植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CAS...植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。展开更多
呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~...呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~2020年牧草生长季(5~8月)的牧草产量,并分别用各牧业旗生态观测站的实测产量数据对模拟产量数据进行精度验证。本研究首先阐释CASA模型的基本原理及模型框架,介绍CASA模型中估算牧草产量的两个关键参量:植被吸收的光合有效辐射和实际光能利用率。通过太阳总辐射和植物光合有效辐射吸收比例确定植被吸收的光合有效辐射,通过温度胁迫因子、水分胁迫因子、最大光能利用率确定实际光能利用率,最终计算出2001~2020年呼伦贝尔市牧业五旗牧草生长季的NPP。根据2001~2020年呼伦贝尔地区逐年的土地覆盖数据提取草原用地,经空间统计分析提取出牧业五旗的牧草生长季(5~8月)逐年平均NPP,进而转换成牧草产量。对比分析牧草估测产量与实测产量之间的相关性系数、均方根误差、平均绝对误差三种精度评价指标,验证CASA模型估产精度。实测产量值与估测产量值的相关性系数分布区间为0.76~0.82、均方根误差区间为20.42~24.21 g/m2、平均绝对误差区间为17.94~23.19 g/m2,三项误差指标均在合理误差范围内。结果表明CASA模型模拟出的牧草产量数据精度较高,可为呼伦贝尔畜牧业高质量发展提供技术支撑。Hulunbuir grassland is a famous natural pasture, and animal husbandry is also a basic industry in Hulunbuir grassland area, giving full play to the advantages of animal husbandry is of great significance to improve the income of herdsmen in the grassland area. In order to obtain the long time series pasture yield data, this paper estimates the pasture yield of the pasture growing season (May-August) of the five flags of Hulunbuir City pastoralism from 2001 to 2020 based on CASA model using geographic information and remote sensing technology, and verifies the accuracy of the simulated yield data with the measured yield data from the ecological observatory of each flag of pastoralism, respectively. In this study, the basic principles and model framework of CASA model were firstly explained, and two key parameters for estimating pasture yield in CASA model were introduced: photosynthetically active radiation absorbed by vegetation and actual light energy utilization rate. The photosynthetically active radiation absorbed by vegetation was determined by the ratio of total solar radiation and plant photosynthetically active radiation absorption, and the actual light energy utilization was determined by the temperature stress factor, moisture stress factor, and maximum light energy utilization, and the NPP of the growing season of forage grass in the five flags of pastoral industry in Hulunbuir City from 2001 to 2020 was finally calculated. Based on the year-by-year land-cover data of Hulunbuir area from 2001 to 2020, the NPP of the growing season of pasture grass was extracted. Grassland land was extracted, and the yearly average NPP of the pasture growing season (May-August) in the five banners of pastoralism was extracted by spatial statistical analysis, and then converted into pasture yield. The correlation coefficient, root-mean-square error, and average absolute error of three accuracy evaluation indexes between the estimated and measured pasture yield were comparatively analyzed to verify the accuracy of CASA model estimation. The distribution range of correlation coefficient between measured and estimated yield values was 0.76~0.82, the root mean square error range was 20.42~24.21 g/m2, and the average absolute error range was 17.94~23.19 g/m2, and the three kinds of error indexes were all within the reasonable error range. The results showed that the CASA model simulated pasture yield data with high accuracy, which can provide technical support for the high-quality development of Hulunbuir’s livestock industry.展开更多
以陕西省为研究对象,运用遥感和GIS手段,结合MODIS/NDVI数据、气象数据以及植被类型数据,应用CASA模型估算得到陕西省2013年的NPP数据。结果表明,2013年陕西省NPP总量为8.87×107g C/a,平均值为469.58 g C/(m2·a),NPP最高值为7...以陕西省为研究对象,运用遥感和GIS手段,结合MODIS/NDVI数据、气象数据以及植被类型数据,应用CASA模型估算得到陕西省2013年的NPP数据。结果表明,2013年陕西省NPP总量为8.87×107g C/a,平均值为469.58 g C/(m2·a),NPP最高值为723.06 g C/(m2·a),其空间分布特点表现为显著的纬度分布,南高北低,陕南>关中>陕北;NPP时间分布表现为明显的季节变化,呈单峰型曲线;植被类型NPP表现为阔叶林>针叶林>耕地>草地,并且各植被类型最大值出现月份不一致。展开更多
文摘植被净初级生产力(Net Primary Productivity,NPP)是反映陆地生态系统碳封存能力和环境变化的直接指标,受气候变化与人类活动的共同影响,且在不同地形上有分异性。然而,人类活动及地形对秦巴山区植被NPP变化的影响研究尚且不足。采用CASA模型,综合利用线性趋势分析、转移矩阵和残差分析等方法研究了秦巴山区2001—2022年长时序NPP时空动态和地形效应,并进一步探讨了气候变化和人类活动对NPP变化的相对贡献率,主要结论如下:①秦巴山区2001—2022年的NPP空间分布表现为中间高,四周低,均值为585.11g C/m^(2),并以4.30g C m^(-2)a^(-1)的速度增加。②林地有最高的年NPP均值,而退耕还林区域具有最高的NPP增长速率(8.17g C m^(-2)a^(-1)),表明退耕还林是秦巴山区NPP增长的有效措施;③NPP随海拔和坡度变化具有明显的分异性。在海拔3400m以下,植被NPP随着高程的增加而增加,而当高程超过3400m时,植被NPP显著减少,坡度在10°—40°范围内植被NPP的多年均值和变化趋势较高;④秦巴山区NPP变化是气候变化和人类活动共同作用的结果,二者对NPP变化的相对贡献率分别为37.81%和62.19%,其中人类活动导致陇南等生态脆弱区NPP显著提高。
文摘呼伦贝尔草原是著名的天然牧场,畜牧业也是呼伦贝尔草原地区的基础性产业,充分发挥畜牧业优势对于提高草原地区牧民收入有着重大意义。为获得长时序牧草产量数据,本文利用地理信息和遥感技术,基于CASA模型估算出呼伦贝尔市牧业五旗2001~2020年牧草生长季(5~8月)的牧草产量,并分别用各牧业旗生态观测站的实测产量数据对模拟产量数据进行精度验证。本研究首先阐释CASA模型的基本原理及模型框架,介绍CASA模型中估算牧草产量的两个关键参量:植被吸收的光合有效辐射和实际光能利用率。通过太阳总辐射和植物光合有效辐射吸收比例确定植被吸收的光合有效辐射,通过温度胁迫因子、水分胁迫因子、最大光能利用率确定实际光能利用率,最终计算出2001~2020年呼伦贝尔市牧业五旗牧草生长季的NPP。根据2001~2020年呼伦贝尔地区逐年的土地覆盖数据提取草原用地,经空间统计分析提取出牧业五旗的牧草生长季(5~8月)逐年平均NPP,进而转换成牧草产量。对比分析牧草估测产量与实测产量之间的相关性系数、均方根误差、平均绝对误差三种精度评价指标,验证CASA模型估产精度。实测产量值与估测产量值的相关性系数分布区间为0.76~0.82、均方根误差区间为20.42~24.21 g/m2、平均绝对误差区间为17.94~23.19 g/m2,三项误差指标均在合理误差范围内。结果表明CASA模型模拟出的牧草产量数据精度较高,可为呼伦贝尔畜牧业高质量发展提供技术支撑。Hulunbuir grassland is a famous natural pasture, and animal husbandry is also a basic industry in Hulunbuir grassland area, giving full play to the advantages of animal husbandry is of great significance to improve the income of herdsmen in the grassland area. In order to obtain the long time series pasture yield data, this paper estimates the pasture yield of the pasture growing season (May-August) of the five flags of Hulunbuir City pastoralism from 2001 to 2020 based on CASA model using geographic information and remote sensing technology, and verifies the accuracy of the simulated yield data with the measured yield data from the ecological observatory of each flag of pastoralism, respectively. In this study, the basic principles and model framework of CASA model were firstly explained, and two key parameters for estimating pasture yield in CASA model were introduced: photosynthetically active radiation absorbed by vegetation and actual light energy utilization rate. The photosynthetically active radiation absorbed by vegetation was determined by the ratio of total solar radiation and plant photosynthetically active radiation absorption, and the actual light energy utilization was determined by the temperature stress factor, moisture stress factor, and maximum light energy utilization, and the NPP of the growing season of forage grass in the five flags of pastoral industry in Hulunbuir City from 2001 to 2020 was finally calculated. Based on the year-by-year land-cover data of Hulunbuir area from 2001 to 2020, the NPP of the growing season of pasture grass was extracted. Grassland land was extracted, and the yearly average NPP of the pasture growing season (May-August) in the five banners of pastoralism was extracted by spatial statistical analysis, and then converted into pasture yield. The correlation coefficient, root-mean-square error, and average absolute error of three accuracy evaluation indexes between the estimated and measured pasture yield were comparatively analyzed to verify the accuracy of CASA model estimation. The distribution range of correlation coefficient between measured and estimated yield values was 0.76~0.82, the root mean square error range was 20.42~24.21 g/m2, and the average absolute error range was 17.94~23.19 g/m2, and the three kinds of error indexes were all within the reasonable error range. The results showed that the CASA model simulated pasture yield data with high accuracy, which can provide technical support for the high-quality development of Hulunbuir’s livestock industry.
文摘以陕西省为研究对象,运用遥感和GIS手段,结合MODIS/NDVI数据、气象数据以及植被类型数据,应用CASA模型估算得到陕西省2013年的NPP数据。结果表明,2013年陕西省NPP总量为8.87×107g C/a,平均值为469.58 g C/(m2·a),NPP最高值为723.06 g C/(m2·a),其空间分布特点表现为显著的纬度分布,南高北低,陕南>关中>陕北;NPP时间分布表现为明显的季节变化,呈单峰型曲线;植被类型NPP表现为阔叶林>针叶林>耕地>草地,并且各植被类型最大值出现月份不一致。
