Estimating fire-related parameters in boreal forest using SPOT VEGETATION

Abstract

The majority of burning in the boreal forest zone consists of stand replacement fires larger than 10 km 2 occurring in remote, sparsely populated regions. Satellite remote sensing using coarse resolution ( c 1 km) sensors is thus well suited in documenting the spatial and temporal distribution of fires in this zone. The purpose of this study was to investigate the utility of the SPOT VEGETATION (VGT) sensor for estimating three key parameters related to boreal forest fire: burned area, postfire regeneration age, and aboveground biomass. Based on a sample of fires across Canada, the best overall discrimination of burned forest was provided by a normalized short-wave-based vegetation index (SWVI) that combines near-infrared (NIR) and short-wave infrared (SWIR) channels from VGT. Multitemporal differencing of this index from anniversary date VGT composites was combined synergistically with active fire locations from NOAA/AVHRR to map Canadian forest that burned during 1998 and 1999. National burned area estimates for both years were within 15% of those compiled by the Canadian Interagency Forest Fire Centre. The normalized index also was correlated (R=.68) with the age of regenerating forests in Saskatchewan and Manitoba that burned between 1949 and 1998. An artificial neural network (ANN) model developed using temporal metrics computed from VGT could predict the age of these forests with an RMS error of 7 years (R=.83). By contrast, forest biomass based on Canada's Forest Inventory (CanFI) was estimated with relatively poor accuracy (RMS = 32 tons/ha) from VGT reflectance and terrestrial ecozone using a network model. We conclude that the VGT instrument is effective for mapping large boreal burns at the end of a fire season and approximating the age of regenerating burns less than about 30 years old. This information can be useful to supplement conventional groundbased data sets in remote areas where coverage may be incomplete. D

Separability Analysis of Sentinel-2A Multi-Spectral Instrument (MSI) Data for Burned Area Discrimination

“…It supports the findings of previous studies that the NIR provides high burned-unburned discrimination in a variety of ecosystems including, for example, boreal forest [23,78,79], savanna [23,75,80] and temperate forest [36]. As observed in other studies, the visible bands provide low burned-unburned discrimination [23,75,81,82].…”

Calibrating Satellite-Based Indices of Burn Severity from UAV-Derived Metrics of a Burned Boreal Forest in NWT, Canada

“…The NBR exhibits a large drop after burning, owing to the removal of leafy vegetation, which decreases NIR scattering, SWIR water absorption and canopy shadowing [22,45]. In this study, we similarly found that dNBR was effective for predicting CBI (Table 4), but also demonstrated that Landsat indices were even more closely related to specific indicators of burn severity derived from UAV mapping (Figure 4).…”

Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest

“…It reflects surface canopy moisture and forest humidity [55,56]. The NDWI has been used for monitoring water stress [57], and for mapping burnt areas in boreal forest [58]. Thus, the changing pattern of the NDWI with the seasons could distinguish areas of natural vegetation, farmland, and soil, in relation to moisture.…”