模型。各模型虽然存在一定误差,但不失为一种快速高效进行红树林生物量估算的手段,极具现实意义。
(3)在像元尺度上,应用多元逐步回归分析方法进行红树林生物量估算明显优于应用KNN(K-Nearest Neighbor)方法。所以在像元尺度上,可以考虑应用元逐步回归分析方法来估算红树林生物量。
(4)应用KNN方法估测红树林生物量,K值取10优于K值取5,且估算精度随尺度的增大而增大,在像元尺度上最差。
(5)无论是多元逐步回归方法还是KNN(K-Nearest Neighbor)方法,生物量估算精度的排序为:红海榄和木榄>白骨壤>桐花树>混合树种群落。
(6)各种红树林地上部分生物量碳贮量转换系数为:木榄47.47%、红海榄43.18%、白骨壤41.47%、桐花树41.84%、秋茄42.32%、混合43.26%。
遥感影像的纹理特征和KNN计算方法在森林生物量研究中应用极少,本研究将其引入红树林湿地生物量的研究尚属首次,且取得一定的成果。红树林生物量和碳贮量遥感估算模型为红树林生态系统的研究提供一种方便快捷的技术手段。
Mangrove is located in tides swamp, distributed in intertidal zone around seashore,so sample survey is too difficult for measure trees for mangrove that the study on mangrove biomass is limited severely. In order to improve the efficiency of the study on mangrove biomass, by Landsat TM imagines,with large number of sample survey about mangrove biomass in Beibu Gulf coast, this paper studied the estimation models of aboveground biomass and carbon storage of five kinds of mangrove community (Rhizophora stylosa,Bruguiera gymnoihiza, Vicennia mariana, Aegiceras corniculatum, Kandelia candel) and mixed species community.In this paper, multiple regression analysis and KNN(K-Nearest Neighbor) methods were used, and the two methods were compared and evaluated.
The results were as follows:
(1) There is a strong relationship between mangrove biomass and texture features,however, a weak relationship between mangrove biomass and variables usually used in biomass estimation by remote sensing, such as NDVI,TM3,TM4.
(2) This paper build the aboveground biomass regression models of Rhizophora stylosa, Bruguiera gymnoihiza, Vicennia mariana, Aegiceras corniculatum, mixed species,but not Kandelia candel forest. Though models have certain errors, provide a method for mangrove biomass estimation using remote sensing data. The study is of great practical significance.
(3) In pixel scale, the multiple regression analysis is better than KNN in biomass estimation using remote sensing data.
(4)Estimatiing mangrove biomass by KNN methods, the accuracy increases by the scale, and K=10 is better than K=5 in the accuracy.
(5) The order of the accuracy of biomass estimation by both the multiple regression analysis and KNN(K-Nearest Neighbor) is that:Rhizophora stylosa and Bruguiera gymnoihiza>Vicennia mariana>Aegiceras corniculatum>mixed species, while multiple regression analysis Rhizophora stylosa is slightly better than Bruguiera gymnoihiza, while KNN, Bruguiera gymnoihiza is slightly better than Rhizophora stylosa.
(6) The conversion coefficients between biomass and carbon storage of mangroves are:Bruguiera gymnoihiza:47.47%,Rhizophora stylosa:43.18%, Vicennia mariana: 41.47%, Aegiceras corniculatum:41.84%,Kandelia candel:42.32%,mixed species 43.26%。
The texture features extracted remote sensing and KNN methods rarely used in forest biomass estimation, are used in the study on estimation mangrove biomass and carbon storage firstly, and getting certain findings. The estimation models of mangrove biomass and carbon storage provide a convenient and efficient method for mangrove ecosystem field.
研究结果如下:
(1)红树林生物量与多个纹理特征存在较好的相关关系,而与一些常被用于生物量遥感估算的变量NDVI、TM3、TM4等相关性不大。
(2)得到像元尺度上红海榄、木榄、白骨壤、桐花树4种群落以及混合群落的地上生物量遥感估算模型,秋茄群落未能得到模型。各模型虽然存在一定误差,但不失为一种快速高效进行红树林生物量估算的手段,极具现实意义。
(3)在像元尺度上,应用多元逐步回归分析方法进行红树林生物量估算明显优于应用KNN(K-Nearest Neighbor)方法。所以在像元尺度上,可以考虑应用元逐步回归分析方法来估算红树林生物量。
(4)应用KNN方法估测红树林生物量,K值取10优于K值取5,且估算精度随尺度的增大而增大,在像元尺度上最差。
(5)无论是多元逐步回归方法还是KNN(K-Nearest Neighbor)方法,生物量估算精度的排序为:红海榄和木榄>白骨壤>桐花树>混合树种群落。
(6)各种红树林地上部分生物量碳贮量转换系数为:木榄47.47%、红海榄43.18%、白骨壤41.47%、桐花树41.84%、秋茄42.32%、混合43.26%。
遥感影像的纹理特征和KNN计算方法在森林生物量研究中应用极少,本研究将其引入红树林湿地生物量的研究尚属首次,且取得一定的成果。红树林生物量和碳贮量遥感估算模型为红树林生态系统的研究提供一种方便快捷的技术手段。
Mangrove is located in tides swamp, distributed in intertidal zone around seashore,so sample survey is too difficult for measure trees for mangrove that the study on mangrove biomass is limited severely. In order to improve the efficiency of the study on mangrove biomass, by Landsat TM imagines,with large number of sample survey about mangrove biomass in Beibu Gulf coast, this paper studied the estimation models of aboveground biomass and carbon storage of five kinds of mangrove community (Rhizophora stylosa,Bruguiera gymnoihiza, Vicennia mariana, Aegiceras corniculatum, Kandelia candel) and mixed species community.In this paper, multiple regression analysis and KNN(K-Nearest Neighbor) methods were used, and the two methods were compared and evaluated.
The results were as follows:
(1) There is a strong relationship between mangrove biomass and texture features,however, a weak relationship between mangrove biomass and variables usually used in biomass estimation by remote sensing, such as NDVI,TM3,TM4.
(2) This paper build the aboveground biomass regression models of Rhizophora stylosa, Bruguiera gymnoihiza, Vicennia mariana, Aegiceras corniculatum, mixed species,but not Kandelia candel forest. Though models have certain errors, provide a method for mangrove biomass estimation using remote sensing data. The study is of great practical significance.
(3) In pixel scale, the multiple regression analysis is better than KNN in biomass estimation using remote sensing data.
(4)Estimatiing mangrove biomass by KNN methods, the accuracy increases by the scale, and K=10 is better than K=5 in the accuracy.
(5) The order of the accuracy of biomass estimation by both the multiple regression analysis and KNN(K-Nearest Neighbor) is that:Rhizophora stylosa and Bruguiera gymnoihiza>Vicennia mariana>Aegiceras corniculatum>mixed species, while multiple regression analysis Rhizophora stylosa is slightly better than Bruguiera gymnoihiza, while KNN, Bruguiera gymnoihiza is slightly better than Rhizophora stylosa.
(6) The conversion coefficients between biomass and carbon storage of mangroves are:Bruguiera gymnoihiza:47.47%,Rhizophora stylosa:43.18%, Vicennia mariana: 41.47%, Aegiceras corniculatum:41.84%,Kandelia candel:42.32%,mixed species 43.26%。
The texture features extracted remote sensing and KNN methods rarely used in forest biomass estimation, are used in the study on estimation mangrove biomass and carbon storage firstly, and getting certain findings. The estimation models of mangrove biomass and carbon storage provide a convenient and efficient method for mangrove ecosystem field.
引文
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