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

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. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.

[1]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[2]  Daniel G. Neary,et al.  Fire effects on belowground sustainability: a review and synthesis , 1999 .

[3]  C. Daly,et al.  A knowledge-based approach to the statistical mapping of climate , 2002 .

[4]  Philip N. Omi,et al.  Effect of thinning and prescribed burning on crown fire severity in ponderosa pine forests , 2002 .

[5]  Dara Entekhabi,et al.  Impact of Hillslope-Scale Organization of Topography, Soil Moisture, Soil Temperature, and Vegetation on Modeling Surface Microwave Radiation Emission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Donald McKenzie,et al.  Climatic Change, Wildfire, and Conservation , 2004 .

[7]  Sreerama K. Murthy,et al.  Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey , 1998, Data Mining and Knowledge Discovery.

[8]  Frederick J. Swanson,et al.  Landform Effects on Ecosystem Patterns and Processes , 1988 .

[9]  T. Swetnam,et al.  Warming and Earlier Spring Increase Western U.S. Forest Wildfire Activity , 2006, Science.

[10]  Jörg Kaduk,et al.  Comparing global models of terrestrial net primary productivity (NPP): comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI) , 1999 .

[11]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[12]  S. Running,et al.  Remote Sensing of Forest Fire Severity and Vegetation Recovery , 1996 .

[13]  Subodh Sharma,et al.  Influence of slope aspect on soil physico-chemical and biological properties in the mid hills of central Nepal , 2010 .

[14]  Willem J. D. van Leeuwen,et al.  Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data , 2008 .

[15]  R. O'Neill,et al.  Landscape Ecology Explained@@@Landscape Ecology in Theory and Practice: Pattern and Process , 2001 .

[16]  Chino Well Fire: A Hydrologic Evaluation of Rainfall and Runoff from the Mud Canyon Watershed , 1999 .

[17]  Nicholas C. Coops,et al.  Assessing forest productivity in Australia and New Zealand using a physiologically-based model driven with averaged monthly weather data and satellite-derived estimates of canopy photosynthetic capacity , 1998 .

[18]  S. P. Rupp Effects of grazing and trampling by Rocky Mountain elk (Cervus elaphus nelsoni) on the vegetative community of Bandelier National Monument, New Mexico , 2000 .

[19]  C. Daly,et al.  A Statistical-Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain , 1994 .

[20]  F. Ramsey,et al.  The statistical sleuth : a course in methods of data analysis , 2002 .

[21]  I. Moore,et al.  Digital terrain modelling: A review of hydrological, geomorphological, and biological applications , 1991 .

[22]  Michael R. Raupach,et al.  The influence of topography on meteorogical variables and surface-atmosphere interactions , 1997 .

[23]  M. Goodchild,et al.  Geographic Information Systems and Science (second edition) , 2001 .

[24]  D. Hall,et al.  Landsat digital analysis of the initial recovery of burned tundra at Kokolik River, Alaska , 1980 .

[25]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

[26]  P. White,et al.  Natural disturbance and patch dynamics: an introduction. , 1985 .

[27]  S. Schneider,et al.  Climate Change 2007 Synthesis report , 2008 .

[28]  Michael C Wimberly,et al.  Assessing fuel treatment effectiveness using satellite imagery and spatial statistics. , 2009, Ecological applications : a publication of the Ecological Society of America.

[29]  Claudia Notarnicola,et al.  Topographical and ecohydrological controls on land surface temperature in an alpine catchment , 2010 .

[30]  Jay D. Miller,et al.  Forest surveys and wildfire assessment in the Los Alamos Region; 1998-1999 , 2000 .

[31]  Scott L. Stephens,et al.  Evaluation of the effects of silvicultural and fuels treatments on potential fire behaviour in Sierra Nevada mixed-conifer forests , 1998 .

[32]  T. Swetnam,et al.  Historical Fire Regime Patterns in the Southwestern United States Since AD 1700 , 1996 .

[33]  Carl N. Skinner,et al.  Basic principles of forest fuel reduction treatments , 2005 .

[34]  C. Skinner,et al.  An Assessment of Factors Associated with Damage to Tree Crowns from the 1987 Wildfires in Northern California , 1995, Forest Science.

[35]  Stuart E. Marsh,et al.  Evaluating Post-wildfire Vegetation Regeneration as a Response to Multiple Environmental Determinants , 2010 .

[36]  William L. Baker,et al.  The landscape ecology of large disturbances in the design and management of nature reserves , 1992, Landscape Ecology.

[37]  N. Benson,et al.  Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio , 2006 .

[38]  A. Huete,et al.  Dependence of NDVI and SAVI on sun/sensor geometry and its effect on fAPAR relationships in Alfalfa , 1995 .

[39]  Jason J. Moghaddas,et al.  Fire treatment effects on vegetation structure, fuels, and potential fire severity in western U.S. forests. , 2009, Ecological applications : a publication of the Ecological Society of America.

[40]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[41]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[42]  T. Swetnam,et al.  Fire history and climatic patterns in ponderosa pine and mixed-conifer forests of the Jemez Mountains, Northern New Mexico , 1996 .

[43]  Anna Barbati,et al.  Evaluating the Effects of Environmental Changes on the Gross Primary Production of Italian Forests , 2009, Remote. Sens..

[44]  Monica G. Turner,et al.  Prefire Heterogeneity, Fire Severity, and Early Postfire Plant Reestablishment in Subalpine Forests , 1999 .

[45]  M. Brandon,et al.  Macrogeomorphic evolution of the post-Triassic Appalachian mountains determined by deconvolution of the offshore basin sedimentary record , 1996 .

[46]  S. K. Jenson,et al.  Extracting topographic structure from digital elevation data for geographic information-system analysis , 1988 .

[47]  J. R. Jensen Biophysical Remote Sensing , 1983 .

[48]  T. Avery,et al.  Fundamentals of Remote Sensing and Airphoto Interpretation , 1992 .

[49]  Jay D. Miller,et al.  Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR) , 2007 .

[50]  D. Breshears,et al.  Soil Morphology of Canopy and Intercanopy Sites in a Piñon-Juniper Woodland , 1996 .

[51]  Anke Jentsch,et al.  The Search for Generality in Studies of Disturbance and Ecosystem Dynamics , 2001 .

[52]  G. De’ath,et al.  CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .

[53]  P. Chavez Image-Based Atmospheric Corrections - Revisited and Improved , 1996 .

[54]  Peter Z. Fulé,et al.  Comparing ecological restoration alternatives: Grand Canyon, Arizona , 2002 .

[55]  W. Hargrove,et al.  EFFECTS OF FIRE SIZE AND PATTERN ON EARLY SUCCESSION IN YELLOWSTONE NATIONAL PARK , 1997 .

[56]  C. Allen,et al.  ECOLOGICAL RESTORATION OF SOUTHWESTERN PONDEROSA PINE ECOSYSTEMS: A BROAD PERSPECTIVE , 2002 .

[57]  J. Allan,et al.  The influence of catchment land use on stream integrity across multiple spatial scales , 1997 .

[58]  David P. Roy,et al.  Remote sensing of fire severity: assessing the performance of the normalized burn ratio , 2006, IEEE Geoscience and Remote Sensing Letters.

[59]  W. Leeuwen,et al.  Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel , 2010 .

[60]  Y. Richard,et al.  A statistical study of NDVI sensitivity to seasonal and interannual rainfall variations in Southern Africa , 1998 .

[61]  S. A. Lewis,et al.  Remote sensing techniques to assess active fire characteristics and post-fire effects , 2006 .

[62]  M. Turner,et al.  Landscape dynamics in crown fire ecosystems , 1994, Landscape Ecology.

[63]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[64]  Martin Herold,et al.  The spatiotemporal form of urban growth: measurement, analysis and modeling , 2003 .

[65]  Jim InnesJ. Innes,et al.  Comparison of thinning and prescribed fire restoration treatments to Sierran mixed-conifer historic conditions , 2007 .

[66]  C. Tucker,et al.  Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999 , 2001 .

[67]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[68]  R. Myneni,et al.  On the relationship between FAPAR and NDVI , 1994 .

[69]  Stuart E. Marsh,et al.  Broad-Scale Environmental Conditions Responsible for Post-Fire Vegetation Dynamics , 2010, Remote. Sens..

[70]  W. Romme,et al.  The Interaction of Fire, Fuels, and Climate across Rocky Mountain Forests , 2004 .

[71]  Walter J. Rawls,et al.  Soil Water Retention as Related to Topographic Variables , 2001 .

[72]  M. Flannigan,et al.  Climate change and forest fires. , 2000, The Science of the total environment.

[73]  Willem J. D. van Leeuwen,et al.  Monitoring the Effects of Forest Restoration Treatments on Post-Fire Vegetation Recovery with MODIS Multitemporal Data , 2008, Sensors.

[74]  C. Allen,et al.  Runoff and erosion on the Pajarito Plateau: observations from the field , 1996, Jemez Mountains Region.

[75]  P. Legendre,et al.  Spatial relationships between soil moisture patterns and topographic variables at multiple scales in a humid temperate forested catchment , 2010 .

[76]  D. Verbyla,et al.  Evaluation of remotely sensed indices for assessing burn severity in interior Alaska using Landsat TM and ETM , 2005 .

[77]  R. Sun,et al.  The topographic controls on the decadal-scale erosion rates in Qilian Shan Mountains, N.W. China , 2010 .

[78]  Guirui Yu,et al.  Impacts of precipitation seasonality and ecosystem types on evapotranspiration in the Yukon River Basin, Alaska , 2010 .