安吉县毛竹林碳储量时空变异遥感研究
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摘要
竹林是中国亚热带地区特殊的森林类型,在区域生态系统中具有重要地位和作用。竹林资源巨大的生物量、碳储量在维护区域生态环境和全球碳平衡中的作用已得到广泛证实。国内外对竹林资源的研究也逐渐深入,结合遥感资源对竹林信息提取的方法研究已取得较多成果,国内对竹类生物量及单株生物量的空间分配规律也进行了较为细致的研究。但就大尺度、时间序列毛竹林地上生物量/碳储量的空间自相关与异质性等研究还不足,不足以从空间上对毛竹林生物量/碳储量进行分析和评价,难以确切地反映毛竹林在时间序列中的变化过程和特征。因此,这方面的研究具有重要意义。
本研究以浙江省安吉县为试验区,采用近30年(1986到2008)5期(1986、1991、1998、2004、2008)Landsat TM卫星遥感影像,在GIS和GS+等相关软件的支持下,对全县毛竹林动态变化及其碳储量时空演变等进行了研究,主要包括以下几个方面:
1.在对5期遥感影像进行空间配准、大气校正和地形校正的基础上,采用最大似然法对安吉县土地利用类型进行分类,并从中提取研究区的毛竹林遥感信息。
2.采用动态度等指标,分析了安吉县各乡镇毛竹林动态变化程度;构建了5个时期土地利用转移矩阵,分析安吉县近30年土地利用覆盖变化对毛竹林面积扩张的贡献。
3、利用2008年遥感影像与对应的样地调查数据,构建毛竹林地上碳储量估算模型,并以该模型为基础对其他4个时期的毛竹林地上碳储量进行估测。
4.结合地统计学相关原理,计算得到5个时期碳储量的半方差图,并用不同模型对半方差图进行理论模拟,最后,通过最佳变异模型对各时期毛竹林碳储量的空间异质性进行分析和比较,从而获得碳储量时空演变特性。
通过研究,本文主要得到以下结论:
1.各个时期影像分类总体精度和毛竹林信息提取的精度比较好,其中总体分类精度都在85%以上,而毛竹林Kappa系数为0.80~0.95。满足竹林遥感信息提取的需要。
2.在近30年时间里,除昆铜乡毛竹林面积呈负增长外(变化幅度为-8.49%),其他各乡镇的毛竹林面积呈上升趋势,变化幅度为14%~86%,以孝丰镇增长幅度最大,天荒坪镇增长幅度最小。
3.安吉县毛竹林总面积由1986年占全县总面积的28.89%增加到2008年的38.48%,呈逐年增加的趋势,其中针叶林、阔叶林以及农业用地的减少对毛竹林总面积增加的贡献最大。
4.毛竹林碳储量/碳密度遥感估算结果表明,从1986年到2008年的近30年的时间里,安吉县毛竹林碳储量/碳密度基本呈逐渐增大趋势,5个时期的碳密度分别是11.50Mg/ha,15.09 Mg/ha,18.35 Mg/ha,23.41 Mg/ha,22.46 Mg/ha。在空间分布上,除1986年碳储量/碳密度空间分布较为均匀外,其余4个时期毛竹林碳储量/碳密度分布格局有较多相似之处,基本呈西南、东南向东北、西北减少的趋势。另外,相对于2008年,各个乡镇碳密度增长速度都超过50%,部分乡镇如杭垓、章村增长速度超过100%。
5.安吉县毛竹林碳储量半方差函数计算和模拟表明,除1991年的理论最优模型为球状模型外,其余4个时期均可采用指数模型进行模拟。
6、对理论模型结构分析表明,安吉县毛竹林碳储量空间自相关范围以1986年最大为9870m,1991以后空间自相关范围明显减小,在2000~3000m之间; 5个时期的碳储量/碳密度具有中等空间自相关性特征,其空间结构比C C0 +C分别是50.1%、72.4%、66.8%、67.3%和65.6%,说明地形、土壤、气候等结构性因素对安吉县毛竹林碳储量空间异质性起主要作用。在不考虑1986年的前提下,从1991年到2008年,随机因素所占的比例呈增加趋势,即随机方差C0所占的比例在增加,这一点与安吉县近年毛竹林人为经营干扰情况是一致的。
Bamboo forest is a special forest type mainly distributed in semi-tropical areas of China. It has been widely confirmed that bamboo forests with huge biomass or carbon storage play important roles in protecting regional ecological environment and maintaining global carbon balance. As studies on bamboo forests increased, many methods used to extract bamboo forest information based on remote sensing data were proposed and a number of publications focus on estimating bamboo biomass and its spatial distribution. However, study on the spatial autocorrelation and heterogeneous of Moso bamboo aboveground biomass (AGB)/ aboveground carbon (AGC) storage in a large scale and time-series is rare. Therefore, it is important to evaluate and analyze the spatial change characteristic of bamboo forest AGB/AGC, and this study is significant.
In this study, based on remote sensing images with five different times, RS, GIS and GS+ were used to study spatial heterogeneous characteristics and changes in carbon storage of Moso bamboo in Anji, Zhejiang province, China. This study includes the following aspects:
1. Maximum likelihood method was used to extract Moso bamboo forest information based on Landsat TM images.
2. Rangeability and dynamical degree were used to analyze the spatial dynamic change character of bamboo forest. Effects of land cover change on bamboo forest were been analyzed.
3. Based on the integration of Landsat TM and field inventory data, aboveground carbon storage estimation model was built and then used to estimate aboveground carbon storage in different periods.
4. The semivariogram of carbon storage were calculated using geostatistical software GS+ and these graphs were fitted using different statistical models. The optimal model was used to analyze the spatial heterogeneity of Moso bamboo forest carbon storage in five periods.
This study mainly gets the following conclusions:
1. Classification accuracy of each image and Moso bamboo forest was high enough. The overall classification accuracy of each TM image was higher than 85%, and kappa coefficient of Moso bamboo forest ranged from 0.80 to 0.95. The classification accuracy is suitable for application.
2. Except decrease in bamboo forest area of Kuntong (rangeability is -8.49%), the bamboo forest areas of other towns were increasing during 1986 and 2008, and the rangeability ranged from 14% to 86%. The rangeability of Xiaofeng was the largest, and the rangeability of Tianhuangping was the smallest.
3. Area percent of Moso bamboo forests in Anji increased from 28.89% in 1986 to 38.48% in 2008. The main reason is that the conversions of conifer and broad-leave forests to bamboo forests.
4. Results showed that carbon storage of Moso bamboo forests notablely increased from 1986 to 2008. The aboveground carbon density in 5 different periods were 11.50Mg/ha, 15.09 Mg/ha, 18.35 Mg/ha, 23.41 Mg/ha, 22.46 Mg/ha, respectively. Except that spatial distribution of carbon density in 1986 is even, there are similarities in carbon density spatial distribution for the other four periods. Large carbon density exists in southwest and southeast and small carbon density exists in northeast and northwest. Increase rate of carbon density in each town is over 50% from 1986 to 2008.
5. Results of semi-variance function simulation showed that the optimal semi-variance function for 1991 is spherical model and the optimal semi-variance functions for the other four periods are exponential models.
6. Analysis on theoretical model structure showed that a range of spatial autocorrelation in 1986 was the largest (9870 m) and it obviously decreased ranged between 2000 and 3000m after 1991. Spatial autocorrelation characters of carbon density in five periods were medium, and their spatial structure ratio (C/C0+C) were 50.1%、72.4%、66.8%、67.3% and 65.6% respectively. It implies that those structural factors, such as terrain, soil, climate, play an important role in spatial heterogeneity of Moso bamboo forest carbon storage. Except for 1986, ratio of random factors increased from 1991 to 2008, that is, ratio of random variance (C0) increased. The result is consistent with Moso bamboo forests severely disturbed by human management in recent years.
本研究以浙江省安吉县为试验区,采用近30年(1986到2008)5期(1986、1991、1998、2004、2008)Landsat TM卫星遥感影像,在GIS和GS+等相关软件的支持下,对全县毛竹林动态变化及其碳储量时空演变等进行了研究,主要包括以下几个方面:
1.在对5期遥感影像进行空间配准、大气校正和地形校正的基础上,采用最大似然法对安吉县土地利用类型进行分类,并从中提取研究区的毛竹林遥感信息。
2.采用动态度等指标,分析了安吉县各乡镇毛竹林动态变化程度;构建了5个时期土地利用转移矩阵,分析安吉县近30年土地利用覆盖变化对毛竹林面积扩张的贡献。
3、利用2008年遥感影像与对应的样地调查数据,构建毛竹林地上碳储量估算模型,并以该模型为基础对其他4个时期的毛竹林地上碳储量进行估测。
4.结合地统计学相关原理,计算得到5个时期碳储量的半方差图,并用不同模型对半方差图进行理论模拟,最后,通过最佳变异模型对各时期毛竹林碳储量的空间异质性进行分析和比较,从而获得碳储量时空演变特性。
通过研究,本文主要得到以下结论:
1.各个时期影像分类总体精度和毛竹林信息提取的精度比较好,其中总体分类精度都在85%以上,而毛竹林Kappa系数为0.80~0.95。满足竹林遥感信息提取的需要。
2.在近30年时间里,除昆铜乡毛竹林面积呈负增长外(变化幅度为-8.49%),其他各乡镇的毛竹林面积呈上升趋势,变化幅度为14%~86%,以孝丰镇增长幅度最大,天荒坪镇增长幅度最小。
3.安吉县毛竹林总面积由1986年占全县总面积的28.89%增加到2008年的38.48%,呈逐年增加的趋势,其中针叶林、阔叶林以及农业用地的减少对毛竹林总面积增加的贡献最大。
4.毛竹林碳储量/碳密度遥感估算结果表明,从1986年到2008年的近30年的时间里,安吉县毛竹林碳储量/碳密度基本呈逐渐增大趋势,5个时期的碳密度分别是11.50Mg/ha,15.09 Mg/ha,18.35 Mg/ha,23.41 Mg/ha,22.46 Mg/ha。在空间分布上,除1986年碳储量/碳密度空间分布较为均匀外,其余4个时期毛竹林碳储量/碳密度分布格局有较多相似之处,基本呈西南、东南向东北、西北减少的趋势。另外,相对于2008年,各个乡镇碳密度增长速度都超过50%,部分乡镇如杭垓、章村增长速度超过100%。
5.安吉县毛竹林碳储量半方差函数计算和模拟表明,除1991年的理论最优模型为球状模型外,其余4个时期均可采用指数模型进行模拟。
6、对理论模型结构分析表明,安吉县毛竹林碳储量空间自相关范围以1986年最大为9870m,1991以后空间自相关范围明显减小,在2000~3000m之间; 5个时期的碳储量/碳密度具有中等空间自相关性特征,其空间结构比C C0 +C分别是50.1%、72.4%、66.8%、67.3%和65.6%,说明地形、土壤、气候等结构性因素对安吉县毛竹林碳储量空间异质性起主要作用。在不考虑1986年的前提下,从1991年到2008年,随机因素所占的比例呈增加趋势,即随机方差C0所占的比例在增加,这一点与安吉县近年毛竹林人为经营干扰情况是一致的。
Bamboo forest is a special forest type mainly distributed in semi-tropical areas of China. It has been widely confirmed that bamboo forests with huge biomass or carbon storage play important roles in protecting regional ecological environment and maintaining global carbon balance. As studies on bamboo forests increased, many methods used to extract bamboo forest information based on remote sensing data were proposed and a number of publications focus on estimating bamboo biomass and its spatial distribution. However, study on the spatial autocorrelation and heterogeneous of Moso bamboo aboveground biomass (AGB)/ aboveground carbon (AGC) storage in a large scale and time-series is rare. Therefore, it is important to evaluate and analyze the spatial change characteristic of bamboo forest AGB/AGC, and this study is significant.
In this study, based on remote sensing images with five different times, RS, GIS and GS+ were used to study spatial heterogeneous characteristics and changes in carbon storage of Moso bamboo in Anji, Zhejiang province, China. This study includes the following aspects:
1. Maximum likelihood method was used to extract Moso bamboo forest information based on Landsat TM images.
2. Rangeability and dynamical degree were used to analyze the spatial dynamic change character of bamboo forest. Effects of land cover change on bamboo forest were been analyzed.
3. Based on the integration of Landsat TM and field inventory data, aboveground carbon storage estimation model was built and then used to estimate aboveground carbon storage in different periods.
4. The semivariogram of carbon storage were calculated using geostatistical software GS+ and these graphs were fitted using different statistical models. The optimal model was used to analyze the spatial heterogeneity of Moso bamboo forest carbon storage in five periods.
This study mainly gets the following conclusions:
1. Classification accuracy of each image and Moso bamboo forest was high enough. The overall classification accuracy of each TM image was higher than 85%, and kappa coefficient of Moso bamboo forest ranged from 0.80 to 0.95. The classification accuracy is suitable for application.
2. Except decrease in bamboo forest area of Kuntong (rangeability is -8.49%), the bamboo forest areas of other towns were increasing during 1986 and 2008, and the rangeability ranged from 14% to 86%. The rangeability of Xiaofeng was the largest, and the rangeability of Tianhuangping was the smallest.
3. Area percent of Moso bamboo forests in Anji increased from 28.89% in 1986 to 38.48% in 2008. The main reason is that the conversions of conifer and broad-leave forests to bamboo forests.
4. Results showed that carbon storage of Moso bamboo forests notablely increased from 1986 to 2008. The aboveground carbon density in 5 different periods were 11.50Mg/ha, 15.09 Mg/ha, 18.35 Mg/ha, 23.41 Mg/ha, 22.46 Mg/ha, respectively. Except that spatial distribution of carbon density in 1986 is even, there are similarities in carbon density spatial distribution for the other four periods. Large carbon density exists in southwest and southeast and small carbon density exists in northeast and northwest. Increase rate of carbon density in each town is over 50% from 1986 to 2008.
5. Results of semi-variance function simulation showed that the optimal semi-variance function for 1991 is spherical model and the optimal semi-variance functions for the other four periods are exponential models.
6. Analysis on theoretical model structure showed that a range of spatial autocorrelation in 1986 was the largest (9870 m) and it obviously decreased ranged between 2000 and 3000m after 1991. Spatial autocorrelation characters of carbon density in five periods were medium, and their spatial structure ratio (C/C0+C) were 50.1%、72.4%、66.8%、67.3% and 65.6% respectively. It implies that those structural factors, such as terrain, soil, climate, play an important role in spatial heterogeneity of Moso bamboo forest carbon storage. Except for 1986, ratio of random factors increased from 1991 to 2008, that is, ratio of random variance (C0) increased. The result is consistent with Moso bamboo forests severely disturbed by human management in recent years.
引文
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