Tree-rings, terrestrial and drone Light Detection and Ranging (LiDAR) techniques to estimate woody cover biomass as a proxy for carbon storage. Developing a streamlined methodology within an open source language (R) to estimate tree carbon storage and growth while testing the accuracies of our results. We will further evaluate the differences in tree species growth, net primary productivity (NPP) and the drought resilience within a sky island plot at Mt. Bigelow Santa Catalina Mountains.

 Using a previously produced map compared to with the aid of drone imagery to classify and upscale woody cover species

Testing a novel method in sapwood boundary detection while assessing species differences in drought resiliency with tree-ring and climate data. Sapwood boundaries are important measurements in understanding differences in tree species development, water flow and drought resiliency.  

Developing woody cover species maps to inform land use impacts. Applying a previously developeded methods in classifying woody cover species with further analysis to understand the impacts of grazing on woody cover through assessing species density, percent cover and crown area. The areas of focus are at Santa Rita Experimental Range (SRER) and Walnut Gultch Experimental Watershed (WGER).

Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover are only among other reasons. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. Our study produced highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data.

Explore Results: Woody Classifications, Cacti Index

The Colorado River Basin (CRB) includes seven states and provides municipal and industrial water to millions of people across all major southwestern cities both inside and outside the basin. Agriculture is the largest part of the CRB economy and crop production depends on irrigation, which accounts for about 74% of the total water demand cross the region. A better understanding of irrigation water demands is critically needed as temperatures continue to rise and drought intensifies, potentially leading to water shortages across the region. Yet, past research on irrigation dynamics has generally utilized relatively low spatiotemporal resolution datasets and has often overlooked the relationship between climate and management decisions such as land fallowing, i.e., the practice of leaving cultivated land idle for a growing season. We produced annual estimates of fallow and active cropland extent at high spatial resolution (30 m) from 2001 to 2017 by applying the fallow-land algorithm based on neighborhood and temporal anomalies (FANTA) in Google Earth Engine (GEE). We specifically focused on diverse CRB agricultural regions: the lower Colorado River planning (LCRP) area and the Pinal and Phoenix active management areas (PPAMA). Our findings indicate that increasing aridity across the region may result in reduced cropland productivity and increased land fallowing for some regions, particularly those with junior water rights. 

Wrote and manage an optimization and automation of photovoltaic production data monitoring pipeline through script programming. I utilize data science and information technology to assure fleet optimal performance, monitor solar savings and quantify environmental impact. 

Geophagy is the practice of ingesting soil and has been observed worldwide in many terrestrial vertebrates. Antelope Jackrabbits (Lepus alleni) are a large and poorly studied hares in the desert scrub through neotropical savannas of southwestern USA and northwest Mexico. Typically herbivorous, Antelope Jackrabbits have been noted to practice geophagy but reasons for the behavior have not been clarified. We evaluated Lepus alleni geophagy as a function of micronutrient supply. Evidence of soil digging and ingestion by Antelope Jackrabbits was collected from motion sensing cameras and soil at these areas was collected. Fifteen soil samples from ingested sites and fifteen control samples from non-ingested sites was collected and analyze for water content, Iron (Fe), Potassium (K), Calcium (Ca) and Sodium (NA). Calcium and sodium was higher in areas where soil was ingested whereas potassium, water and iron content did not differ. In conclusion, the evidence suggests that Antelope Jackrabbits are ingesting soil to uptake necessary nutrients such as sodium and calcium.

Geospatial Data Processing and Development

Processing geospatial datasets such as classified cover and soil types to assess mine tailings. Developed a GEE visualizer for exploration. Project for the University of Arizona Dr. Flurin Babst, Dr. Willem Van Leeuwen with ARSC 

Geospatial Data Processing and Development

Processing geospatial datasets to subset high severity fire risk, climate vulnerability and future vulnerability within similar vegetation types. Developed a GEE visualizer for exploration. Project for the University of Arizona Dr. Don Falk with ARSC 

Drone Imagery, Phantom 4 RTK

A collection of drone images that were taken during the June and October of 2023 to document pre and post monsoon dynamics. Nadir and Oblique photos were used to create structure for motion point clouds to create digital elevation models and canopy height models. Drone images taken for the University of Arizona Dr. Mitchel P McClaran and Dr. Guillermo E. Ponce-Campos with ARSC 

Drone Imagery, DJI Mavic Pro 2

A collection of drone images and HD video taken at 9am to document bio safe dye injections to gather water-travel data to inform laboratory experiments in collaboration with the US Geological Survey and University of Arizona researchers. Drone images taken for the University of Arizona Dr. David Quanrud with ARSC