This tabular, machine-readable CSV file contains annual phenometrics at locations in ponderosa pine ecosystems across Arizona and New Mexico that experienced stand-clearing, high-severity fire. The locations represent areas of vegetative recovery towards pre-fire (coniferous/pine) vegetation communities or towards novel grassland, shrubland, or deciduous replacements. Each sampled area is associated with the point location (latitude/longitude) as well as multiple calendar year phenometrics derived from the time-series of normalized difference vegetation index (NDVI) values in the phenology software package Timesat v3.2.

These data consist of environmental covariates, measured plot-level and tree characteristics for seven coniferous tree species across the southwestern United States. The objectives of the study were to assess how growth characteristics of conifer tree species vary across environmental gradients and across the different tree species. These data represent conifer growth under a variety of stand and site characteristics. These data were collected in the summer of 2019, from sites across Nevada, Arizona, New Mexico and Colorado, and collected by field crews directed by Matt Petrie (University of Nevada Las Vegas), Rob Hubbard (USDA Forest Service), Tom Kolb (Northern Arizona University) and John Bradford (U.S. Geological Survey). We selected six locations to encompass a wide range of regional climate conditions. Within each location, we selected sites to capture diversity in local factors expected to influence regeneration including topography, adult tree density, vegetation characteristics, management action, and disturbance. To include more and less sheltered forest microsites in each plot, we located the center of each plot at the boundary between a moderately sized forest interspace and a higher density area, using a spherical densiometer to estimate the midpoint of this boundary for each site based on canopy cover. Interspace sizes differed for each forest site, such that in a dense forest stand a moderate interspace was smaller (∼ 10-100 m−2) than that of a thinned forest stand, where a moderate interspace was in some cases > 1.0 ha in area (10,000 m2). Sites were located on shallow slopes when possible (< 10◦). Recognizing the important influence of soil properties and soil parent material on regeneration, we selected sites with similar texture (sandy loams, loamy sands) at each study location to minimize edaphic influence between our regional study locations. We did not pre-evaluate regeneration in the field prior to setting plot boundaries. We note that some of our sites were located in different environments (differing canopy covers, understory vegetation and debris, adult tree densities, etc.) within the same forest management unit, and other sites were located in nearby managed and unmanaged forest stands. These data can be used to assess how environmental conditions and site characteristics may influence conifer tree regeneration.

These data consist of environmental covariates, measured plot-level and tree characteristics for seven coniferous tree species across the southwestern United States. The objectives of the study were to assess how growth characteristics of conifer tree species vary across environmental gradients and across the different tree species. These data represent conifer growth under a variety of stand and site characteristics. These data were collected in the summer of 2019, from sites across Nevada, Arizona, New Mexico and Colorado, and collected by field crews directed by Matt Petrie (University of Nevada Las Vegas), Rob Hubbard (USDA Forest Service), Tom Kolb (Northern Arizona University) and John Bradford (U.S. Geological Survey). We selected six locations to encompass a wide range of regional climate conditions. Within each location, we selected sites to capture diversity in local factors expected to influence regeneration including topography, adult tree density, vegetation characteristics, management action, and disturbance. To include more and less sheltered forest microsites in each plot, we located the center of each plot at the boundary between a moderately sized forest interspace and a higher density area, using a spherical densiometer to estimate the midpoint of this boundary for each site based on canopy cover. Interspace sizes differed for each forest site, such that in a dense forest stand a moderate interspace was smaller (∼ 10-100 m−2) than that of a thinned forest stand, where a moderate interspace was in some cases > 1.0 ha in area (10,000 m2). Sites were located on shallow slopes when possible (< 10◦). Recognizing the important influence of soil properties and soil parent material on regeneration, we selected sites with similar texture (sandy loams, loamy sands) at each study location to minimize edaphic influence between our regional study locations. We did not pre-evaluate regeneration in the field prior to setting plot boundaries. We note that some of our sites were located in different environments (differing canopy covers, understory vegetation and debris, adult tree densities, etc.) within the same forest management unit, and other sites were located in nearby managed and unmanaged forest stands. These data can be used to assess how environmental conditions and site characteristics may influence conifer tree regeneration.

These are terrestrial laser scanner datasets collected in forested areas west of Flagstaff, Arizona in 2015 and 2016. For each of the two scanners, six treatment areas were scanned, with four of them overlapping one another (Figure 1). These data are composed of individual scans referenced to one another using reflective targets, and geolocated using differentially corrected GPS and RTK locations of scan locations (Figure 3). There were overall large differences in point density among the two scanners (Figure 2).

These are terrestrial laser scanner datasets collected in forested areas west of Flagstaff, Arizona in 2015 and 2016. For each of the two scanners, six treatment areas were scanned, with four of them overlapping one another (Figure 1). These data are composed of individual scans referenced to one another using reflective targets, and geolocated using differentially corrected GPS and RTK locations of scan locations (Figure 3). There were overall large differences in point density among the two scanners (Figure 2).

These data consist of environmental covariates and estimated plot-level mortality of ponderosa pine trees. Environmental covariates include growing season temperature and soil moisture, and values are summarized into long-term mean conditions, and anomalies observed between forest inventory sampling events for each plot. Data also include plot locations (with uncertainty introduced by the US Forest Service to maintain private property rights), plot basal area, and several variables related to estimated mortality rate of ponderosa pine trees under various assumptions about basal area conditions.

These data consist of environmental covariates and estimated plot-level mortality of ponderosa pine trees. Environmental covariates include growing season temperature and soil moisture, and values are summarized into long-term mean conditions, and anomalies observed between forest inventory sampling events for each plot. Data also include plot locations (with uncertainty introduced by the US Forest Service to maintain private property rights), plot basal area, and several variables related to estimated mortality rate of ponderosa pine trees under various assumptions about basal area conditions.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.

Post-fire shifts in vegetation composition will have broad ecological impacts. However, information characterizing post-fire recovery patterns and their drivers are lacking over large spatial extents. In this analysis we used Landsat imagery collected when snow cover (SCS) was present, in combination with growing season (GS) imagery, to distinguish evergreen vegetation from deciduous vegetation. We sought to (1) characterize patterns in the rate of post-fire, dual season Normalized Difference Vegetation Index (NDVI) across the region, (2) relate remotely sensed patterns to field-measured patterns of re-vegetation, and (3) identify seasonally-specific drivers of post-fire rates of NDVI recovery. Rates of post-fire NDVI recovery were calculated for both the GS and SCS for more than 12,500 burned points across the western United States. Points were partitioned into faster and slower rates of NDVI recovery using thresholds derived from field plot data (n=230) and their associated rates of NDVI recovery. We found plots with conifer saplings had significantly higher SCS NDVI recovery rates relative to plots without conifer saplings, while plots with ≥50% grass/forbs/shrubs cover had significantly higher GS NDVI recovery rates relative to plots with <50%. GS rates of NDVI recovery were best predicted by burn severity and anomalies in post-fire maximum temperature. SCS NDVI recovery rates were best explained by aridity and growing degree days. This study is the most extensive effort, to date, to track post-fire forest recovery across the western U.S. Isolating patterns and drivers of evergreen recovery from deciduous recovery will enable improved characterization of forest ecological condition across large spatial scales.