在模拟兴隆山自然保护区近20年来土地利用/覆盖变化(land use and land cover change,LUCC)的过程中,提出了按照驱动力差异将研究区划分成不同亚区后分别进行模拟的思路.首先将研究区划分成了4个亚区,并对分区的合理性进行了检验,然后采用空间马尔科夫模型预测了研究区的土地利用变化趋势.结果表明:1)对于山地景观,因为局部地形影响以及水热条件组合的不同,LUCC驱动力表现出较大差异,因此在进行山地LUCC预测时需要按照驱动力的差异将研究区划分成不同亚区后分别进行预测;2)4个亚区的演化趋势并不一致,说明在兴隆山自然保护区今后的保育过程中应该针对具体区域具体分析,对不同亚区采取不同的保护措施;3)研究区林地面积趋于减少,耕地面积趋于增加,因此,应严格执行保护区管理条例,加强林地管护.
Capacity of carbon sequestration in forest ecosystem largely depends on the trend of net primary production (NPP) and the length of ecosystem carbon residence time. Retrieving spatial patterns of ecosystem carbon residence time is important and necessary for accurately predicting regional carbon cycles in the future. In this study, a data-model fusion method that combined a process-based regional carbon model (TECO-R) with various ground-based ecosystem observations (NPP, biomass, and soil organic carbon) and auxiliary data sets (NDVI, meteorological data, and maps of vegetation and soil texture) was applied to estimate spatial patterns of ecosystem carbon residence time in Chinese forests at steady state. In the data-model fusion, the genetic algorithm was used to estimate the optimal model parameters related with the ecosystem carbon residence time by minimizing total deviation between modeled and observed values. The results indicated that data-model fusion technology could effectively retrieve model parameters and simulate carbon cycling processes for Chinese forest ecosystems. The estimated carbon residence times were highly heterogenous over China, with most of regions having values between 24 and 70 years. The deciduous needleleaf forest and the evergreen needleleaf forest had the highest averaged carbon residence times (73.8 and 71.3 years, respectively), the mixed forest and the deciduous broadleaf forest had moderate values (38.1 and 37.3 years, respectively), and the evergreen broadleaf forest had the lowest value (31.7 years). The averaged carbon residence time of forest ecosystems in China was 57.8 years.
ZHOU Tao1,2, SHI PeiJun1,2, JIA GenSuo3, LI XiuJuan1,2 & LUO YiQi4 1 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
