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Quantifying the Environmental Impact of Forest Fires

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 June 2014) | Viewed by 125277

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Dr. Ioannis Gitas

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Guest Editor

Lab of Forest Management and Remote Sensing, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: forest fires; land-use/land-cover mapping; pre-fire planning and post-fire assessment; remote sensing; GIS; forest management
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Prof. Dr. Kevin Tansey

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Guest Editor

Centre for Landscape & Climate Research, Leicester Institute for Space & Earth Observation, School of Geography, Geology & the Environment, University of Leicester, Leicester, UK
Interests: radar; InSAR; LiDAR; multispectral; hyperspectral; lithological mapping; image classification; structural mapping; vegetation mapping; hydrocarbon seep mapping; landscape modelling
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Special Issue Information

Dear Colleagues,

Since the initiation of the Landsat program (1972), several projects have been conducted to test the potential efﬁcacy and reliability of satellite data in collecting information related to post-ﬁre management. Consequently, there is a considerable amount of literature on the location and mapping of the area affected by ﬁre at different scales ranging from local to global.

However, during the last decade, the range of applications has increased signiﬁcantly, including the assessment of the short-term and long-term impact caused by ﬁre to the environment, as a result of the following:

an increase in the number of sensors with different characteristics suitable for studying aspects of ﬁre, some of which have been designed speciﬁcally for ﬁre monitoring;
improvement of our understanding of the role of ﬁre in ecosystems functioning;
progress in computer technology (hardware, software);
development of new advanced digital image analysis techniques; and
the improved access to and availability of satellite data and derived products.

As a result, currently, remotely sensed data are used regularly and even operationally to detect fire scars. Moreover, such data are now being used more and more to make observations of landscapes before and after fire and also to characterise the impact of multi-frequency fire disturbance events. Also, satellite data is being used as input into hydrological models to indicate periods of time when a fire disturbed area is flooded. Furthermore, there is integration of these satellite observations into dynamic vegetation models and atmospheric emission models (GFEDv4).

Although optical satellite data are leading the way, there is a growing interest in radar satellites providing information in particularly cloudy regions of the world.

As we attempt to model the Earth System it is important that the impact of forest fires on the Earth System is fully understood and quantified. These impacts can be on climate, the biosphere, ecosystem functioning, society and livelihood. Fire disturbance has been identified by climate modellers as an Essential Climate Variable. Forest disturbance and the associated carbon flux needs to be measured and reported under the United Nations REDD+programme. Furthermore, we have been very good at understanding the short term impacts of fire on forests, but less good at understanding the response of vegetation under different fire frequency and severity scenarios.

The European Association of Remote Sensing Laboratories (EARSeL)’s Special Interest Group (SIG) on Forest Fires was created in 1995, following the initiative of several researchers studying ﬁres in Mediterranean Europe. It has promoted eight technical meetings and several specialized publications since then, and represents one of the most active groups within the EARSeL. The SIG has tried to foster interaction among scientists and managers who are interested in using remote sensing data and techniques to improve the traditional methods of ﬁre risk estimation and the assessment of ﬁre effects.

The 9th International Workshop of the group, will take place in Coombe Abbey, Warwickshire on 15-17 October 2013. The workshop is organised by the University of Leicester in collaboration with the Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki.

The workshop will draw out the state of the art research being undertaken to identify and quantify these impacts.

More specifically, the workshop and the proposed special issue aim to focus on quantifying the environmental impact of fire at a number of scales and will provide a set of papers that will be of great reference for many users of satellite-derived fire products. We invite you to submit articles on the following topics:

Characterising the impact of fire severity and fire frequency across vegetation type
Validation methods for burned area mapping
Monitoring and modelling vegetation recovery after fire disturbance
Scaling from regional to global burned area maps
Mapping forest fires for REDD+ MRV
Using active fire mapping and fire radiative energy to inform on fire severity and impact

Dr. Ioannis Gitas
Dr. Kevin Tansey
Guest Editors

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Published Papers (12 papers)

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66226 KiB

Open AccessArticle

Integration of Optical and SAR Data for Burned Area Mapping in Mediterranean Regions

by Daniela Stroppiana, Ramin Azar, Fabiana Calò, Antonio Pepe, Pasquale Imperatore, Mirco Boschetti, João M. N. Silva, Pietro A. Brivio and Riccardo Lanari

Remote Sens. 2015, 7(2), 1320-1345; https://doi.org/10.3390/rs70201320 - 26 Jan 2015

Cited by 77 | Viewed by 10915

Abstract

The aim of this paper is to investigate how optical and Synthetic Aperture Radar (SAR) data can be combined in an integrated multi-source framework to identify burned areas at the regional scale. The proposed approach is based on the use of fuzzy sets theory and a region-growing algorithm. Landsat TM and (C-band) ENVISAT Advanced Synthetic Aperture Radar (ASAR) images acquired for the year 2003 have been processed to extract burned area maps over Portugal. Pre-post fire SAR backscatter temporal difference has been integrated with optical spectral indices to the aim of reducing confusion between burned areas and low-albedo surfaces. The output fuzzy score maps have been compared with reference fire perimeters provided by the Fire Atlas of Portugal. Results show that commission and omission errors in the output burned area maps are a function of the threshold applied to the fuzzy score maps; between the two extremes of the greatest producer’s accuracy (omission error < 10%) and user’s accuracy (commission error < 5%), an intermediate threshold value provides errors of about 20% over the study area. The integration of SAR backscatter allowed reducing local commission errors from 65.4% (using optical data, only) to 11.4%, showing to significantly mitigate local errors due to the presence of cloud shadows and wetland areas. Overall, the proposed method is flexible and open to further developments; also in the perspective of the European Space Agency (ESA) Sentinel missions operationally providing SAR and optical datasets. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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10793 KiB

Open AccessArticle

Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

by Eleni Dragozi, Ioannis Z. Gitas, Dimitris G. Stavrakoudis and John B. Theocharis

Remote Sens. 2014, 6(12), 12005-12036; https://doi.org/10.3390/rs61212005 - 3 Dec 2014

Cited by 49 | Viewed by 8454

Abstract

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image analysis techniques: (a) an efficient feature selection method based on the Fuzzy Complementary Criterion (FuzCoC) and (b) the Support Vector Machine (SVM) classifier. In both cases (pixel and object-based), a number of higher-order spectral and spatial features were produced from the original image. The proposed methodology was tested in areas of Greece recently affected by severe forest fires, namely, Parnitha and Rhodes. The extensive comparative analysis indicates that the SVM object-based scheme exhibits higher classification accuracy than the respective pixel-based one. Additionally, the accuracy increased with the addition of derivative features and subsequent implementation of the FuzCoC feature selection (FS) method. Apart from the positive effect in the classification accuracy, the application of the FuzCoC FS method significantly reduces the computational requirements and facilitates the manipulation of the large data volume. In both cases (pixel and objet) the results confirmed that the use of an efficient feature selection method is a prerequisite step when extra information through higher-order features is added to the classification process of VHR imagery for burned area mapping. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1117 KiB

Open AccessArticle

Development of Methods for Detection and Monitoring of Fire Disturbance in the Alaskan Tundra Using a Two-Decade Long Record of Synthetic Aperture Radar Satellite Images

by Liza K. Jenkins, Laura L. Bourgeau-Chavez, Nancy H. F. French, Tatiana V. Loboda and Brian J. Thelen

Remote Sens. 2014, 6(7), 6347-6364; https://doi.org/10.3390/rs6076347 - 8 Jul 2014

Cited by 19 | Viewed by 8053

Abstract

Using the extensive archive of historical ERS-1 and -2 synthetic aperture radar (SAR) images, this analysis demonstrates that fire disturbance can be effectively detected and monitored in high northern latitudes using radar technology. A total of 392 SAR images from May to August spanning 1992–2010 were analyzed from three study fires in the Alaskan tundra. The investigated fires included the 2007 Anaktuvuk River Fire and the 1993 DCKN178 Fire on the North Slope of Alaska and the 1999 Uvgoon Creek Fire in the Noatak National Preserve. A 3 dB difference was found between burned and unburned tundra, with the best time for burned area detection being as late in the growing season as possible before frozen ground conditions develop. This corresponds to mid-August for the study fires. In contrast to electro-optical studies from the same region, measures of landscape recovery as detected by the SAR were on the order of four to five years instead of one. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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2956 KiB

Open AccessArticle

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

by Lidia Vlassova, Fernando Pérez-Cabello, Marcos Rodrigues Mimbrero, Raquel Montorio Llovería and Alberto García-Martín

Remote Sens. 2014, 6(7), 6136-6162; https://doi.org/10.3390/rs6076136 - 30 Jun 2014

Cited by 86 | Viewed by 12430

Abstract

The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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978 KiB

Open AccessArticle

An Object-Based Approach for Fire History Reconstruction by Using Three Generations of Landsat Sensors

by Thomas Katagis, Ioannis Z. Gitas and George H. Mitri

Remote Sens. 2014, 6(6), 5480-5496; https://doi.org/10.3390/rs6065480 - 12 Jun 2014

Cited by 8 | Viewed by 7085

Abstract

In this study, the capability of geographic object-based image analysis (GEOBIA) in the reconstruction of the recent fire history of a typical Mediterranean area was investigated. More specifically, a semi-automated GEOBIA procedure was developed and tested on archived and newly acquired Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images in order to accurately map burned areas in the Mediterranean island of Thasos. The developed GEOBIA ruleset was built with the use of the TM image and then applied to the other two images. This process of transferring the ruleset did not require substantial adjustments or any replacement of the initially selected features used for the classification, thus, displaying reduced complexity in processing the images. As a result, burned area maps of very high accuracy (over 94% overall) were produced. In addition to the standard error matrix, the employment of additional measures of agreement between the produced maps and the reference data revealed that “spatial misplacement” was the main source of classification error. It can be concluded that the proposed approach can be potentially used for reconstructing the recent (40-year) fire history in the Mediterranean, based on extended time series of Landsat or similar data. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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4928 KiB

Open AccessArticle

Forest Fire Severity Assessment Using ALS Data in a Mediterranean Environment

by Antonio Luis Montealegre, María Teresa Lamelas, Mihai A. Tanase and Juan De la Riva

Remote Sens. 2014, 6(5), 4240-4265; https://doi.org/10.3390/rs6054240 - 8 May 2014

Cited by 51 | Viewed by 9032

Abstract

Mediterranean pine forests in Spain experience wildland fire events with different frequencies, intensities, and severities which result in diverse socio-ecological consequences. In order to predict fire severity, spectral indices derived from remotely sensed images have been used extensively. Such spectral indices are usually used in combination with ground sampling to relate detected radiometric changes to actual fire effects. However, the potential of the tridimensional information captured by Airborne Laser Scanners (ALS) to severity mapping has been less explored. With the objective of addressing this question, in this paper, explanatory variables extracted from ALS point clouds are related to field estimations of the Composite Burn Index collected in four fires located in Aragón (Spain). Logistic regression models were developed and statistically tested and validated to map fire severity with up to 85.5% accuracy. The canopy relief ratio and the percentage of all returns above one meter height were the most significant variables and were therefore used to create a continuous map of severity levels. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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826 KiB

Open AccessArticle

Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products

by Marc Padilla, Stephen V. Stehman, Javier Litago and Emilio Chuvieco

Remote Sens. 2014, 6(3), 2050-2068; https://doi.org/10.3390/rs6032050 - 6 Mar 2014

Cited by 36 | Viewed by 7759

Abstract

Temporal stability, defined as the change of accuracy through time, is one of the validation aspects required by the Committee on Earth Observation Satellites’ Land Product Validation Subgroup. Temporal stability was evaluated for three burned area products: MCD64, Globcarbon, and fire_cci. Traditional accuracy measures, such as overall accuracy and omission and commission error ratios, were computed from reference data for seven years (2001–2007) in seven study sites, located in Angola, Australia, Brazil, Canada, Colombia, Portugal, and South Africa. These accuracy measures served as the basis for the evaluation of temporal stability of each product. Nonparametric tests were constructed to assess different departures from temporal stability, specifically a monotonic trend in accuracy over time (Wilcoxon test for trend), and differences in median accuracy among years (Friedman test). When applied to the three burned area products, these tests did not detect a statistically significant temporal trend or significant differences among years, thus, based on the small sample size of seven sites, there was insufficient evidence to claim these products had temporal instability. Pairwise Wilcoxon tests comparing yearly accuracies provided a measure of the proportion of year-pairs with significant differences and these proportions of significant pairwise differences were in turn used to compare temporal stability between BA products. The proportion of year-pairs with different accuracy (at the 0.05 significance level) ranged from 0% (MCD64) to 14% (fire_cci), computed from the 21 year-pairs available. In addition to the analysis of the three real burned area products, the analyses were applied to the accuracy measures computed for four hypothetical burned area products to illustrate the properties of the temporal stability analysis for different hypothetical scenarios of change in accuracy over time. The nonparametric tests were generally successful at detecting the different types of temporal instability designed into the hypothetical scenarios. The current work presents for the first time methods to quantify the temporal stability of BA product accuracies and to alert product end-users that statistically significant temporal instabilities exist. These methods represent diagnostic tools that allow product users to recognize the potential confounding effect of temporal instability on analysis of fire trends and allow map producers to identify anomalies in accuracy over time that may lead to insights for improving fire products. Additionally, we suggest temporal instabilities that could hypothetically appear, caused by for example by failures or changes in sensor data or classification algorithms. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1124 KiB

Open AccessArticle

Classification of Plot-Level Fire-Caused Tree Mortality in a Redwood Forest Using Digital Orthophotography and LiDAR

by Brian D. Bishop, Brian C. Dietterick, Russell A. White and Tom B. Mastin

Remote Sens. 2014, 6(3), 1954-1972; https://doi.org/10.3390/rs6031954 - 4 Mar 2014

Cited by 18 | Viewed by 7389

Abstract

Aerial and satellite imagery are widely used to assess the severity and impact of wildfires. Light detection and ranging (LiDAR) is a newer remote sensing technology that has demonstrated utility in measuring vegetation structure. Combined use of imagery and LiDAR may improve the assessment of wildfire impacts compared to imagery alone. Estimation of tree mortality at the plot scale could serve for more rapid, broad-scale, and lower cost post-fire assessments than feasible through field assessment. We assessed the accuracy of classifying color-infrared imagery in combination with post-fire LiDAR, and with differenced (pre- and post-fire) LiDAR, in estimating plot percent mortality in a second-growth coast redwood forest near Santa Cruz, CA. Percent mortality of trees greater than 25.4 cm DBH in 47 permanent 0.08 ha plots was categorized as low (<25%), moderate (25%–50%), or high (>50%). The model using Normalized Difference Vegetation Index (NDVI) from National Agricultural Imagery Program (NAIP) was 74% accurate; the model using NDVI and post-fire LiDAR was 85% accurate, while the model using NDVI and differenced LiDAR was 83% accurate. The addition of post-fire LiDAR data provided a modest increase in accuracy compared to imagery alone, which may not justify the substantial cost of data acquisition. The method demonstrated could be applied to rapidly estimate tree mortality resulting from wildfires at fine to moderate scale. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1799 KiB

Open AccessArticle

A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio

by Sean A. Parks, Gregory K. Dillon and Carol Miller

Remote Sens. 2014, 6(3), 1827-1844; https://doi.org/10.3390/rs6031827 - 27 Feb 2014

Cited by 275 | Viewed by 20395 | Correction

Abstract

Satellite-inferred burn severity data have become increasingly popular over the last decade for management and research purposes. These data typically quantify spectral change between pre-and post-fire satellite images (usually Landsat). There is an active debate regarding which of the two main equations, the delta normalized burn ratio (dNBR) and its relativized form (RdNBR), is most suitable for quantifying burn severity; each has its critics. In this study, we propose and evaluate a new Landsat-based burn severity metric, the relativized burn ratio (RBR), that provides an alternative to dNBR and RdNBR. For 18 fires in the western US, we compared the performance of RBR to both dNBR and RdNBR by evaluating the agreement of these metrics with field-based burn severity measurements. Specifically, we evaluated (1) the correspondence between each metric and a continuous measure of burn severity (the composite burn index) and (2) the overall accuracy of each metric when classifying into discrete burn severity classes (i.e., unchanged, low, moderate, and high). Results indicate that RBR corresponds better to field-based measurements (average R² among 18 fires = 0.786) than both dNBR (R² = 0.761) and RdNBR (R² = 0.766). Furthermore, the overall classification accuracy achieved with RBR (average among 18 fires = 70.5%) was higher than both dNBR (68.4%) and RdNBR (69.2%). Consequently, we recommend RBR as a robust alternative to both dNBR and RdNBR for measuring and classifying burn severity. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1748 KiB

Open AccessArticle

Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX)

by Lennert Schepers, Birgen Haest, Sander Veraverbeke, Toon Spanhove, Jeroen Vanden Borre and Rudi Goossens

Remote Sens. 2014, 6(3), 1803-1826; https://doi.org/10.3390/rs6031803 - 27 Feb 2014

Cited by 105 | Viewed by 13293

Abstract

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R² maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R² up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R² = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R² = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R² = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1135 KiB

Open AccessArticle

A Comparative Analysis of EO-1 Hyperion, Quickbird and Landsat TM Imagery for Fuel Type Mapping of a Typical Mediterranean Landscape

by Giorgos Mallinis, Georgia Galidaki and Ioannis Gitas

Remote Sens. 2014, 6(2), 1684-1704; https://doi.org/10.3390/rs6021684 - 20 Feb 2014

Cited by 37 | Viewed by 10301

Abstract

Forest fires constitute a natural disturbance factor and an agent of environmental change with local to global impacts on Earth’s processes and functions. Accurate knowledge of forest fuel extent and properties can be an effective component for assessing the impacts of possible future wildfires on ecosystem services. Our study aims to evaluate and compare the spectral and spatial information inherent in the EO-1 Hyperion, Quickbird and Landsat TM imagery. The analysis was based on a support vector machine classification approach in order to discriminate and map Mediterranean fuel types. The fuel classification scheme followed a site-specific fuel model within the study area, which is suitable for fire behavior prediction and spatial simulation. The overall accuracy of the Quickbird-based fuel type mapping was higher than 74% with a quantity disagreement of 9% and an allocation disagreement of 17%. Both classifications from the Hyperion and Landsat TM fuel type maps presented approximately 70% overall accuracy and 16% allocation disagreement. The McNemar’s test indicated that the overall accuracy differences between the three produced fuel type maps were not significant (p < 0.05). Based on both overall and individual higher accuracies obtained with the use of the Quickbird image, this study suggests that the high spatial resolution might be more decisive than the high spectral resolution in Mediterranean fuel type mapping. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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1723 KiB

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

by Kunpeng Yi, Hiroshi Tani, Jiquan Zhang, Meng Guo, Xiufeng Wang and Guosheng Zhong

Remote Sens. 2013, 5(12), 6938-6957; https://doi.org/10.3390/rs5126938 - 12 Dec 2013

Cited by 27 | Viewed by 8302

Abstract

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 MK 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. Full article

(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)

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