Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China

Abstract

This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing'an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post-and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The 6939MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing'anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.

Assessing Re-Composition of Xing’an Larch in Boreal Forests after the 1987 Fire, Northeast China

“…While forest/non-forest types were fairly discriminated, detailed tree species composition cannot be effectively extracted because of their spectral similarity. With vegetation indices, various studies reported that greenness of boreal forests can grow back to pre-fire conditions in 5-20 years depending on fire severities and imagery resolutions [9,10,19]. This study revealed, however, that Xing'an larch re-composition had not been fully recovered in a 30-year span.…”

“…mongolica). Other deciduous species such as aspen and willow and coniferous species such as spruce only grow in less than 10% of the region [9]. As Xing'an larch is the primary concern of this study, these tree species are not examined to avoid unnecessary classification uncertainties.…”

“…If more frequent observations were available, the confusion among tree species could be reduced from better defined trajectories in a growing cycle. Reference [9] extracted trajectories of the post-fire stand regrowth index from the 15-day AVHRR Global Inventory Modeling and Process (GIMMS) NDVI In the confusion matrix (Table 2), the overall accuracy of classification reaches 86.16% with a Kappa value of 0.80. Non-forest covers were optimally classified from satellite time series because of their distinctively different reflectance from tree species.…”

“…If more frequent observations were available, the confusion among tree species could be reduced from better defined trajectories in a growing cycle. Reference [9] extracted trajectories of the post-fire stand regrowth index from the 15-day AVHRR Global Inventory Modeling and Process (GIMMS) NDVI product in 1984-2006. Its 8-km grid size, however, is too coarse to classify individual tree species in this study.…”

“…Kong and Liu [8] manually extracted burned areas from Landsat imagery and examined fire disturbances to landscape in the northern Xing'an Mountains. The Normalized Difference Vegetation Index (NDVI), a greenness indictor, was commonly used to approximate its regrowth after a fire event [9]. However, it often saturated before reaching the point where a forest recovered to its maximum biomass [10].…”