A multitemporal dataset of medium-resolution imagery was used to document a tree cover loss process in three forest reserves of Zambia. This degradation process was attributed to charcoal production with a high degree of certainty, as evidence of kiln scars was found in more than 85% of sites sampled with high-resolution imagery across the study areas. The spatial and temporal pattern of mapped kiln scars exposed an intense and fast-paced degradation process, with kiln densities reaching 2.3 kiln/ha, removal of about 79.3% of the aboveground biomass and reductions of 74.2% of tree cover. The analysis revealed that this forest degradation process progressively moves further away from urban centers. In the year 2010, charcoal production took place around 190 km away from Lusaka, whereas extraction areas in 2020 were located around 350 km from this city. These results underline the negative impact of charcoal production on forest resources and question its characterization as localized and periurban. The post-disturbance LCLUC trajectories of degraded woodlands in forest reserves revealed a partial conversion to agricultural land over time, with less than 25% of these woodlands cultivated seven years after charcoal production. The disaggregation of the supply sources of charcoal for the city of Lusaka based on consumption figures and remote sensing deforestation records showed that the charcoal generated as a byproduct of agricultural expansion is not enough to meet the annual charcoal demand of Lusaka. On the contrary, the majority (65%) of this charcoal is the result of a forest degradation process that alters 197.4 km² of miombo woodlands annually. These findings highlight the role of charcoal production as a direct driver of forest degradation and suggest that forest degradation resulting from charcoal production has surpassed deforestation due to agricultural expansion as the main tree cover loss process in Zambia. These results provide data-driven evidence to improve the characterization of forest degradation resulting from charcoal production across the woodlands of southern Africa and aid the REDD + monitoring, reporting and verification systems in compliance with international reporting commitments.

Context Widespread conversion of land to intensive agriculture across Europe has led to a significant decrease in the availability of natural habitat. Besides direct local effects, this process also disrupts connectivity between shrinking habitat patches. This pattern has ignited various, self-reinforcing ecological mechanisms that are threatening the resilience of natural communities and the ecosystem services they provide. In response, the European Union (EU) has been promoting policies to increase the availability of natural habitats with minimal impacts on agricultural yields. For instance, the EU Biodiversity Strategy 2030 set a target of incorporating high diversity landscape features into 10% of agricultural land.. Objectives In this study, we explore potential ecological benefits of habitat connectivity enhancement—stemming from the expansion of “Small Woody Features” (SWF); woody habitats within the agricultural matrix of the most densely cultivated areas (i.e., crop areas >90%). Methods Using a spatially explicit algorithm and data from farmers’ declarations, we simulated plausible scenarios of progressive expansion of SWF following alternative sets of spatial rules. To compare the ecological outcomes of different scenarios, we focused on a single metric of connectivity, the Equivalent Connected Area, which we measured at each step of our SWF expansion simulations. Results We found that expanding SWF along the existing boundaries of agricultural field parcels maximises gains in connectivity. Our findings suggest that in intensely farmed croplands a strategic expansion of current SWF to 11.6% could yield the maximum relative gain in ecological connectivity. This implies an average increase of 6.5% from current woody habitat cover in these landscapes. Conclusions The initial spatial configuration and shape complexity of woody habitat coverage has a substantial influence on the dynamics between habitat expansion and connectivity enhancement. As a result, the connectivity of woody habitats would not benefit equally from the expansion of existing SWF habitats across all agricultural landscapes; some would require the strategic conversion of strips of agricultural land to “woody features” to effectively enhance connectivity.

This study presents an operational and robust method for detecting and dating cereal harvest events using temporal stacks of Copernicus Sentinel-2 imagery and crop and fields border information from ancillary records. The proposed approach is exempt from training data, thereby enabling its application across diverse geographical contexts. The method was used to generate 10 m resolution maps of harvest dates for all wheat and barley fields in 2021, 2022, and 2023 in Castilla y León, a major cereal-producing region of Spain. This work also investigates the use of a reference dataset derived from real time kinematic records (RTK) in agricultural machinery as an alternative source of large-scale in situ data reference as for Earth observation-based agricultural products. The initial comparison of annual harvest date maps with the RTK-based reference datasets revealed that the temporal lag in the detection of harvest events between Earth observation-derived maps and reference harvest dates was less than 10 days for 65.7% of fields, while the temporal lag was between 10 and 30 days for 26.1% of the fields. The 3-year average root mean square error of the lag between harvest dates in the reference dataset and maps was 16.1 days. An in-depth visual analysis of the Sentinel-2 temporal series was carried out to understand and evaluate the potential and limitations of the RTK-based reference dataset. The visual inspection of a representative sample of 668 fields with large temporal lags revealed that the date of harvest of 41.11% of these fields had been correctly identified in the Sentinel-2 based maps and 16.43% of them had been incorrectly identified. The visual inspection could not find evidence of harvest in 10.52% of the analyzed fields. Monte Carlo simulations were parameterized using the findings of the visual inspection to build a series of synthetic reference datasets. Accuracy metrics calculated from synthetic datasets revealed that the quality of the harvest maps was higher than what the initial comparison against the RTK-based reference dataset suggested. The date of harvest was registered within 10 days in both the maps and the synthetic reference datasets for 90.5% of the fields, the root mean squared error of the comparison was 9.5 days, and harvest dates were registered in the Sentinel-2 based maps 2 days (median) after the dates registered in the reference dataset. These results highlight the feasibility of mapping harvest dates in cereal fields with time series of high-resolution satellite imagery and expose the potential use of alternative sources of calibration and validation datasets for Earth observation products. More generally, these results contribute to defining plausible targets for monitoring of agricultural practices with Earth observation data.