Examining Trends in Post-Disturbance Ecosystem Dynamics in the Southwestern United States and Northwestern Mexico Using Remote Sensing Time-Series Data and Land Cover Change Detection

Abstract

New forms of disturbance, and alteration of current disturbance regimes in arid and semiarid ecosystems, have resulted in the modification and degradation of large regions. This research explores vegetation response as a consequence of two different disturbance events in the southwestern US and northwestern Mexico. This topic was explored in this dissertation utilizing remotely sensed geospatial information in three separate studies.The first study explores the development of methods to assess the effectiveness of pre-fire restoration efforts, by evaluating vegetation response as a function of local environmental variables. Here I evaluated three fire locations at Bandelier National Monument (New Mexico). My models explain post-fire vegetation response as a function of environmental inputs and pre-fire site conditions (restored, unrestored and control areas). However, further analysis will be needed to better understand the effect of pre-fire restoration techniques on post-fire vegetation response.My second study explores the development of monitoring practices using remotely sensed data to assess land cover dynamics through time. The study area was the arid agro-ecosystem of La Costa de Hermosillo (LCH) in northwestern Mexico. My results show a continuous tendency towards a decrease in agriculture from 1988 until 2009. Detailed change detection demonstrates high rates of change from agriculture to other land cover classes in areas with dense agricultural developments. Implementation of these monitoring protocols would help with the application of restoration practices.The third study we used remote sensing time series data to assess phenological trends and variability among land cover types in relation to climatic variability within communities present in a heavily impacted agro-ecosystem (LCH). My analysis comprised three different agricultural land cover types including abandoned agricultural fields, and four additional natural land cover classes. I found that productivity has not increased in abandoned fields (since abandonment). Furthermore, I found that the models developed in this study significantly explain phenological variability as a function of climatic variability.These studies suggest that the use of remote sensing tools could effectively contribute to our ability to monitor vegetation dynamics in arid ecosystems. The implementation of methodologies generated in this work would significantly inform managers in decision making processes.