Abstract
Forest structural data are essential for assessing biophysical processes and changes, and promoting sustainable forest management. For 18+ years, the Multi-Angle Imaging SpectroRadiometer (MISR) instrument has been observing the land surface reflectance anisotropy, which is known to be related to vegetation structure. This study sought to determine the performance of a new MISR-High Resolution (HR) dataset, recently produced at a full 275 m spatial resolution, and consisting of 36 Bidirectional Reflectance Factors (BRF) and 12 Rahman–Pinty–Verstraete (RPV) parameters, to estimate the mean tree height (Hmean) and canopy cover (CC) across structurally diverse, heterogeneous, and fragmented forest types in South Africa. Airborne LiDAR data were used to train and validate Random Forest models which were tested across various MISR-HR scenarios. The combination of MISR multi-angular and multispectral data was consistently effective in improving the estimation of structural parameters, and produced the lowest relative root mean square error (rRMSE) (33.14% and 38.58%), for Hmean and CC respectively. The combined RPV parameters for all four bands yielded the best results in comparison to the models of the RPV parameters separately: Hmean (R2 = 0.71, rRMSE = 34.84%) and CC (R2 = 0.60, rRMSE = 40.96%). However, the combined RPV parameters for all four bands in comparison to the MISR-HR BRF 36 band model it performed poorer (rRMSE of 5.1% and 6.2% higher for Hmean and CC, respectively). When considered separately, savanna forest type had greater improvement when adding multi-angular data, with the highest accuracies obtained for the Hmean parameter (R2 of 0.67, rRMSE of 31.28%). The findings demonstrate the potential of the optical multi-spectral and multi-directional newly processed data (MISR-HR) for estimating forest structure across Southern African forest types.
Highlights
Forests provide a broad range of ecosystem services, e.g., carbon sequestration, water regulation, fuelwood and timber production [1,2]
This section documents the variability of the LiDAR-based structural parameters across the study sites (Table 1), the performance of the retrieval of canopy structural parameters for all Multi-Angle Imaging SpectroRadiometer (MISR)-High Resolution (HR) bi-directional reflectance factor (BRF) and RPV model scenarios, for all vegetation types combined (Tables 2 and 3, Figures 5–7), and for the scenarios 1 and 4, for the three vegetation types separately (Figure 8)
We investigated the use of MISR High-Resolution (HR) data products at 275 m spatial resolution—the 36 bi-directional reflectance factor data channels and 12 Rahman-Pinty-Verstraete model parameters (ρ0, k, Θ in each of the 4 spectral bands)—to retrieve vegetation structural variables for different forest types, using Random Forest models
Summary
Forests provide a broad range of ecosystem services, e.g., carbon sequestration, water regulation, fuelwood and timber production [1,2]. Remote sensing (RS) technologies produce valuable data for mapping forest structural parameters at a variety of spatial scales. They are useful for providing dense and frequent coverage over large areas, which enables the monitoring of forest structural parameters cost-effectively [11,12]. The BRDF of a surface target is a function describing the ratio of the spectral radiance reflected in a given direction to the irradiance received by this target according to a specific illumination geometry; it measures reflectance changes with the illumination and observation geometry [23]. Reflectance anisotropy has often been ignored or considered to be a source of noise for mapping forests [32,33]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
