Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time.

Ecological processes are centered to water availability in drylands, however less known nutrient stoichiometry can help explain much of their structure and ecological interactions. Here we look to the foliar stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) of 38 dominant plant species from the Sonoran Desert, grouped in four different functional types to describe ecological characteristics and processes. We found that foliar N, C:N, C:P and N:P stoichiometric ratios, but not P, were higher than those known to most other ecosystems and indicate P but not N limitations in leaves. Biological N fixers (BNF) had even higher leaf N concentrations, but bio-elemental concentrations and stoichiometry ratios were not different to other non-N fixing legume species which underscores the need to understand the physiological mechanisms for high N, and to how costly BNF can succeed in P-limiting drylands environments. Stoichiometry ratios, and to lesser extent elemental concentrations, were able to characterize BNF and colonizing strategies in the Sonoran Desert, as well as explain leaf attribute differences, ecological processes and biogeochemical niches in this dryland ecosystem, even when no direct reference is made to other water-limitation strategies.

Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response. We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. OPEN ACCESSRemote Sensing 2012, 4 599Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.

2012) Mapping and monitoring riparian vegetation distribution, structure and composition with regression tree models and post-classification change metrics, International Journal of Remote Sensing, 33:13, 4266-4290, Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring pro tocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with phys iognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.