Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands, Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.

Forests are widely recognized as critical to combating climate change due to their ability to sequester and store carbon in the form of biomass. In recent years, the combined use of data from ground-based forest inventories and remotely sensed data from light detection and ranging (lidar) has proven useful for large-scale assessment of forest biomass, but airborne lidar is expensive and data acquisition is infeasible for many countries. By contrast, the spaceborne Global Ecosystem Dynamics Investigation (GEDI) lidar instrument has collected freely available data for most of the world’s temperate and tropical forests since 2019. GEDI’s biomass products rely on models calibrated with a global network of field plots paired with GEDI waveforms simulated from airborne lidar to predict biomass. While this calibration strategy minimizes spatial and temporal offsets between field measurements and corresponding lidar returns, calibration data are sparse in many regions. Paraguay’s forests are known to be poorly represented in GEDI’s current calibration dataset, and here we demonstrate that local models calibrated opportunistically with on-orbit GEDI data and field surveys from Paraguay’s national forest inventory can be used with GEDI’s statistical estimators of aboveground biomass density (AGBD). We specify a protocol for opportunistically matching GEDI observations with field plots to calibrate a field-to-GEDI biomass model for use in GEDI’s hybrid statistical framework. Country-specific calibration using on-orbit data resulted in relatively accurate and unbiased predictions of footprint-level biomass, and importantly, supported the assumption underlying model-based inference that the model must ‘apply’ to the area of interest. Using a locally calibrated biomass model, we estimate that the mean AGBD in Paraguay is 65.55 Mg ha⁻¹, which coincides well with the design-based approach employed by the national forest inventory. The GEDI estimates for individual forest strata range from 52.34 Mg ha⁻¹ to 103.88 Mg ha⁻¹. On average, the standard errors are 47% lower for estimates based on GEDI than the forest inventory, representing a significant gain in precision. Our research demonstrates that GEDI can be used by national forest inventories in countries that seek reliable estimates of AGBD, and that local calibration using existing field plots may be more appropriate in some applications than using GEDI global models, especially in regions where those models are sparsely calibrated.

Protected areas in Guatemala provide habitat for diverse tropical ecosystems, contain ancient archeological sites, sequester carbon, and support economic activity through eco‐tourism. However, many of the forests in these protected areas have been converted to other uses or degraded by human activity, and therefore are considered “paper parks”. In this study, we analyzed time series of satellite data to monitor deforestation, degradation, and natural disturbance throughout Guatemala from 2000 to 2017. A recently developed methodology, Continuous Degradation Detection (CODED), was used to detect forest disturbances of varying size and magnitude. Through sample‐based statistical inference, we estimated that 854 137 ha (± 83 133 ha) were deforested and 1 012 947 ha (±139 512 ha) of forest was disturbed but not converted during our study period. Forest disturbance in protected areas ranged from under 1% of a park's area to over 95%. Our estimate of the extent of deforestation is similar to previous studies, however, degradation and natural disturbance affect a larger area. These results suggest that the total amount of forest disturbance can be significantly underestimated if degradation and natural disturbance are not taken into account. As a consequence, we found that the protected areas of Guatemala are more affected by disturbance than previously realized.