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Agrarian movements around the world use agroecology to build sovereignty and steward dynamic ecosystems. Research has shown that agroecological farmers steward more resilient crops, more resilient soil biomes, and greater biodiversity than conventional agriculture. GIS and remote sensing offer many tools to detect the impacts of these farmers on the environment, but it is less clear how such technologies fit into agroecological goals. This study asks: what values, experiences and knowledge do smallholder producers in Bernalillo County embody in their soil stewardship practices? Also, what experience or knowledge do smallholder producers in Bernalillo County have about remote sensing, and would they use remotely sensed data to understand the impact of their soil stewardship practices? To answer these questions, I look to political ecology and feminist theory for understanding the embodied knowledge and practices that are fundamental to agroecology. I find that the practice of farmers in this study embody the same values and methods of agroecology farmers worldwide. Additionally, the remote sensing process could be useful to detect the impacts of farmer practices on soil dynamics in Bernalillo County.

New Mexico's drylands are undergoing notable vegetation changes due to rising aridity, higher temperatures, and shifts in precipitation with changing climate. Mapping drylands through remote sensing is challenging compared to mesic systems due to the irregular patterns of rainfall-driven phenology and weaker vegetation absorption signals. This study aims to enhance vegetation mapping in New Mexico, utilizing a high-resolution, frequently revisited PlanetScope dataset with multispectral capabilities, specifically chosen for its suitability for time-series analysis. The challenge with high spatial and temporal data lies in the overwhelming volume, leading to issues like the curse of dimensionality, overfitting, data redundancy, collinearity, and visualization problems. This study assesses the effectiveness of dimensionality reduction techniques such as Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Fourier Transform (FT), and Independent Component Analysis (ICA) in transforming phenological time series data for vegetation classification. A null model, without phenological data, serves as a comparison baseline. The classification model related known field plots to a series of variables using a random forest (RF) algorithm in an object-based image analysis (OBIA) framework to map vegetation at the Sevilleta National Wildlife Refuge and East Jemez Area in New Mexico, USA. The study highlighted the importance of using phenological data, where the PCA method had the highest overall accuracy at 53.52%, whereas the null model had 46.71%. PCA might be more appropriate for supervised classification than other methods because it reduces the influence of noise in the data. However, the MNF model performed better in the discrimination of vegetation classes.

New Mexico experienced substantial impacts of regional-scale drought from 2011-2014. Global climate change may make such events a new normal for the southwest: drought events are expected to increase in both frequency and severity over the coming century. While semiarid grasslands recover quickly from short-term drought, the cumulative impacts of climate change may reduce the resiliency of these systems over time. Remote sensing methods can allow efficient and cost-effective comparison of ecosystem recovery from drought events over time using long-running imaging systems like Landsat. We investigate the efficacy of using multi-endmember spectral mixture analysis (MESMA) to quantify the impacts of drought events on the fractional cover of green vegetation (GV), non-photosynthetic vegetation (NPV), and soil (S). Field spectra of dominant vegetation species were collected at the Sevilleta National Wildlife Refuge roughly monthly over six field sessions from May through September 2019. Four endmember selection methods were tested to optimize the spectral library, as well as three thresholding adjustments to unmix Landsat imagery from 2009 (five years pre-drought), 2014 (final year of drought), and 2019 (five years post-drought). The best fit model had high levels of agreement for all three classes with R2 values of 0.89 (NPV), 0.71 (GV), and 0.81 (S), respectively. Image differencing showed increases in S and decreases in NPV fractions throughout the study area that were unaccompanied by a return to baseline cover in the post-drought period, contradicting ground-based observations of full recovery of grassland vegetation.

This research examines the urban growth and land use pattern of Albuquerque in the next 20 years, for the year 2035, based on past urbanization and land use. Additionally, it compares possible urban growth and land use patterns for two scenarios i. Business as usual scenario, where urbanization pattern of Albuquerque is based on historic data without any explicit definition of areas specifically designated for development and ii. Expansion scenario, where three areas around Albuquerque specifically designated for development (Mesa del Sol, Volcano Mesa and Santolina) is explicitly defined in the model. The two scenarios are further examined based on possible high and low growth rate to expose the upper and lower bounds of future development. SLEUTH, a cellular automata based dynamic urban growth model, was used in the analysis and future urban growth prediction. The SLEUTH model was first calibrated for Albuquerque and its input variables (specifically exclusion layer), and self-modification rule were modified to simulate the two scenarios and the growth conditions respectively. The results indicated, with a very high certainty, that for any scenario in any growth rates, urban expansion would occur in the in-fills fringes of the current urban extension of the city. Among the areas designated for urban development, results showed a high probability of urban growth occurring primarily in Volcano Mesa, followed by Mesa del Sol and with a low probability of urban growth occurring in Santolina. This was true for both scenarios in high and low growth rate.

This study examines how land-use policies can play a role in manifesting physical landscape changes. In 2000, Congress enacted a unique experiment in public lands management by creating the Valles Caldera National Preserve and its governing body, the Valles Caldera Trust (VCT). The management approach enacted by Valles Caldera Trust marked a significant departure in land-use intensity. To assess how these changes in management have affected land-use intensity and resulting cover change, this research seeks to identify landscape-level changes that taken place during a period of ten years prior to and following the formation of the Valles Caldera National Preserve (i.e., 1989-2013). Remote sensing techniques were used to quantify land cover transitions. Landsat image data (30m) from 1989, 1999, 2003, and 2013 were compared to identify changes that have taken place between the time periods 1989-1999, a period just prior to the purchase of the Baca Ranch by the federal government, and 10 years of management by the Valles Caldera Trust, 2003-2013. In order to do this, pre-classification change detection was used to quantify changes that have taken place during each period and a range of historical evidence used to classify those changes by disturbance regime. Results demonstrate an increase in wetland and rangeland recovery under VCT management based on evaluating several ancillary datasets. This leads to the conclusion that adaptive management strategies were beneficial for these cover types. The impact of the VCTs adaptive management strategies on forests is difficult to understand due to large wildfire-induced cover changes that occurred during VCT management.'