Relationships between Satellite-Based Spectral Burned Ratios and Terrestrial Laser Scanning

Abstract

Three-dimensional point data acquired by Terrestrial Lidar Scanning (TLS) is used as ground observation in comparisons with fire severity indices computed from Landsat satellite multi-temporal images through Google Earth Engine (GEE). Forest fires are measured by the extent and severity of fire. Current methods of assessing fire severity are limited to on-site visual inspection or the use of satellite and aerial images to quantify severity over larger areas. On the ground, assessment of fire severity is influenced by the observers’ knowledge of the local ecosystem and ability to accurately assess several forest structure measurements. The objective of this study is to introduce TLS to validate spectral burned ratios obtained from Landsat images. The spectral change was obtained by an image compositing technique through GEE. The 32 plots were collected using TLS in Wood Buffalo National Park, Canada. TLS-generated 3D points were converted to voxels and the counted voxels were compared in four height strata. There was a negative linear relationship between spectral indices and counted voxels in the height strata between 1 to 5 m to produce R2 value of 0.45 and 0.47 for unburned plots and a non-linear relationship in the height strata between 0 to 0.5m for burned plots to produce R2 value of 0.56 and 0.59. Shrub or stand development was related with the spectral indices at unburned plots, and vegetation recovery in the ground surface was related at burned plots. As TLS systems become more cost efficient and portable, techniques used in this study will be useful to produce objective assessments of structure measurements for fire refugia and ecological response after a fire. TLS is especially useful for the quick ground assessments which are needed for forest fire applications.

Feasibility of Burned Area Mapping Based on ICESAT−2 Photon Counting Data

“…Fire perimeters downloaded from the Cal Fire (https://frap.fire.ca.gov/mapping/gis-data/) were used to mask out unburned pixels. Based on the suggested value from United States Geological Survey (USGS) [14,38], as shown in Table 3, a threshold of dNBR = 0.1 was further used to classify pixels into burned (>0.1) and unburned (<0.1) ( Figure 4), removing those unburned islands within the fire perimeters. Landsat 8 images were also used to derive burned map in the same way.…”

Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?

“…Thus, the Forests special issue "3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function" was conceptualized by the authors of this paper and finally hosted 10 peer-reviewed contributions in which 3D sources of remote sensing data were applied either as a preliminary or auxiliary sources of information to understand, classify, augment, model and predict forest ecological attributes. Geographically, the contributions published within this special issue were well distributed around the globe, including China (four contributions) [32][33][34][35], Canada [36], Germany [37], India [38], Iran [39], Panama [40] and the United States [41]. The geographical distribution of the countries in which the published contributions were carried out are summarized in Figure 1.…”

“…In terms of global climatic regimes and ecological biomes, the temperate biome included the majority of works with seven studies [33][34][35][36][37]39,41], followed by sub-tropical [32,38] and tropical [40] biomes. The topics covered within the published contributions can be divided into multiple groups: There were studies with rather classical applications such as single tree-level prediction of forest structural attributes by terrestrial laser scanning or visual estimation from Google Street View [33,41] and area-based prediction of forest structural attributes by space-borne stereo imagery, laser scanning or combination of passive optical with multi-frequency SAR data [34,35,39].…”

“…As an example, Ataee et al [39] proved that a combination of space-borne SAR and optical data could improve performance and reduce uncertainties in the retrieval of tree volume. A number of works conducted on novel domains were also published, including a correlation between forest spectral burned ratios and height metrics derived from terrestrial laser scanning (using rather conventional height metrics for a novel application) [36], followed by other papers on hitherto rarely-studied topics like association between post-harvest tree root collar geometry and stump height by terrestrial laser scanning [37] and combining space-borne spectral and 3D data for fractional cover mapping of invasive alien woody species [38]. Moreover, Vallejos et al [40] focused on a crucial, yet often neglected, source of statistical problem caused when working with optical remote sensing on quantitative ecological data, co-dispersion errors and data noise, whose results can be directly generalized to any existing source of 3D data.…”

“…In terms of forest ecological attributes, the published papers represent a wide variety of attributes and thereby reflect the utterly diverse range of forest ecological attributes. As partly discussed above, the variables range from continuous variables that are commonly subject to regression modeling (allometric tree and stand structural attributes, root geometry, edaphic variables) [33][34][35]37,[39][40][41] to categorical attributes that are relevant for classification approaches (fractional cover estimations of invasive species, landscape fragmentation) [32,38] and even to the use of vegetation indices and ratios [32,36]. One may, however, note that the current and potential applications of 3D remote sensing data in forest ecological domain go far beyond those covered here, with some examples being characterization of flora and fauna, coastal ecosystems, plant biodiversity (i.e., species richness), abiotic and biotic forest disturbances, sample size/sampling grid optimization for reference data surveys and many more.…”