Linking Terrestrial LiDAR Scanner and Conventional Forest Structure Measurements with Multi-Modal Satellite Data

Abstract

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.

Underestimation of Global Photosynthesis in Earth System Models Due to Representation of Vegetation Structure

“…Arguably, however, this problem also needs addressing using observations that are more directly related to forest structure such as space borne LiDAR from missions such as NASA GEDI (Hancock et al, 2019), or long-wavelength RADAR from JAXA's ALOS PALSAR the upcoming ESA Biomass mission. Terrestrial and airborne observations will also be critically important (Ferraz et al, 2018;Longo et al, 2016;Rödig et al, 2018) and the increased interest in terrestrial scanning LiDAR may help to answer some of these questions (Disney et al, 2010;Mulatu et al, 2019).…”

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

“…A future pillar of research should also specifically concentrate on strengthening data assimilation and integrated use of multiple high-and medium-spatial resolution data sources. Recently published examples of such data assimilations are UAV with freely available optical data [42] and terrestrial laser scanning with multimodal space-borne data [43] for retrieving forest structural attributes. For forest ecological applications, this would concretely mean enhanced potentials for important practical applications like large-area calibrations of local models, monitoring remote and inaccessible mountainous forest ecosystems, calibrating small-area observations with large-area data on animal movements, and studying large-area habitat fragmentations.…”

Mapping of the Canopy Openings in Mixed Beech-Fir Forest at Sentinel-2 Subpixel level Using UAV and Machine Learning Approach

“…UAV (Unmanned Aerial Vehicle) based LiDAR has a lower cost, more convenient operation and more flexible flight route design than airborne LiDAR, as well as unique advantages in the high-precision acquisition of high-density point clouds [19]. Satellite remote sensing, medium resolution data from Landsat, is traditionally a very appropriate source of information for crown cover estimation, most often comprising the supervised classification approaches based on ground truth validation sets obtained from field measurements, airborne or UAV aero photo imagery, LiDAR or very high-resolution satellite imagery [22][23][24][25][26][27][28]. The main drawback of the medium resolution satellite data such as Landsat 8 is that its spatial resolution of 30 m is too coarse for precise forest gaps detection.…”