Across five post-fire growing seasons, spectral reflectance of all Landsat bands were significantly lower at burned sites than the unburned, except near the infrared band NIR which saw higher reflectance in burned sites howbeit most results were not statistically significant. Moreover, for the aforementioned non-NIR bands, the difference of spectral reflectance between treatments generally displayed a decreasing trend. For the fourth season some statistics became weaker Red, SWIR2 and eventually in the fifth season all statistics were non-significant, implying the fact that the burned communities had almost fully recovered.
Notice the anomalies which occurred following where the converging trend was disturbed. This is caused by the water stress in , which affected the post-fire recovery.
However, the converging trend continued consistently after More research is needed to quantify the impact of drought, fire and herbivores, the three most common and interconnected disturbances in shaping the grasslands. Responses from the three visible bands especially the red band are significantly smaller at burned sites than the unburned. This is because burned sites tend to have more live component green vegetation and less dead material standing dead and litter.
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Chlorophyll and other pigments in healthy green vegetation absorbing red spectrum for photosynthesis, making reflectance in that region significantly lower than the unburned sites that have less live component. On the other hand, the light-coloured standing dead and other non-photosynthetic vegetation NPV have higher reflectance at visible bands and appear brighter. However, across time the difference of reflectance in visible bands between treatments became less pronounced, from 3. As the grasslands recovered, burned sites started to build up the dead material and the contrast in the visible bands was getting less obvious.
NIR is the only band which showed increased reflectance at burned sites, simply because of the more healthy live component at burned sites. Though we expect to see a consistent and significantly higher reflectance in NIR at burned sites, satellite data suggested otherwise.
Yang et al. The insignificant result for other growing seasons may be due to limited sample size, and large variations in the live component at the burned sites.
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In SWIR2 region, solar radiation can be significantly absorbed by the water content in green vegetation or soils. Meanwhile, research   also suggests that dry soil exposure after burning would increase SWIR2 reflection. Water absorption in SWIR1 is considerably weaker.
However, this study showed the opposite relationship. There was significant decrease in both SWIR bands at burned sites. This can be explained by the increased composition in green vegetation for the burned sites which had higher water content than unburned sites.
As a result, SWIR regions in burned sites had more absorption and less reflectance. Although for unburned sites the significant amount of dead material helped to retain water. Furthermore, Landsat 8 imagery acquired five days after the burn also indicated lower SWIR responses for burned sites. This is probably due to the presence of charred soils found at the burned sites which had lower reflectance in both SWIR regions than unburned sites where senescent grasses were dominant . Overall, we observe lower reflectance in SWIR region for burned sites from right after the burn, till the third year.
This finding contradicts , probably due to the difference in ecosystems and vegetation types. Performance of Vis. Although individual spectral bands demonstrated promising capability in separating treatments, commonly used broadband vegetation and fire indices showed mixed results with most statistics non-significant, demonstrating the pressing and challenging issue of finding a suitable vegetation index in monitoring vegetation recovery from the fire. Detailed results can be summarized in Table 4. The algorithm of NDVI involves NIR and red bands, and has been widely used in the literature as an important index for studying green vegetation as well as fires.
NDVI for burned sites in the second and fourth growing season also increased but was not statistically significant.
Tallgrass Prairie Ecosystem // LandScope America
It can distinguish green vegetation from soil based on their contrasting reflectance signatures at these two bands. This study confirms its performance in such ability. NBR at burned sites was as much as 1. Though its performance varied from weak in the first year to not significant in the second year, and became strong in the third year.
This result proves NBR as a reliable long-term fire index suitable for studying grassland fires. Its weak performance may be affected by high percentage of dead material dominant in GNP grasslands. We see these indices are greater at burned sites. From a theoretic perspective, they should have good performance for the first two years because percentage of bare soil exposure is relatively high at burned sites. However, it is interesting to find both indices can only distinguish burned sites in the third year with statistical significance. This is also when bare soil percentage was lowest in burned sites.
The poor performance may due to a few reasons.
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The soil brightness correction factor parameter L for calculating SAVI may need fine-tuning in this study. Meanwhile, these indices are developed to overcome the influence of soils at the cost of being less sensitive to vegetation changes that are essential in studying post-fire vegetation recovery. However, this study finds that they are not suitable for grasslands fire study in GNP. Possibly because grassland ecosystems have a higher turnover rate and grass communities are well adapted to disturbances like fire.
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As a result, their fast recovery will block the char signal from being captured by remote sensors. MIRBI also performed well in this study, showing significantly greater values in the burned sites than the unburned, though not significant in the third year. Removing the limitation of sample size, with only satellite imagery data, the difference between burned and unburned sites is shown clearly for within years and an overall decreasing trend from to , reflecting the climate variation of the same period.
Grasslands recovery is closely tied to climatic variables. Though both burned and unburned grassland underwent a similar recovery scenario, yet they carry significant differences. This result may suggest a fire regime with a period of four years to be ideal for the prairie ecosystem. This either means the burned site has an earlier start to the growing season, or the burned site always tends to grow greener vegetation. Since there is no apparent phase shift a. However,   indeed reported the greenup being advanced by one week or as much as one month at burned sites due to relatively warmer soil temperatures during the day.
Therefore to clarify this, better satellite remote sensing datasets need to be analyzed, especially with fine-tuned temporal resolution during the greenup period for the study area. Nonetheless, we can certainly observe the impact of grassland fire even at the fifth growing season, with both climate variation and a minor disturbance water-stress condition in considered.
Figure 9. Notice there are more scenes available for year This study found that vegetation could recover quickly from the spring burning. Starting from the first post-fire growing season, the grasslands ecosystem has begun the quick process of regeneration, and even resulted in establishment on previously bare soil. This demonstrates the strong resilience of the mixed prairie due to its adaption to frequent grassland fires in the past.
Fire effectively removed the dominant dead component in the ecosystem and promoted the regeneration of the grasslands, indicated by the increased green live component in the burned communities. However, the difference between burned and unburned communities get less pronounced across time as vegetation recovers. In the third post-fire growing season, the burned communities started to converge back to the physiology of the unburned communities. Results suggested that remote sensing product is effective in detecting post-fire vegetation recovery. Some vegetation indices are sensitive in such detection.
NDVI as a widely used vegetation index is a good indicator, suggesting the possibility of studying fire ecology in an even longer time frame in light of the rich and cost-effective remote sensing data archive available. Overall, Landsat product demonstrated its effectiveness in studying grasslands post-fire recovery, with the most sensitive bands being NIR, red, and two SWIR bands.
Landsat NIR and red bands are the primary and secondary strong predictors of variation in the live component if green vegetation is the main focus of vegetation post-fire recovery. But overall sensitive wavelength windows for SRIbio and NDIbio are less concentrated than that of the live component, indicating the challenge of developing the best remote sensing index for quantifying live-dead dynamics of grasslands post-fire recovery.
General Features of Grasslands
Li and Guo  have reviewed the remote sensing approaches in quantifying non-photosynthetic vegetation dead materials. They mentioned the claim from related researchers   on the feasibility of detecting NPV based on wavelengths from nm to nm. However, in reality NPV estimation is always complicated due to the presence of water, soil mineralogy, and soil organic carbon   .