An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p < 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer’s accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

On frontiers dominated by illicit activities such as narcotrafficking, criminal organizations’ usurpation of land and resources is profoundly changing rural livelihoods and prospects for biodiversity conservation. Prior work has demonstrated how drug trafficking catalyzes forest loss and smallholder dispossession but does not make clear the extent to which the long-term control of land is moved from state, Indigenous, or smallholders to criminal or other actors. This study attempts to describe those shifts. Specifically: we develop a typology of land control, and use it to track how drug trafficking initiates shifts from public lands and Indigenous territories to private large holdings. We examine an array of secondary sources indicating shifts in land control related to narcotrafficking, including illegal land seizure documents, news media, and surveys of land managers. In absence of formal land registries, frontier actors may signal their control over land through land use change. After establishing where changes in land control have taken place, we analyzed land use and resulting changes in spatial patterns of forest loss. We found that large scale sustained forest losses (over 713,244 ha and 417,329 ha), in Guatemala and Honduras, respectively, from 2000–2019) corresponds with areas undergoing shifts in control towards large landowners, often related to narcotrafficking. Incomplete empirical data on land control prevent comprehensive attribution of all sustained forest loss related to narcotrafficking. Yet the limited evidence gathered here indicates drug trafficking activities initiate widespread and sustained shifts and consolidation of who controls land and resources at the frontier. Our work suggests that in Central America and likely elsewhere, control over land—quite separate from property rights—is the key factor in understanding social and ecological change.

Fire has historically played an important role in shaping the structure and composition of Sonoran semi-desert grassland vegetation. Yet, human use and land management activities have significantly altered arid grassland ecosystems over the last century, often producing novel fuel conditions. The variety of continuously updated satellite remote sensing systems provide opportunities for efficiently mapping combustible fine-fuels and fuel-types (e.g., grass, shrub, or tree cover) over large landscapes that are helpful for evaluating fire hazard and risk. For this study, we compared field ceptometer leaf area index (LAI) measurements to conventional means for estimating fine-fuel biomass on 20, 50 m × 20 m plots and 431, 0.5 m × 0.5 m quadrats on the Buenos Aires National Wildlife Refuge (BANWR) in southern Arizona. LAI explained 65% of the variance in fine-fuel biomass using simple linear regression. An additional 19% of variance was explained from Random Forest regression tree models that included herbaceous plant height and cover as predictors. Field biomass and vegetation measurements were used to map fine-fuel and vegetation cover (fuel-type) from plots on BANWR comparing outcomes from multi-date (peak green and dormant period) Worldview-3 (WV3) and Landsat Operational Land Imager (OLI) imagery. Fine-fuel biomass predicted from WV3 imagery combined with terrain information from a digital elevation model explained greater variance using regression tree models (65%) as compared to OLI models (58%). Vegetation indices developed using red-edge bands as well as modeled bare ground and herbaceous cover were important to improve WV3 biomass estimates. Land cover classification for 11 cover categories with high spatial resolution WV3 imagery showed 80% overall accuracy and highlighted areas dominated by non-native grasses with 87% user’s class accuracy. Mixed native and non-native grass and shrublands showed 59% accuracy and less common areas dominated by native grasses on plots showed low class accuracy (23%). Digital data layers from WV3 models showed a significantly positive relationship (r² = 0.68, F = 119.2, p < 0.001) between non-native grass cover (e.g., Eragrostis lehmanniana) and average fine-fuel biomass within refuge fire management units. Overall, both WV3 and OLI produced similar fine-fuel biomass estimates although WV3 showed better model performance and helped characterized fine-scale changes in fuel-type and continuity across the study area.

The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R${}^2$ = 0.52, RMSE = 218 kg/ha) and last year (R${}^2$ = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world.

Robust models of wildlife population size, spatial distribution, and habitat relationships are needed to more effectively monitor endangered species and prioritize habitat conservation efforts. Remotely sensed data such as airborne laser altimetry (LiDAR) and digital color infrared (CIR) aerial photography combined with well‐designed field studies can help fill these information voids. We used point count‐based distance sampling survey data and LiDAR‐fusedCIRaerial photography to model density of the Golden‐cheeked Warbler (Setophaga chrysoparia), an endangered songbird, on the 10 000‐ha Balcones Canyonlands National Wildlife Refuge (BCNWR). We developed a novel set of candidate models to explain Golden‐cheeked Warbler detection probability and density using habitat covariates characterizing vegetation structure, composition, and complexity as well as habitat fragmentation, topography, and human infrastructure. We had the most model support for covariates calculated using focal means representing a 3.2 ha territory size (100 m radius) vs. 1.8 and 7.0 ha territory sizes. Detection probability decreased with canopy cover and increased with topographic roughness. Golden‐cheeked Warbler density increased with canopy cover, was highest at a 7:3 ratio of Ashe juniper (Juniperus ashei) to broadleaf tree canopy cover, and decreased with global solar radiation. Predicted warbler densities using 3 min point counts were similar to six estimates from independently collected warbler territory mapping onBCNWRwith a mean difference of 6% and a Root Mean Squared Error of 1.88 males/40 ha. The total population size forBCNWRwas estimated at 884 Golden‐cheeked Warbler males (95%CI662, 1206) and predicted densities across the refuge ranged from 0.0 to 0.50 male warblers per ha. On the basis of observed habitat relationships, we defined high quality habitat as having at least 60% canopy cover with Ashe juniper comprising 50–90% of the canopy. We estimated 48% of the area atBCNWRmanaged for Golden‐cheeked Warblers was in high quality habitat conditions and identified patches within the lower habitat quality areas (14% of warbler management areas) that had the greatest potential to become high quality habitat with management. Our approach combined robust wildlife surveys with highly scalable remotely sensed data to examine habitat relationships, estimate population size, and identify existing areas of high quality habitat. This method can be applied to other species of conservation interest and can be used with multiple years of remotely sensed data to assess changes in habitat at local to regional scales.