遥感数据时间分辨率对土地覆盖变化监测的影响

Effect of Satellite Temporal Resolution on Land Cover Change Detection

由于土地覆盖通常存在年际变化和季节变化,区分人为活动引起的变化与这种自然变化需要两期数据时间一致。论文利用黑龙江省1975、1990、2000、和2005代表年Landsat数据,分析了数据时间差在16 d MODIS-NDVI时间序列的误差。研究发现,Landsat数据4个代表年的年际差分别为1975±4 a,1990±4.5 a,2000±1.5 a,2005±1.5 a,季节差为47 d。Landsat季节差对MODIS-NDVI的影响是:当季节差为16～48 d时,东北地区耕地与草地以生长高峰期为参照的MODIS-NDVI变化最高可达0.4以上,林地MODIS-NDVI变化0.1～0.2;当时间差<16 d时,如果仍以生长高峰期为绝对参照,东北大部分地区误差<0.1,只有少数耕地误差0.1～0.2。如果以两个时间为相对参照,则<16 d的误差也可能导致较大的MODIS-NDVI误差,其大小有明显的季节性。在生长高峰期7月12日至8月28日,8 d±8 d的时间差导致的NDVI差大于0.1的像元仅2.93%,是变化检测最佳时期;但在除此之外的6月10日至9月29日间,8 d±8 d时间差导致NDVI差大于0.1的像元比例超过11.42%,选择Landsat必须避开这一敏感期。结论:利用高时间分辨率MODIS-NDVI可在3个方面提高Landsat变化检测效果:①辅助选择最佳时间;②当无法选择最佳时间时,评估时间差可能引起的误差和方向;③直接使用生物物理参数作为变化检测的指标之一。

Temporal consistency is one of the pre-requisite when two satellite images are used for land cover change detection, since both inter-annual （yearly） and intra-annual （seasonal） changes are quite common for some land covers. Global Land Survey （GLS） images, mainly based on Land- sat MSS/TM/ETM ＋, have been widely used for land cover change detection due to its 40-year global coverage, relatively high quality, 30 80 m spatial resolution, and open policy. However, few works have been conducted to investigate the effect of lower temporal resolution on change de- tection. There may be two reasons for this oversight： for some applications, land cover change can be detected using classification, which may circumvent the temporal inconsistency problem; for oth- ers, the difference resulted from minor temporal change may be regarded as negligible. This paper obtained the actual year and date of GLS Landsat data from Global Land Cover Facility （GLCF） for nominal year （epoch） 1975, 1990, 2000 and 2005, and analyzed date in- consistency effect on change detection using 16-day MODIS-NDVI serials. The investigation showed that real year （year-difference, YD） covered 1975 ± 4, 1990 ± 4.5, 2000 ± 1.5 and 2005 ± 1.5 for epoch 1975, 1990, 2000 and 2005, respectively. And the average date difference （DD） was 47 days, roughly 3 composite periods for 16-day MODIS-NDVI. What do the Landsat YD and DD mean to MODIS-NDVI？ For YD, the study in NECT using 2000 -2008 MODIS-NDVI showed that, 4 years among 9 years, 7% sparse grasslands have a 〉 7% yearly variance. For DD, when using growth peak （mid-summer） as reference, 16-48 d difference may lead to MO- DIS-NDVI difference over 0. 4 for cropland and grassland, 0. 1 - 0.2 for forest, while 〈 16 d difference will lead to 〈 0. 1 MODIS-NDVI difference. However, when using actual MODIS-NDVI date as reference, even a DD 〈 16 d may also lead to MODIS-NDVI difference with seasonal patterns： only 2.93% pixels with a more than 0.1 NDVI difference resulted from DD 〈 16 d dur- ing July 12 to August 28, indicating a perfect period for change detection. Yet this number in- creased to 11.42% during June 10- September 29 （excluding July 12 to August 28 ） , showing a worst period for change detection. This study concluded that the difference resulted from minor temporal change can not be re- garded as negligible in certain cases, and MODIS-like high temporal data can improve change de- tection using low temporal Landsat-like data, in three aspects ： 1 ） when there are enough high- spatial and low-temporal resolution images, MODIS-like data serials can be used to choose data with optimal time for change detection; 2） when there are not enough such images and second- best data are used, MODIS-like data serials can be used to estimate resulted NDVI difference; and 3） when change is only quantitative and simple classification fails to detect, high temporal bio-physiological parameters such as MODIS-NDVI can be directly used to change detection.