Thermal Imaging of Increment Cores to Assess  Conducting Sapwood Width and Drought Sensitivity in U.S. Southwestern Conifers

We present a novel in situ thermal imaging method to detect conducting versus non-conducting wood by visualizing temperature changes along increment cores. Applied to Pinus ponderosa (PIPO), Pinus strobiformis (PISF), and Pseudotsuga menziesii (PSME), thermal estimates of sapwood depth strongly matched traditional methods (R² = 0.99, 0.98, 0.96) with a mean difference of 0.13 ± 0.06 cm. Species-specific differences were minor, with PIPO showing the largest deviation (0.32 cm). This cost-effective, user-friendly method enhances dendroecological studies by improving sapwood detection, supporting integration of water, growth, and carbon dynamics to guide forest management.

CHEMICAL COMPOSITION AND MOISTURE CONTENT OF SOIL AT SITES OF GEOPHAGY BY THE ANTELOPE JACKRABBIT (LEPUS ALLENI)

We evaluated the composition of soil involved in antelope jackrabbit (Lepus alleni) geophagy as a potential source of micronutrient supplementation. We identified evidence of soil digging and ingestion by antelope jackrabbits at sites with the assistance of motion-sensing cameras. Fifteen soil samples from sites with evidence of soil ingestion and 15 control samples from noningested sites were collected and analyzed for water content, iron, potassium, calcium, and sodium. We found more calcium and sodium in areas where soils were ingested, whereas potassium, water, and iron content did not differ. In conclusion, the evidence may suggest that antelope jackrabbits ingested soil to uptake necessary nutrients such as sodium and calcium.

Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

Mapping woody vegetation is critical for monitoring encroachment in semi-arid grasslands, yet species-level discrimination is challenging due to small, sparse canopies, species similarities, overlapping crowns, and evergreen–deciduous differences. Coarse-resolution studies often yield low accuracies. This study evaluated multi-temporal airborne hyperspectral imagery and LiDAR for tree species classification at ~1 m resolution. Using a machine learning workflow, combining spectral indices and canopy height models achieved 95.28% overall accuracy and 94.17% kappa. Five woody species were identified with producer accuracies of 86.12–98.38%. Results highlight the value of fusing spectral and structural data, with multi-temporal imagery further improving classification reliability. 

Climate and Socioeconomic Factors Drive Irrigated Agriculture Dynamics in the Lower Colorado River Basin

The Colorado River Basin (CRB) sustains agriculture and urban water use, with irrigation comprising ~74% of demand. Rising temperatures and drought heighten the need to understand irrigation dynamics. Using the FANTA algorithm, we mapped annual fallow and active cropland (30 m, 2001–2017) in the Lower Colorado River Planning area (LCRP) and Pinal–Phoenix Active Management Areas (PPAMA). Classification accuracy exceeded 87%. Results show cropland extent and productivity respond differently to climate, water rights, and crop prices: LCRP productivity correlated with cool-season vapor pressure deficit, while PPAMA productivity and extent correlated with aridity. Junior water rights amplified sensitivity, indicating vulnerability under intensifying drought.