Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics

Abstract

Ecosystem response to disturbance is influenced by environmental conditions at a number of scales. Changes in climate have altered fire regimes across the western United States, and have also likely altered spatio-temporal patterns of post-fire vegetation regeneration. Fire occurrence data and a vegetation index (NDVI) derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) were used to monitor post-fire vegetation from 1989 to 2007. We first investigated differences in post-fire rates of vegetation regeneration between ecoregions. We then related precipitation, temperature, and elevation records at four temporal scales to rates of post-fire vegetation regeneration to ascertain the influence of climate on post-fire vegetation dynamics. We found that broad-scale climate factors are an important influence on post-fire vegetation regeneration. Most notably, higher rates of post-fire regeneration occurred with warmer minimum temperatures. Increases in precipitation also resulted in higher rates of post-fire vegetation growth. While explanatory power was slight, multiple statistical approaches provided evidence for real ecological drivers of post-fire regeneration that should be investigated further at finer scales. The sensitivity of post-disturbance vegetation dynamics to climatic drivers has important ramifications for the management of ecosystems under changing climatic conditions. Shifts in temperature and precipitation regimes are likely to result in changes in post-disturbance dynamics, which could represent important feedbacks into the global climate system.

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

“…These orbital data enable several types of fire study, such as active fire detection, which registers the instantaneous temperature of objects in combustion using spectral bands between 3 and 11 μm [10,11]; burned area estimation based on changes in the spectral characteristics of vegetation before and after the occurrence of fires, which uses the spectral bands in the visible, near-infrared and mid-infrared regions of the electromagnetic spectrum [12]; fire patterns and severity; and studies of vegetation regeneration and risk analysis [5,10,13]. Additionally, the mapping of burned areas and acquisition of quantitative information about the spatial and temporal distribution of fire events are fundamental, not only to enable more precise estimations of environmental impacts and LULCC monitoring but because these are required input data in climate models [14,15].…”

Monitoring Post Disturbance Forest Regeneration with Hierarchical Object-Based Image Analysis

“…Because the image resolution is not optimal for seedling crown identification, we conclude that developing relationships between forest inventory variables including crown diameter and height with image-derived parameters are not feasible for the seedling polygons, but can be feasible for the mature forest polygons. Climate change is impacting post-disturbance regeneration rates [52]. The method demonstrated here captures the spatial patterns of post-disturbance regeneration of forests, a basic knowledge parameters for adaptive management to landscape stewardship under climate change [53].…”

Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

“…Remote sensing techniques have been extensively used to monitor and assess diverse aspects of wildfire events [14,39], ranging from spatial studies to temporal assessment of processes [30,32,40,41]. Previous research has taken advantage of the temporal, spectral, and spatial characteristics of Landsat and MODIS to study post-and pre-wildfire vegetation responses [40,42].…”

“…The use of remote sensing technologies constitutes a good source of information and data to assess vegetation response and trends [29][30][31][32][33]. Remote sensing tools provide the means to assess biophysical variables such as location, spectral signature of objects, chlorophyll absorption characteristics, moisture content of vegetation and soil, elevation and topography [34].…”