Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

Veraverbeke, Sander; Gitas, Ioannis Z.; Katagis, Thomas; Polychronaki, Anastasia; Somers, Ben; Goossens, Rudi

doi:10.1016/j.isprsjprs.2011.12.007

Cited by 108 publications

(54 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In previous studies, a variety of spectral indices (such as DI by Healey et al 2005, NDVI and EVI by Stroppiana et al 2012 and NDVI, DVI, RVI, SAVI, etc. by Veraverbeke et al 2012) have been utilized and compared, and are being proven to achieve high accuracies. However, discussion on the causes of the variations in accuracies under certain circumstances still remains to be further explored.…”

Section: Discussionmentioning

confidence: 99%

Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

Forest fire is one of the dominant disturbances in boreal forests. It is the primary process responsible for organizing the physical and biological attributes of the boreal biome, shaping landscape diversity and influencing biogeochemical cycles. The Greater Hinggan Mountain of China is rich in forest resources while suffers from a high incidence of forest fires simultaneously. In this study, focusing on the most serious forest fire in the history of P. R. China which occurred in this region, we made use of two Landsat-5 TM (Thematic Mapper) images, and proposed to map the overall burned area and burned forest area by multiple methods. During the mapping, the fire perimeter, as well as rivers, roads and urban areas were first extracted and masked visually, and then four indices of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Vegetation Fractional Cover and Disturbance Index were calculated. For each index, the optimal threshold for separating burned from unburned forest area was determined using their histograms. For comparison, threshold segmentation using single-band reflectance was performed, in addition to a Maximum Likelihood Classifier (MLC) based supervised classification of all features and forest area alone; their accuracies were also evaluated and analysed. Among all the methods compared here, mapping by EVI threshold segmentation proved to be optimal by the comparisons of overall accuracy (99.78%) and the kappa coefficient (0.9946). Finally, the calculated burned area and burned forest area were compared with the values from official statistics. Compared with the classical methods used to report official statistics on burned areas, the remote sensing-based mapping is more objective and efficient, less labour-and time-consuming, and more repeatable.

show abstract

Section: Discussionmentioning

confidence: 99%

Mapping a burned forest area from Landsat TM data by multiple methods

Chen

Moriya

Sakai

et al. 2014

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…However, Figure 5e shows that there was a sharp decrease when the fire occurred in May 1987. Following the fire event, there was a rapid increase from June to August (Figure 5f-h), which was likely the NDVI response of the understory vegetation (e.g., herbaceous and shrubs) to temperature or precipitation variability [34,35]. From September to November, the Great Xing'an Mountain entered autumn; NDVI values decreased in both years with the spread of colored foliage and the percentage of fallen leaves increasing.…”

Section: Fire Damage Assessmentmentioning

confidence: 97%

Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China

Tani

Zhang

et al. 2013

Remote Sensing

View full text Add to dashboard Cite

This paper describes the long-term effects on vegetation following the catastrophic fire in 1987 on the northern Great Xing'an Mountain by analyzing the AVHRR GIMMS 15-day composite normalized difference vegetation index (NDVI) dataset. Both temporal and spatial characteristics were analyzed for natural regeneration and tree planting scenarios from 1984 to 2006. Regressing post-fire NDVI values on the pre-fire values helped identify the NDVI for burnt pixels in vegetation stands. Stand differences in fire damage were classified into five levels: Very High (VH), High (H), Moderate (M), Low (L) and Slight (S). Furthermore, intra-annual and inter-annual post-fire vegetation recovery trajectories were analyzed by deriving a time series of NDVI and relative regrowth index (RRI) values for the entire burned area. Finally, spatial pattern and trend analyses were conducted using the pixel-based post-fire annual stands regrowth index (SRI) with a nonparametric Mann-Kendall (MK) statistics method. The results show that October was a better period compared to other months for distinguishing the post-and pre-fire vegetation conditions using the NDVI signals in boreal forests of China because colored leaves on grasses and shrubs fall down, while the leaves on healthy trees remain green in October. The 6939MK statistics method is robustly capable of detecting vegetation trends in a relatively long time series. Because tree planting primarily occurred in the severely burned area (approximately equal to the Medium, High and Very High fire damage areas) following the Daxing'anling fire in 1987, the severely burned area exhibited a better recovery trend than the lightly burned regions. Reasonable tree planting can substantially quicken the recovery and shorten the restoration time of the target species. More detailed satellite analyses and field data will be required in the future for a more convincing validation of the results.

show abstract

“…Applications of remote sensing aimed at monitoring structural attributes of forests that were listed above have been driven largely by using empirical models to calibrate remotely sensed data with in situ data in either boreal or other forest ecosystems (Table 7, [8,28,112,187,196,[202][203][204][205][206][207][208][209][210][211][212][213]). For example, regression-based prediction has been a widely accepted approach to mapping regional forest attributes using linear regression [196,203,204,213], nonlinear regression [187,[209][210][211]214,215], partial least squares regression [216] and regression tree algorithms [26,217].…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

“…For example, regression-based prediction has been a widely accepted approach to mapping regional forest attributes using linear regression [196,203,204,213], nonlinear regression [187,[209][210][211]214,215], partial least squares regression [216] and regression tree algorithms [26,217]. Recently, non-parametric regression approaches, such as Reduced Major Axis (RMA) regression, k-Nearest Neighbor (k-NN), Gradient Nearest Neighbor (GNN) and Random Forest (RF) regressions, have received considerable attention for the estimation of structural forest attributes, because these approaches can account for mapping uncertainty and involve a large number of response variables with analytical and operational flexibility [28,209,218].…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

“…Some common remote sensing approaches used to estimate post-fire forest patterns in boreal regions; RSR, Reduced Simple Ratio [202]; SWVI, Shortwave Vegetation Index; GLAS, Geoscience Laser Altimetry System on ICESAT. Other examples of remote sensing approaches for monitoring forest structure in undisturbed boreal forests, as well as other post-fire forest ecosystems can be found in [8,28,[203][204][205][206][207]. Landsat and SAR data [209] Similar to mapping forest succession, some studies mentioned that the inclusion of derived spectral indices, raw spectral bands, biophysical variables and time series data can improve the estimation of structural attributes of post-fire forests.…”

Section: Measurement Of Other Variables In Forest Structurementioning

confidence: 99%

See 1 more Smart Citation

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

2013

View full text Add to dashboard Cite

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.

show abstract

Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

Cited by 108 publications

References 79 publications

Mapping a burned forest area from Landsat TM data by multiple methods

Mapping a burned forest area from Landsat TM data by multiple methods

Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China

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

Contact Info

Product

Resources

About