Development of indicators of vegetation recovery based on time series analysis of SPOT Vegetation data

Large-scale wild fires have direct impacts on natural ecosystems and play a major role in the vegetation ecology and carbon budget. Accurate methods for describing post-fire development of vegetation are therefore essential for the understanding and monitoring of terrestrial ecosystems. Time series analysis of satellite imagery offers the potential to quantify these parameters with spatial and temporal accuracy. Current research focuses on the potential of time series analysis of SPOT Vegetation S10 data (1999-2001) to quantify the vegetation recovery of large-scale burns detected in the framework of GBA2000. The objective of this study was to provide quantitative estimates of the spatio-temporal variation of vegetation recovery based on remote sensing indicators. Southern Africa was used as a pilot study area, given the availability of ground and satellite data. An automated technique was developed to extract consistent indicators of vegetation recovery from the SPOT-VGT time series. Reference areas were used to quantify the vegetation regrowth by means of Regeneration Indices (RI). Two kinds of recovery indicators (time and value- based) were tested for RI's of NDVI, SR, SAVI, NDWI, and pure band information. The effects of vegetation structure and temporal fire regime features on the recovery indicators were subsequently analyzed. Statistical analyses were conducted to assess whether the recovery indicators were different for different vegetation types and dependent on timing of the burning season. Results highlighted the importance of appropriate reference areas and the importance of correct normalization of the SPOT-VGT data.

[1]  C. Ricotta,et al.  Monitoring the landscape stability of Mediterranean vegetation relation to fire with a fractal algorithm , 1998 .

[2]  C. Field,et al.  Remote Sensing of Terrestrial Photosynthesis1 , 1995 .

[3]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[4]  Emilio Chuvieco,et al.  Assessment of vegetation regeneration after fire through multitemporal analysis of AVIRIS images in the Santa Monica Mountains , 2002 .

[5]  Xavier Pons,et al.  MONITORING OF PLANT COMMUNITY REGENERATION AFTER FIRE BY REMOTE SENSING . , 2002 .

[6]  J. Pereira,et al.  Radiometric analysis of SPOT-VEGETATION images for burnt area detection in Northern Australia , 2002 .

[7]  Ana C. L. Sá,et al.  An estimate of the area burned in southern Africa during the 2000 dry season using SPOT-VEGETATION satellite data , 2003 .

[8]  Xavier Pons,et al.  Spatial patterns of forest fires in Catalonia (NE of Spain) along the period 1975–1995: Analysis of vegetation recovery after fire , 2001 .

[9]  Marco Marchetti,et al.  A qualitative approach to the mapping of post-fire regrowth in Mediterranean vegetation with Landsat TM data , 1995 .

[10]  Dar A. Roberts,et al.  Post-fire recovery of leaf area index in California chaparral: A remote sensing-chronosequence approach , 2004 .

[11]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[12]  B. Rock,et al.  Detection of changes in leaf water content using Near- and Middle-Infrared reflectances , 1989 .

[13]  O. Viedma,et al.  Modeling rates of ecosystem recovery after fires by using landsat TM data , 1997 .

[14]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[15]  W. Ripple,et al.  Assessing wildfire effects with Landsat thematic mapper data , 1998 .

[16]  Tansey Kevin,et al.  Implementation of Regional Burnt Area Algorithms for the GBA2000 Initiative. , 2002 .