Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests

Kasischke, E. S.; French, N. H. F.

doi:10.1080/014311697217684

Cited by 52 publications

(39 citation statements)

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“…Our study re-emphasized the value of using post-fire time-series vegetation data. We concur with Kasischke and French [29] that although the AVHRR time-series presents challenges to long-term vegetation monitoring, the long time-series of vegetation data is invaluable for monitoring global vegetation dynamics. Research of this type should be pursued with newer sensors that provide better spectral continuity, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), as these sensors accumulate a longer time-series.…”

Section: Discussionsupporting

confidence: 62%

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

Casady

Marsh

2010

Remote Sensing

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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.

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Section: Discussionsupporting

confidence: 62%

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

Casady

Marsh

2010

Remote Sensing

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“…Goetz et al [225] used two NDVI time series derived from the Pathfinder AVHRR Land (PAL) and the Global Inventory Modeling and Mapping Studies (GIMMS) AVHRR to investigate the recovery of vegetation after fires in the boreal forests of Canada. Their results indicated that the recovery rates based on NDVI of Canadian boreal forest were different between the PAL and GIMMS datasets, but both were consistently shorter than previous studies, e.g., [81,222]. This is probably because the previous studies in North America only emphasized the most impacted pixels within fire perimeters [225], which might require a longer period to return to pre-fire conditions [221].…”

Section: Tracking Patterns Of Forest Recovery After Firecontrasting

confidence: 42%

“…In addition to field-based observations, the evaluation of satellite datasets in monitoring post-fire forest recovery should include comparisons of independent observations at the stage of results, for example, comparing detected trends of different optical datasets [225,227] and optical and SAR/LiDAR datasets in different regions [183]. Finally, as noted by some authors (e.g., [221,222,225,227]), analyzing patterns of vegetation cover in boreal forests using remote sensing data requires the development of approaches to account for variations in spatial and spectral resolution of remotely sensed data, environmental conditions (e.g., clouds and haze, soil moisture, albedo, latitude, topography, climate), vegetation characteristics (e.g., species composition, land cover type, vegetation phenology) and disturbance regimes (e.g., fire and burn severity, fire type, fire frequency). A useful approach might be the stratification of those factors with similar conditions prior to applying remote sensing tools (Table 9).…”

Section: Tracking Patterns Of Forest Recovery After Firementioning

confidence: 99%

“…Many studies have addressed this issue by using moderate-to-low resolution time series NDVI, SAVI, EVI, albedo, NDVI-based Net Primary Productivity (NPP), fraction of absorbed photosynthetically active radiation (fAPAR) and, recently, the vegetation optical depth (VOD) parameter from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E) sensor as surrogates representing the recovery of vegetation after fire disturbances in both the Siberian boreal forest [219,221] and North American boreal forest [81,83,167,183,208,[222][223][224][225][226] (Table 8). NDSWIR to recover fully [221] With respect to the post-fire recovery of NDVI [83,221,222,225,227], Normalized Difference Shortwave Infrared Index (NDSWIR) [221], NDVI-based NPP [81] and fAPAR [219], the observations of the recovery of vegetation from these analyses varied greatly, even in the same eco-region, depending upon data resolution, pre-burn vegetation, burn severity and the temporal and spatial variability of vegetation indices within burned and unburned areas (Table 8). Kasischke and French [222] used AVHRR-NDVI time series spanning over three years (1990)(1991)(1992) to study 14 test sites in Alaska and found that patterns of forest recovery were defined by pre-fire vegetation and the timing of the wildfire during the growing season.…”

Section: Tracking Patterns Of Forest Recovery After Firementioning

confidence: 99%

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Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

2013

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Abstract:The frequency and severity of forest fires, coupled with changes in spatial and temporal precipitation and temperature patterns, are likely to severely affect the characteristics of forest and permafrost patterns in boreal eco-regions. Forest fires, however, are also an ecological factor in how forest ecosystems form and function, as they affect the rate and characteristics of tree recruitment. A better understanding of fire regimes and forest recovery patterns in different environmental and climatic conditions will improve the management of sustainable forests by facilitating the process of forest resilience. Remote sensing has been identified as an effective tool for preventing and monitoring forest fires, as well as being a potential tool for understanding how forest ecosystems respond to them. However, a number of challenges remain before remote sensing practitioners will be able to better understand the effects of forest fires and how vegetation responds afterward. This article attempts to provide a comprehensive review of current research with respect to remotely sensed data and methods used to model post-fire effects and forest recovery patterns in boreal forest regions. The review reveals that remote sensing-based monitoring of post-fire effects and forest recovery patterns in boreal forest regions is not only limited by the gaps in both field data and remotely sensed data, but also the complexity of far-northern fire regimes, climatic conditions and environmental conditions. We expect that the integration of different remotely sensed data coupled with field campaigns can provide an important data source to support the monitoring of post-fire effects and forest recovery patterns. Additionally, the variation and stratification of pre-and post-fire vegetation and environmental conditions should be considered to achieve a reasonable, operational model for monitoring post-fire effects and forest patterns in boreal regions.

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“…However, fires in the boreal region are subject to large annual fluctuations which are associated with weather patterns such as low precipitation (Jupp et al 2006) and high temperature. Some estimates indicate that between 5 and 10 million ha of boreal forest burn each year, with most of the area being burned in fires bigger than 50 000 ha (Kasischke and French 1997). With such high rates of change in boreal ecosystems, and the remoteness of some burned areas and their huge extent, it is clear that traditional studies of forest succession based on field surveys should be complemented by analyses with remote sensing data which provide timely information on forest ecosystem status for large areas and in a cost efficient manner .…”

Section: Introductionmentioning

confidence: 99%

Studying the change in fAPAR after forest fires in Siberia using MODIS

Cuevas-González

Gerard

Balzter

et al. 2008

International Journal of Remote Sensing

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Disturbance events such as fire have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. This paper focuses on establishing whether characteristic spatio-temporal patterns of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) occur in the initial two years after a fire event in Siberian boreal forests. Time-series of MODIS fAPAR were used to study post-fire dynamics during the year of the fire and the following two years. Three forest types (evergreen needle-leaf, deciduous needle-leaf and deciduous broadleaf) grouped in three latitudinal regions, ranging from 51° N to 65° N, were studied by analysing a sample of fourteen burned areas. For each of the burned areas an adjacent unburned control plot was selected with the aim of separating inter-annual variations caused by climate from changes in fAPAR behaviour due to a burn. The results suggest that (i) the forest types exhibit characteristic fAPAR change trajectories shortly after the fire, (ii) the differences in the fAPAR trajectories are related to the forest type, (iii) fAPAR changes are not significantly different among the latitudinal regions, and (iv) the limited temporal variability observed among the three years of observations indicates that fAPAR varies very little in the initial years after a fire event.

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Constraints on using AVHRR composite index imagery to study patterns of vegetation cover in boreal forests

Cited by 52 publications

References 0 publications

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

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

Remote Sensing Techniques in Monitoring Post-Fire Effects and Patterns of Forest Recovery in Boreal Forest Regions: A Review

Studying the change in fAPAR after forest fires in Siberia using MODIS

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