为了降低森林资源监测过程中由于时空不同造成的误差影响,收集了2004年以来浙江省森林资源年度监测和2014年以来省、市联动监测样地数据,统计估算了各树种组的分径级各生长月的蓄积月生长率和11个设区市的各年度蓄积生长率,提出了森林蓄积年生长量精准监测的时空误差校正方法。研究结果表明:1)时间尺度上,除11月至次年3月的低速生长季外,各树种组4月与10月的月生长率占比在7%~10%之间,其余各月的占比在10%~17%之间;2)空间尺度上,除嘉兴市情况特殊外,其余10市的蓄积年生长率与省平均值的比值在0.95~1.06之间;3)时间误差的校正基准月确定在6—8月期间为好,可先校正原始调查数据,然后进行统计汇总;4)空间误差的校正,可在先进行原始调查数据时间误差校正的基础上,再对各设区市的统计汇总数据进行统一校正。

In order to reduce the influence of error value caused by spatio-temporal difference in forest resources monitoring,a spatio-temporal error correction method of annual growth rate of forest volume (AGRFV) was studied in this paper. Both the date of AGRFV from 2004 to 2014 in the Zhejiang Province Forest Resources Survey and the date of AGRFV from 2014 to 2019 in the Zhejiang Province & Eleven Cities Forest Resources Survey were collected and analyzed. The monthly growth rate of forest volume (MGRFV) and the AGRFV of all 12 groups,which are divided into different groups according to the breast high were statistically analyzed and estimated.The results showed that:(1) on the time scale,for all the 4 tree species,the ratio of monthly growth rate to annual growth rate (RMGRAGR) in April or October was 7%~10%,the RMGRAGR in May,June,July,August or September was 10%~17%,and the RMGRAGR between November to next March was lower because of the low growth rate; (2) on the spatial scale,except for Jiaxingin special situation,the ratio of AGRFV in each of other 10 cities to the provincial average value were between 0.95~1.06; (3) for the temporal error,it was better to set the correction base month as June,July or August,and the steps of calibration process were firstly original survey data correction and then statistical summary; (4) the spatial error can be corrected after the temporal error correction of the original survey data,and then the statistical summary data of eachcity can be corrected.