Remote Sensing Monitoring of Vegetation Dynamic Changes after Fire in the Greater Hinggan Mountain Area: The Algorithm and Application for Eliminating Phenological Impacts

Abstract

Fires are frequent in boreal forests affecting forest areas. The detection of forest disturbances and the monitoring of forest restoration are critical for forest management. Vegetation phenology information in remote sensing images may interfere with the monitoring of vegetation restoration, but little research has been done on this issue. Remote sensing and the geographic information system (GIS) have emerged as important tools in providing valuable information about vegetation phenology. Based on the MODIS and Landsat time-series images acquired from 2000 to 2018, this study uses the spatio-temporal data fusion method to construct reflectance images of vegetation with a relatively consistent growth period to study the vegetation restoration after the Greater Hinggan Mountain forest fire in the year 1987. The influence of phenology on vegetation monitoring was analyzed through three aspects: band characteristics, normalized difference vegetation index (NDVI) and disturbance index (DI) values. The comparison of the band characteristics shows that in the blue band and the red band, the average reflectance values of the study area after eliminating phenological influence is lower than that without eliminating the phenological influence in each year. In the infrared band, the average reflectance value after eliminating the influence of phenology is greater than the value with phenological influence in almost every year. In the second shortwave infrared band, the average reflectance value without phenological influence is lower than that with phenological influence in almost every year. The analysis results of NDVI and DI values in the study area of each year show that the NDVI and DI curves vary considerably without eliminating the phenological influence, and there is no obvious trend. After eliminating the phenological influence, the changing trend of the NDVI and DI values in each year is more stable and shows that the forest in the region was impacted by other factors in some years and also the recovery trend. The results show that the spatio-temporal data fusion approach used in this study can eliminate vegetation phenology effectively and the elimination of the phenology impact provides more reliable information about changes in vegetation regions affected by the forest fires. The results will be useful as a reference for future monitoring and management of forest resources.

Recognition of Areca Leaf Yellow Disease Based on PlanetScope Satellite Imagery

“…Feature selection is needed to remove these negative features. Previous studies reveal that the green band is sensitive to plant chlorophyll content [18]; the blue band is sensitive to the reaction between chlorophyll and leaf pigment [18]; the red band is an important indicator of plant vitality [19]; PSRI can monitor the onset of plant senescence and canopy stress [42]; and EVI is sensitive to high biomass areas and has a stronger ability to identify crops compared to NDVI [43,44]. Thus, the Green, Blue, Red, PSRI and EVI feature variables were selected through the CA and independent t-testing.…”

A New Application of the Disturbance Index for Fire Severity in Coastal Dunes

“…dDI allows for the effects of soil brightness to be mitigated with a scaling factor to reduce its overall contribution to the resulting index, and the applied factor (0.5) shows good results for the heterogenous soils and landscapes of Kangaroo Island. Whereas others have successfully applied the normalised version of DI [45][46][47][48][49][50][51]113,114], the prerequisite information of representative pixel values of target landscapes for normalisation limits its broadscale application. The dDI presented here is a direct pixel to pixel comparison, requiring no region-specific adjusted thresholds or mean values for normalisation.…”

“…Healey et al [45] developed the Disturbance Index (DI) to track both short and long-term changes in forests by exploiting the differences between brightness compared to greenness and wetness in a cleared forest. TCT outputs are normalised to mean and standard deviation values of pre-disturbance pixels and combined into a single value that represents the normalised difference from a representative mean value [46][47][48][49][50]. The full spectral resolution of an image that is combined with TCT and the normalised DI helps to minimise the effects of different soil spectra and varying vegetation types or phenology that can be influential in multi-band indices [50][51][52].…”

Interannual evolution of hydrosedimentary connectivity induced by forest cover change in a snow‐dominated mountainous catchment

“…Thus, our framework would be (i) easily transferrable to any forested watershed covered by detailed spatial historical records on disturbance types (e.g., BC, Alberta) and (ii) most likely transferrable to forested watersheds not covered by detailed spatial historical records, by using an adapted indicator of historical forest change, which might be converted into tabulated impedance. In this case, the use of satellite‐derived imagery would be a relevant alternative; these techniques have proven to be effective for detecting different kinds of disturbances and for monitoring diachronic vegetation cover change and recovery (Aires, da Silva, Moreira, Ribeiro, & Ribeiro, 2020; Guindon et al, 2014; Huang et al, 2020, Martini et al, 2020; Khodaee et al, 2020). A variety of indices can be used, for example, the normalized difference vegetation index (NDVI), the normalized burn ratio index (NBRI), or the disturbance index (DI).…”