Accurate information on the global distribution and the three-dimensional (3D) structure of Earth’s forests is needed to assess forest biomass stocks and to project the future of the terrestrial Carbon sink. In spite of its importance, the 3D structure of forests continues to be the most crucial information gap in the observational archive. The Global Ecosystem Dynamics Investigation (GEDI) Light Detection and Ranging (LiDAR) sensor is providing an unprecedented near-global sampling of tropical and temperate forest structural properties. The integration of GEDI measurements with spatially-contiguous observations from polar orbiting optical satellite data therefore provides a unique opportunity to produce wall-to-wall maps of forests’ 3D structure. Here, we utilized Visible Infrared Imaging Radiometer Suite (VIIRS) annual metrics data to extrapolate GEDI-derived forest structure attributes into 1-km resolution contiguous maps of tree height (TH), canopy fraction cover (CFC), plant area index (PAI), and foliage height diversity (FHD) for the conterminous US (CONUS). The maps were validated using an independent subset of GEDI data. Validation results for TH (r² = 0.8; RMSE = 3.35 m), CFC (r² = 0.79; RMSE = 0.09), PAI (r² = 0.76; RMSE = 0.41), and FHD (r² = 0.83; RMSE = 0.25) demonstrated the robustness of VIIRS data for extrapolating GEDI measurements across the nation or even over larger areas. The methodology developed through this study may allow multi-decadal monitoring of changes in multiple forest structural attributes using consistent satellite observations acquired by orbiting and forthcoming VIIRS instruments.

Few studies have evaluated the precision of IKONOS stereo data for measuring forest canopy height. The high cost of airborne light detection and ranging (LiDAR) data collection for large area studies and the present lack of a spaceborne instrument lead to the need to explore other low cost options. The US Government currently has access to a large archive of commercial high-resolution imagery, which could be quite valuable to forest structure studies. At 1 m resolution, we here compared canopy height models (CHMs) and height data derived from Goddard’s airborne LiDAR Hyper-spectral and Thermal Imager (G-LiHT) with three types of IKONOS stereo derived digital surface models (DSMs) that estimate CHMs by subtracting National Elevation Data (NED) digital terrain models (DTMs). We found the following in three different forested regions of the US after excluding heterogeneous and disturbed forest samples: (1) G-LiHT DTMs were highly correlated with NED DTMs with R² > 0.98 and root mean square errors (RMSEs) < 2.96 m; (2) when using one visually identifiable ground control point (GCP) from NED, G-LiHT DSMs and IKONOS DSMs had R² > 0.84 and RMSEs of 2.7 to 4.1 m; and (3) one GCP CHMs for two study sites had R² > 0.7 and RMSEs of 2.6 to 3 m where data were collected less than four years apart. Our results suggest that IKONOS stereo data are a useful LiDAR alternative where high-quality DTMs are available.

There is a great deal of debate on the extent, causes, and even the reality of land degradation in the Sahel. Investigations carried out before approximately 2000 using remote sensing data suggest widespread reductions in biological productivity, while studies extending beyond 2000 consistently reveal a net increase in vegetation production, strongly related to the recovery of rainfall following the extreme droughts of the 1970s and 1980s, and thus challenging the notion of widespread, long-term, subcontinental-scale degradation. Yet, the spatial variations in the rates of vegetation recovery are not fully explained by rainfall trends. It is hypothesized that, in addition to rainfall, other meteorological variables and human land use have contributed to vegetation dynamics. Throughout most of the Sahel, the interannual variability in growing season ΣNDVI_gs (measured from satellites, used as a proxy of vegetation productivity) was strongly related to rainfall, humidity, and temperature (mean r² = 0.67), but with rainfall alone was weaker (mean r² = 0.41). The mean and upper 95th quantile (UQ) rates of change in ΣNDVI_gs in response to climate were used to predict potential ΣNDVI_gs—that is, the ΣNDVI_gs expected in response to climate variability alone, excluding any anthropogenic effects. The differences between predicted and observed ΣNDVI_gs were regressed against time to detect any long-term (positive or negative) trends in vegetation productivity. Over most of the Sahel, the trends did not significantly depart from what is expected from the trends in meteorological variables. However, substantial and spatially contiguous areas (~8% of the total area of the Sahel) were characterized by negative, and, in some areas, positive trends. To explore whether the negative trends were human-induced, they were compared with the available data of population density, land use, and land biophysical properties that are known to affect the susceptibility of land to degradation. The spatial variations in the trends of the residuals were partly related to soils and tree cover, but also to several anthropogenic pressures.

Consistent and spatially explicit periodic monitoring of forest structure is essential for estimating forest-related carbon emissions, analyzing forest degradation, and supporting sustainable forest management policies. To date, few products are available that allow for continental to global operational monitoring of changes in canopy structure. In this study, we explored the synergy between the NASA’s spaceborne Global Ecosystem Dynamics Investigation (GEDI) waveform LiDAR and the Visible Infrared Imaging Radiometer Suite (VIIRS) data to produce spatially explicit and consistent annual maps of canopy height (CH), percent canopy cover (PCC), plant area index (PAI), and foliage height diversity (FHD) across the conterminous United States (CONUS) at a 1-km resolution for 2013–2020. The accuracies of the annual maps were assessed using forest structure attribute derived from airborne laser scanning (ALS) data acquired between 2013 and 2020 for the 48 National Ecological Observatory Network (NEON) field sites distributed across the CONUS. The root mean square error (RMSE) values of the annual canopy height maps as compared with the ALS reference data varied from a minimum of 3.31-m for 2020 to a maximum of 4.19-m for 2017. Similarly, the RMSE values for PCC ranged between 8% (2020) and 11% (all other years). Qualitative evaluations of the annual maps using time series of very high-resolution images further suggested that the VIIRS-derived products could capture both large and “more” subtle changes in forest structure associated with partial harvesting, wind damage, wildfires, and other environmental stresses. The methods developed in this study are expected to enable multi-decadal analysis of forest structure and its dynamics using consistent satellite observations from moderate resolution sensors such as VIIRS onboard JPSS satellites.